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false | 2023-07-07 04:44:37 | Python: Re-import module | [
"python",
"til"
] | [
"TIL"
] | :icons: font
I often write little Python scripts that import code from other local modules and a common problem I have when using the Python REPL is that I update the code in the other modules and then can't use the new functionality without restarting the REPL and re-importing everything.
At least so I thought!
It turns out there is a way to refresh those modules and that's what we'll be exploring in this blog post.
Let's say that we have the file `person.py` that contains the code shown below:
.person.py
[source, python]
----
class Person:
def __init__(self, name):
self.name = name
def say_hello(self):
print(f"Hello {self.name}")
----
Launch the Python REPL by typing `python` and then import the class and run the `say_hello` function:
[source, python]
----
from person import Person
Person(name="Mark").say_hello()
----
.Output
[source, text]
----
Hello Mark
----
Now let's say that we add a `say_goodbye` function to the class so that it now looks like this:
.person.py
[source, python]
----
class Person:
def __init__(self, name):
self.name = name
def say_hello(self):
print(f"Hello {self.name}")
def say_goodbye(self):
print(f"Good Bye {self.name}")
----
If we try to use that function, it's not available:
[source, python]
----
from person import Person
Person(name="Mark").say_goodbye()
----
.Output
[source, text]
----
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'Person' object has no attribute 'say_goodbye'
----
Even running `from person import Person` again doesn't help.
Help does come, however, from the https://docs.python.org/3/library/importlib.html[importlib^] library, which we can use to re-import the module.
I initially tried doing this:
[source, python]
----
import importlib
importlib.reload(person)
----
And then I ran the `say_goodbye` function again, to no avail:
.Output
[source, text]
----
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'person' is not defined. Did you mean: 'Person'?
----
To solve that problem, I had to first import `person` and then reload:
[source, python]
----
import person
importlib.reload(person)
----
I ran the goodbye command again, but still got the same error:
[source, python]
----
Person(name="Mark").say_goodbye()
----
.Output
[source, text]
----
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'Person' object has no attribute 'say_goodbye'
----
It turns out that we need to re-run the import of the `Person` class again, after the reload of `person`, meaning that the full re-importing code looks like this:
[source, python]
----
import person
importlib.reload(person)
from person import Person
----
And now let's call goodbye:
[source, python]
----
Person(name="Mark").say_goodbye()
----
.Output
[source, text]
----
Good Bye Mark
---- | In this post, we're going to learn how to re-import a local Python module | uploads/2023/07/python-reimport-module.png | [
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] | python-reimport-module | https://markhneedham.com/blog/2023/07/07/python-reimport-module |
false | 2023-07-24 04:44:37 | VSCode: Adding Poetry Python Interpreter | [
"poetry",
"vscode",
"python",
"til"
] | [
"TIL"
] | :icons: font
I've been trying out Python's https://python-poetry.org/[Poetry^] dependency management tool recently and I really like it, but couldn't figure out how to get it setup as VSCode's Python interpreter.
In this blog post, we'll learn how to do that.
One way to add the Python interpreter in VSCode is to press `Cmd+Shift+p` and then type `Python Interpreter`.
If you select the first result, you'll see something like the following:
.The Python interpreter modal window in VSCode
image::{{<siteurl>}}/uploads/2023/07/python-interpreter.png[]
When I create a virtual environment directly it'll usually appear on the list, but Poetry wasn't.
I went a-searching and came across https://stackoverflow.com/questions/59882884/vscode-doesnt-show-poetry-virtualenvs-in-select-interpreter-option[a StackOverflow thread where people were experiencing the same problem^].
I tried some of the suggestions, but they mostly didn't work for me.
I did, however, realise that I'd be able to add the interpreter manually if I knew its path, which led me to https://github.com/python-poetry/poetry/pull/731[this PR^].
This led me to the following command which gives us that answer:
[source, bash]
----
poetry env info
----
.Output
[source, text]
----
Virtualenv
Python: 3.11.4
Implementation: CPython
Path: /Users/markhneedham/projects/duckdb-0.8/sentiment-analysis/.venv
Executable: /Users/markhneedham/projects/duckdb-0.8/sentiment-analysis/.venv/bin/python
Valid: True
System
Platform: darwin
OS: posix
Python: 3.11.4
Path: /opt/homebrew/opt/python@3.11/Frameworks/Python.framework/Versions/3.11
Executable: /opt/homebrew/opt/python@3.11/Frameworks/Python.framework/Versions/3.11/bin/python3.11
----
If we want to only get the path, we can pass in `--path`:
[source, bash]
----
poetry env info --path
----
.Output
[source, text]
----
/Users/markhneedham/projects/duckdb-0.8/sentiment-analysis/.venv
----
We can then pipe that to our clipboard:
[source, bash]
----
poetry env info --path | pbcopy
----
And now we can go back to the Python interpreter window and paste in the result.
Job done! | In this post, we're going to learn how to configure Poetry as a Python interpreter in VSCode. | uploads/2023/07/vscode-poetry-interpreter.png | [
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] | vscode-poetry-python-interpreter | https://markhneedham.com/blog/2023/07/24/vscode-poetry-python-interpreter |
false | 2023-07-12 04:44:37 | Redpanda: Viewing consumer group offsets from __consumer_offsets | [
"redpanda",
"til"
] | [
"TIL"
] | https://docs.redpanda.com/docs/home[Redpanda^] supports consumer groups, which are sets of consumers that cooperate to consume data from topics.
The consumers in a group are assigned a partition and they keep track of the last consumed offset in the https://docs.redpanda.com/docs/develop/consume-data/consumer-offsets/[`__consumer_offsets` topic].
I wanted to see how many messages had been consumed by a consumer group and that's what we'll explore in this post.
My first thought was to query the `__consumer_offsets` topic using `rpk topic consume`.
The following code gets three messages from this topic:
[source, bash]
----
rpk topic consume __consumer_offsets --brokers localhost:9092 -n 3
----
.Output
[source, json]
----
{
"topic": "__consumer_offsets",
"key": "\u0000\u0002\u0000\u0019massaged-delays-consumer2",
"value": "\u0000\u0003\u0000\u0008consumer\u0000\u0000\u0000\u0001\u0000\u0005range\u0000,rdkafka-5f35e588-5e24-42a0-b2e3-7913d85ac9c8\u0000\u0000\u0001\ufffd@s\ufffd\ufffd\u0000\u0000\u0000\u0001\u0000\u0003\u0000,rdkafka-5f35e588-5e24-42a0-b2e3-7913d85ac9c8\ufffd\ufffd\u0000\u0007rdkafka\u0000\u000c192.168.96.1\u0000\u0004\ufffd\ufffd\u0000\u0000\ufffd\ufffd\u0000\u0000\u0000\u001f\u0000\u0001\u0000\u0000\u0000\u0001\u0000\u000fmassaged-delays\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000#\u0000\u0000\u0000\u0000\u0000\u0001\u0000\u000fmassaged-delays\u0000\u0000\u0000\u0001\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000",
"timestamp": 1689003484597,
"partition": 0,
"offset": 0
}
{
"topic": "__consumer_offsets",
"key": "\u0000\u0001\u0000\u0019massaged-delays-consumer2\u0000\u000fmassaged-delays\u0000\u0000\u0000\u0000",
"value": "\u0000\u0003\u0000\u0000\u0000\u0000\u0000\u0000\u0001Z\ufffd\ufffd\ufffd\ufffd\u0000\u0000\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd",
"timestamp": 1689003486581,
"partition": 0,
"offset": 1
}
{
"topic": "__consumer_offsets",
"key": "\u0000\u0002\u0000\u0019massaged-delays-consumer2",
"value": "\u0000\u0003\u0000\u0008consumer\u0000\u0000\u0000\u0002\ufffd\ufffd\ufffd\ufffd\u0000\u0000\u0001\ufffd@t\ufffdF\u0000\u0000\u0000\u0000",
"timestamp": 1689003547718,
"partition": 0,
"offset": 2
}
----
I wanted to filter these messages to find the latest offset for the consumer group with the name `massaged-delays-consumer2`, but I couldn't for the life of me get that to work.
My next thought was to use kcat, but that was even less successful:
[source, bash]
----
kcat -C -b localhost:9092 -t __consumer_offsets -p 0 -e -q
----
.Output
[source, bash]
----
consumerrange,rdkafka-5f35e588-5e24-42a0-b2e3-7913d85ac9c8�@s�,rdkafka-5f35e588-5e24-42a0-b2e3-7913d85ac9c8��rdkafka
192.168.96.1����massaged-delays#massaged-delays
Z������������
consumer�����@t�F
consumerrange,rdkafka-a7ffba78-1b21-4f34-bdd5-fe70fd5d43cf�@w0�,rdkafka-a7ffba78-1b21-4f34-bdd5-fe70fd5d43cf��rdkafka
192.168.96.1����massaged-delays#massaged-delays
consumer�����@w�
consumerrange7kafka-python-2.0.2-637f42b6-95f0-47e0-abe6-d4b27c02f1e3�E�̭7kafka-python-2.0.2-637f42b6-95f0-47e0-abe6-d4b27c02f1e3��kafka-python-2.0.2
notifications! 192.168.96.1��'
consumer�����E��
consumerrange7kafka-python-2.0.2-d0841fce-363c-4683-b285-7e46d73b5a32�E���7kafka-python-2.0.2-d0841fce-363c-4683-b285-7e46d73b5a32��kafka-python-2.0.2
notifications! 192.168.96.1��'
consumer�����E�6�
consumerrange7kafka-python-2.0.2-d1101589-bd3e-4270-8b70-f1fe8ca88595�E�Y�7kafka-python-2.0.2-d1101589-bd3e-4270-8b70-f1fe8ca88595��kafka-python-2.0.2
notifications! 192.168.96.1��'
consumer�����E�
consumerrange7kafka-python-2.0.2-f709bfa7-d592-4ebb-9c87-9cea6ff43c91�E�o�7kafka-python-2.0.2-f709bfa7-d592-4ebb-9c87-9cea6ff43c91��kafka-python-2.0.2
notifications! 192.168.96.1��'
consumerrange7kafka-python-2.0.2-72b0f695-03b0-461f-885b-d2f37efaea0e�E���7kafka-python-2.0.2-72b0f695-03b0-461f-885b-d2f37efaea0e��kafka-python-2.0.2
notifications! 192.168.96.1��'
consumerrange7kafka-python-2.0.2-e3a53b7d-c0a2-4db7-b8d7-f9b58d2bfba5�F�@�7kafka-python-2.0.2-e3a53b7d-c0a2-4db7-b8d7-f9b58d2bfba5��kafka-python-2.0.2
notifications! 192.168.96.1��'
consumer�����F��
consumerrange7kafka-python-2.0.2-2f06ad87-2ddd-4733-9be6-ba43370d26ec�I<7kafka-python-2.0.2-2f06ad87-2ddd-4733-9be6-ba43370d26ec��kafka-python-2.0.2
notifications! 192.168.96.1��'
consume�����IE��
consumer range7kafka-python-2.0.2-8f74599d-8e57-4836-beef-318b23752144�IG�7kafka-python-2.0.2-8f74599d-8e57-4836-beef-318b23752144��kafka-python-2.0.2
notifications! 192.168.96.1��'
consumer
�����IJ�)
consumer
range7kafka-python-2.0.2-f01cf447-0066-437b-8654-c0be65b00a57�IK�7kafka-python-2.0.2-f01cf447-0066-437b-8654-c0be65b00a57��kafka-python-2.0.2
notifications!
----
I called upon ChatGPT, which pointed me to the `rpk group command`, which is exactly what we need.
[NOTE]
====
ChatGPT made up some completely random parameters for `rpk group`, but hopefully this post will be used for the training of future models!
====
We can list the consumer groups by running the following command:
[source, bash]
----
rpk group list
----
.Output
[source, text]
----
BROKER GROUP
0 demo-group
0 demo-group2
0 demo-group4
0 demo-group5
0 demo-group6
0 demo-group7
0 demo-group8
0 demo-group9
0 demo-group91
0 demo-group92
0 demo-group93
0 demo-group94
0 demo-group95
0 events-consumer-group
0 massaged-delays-consumer
0 massaged-delays-consumer2
0 massaged-delays-consumer3
0 massaged-delays-consumer4
----
I hope you like my excellent consumer group naming convention!
Next, let's check what's happening with `massaged-delays-consumer4`:
[source, bash]
----
rpk group describe massaged-delays-consumer4
----
.Output
[source, text]
----
GROUP massaged-delays-consumer4
COORDINATOR 0
STATE Empty
BALANCER
MEMBERS 0
TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG MEMBER-ID CLIENT-ID HOST
massaged-delays 0 165 1384 1219
----
From this output, we learn that there are 1,384 messages in this topic and only 165 have been consumed by the consumer group.
We better get on it! | In this post, we're going to learn how to find the latest offset for a consumer group when using Redpanda. | uploads/2023/07/redpanda-banner.png | [
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] | redpanda-consumer-group-offsets | https://markhneedham.com/blog/2023/07/12/redpanda-consumer-group-offsets |
false | 2023-07-13 04:44:37 | Puppeteer: Unsupported command-line flag: --enabled-blink-features=IdleDetection. | [
"puppeteer",
"javascript",
"til"
] | [
"TIL"
] | :icons: font
In many of the https://www.youtube.com/watch?v=6Ck1d5iswm4&list=PLihIrF0tCXdc35Lq865Z0jsnwaMol9Zn7[StarTree recipe videos^] that I've worked on, I show how to write queries in the Pinot UI.
If I wrote these queries manually there'd be way too many typos, so I drive the UI using a script.
I've recently been exploring whether I can do this using a Node.js library called https://pptr.dev/[Puppeteer^] and wanted to share a warning message that I ran into early doors.
I installed Puppeteer using the following command:
[source, bash]
----
npm i puppeteer-core
----
I then created the file `drive_pinot.mjs` and added the following code, which opens the Pinot query console on the first tab:
.drive_pinot.mjs
[source, javascript]
----
import puppeteer from 'puppeteer-core';
async function run() {
const browser = await puppeteer.launch({
headless: false,
executablePath: '/Applications/Google Chrome.app/Contents/MacOS/Google Chrome',
ignoreDefaultArgs: ['--enable-automation'],
defaultViewport: null,
args: [
'--window-size=1920,1080',
'--disable-infobars',
],
});
const [page] = await browser.pages(); // <1>
await page.goto('http://localhost:9000/#/query'); // <2>
await page.waitForSelector('.CodeMirror');
// await browser.close();
}
run();
----
<.> Get the first tab
<.> Open the Pinot query console
We can run that script like this:
[source, bash]
----
node drive_pinot.mjs
----
The script launched fine, but the Chrome browser had the following warning underneath the URL bar:
[source, text]
----
You are using an unsupported command-line flag: --enabled-blink-features=IdleDetection. Stability and security will suffer.
----
I didn't set any blink features, so I have no idea where this error came from.
By trial and error, I did work out that I could get rid of it by opening the Pinot query console on a new tab and then closing the initial tab.
Our code, therefore, looks like this:
.drive_pinot.mjs
[source, javascript]
----
import puppeteer from 'puppeteer-core';
async function run() {
const browser = await puppeteer.launch({
headless: false,
executablePath: '/Applications/Google Chrome.app/Contents/MacOS/Google Chrome', // Add your path here
ignoreDefaultArgs: ['--enable-automation'], // exclude this switch
defaultViewport: null, // required for --window-size
args: [
'--window-size=1920,1080', // set the window size
'--disable-infobars',
],
});
const page = await browser.newPage(); // <1>
await page.goto('http://localhost:9000/#/query');
const [initPage] = await browser.pages();
initPage.close() // <2>
await page.waitForSelector('.CodeMirror');
// await browser.close();
}
run();
----
<.> Open a new browser tab
<.> Close the initial one
And now the error message has miraculously gone! | In this post, we're going to learn how to work around a Chrome browser warning when using Puppeteer. | uploads/2023/07/puppeteer-banner.png | [
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] | puppeteer-unsupported-flag-enabled-blink-features-idledetection | https://markhneedham.com/blog/2023/07/13/puppeteer-unsupported-flag-enabled-blink-features-idledetection |
false | 2023-07-25 04:44:37 | Confluent Kafka: DeprecationWarning: AvroProducer has been deprecated. Use AvroSerializer instead. | [
"python",
"kafka",
"avro",
"til"
] | [
"TIL"
] | I've been creating a demo showing how to ingest Avro-encoded data from Apache Kafka into Apache Pinot and ran into a deprecation warning.
In this blog post, I'll show how to update code using the Confluent Kafka Python client to get rid of that warning.
I started by installing the following libraries:
[source, bash]
----
pip install confluent-kafka avro urllib3 requests
----
And then my code to publish an Avro encoded event to Kafka looked like this:
[source, python]
----
from confluent_kafka.avro import AvroProducer
from confluent_kafka import avro
schema_name = "telemetry.avsc"
producer_config = {
'bootstrap.servers': 'localhost:9092',
'schema.registry.url': 'http://localhost:8081',
'broker.address.family': 'v4'
}
value_schema = avro.load(schema_name)
producer = AvroProducer(producer_config, default_value_schema=value_schema)
event = {
# all my fields
}
producer.produce(topic="telemetry", value=event)
----
When I ran this script data did make its way into Kafka, but I also got the following warning on the `AvroProducer` line:
.Output
[source, text]
----
DeprecationWarning: AvroProducer has been deprecated. Use AvroSerializer instead.
----
It took me a little while to figure out where `AvroSerializer` lived and how to use it, but I eventually ended up with the following code for creating the producer:
[source, python]
----
from confluent_kafka import Producer
from confluent_kafka.schema_registry import SchemaRegistryClient
from confluent_kafka.schema_registry.avro import AvroSerializer
from confluent_kafka.serialization import (
SerializationContext,
MessageField,
)
schema_registry_conf = {'url': 'http://localhost:8081'}
schema_registry_client = SchemaRegistryClient(schema_registry_conf)
with open("telemetry.avsc") as f:
value_schema = f.read()
avro_serializer = AvroSerializer(schema_registry_client, value_schema)
producer_conf = {'bootstrap.servers': 'localhost:9092'}
producer = Producer(producer_conf)
----
I ran this code and got the following error message:
.Output
[source, text]
----
Traceback (most recent call last):
File "/Users/markhneedham/projects/hugo-blog/blog/content/2023/07/25/new.py", line 5, in <module>
from confluent_kafka.schema_registry.avro import AvroSerializer
File "/Users/markhneedham/projects/hugo-blog/blog/content/2023/07/25/.venv/lib/python3.11/site-packages/confluent_kafka/schema_registry/avro.py", line 22, in <module>
from fastavro import (parse_schema,
ModuleNotFoundError: No module named 'fastavro'
----
Let's get `fastavro` installed:
[source, bash]
----
pip install fastavro
----
When I ran the script again, the deprecation warning was gone, but I got the following error message instead:
.Output
[source, text]
----
Traceback (most recent call last):
File "/Users/markhneedham/projects/hugo-blog/blog/content/2023/07/25/new.py", line 47, in <module>
producer.produce(topic="telemetry", value=event)
TypeError: a bytes-like object is required, not 'dict'
----
In the previous API, our messages were encoded inside the producer, but here we need to do it explicitly using the Avro serialiser.
We, therefore, need to update this line:
[source, python]
----
producer.produce(topic="telemetry", value=event)
----
To read like this:
[source, python]
----
producer.produce(
topic="telemetry",
value=avro_serializer(event, SerializationContext("telemetry", MessageField.VALUE)),
)
----
Now if I run the script, messages make their way into Kafka and I don't have any warnings or errors!
[NOTE]
====
If you want to see the entirety of both code samples, I've included them both in https://gist.github.com/mneedham/2181b1dd8ddbcdf6d31da3ff569ca1ba[a GitHub Gist^].
==== | In this post, we're going to learn how to fix a deprecation warning when using the Confluent Kafka Python library. | uploads/2023/07/kafka-deprecation-banner.png | [
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] | confluent-kafka-avroproducer-deprecated-use-avroserializer | https://markhneedham.com/blog/2023/07/25/confluent-kafka-avroproducer-deprecated-use-avroserializer |
false | 2023-09-03 00:44:37 | JupyterLab 4.0.5: Scroll output with keyboard shortcut | [
"jupyterlab",
"til"
] | [
"TIL"
] | :icons: font
In the latest version of Jupyter Notebook/Lab (at least), the output of each cell is shown in full, regardless of how long it is.
I wanted to limit the height of the output and then scroll through it within that inner window, ideally by triggering a keyboard shortcut.
I learnt how to do this with the help of https://stackoverflow.com/questions/59025144/how-do-i-set-a-shortcut-to-enable-scrolling-for-outputs-in-jupyter-lab[Stack Overflow].
First, you need to open the settings editor by typing `Cmd + ,` on a Mac or by clicking on that screen from the top menu:
.Settings > Settings Editor
image::{{<siteurl>}}/uploads/2023/09/settings-editor.png[]
Next, click on `JSON Settings Editor` in the top right-hand corner:
.JSON Settings Editor
image::{{<siteurl>}}/uploads/2023/09/json-settings-editor.png[]
And then paste the following JSON into the `shortcuts` array:
[source, json]
----
{
"command": "notebook:enable-output-scrolling",
"keys": [
"S"
],
"selector": ".jp-Notebook:focus",
"args": {}
},
{
"command": "notebook:disable-output-scrolling",
"keys": [
"Alt S"
],
"selector": ".jp-Notebook:focus",
"args": {}
},
----
This is how it should look like once you've done that:
.Keyboard shortcuts added
image::{{<siteurl>}}/uploads/2023/09/json-edited.png[]
You can then add a scrollbar to an output cell by pressing `s` or remove the scrollbar by pressing `Alt + s`.
Below is an example of what happens when you toggle output scrolling:
.No scrollbar vs Scrollbar
image::{{<siteurl>}}/uploads/2023/09/scrollbar-noscrollbar.png[] | In this post, we'll learn how to make the output of a Jupyterlab cell scrollable with a keyboard shortcut. | uploads/2023/09/jupyter-scroll.png | [
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] | jupyterlab-scroll-output-keyboard-shortcut | https://markhneedham.com/blog/2023/09/03/jupyterlab-scroll-output-keyboard-shortcut |
false | 2023-09-05 00:44:37 | Quix Streams: Process certain number of Kafka messages | [
"quixstreams",
"python",
"til"
] | [
"TIL"
] | :icons: font
In a recent demo, I wanted to use Quix Streams to process a specified number of messages from a Kafka topic, write a message to another stream, and then exit the Quix app.
This is an unusual use of Quix Streams, so it took me a while to figure out how to do it.
Let's assume we have a Kafka broker running.
We'll create a couple of topics using the `rpk` tool:
[source, bash]
----
rpk topic create events -p1
rpk topic create massaged-events -p1
----
Now, we're going to start by installing Quix Streams and Click, a library for processing command line arguments:
[source, bash]
----
pip install quixstreams click
----
We're going to start by creating a version of the application that processes all messages.
Create a file called `massage.py` and add the following imports
[source, python]
----
import quixstreams as qx
from quixstreams import StreamConsumer, EventData, CommitMode
import json
import click
----
Next, let's create a 'main' method that's going to bootstrap the app:
[source, python]
----
@click.command()
def run_app():
global client, topic_consumer, producer
client = qx.KafkaStreamingClient('127.0.0.1:9092') # <.>
topic_consumer = client.get_topic_consumer( # <.>
topic="events",
auto_offset_reset=qx.AutoOffsetReset.Earliest,
consumer_group="events-consumer",
commit_settings=CommitMode.Manual
)
producer = client.get_raw_topic_producer("massaged-events") # <.>
print("Listening to streams. Press CTRL-C to exit.")
topic_consumer.on_stream_received = on_stream_received_handler
topic_consumer.subscribe()
qx.App.run(before_shutdown=before_shutdown)
----
<.> Create Kafka client
<.> Create consumer for the `events` topic
<.> Create producer for the `massaged-events` topic
We then need to add the following functions to process each message and handle the shutdown of the app:
[source, python]
----
def on_event_data_received_handler(stream: StreamConsumer, data: EventData):
with data:
payload = json.loads(data.value)
payload["count"] *= 2 # <.>
message = qx.RawMessage(json.dumps(payload, indent=2).encode('utf-8')) # <.>
message.key = str(payload["id"]).encode('utf-8')
producer.publish(message) #<.>
topic_consumer.commit()
def on_stream_received_handler(stream_received: StreamConsumer):
stream_received.events.on_data_received = on_event_data_received_handler
def before_shutdown():
print('before shutdown')
topic_consumer.dispose()
producer.dispose()
----
<.> Multiply the `count` property by 2
<.> Create a new message
<.> Publish the message to the `massaged-events` topic
Finally, let's call the 'main' function:
[source, python]
----
if __name__ == "__main__":
run_app()
----
If we run this script, it will process any messages received by the `events` topic and write a new message to `massaged-events` with the `count` property doubled.
Let's ingest a message into Kafka to see if it works:
[source, bash]
----
echo '{"id": 1, "count": 4}' |
jq -cr --arg sep ø '[.id, tostring] | join($sep)' |
kcat -P -b localhost:9092 -t events -Kø
----
And now we'll check the contents of the `events` and `massaged-events` topics:
[source, bash]
----
kcat -C -b localhost:9092 -t events -e | jq -c
----
.Output
[source, json]
----
{"id":1,"count":4}
----
[source, bash]
----
kcat -C -b localhost:9092 -t massaged-events -e | jq -c
----
.Output
[source, json]
----
{"id":1,"count":8}
----
So far, so good.
If we write any more messages to the `events` topic they will automatically be processed as well.
But now we want to update our script so that we can specify how many messages to consume before stopping.
If we then run the script again, it will continue from where we left off because our topic consumer was created with a consumer group that's keeping track of the last read offset.
We can return that offset by running the following command:
[source, bash]
----
rpk group describe events-consumer
----
.Output
[source, text]
----
GROUP events-consumer
COORDINATOR 0
STATE Empty
BALANCER
MEMBERS 0
TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG MEMBER-ID CLIENT-ID HOST
events 0 1 1 0
----
Let's update our imports:
[source, python]
----
from quixstreams import StreamConsumer, EventData, CancellationTokenSource, CommitMode
import threading
----
And now we'll update the `run_app` function to look like this:
[source, python]
----
@click.command()
@click.option('--number-events', default=1)
def run_app(number_events):
global client, topic_consumer, producer
global events_to_consume, events_consumed, thread_lock, cancellation_thread
client = qx.KafkaStreamingClient('127.0.0.1:9092')
topic_consumer = client.get_topic_consumer(
topic="events",
auto_offset_reset=qx.AutoOffsetReset.Earliest,
consumer_group="events-consumer",
commit_settings=CommitMode.Manual
)
producer = client.get_raw_topic_producer("massaged-events")
thread_lock = threading.Lock()
cts = CancellationTokenSource() # <.>
cancellation_thread = threading.Thread(target=lambda: cts.cancel()) # <.>
events_to_consume = number_events
events_consumed = 0
print("Listening to streams. Press CTRL-C to exit.")
topic_consumer.on_stream_received = on_stream_received_handler
topic_consumer.subscribe()
qx.App.run(cts.token, before_shutdown=before_shutdown)
if cancellation_thread.is_alive(): # <.>
cancellation_thread.join()
----
<.> Cancellation token used to stop message processing
<.> Cancellation thread which will trigger the cancellation token
<.> Join the cancellation thread to the main thread before exiting
And the `on_event_data_received_handler` needs to be updated to keep track the messages consumed:
[source, python]
----
def on_event_data_received_handler(stream: StreamConsumer, data: EventData):
global events_consumed
with data:
payload = json.loads(data.value)
payload["count"] *= 2
message = qx.RawMessage(json.dumps(payload, indent=2).encode('utf-8'))
message.key = str(payload["id"]).encode('utf-8')
producer.publish(message)
topic_consumer.commit()
with thread_lock:
events_consumed += 1 # <.>
if events_consumed >= events_to_consume: # <.>
if not cancellation_thread.is_alive():
cancellation_thread.start() # <.>
print("Cancellation token triggered")
return
----
<.> Increment the number of messages consumed
<.> Check if we've exceeded the count
<.> Trigger the cancellation thread, which will cancel the token
We can then call our Python script like this to process one event:
[source, bash]
----
python massage.py --number-events 1
----
Let's now add another message to Kafka:
[source, bash]
----
echo '{"id": 42, "count": 9000}' |
jq -cr --arg sep ø '[.id, tostring] | join($sep)' |
kcat -P -b localhost:9092 -t events -Kø
----
We'll see the following output from `massage.py`:
.Output
[source, text]
----
Listening to streams. Press CTRL-C to exit.
Cancellation token triggered
before shutdown
----
And if we look at the `massaged-events` topic, it now has the following message:
.Output
[source, json]
----
{"id":42,"count":18000}
----
You can find the full code in this https://gist.github.com/mneedham/d877080aa5247006a1272a2da464185f[GitHub Gist^]. | In this post, we'll learn how to process a specified number of messages in a Kafka topic using Quix Streams. | uploads/2023/09/quix-messages-kafka-banner.png | [
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] | quix-streams-process-n-kafka-messages | https://markhneedham.com/blog/2023/09/05/quix-streams-process-n-kafka-messages |
false | 2023-09-29 00:44:37 | GPT 3.5 Turbo vs GPT 3.5 Turbo Instruct | [
"openai",
"langchain",
"til"
] | [
"TIL"
] | :icons: font
Last week OpenAI sent out the following email introducing the `gpt-3.5-turbo-instruct` large language model:
.Open AI announce gpt-3.5-turbo-instruct LLM
image::{{<siteurl>}}/uploads/2023/09/openai-email.png[width="500px"]
I've never completely understood the difference between the chat and instruct models, so this seemed like a good time to figure it out.
In this blog post, we're going to give the models 5 tasks to do and then we'll see how they get on.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/C-JV0VEzn-0?si=dMTX8FpWIBgfqySZ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
You can also find all the code at the https://github.com/mneedham/LearnDataWithMark/blob/main/gpt-instruct/notebooks/GPT-Instruct-Tutorial.ipynb[GPT-Instruct-Tutorial.ipynb notebook^].
====
== Setup
First things first, let's install some libraries:
[source, bash]
----
pip install langchain openai python-dotenv wikipedia
----
I've got my OpenAI key in a file called `.env`, so let's first import that:
[source, python]
----
import dotenv
dotenv.load_dotenv()
----
And now we're going to create chat and instruct models.
First, chat:
[source, python]
----
from langchain.chat_models import ChatOpenAI
turbo = ChatOpenAI(model_name="gpt-3.5-turbo")
----
And then instruct:
[source, python]
----
from langchain.llms import OpenAI
turbo_instruct = OpenAI(model_name="gpt-3.5-turbo-instruct")
----
== Answering factual questions 🤓
It's time to put the models through their paces.
Task number 1 is to answer a factual question:
[source, python]
----
question = "Did Microsoft acquire OpenAI?"
----
We can get the chat LLM's answer by running the following:
[source, python]
----
print(turbo.predict(question))
----
.Output
[source, text]
----
No, Microsoft has not acquired OpenAI. However, Microsoft has provided financial support to OpenAI and the two companies have formed a partnership to work together on AI research and development.
CPU times: user 32.8 ms, sys: 4.46 ms, total: 37.3 ms
Wall time: 3.28 s
----
Now it's instruct's turn:
[source, python]
----
print(turbo_instruct.predict(question))
----
.Output
[source, text]
----
No, Microsoft did not acquire OpenAI. Microsoft and OpenAI have a partnership where Microsoft invests $1 billion in OpenAI's research and development efforts.
CPU times: user 5.79 ms, sys: 3.06 ms, total: 8.85 ms
Wall time: 866 ms
----
Instruct is a bit more concise, but they more or less say the same thing.
== Replying with empathy 💕
Next up, replying with empathy.
Let's pretend I'm sad and need cheering up.
What will they suggest?
[source, python]
----
question = "I'm feeling sad today. Can you cheer me up?"
----
Chat is first up:
[source, python]
----
print(turbo.predict(question))
----
.Output
[source, text]
----
I'm sorry to hear that you're feeling sad today, but I'll do my best to cheer you up! Remember that it's normal to have ups and downs, and this feeling won't last forever. Here are a few things that might brighten your day:
1. Reach out to a loved one: Talking to someone you trust about how you're feeling can provide support and comfort.
2. Do something you enjoy: Engaging in activities you love, such as listening to music, reading a book, or watching a funny movie, can help lift your spirits.
3. Practice self-care: Take some time to focus on yourself. Whether it's taking a relaxing bath, going for a walk in nature, or treating yourself to your favorite snack, prioritize your well-being.
4. Surround yourself with positivity: Surround yourself with things that make you happy. This could be listening to uplifting music, reading inspirational quotes, or looking at photos that bring back good memories.
5. Help others: Sometimes, doing something kind for someone else can give you a sense of fulfillment and joy. Consider volunteering, helping a friend, or even offering a kind word or gesture to brighten someone else's day.
Remember, it's okay to feel sad, but always know that brighter days are ahead. Take care of yourself and seek support when you need it.
CPU times: user 6.18 ms, sys: 3.23 ms, total: 9.41 ms
Wall time: 13.8 s
----
I think there are some decent suggestions there, although it does take a long time to produce the answer - just under 14 seconds.
Let's see how instruct gets on:
[source, python]
----
print(turbo_instruct.predict(question))
----
.Output
[source, text]
----
Of course! Here are a few things that might make you smile:
1. Look at some funny memes or watch a funny video. Laughter is the best medicine!
2. Call or text a friend or family member and have a chat. Sometimes just talking to someone can help lift our spirits.
3. Take a walk outside and enjoy some fresh air and sunshine. Nature has a way of making us feel better.
4. Treat yourself to something nice, like your favorite snack or a warm cup of tea.
5. Put on some upbeat music and dance around your room. It's hard to be sad when you're dancing!
Remember, it's okay to feel sad sometimes. But know that it won't last forever and there are always things we can do to make ourselves feel better. Sending you virtual hugs and positive vibes!
CPU times: user 5.34 ms, sys: 2.37 ms, total: 7.71 ms
Wall time: 2.43 s
----
The suggestions are pretty similar, but less detailed.
It is, however, 5 times faster, which was surprising to me.
I thought instruct would give short answers and that it would be quicker because of that, but there seems to be maybe a more fundamental difference that's making it generate answers more quickly.
== Code generation 💻
Time for task number 3, which is a code generation task.
We're going to ask the models to create a function that iterates through a CSV file and prints out every other line.
[source, python]
----
question = "Can you write a Python function that reads a CSV file and prints out every other row?"
----
Over to you, chat:
[source, python]
----
print(turbo.predict(question))
----
.Output
[source, text]
----
Yes, here's an example of a Python function that reads a CSV file and prints out every other row:
```python
import csv
def print_every_other_row(csv_file):
with open(csv_file, 'r') as file:
reader = csv.reader(file)
row_num = 0
for row in reader:
if row_num % 2 == 0:
print(row)
row_num += 1
# Example usage
print_every_other_row('example.csv')
```
In this function, we use the `csv` module to read the CSV file. The `csv.reader` object allows us to iterate over the rows of the file. We keep a counter `row_num` to keep track of the row number, and if `row_num` is even (i.e., every other row), we print the row.
CPU times: user 5.41 ms, sys: 3.07 ms, total: 8.48 ms
Wall time: 9.23 s
----
That looks good to me and having tested it, the code does do what I asked!
Let's see what instruct comes up with:
[source, python]
----
print(turbo_instruct.predict(question))
----
.Output
[source, text]
----
def print_every_other_row(file_name):
# open the CSV file
with open(file_name, 'r') as csv_file:
# use the csv module to read the file
reader = csv.reader(csv_file)
# loop through each row in the file
for i, row in enumerate(reader):
# check if the row is odd
if i % 2 == 1:
# print the row
print(row)
CPU times: user 7.04 ms, sys: 3.57 ms, total: 10.6 ms
Wall time: 3.4 s
----
This time we get just the function.
I'd say this function is cleaner than the other one, but it has missed the `import csv` line that we need to use the CSV module.
== Analysing sentiment 😊
Let's make things a bit more interesting and see if the models can analyse the sentiment of a sentence.
[source, python]
----
from langchain.prompts import PromptTemplate
prompt = PromptTemplate.from_template("Analyse the sentiment of the following text: {text}")
question = prompt.format(text="The DuckDB team is happy to announce the latest DuckDB release (0.9.0).")
----
We're gonna run this one three times each to see if the responses differ between runs:
[source, python]
----
for _ in range(0,3):
print(turbo.predict(question))
----
.Output
[source, text]
----
The sentiment of the given text is positive.
The sentiment of the given text is positive.
The sentiment of the following text is positive. The use of words like "happy" and "announce" indicates a positive sentiment. Additionally, the mention of the latest release suggests excitement and satisfaction.
CPU times: user 16.7 ms, sys: 5.17 ms, total: 21.8 ms
Wall time: 5.88 s
----
And instruct:
[source, python]
----
for _ in range(0,3):
print(turbo_instruct.predict(question))
----
.Output
[source, text]
----
Positive
Positive
Positive
CPU times: user 15.9 ms, sys: 4.64 ms, total: 20.6 ms
Wall time: 1.21 s
----
There's quite a big difference in the responses this time.
Chat returns a full sentence every time, including an explanation on the 3rd try.
Instruct returns a single word each time.
== Summarising a Wikipedia page 📄
And now it's time for our last task - summarising a Wikipedia page.
And that page is going to be the one about the Laver Cup, a team tennis tournament that was played last weekend.
[source, python]
----
from langchain.document_loaders import WikipediaLoader
docs = WikipediaLoader(query="Laver Cup", load_max_docs=1).load()
docs[0].page_content
----
Below is a sample of the data on that page:
.Output
[source, text]
----
'The Laver Cup is an international indoor hard court men\'s team tennis tournament between Team Europe and Team World, the latter of which is composed of players from all other continents except Europe. Usually held annually since 2017, the tournament is intended to be the Ryder Cup of the tennis world. It normally takes place two weeks after the US Open, with the location rotating between various host cities (that usually do not have an ATP Tour event); alternating yearly between European cities and cities in the rest of the world. In addition to the guaranteed participation fees which are based upon the players\' ATP rankings, each member of the winning team gets $250,000 in prize money, but the tournament itself does not count towards the players\' point totals in the ATP Tour for that year.In May 2019, the Laver Cup became an officially sanctioned ATP Tour event.'
----
There's a lot more after that, but let's see how well our LLMs get on if we ask them to find 5 interesting things from the page.
[source, python]
----
prompt = PromptTemplate.from_template("Give me 5 interesting things from this text: {text}")
question = prompt.format(text=docs[0].page_content)
----
Chat, you're up:
[source, python]
----
print(turbo.predict(question))
----
.Output
[source, text]
----
1. The Laver Cup is an international indoor hard court men's team tennis tournament that takes place annually since 2017.
2. The tournament is intended to be the Ryder Cup of the tennis world, modeled after the biennial golf tournament between the United States and Europe.
3. The location of the tournament rotates between various host cities, alternating yearly between European cities and cities in the rest of the world.
4. Matches during the Laver Cup tournament differ from conventional 3-set matches played on the ATP Tour, with a 10-point "match tiebreak" being played instead of a deciding final set.
5. The tournament is named after Australian tennis player Rod Laver, who is considered one of the greatest players in the sport's history and has achieved the Grand Slam twice.
CPU times: user 32.7 ms, sys: 4.44 ms, total: 37.2 ms
Wall time: 8.87 s
----
And now instruct:
[source, python]
----
print(turbo_instruct.predict(question))
----
.Output
[source, text]
----
1. The Laver Cup is named after Australian tennis legend Rod Laver, who is widely regarded as one of the greatest players in the sport's history.
2. The tournament is intended to be the equivalent of the Ryder Cup in golf, with the best players from Europe competing against the best players from the rest of the world.
3. Matches during the Laver Cup follow a unique format, with a "match tiebreak" being played if the match is tied at one set all.
4. The winning team receives a prize of $250,000, in addition to guaranteed participation fees based on players' ATP rankings.
5. The idea for the Laver Cup was inspired by Roger Federer, and was created in partnership with his management company, a Brazilian businessman, and Tennis Australia.
CPU times: user 17.3 ms, sys: 17.7 ms, total: 35 ms
Wall time: 2.11 s
----
Both results are pretty good and have pulled out the main points.
Interestingly, it seems like the Instruct model might not stick around for long if the following tweet is anything to go by:
.Logan Kilpatrick tweet
image::{{<siteurl>}}/uploads/2023/09/logan-kilpatrick-tweet.png[width="500px"] | In this post, we'll learn about the differences between the GPT 3.5 chat and instruct large language models. | uploads/2023/09/turbo-banner.png | [
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] | openai-gpt-chat-vs-instruct | https://markhneedham.com/blog/2023/09/29/openai-gpt-chat-vs-instruct |
false | 2023-09-07 00:44:37 | How to run a Kotlin script | [
"kotlin",
"til"
] | [
"TIL"
] | :icons: font
I was recently helping Tim get a https://github.com/tlberglund/pinot-movie-ratings[Pinot data-loading Kotlin script^] working and it took me a while to figure out the best way to run it.
In this blog post, I'll share the solution we came up with.
But first things first, we need to install Kotlin if it's not already installed.
I used a library called https://sdkman.io/[SDKMAN6] for all things JVM, so I'm gonna run the following command:
[source, bash]
----
sdk install kotlin
----
And now for the Kotlin script.
Our script prints out a JSON document representing a person:
.script.kts
[source, kotlin]
----
@file:DependsOn("com.google.code.gson:gson:2.8.6") // <.>
import com.google.gson.Gson
data class Person(val name: String)
class Blog {
companion object {
@JvmStatic
fun main(args: Array<String>) {
println(
Gson().toJson(Person("Mark Needham"))
)
}
}
}
println("Script started")
println("Before main function call")
Blog.main(arrayOf()) // <.>
println("After main function call")
----
<.> Install GSON as a dependency
<.> Run the `Blog` main class
After trying a few different approaches, I came https://github.com/kscripting/kscript[kscript^], a wrapper around `kotlinc` that takes care of compilation and dependency management for us.
We can install it like this:
[source, bash]
----
sdk install kscript
----
And then run our script:
[source, bash]
----
kscript script.kts
----
.Output
[source, text]
----
[kscript] Resolving com.google.code.gson:gson:2.8.6...
Script started
Before main function call
{"name":"Mark Needham"}
After main function call
----
And that's it! | In this post, we'll learn how to run a Kotlin script. | uploads/2023/09/kotlin-banner.png | [
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] | how-to-run-kotlin-script | https://markhneedham.com/blog/2023/09/07/how-to-run-kotlin-script |
false | 2023-09-12 00:44:37 | kcat: SASL - Java JAAS configuration is not supported | [
"kcat",
"kafka",
"til"
] | [
"TIL"
] | :icons: font
I've been updating the StarTree Kafka SASL recipe to use Pinot 0.12 and ran into an error while trying to have it use `kcat` to ingest data into Kafka.
In this blog post, we'll learn how I did this.
The initial recipe was ingesting data into Kafka using `kafka-console-consumer.sh`, which uses the Java Kafka client.
I'm using this Kafka client config file:
.kafka-config/kafka_client.conf
[source, properties]
----
security.protocol=SASL_PLAINTEXT
sasl.mechanism=PLAIN
sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required \
username="alice" \
password="alice-secret";
----
And, we use this script to ingest data from a data generator:
[source, bash]
----
python datagen.py |
docker exec -i kafka-sasl /opt/kafka/bin/kafka-console-consumer.sh \
--bootstrap-server localhost:9093 \
--consumer.config /etc/kafka/kafka_client.conf \
--topic events \
--from-beginning
----
This all works fine, so I tried to update the script to use `kcat` instead:
[source, bash]
----
python datagen.py |
kcat -P -b localhost:9092 -F kafka-config/kafka_client.conf -t events
----
But this time it's not happy and we see the following error:
.Output
[source, text]
----
% Reading configuration from file kafka-config/kafka_client.conf
% ERROR: kafka-config/kafka_client.conf:3: Java JAAS configuration is not supported, see https://github.com/edenhill/librdkafka/wiki/Using-SASL-with-librdkafka for more information.
----
ChatGPT helped me translate my original configuration to the following:
.kafka-config/kafka_client_kcat.conf
[source, properties]
----
security.protocol=SASL_PLAINTEXT
sasl.mechanisms=PLAIN
sasl.username=alice
sasl.password=alice-secret
----
And then I updated my script to use that file:
```bash
python datagen.py |
kcat -P -b localhost:9092 -F kafka-config/kafka_client_kcat.conf -t events
```
If we run that for a few seconds, we can then check if any data has been ingested:
[source, text]
----
kcat -C -b localhost:9092 \
-F kafka-config/kafka_client_kcat.conf \
-t events \
-c 5
----
.Output
[source, json]
----
{"ts": 1694529896154, "uuid": "1c192aca-05e4-497c-823c-6de697b38ebc", "count": 153}
{"ts": 1694529896154, "uuid": "1336fc02-6e2a-47da-bbdb-b960356f6cac", "count": 674}
{"ts": 1694529896154, "uuid": "ce4878f0-129a-4aad-b65f-d06b5817c5cd", "count": 231}
{"ts": 1694529896154, "uuid": "006d29dd-e6a2-4dec-b9dd-49ee72406aeb", "count": 287}
{"ts": 1694529896154, "uuid": "86693165-5194-41a5-9531-c61453b0d565", "count": 931}
----
Success! | In this post, we'll learn how to ingest data into Kafka using kcat with SASL. | uploads/2023/09/kcat-sasl-banner.png | [
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] | kcat-sasl-java-jaas-not-supported | https://markhneedham.com/blog/2023/09/12/kcat-sasl-java-jaas-not-supported |
false | 2023-09-14 00:44:37 | FAISS: Exploring Approximate Nearest Neighbours Cell Probe Methods | [
"python",
"faiss",
"til"
] | [
"TIL"
] | :icons: font
I've been learning about vector search in recent weeks and I came across FaceBook's https://faiss.ai/[FAISS library^].
I wanted to learn the simplest way to do approximate nearest neighbours, and that's what we'll be exploring in this blog post.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/iY7HuG1r5YM?si=rdN5fF22veQVpvk_" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
You can also find all the code at the https://github.com/mneedham/LearnDataWithMark/blob/main/faiss-ann/notebooks/ANN-Tutorial.ipynb[ANN-Tutorial.ipynb notebook^].
====
First things first, let's install some libraries:
[source, bash]
----
pip install faiss-cpu pandas numpy
----
We'll be using the following imports:
[source, python]
----
import faiss
import copy
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly_functions import generate_distinct_colors, zoom_in, create_plot, plot_points
----
https://github.com/mneedham/LearnDataWithMark/blob/main/faiss-ann/notebooks/plotly_functions.py[`plotly_functions`^] contains a bunch of helper functions for making it easier to create charts with plot.ly.
== Create vectors
We're going to create 10,000 2D vectors to keep things simple.
[source, python]
----
dimensions = 2
number_of_vectors = 10_000
vectors = np.random.random((number_of_vectors, dimensions)).astype(np.float32)
----
Next, let's create a search vector, whose neighbours we're going to find:
[source, python]
----
search_vector = np.array([[0.5, 0.5]])
----
== Creating a cell probe index
The simplest version of approximate nearest neighbours in FAISS is to use one of the cell probe methods.
These methods partition the vector space into a configurable number of cells using the K-means algorithm.
When we look for our search vector's neighbours, it's going to find the centroid closest to the search vector and then search for all the other vectors that belong to the same cell as that centroid.
We can create a cell probe index like this:
[source, python]
----
cells = 10
quantizer = faiss.IndexFlatL2(dimensions)
index = faiss.IndexIVFFlat(quantizer, dimensions, cells)
----
To create the centroids, we need to call the `train` function:
[source, python]
----
index.train(vectors)
----
We can then find the centroids by querying the quantizer::
[source, python]
----
centroids = index.quantizer.reconstruct_n(0, index.nlist)
centroids
----
.Output
[source, text]
----
array([[0.8503718 , 0.46587527],
[0.14201212, 0.80757564],
[0.831061 , 0.82165515],
[0.5756452 , 0.54481953],
[0.5543639 , 0.1812697 ],
[0.84584594, 0.16083847],
[0.259557 , 0.5097532 ],
[0.23731372, 0.12491277],
[0.47171366, 0.8513159 ],
[0.08305518, 0.30214617]], dtype=float32)
----
== Visualising cells and centroids
Next, let's look at how we can visualise how the vector space has been split.
We can work out which cell each vector has been assigned to by calling the `search` function on the quantizer:
[source, python]
----
_, cell_ids = index.quantizer.search(vectors, k=1)
cell_ids = cell_ids.flatten()
cell_ids[:10]
----
.Output
[source, python]
----
array([0, 4, 3, 1, 1, 8, 9, 4, 0, 9])
----
So far so good.
Now let's create a plot visualising that:
[source, python]
----
color_map = generate_distinct_colors(index.nlist) # <.>
fig_cells = create_plot()
unique_ids = np.unique(cell_ids)
for uid in unique_ids: # <.>
mask = (cell_ids == uid)
masked_vectors = vectors[mask]
plot_points(fig_cells, masked_vectors, color_map[uid], "Cell {}".format(uid), size=6) # <.>
plot_points(fig_cells, centroids, symbol="diamond-tall", color="black", size=15, showlegend=False) # <.>
plot_points(fig_cells, search_vector, symbol="x", color="black", size=15, label="Search Vector") # <.>
fig_cells
----
<.> Get a list of unique colours for each cell
<.> Iterate over the cells
<.> Plot each vector with the colour assigned to its cell id
<.> Plot the centroid of each cell
<.> Plot the search vector
The resulting visualisation is shown below:
.Vectors and their cell assignments
image::{{<siteurl>}}/uploads/2023/09/ann-plot.png[]
When creating the index, we need to specify how many partitions (or cells) we want to divide the vector space into.
== Searching for our vector
It's time to search for our vector.
We'll start by adding the vectors to the index:
[source, python]
----
index.add(vectors)
----
And now let's call the `search` function:
[source, python]
----
distances, indices = index.search(search_vector, k=10)
df_ann = pd.DataFrame({
"id": indices[0],
"vector": [vectors[id] for id in indices[0]],
"distance": distances[0],
})
df_ann
----
.df_ann
[format="csv", options="header"]
|===
include::content/2023/09/14/df_ann.csv[]
|===
We've got a bunch of vectors that are very close to the search vector.
When we ran the `search` function, FAISS first looked for the cell in which it needed to search.
We can figure out which cell it used by asking the quantizer:
[source, python]
----
_, search_vectors_cell_ids = index.quantizer.search(search_vector, k=1)
unique_searched_ids = search_vectors_cell_ids[0]
unique_searched_ids
----
.Output
[source, text]
----
array([3])
----
So the nearest cell to `0.5, 0.5` is the one with index 3.
If we wanted to find the nearest two cells, we could pass in a different `k` value.
We can visualise the nearest neighbours that it's found by running the following code:
[source, python]
----
fig_search = create_plot()
for uid in unique_searched_ids: # <.>
mask = (cell_ids == uid)
masked_vectors = vectors[mask]
plot_points(fig_search, masked_vectors, color_map[uid], label="Cell {}".format(uid)) # <.>
plot_points(fig_search, centroids[uid].reshape(1, -1), symbol="diamond-tall", color="black", size=10, label="Centroid for Cell {}".format(uid), showlegend=False) # <.>
plot_points(fig_search, points=search_vector, color='black', label="Search Vector", symbol="x", size=10)
ann_vectors = np.array(df_ann["vector"].tolist())
plot_points(fig_search, points=ann_vectors, color='black', label="Approx Nearest Neighbors") # <.>
fig_search
----
<.> Iterate over the cells used in the search (i.e. only cell with index=3)
<.> Plot the vectors in this cell
<.> Plot the centroid for the cell
<.> Plot the nearest neighbours
The resulting visualisation is shown below:
.Approximate nearest neighbours
image::{{<siteurl>}}/uploads/2023/09/ann-search-plot.png[]
== Brute Force vs ANN
It looks like ANN has done pretty well, but let's compare it to the brute force approach where we compare the search vector with every other vector to find its neighbours.
We can create a brute force index like this:
[source, python]
----
brute_force_index = faiss.IndexFlatL2(dimensions)
brute_force_index.add(vectors)
----
And then search like this:
[source, python]
----
distances, indices = brute_force_index.search(search_vector, k=10)
pd.DataFrame({
"id": indices[0],
"vector": [vectors[id] for id in indices[0]],
"distance": distances[0],
"cell": [cell_ids[id] for id in indices[0]]
})
----
.Brute Force
[format="csv", options="header"]
|===
include::content/2023/09/14/brute_force.csv[]
|===
The results are the same as we got with ANN and we can see that all the neighbours belong to cell 3, which was the one used by ANN.
We can actually tweak ANN to search across more than 1 cell by setting the `nprobe` attribute.
For example, if we wanted to search the two closest cells, we would do this:
[source,python]
----
index.nprobe = 2
----
And then re-run the search code above.
The result for this dataset wouldn't change since it's relatively small and has low dimensionality, but with bigger datasets this is a useful thing to play around with. | In this post, we'll learn how to do approximate nearest neighbours with FaceBook's FAISS vector search library. | uploads/2023/09/faiss-banner.png | [
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] | faiss-approximate-nearest-neighbors-cell-probe | https://markhneedham.com/blog/2023/09/14/faiss-approximate-nearest-neighbors-cell-probe |
false | 2023-08-20 00:44:37 | JupyterLab 4.0.5: Adding execution time to cell | [
"jupyterlab",
"til"
] | [
"TIL"
] | :icons: font
I've been using Jupyter Lab notebooks in some of my recent videos on https://www.youtube.com/@learndatawithmark/videos[Learn Data with Mark^] and I wanted to show cell execution timings so that viewers would have an idea of how long things were taking.
I thought I'd need to use a custom timer, but it turns out there's quite a nice plug-in, which we'll learn about in this blog post.
The plug-in is called https://github.com/deshaw/jupyterlab-execute-time[`jupyterlab-execute-time`^] and it shows a live view of the time that a cell takes to execute, as well as showing the execution time afterward.
.The jupyterlab-execute-time plugin-in in action
image::{{<siteurl>}}/uploads/2023/08/cell-execution-time.gif[]
We can install the library directly using pip:
[source, bash]
----
pip install jupyterlab_execute_time
----
Or Poetry, if that's your thing:
[source, bash]
----
poetry add jupyterlab_execute_time
----
Alternatively, we can use the extension manager inside Jupyter Lab, which you can launch by typing `Cmd + Shift + X` on a Mac.
Once it pops up, search for `jupyterlab-execute-time`, as shown below:
.Searching for the jupyterlab-execute-time plug-in
image::{{<siteurl>}}/uploads/2023/08/time-extension-manual-search.png[]
And then click on 'Install'.
Regardless of the installation option used, you should then see the plugin under the 'Installed' section:
.jupyterlab-execute-time plug-in installed
image::{{<siteurl>}}/uploads/2023/08/time-extension-installed.png[]
And here's a screenshot showing the timings for a few cells in a notebook that I'm currently working on:
.Multiple cell timings
image::{{<siteurl>}}/uploads/2023/08/jupyterlab-time-execution-cell.png[] | In this post, we'll learn how to add execution time to cells in JupyterLab. | uploads/2023/07/jupyterlab-banner.png | [
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] | jupyterlab-time-cell-execution | https://markhneedham.com/blog/2023/08/20/jupyterlab-time-cell-execution |
false | 2023-08-21 00:44:37 | Python: TypeError: Instance and class checks can only be used with @runtime_checkable protocols | [
"python",
"til"
] | [
"TIL"
] | :icons: font
I've been playing around with https://www.trychroma.com/[ChromaDB^] and I wanted to programatically get a list of the embedding functions, which was a little trickier thna I expected.
In this blog post, we'll explore how I failed and then succeeded at this task.
But first, let's install ChromaDB:
[source, bash]
----
pip install chromadb
----
The embedding functions live in the `chromadb.utils.embedding_functions` module.
So my first thought was that I could list all the things defined in that module and then check which ones were a sub class of `EmbeddingFunction`:
[source, python]
----
import chromadb.utils.embedding_functions as ef
[
cls_name in dir(ef)
for cls_name in dir(ef)
if issubclass(getattr(ef, cls_name), ef.EmbeddingFunction)
]
----
If we run this, we'll see the following error:
[source, text]
----
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 4, in <listcomp>
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/chroma-example-SSMwZxEc-py3.11/lib/python3.11/site-packages/typing_extensions.py", line 662, in __subclasscheck__
raise TypeError(
TypeError: Instance and class checks can only be used with @runtime_checkable protocols
----
From my understanding, we'd need `EmbeddingFunction` to have a `@runtime_checkable` annotation for this technique to work.
ChatGPT guided me to the following alternative, where we check if `EmbeddingFunction` exists in in the base classes of a given class via the `__bases__` attribute:
[source, python]
----
import inspect
[
cls_name
for cls_name in dir(ef)
if inspect.isclass(getattr(ef,cls_name)) and ef.EmbeddingFunction in getattr(ef,cls_name).__bases__
]
----
If we run that piece of code, we'll see the following output:
[source, text]
----
['CohereEmbeddingFunction',
'GooglePalmEmbeddingFunction',
'GoogleVertexEmbeddingFunction',
'HuggingFaceEmbeddingFunction',
'InstructorEmbeddingFunction',
'ONNXMiniLM_L6_V2',
'OpenAIEmbeddingFunction',
'SentenceTransformerEmbeddingFunction',
'Text2VecEmbeddingFunction']
---- | In this post, we'll learn how to work around an exception when trying to find Python classes that extend a sub class. | uploads/2023/08/python-runtime-banner.png | [
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] | python-typeerrr-instance-class-check-runtime-checkable | https://markhneedham.com/blog/2023/08/21/python-typeerrr-instance-class-check-runtime-checkable |
false | 2023-08-23 00:44:37 | pyarrow: pyarrow.lib.ArrowNotImplementedError: Filter argument must be boolean type | [
"python",
"pyarrow",
"til"
] | [
"TIL"
] | :icons: font
I wanted to filter a table in pyarrow table recently and ran into troubles when trying to use the filter syntax that I'm used to from DuckDB.
In this blog post I'll explain my mistake and how to fix it.
First, let's install `pyarrow`:
[source, bash]
----
pip install pyarrow
----
And now we're going to create a table that has a few countries and their corresponding continents:
[source, python]
----
import pyarrow as pa
countries = pa.Table.from_arrays(
[
pa.array(['India', 'Pakistan', 'Belgium', 'Finland'], pa.string()),
pa.array(['Asia', 'Asia', 'Europe', 'Europe'], pa.string())
],
names=['Country', 'Continent']
)
----
Let's say we want to find just the rows where the continent is Europe.
I initially tried to do that using the following syntax:
[source, python]
----
countries.filter("Continent = 'Europe'")
----
.Output
[source, text]
----
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pyarrow/table.pxi", line 3154, in pyarrow.lib.Table.filter
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/ch07-YVG4Qrie-py3.11/lib/python3.11/site-packages/pyarrow/compute.py", line 259, in wrapper
return func.call(args, options, memory_pool)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/_compute.pyx", line 367, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Filter argument must be boolean type
----
Hmmm, that didn't work so well.
Instead we need to construct a filter predicate using some functions from the `pyarrow.compute` module, so let's import that:
[source, python]
----
import pyarrow.compute as pc
----
And now we have (at least) two ways to write the filtering statement.
We could use `pc.equal` like this:
[source, python]
----
countries.filter(pc.equal(countries["Continent"], "Europe"))
----
Or `pc.field` like this:
[source, python]
----
countries.filter(pc.field("Continent") == "Europe")
----
Either way we get the same result:
.Output
[source, text]
-----
pyarrow.Table
Country: string
Continent: string
----
Country: [["Belgium","Finland"]]
Continent: [["Europe","Europe"]]
----- | In this post, we'll learn how to filter columns using Apache Arrow in Python. | uploads/2023/08/arrow-filter-banner.png | [
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] | pyarrow-filter-argument-boolean-type | https://markhneedham.com/blog/2023/08/23/pyarrow-filter-argument-boolean-type |
false | 2023-01-06 02:44:37 | Exporting CSV files to Parquet file format with Pandas, Polars, and DuckDB | [
"python",
"polars",
"pandas",
"duckdb"
] | [
"Python"
] | I was recently trying to convert a CSV file to Parquet format and came across https://stackoverflow.com/questions/50604133/convert-csv-to-parquet-file-using-python[a StackOverflow post^] that described a collection of different options.
My CSV file was bigger than the amount of memory I had available, which ruled out some of the methods.
In this blog post we're going to walk through some options for exporting big CSV files to Parquet format.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/aexszHMKdy8" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
====
== The Dataset
We're going to be exploring this problem using a CSV file that contains the rankings of male tennis players going back to the 1970s.
This file is 270MB in size and contains about 12 million records.
A sample is shown below:
[source, bash]
----
head -n10 output/atp_rankings.csv
----
.Output
[source, text]
----
ranking_date,rank,player,points
19900101,1,100656,2913
19900101,2,101414,2279
19900101,3,101222,2111
19900101,4,100763,1398
19900101,5,100581,1354
19900101,6,102021,1328
19900101,7,101381,1217
19900101,8,101736,1160
19900101,9,101309,1039
----
== Pandas
The simplest way to convert this file to Parquet format would be to use Pandas, as shown in the script below:
.scripts/duck_to_parquet.py
[source, python]
----
import pandas as pd
pd.read_csv("/output/atp_rankings.csv").to_parquet(
"/output/pandas_atp_rankings.parquet")
----
This code loads the file into memory before converting it to Parquet format.
We're going to run it using a Docker image that I've created that contains a Python runtime along with a bunch of pre-installed libraries.
The Dockerfile is shown below:
.Dockerfile
[source, text]
----
FROM python:3.11
WORKDIR /usr/src/app
COPY requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD [ "python"]
----
`requirements.txt` contains the following:
requirements.txt
[source, text]
----
pandas
fastparquet
polars
duckdb
----
Let's run the Pandas code with 1GB of RAM assigned to the Docker container:
[source, bash]
----
docker run -it \
-v $PWD/scripts:/scripts \
-v $PWD/output:/output \
-m 1024m \
--name python-tennis \
--rm python-tennis:0.0.1 \
python /scripts/pandas_to_parquet.py
----
This will take 10-15 seconds and at the end of that we'll have the data in Parquet format:
[source, bash]
----
du -h output/pandas_atp_rankings.parquet
----
.Output
[source, text]
----
51M output/pandas_atp_rankings.parquet
----
So far, so good.
But what about if we reduce the memory to 100MB?
[source, bash]
----
docker run -it \
-v $PWD/scripts:/scripts \
-v $PWD/output:/output \
-m 100m \
--name python-tennis \
--rm python-tennis:0.0.1 \
python /scripts/pandas_to_parquet.py
----
This time the container exits with the following exit code:
[source, bash]
----
echo $?
----
.Output
[source, text]
----
137
----
This is the error code that indicates Docker terminated the container due to an out of memory exception.
Let's see if we can fix that using some other tools.
== Polars
First up is https://pola-rs.github.io/polars-book/user-guide/[Polars^], a DataFrames library implemented in Rust using Apache Arrow Columnar Format as the memory model.
Polars has a neat function called `scan_csv` that lets us process CSV files without loading everything into memory.
The equivalent script to convert CSV to Parquet therefore looks like this:
.scripts/polars_to_parquet.py
[source, python]
----
import polars as pl
pl.scan_csv("/output/atp_rankings.csv").sink_parquet(
"/output/polars_atp_rankings.parquet",
compression="zstd",
row_group_size=100_000
)
----
The `sink_parquet` function lets us define the compression algorithm to use as well as the size to use for each group size, which is nice.
There are some other parameters you can set as well, but I left those as they were.
We're going to run this script using Docker, but we're also going to track the memory usage of the container so we can see what's going on.
We'll do this using the following function that is a wrapper around `docker stats`:
[source, bash]
----
check_memory_usage() {
maxTime=${1:=30}
start=$(date +%s)
hasStarted=0
while true; do
if [ $(date +%s) -gt `expr ${start} + ${maxTime}` ]; then
break;
fi
stats=$(docker stats --format '{{.Name}}\t{{.MemPerc}}\t{{.MemUsage}}' --no-stream)
if [ -z "${stats}" ]; then
if [ ${hasStarted} -eq 1 ]; then
break;
fi
continue;
fi
hasStarted=1
echo "$(date "+%Y-%m-%d %H:%M:%S")\t${stats}"
done
}
----
Now let's run the Polars code:
[source, bash]
----
docker run -it -d \
-v $PWD/scripts:/scripts \
-v $PWD/output:/output \
-m 100m \
--name python-tennis \
--rm python-tennis:0.0.1 \
python /scripts/polars_to_parquet.py &>/dev/null && check_memory_usage
----
If we run this command, we'll see the following output:
.Output
[source, text]
----
2023-01-06 17:10:58 python-tennis 62.79% 62.79MiB / 100MiB
2023-01-06 17:11:00 python-tennis 72.71% 72.71MiB / 100MiB
2023-01-06 17:11:02 python-tennis 74.48% 74.48MiB / 100MiB
2023-01-06 17:11:04 python-tennis 74.69% 74.69MiB / 100MiB
2023-01-06 17:11:06 python-tennis 82.39% 82.39MiB / 100MiB
2023-01-06 17:11:08 python-tennis 82.64% 82.64MiB / 100MiB
2023-01-06 17:11:10 python-tennis 82.65% 82.65MiB / 100MiB
----
And we have another Parquet file!
[source, bash]
----
du -h output/polars_atp_rankings.parquet
----
.Output
[source, text]
----
25M output/polars_atp_rankings.parquet
----
== DuckDB
Finally, let's have a look at how to do the same thing with DuckDB, an in-process SQL OLAP database management system.
A script that uses DuckDB's Python client looks like this:
.scripts/duck_to_parquet.py
[source, python]
----
import duckdb
con = duckdb.connect(database=':memory:')
con.execute("SET memory_limit='100MB'")
con.execute("""
COPY (SELECT * FROM '/output/atp_rankings.csv')
TO '/output/duck_atp_rankings.parquet'
(FORMAT PARQUET, CODEC 'SNAPPY', ROW_GROUP_SIZE 100000);
""")
----
I found when using DuckDB that I needed to tell it the memory limit explicitly, otherwise it was trying to use more than that and I was ending up with incomplete and invalid Parquet files.
As with Polars, we can define the compression algorithm (called codec in this case) and the row group size.
Let's give this one a whirl:
[source, bash]
----
docker run -it -d \
-v $PWD/scripts:/scripts \
-v $PWD/output:/output \
-m 100m \
--name python-tennis \
--rm python-tennis:0.0.1 \
python /scripts/duck_to_parquet.py &>/dev/null && check_memory_usage
----
.Output
[source, text]
----
2023-01-06 17:15:47 python-tennis 77.29% 77.29MiB / 100MiB
2023-01-06 17:15:49 python-tennis 98.82% 98.82MiB / 100MiB
2023-01-06 17:15:51 python-tennis 98.59% 98.59MiB / 100MiB
2023-01-06 17:15:53 python-tennis 99.47% 99.47MiB / 100MiB
2023-01-06 17:15:55 python-tennis 98.94% 98.94MiB / 100MiB
----
And we have one final Parquet file!
[source, bash]
----
du -h output/duck_atp_rankings.parquet
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.Output
[source, text]
----
51M output/duck_atp_rankings.parquet
----
This file is a bit bigger than the Polars one, which I think is because we used a different compression format.
== Conclusion.
Pandas is a great choice for converting CSV files that fit in memory, but if we want to work with really big files Polars and DuckDB are ready and waiting to step in - both work equally well in my experiments! | In this post we'll learn how to export bigger-than-memory CSV files from CSV to Parquet format using Pandas, Polars, and DuckDB. | uploads/2023/01/csv-parquet-banner.png | [
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] | export-csv-parquet-pandas-polars-duckdb | https://markhneedham.com/blog/2023/01/06/export-csv-parquet-pandas-polars-duckdb |
false | 2023-01-24 02:44:37 | Flink SQL: Could not execute SQL statement. Reason: java.io.IOException: Corrupt Debezium JSON message | [
"flink",
"kafka"
] | [
"Flink"
] | As part of a https://github.com/mneedham/pizza-shop-workshop[JFokus workshop^] that I'm working on I wanted to create a Flink table around a Kafka stream that I'd populated from MySQL with help from Debezium.
In this blog post I want to show how to do this and explain an error that I encountered along the way.
To start, we have a products table in MySQL that's publishing events to Apache Kafka.
We can see the fields in this event by running the following command:
[source, bash]
----
kcat -C -b localhost:29092 -t mysql.pizzashop.products -c1 | jq 'keys'
----
[source, json]
----
[
"payload",
"schema"
]
----
Let's first have a look at the payload:
[source, bash]
----
kcat -C -b localhost:29092 -t mysql.pizzashop.products -c1 | jq '.payload'
----
.Output
[source, json]
----
{
"before": null,
"after": {
"id": 1,
"name": "Moroccan Spice Pasta Pizza - Veg",
"description": "A pizza loaded with a spicy combination of Harissa sauce and delicious pasta.",
"category": "veg pizzas",
"price": 335,
"image": "https://www.dominos.co.in//files/items/MoroccanSpicePPVG_N.jpg",
"created_at": "2023-01-24T12:53:48Z",
"updated_at": 1674564828000
},
"source": {
"version": "1.8.1.Final",
"connector": "mysql",
"name": "mysql",
"ts_ms": 1674565167817,
"snapshot": "true",
"db": "pizzashop",
"sequence": null,
"table": "products",
"server_id": 0,
"gtid": null,
"file": "binlog.000002",
"pos": 156,
"row": 0,
"thread": null,
"query": null
},
"op": "r",
"ts_ms": 1674565167827,
"transaction": null
}
----
`before` is null because there wasn't anything there before.
If we'd done an update to this record we'd see a `before` entry that contained all the fields that are under the `after` property.
And now, we'll zoom in on the schema:
[source, bash]
----
kcat -C -b localhost:29092 -t mysql.pizzashop.products -c1 | jq -c '.schema'
----
.Output
[source, json]
----
{"type":"struct","fields":[{"type":"struct","fields":[{"type":"int64","optional":false,"field":"id"},{"type":"string","optional":true,"field":"name"},{"type":"string","optional":true,"field":"description"},{"type":"string","optional":true,"field":"category"},{"type":"double","optional":true,"field":"price"},{"type":"string","optional":true,"field":"image"},{"type":"string","optional":true,"name":"io.debezium.time.ZonedTimestamp","version":1,"field":"created_at"},{"type":"int64","optional":true,"name":"org.apache.kafka.connect.data.Timestamp","version":1,"default":0,"field":"updated_at"}],"optional":true,"name":"mysql.pizzashop.products.Value","field":"before"},{"type":"struct","fields":[{"type":"int64","optional":false,"field":"id"},{"type":"string","optional":true,"field":"name"},{"type":"string","optional":true,"field":"description"},{"type":"string","optional":true,"field":"category"},{"type":"double","optional":true,"field":"price"},{"type":"string","optional":true,"field":"image"},{"type":"string","optional":true,"name":"io.debezium.time.ZonedTimestamp","version":1,"field":"created_at"},{"type":"int64","optional":true,"name":"org.apache.kafka.connect.data.Timestamp","version":1,"default":0,"field":"updated_at"}],"optional":true,"name":"mysql.pizzashop.products.Value","field":"after"},{"type":"struct","fields":[{"type":"string","optional":false,"field":"version"},{"type":"string","optional":false,"field":"connector"},{"type":"string","optional":false,"field":"name"},{"type":"int64","optional":false,"field":"ts_ms"},{"type":"string","optional":true,"name":"io.debezium.data.Enum","version":1,"parameters":{"allowed":"true,last,false,incremental"},"default":"false","field":"snapshot"},{"type":"string","optional":false,"field":"db"},{"type":"string","optional":true,"field":"sequence"},{"type":"string","optional":true,"field":"table"},{"type":"int64","optional":false,"field":"server_id"},{"type":"string","optional":true,"field":"gtid"},{"type":"string","optional":false,"field":"file"},{"type":"int64","optional":false,"field":"pos"},{"type":"int32","optional":false,"field":"row"},{"type":"int64","optional":true,"field":"thread"},{"type":"string","optional":true,"field":"query"}],"optional":false,"name":"io.debezium.connector.mysql.Source","field":"source"},{"type":"string","optional":false,"field":"op"},{"type":"int64","optional":true,"field":"ts_ms"},{"type":"struct","fields":[{"type":"string","optional":false,"field":"id"},{"type":"int64","optional":false,"field":"total_order"},{"type":"int64","optional":false,"field":"data_collection_order"}],"optional":true,"field":"transaction"}],"optional":false,"name":"mysql.pizzashop.products.Envelope"}
----
There's a lot there, let's get a list of fields in the schema:
[source, bash]
----
kcat -C -b localhost:29092 -t mysql.pizzashop.products -c1 | jq -c '.schema .fields[] .field'
----
.Output
[source, json]
----
"before"
"after"
"source"
"op"
"ts_ms"
"transaction"
----
Let's select the schema for the `before` field:
[source, bash]
----
kcat -C -b localhost:29092 -t mysql.pizzashop.products -c1 | jq '.schema .fields[] | select(.field == "before")'
----
.Output
[source, json]
----
{
"type": "struct",
"fields": [
{
"type": "int64",
"optional": false,
"field": "id"
},
{
"type": "string",
"optional": true,
"field": "name"
},
{
"type": "string",
"optional": true,
"field": "description"
},
{
"type": "string",
"optional": true,
"field": "category"
},
{
"type": "double",
"optional": true,
"field": "price"
},
{
"type": "string",
"optional": true,
"field": "image"
},
{
"type": "string",
"optional": true,
"name": "io.debezium.time.ZonedTimestamp",
"version": 1,
"field": "created_at"
},
{
"type": "int64",
"optional": true,
"name": "org.apache.kafka.connect.data.Timestamp",
"version": 1,
"default": 0,
"field": "updated_at"
}
],
"optional": true,
"name": "mysql.pizzashop.products.Value",
"field": "before"
}
----
Next, we're going to launch the Flink CLI and create a `Products` table with the `mysql.pizzashop.products` topic as its source:
[source, sql]
----
CREATE TABLE Products (
`event_time` TIMESTAMP(3) METADATA FROM 'timestamp',
`partition` BIGINT METADATA VIRTUAL,
`offset` BIGINT METADATA VIRTUAL,
`id` STRING,
`name` STRING,
`description` STRING,
`category` STRING,
`price` DOUBLE,
`image` STRING,
`createdAt` STRING
) WITH (
'connector' = 'kafka',
'topic' = 'mysql.pizzashop.products',
'properties.bootstrap.servers' = 'kafka:9092',
'properties.group.id' = 'testGroup',
'scan.startup.mode' = 'earliest-offset',
'format' = 'debezium-json'
);
----
.Output
[source, text]
----
[INFO] Execute statement succeed.
----
Now let's try to query the table:
[source, sql]
----
SELECT *
FROM Products;
----
This results in the following error:
.Output
[source, text]
----
[ERROR] Could not execute SQL statement. Reason:
java.io.IOException: Corrupt Debezium JSON message '{"schema":{"type":"struct","fields":[{"type":"struct","fields":[{"type":"int64","optional":false,"field":"id"},{"type":"string","optional":true,"field":"name"},{"type":"string","optional":true,"field":"description"},{"type":"string","optional":true,"field":"category"},{"type":"double","optional":true,"field":"price"},{"type":"string","optional":true,"field":"image"},{"type":"string","optional":true,"name":"io.debezium.time.ZonedTimestamp","version":1,"field":"created_at"},{"type":"int64","optional":true,"name":"org.apache.kafka.connect.data.Timestamp","version":1,"default":0,"field":"updated_at"}],"optional":true,"name":"mysql.pizzashop.products.Value","field":"before"},{"type":"struct","fields":[{"type":"int64","optional":false,"field":"id"},{"type":"string","optional":true,"field":"name"},{"type":"string","optional":true,"field":"description"},{"type":"string","optional":true,"field":"category"},{"type":"double","optional":true,"field":"price"},{"type":"string","optional":true,"field":"image"},{"type":"string","optional":true,"name":"io.debezium.time.ZonedTimestamp","version":1,"field":"created_at"},{"type":"int64","optional":true,"name":"org.apache.kafka.connect.data.Timestamp","version":1,"default":0,"field":"updated_at"}],"optional":true,"name":"mysql.pizzashop.products.Value","field":"after"},{"type":"struct","fields":[{"type":"string","optional":false,"field":"version"},{"type":"string","optional":false,"field":"connector"},{"type":"string","optional":false,"field":"name"},{"type":"int64","optional":false,"field":"ts_ms"},{"type":"string","optional":true,"name":"io.debezium.data.Enum","version":1,"parameters":{"allowed":"true,last,false,incremental"},"default":"false","field":"snapshot"},{"type":"string","optional":false,"field":"db"},{"type":"string","optional":true,"field":"sequence"},{"type":"string","optional":true,"field":"table"},{"type":"int64","optional":false,"field":"server_id"},{"type":"string","optional":true,"field":"gtid"},{"type":"string","optional":false,"field":"file"},{"type":"int64","optional":false,"field":"pos"},{"type":"int32","optional":false,"field":"row"},{"type":"int64","optional":true,"field":"thread"},{"type":"string","optional":true,"field":"query"}],"optional":false,"name":"io.debezium.connector.mysql.Source","field":"source"},{"type":"string","optional":false,"field":"op"},{"type":"int64","optional":true,"field":"ts_ms"},{"type":"struct","fields":[{"type":"string","optional":false,"field":"id"},{"type":"int64","optional":false,"field":"total_order"},{"type":"int64","optional":false,"field":"data_collection_order"}],"optional":true,"field":"transaction"}],"optional":false,"name":"mysql.pizzashop.products.Envelope"},"payload":{"before":null,"after":{"id":1,"name":"Moroccan Spice Pasta Pizza - Veg","description":"A pizza loaded with a spicy combination of Harissa sauce and delicious pasta.","category":"veg pizzas","price":335.0,"image":"https://www.dominos.co.in//files/items/MoroccanSpicePPVG_N.jpg","created_at":"2023-01-24T12:53:48Z","updated_at":1674564828000},"source":{"version":"1.8.1.Final","connector":"mysql","name":"mysql","ts_ms":1674565167817,"snapshot":"true","db":"pizzashop","sequence":null,"table":"products","server_id":0,"gtid":null,"file":"binlog.000002","pos":156,"row":0,"thread":null,"query":null},"op":"r","ts_ms":1674565167827,"transaction":null}}'.
----
A bit of Googling led me to https://stackoverflow.com/questions/74779675/flink-failed-to-deserialize-json-produced-by-debezium[this StackOverflow question^], but my issue didn't seem to match those on that thread.
Instead the problem is actually that we need to specify an extra property for the table, as described in https://nightlies.apache.org/flink/flink-docs-master/docs/connectors/table/formats/debezium/[the documentation^]:
[quote]
____
In order to interpret such messages, you need to add the option 'debezium-json.schema-include' = 'true' into above DDL WITH clause (false by default). Usually, this is not recommended to include schema because this makes the messages very verbose and reduces parsing performance.
____
Let's fix our table by adding `'debezium-json.schema-include' = 'true'` to the `WITH` part of the `CREATE` clause:
[source, sql]
----
DROP TABLE Products;
CREATE TABLE Products (
`event_time` TIMESTAMP(3) METADATA FROM 'timestamp',
`partition` BIGINT METADATA VIRTUAL,
`offset` BIGINT METADATA VIRTUAL,
`id` STRING,
`name` STRING,
`description` STRING,
`category` STRING,
`price` DOUBLE,
`image` STRING,
`createdAt` STRING
) WITH (
'connector' = 'kafka',
'topic' = 'mysql.pizzashop.products',
'properties.bootstrap.servers' = 'kafka:9092',
'properties.group.id' = 'testGroup',
'scan.startup.mode' = 'earliest-offset',
'format' = 'debezium-json',
'debezium-json.schema-include' = 'true'
);
----
If we query the table again, this time it works! | In this post we'll learn how to work around the Corrupt Debezium JSON message when using Flink SQL. | uploads/2023/01/flink-corruption-banner.png | [
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] | flink-sql-could-not-execute-sql-statement-corrupt-debezium-message | https://markhneedham.com/blog/2023/01/24/flink-sql-could-not-execute-sql-statement-corrupt-debezium-message |
false | 2023-01-24 02:44:37 | Flink SQL: Exporting nested JSON to a Kafka topic | [
"flink",
"kafka"
] | [
"Flink"
] | I've been playing around with Flink as part of https://github.com/mneedham/pizza-shop-workshop[a workshop that I'm doing at JFokus^] in a couple of weeks and I wanted to export some data from Flink to Apache Kafka in a nested format.
In this blog post we'll learn how to do that.
== Setup
We're going to be using the following Docker Compose config:
.docker-compose.yml
[source, yaml]
----
include::content/2023/01/24/docker/docker-compose.yml[]
----
The Flink config used here is adapted Francesco Tisiot's repository at https://github.com/aiven/sql-cli-for-apache-flink-docker/[github.com/aiven/sql-cli-for-apache-flink-docker/], so thanks Francesco!
Let's get started:
[source, bash]
----
docker-compose up
----
== Data Generation
We're going to generate data using the https://github.com/mneedham/livestream-data-generator[github.com/mneedham/livestream-data-generator] repository, which simulates users joining and leaving live stream events.
Once we've checked out that repository, we can install the dependencies:
[source, bash]
----
pip install -r requirements.txt
----
And then run the data generator:
[source, bash]
----
python loop.py \
--timeout 1 \
--users 1000 \
--events 100 \
--max-start-delay 0 \
--min-event-length 60 \
--max-event-length 180
----
We'll see output similar to this:
.Output
[source, json]
----
{"eventTime": "2023-01-23T10:15:24.089789", "eventId": "89653462-d58c-4751-974b-cc94d9fa9a11", "userId": "cf29d9e5-4f52-43cf-99c6-b6138ae612eb", "name": "Beverly Kelley", "lat": "51.5085", "lng": "-0.1257", "city": "London", "region": "England", "action": "Join"}
{"eventTime": "2023-01-23T10:15:24.048042", "eventId": "3bf77680-7664-44a2-b5eb-7281fb759999", "userId": "31ab115f-b7a4-48a6-a282-5e5a3c15ddf0", "name": "Jeffery Adams", "lat": "32.5530", "lng": "-92.0422", "city": "Monroe", "region": "Louisiana", "action": "Join"}
{"eventTime": "2023-01-23T10:15:24.033714", "eventId": "0283e8e6-14a7-4d8e-8aab-5d40d38eb52d", "userId": "0acad44c-2545-4c81-bd3f-33385d21160f", "name": "Alexander Fuller", "lat": "43.2501", "lng": "-79.8496", "city": "Hamilton", "region": "Ontario", "action": "Join"}
{"eventTime": "2023-01-23T10:15:23.979862", "eventId": "bf061b6c-b03d-43d7-9d04-f4f8c71a9ab0", "userId": "0a9f8527-a2b4-4c5f-b82b-2a3930182c62", "name": "Julie Grant", "lat": "35.6910", "lng": "139.7679", "city": "Tokyo", "region": "Tokyo", "action": "Join"}
{"eventTime": "2023-01-23T10:15:24.075753", "eventId": "868320eb-96b2-496f-af72-9cd83d9726c0", "userId": "fc20e945-f4ce-4c71-9a55-8f7253583c1c", "name": "Natalie Martinez", "lat": "39.9690", "lng": "-83.0114", "city": "Columbus", "region": "Ohio", "action": "Join"}
----
== Ingesting data into Kafka
Let's now ingest that data into Kafka, using the https://stedolan.github.io/jq/[jq^] and https://docs.confluent.io/platform/current/app-development/kafkacat-usage.html[kcat^] command line tools:
[source, bash]
----
python loop.py \
--timeout 1 \
--users 1000 \
--events 100 \
--max-start-delay 0 \
--min-event-length 60 \
--max-event-length 180 |
jq -cr --arg sep 😊 '[.eventId, tostring] | join($sep)' |
kcat -P -b localhost:29092 -t events -K😊
----
[NOTE]
====
I've created a video showing how ingest data using this technique on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], which is embedded below.
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/izSk4vjmf_E" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
====
We can also use kcat to check that the data has made its way into Kafka:
[source, bash]
----
kcat -C -b localhost:29092 -t events -c3 | jq
----
.Output
[source, json]
----
{
"eventTime": "2023-01-24T11:13:05.213589",
"eventId": "ebfec380-c3f1-4471-b30b-da822db57117",
"userId": "983aa5c2-4f98-4507-937b-d60f41e1407e",
"name": "Joanne Walters",
"lat": "42.2399",
"lng": "-83.1508",
"city": "Dearborn",
"region": "Michigan",
"action": "Join"
}
{
"eventTime": "2023-01-24T11:13:05.216398",
"eventId": "9cc0a7f5-0638-4b27-a897-2a61ddab98ac",
"userId": "1043a9d5-e722-4d86-91f6-8926046050d5",
"name": "Michael Miller",
"lat": "34.0498",
"lng": "-117.4706",
"city": "Fontana",
"region": "California",
"action": "Join"
}
{
"eventTime": "2023-01-24T11:13:05.291973",
"eventId": "2bb4a79b-f5ec-488e-84ee-72bf2fb8c293",
"userId": "cd0e36ff-9171-41b5-91d2-bd32feaae17c",
"name": "David Trujillo",
"lat": "35.9139",
"lng": "47.0239",
"city": "Dīvāndarreh",
"region": "Kordestān",
"action": "Join"
}
----
So far, so good.
== Transforming stream with Flink
Now let's say that we want to tranform these events to have a nested structure like this:
[source, json]
----
{
"event": {
"time": "2023-01-24T11:13:05.213589",
"id": "ebfec380-c3f1-4471-b30b-da822db57117"
},
"user": {
"id": "983aa5c2-4f98-4507-937b-d60f41e1407e",
"name": "Joanne Walters",
"lat": "42.2399",
"lng": "-83.1508",
"city": "Dearborn",
"region": "Michigan",
},
"action": "Join"
}
----
We're going to do this using https://flink.apache.org/flink-architecture.html[Flink^], a popular stateful stream processor.
We can interact with Flink via its SQL client:
[source, bash]
----
docker exec -it flink-sql-client /opt/sql-client/sql-client.sh
----
Create a table on the `events` stream:
[source, sql]
----
CREATE TABLE Events (
`event_time` TIMESTAMP(3) METADATA FROM 'timestamp',
`partition` BIGINT METADATA VIRTUAL,
`offset` BIGINT METADATA VIRTUAL,
`eventTime` STRING,
`eventId` STRING,
`userId` STRING,
`name` STRING,
`lat` DOUBLE,
`lng` DOUBLE,
`city` STRING,
`region` STRING,
`action` STRING
) WITH (
'connector' = 'kafka',
'topic' = 'events',
'properties.bootstrap.servers' = 'kafka:9092',
'properties.group.id' = 'eventsGroup=',
'scan.startup.mode' = 'earliest-offset',
'format' = 'json'
);
----
We can query this table to view some of the events:
[source, sql]
----
select eventTime, eventId, userId, name, lat, lng, city, region, action
FROM Events
LIMIT 5;
----
.Output
[format="csv", options="header"]
|===
include::content/2023/01/24/data/events.csv[]
|===
We can use the `map` function to massage this data into the nested structure.
The following query does this:
[source, sql]
----
SELECT map[
'time', eventTime,
'id', eventId
] AS event,
map [
'id', userId,
'name', name,
'lat', lat,
'lng', lng,
'city', city,
'region', region
] AS `user`,
action
FROM Events
LIMIT 1;
----
If we run this query, we'll get the following exception:
.Output
[source, text]
----
[ERROR] Could not execute SQL statement. Reason:
org.apache.calcite.sql.validate.SqlValidatorException: Parameters must be of the same type
----
The problem is that it expects all the value to be of the same type and the `lat` and `lng` fields are doubles.
The easiest solution is to cast these values to strings, as shown below:
[source, sql]
----
SELECT map[
'time', eventTime,
'id', eventId
] AS event,
map [
'id', userId,
'name', name,
'lat', CAST(lat AS STRING),
'lng', CAST(lng AS STRING),
'city', city,
'region', region
] AS `user`,
action
FROM Events
LIMIT 1;
----
Now let's create a table that exports its contents to another Kafka topic called `events-nested`;
[source, sql]
----
CREATE TABLE EventsNested (
`user` MAP<STRING,STRING>,
`event` MAP<STRING,STRING>,
`action` STRING
) WITH (
'connector' = 'kafka',
'topic' = 'events-nested',
'properties.bootstrap.servers' = 'kafka:9092',
'properties.group.id' = 'testGroup',
'value.format' = 'json'
);
----
And now let's ingest the previous query into that table:
[source, sql]
----
INSERT INTO EventsNested
SELECT map[
'time', eventTime,
'id', eventId
] AS event,
map [
'id', userId,
'name', name,
'lat', CAST(lat AS STRING),
'lng', CAST(lng AS STRING),
'city', city,
'region', region
] AS `user`,
action
FROM Events;
----
.Output
[source, text]
----
[INFO] Submitting SQL update statement to the cluster...
[INFO] SQL update statement has been successfully submitted to the cluster:
Job ID: f2e6f707a9c69b84f4d6e9ca7bc34fc6
----
We can then check that data is making its way into that topic using kcat:
[source, bash]
----
kcat -C -b localhost:29092 -t events-nested -c1 | jq
----
.Output
[source, json]
----
{
"user": {
"time": "2023-01-24T11:35:21.017729",
"id": "6a8fbce6-aa20-46eb-a201-7580762c2a16"
},
"event": {
"city": "Brisbane",
"lng": "153.0281",
"id": "ff8663b0-4376-448a-a443-c14bb0cee0cc",
"region": "Queensland",
"name": "Terri Simmons",
"lat": "-27.4679"
},
"action": "Join"
}
----
Job done!
=== In Summary
The map structure is very helpful for creating nested structures, but it took me a little while to figure out how to use it.
Hopefully this blog post will save you going through that same journey of exploration. | In this post we'll learn how to export nested documents from Flink to Apache Kafka. | uploads/2023/01/flink-banner.png | [
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] | flink-sql-export-nested-json-kafka | https://markhneedham.com/blog/2023/01/24/flink-sql-export-nested-json-kafka |
false | 2023-06-05 02:44:37 | Python: Padding a string | [
"til",
"python"
] | [
"TIL"
] | I've been writing some scripts to parse data from Apache Pinot's HTTP API and I wanted to format the values stored in a map to make them more readable.
In this blog post, we'll look at some ways that I did that.
I started with a map that looked a bit like this:
[source, python]
----
segments = {
"events3__4__1__20230605T1335Z": "CONSUMED",
"events3__4__20__20230605T1335Z": "CONSUMING"
}
----
And then I iterated over and printed each item like this:
[source, python]
----
for segment_id, status in segments.items():
print(segment_id, status)
----
.Output
[source, text]
----
events3__4__1__20230605T1335Z CONSUMED
events3__4__20__20230605T1335Z CONSUMING
----
I wanted to have the `segment_id` be a fixed width so that the statuses would be aligned.
One way that we can do this is with an f-string.
If we want to add padding to the right with a fixed width of 35 columns, it'd be like this:
[source, python]
----
for segment_id, status in segments.items():
print(f"{segment_id: <35}", status)
----
.Output
[source, text]
----
events3__4__1__20230605T1335Z CONSUMED
events3__4__20__20230605T1335Z CONSUMING
----
And if we want to add padding to the left:
[source, python]
----
for segment_id, status in segments.items():
print(f"{segment_id: >35}", status)
----
.Output
[source, text]
----
events3__4__1__20230605T1335Z CONSUMED
events3__4__20__20230605T1335Z CONSUMING
----
That works well if we want to fill with spaces, but what if we want to specify a filler character?
In that case, we can use `ljust` and `rjust`:
[source, python]
----
for segment_id, status in segments.items():
print(segment_id.ljust(35, "."), status)
----
.Output
[source, text]
----
events3__4__1__20230605T1335Z...... CONSUMED
events3__4__20__20230605T1335Z..... CONSUMING
----
And padding from the left:
[source, python]
----
for segment_id, status in segments.items():
print(segment_id.rjust(35, "-"), status)
----
.Output
[source, text]
----
------events3__4__1__20230605T1335Z CONSUMED
-----events3__4__20__20230605T1335Z CONSUMING
---- | In this post we'll learn how to pad the left and right of a string in Python. | uploads/2023/06/python-padding-banner.png | [
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] | python-pad-string | https://markhneedham.com/blog/2023/06/05/python-pad-string |
false | 2023-06-02 02:44:37 | DuckDB: Generate dummy data with user defined functions (UDFs) | [
"duckdb",
"til"
] | [
"DuckDB"
] | In the 0.8 release of DuckDB, they added functionality that lets you add your own functions when using the Python package
I wanted to see if I could use it to generate dummy data so that's what we're going to do in this blog post.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/EVLDg-RNjoc" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
====
We're going to do this using DuckDB's Python package.
We'll install that, along with the Faker library, by running the following:
[source, bash]
----
pip install duckdb faker
----
I then created a function to generate a fake person:
[source, python]
----
import faker
fake = faker.Faker()
def generate_person():
person = {
'name': fake.name(),
'city': fake.city(),
'state': fake.state(),
'zip_code': fake.zipcode(),
'country': fake.country(),
'email': fake.email(),
'job': fake.job(),
'company': fake.company(),
'ssn': fake.ssn(),
'birthdate': fake.date_of_birth(),
'phone_number': fake.phone_number()
}
return person
----
Now we need to create a DuckDB database and register the function, which we'll do with the following code:
[source, python]
----
import duckdb
from duckdb.typing import *
con.create_function(
'generate_person',
generate_person,
[],
duckdb.struct_type({
'name': 'VARCHAR',
'city': 'VARCHAR',
'state': 'VARCHAR',
'zip_code': 'VARCHAR',
'country': 'VARCHAR',
'email': 'VARCHAR',
'job': 'VARCHAR',
'company': 'VARCHAR',
'ssn': 'VARCHAR',
'birthdate': 'DATE',
'phone_number': 'VARCHAR'
})
)
----
A dictionary in Python maps to the `duckdb.struct_type` type in DuckDB.
We can then pass in a map of the fields and their data types.
We can then use that function in a query like this:
[source, python]
----
con.sql("""
CREATE OR REPLACE TABLE people AS
SELECT person.* FROM (
SELECT generate_person(random()) AS person
FROM generate_series(1,10000)
)
""")
----
When I ran this script it was returning the same person repeatedly, which makes me think the function was being cached.
To work around that, I add a `seed` parameter to the function, resulting in the following code:
[source, python]
----
def generate_person(seed):
person = {
'name': fake.name(),
'city': fake.city(),
'state': fake.state(),
'zip_code': fake.zipcode(),
'country': fake.country(),
'email': fake.email(),
'job': fake.job(),
'company': fake.company(),
'ssn': fake.ssn(),
'birthdate': fake.date_of_birth(),
'phone_number': fake.phone_number()
}
return person
con.create_function(
'generate_person',
generate_person,
[DOUBLE],
duckdb.struct_type({
'name': 'VARCHAR',
'city': 'VARCHAR',
'state': 'VARCHAR',
'zip_code': 'VARCHAR',
'country': 'VARCHAR',
'email': 'VARCHAR',
'job': 'VARCHAR',
'company': 'VARCHAR',
'ssn': 'VARCHAR',
'birthdate': 'DATE',
'phone_number': 'VARCHAR'
})
)
con.sql("""
CREATE OR REPLACE TABLE people AS
SELECT person.* FROM (
SELECT generate_person(random()) AS person
FROM generate_series(1,10000)
)
""")
----
And now it works! | In this post we'll learn how to create dummy data with DuckDB user defined functions (UDFs). | uploads/2023/02/dbz-banner.png | [
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] | duckdb-dummy-data-user-defined-functions | https://markhneedham.com/blog/2023/06/02/duckdb-dummy-data-user-defined-functions |
false | 2023-06-20 02:44:37 | DuckDB/SQL: Convert string in YYYYmmdd format to Date | [
"duckdb",
"til"
] | [
"TIL"
] | I've been working with a data set that represents dates as strings in the format 'YYYYmmdd' and I wanted to convert those values to Dates in DuckDB.
In this blog post, we'll learn how to do that.
Let's create a small table with a single column that represents date of births:
[source, sql]
----
create table players (dob VARCHAR);
insert into players values('20080203'), ('20230708');
----
We can write the following query to return the rows in the table:
[source, sql]
----
select * from players;
----
.Output
[%header,format=csv]
|===
dob
20080203
20230708
|===
At the moment the date of birth is a string, but we can use the `strptime` function to convert it to a timestamp:
[source, sql]
----
SELECT strptime(dob, '%Y%m%d') AS dob
FROM players;
----
.Output
[%header,format=csv]
|===
dob
"2008-02-03 00:00:00"
"2023-07-08 00:00:00"
|===
I don't really care about the time component though, so let's get rid of that by casting it to a `DATE`:
[source, sql]
----
SELECT cast(strptime(dob, '%Y%m%d') AS DATE) AS dob
FROM players;
----
.Output
[%header,format=csv]
|===
dob
2008-02-03
2023-07-08
|===
Job done! | In this post we'll learn how to convert a date string to a Date type in DuckDB. | uploads/2023/06/duckdb-date-banner.png | [
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] | duckdb-sql-string-date | https://markhneedham.com/blog/2023/06/20/duckdb-sql-string-date |
false | 2023-06-27 04:44:37 | Python: Get the first item from a collection, ignore the rest | [
"python",
"til"
] | [
"TIL"
] | :icons: font
When writing Python scripts, I often find myself wanting to take the first item from a collection and ignore the rest of the values.
I usually use something like `values[0]` to take the first value from the list, but I was curious whether I could do better by using https://blog.teclado.com/destructuring-in-python/[destructuring^].
That's what we're going to explore in this blog post.
We'll start with a list that contains some names:
[source, python]
----
values = ["Michael", "Ryan", "Karin", "Mark", "Jennifer", "Will"]
----
We can destructure this list to get the first item and assign the rest to another variable like this:
[source, python]
----
first, *rest = values
print(first)
print(rest)
----
.Output
[source, text]
----
Michael
['Ryan', 'Karin', 'Mark', 'Jennifer', 'Will']
----
If we don't care about the other values, we could use `_` instead of `rest`:
[source, python]
----
first, *_ = values
----
The other values will still be assigned to a variable named `_`, but this seems to be a convention for throwing away data in Python.
What about if we try to get the first item when the list is empty?
[source, python]
----
values = []
first, *_ = values
----
.Output
[source, text]
----
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: not enough values to unpack (expected at least 1, got 0)
----
One way around this would be to check that the collection contains some values first:
[source, python]
----
values = []
if len(values) > 0:
first, *_ = values
else:
print("values is empty")
----
.Output
[source, text]
----
values is empty
----
Hmmm, a bit verbose.
We could convert that into a one-liner if we aren't worried about printing the message when it's empty:
[source, python]
----
values = []
first, *_ = values if len(values) > 0 else [None]
print(first)
print(rest)
----
.Output
[source, text]
----
None
[]
----
Not bad, but probably not better than my usual technique, which would read like this:
[source, python]
----
values = []
first = values[0] if len(values) > 0 else None
print(first)
---- | In this post, we're going to explore some ways that we can get the first item from a collection while throwing away the other values. | uploads/2023/06/python-first-item-banner.png | [
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] | python-get-first-item-collection-ignore-rest | https://markhneedham.com/blog/2023/06/27/python-get-first-item-collection-ignore-rest |
false | 2023-06-28 04:44:37 | Python: All about the next function | [
"python",
"til"
] | [
"TIL"
] | Yesterday I wrote a blog post about some different ways to https://www.markhneedham.com/blog/2023/06/27/python-get-first-item-collection-ignore-rest/[take the first element from a Python list^].
Afterward I was chatting to my new rubber duck, ChatGPT, which suggested the `next` function on an iterator as an alternative approach.
And so that's what we're going to explore in this blog post.
The `next` function gets the first value from an iterator and optionally returns a provided default value if the iterator is empty.
Let's try it out on our example:
[source, python]
----
values = ["Michael", "Ryan", "Karin", "Mark", "Jennifer", "Will"]
print(next(iter(values)))
----
.Output
[source, text]
----
Michael
----
That works well.
We could extract the other values as well if we wanted:
[source, python]
----
values = iter(["Michael", "Ryan", "Karin", "Mark", "Jennifer", "Will"])
print(next(values))
print(next(values))
print(next(values))
----
.Output
[source, text]
----
Michael
Ryan
Karin
----
And the empty case?
[source, python]
----
values = []
print(next(iter(values)))
----
.Output
[source, text]
----
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration
----
The `StopIteration` exception gets thrown if you try to read from an iterator that's been exhausted.
We created an empty iterator, so that was to be expected.
Let's instead pass in a default value:
[source, python]
----
values = []
print(next(iter(values), None))
----
.Output
[source, text]
----
None
----
This time it returns the default value instead of throwing the exception and I think this is probably a nicer solution than the one we ended up with in the last blog post, which was as follows:
[source, python]
----
values = []
first, *_ = values if len(values) > 0 else [None]
---- | In this post, we're going to learn about the Python next function, which can be used to extract values from an iterator. | uploads/2023/06/python-next-banner.png | [
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] | python-next-function-iterator | https://markhneedham.com/blog/2023/06/28/python-next-function-iterator |
false | 2023-06-19 02:44:37 | Hugging Face: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated | [
"duckdb",
"til",
"generative-ai",
"hugging-face"
] | [
"TIL"
] | I've been trying out some of the https://huggingface.co/[Hugging Face^] tutorials and came across an interesting warning message while playing around with the https://huggingface.co/google/flan-t5-large#usage[google/flan-t5-large model^].
In this blog post, we'll learn how to get rid of that warning.
I was running a variation of the getting started example:
[source, python]
----
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
input_text = "Who is the UK Prime Minister? Explain step by step"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
----
The output from running this fragment of code is shown below:
[source, text]
----
<pad>The Prime Minister of the United Kingdom is Theresa May. Theresa May is the
----
To be fair, we have had a lot of Prime Ministers over the last few years, but it hasn't been Theresa May for several years!
In any case, I also got the following error message when it executed the `outputs =` line:
[source, text]
----
/Users/markhneedham/projects/docs-bot/env/lib/python3.11/site-packages/transformers/generation/utils.py:1353: UserWarning: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.
warnings.warn(
----
I'm not actually setting a maximum length, but we seem to end up down this code path if no maximum is set.
You can also see that the output is chopped off.
We can fix that by setting the parameter `max_new_tokens`:
[source, python]
----
outputs = model.generate(input_ids, max_new_tokens=1000)
print(tokenizer.decode(outputs[0]))
----
The new output is shown below:
[source, text]
----
<pad> The Prime Minister of the United Kingdom is Theresa May. Theresa May is the wife of David Cameron. David Cameron is the Prime Minister of the United Kingdom. So, the final answer is Theresa May.</s>
----
The accuracy is pretty bad, but at least it's not chopping off the output anymore. | In this post, we'll learn how to set the maximum token length when using the google/flan-t5-large model in Hugging Face. | uploads/2023/06/huggingface-maxlength.png | [
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] | huggingface-max-length-generation-length-deprecated | https://markhneedham.com/blog/2023/06/19/huggingface-max-length-generation-length-deprecated |
false | 2023-06-21 02:44:37 | Chroma/LangChain: Index not found, please create an instance before querying | [
"chroma",
"chromadb",
"langchain",
"til",
"generative-ai"
] | [
"TIL"
] | Somewhat belatedly, I've been playing around with https://github.com/hwchase17/langchain[LangChain^] and https://huggingface.co/[HuggingFace^] to spike a tool that lets me ask question about https://podcasts.apple.com/gb/podcast/real-time-analytics-with-tim-berglund/id1680445905[Tim Berglund's Real-Time Analytics podcast^].
I'm using the https://www.trychroma.com/[Chroma^] database to store vectors of chunks of the transcript so that I can find appropriate sections to feed to the Large Language Model to help with answering my questions.
I ran into an initially perplexing error while building this out, which we're going to explore in this blog post.
I have a script called `generate_embeddings.py` that I use to generate embeddings of 256 length chunks of each podcast episode:
.generate_embeddings.py
[source, python]
----
from langchain.document_loaders import (
YoutubeLoader
)
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
splitter = CharacterTextSplitter(
chunk_size=256,
chunk_overlap=50,
separator=" "
)
# YouTube video IDs
files = [
"nCLN15W_WOc",
"K14Kn0D-I4Y"
]
# Create chunks of text
data = []
for file in files:
yt_loader = YoutubeLoader(file)
yt_data = yt_loader.load()
data += splitter.split_documents(yt_data)
# Create embeddings for those chunks and store them in Chroma
hf_embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
store = Chroma.from_documents(
data, hf_embeddings,
[f"{item.metadata['source']}-{index}" for index, item in enumerate(data)],
collection_name="transcript",
persist_directory='db',
)
store.persist()
----
This takes a couple of seconds to run and then I wanted to see which chunks it would return if I asked about Anna McDonald, who was the guest for the last two episodes:
[source, python]
----
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
hf_embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
store = Chroma(persist_directory="db", embedding_function=hf_embeddings)
print(
store.similarity_search("Who is Anna McDonald?", top_n=2)
)
----
When I ran this, I got this error:
.Error
[source, text]
----
NoIndexException: Index not found, please create an instance before querying
Exception ignored in: <function PersistentDuckDB.__del__ at 0x2acde7920>
----
There's a whole thread on different ways that this can happen on https://github.com/hwchase17/langchain/issues/3011[LangChain's GitHub repository^], but it turns out my problem was that I hadn't specified the collection name.
Let's fix that:
[source, python]
----
hf_embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
store = Chroma(collection_name="transcript", persist_directory="db", embedding_function=hf_embeddings)
for row in store.similarity_search("Who is Anna McDonald?", top_n=2):
print(row)
----
And now if we run the script, we'll see the following output:
.Output
[source, text]
----
page_content="- Yeah, and you're great at explaining it. So, if you wanna know who Anna is, and you wanna know the\nbasics of Kafka Streams... Back to that episode. I'll just say you are a Customer\nSuccess Technical Architect at Confluent. - Almost got it this time." metadata={'source': 'nCLN15W_WOc'}
page_content='- Anna McDonald is a world-class,\nno universe-class expert in Kafka, and in\nparticular, Kafka Streams. Kafka Streams is an important\npart of the ecosystem and I wanted her to give us\nan introduction to the topic. Good, solid foundation in Kafka\nStreams on' metadata={'source': 'K14Kn0D-I4Y'}
page_content="and I'm joined here today\nby my friend, Anna McDonald. Anna is a customer success\ntechnical architect at Confluent. - Bravo (clapping) - I got it. Better known as the Duchess of Siesta. Anna, welcome to the\nReal-Time Analytics Podcast. - Thank you very" metadata={'source': 'K14Kn0D-I4Y'}
page_content="- What up? - My guest today- - I learned how to book a... Oh, sorry, I was just gonna say, I learned how to book a\nconference room sort of today, so I can do these. - So now we can do it more. - That's right. - My guest today has been Anna McDonald. Anna," metadata={'source': 'nCLN15W_WOc'}
---- | In this post we'll learn how to work around the index not found error when using the Chroma database to do similarity search. | uploads/2023/06/langchain-chroma-banner.png | [
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] | chroma-index-not-found-create-instance-querying | https://markhneedham.com/blog/2023/06/21/chroma-index-not-found-create-instance-querying |
false | 2023-06-07 02:44:37 | DuckDB/SQL: Pivot - 0 if null | [
"duckdb",
"sql",
"til"
] | [
"TIL"
] | :icons: font
I've been learning all about the https://duckdb.org/docs/sql/statements/pivot.html[PIVOT function^] that was recently added in DuckDB and I ran into an issue where lots of the cells in my post PIVOT table were null values.
In this blog post, we'll learn how to replace those nulls with 0s (or indeed any other value).
== Setup
I'm working with https://github.com/JeffSackmann/tennis_atp[Jeff Sackmann's tennis dataset^], which I loaded by running the following query:
[source, sql]
----
CREATE OR REPLACE TABLE matches AS
SELECT * FROM read_csv_auto(
list_transform(
range(1968, 2023),
y -> 'https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_' || y || '.csv'
),
types={'winner_seed': 'VARCHAR', 'loser_seed': 'VARCHAR'}
);
----
I then created a view on top of that table that contains matches played by top 10 ranked players in the last few years:
[source, sql]
----
CREATE OR REPLACE VIEW top10Players AS
FROM matches
SELECT winner_name AS player,
winner_ioc AS country,
surface,
datepart('year', tourney_date) AS matchYear,
round,
count(*) AS matches
WHERE matchYear >= 2020
AND tourney_level IN ('G')
AND winner_name IN (
from matches
select distinct winner_name
where winner_rank <=10 AND datepart('year', tourney_date) >= 2020
)
GROUP BY ALL
ORDER BY count(*) DESC;
----
Now that we've got that setup, it's time to pivot.
== Every day I'm PIVOTing
I wrote the following query to show the number of matches won by players from a particular country across a bunch of years.
[source, sql]
----
PIVOT top10Players
ON matchYear
USING SUM(matches)
GROUP BY country
ORDER BY "2022" DESC;
----
.Output
[options="header"]
|====================================
| country | 2020 | 2021 | 2022 | 2023
| ESP | 17 | 23 | 41 | 4
| ITA | 11 | 23 | 24 | 3
| RUS | 19 | 29 | 22 | 6
| CAN | 8 | 21 | 15 | 5
| NOR | 4 | 6 | 13 | 1
| SRB | 16 | 27 | 11 | 7
| GRE | 9 | 13 | 10 | 6
| GBR | 2 | 6 | 10 | 2
| USA | 6 | 6 | 8 | 1
| GER | 14 | 17 | 8 | 1
| ARG | 8 | 11 | 7 | 1
| BEL | 5 | | 6 |
| DEN | | | 6 | 3
| POL | 2 | 6 | 5 | 3
| FRA | 3 | 4 | 4 |
| AUT | 17 | 3 | |
| SUI | 5 | 7 | |
|====================================
You can see that towards the end of the table we have empty cells.
This is because players from those countries didn't win any matches in that time period.
We'd usually replace null values using the `coalesce` function, which is exactly what we're going to do here.
But what I really like about DuckDB is that it treats the whole PIVOT query as if it was any other sub query or Common Table Expression.
i.e. we can include it in a `FROM` clause and then operate on the resulting columns.
To make this even cleaner, I'm going to use the `FROM...SELECT` and `SELECT * REPLACE` clauses, described in the https://duckdb.org/2022/05/04/friendlier-sql.html[Friendlier SQL with DuckDB^] blog post.
The query is shown below:
[source, sql]
----
FROM (
PIVOT top10Players
ON matchYear
USING SUM(matches)
GROUP BY country
ORDER BY "2022" DESC
)
SELECT * REPLACE(
coalesce("2020", 0) AS "2020",
coalesce("2021", 0) AS "2021",
coalesce("2022", 0) AS "2022",
coalesce("2023", 0) AS "2023"
);
----
.Output
[options="header"]
|====================================
| country | 2020 | 2021 | 2022 | 2023
| ESP | 17 | 23 | 41 | 4
| ITA | 11 | 23 | 24 | 3
| RUS | 19 | 29 | 22 | 6
| CAN | 8 | 21 | 15 | 5
| NOR | 4 | 6 | 13 | 1
| SRB | 16 | 27 | 11 | 7
| GRE | 9 | 13 | 10 | 6
| GBR | 2 | 6 | 10 | 2
| USA | 6 | 6 | 8 | 1
| GER | 14 | 17 | 8 | 1
| ARG | 8 | 11 | 7 | 1
| BEL | 5 | 0 | 6 | 0
| DEN | 0 | 0 | 6 | 3
| POL | 2 | 6 | 5 | 3
| FRA | 3 | 4 | 4 | 0
| AUT | 17 | 3 | 0 | 0
| SUI | 5 | 7 | 0 | 0
|====================================
Our table is now filled with 0s instead of nulls, you love to see it! | In this post we'll learn how to replace null values when using the PIVOT function in DuckDB. | uploads/2023/06/duckdb-pivot-nulls.png | [
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] | duckdb-sql-pivot-0-if-null | https://markhneedham.com/blog/2023/06/07/duckdb-sql-pivot-0-if-null |
false | 2023-06-30 04:44:37 | Detecting and splitting scenes in a video | [
"video-editing",
"til"
] | [
"TIL"
] | When editing videos for my YouTube channel, https://www.youtube.com/@learndatawithmark[Learn Data with Mark^], I spend a bunch of time each week chopping up a screencast into scenes that I then line up with a separately recorded voice-over.
I was curious whether I could automate the chopping-up process and that's what we're going to explore in this blog post.
I started out by asking ChatGPT the following question:
.ChatGPT Prompt
[source, text]
----
I want to chop up a demo for a YouTube video into smaller segments.
Ideally, I want to do the cuts when the screen is cleared.
Is there a Python library that will let me iterate over a video file and detect changes between frames?
----
ChatGPT pointed me to a library called https://www.scenedetect.com/cli[PySceneDetect^] and that's where our adventure begins.
If you want to try this out yourself, I've created the following Poetry file containing all the dependencies that I used:
.pyproject.toml
[source, yml]
----
[tool.poetry]
name = "scene-detection"
version = "0.1.0"
description = ""
authors = ["Mark Needham <m.h.needham@gmail.com>"]
readme = "README.md"
packages = [{include = "scene_detection"}]
[tool.poetry.dependencies]
python = "^3.11"
opencv-python-headless = "^4.7.0.72"
scikit-image = "^0.21.0"
scenedetect = "^0.6.1"
pandas = "^2.0.3"
matplotlib = "^3.7.1"
plotly = "^5.15.0"
kaleido = "0.2.1"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
----
If you create that file in a directory locally, you can install everything by running this command:
[source, bash]
----
poetry install
----
You should now have a command line tool called `scenedetect`.
After trying a few command that didn't seem to do anything, I came across an example that computes the difference between adjacent frames (`detect-content`):
[source, bash]
----
scenedetect --input BetterSQL-Demo.mp4 \
-s better-sql.stats-detectcontent.csv \
detect-content \
list-scenes
----
It writes those differences to `better-sql.stats-detectcontent.csv` and then I use the `list-scenes` command to output the detected scenes.
The output is shown below:
.Truncated Output
[source, text]
----
[PySceneDetect] Scene List:
-----------------------------------------------------------------------
| Scene # | Start Frame | Start Time | End Frame | End Time |
-----------------------------------------------------------------------
| 1 | 1 | 00:00:00.000 | 7101 | 00:01:58.350 |
-----------------------------------------------------------------------
----
It's only detected one scene, which isn't quite right.
There are quite a few transitions in the video where I got from one code example to a blank screen and I want each of those to be a new scene.
Let's have a look at the first few lines of the CSV file:
[source, bash]
----
head -n5 better-sql.stats-detectcontent.csv
----
.The first few lines of `better-sql.stats-detectcontent.csv`
[%header,format=csv]
|===
Frame Number,Timecode,content_val,delta_edges,delta_hue,delta_lum,delta_sat
2,00:00:00.017,4.5211226851851856e-05,0.0,0.0,0.00013563368055555556,0.0
3,00:00:00.033,0.0,0.0,0.0,0.0,0.0
4,00:00:00.050,0.0,0.0,0.0,0.0,0.0
5,00:00:00.067,0.0,0.0,0.0,0.0,0.0
|===
I don't know what most of these values mean, but the documentation suggested that we create a chart plotting the `Frame Number` against the `content_val`.
We can do that using plot.ly:
.visual.py
[source, python]
----
import pandas as pd
import plotly.graph_objects as go
import plotly.io as pio
data = pd.read_csv('better-sql.stats-detectcontent.csv')
fig = go.Figure(data=go.Scatter(x=data['Timecode'], y=data['content_val'], mode='lines'))
fig.update_layout(
title='Line Chart of Content Value',
xaxis_title='Frame Number',
yaxis_title='Content Value',
autosize=True
)
pio.write_image(fig, 'figure.svg', width=1000, height=600)
----
The resulting image is shown below:
.A plot of Frame Number vs Content Value
image::{{<siteurl>}}/uploads/2023/06/scenedetect-plot.svg[]
We can see that most of the time there are minimal changes between pairs of frames, but on around 15 occasions there is a change bigger than 1.
Let's try running `scenedetect` again, but this time with the threshold set to `1`:
[source, bash]
----
scenedetect --input BetterSQL-Demo.mp4 \
-s better-sql.stats-detectcontent.csv \
detect-content -t 1 \
list-scenes
----
Running it with this threshold tells `scenedetect` to create a new scene whenever the threshold is exceeded.
The output of running the command is shown below:
.Truncated Output
[source, text]
----
[PySceneDetect] Scene List:
-----------------------------------------------------------------------
| Scene # | Start Frame | Start Time | End Frame | End Time |
-----------------------------------------------------------------------
| 1 | 1 | 00:00:00.000 | 509 | 00:00:08.483 |
| 2 | 510 | 00:00:08.483 | 967 | 00:00:16.117 |
| 3 | 968 | 00:00:16.117 | 1394 | 00:00:23.233 |
| 4 | 1395 | 00:00:23.233 | 1763 | 00:00:29.383 |
| 5 | 1764 | 00:00:29.383 | 2319 | 00:00:38.650 |
| 6 | 2320 | 00:00:38.650 | 2808 | 00:00:46.800 |
| 7 | 2809 | 00:00:46.800 | 2887 | 00:00:48.117 |
| 8 | 2888 | 00:00:48.117 | 4057 | 00:01:07.617 |
| 9 | 4058 | 00:01:07.617 | 5308 | 00:01:28.467 |
| 10 | 5309 | 00:01:28.467 | 5368 | 00:01:29.467 |
| 11 | 5369 | 00:01:29.467 | 5449 | 00:01:30.817 |
| 12 | 5450 | 00:01:30.817 | 6819 | 00:01:53.650 |
| 13 | 6820 | 00:01:53.650 | 6879 | 00:01:54.650 |
| 14 | 6880 | 00:01:54.650 | 7101 | 00:01:58.350 |
-----------------------------------------------------------------------
----
It detected 14 different scenes, which sounds promising.
Let's now run it one more time, but this time with the `split-video` command appended so that it will split the video up into segments:
[source, bash]
----
scenedetect --input BetterSQL-Demo.mp4 \
-s better-sql.stats-detectcontent.csv \
detect-content -t 1 \
list-scenes split-video
----
Let's check the resulting file listing:
[source, bash]
----
du -h BetterSQL-Demo-Scene*.mp4
----
.Output
[source, text]
----
284K BetterSQL-Demo-Scene-001.mp4
324K BetterSQL-Demo-Scene-002.mp4
224K BetterSQL-Demo-Scene-003.mp4
216K BetterSQL-Demo-Scene-004.mp4
276K BetterSQL-Demo-Scene-005.mp4
388K BetterSQL-Demo-Scene-006.mp4
144K BetterSQL-Demo-Scene-007.mp4
576K BetterSQL-Demo-Scene-008.mp4
2.0M BetterSQL-Demo-Scene-009.mp4
176K BetterSQL-Demo-Scene-010.mp4
276K BetterSQL-Demo-Scene-011.mp4
816K BetterSQL-Demo-Scene-012.mp4
116K BetterSQL-Demo-Scene-013.mp4
196K BetterSQL-Demo-Scene-014.mp4
----
We can have a look at some of the frames in the extracted scenes by calling the `save-images` command like this:
[source, bash]
----
scenedetect --input BetterSQL-Demo.mp4 \
-s better-sql.stats-detectcontent.csv \
detect-content -t 1 \
list-scenes save-images
----
Let's have a look at a couple of examples:
.Last frame of scene 1
image::{{<siteurl>}}/uploads/2023/06/BetterSQL-Demo-Scene-001-03.jpg[]
.First frame of scene 2
image::{{<siteurl>}}/uploads/2023/06/BetterSQL-Demo-Scene-002-01.jpg[]
That looks good, that's exactly the cut that I would have made.
And it does seem to pick up all the switches to a completely blank screen as new scenes.
An interesting cut that it found that is quite clever is the following one:
.Last frame of scene 6
image::{{<siteurl>}}/uploads/2023/06/BetterSQL-Demo-Scene-006-03.jpg[]
.First frame of scene 7
image::{{<siteurl>}}/uploads/2023/06/BetterSQL-Demo-Scene-007-01.jpg[]
Pretty cool, I'd say it's done a great job.
I'm gonna give this a go on my next video to see if it saves me time, but here's hoping! | In this post, we're going to learn how to split a video into scenes using the scenedetect library. | uploads/2023/06/detect-split-scenes.png | [
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] | detecting-splitting-scenes-video | https://markhneedham.com/blog/2023/06/30/detecting-splitting-scenes-video |
false | 2023-06-08 02:44:37 | Creating LinkedIn Carousel/Slides | [
"linkedin",
"til"
] | [
"TIL"
] | If you've been using LinkedIn recently, you've likely seen those posts where people post slides in a kind of carousel that you can horizontally scroll.
I wanted to create one to explain Apache Pinot's Upserts feature, but I wasn't sure how to create one.
Since it's 2023, I started by asking ChatGPT:
.ChatGPT doesn't know about LinkedIn Carousel
image::{{<siteurl>}}/uploads/2023/06/chatgpt-carousel.png[]
Unfortunately ChatGPT doesn't know how to do it, but https://www.linkedin.com/in/harrison-avisto-42996077/[Harrison Avisto^] does and was happy to teach me.
The basic idea is that you need to upload a PDF when you're creating your post, as showing in the animation below:
.Uploading a PDF
image::{{<siteurl>}}/uploads/2023/06/linkedin-post.gif[]
So the next question is, how do you go about creating those PDFs?
I'm sure there are lots of ways to do this, but I've come across two that I think are pretty accessible.
The first is to create a 'LinkedIn Carousel' using https://www.canva.com/[canva.com] and you can do this on a free account.
.A LinkedIn Carousel on canva.com
image::{{<siteurl>}}/uploads/2023/06/carousel-canvas.png[]
You can then create a bunch of images and then download as PDF, as shown below:
.Download as PDF on canva.com
image::{{<siteurl>}}/uploads/2023/06/canva-pdf.png[]
If you don't want to use canva, you can do a similar thing with Google Slides.
Create a new slides document as normal and then go to `File > Page Setup` and change the dimensions of the slide/canvas:
.Google Slides Canvas size
image::{{<siteurl>}}/uploads/2023/06/google-slides.png[]
Again, create slides like normal and export as PDF when you're done.
And here's the one I made:
++++
<iframe src="https://www.linkedin.com/embed/feed/update/urn:li:ugcPost:7072557847632306176" height="640" width="504" frameborder="0" allowfullscreen="" title="Embedded post"></iframe>
++++ | In this post we'll learn how to create a LinkedIn carousel using Google Slides and canva.com | uploads/2023/06/linkedin-banner.png | [
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] | linkedin-slides-carousel | https://markhneedham.com/blog/2023/06/08/linkedin-slides-carousel |
false | 2023-06-06 02:44:37 | Python: Working with tuples in lambda expressions | [
"python",
"til"
] | [
"TIL",
"python"
] | I'm still playing around with data returned by Apache Pinot's HTTP API and I wanted to sort a dictionary of segment names by partition id and index.
In this blog post we're going to look into how to do that.
We'll start with the following dictionary:
[source, python]
----
segments = {
"events3__4__1__20230605T1335Z": "CONSUMED",
"events3__4__13__20230605T1335Z": "CONSUMED",
"events3__4__20__20230605T1335Z": "CONSUMING"
}
----
As I mentioned above, I want to sort the dictionary's items by partition id and index, which are embedded inside the key name.
In the following key:
[source, text]
----
events3__4__13__20230605T1335Z
----
The partition id is `4` and the index is `13`.
To sort by these values, we need to split on `__` and then grab the 1st and 2nd indexes of the corresponding list, before calling the `int` function to have those values treated as numbers rather than strings.
The code looks like this:
[source, python]
----
sorted_segment_ids = sorted(
segments.items(),
key=lambda item: (
int(item[0].split("__")[1]),
int(item[0].split("__")[2])
)
)
for segment_id, server_names in sorted_segment_ids:
print(f'{segment_id: <35}', server_names)
----
.Output
[source, text]
----
events3__4__1__20230605T1335Z CONSUMED
events3__4__13__20230605T1335Z CONSUMED
events3__4__20__20230605T1335Z CONSUMING
----
That works, but we have some repetition in the code and it's not particularly readable.
It would be slightly easier if we could destructure the `item` tuple, but you can't do that in lambdas in Python 3.
Instead, we can use an assignment expression, which assigns a value to a variable name and also returns that value.
The updated code looks like this:
[source, python]
----
sorted_segment_ids = sorted(
segments.items(),
key=lambda item: ((
seg_id := item[0],
seg_parts := seg_id.split("__"),
partition := int(seg_parts[1]),
index := int(seg_parts[2])
)[2:])
)
for segment_id, server_names in sorted_segment_ids:
print(f'{segment_id: <35}', server_names)
----
Our lambda function returns a 4 value tuple, but we don't need the first two, so we filter those out with the `[2:]` slice operator.
We could also convert the `partition` and `index` expressions into one list comprehension, like this:
[source, python]
----
sorted_segment_ids = sorted(
segments.items(),
key=lambda item: ((
seg_id := item[0],
seg_parts := seg_id.split("__"),
[int(val) for val in seg_parts[1:3]]
)[2:])
)
for segment_id, server_names in sorted_segment_ids:
print(f'{segment_id: <35}', server_names)
----
It's still kinda verbose though, although I think it is easier to understand what's going on.
I still don't like having to use the slice operator though, so perhaps I should just bite the bullet and use a function instead!
That would look like this:
[source, python]
----
def sort_fn(item):
seg_id, *_ = item
seg_parts = seg_id.split("__"),
return [int(val) for val in seg_parts[1:3]]
sorted_segment_ids = sorted(segments.items(), key=sort_fn)
for segment_id, server_names in sorted_segment_ids:
print(f'{segment_id: <35}', server_names)
---- | In this post we'll learn how to work with tuples inside Python lambda expressions. | uploads/2023/06/python-lambdas-banner.png | [
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] | python-lambda-expression-tuple | https://markhneedham.com/blog/2023/06/06/python-lambda-expression-tuple |
false | 2023-06-06 02:44:37 | Kafka/Kubernetes: Failed to resolve: nodename nor servname provided, or not known | [
"til",
"kubernetes",
"kafka"
] | [
"TIL"
] | I've been trying out the https://docs.pinot.apache.org/basics/getting-started/kubernetes-quickstart[Running Pinot in Kubernetes^] tutorial and ran into a problem trying to write data to Kafka.
In this blog we'll explore how I got around that problem.
I'm using Helm with Kubernetes and started a Kafka service by running the following:
[source, bash]
----
helm repo add kafka https://charts.bitnami.com/bitnami
helm install -n pinot-quickstart kafka kafka/kafka --set replicas=1,zookeeper.image.tag=latest
----
I waited until the service had started and then ran the following command to port forward the Kafka service's port 9092 to port 9092 on my host OS:
[source, bash]
----
kubectl port-forward service/kafka-headless 9092:9092 -n pinot-quickstart
----
I then ran a script that attempted to write some data into Kafka:
[source, bash]
----
python datagen.py --sleep 0.0001 2>/dev/null |
jq -cr --arg sep ø '[.uuid, tostring] | join($sep)' |
kcat -P -b localhost:9092 -t events -Kø
----
This hung for a few seconds before throwing the following error:
.Output
[source, text]
----
%3|1685969550.862|FAIL|rdkafka#producer-1| [thrd:kafka-0.kafka-headless.pinot-quickstart.svc.cluster.local:9092/]: kafka-0.kafka-headless.pinot-quickstart.svc.cluster.local:9092/0: Failed to resolve 'kafka-0.kafka-headless.pinot-quickstart.svc.cluster.local:9092': nodename nor servname provided, or not known (after 5002ms in state CONNECT)
----
I'd assumed that all I needed to do was map the port, but it seems like it tries to resolve the hostname, which isn't known by the host OS.
A workaround for this issue is to https://github.com/bitnami/charts/issues/1021[update your `/etc/hosts`^] file:
```
127.0.0.1 kafka-0.kafka-headless.pinot-quickstart.svc.cluster.local
```
If your Kafka has a different host name, be sure to change the second argument appropriately. | In this post we'll learn how to work around a problem when trying to write from the host OS to Kafka running inside Kubernetes. | uploads/2023/06/k8s-kafka-banner.png | [
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] | kafka-kubernetes-failed-resolve-nodename-servname-not-known | https://markhneedham.com/blog/2023/06/06/kafka-kubernetes-failed-resolve-nodename-servname-not-known |
false | 2023-06-23 04:44:37 | Running a Hugging Face Large Language Model (LLM) locally on my laptop | [
"hugging-face",
"langchain",
"til",
"generative-ai"
] | [
"TIL"
] | :icons: font
I've been playing around with a bunch of Large Language Models (LLMs) on Hugging Face and while the free inference API is cool, it can sometimes be busy, so I wanted to learn how to run the models locally.
That's what we'll be doing in this blog post.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/Ay5K4tog5NQ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
====
You'll need to install the following libraries if you want to follow along:
[source, bash]
----
pip install 'langchain[llms]' huggingface-hub langchain transformers
----
The first step is to choose a model that you want to download.
I quite like https://huggingface.co/lmsys/fastchat-t5-3b-v1.0[lmsys/fastchat-t5-3b-v1.06] so we're gonna use that one for the rest of the post.
## Downloading the LLM
We can https://stackoverflow.com/questions/67595500/how-to-download-model-from-huggingface[download a model^] by running the following code:
[source, python]
----
from huggingface_hub import hf_hub_download
HUGGING_FACE_API_KEY = "<hugging-face-api-key-goes-here>"
# Replace this if you want to use a different model
model_id = "lmsys/fastchat-t5-3b-v1.0"
filenames = [ <1>
"pytorch_model.bin", "added_tokens.json", "config.json", "generation_config.json",
"special_tokens_map.json", "spiece.model", "tokenizer_config.json"
]
for filename in filenames:
downloaded_model_path = hf_hub_download(
repo_id=model_id,
filename=filename,
token=HUGGING_FACE_API_KEY
)
print(downloaded_model_path)
print(downloaded_model_path)
----
<.> I worked out the filenames by browsing https://huggingface.co/lmsys/fastchat-t5-3b-v1.0/tree/main[Files and versions^] on the Hugging Face UI.
This model is almost 7GB in size, so you probably want to connect your computer to an ethernet cable to get maximum download speed!
As well as downloading the model, the script prints out the location of the model.
In my case it's the following:
.Output
[source, text]
----
/Users/markhneedham/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/pytorch_model.bin
/Users/markhneedham/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/added_tokens.json
/Users/markhneedham/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/config.json
/Users/markhneedham/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/generation_config.json
/Users/markhneedham/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/special_tokens_map.json
/Users/markhneedham/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/spiece.model
/Users/markhneedham/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/tokenizer_config.json
----
[WARNING]
====
Any files that you don't explicitly download like this will be downloaded the first time that you use the model.
I'm downloading everything separately so that I don't have to unexpectedly have to wait for things later on!
====
## Running the LLM
We're now going to use the model locally with LangChain so that we can create a repeatable structure around the prompt.
Let's first import some libraries:
[source, python]
----
from langchain.llms import HuggingFacePipeline
from langchain import PromptTemplate, LLMChain
----
And now we're going to create an instance of our model:
[source, python]
----
model_id = "lmsys/fastchat-t5-3b-v1.0"
llm = HuggingFacePipeline.from_model_id(
model_id=model_id,
task="text2text-generation",
model_kwargs={"temperature": 0, "max_length": 1000},
)
----
[NOTE]
====
The value that we use for `task` needs to match the label of the model that's just underneath the model name on the Hugging Face UI.
image::{{<siteurl>}}/uploads/2023/07/task-type.png[title='The task type for this model']
====
Now let's create a template for what we want the LLM to do when we send it a prompt:
[source, python]
----
template = """
You are a friendly chatbot assistant that responds conversationally to users' questions.
Keep the answers short, unless specifically asked by the user to elaborate on something.
Question: {question}
Answer:"""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
----
Next, let's create a little function that asks a question and prints the response:
[source, python]
----
def ask_question(question):
result = llm_chain(question)
print(result['question'])
print("")
print(result['text'])
----
We'll also create a (ChatGPT generated) Timer context manager to make it easier to see how long it takes to answer each question:
[source, python]
----
import time
class TimerError(Exception):
"""A custom exception used to report errors in use of Timer class"""
class Timer:
def __init__(self):
self._start_time = None
def __enter__(self):
if self._start_time is not None:
raise TimerError(f"Timer is running. Use .stop() to stop it")
self._start_time = time.perf_counter()
def __exit__(self, exc_type, exc_val, exc_tb):
if self._start_time is None:
raise TimerError(f"Timer is not running. Use .start() to start it")
elapsed_time = time.perf_counter() - self._start_time
self._start_time = None
print(f"Elapsed time: {elapsed_time:0.4f} seconds")
----
Now let's see how well the model knows London:
[source, python]
----
with Timer():
ask_question("Describe some famous landmarks in London")
----
.Output
[source, text]
----
Describe some famous landmarks in London
<pad> Some famous landmarks in London include:
* Buckingham Palace
* St. Paul's Cathedral
* The Tower of London
* The London Eye
* The London Eye is a giant wheel that flies over London.
Elapsed time: 17.7592 seconds
----
I'm not sure about that last bullet, but I do like the idea of a giant wheel flying over the city!
Let's try something else:
[source, python]
----
with Timer():
ask_question("Tell me about Apache Kafka in a few sentences.")
----
.Output
[source, text]
----
Tell me about Apache Kafka in a few sentences.
<pad> Apache Kafka is a distributed streaming platform that allows for the real-time processing of large amounts of data. It is designed to be scalable, fault-tolerant, and easy to use.
Elapsed time: 15.7795 seconds
----
Not too bad.
It doesn't do so well if I ask about Apache Pinot though!
[source, python]
----
with Timer():
ask_question("Tell me about Apache Pinot in a few sentences.")
----
.Output
[source, text]
----
Tell me about Apache Pinot in a few sentences.
<pad> Apache Pinot is a Java framework for building web applications that can handle a wide range of tasks, including web development, database management, and web application testing.
Elapsed time: 13.6518 seconds
----
It's also nowhere near as fast as ChatGPT, but my computer isn't as good as the ones that they use!
Having said that, it is pretty cool to be able to run this type of thing on your own machine and I think it could certainly be useful if you want to ask questions about your own documents that you don't want to send over the internet. | In this post, we'll learn how to download a Hugging Face Large Language Model (LLM) and run it locally. | uploads/2023/06/huggingface-local-llm-banner.png | [
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] | hugging-face-run-llm-model-locally-laptop | https://markhneedham.com/blog/2023/06/23/hugging-face-run-llm-model-locally-laptop |
false | 2023-06-23 02:44:37 | LangChain: 1 validation error for LLMChain - value is not a valid dict (type=type_error.dict) | [
"gpt4all",
"langchain",
"til",
"generative-ai"
] | [
"TIL"
] | I surely can't be the first to make the mistake that I'm about to describe and I expect I won't be the last!
I'm still swimming in the LLM waters and I was trying to get GPT4All to play nicely with https://python.langchain.com/docs/get_started/introduction.html[LangChain^].
I wrote the following code to create an LLM chain in LangChain so that every question would use the same prompt template:
[source, python]
----
from langchain import PromptTemplate, LLMChain
from gpt4all import GPT4All
llm = GPT4All(
model_name="ggml-gpt4all-j-v1.3-groovy",
model_path="/Users/markhneedham/Library/Application Support/nomic.ai/GPT4All/"
)
template = """
You are a friendly chatbot assistant that responds in a conversational
manner to users questions. Keep the answers short, unless specifically
asked by the user to elaborate on something.
Question: {question}
Answer:"""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
----
When I ran this code, I got the following error:
.Output
[source, text]
----
Traceback (most recent call last):
File "/Users/markhneedham/projects/docs-bot/gpt4all_chain.py", line 26, in <module>
llm_chain = LLMChain(prompt=prompt, llm=llm)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/markhneedham/projects/docs-bot/env/lib/python3.11/site-packages/langchain/load/serializable.py", line 65, in __init__
super().__init__(**kwargs)
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for LLMChain
llm
value is not a valid dict (type=type_error.dict)
----
It took me a long time to work out that the issue is that the value we've passed in as `llm` is invalid as it doesn't extend LangChain's `BaseLanguageModel` class.
And the reason for that is that I accidentally passed in the `GPT4All` class, rather than the one with the same name from the LangChain library.
Let's fix that:
[source, python]
----
from langchain.llms import GPT4All
llm = GPT4All(
model="/Users/markhneedham/Library/Application Support/nomic.ai/GPT4All/ggml-gpt4all-j-v1.3-groovy",
)
----
And now we can run that code without any errors! | In this post we'll learn about an error message when passing the wrong GPT4All model to LangChain. | uploads/2023/06/langchain-invalid-model-banner.png | [
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] | langchain-validation-error-llmchain-value-not-valid-dict | https://markhneedham.com/blog/2023/06/23/langchain-validation-error-llmchain-value-not-valid-dict |
false | 2023-06-12 02:44:37 | GitHub: Get a CSV containing my pull requests (PRs) | [
"github",
"til"
] | [
"TIL"
] | :icons: font
I wanted to get a list of my GitHub pull requests (PRs) and commits, which was surprisingly difficult to figure out how to do.
I'm sure it must be possible to get this data from the API, but it was a lot easier to figure out how to do so with the https://github.com/cli/cli[GitHub CLI^].
[NOTE]
====
This blog post explains how to use the GitHub CLI on the Mac OS terminal.
If you're trying to do this on Windows, see https://reshmeeauckloo.com/posts/github_retrieve_all_prs_githubcli/[Get a CSV of all my pull requests from Github using Github CLI and PowerShell^].
====
So I installed that and it had to be authorised via the GitHub website the first time.
I could then get a list of my PRs by running the following command:
[source, bash]
----
gh search prs \
--author=@me \
--limit 10 \
--json title,repository,closedAt,url | jq -c '.[]'
----
.Output
[source, json]
----
{"closedAt":"2023-06-12T13:09:43Z","repository":{"name":"dev.startree.ai","nameWithOwner":"startreedata/dev.startree.ai"},"title":"RT data ingestion explanation","url":"https://github.com/startreedata/dev.startree.ai/pull/173"}
{"closedAt":"0001-01-01T00:00:00Z","repository":{"name":"sourcegraph","nameWithOwner":"sourcegraph/sourcegraph"},"title":"Small typo","url":"https://github.com/sourcegraph/sourcegraph/pull/52604"}
{"closedAt":"2023-05-13T16:40:59Z","repository":{"name":"venkat","nameWithOwner":"mneedham/venkat"},"title":"pull out language","url":"https://github.com/mneedham/venkat/pull/3"}
{"closedAt":"2023-05-05T06:00:29Z","repository":{"name":"dev.startree.ai","nameWithOwner":"startreedata/dev.startree.ai"},"title":"Update README.md","url":"https://github.com/startreedata/dev.startree.ai/pull/149"}
{"closedAt":"2023-05-10T01:22:35Z","repository":{"name":"risingwave-docs","nameWithOwner":"risingwavelabs/risingwave-docs"},"title":"Update risingwave-kubernetes.md","url":"https://github.com/risingwavelabs/risingwave-docs/pull/792"}
{"closedAt":"2023-04-04T13:08:18Z","repository":{"name":"pinot-recipes","nameWithOwner":"startreedata/pinot-recipes"},"title":"Update README.md","url":"https://github.com/startreedata/pinot-recipes/pull/10"}
{"closedAt":"2023-03-30T10:07:52Z","repository":{"name":"pinot-recipes","nameWithOwner":"startreedata/pinot-recipes"},"title":"Update README.md","url":"https://github.com/startreedata/pinot-recipes/pull/8"}
{"closedAt":"2023-03-30T10:07:13Z","repository":{"name":"pinot-recipes","nameWithOwner":"startreedata/pinot-recipes"},"title":"Update README.md","url":"https://github.com/startreedata/pinot-recipes/pull/7"}
{"closedAt":"0001-01-01T00:00:00Z","repository":{"name":"pinot","nameWithOwner":"apache/pinot"},"title":"Consumer lag unknown","url":"https://github.com/apache/pinot/pull/10310"}
{"closedAt":"2023-02-13T23:30:29Z","repository":{"name":"pinot","nameWithOwner":"apache/pinot"},"title":"Bug with time based segment relocator on real-time segments","url":"https://github.com/apache/pinot/pull/10272"}
----
I want those in CSV format though, which we can do by putting all the values into an array and then calling `@csv`:
[source, bash]
----
gh search prs \
--author=@me \
--limit 10 \
--json title,repository,closedAt,url |
jq -r '.[]| [.title, .url, .repository.nameWithOwner, .closedAt] | @csv'
----
.Output
|===
|RT data ingestion explanation |https://github.com/startreedata/dev.startree.ai/pull/173 |startreedata/dev.startree.ai |2023-06-12T13:09:43Z
|Small typo |https://github.com/sourcegraph/sourcegraph/pull/52604 |sourcegraph/sourcegraph |0001-01-01T00:00:00Z
|Pull out language |https://github.com/mneedham/venkat/pull/3 |mneedham/venkat |2023-05-13T16:40:59Z
|Update README.md |https://github.com/startreedata/dev.startree.ai/pull/149 |startreedata/dev.startree.ai |2023-05-05T06:00:29Z
|Update risingwave-kubernetes.md |https://github.com/risingwavelabs/risingwave-docs/pull/792 |risingwavelabs/risingwave-docs |2023-05-10T01:22:35Z
|Update README.md |https://github.com/startreedata/pinot-recipes/pull/10 |startreedata/pinot-recipes |2023-04-04T13:08:18Z
|Update README.md |https://github.com/startreedata/pinot-recipes/pull/8 |startreedata/pinot-recipes |2023-03-30T10:07:52Z
|Update README.md |https://github.com/startreedata/pinot-recipes/pull/7 |startreedata/pinot-recipes |2023-03-30T10:07:13Z
|Consumer lag unknown |https://github.com/apache/pinot/pull/10310 |apache/pinot |0001-01-01T00:00:00Z
|Bug with time based segment relocator on real-time segments |https://github.com/apache/pinot/pull/10272 |apache/pinot |2023-02-13T23:30:29Z
|===
You can see that I don't actually create that many pull requests as I tend to commit things straight to main!
As a bonus, let's have a look at my commits as well:
[source, bash]
----
gh search commits \
--author=@me --limit 1 \
--author-date=">2022-01-01" --order desc --sort author-date \
--visibility public --json repository,sha,commit |
jq '.[]'
----
.Output
[source, json]
----
{
"commit": {
"author": {
"date": "2023-06-12T10:59:13+01:00",
"email": "m.h.needham@gmail.com",
"name": "Mark Needham"
},
"comment_count": 0,
"committer": {
"date": "2023-06-12T10:59:13+01:00",
"email": "m.h.needham@gmail.com",
"name": "Mark Needham"
},
"message": "Merge branch 'main' of github.com:startreedata/pinot-recipes",
"tree": {
"sha": "71da7e300e8ed035c6877422e8dd30266d7abc2c"
}
},
"repository": {
"description": "This repository contains recipes for Apache Pinot.",
"fullName": "startreedata/pinot-recipes",
"id": "R_kgDOGdrbcQ",
"isFork": false,
"isPrivate": false,
"name": "pinot-recipes",
"owner": {
"id": "MDEyOk9yZ2FuaXphdGlvbjc2NjAxMzMz",
"is_bot": false,
"login": "startreedata",
"type": "Organization",
"url": "https://github.com/startreedata"
},
"url": "https://github.com/startreedata/pinot-recipes"
},
"sha": "373dc891a04ecb9dba009394ce9266a07d8ac9c0"
}
----
Let's pull out just the bits we want:
[source, bash]
----
gh search commits \
--author=@me --limit 10 \
--author-date=">2022-01-01" --order desc --sort author-date \
--visibility public --json repository,sha,commit |
jq -r '.[] | [.commit.author.date, .commit.message, .repository.fullName] | @csv'
----
.Output
|===
|2023-06-12T10:59:13+01:00 |Merge branch 'main' of github.com:startreedata/pinot-recipes |startreedata/pinot-recipes
|2023-06-12T10:59:01+01:00 |fixed host |startreedata/pinot-recipes
|2023-06-09T09:30:53+01:00 |update |mneedham/hugo-blog
|2023-06-09T07:48:13+01:00 |updates |mneedham/mneedham.github.io
|2023-06-09T07:40:23+01:00 |linkedin |mneedham/mneedham.github.io
|2023-06-09T07:03:28+01:00 |updates |mneedham/hugo-blog
|2023-06-09T06:56:00+01:00 |duckdb |mneedham/mneedham.github.io
|2023-06-06T16:46:23+01:00 |Update README.md |startreedata/pinot-recipes
|2023-06-06T16:45:41+01:00 |Update README.md |startreedata/pinot-recipes
|2023-06-06T16:45:22+01:00 |Update README.md |startreedata/pinot-recipes
|=== | In this post we'll learn how to get a list of our latest PRs using the GitHub CLI tool. | uploads/2023/05/git-prs-banner.png | [
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] | github-list-pull-requests-csv | https://markhneedham.com/blog/2023/06/12/github-list-pull-requests-csv |
false | 2023-06-13 02:44:37 | DuckDB/SQL: Transpose columns to rows with UNPIVOT | [
"duckdb",
"til"
] | [
"TIL"
] | :icons: font
I've been playing around with the https://www.kaggle.com/datasets/hugomathien/soccer[Kaggle European Soccer dataset^], which contains, amongst other things, players and their stats in the FIFA video game.
I wanted to compare the stats of Ronaldo and Messi, which is where this story begins.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/zhVSXLzy8EY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
====
We will of course be using DuckDB and since the dataset is a SQLite database, we'll need to install the SQLite extension"
[source, sql]
----
INSTALL sqlite;
LOAD sqlite;
----
Next, let's create tables for `Player` and `Player_Attribute`:
[source, sql]
----
CREATE OR REPLACE TABLE Player (
id BIGINT,
player_api_id BIGINT,
player_name VARCHAR,
player_fifa_api_id BIGINT,
birthday VARCHAR,
height float,
weight BIGINT
);
INSERT INTO Player
FROM sqlite_scan('database.sqlite', 'Player');
CREATE OR REPLACE TABLE Player_Attributes AS
FROM sqlite_scan('database.sqlite', 'Player_Attributes');
----
I want to join these two tables together and get the latest stat for each player, which we can do by creating the following view that uses a window function:
[source, sql]
----
CREATE OR REPLACE VIEW PlayerStats AS
FROM Player
JOIN (
select *,
ROW_NUMBER()
OVER (partition by player_api_id ORDER BY date DESC) rn
FROM Player_Attributes
) AS attrs ON attrs.player_api_id = Player.player_api_id AND rn = 1
SELECT * EXCLUDE(rn, id, player_api_id, player_fifa_api_id);
----
If we want to find the stats for one player, we can write a query like this:
[source, sql]
----
FROM PlayerStats WHERE player_name = 'Cristiano Ronaldo';
----
.Output
[options="header"]
|===
|Player Name |Birthday |Height |Weight |... |GK Handling |GK Kicking |GK Positioning |GK Reflexes
|Cristiano Ronaldo |1985-02-05 00:00:00 |185.42 |176 |... |11 |15 |14 |11
|===
We can see from the `...` that some of the columns have been truncated.
This table has only 9 columns, but there are more than 30 different stats for each player.
We'd need a very wide monitor to be able to see all of those in one go.
Alternatively, we could display the stats vertically, which is where the `UNPIVOT` function comes in.
We can write the following query to see Ronaldo's stats as rows instead of columns:
[source, sql]
----
UNPIVOT (FROM PlayerStats WHERE player_name = 'Cristiano Ronaldo')
ON COLUMNS (*)
INTO NAME stat1 VALUE "Cristiano Ronaldo";
----
.Output
[options="header"]
|==========================================
| stat1 | Cristiano Ronaldo
| player_name | Cristiano Ronaldo
| birthday | 1985-02-05 00:00:00
| height | 185.42
| weight | 176
| date | 2015-10-16 00:00:00
| overall_rating | 93
| potential | 93
| preferred_foot | right
| attacking_work_rate | high
| defensive_work_rate | low
| crossing | 82
| finishing | 95
| heading_accuracy | 86
| short_passing | 81
| volleys | 87
| dribbling | 93
| curve | 88
| free_kick_accuracy | 77
| long_passing | 72
| ball_control | 91
| acceleration | 91
| sprint_speed | 93
| agility | 90
| reactions | 92
| balance | 62
| shot_power | 94
| jumping | 94
| stamina | 90
| strength | 79
| long_shots | 93
| aggression | 62
| interceptions | 29
| positioning | 93
| vision | 81
| penalties | 85
| marking | 22
| standing_tackle | 31
| sliding_tackle | 23
| gk_diving | 7
| gk_handling | 11
| gk_kicking | 15
| gk_positioning | 14
| gk_reflexes | 11
|==========================================
That's much easier to read.
And if we want to compare Ronaldo's stats against another play, say, Lionel Messi, we can JOIN `UNPIVOT` clauses together:
[source, sql]
----
FROM (
UNPIVOT (FROM PlayerStats WHERE player_name = 'Cristiano Ronaldo')
ON COLUMNS (*)
INTO NAME stat1 VALUE "Cristiano Ronaldo"
) p1
JOIN (
UNPIVOT (FROM PlayerStats WHERE player_name = 'Lionel Messi')
ON COLUMNS (*)
INTO NAME stat2 VALUE "Lionel Messi"
) p2 ON p1.stat1 = p2.stat2
SELECT stat1 AS stat, * EXCLUDE(stat1, stat2);
----
.Output
[options="header"]
|================================================================
| stat | Cristiano Ronaldo | Lionel Messi
| player_name | Cristiano Ronaldo | Lionel Messi
| birthday | 1985-02-05 00:00:00 | 1987-06-24 00:00:00
| height | 185.42 | 170.18
| weight | 176 | 159
| date | 2015-10-16 00:00:00 | 2015-12-17 00:00:00
| overall_rating | 93 | 94
| potential | 93 | 94
| preferred_foot | right | left
| attacking_work_rate | high | medium
| defensive_work_rate | low | low
| crossing | 82 | 80
| finishing | 95 | 93
| heading_accuracy | 86 | 71
| short_passing | 81 | 88
| volleys | 87 | 85
| dribbling | 93 | 96
| curve | 88 | 89
| free_kick_accuracy | 77 | 90
| ball_control | 91 | 96
| acceleration | 91 | 95
| sprint_speed | 93 | 90
| agility | 90 | 92
| reactions | 92 | 92
| balance | 62 | 95
| shot_power | 94 | 80
| jumping | 94 | 68
| stamina | 90 | 75
| strength | 79 | 59
| long_shots | 93 | 88
| aggression | 62 | 48
| interceptions | 29 | 22
| positioning | 93 | 90
| vision | 81 | 90
| penalties | 85 | 74
| marking | 22 | 13
| standing_tackle | 31 | 23
| sliding_tackle | 23 | 21
| gk_diving | 7 | 6
| gk_handling | 11 | 11
| gk_kicking | 15 | 15
| gk_positioning | 14 | 14
| gk_reflexes | 11 | 8
| long_passing | 72 | 79
|================================================================ | In this post we'll learn how to transpose columns to rows using DuckDB's UNPIVOT function. | uploads/2023/06/duckdb-transpose-banner.png | [
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] | duckdb-sql-transpose-columns-to-rows-unpivot | https://markhneedham.com/blog/2023/06/13/duckdb-sql-transpose-columns-to-rows-unpivot |
false | 2023-06-22 04:44:37 | GPT4All/LangChain: Model.__init__() got an unexpected keyword argument 'ggml_model' (type=type_error) | [
"gpt4all",
"langchain",
"til",
"generative-ai"
] | [
"TIL"
] | I'm starting to realise that things move insanely fast in the world of LLMs (Large Language Models) and you will run into issues because you aren't using the latest version of libraries.
I say this because I've been following Sami Maameri's blog post which explains how to run an https://betterprogramming.pub/private-llms-on-local-and-in-the-cloud-with-langchain-gpt4all-and-cerebrium-6dade79f45f6[LLM on your own machine^] and ran into an error, which we'll explore in this blog post.
Sami's post is based around a library called GPT4All, but he also uses LangChain to glue things together.
I first installed the following libraries:
[source, bash]
----
pip install gpt4all langchain pyllamacpp
----
And then launched a Python REPL, into which I pasted the following code:
[source, python]
----
from langchain.llms import GPT4All
llm = GPT4All(model='/Users/markhneedham/Library/Application Support/nomic.ai/GPT4All/ggml-gpt4all-j-v1.3-groovy.bin')
----
The model path that I've used is the location where the GPT4All app downloaded the model, but you can choose to put it elsewhere.
When that second line of code ran, I got the following exception:
[source, output]
----
Exception ignored in: <function Model.__del__ at 0x10ab2b7e0>
Traceback (most recent call last):
File "/Users/markhneedham/projects/docs-bot/env/lib/python3.11/site-packages/pyllamacpp/model.py", line 402, in __del__
if self._ctx:
^^^^^^^^^
AttributeError: 'Model' object has no attribute '_ctx'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for GPT4All
__root__
Model.__init__() got an unexpected keyword argument 'ggml_model' (type=type_error)
----
After a bit of exploration, I came across https://github.com/hwchase17/langchain/issues/3839[this GitHub thread^], which suggests that the GPT4All code had changed and LangChain was passing in the wrong parameters.
I also learnt that it's been fixed for https://github.com/hwchase17/langchain/pull/4567[about a month^], so my version of LangChain was clearly lagging.
Let's check my version:
[source, bash]
----
pip freeze | grep langchain
----
.Output
[source, text]
----
langchain==0.0.142
----
From searching the ttps://pypi.org/project/langchain/#history[release history of LangChain^], I discovered that I installed it on 17th April, since when there have been more than 60 releases!
I updated by running `pip install langchain -U` and now I'm up to date (at least for now!):
.Output
[source, text]
----
langchain==0.0.208
langchainplus-sdk==0.0.16
----
I'm also able to run the example from Sami's blog post:
[source, python]
----
llm("""
You are a friendly chatbot assistant that responds in a conversational
manner to users questions. Keep the answers short, unless specifically
asked by the user to elaborate on something.
Question: Where is Paris?
Answer:""")
----
.Output
[source, text]
----
' The capital city of France and Europe located at 47°23′N 8°54″W'
---- | In this post we'll learn about an error message when using GPT4All with LangChain. | uploads/2023/06/gpt4all-langchain-banner.png | [
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] | gpt4all-langchain-unexpected-keyword-ggml_model | https://markhneedham.com/blog/2023/06/22/gpt4all-langchain-unexpected-keyword-ggml_model |
false | 2023-06-22 02:44:37 | Chroma/LangChain: 'NoneType' object has no attribute 'info' | [
"chroma",
"chromadb",
"langchain",
"til",
"generative-ai"
] | [
"TIL"
] | Following on from https://www.markhneedham.com/blog/2023/06/21/chroma-index-not-found-create-instance-querying/[a blog post that I wrote yesterday^] about doing similarity search with ChromaDB, I noticed an odd error message being printed as the script was exiting.
In this blog post, we'll explore what was going on.
To recap, I have the following code to find chunks of YouTube transcripts that are most similar to an input query:
.test_chroma.py
[source, python]
----
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
hf_embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
store = Chroma(collection_name="transcript", persist_directory="db", embedding_function=hf_embeddings)
result = store.similarity_search("Who is Tim Berglund?", top_n=2)
for row in result:
print(row)
----
When I run this script by typing `python test_chroma.py`, after the similar documents are printed, I get the following exception:
.Output
[source, text]
----
Exception ignored in: <function PersistentDuckDB.__del__ at 0x2abfe39c0>
Traceback (most recent call last):
File "/Users/markhneedham/projects/docs-bot/env/lib/python3.11/site-packages/chromadb/db/duckdb.py", line 445, in __del__
AttributeError: 'NoneType' object has no attribute 'info'
----
I came across the following https://github.com/chroma-core/chroma/issues/364[GitHub thread^] that was discussing the issue and one suggested fix was to set `store = None` at the end of the script:
[source, python]
----
store = None
----
This did indeed work, but further down the thread it was suggested that the bug is fixed in the latest version of Chroma.
I checked my version:
[source, bash]
----
pip freeze | grep chroma
----
.Output
[source, text]
----
chromadb==0.3.21
----
A https://pypi.org/project/chromadb/[quick check on PyPi^] revealed that the latest version is actually 0.3.26, so let's update our version:
[source, bash]
----
pip install --upgrade chromadb
----
Let's check our version:
[source, bash]
----
pip freeze | grep chroma
----
.Output
[source, text]
----
chromadb==0.3.26
----
If I delete the `store = None` line and re-run the script again, the issue has been fixed and I don't get an error message. | In this post we'll learn about an error message when using the Chroma database. | uploads/2023/06/chroma-nonetype-banner.png | [
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0.018208127468824387,
0.025000007823109627,
0.0320802740752697,
-0.01742166467010975
] | chroma-nonetype-object-no-attribute-info | https://markhneedham.com/blog/2023/06/22/chroma-nonetype-object-no-attribute-info |
false | 2023-12-04 00:44:37 | ClickHouse: Tuples - Code: 47. DB::Exception: Missing columns: while processing query: | [
"clickhouse",
"til"
] | [
"TIL"
] | :icons: font
I've been playing around with the https://www.markhneedham.com/blog/2023/11/15/clickhouse-summing-columns-remote-files/[Mid Journey Parquet metadata that I wrote about in my last blog post^] and struggled quite a bit to get the query to do what I wanted.
Come along on a journey with me and we'll figure it out together.
We're querying the metadata of a Parquet file that contains the metadata (I know!) of images created by the Mid Journey generative AI service.
The following query gets us the columns:
[source, sql]
----
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet', ParquetMetadata)
SELECT columns
SETTINGS max_http_get_redirects=1
Format Vertical;
----
.Output
[source, text]
----
Row 1:
──────
columns: [('id','id',1,0,'BYTE_ARRAY','String','SNAPPY',23093988,13133418,'43.13%',['PLAIN','RLE','RLE_DICTIONARY']),('channel_id','channel_id',1,0,'BYTE_ARRAY','String','SNAPPY',112,116,'-3.571%',['PLAIN','RLE','RLE_DICTIONARY']),('content','content',1,0,'BYTE_ARRAY','String','SNAPPY',208657682,46191873,'77.86%',['PLAIN','RLE','RLE_DICTIONARY']),('timestamp','timestamp',1,0,'BYTE_ARRAY','String','SNAPPY',36052113,9046231,'74.91%',['PLAIN','RLE','RLE_DICTIONARY']),('image_id','image_id',1,0,'BYTE_ARRAY','String','SNAPPY',23093988,13118570,'43.19%',['PLAIN','RLE','RLE_DICTIONARY']),('height','height',1,0,'INT64','None','SNAPPY',915584,498549,'45.55%',['PLAIN','RLE','RLE_DICTIONARY']),('width','width',1,0,'INT64','None','SNAPPY',916607,496767,'45.8%',['PLAIN','RLE','RLE_DICTIONARY']),('url','url',1,0,'BYTE_ARRAY','String','SNAPPY',180090922,71430496,'60.34%',['PLAIN','RLE','RLE_DICTIONARY']),('size','size',1,0,'INT64','None','SNAPPY',8286381,5392260,'34.93%',['PLAIN','RLE','RLE_DICTIONARY'])]
----
This isn't that readable, but we can sort that out by using the `arrayJoin` function to pull each column out into its own row.
The metadata about each column is stored in a tuple, so we're also going to use the `toTypeName` function to return the names of each element in the tuple:
[source, sql]
----
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet', ParquetMetadata)
SELECT arrayJoin(columns) AS md, toTypeName(md)
SETTINGS max_http_get_redirects=1
Format Vertical;
----
.Output
[source, text]
----
Row 1:
──────
md: ('id','id',1,0,'BYTE_ARRAY','String','SNAPPY',23093988,13133418,'43.13%',['PLAIN','RLE','RLE_DICTIONARY'])
toTypeName(arrayJoin(columns)): Tuple(name String, path String, max_definition_level UInt64, max_repetition_level UInt64, physical_type String, logical_type String, compression String, total_uncompressed_size UInt64, total_compressed_size UInt64, space_saved String, encodings Array(String))
Row 2:
──────
md: ('channel_id','channel_id',1,0,'BYTE_ARRAY','String','SNAPPY',112,116,'-3.571%',['PLAIN','RLE','RLE_DICTIONARY'])
toTypeName(arrayJoin(columns)): Tuple(name String, path String, max_definition_level UInt64, max_repetition_level UInt64, physical_type String, logical_type String, compression String, total_uncompressed_size UInt64, total_compressed_size UInt64, space_saved String, encodings Array(String))
Row 3:
──────
md: ('content','content',1,0,'BYTE_ARRAY','String','SNAPPY',208657682,46191873,'77.86%',['PLAIN','RLE','RLE_DICTIONARY'])
toTypeName(arrayJoin(columns)): Tuple(name String, path String, max_definition_level UInt64, max_repetition_level UInt64, physical_type String, logical_type String, compression String, total_uncompressed_size UInt64, total_compressed_size UInt64, space_saved String, encodings Array(String))
Row 4:
──────
md: ('timestamp','timestamp',1,0,'BYTE_ARRAY','String','SNAPPY',36052113,9046231,'74.91%',['PLAIN','RLE','RLE_DICTIONARY'])
toTypeName(arrayJoin(columns)): Tuple(name String, path String, max_definition_level UInt64, max_repetition_level UInt64, physical_type String, logical_type String, compression String, total_uncompressed_size UInt64, total_compressed_size UInt64, space_saved String, encodings Array(String))
Row 5:
──────
md: ('image_id','image_id',1,0,'BYTE_ARRAY','String','SNAPPY',23093988,13118570,'43.19%',['PLAIN','RLE','RLE_DICTIONARY'])
toTypeName(arrayJoin(columns)): Tuple(name String, path String, max_definition_level UInt64, max_repetition_level UInt64, physical_type String, logical_type String, compression String, total_uncompressed_size UInt64, total_compressed_size UInt64, space_saved String, encodings Array(String))
Row 6:
──────
md: ('height','height',1,0,'INT64','None','SNAPPY',915584,498549,'45.55%',['PLAIN','RLE','RLE_DICTIONARY'])
toTypeName(arrayJoin(columns)): Tuple(name String, path String, max_definition_level UInt64, max_repetition_level UInt64, physical_type String, logical_type String, compression String, total_uncompressed_size UInt64, total_compressed_size UInt64, space_saved String, encodings Array(String))
Row 7:
──────
md: ('width','width',1,0,'INT64','None','SNAPPY',916607,496767,'45.8%',['PLAIN','RLE','RLE_DICTIONARY'])
toTypeName(arrayJoin(columns)): Tuple(name String, path String, max_definition_level UInt64, max_repetition_level UInt64, physical_type String, logical_type String, compression String, total_uncompressed_size UInt64, total_compressed_size UInt64, space_saved String, encodings Array(String))
Row 8:
──────
md: ('url','url',1,0,'BYTE_ARRAY','String','SNAPPY',180090922,71430496,'60.34%',['PLAIN','RLE','RLE_DICTIONARY'])
toTypeName(arrayJoin(columns)): Tuple(name String, path String, max_definition_level UInt64, max_repetition_level UInt64, physical_type String, logical_type String, compression String, total_uncompressed_size UInt64, total_compressed_size UInt64, space_saved String, encodings Array(String))
Row 9:
───────
md: ('size','size',1,0,'INT64','None','SNAPPY',8286381,5392260,'34.93%',['PLAIN','RLE','RLE_DICTIONARY'])
toTypeName(arrayJoin(columns)): Tuple(name String, path String, max_definition_level UInt64, max_repetition_level UInt64, physical_type String, logical_type String, compression String, total_uncompressed_size UInt64, total_compressed_size UInt64, space_saved String, encodings Array(String))
----
We can explode these tuples so that each element in the tuple has its own column by using the `untuple` function:
[source, sql]
----
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet', ParquetMetadata)
SELECT untuple(arrayJoin(columns)) AS md
LIMIT 1
SETTINGS max_http_get_redirects=1
Format Vertical;
----
[source, text]
----
Row 1:
──────
md.name: id
md.path: id
md.max_definition_level: 1
md.max_repetition_level: 0
md.physical_type: BYTE_ARRAY
md.logical_type: String
md.compression: SNAPPY
md.total_uncompressed_size: 23093988
md.total_compressed_size: 13133418
md.space_saved: 43.13%
md.encodings: ['PLAIN','RLE','RLE_DICTIONARY']
----
So far, so good.
But what about if we want to filter the data so that we say only return the columns that have a `logical_type` of `STRING`?
I tried to do this in a few different ways:
[source, sql]
----
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet', ParquetMetadata)
SELECT untuple(arrayJoin(columns)) AS md
WHERE md.logical_type = 'STRING'
SETTINGS max_http_get_redirects=1
Format Vertical;
----
.Output
[source, text]
----
Received exception:
Code: 47. DB::Exception: Missing columns: 'md.logical_type' while processing query: 'SELECT untuple(arrayJoin(columns)) AS md FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet', ParquetMetadata) WHERE md.logical_type = 'STRING' SETTINGS max_http_get_redirects = 1', required columns: 'md.logical_type' 'columns', maybe you meant: 'columns'. (UNKNOWN_IDENTIFIER)
----
[source, sql]
----
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet', ParquetMetadata)
SELECT untuple(arrayJoin(columns)) AS md
WHERE "md.logical_type" = 'STRING'
SETTINGS max_http_get_redirects=1
Format Vertical;
----
.Output
[source, text]
----
Received exception:
Code: 47. DB::Exception: Missing columns: 'md.logical_type' while processing query: 'SELECT untuple(arrayJoin(columns)) AS md FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet', ParquetMetadata) WHERE `md.logical_type` = 'STRING' SETTINGS max_http_get_redirects = 1', required columns: 'md.logical_type' 'columns', maybe you meant: 'columns'. (UNKNOWN_IDENTIFIER)
----
[source, sql]
----
WITH columns AS (
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet', ParquetMetadata)
SELECT untuple(arrayJoin(columns)) AS md
SETTINGS max_http_get_redirects=1
)
FROM columns
SELECT *
WHERE "md.logical_type" = 'STRING'
Format Vertical;
----
.Output
[source, text]
----
Received exception:
Code: 47. DB::Exception: Missing columns: 'md.logical_type' while processing query: 'SELECT untuple(arrayJoin(columns)) AS md FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet', ParquetMetadata) WHERE `md.logical_type` = 'STRING' SETTINGS max_http_get_redirects = 1', required columns: 'md.logical_type' 'columns', maybe you meant: 'columns'. (UNKNOWN_IDENTIFIER)
----
But no luck - it doesn't seem to recognise the columns created by the `untuple` function.
So my next move was to try filtering on the tuple before I called `untuple`.
[source, sql]
----
WITH columns AS (
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet', ParquetMetadata)
SELECT arrayJoin(columns) AS col
SETTINGS max_http_get_redirects=1
)
FROM columns
SELECT untuple(col) AS md
WHERE col.'logical_type' = 'String'
Format Vertical;
----
.Output
[source, text]
----
Row 1:
──────
md.name: id
md.path: id
md.max_definition_level: 1
md.max_repetition_level: 0
md.physical_type: BYTE_ARRAY
md.logical_type: String
md.compression: SNAPPY
md.total_uncompressed_size: 23093988
md.total_compressed_size: 13133418
md.space_saved: 43.13%
md.encodings: ['PLAIN','RLE','RLE_DICTIONARY']
Row 2:
──────
md.name: channel_id
md.path: channel_id
md.max_definition_level: 1
md.max_repetition_level: 0
md.physical_type: BYTE_ARRAY
md.logical_type: String
md.compression: SNAPPY
md.total_uncompressed_size: 112
md.total_compressed_size: 116
md.space_saved: -3.571%
md.encodings: ['PLAIN','RLE','RLE_DICTIONARY']
Row 3:
──────
md.name: content
md.path: content
md.max_definition_level: 1
md.max_repetition_level: 0
md.physical_type: BYTE_ARRAY
md.logical_type: String
md.compression: SNAPPY
md.total_uncompressed_size: 208657682
md.total_compressed_size: 46191873
md.space_saved: 77.86%
md.encodings: ['PLAIN','RLE','RLE_DICTIONARY']
Row 4:
──────
md.name: timestamp
md.path: timestamp
md.max_definition_level: 1
md.max_repetition_level: 0
md.physical_type: BYTE_ARRAY
md.logical_type: String
md.compression: SNAPPY
md.total_uncompressed_size: 36052113
md.total_compressed_size: 9046231
md.space_saved: 74.91%
md.encodings: ['PLAIN','RLE','RLE_DICTIONARY']
Row 5:
──────
md.name: image_id
md.path: image_id
md.max_definition_level: 1
md.max_repetition_level: 0
md.physical_type: BYTE_ARRAY
md.logical_type: String
md.compression: SNAPPY
md.total_uncompressed_size: 23093988
md.total_compressed_size: 13118570
md.space_saved: 43.19%
md.encodings: ['PLAIN','RLE','RLE_DICTIONARY']
Row 6:
──────
md.name: url
md.path: url
md.max_definition_level: 1
md.max_repetition_level: 0
md.physical_type: BYTE_ARRAY
md.logical_type: String
md.compression: SNAPPY
md.total_uncompressed_size: 180090922
md.total_compressed_size: 71430496
md.space_saved: 60.34%
md.encodings: ['PLAIN','RLE','RLE_DICTIONARY']
----
That's better.
But let's say we only want to return a subset of the columns rather than all of them.
We can do that by running the following query:
[source, sql]
----
WITH columns AS (
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet', ParquetMetadata)
SELECT arrayJoin(columns) AS col
SETTINGS max_http_get_redirects=1
)
FROM columns
SELECT
col.'name' AS name,
col.'physical_type' AS physical_type,
col.'logical_type' AS logical_type,
col.'compression' AS compression
WHERE col.'logical_type' = 'String';
----
.Output
[source, text]
----
┌─name───────┬─physical_type─┬─logical_type─┬─compression─┐
│ id │ BYTE_ARRAY │ String │ SNAPPY │
│ channel_id │ BYTE_ARRAY │ String │ SNAPPY │
│ content │ BYTE_ARRAY │ String │ SNAPPY │
│ timestamp │ BYTE_ARRAY │ String │ SNAPPY │
│ image_id │ BYTE_ARRAY │ String │ SNAPPY │
│ url │ BYTE_ARRAY │ String │ SNAPPY │
└────────────┴───────────────┴──────────────┴─────────────┘
----
Nice!
Alternatively, we can use the `ARRAY JOIN` clause instead of the `arrayJoin` function, which I think is a better choice for this problem.
[source, sql]
----
SELECT
col.name,
col.physical_type,
col.logical_type,
col.compression
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet', ParquetMetadata)
ARRAY JOIN columns AS col
WHERE col.logical_type = 'String'
SETTINGS max_http_get_redirects=1;
----
.Output
[source, text]
----
┌─col.name───┬─col.physical_type─┬─col.logical_type─┬─col.compression─┐
│ id │ BYTE_ARRAY │ String │ SNAPPY │
│ channel_id │ BYTE_ARRAY │ String │ SNAPPY │
│ content │ BYTE_ARRAY │ String │ SNAPPY │
│ timestamp │ BYTE_ARRAY │ String │ SNAPPY │
│ image_id │ BYTE_ARRAY │ String │ SNAPPY │
│ url │ BYTE_ARRAY │ String │ SNAPPY │
└────────────┴───────────────────┴──────────────────┴─────────────────┘
---- | In this post, we'll learn how to filter on tuples using ClickHouse. | uploads/2023/12/clickhouse-tuples.png | [
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] | clickhouse-tuples-missing-columns | https://markhneedham.com/blog/2023/12/04/clickhouse-tuples-missing-columns |
false | 2023-12-11 00:44:37 | Dask: Parallelising file downloads | [
"dask",
"python",
"til"
] | [
"TIL"
] | :icons: font
Before a recent meetup talk that I did showing https://speakerdeck.com/markhneedham/analytics-on-your-laptop-with-clickhouse-local[how to do analytics on your laptop with ClickHouse Local^] at https://www.meetup.com/uk-open-source-data-infrastructure-meetup/events/297395569/[Aiven's Open Source Data Infrastructure Meetup^], I needed to download a bunch of Parquet files from https://huggingface.co/datasets/vivym/midjourney-messages[Hugging Face's midjourney-messages dataset^].
I alternate between using `wget`/`curl` or a Python script to do this type of work.
This time I used Python's `requests` library and I had the following script which downloads the Parquet files that I haven't already downloaded.
.download_parquet_files.py
[source, python]
----
import requests
import os
import shutil
import time
from tqdm import tqdm
def download_file(url, local_filename):
with requests.get(url, stream=True, allow_redirects=True) as r:
r.raise_for_status()
total_length = int(r.headers.get('content-length', 0))
chunk_size = 1024
with open(local_filename, 'wb') as f:
for chunk in tqdm(r.iter_content(chunk_size), total=total_length/chunk_size, desc=local_filename):
if chunk:
f.write(chunk)
return local_filename
def download(file):
url = f"https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/{file}?download=true"
print(f"Downloading {url}")
local_filename = download_file(url, f"data/{file}")
return local_filename
files_to_download = [f"0000{index:0>2}.parquet" for index in range(0, 56)]
for file in files_to_download:
if not os.path.isfile(f"data/{file}"):
download(file)
else:
print(f"File {file} already exists")
----
If we run this script, it downloads each file sequentially.
The output will look like the following:
.Output
[source, text]
----
Downloading https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet?download=true
data/000000.parquet: 155582it [00:14, 10577.06it/s]
Downloading https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000001.parquet?download=true
data/000001.parquet: 154888it [00:14, 10625.43it/s]
Downloading https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000002.parquet?download=true
data/000002.parquet: 147923it [00:13, 11228.27it/s]
5.42s user 1.40s system 15% cpu 43.926 total
----
It's pretty fast considering I haven't got my ethernet cable connected, but I was curious whether parallelising the downloads would speed things up.
I've previously done this using the https://docs.python.org/3/library/multiprocessing.html[multiprocessing^] library, but I found it quite fiddly to set up.
I'd heard about a library called https://www.dask.org/[dask^] on a few podcasts and although it's overkill to use it for this problem, I wanted to see if I could do it.
I didn't have to make too many changes to my script to get it working.
I had to add the following import
[source, python]
----
import dask
----
And update my for loop to use the `dask.delayed` function:
[source, python]
----
lazy_results = []
for file in files_to_download:
if not os.path.isfile(f"data/{file}"):
result = dask.delayed(download)(file)
lazy_results.append(result)
else:
print(f"File {file} already exists")
dask.persist(*lazy_results)
----
I deleted a few of the Parquet files from my machine and then gave it a try:
[source, python]
----
time poetry run python download_parquet_files.py
----
.Output
[source, text]
----
....
Downloading https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet?download=true
Downloading https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000001.parquet?download=true
Downloading https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000002.parquet?download=true
data/000002.parquet: 147923it [00:47, 3084.88it/s]
data/000001.parquet: 154888it [00:48, 3221.52it/s]
data/000000.parquet: 155582it [00:50, 3088.57it/s]
7.27s user 2.52s system 19% cpu 51.266 total
----
Well, that's no good - I made it slower!
So a good lesson for me that parallelising doesn't always make things faster, but along the way, I learnt that dask is very easy to configure.
Now I need to give it a try on some other tasks that can easily be parallelised to see if it fares better on those. | In this post, we'll learn how to use dask to parallelise the download of Parquet files from Hugging Face. | uploads/2023/12/dask-banner.png | [
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] | dash-parallelise-file-downloads | https://markhneedham.com/blog/2023/12/11/dash-parallelise-file-downloads |
false | 2023-12-23 00:44:37 | Experimenting with insanely-fast-whisper | [
"whisper",
"openai",
"til"
] | [
"TIL"
] | :icons: font
I recently came across https://github.com/Vaibhavs10/insanely-fast-whisper[insanely-fast-whisper^], a CLI tool that you can use to transcribe audio files using https://huggingface.co/openai/whisper-large-v3[OpenAI's whisper-large-v3 model^] or other smaller models.
In this blog post, I'll summarise my experience using it to transcribe one of Scott Galloway's podcast episodes.
I listen to podcasts on Player.FM and we'll use the https://player.fm/series/the-prof-g-pod-with-scott-galloway/prof-g-markets-scotts-nine-businesses[Prof G Markets: Scott’s Nine Businesses^] episode.
If we append `.json` to the URL, we'll get the https://player.fm/series/the-prof-g-pod-with-scott-galloway/prof-g-markets-scotts-nine-businesses.json[JSON metadata^] for the podcast.
[source, bash]
----
curl \
-H "Accept: application/json" \
--compressed \
https://player.fm/series/the-prof-g-pod-with-scott-galloway/prof-g-markets-scotts-nine-businesses.json 2>/dev/null
----
.Output
[source, json]
----
{
"type": "episode",
"id": 386048284,
"slug": "prof-g-markets-scotts-nine-businesses",
"explicit": false,
"mediaType": "audio/mpeg",
"rawTitle": "prof g market scott s busi",
"title": "Prof G Markets: Scott’s Nine Businesses",
"url": "https://www.podtrac.com/pts/redirect.mp3/pdst.fm/e/chrt.fm/track/524GE/traffic.megaphone.fm/VMP5922871816.mp3?updated=1701050904",
"publishedAt": 1701075600,
"lookup": "https://player.fm/series/2629939/386048284.json",
"share": "https://player.fm/1Balmje",
"size": 0,
"duration": 3256,
"description": "<p>Scott tells the story of his career in entrepreneurship, from starting a video rental company before business school, to going public with Red Envelope, to founding Prof G Media. He shares what was most meaningful about those experiences, and what was most surprising.</p><p>Learn more about your ad choices. Visit <a href=\"https://podcastchoices.com/adchoices\">podcastchoices.com/adchoices</a></p>",
"image": {
"url": "https://megaphone.imgix.net/podcasts/6769b2f4-3513-11ed-80e4-f37d796024b6/image/3e611f.png?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress",
"type": "image"
},
"series": {
"type": "series",
"id": 2629939,
"slug": "the-prof-g-pod-with-scott-galloway",
"access": "public",
"currentURL": "https://feeds.megaphone.fm/WWO6655869236",
"url": "https://feeds.megaphone.fm/WWO6655869236",
"author": "Vox Media Podcast Network",
"mediaKind": "audio",
"updatedAt": 1703149585,
"fingerprint": "OfxuCQYPnerfKAGIJams50OQEFXQ,Ufh9dcOyNk_0eM",
"descriptionFingerprint": "xN61CKwuMT87BElgYpgS5gqzI5xnI50UvHDxPiKe_9g",
"title": "The Prof G Pod with Scott Galloway",
"home": "https://podcasts.voxmedia.com/show/the-prof-g-show-with-scott-galloway",
"language": "en",
"imageURL": "https://megaphone.imgix.net/podcasts/e36115c4-4db6-11ea-be1c-87cdcc67bd9e/image/Tile.png?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress",
"fetch": {"status": "ok", "confidence": 9},
"fetchStatus": "ok",
"lookup": "https://player.fm/series/2629939.json",
"latestLookup": "https://player.fm/series/2629939/at/1703149585.json",
"relatedLookup": "https://player.fm/related-to/the-prof-g-pod-with-scott-galloway.json",
"share": "https://player.fm/series/the-prof-g-pod-with-scott-galloway",
"stats": {
"numberOfSubscriptions": 1558,
"numberOfEpisodes": 486,
"averageDuration": 2112,
"averageInterval": 144514,
"earliestPublishedAt": 1581525840,
"latestPublishedAt": 1703149200,
"manualSubscriptionsCentile": 0.985,
"shortTrendCentile": 0.12,
"longTrendCentile": 0.99
},
"tags": [
{
"type": "tag",
"id": 506,
"title": "Careers",
"rawTitle": "career",
"language": "en",
"polar": 0.5,
"topic": {"id": 658, "owner": {"id": 3}, "title": "Careers"},
"ancestors": null,
"series": {"amount": 17308, "centile": 0.991},
"sources": ["categories"]
},
{
"type": "tag",
"id": 1218967,
"title": "Section 4 / Westwood One Podcast Network",
"rawTitle": "section-4-westwood-network",
"language": "en",
"polar": 0.5,
"sources": ["owner"]
},
{
"type": "tag",
"id": 225,
"title": "Business Trends",
"rawTitle": "busi-trend",
"language": "en",
"polar": 0.7,
"topic": {
"id": 1695746,
"owner": {"id": 3},
"title": "Business Trends"
},
"ancestors": null,
"series": {"amount": 114, "centile": 0.941},
"sources": ["featured"]
},
{
"type": "tag",
"id": 125,
"title": "Business",
"rawTitle": "busi",
"language": "en",
"polar": 0.5,
"topic": {"id": 932129, "owner": {"id": 3}, "title": "Business"},
"ancestors": null,
"series": {"amount": 112273, "centile": 0.997},
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},
{
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"title": "Entrepreneur",
"rawTitle": "entrepreneur",
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"series": {"amount": 25242, "centile": 0.991},
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}
],
"image": {
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"url": "https://megaphone.imgix.net/podcasts/e36115c4-4db6-11ea-be1c-87cdcc67bd9e/image/Tile.png?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress",
"urlBase": "https://cdn.player.fm/images/26431330/series/u3mGVUW4HakPKQ0o",
"palette": ["4eafb8", "091011"],
"suffix": "jpg"
},
"description": "Bestselling author, professor and entrepreneur Scott Galloway combines business insight and analysis with provocative life and career advice. On Mondays, Prof G Markets breaks down what’s moving the capital markets, teaching the basics of financial literacy so you can build economic security. Wednesdays, during Office Hours, Scott answers your questions about business, career, and life. Thursdays, Scott has a conversation with a blue-flame thinker in the innovation economy. And Scott closes the week on Saturdays with his Webby Award–winning newsletter, No Mercy / No Malice, as read by actor and raconteur George Hahn. To resist is futile… Want to get in touch? Email us, officehours@profgmedia.com",
"backgroundColor": "#D81422",
"network": {"name": "Vox Media Podcast Network"}
},
"versionInfo": "1.1.0"
}
----
We're mostly interested in the `url`, so let's update the command to extract that using `jq`:
[source, bash]
----
curl \
-H "Accept: application/json" \
--compressed \
https://player.fm/series/the-prof-g-pod-with-scott-galloway/prof-g-markets-scotts-nine-businesses.json 2>/dev/null |
jq '.url'
----
[source, text]
----
"https://www.podtrac.com/pts/redirect.mp3/pdst.fm/e/chrt.fm/track/524GE/traffic.megaphone.fm/VMP5922871816.mp3?updated=1701050904"
----
I downloaded that to my machine and then installed insanely-fast-whisper:
[source, bash]
----
pipx install insanely-fast-whisper==0.0.12
----
Once that was installed, I ran it using the `openai/whisper-large-v3` model
[source, bash]
----
insanely-fast-whisper \
--file-name VMP5922871816.mp3 \
--device-id mps \
--model-name openai/whisper-large-v3 \
--batch-size 4 \
--transcript-path profg.jsons
----
.Output
[source, text]
----
🤗 Transcribing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:13:37
Voila!✨ Your file has been transcribed go check it out over here 👉 profg.json
----
You can see from the output that it took just over 13 minutes to transcribe this file, which is 54 minutes long.
This is nowhere near as fast as Vaibhav Srivastav (the author) was able to achieve on a Nvidia A100 - 80GB.
He later posted a LinkedIn message where he showed how to use the tool with the https://huggingface.co/distil-whisper/distil-small.en[distil-whisper/distil-small.en^] model.
++++
<iframe src="https://www.linkedin.com/embed/feed/update/urn:li:ugcPost:7139995906757537792" height="914" width="504" frameborder="0" allowfullscreen="" title="Embedded post"></iframe>
++++
This is a small model and the Hugging Face says the following:
[blockquote]
____
Distil-Whisper was proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. It is a distilled version of the Whisper model that is 6 times faster, 49% smaller, and performs within 1% WER on out-of-distribution evaluation sets.
This is the repository for distil-small.en, a distilled variant of Whisper small.en. It is the smallest Distil-Whisper checkpoint, with just 166M parameters, making it the ideal choice for memory constrained applications (e.g. on-device).
____
I gave this a try by running the following command:
[source, bash]
----
insanely-fast-whisper \
--file-name VMP5922871816.mp3 \
--device-id mps \
--model-name distil-whisper/distil-small.en \
--batch-size 4 \
--transcript-path profg-small.json
----
I'm using an Apple M1 Max with 64GB RAM and below is the amount of time that it took for various batch sizes:
[options="header"]
|===
| Batch Size | Time
|1 |08:54
| 2 | 06:50
|3 | 06:57
|4 | 06:40
|5 | 07:15
| 6 | 07:26
| 8 | 07:30
| 12 | 08:42
|===
And if we want to do this directly in code, we can run the following script:
[source, python]
----
import torch
from transformers import pipeline
import time
pipe = pipeline(
"automatic-speech-recognition",
model="distil-whisper/distil-small.en",
torch_dtype=torch.float16,
device="mps",
model_kwargs={"use_flash_attention_2": False},
)
start = time.time()
outputs = pipe(
"VMP5922871816.mp3",
chunk_length_s=30,
batch_size=4,
return_timestamps=True
)
end = time.time()
print(outputs)
print(end - start)
---- | In this post, we'll learn how to use insanely-fast-whisper to generate a transcript for one of Scott Galloway's podcast episodes. | uploads/2023/12/whisper-banner.png | [
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] | insanely-fast-whisper-experiments | https://markhneedham.com/blog/2023/12/23/insanely-fast-whisper-experiments |
false | 2023-12-13 00:44:37 | ClickHouse: S3Queue Table Engine - DB::Exception: There is no Zookeeper configuration in server config | [
"clickhouse",
"til"
] | [
"TIL"
] | :icons: font
This week I've been making a video showing how to use ClickHouse's https://clickhouse.com/docs/en/engines/table-engines/integrations/s3queue[S3Queue table engine^], which allows streaming import of files in an S3 bucket.
The S3Queue table engine was released in version 23.8, but only received 'production-ready' status in version 23.11.
In this blog post, we'll walk through the steps to getting this to work locally and the mistakes that I made along the way.
I configured an S3 bucket, added 10 files containing 100,000 rows of JSON each, and made sure that I'd set the `AWS_PROFILE` environment variable so that ClickHouse Server could read from the bucket.
I then started the ClickHouse Server:
[source, bash]
----
./clickhouse server
----
Connected to that server using the ClickHouse client:
[source, bash]
----
./clickhouse client -m
----
And tried to configure an S3Queue table engine based on my S3 bucket:
[source, sql]
----
CREATE TABLE ordersQueue (
orderDate DateTime,
gender String,
customerId UUID,
cost Float32,
name String,
creditCardNumber String,
address String,
orderId UUID
)
ENGINE = S3Queue(
'https://s3queue.clickhouse.com.s3.eu-north-1.amazonaws.com/data/*.json',
JSONEachRow
)
SETTINGS
mode = 'ordered',
s3queue_enable_logging_to_s3queue_log = 1;
----
When I ran this query, I got the following error:
.Output
[source, text]
----
Received exception from server (version 23.12.1):
Code: 139. DB::Exception: Received from localhost:9000. DB::Exception: There is no Zookeeper configuration in server config. (NO_ELEMENTS_IN_CONFIG)
----
The mistake I made was not configuring ClickHouse Keeper in the ClickHouse Server config.
ClickHouse Server uses ClickHouse Keeper to keep track of the files that have already been processed.
When running ClickHouse from the binary executable it picks up any XML or YAML config files under the `config.d` directory (relative to where it's run) and then merges them into its global configuration file during the startup process.
So to enable ClickHouse Keeper, we will add the following config:
..config.d/config.yaml
[source, yaml]
----
keeper_server:
tcp_port: 2181
server_id: 1
log_storage_path: 'keeper/coordination/log'
snapshot_storage_path: 'keeper/coordination/snapshots'
coordination_settings:
operation_timeout_ms: 10000
session_timeout_ms: 30000
raft_logs_level: warning
raft_configuration:
server:
id: 1
hostname: '127.0.0.1'
port: 9444
s3queue_log:
database: system
table: s3queue_log
logger:
level: debug
console: true
----
We can then restart the server:
[source, bash]
----
./clickhouse server
----
And if we re-run the `CREATE TABLE` command, it will succeed.
The next step is to create a table to store the data in ClickHouse.
The query to create that table is similar to the previous one, but we use the `MergeTree` engine instead of `S3Queue`:
[source, sql]
----
CREATE TABLE orders (
orderDate DateTime,
gender String,
customerId UUID,
cost Float32,
name String,
creditCardNumber String,
address String,
orderId UUID
)
ENGINE = MergeTree
ORDER BY (customerId, orderDate);
----
The final piece that glues these two tables together is a materialised view.
The materialised view processes rows coming in from S3 files and then writes them into the table:
[source, sql]
----
CREATE MATERIALIZED VIEW ordersConsumer TO orders AS
SELECT *
FROM ordersQueue;
----
We can then query the `orders` table to check that data is being ingested:
[source, sql]
----
FROM orders SELECT count()
----
One thing I found confusing is that this query returned 0 for what seemed like ages and then suddenly the count went up to 1 million in one go.
I learnt that this happens because the materialised view only flushes the data once it's reached a certain number of rows, which is determined by the config properties `min_insert_block_size_rows_for_materialized_views` and `min_insert_block_size_bytes_for_materialized_views`.
If those properties aren't configured, their values default to `min_insert_block_size_rows` and `min_insert_block_size_bytes` respectively.
The default values of `min_insert_block_size_rows` and `min_insert_block_size_bytes` are as follows:
.Output
[source, text]
----
Row 1:
──────
name: min_insert_block_size_bytes
type: UInt64
value: 268402944
Row 2:
──────
name: min_insert_block_size_rows
type: UInt64
value: 1048449
----
So, by default, the materialised view was only getting flushed once it had just over 1 million rows!
We can, however, configure this property at the server level, the same way that we configured ClickHouse Keeper.
I added the following config file and restarted ClickHouse Server:
..config.d/profiles.yaml
[source.yaml]
----
profiles:
default:
min_insert_block_size_rows_for_materialized_views: 1000
min_insert_block_size_bytes_for_materialized_views: 2000
----
I then removed the tables and materialised view and recreated everything.
And this time I could see the data being ingested immediately! | In this post, we'll learn how to ingest data from S3 into ClickHouse. | uploads/2023/12/clickhouse-s3queue.png | [
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] | clickhouse-s3queue-no-zookeeper-configuration | https://markhneedham.com/blog/2023/12/13/clickhouse-s3queue-no-zookeeper-configuration |
false | 2023-12-14 00:44:37 | litellm and llamafile - APIError: OpenAIException - File Not Found | [
"llamafile",
"litellm",
"til"
] | [
"TIL"
] | :icons: font
I wanted to get two of my favourite tools in the LLM world - https://docs.litellm.ai/docs/[llmlite^] and https://github.com/Mozilla-Ocho/llamafile[llamafile^] - to play nicely and ran into an issue that I'll explain in this blog post.
This should be helpful if you're trying to wire up other LLM servers to llmlite, it's not specific to llamafile.
== Setting up llamafile
In case you want to follow along, I https://github.com/Mozilla-Ocho/llamafile/releases[downloaded llamafile^] and MistralAI 7B weights from https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF[TheBloke/Mistral-7B-v0.1-GGUF^].
I then started the llamafile server like this:
[source, bash]
----
./llamafile-server-0.3 -m models/mistral-7b-instruct-v0.1.Q4_K_M.gguf --nobrowser
----
== Installing litellm
Let's install litellm, a library that lets you call many different LLMs as if they had the OpenAI API.
[source, bash]
----
poetry add litellm
----
or
[source, bash]
----
pip install litellm
----
== Calling llamafile from litellm
Right, now we need to figure out how to call llamafile from litellm.
There isn't currently a documentation page for llamafile, but we can follow the instructions for creating https://docs.litellm.ai/docs/providers/custom_openai_proxy[a Custom API Server^], since llamafile provides an Open AI compatible endpoint.
[source, python]
----
import os
from litellm import completion
os.environ["OPENAI_API_KEY"] = "i-am-not-used-but-must-be-here"
messages = [{"content": "Write a limerick about ClickHouse", "role": "user"}]
response = completion(
model="command-nightly",
messages=messages,
api_base="http://localhost:8080/",
custom_llm_provider="openai"
)
----
I ran this code and got the following error:
[source, python]
----
File ~/Library/Caches/pypoetry/virtualenvs/llamafile-playground-PMlWj0HV-py3.11/lib/python3.11/site-packages/litellm/utils.py:4192, in exception_type(model, original_exception, custom_llm_provider, completion_kwargs)
4190 else:
4191 exception_mapping_worked = True
-> 4192 raise APIError(
4193 status_code=original_exception.status_code,
4194 message=f"OpenAIException - {original_exception.message}",
4195 llm_provider="openai",
4196 model=model,
4197 request=original_exception.request
4198 )
4199 else:
4200 # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors
4201 raise APIConnectionError(
4202 __cause__=original_exception.__cause__,
4203 llm_provider=custom_llm_provider,
4204 model=model,
4205 request=original_exception.request
4206 )
APIError: OpenAIException - File Not Found
----
I couldn't make much sense of the stack trace - I'm not sure why it's mentioning trying to find a file to start with, so I turned on debug mode so that I could see the HTTP requests that were being made.
[source, python]
----
import litellm
litellm.set_verbose = True
----
If we re-run the completion function above, we'll see something like the following output:
[source, bash]
----
POST Request Sent from LiteLLM:
curl -X POST \
http://localhost:8080 \
-d '{'model': 'command-nightly', 'messages': [{'content': 'Write a limerick about ClickHouse', 'role': 'user'}]}'
RAW RESPONSE:
File Not Found
----
The debug output doesn't seem quite right to me as I think it's actually appending `/chat/completion` to the base URI, which would mean the request was made to `http://localhost:8080/chat/completion`.
I had a look in the llamafile logs to see if it had registered a request:
[source, bash]
----
{"timestamp":1702535457,"level":"INFO","function":"log_server_request","line":2593,"message":"request","remote_addr":"127.0.0.1","remote_port":50193,"status":404,"method":"POST","path":"/chat/completions","params":{}}
----
The 'File Not Found' message makes more sense now since it was saying that it was getting a 404 when trying to call llamafile.
The mistake I'd made is not including the `v1` suffix in the `api_base` property.
Let's fix that:
[source, python]
----
response = completion(
model="command-nightly",
messages=messages,
api_base="http://localhost:8080/v1",
custom_llm_provider="openai"
)
----
If we run it this time, we'll see the following debug output:
[source, bash]
----
POST Request Sent from LiteLLM:
curl -X POST \
http://localhost:8080/v1 \
-d '{'model': 'command-nightly', 'messages': [{'content': 'Write a limerick about ClickHouse', 'role': 'user'}]}'
RAW RESPONSE:
{"id":"chatcmpl-b5Q6ctuTXM9xgHJPyH7q8oqw5OL1FScH","choices":[{"finish_reason":"stop","index":0,"message":{"content":"There once was a database named ClickHouse\nIt could handle all sorts of data, no doubt\nWith its speed and its might\nIt could handle all queries in sight\nAnd its users were never left in a drought\n","role":"assistant","function_call":null,"tool_calls":null}}],"created":1702536234,"model":"gpt-3.5-turbo-0613","object":"chat.completion","system_fingerprint":null,"usage":{"completion_tokens":53,"prompt_tokens":37,"total_tokens":90}}
----
And let's print out the limerick:
[source, python]
----
print(response.choices[0].message.content)
----
.Output
[source, text]
----
There once was a database named ClickHouse
It could handle all sorts of data, no doubt
With its speed and its might
It could handle all queries in sight
And its users were never left in a drought
---- | In this post, we'll learn how to get litellm and llamafile playing nicely together. | uploads/2023/12/litellm-llamafile.png | [
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] | litellm-apierror-openaiexception-file-not-found | https://markhneedham.com/blog/2023/12/14/litellm-apierror-openaiexception-file-not-found |
false | 2023-12-22 00:44:37 | Generating sample JSON data in S3 with shadowtraffic.io | [
"s3",
"til"
] | [
"TIL"
] | :icons: font
I needed to quickly generate some data to write to S3 for a https://www.youtube.com/watch?v=lnbWFjfZxZ4&t=4s[recent video on the ClickHouse YouTube channel^] and it seemed like a good opportunity to try out https://shadowtraffic.io/[ShadowTraffic].
ShadowTraffic is a tool being built by Michael Drogalis and it simulates production traffic based on a JSON file that you provide.
Michael is documenting the process of building ShadowTraffic on https://michaeldrogalis.substack.com/[his Substack newsletter^].
[NOTE]
====
Michael gave me a free license to use for a few months as a 'thank you' for giving him some feedback on the product, but there is also a free version of the tool.
====
The first thing we need to do is create a JSON file that describes the data that we'd like to generate.
I've adapted the sample e-commerce example to include orders and customer details in the same file.
You can see my file below:
.orders.json
[source, json]
----
{
"generators": [
{
"table": "orders",
"row": {
"customerId": {"_gen": "uuid"},
"name": {"_gen": "string", "expr": "#{Name.full_name}"},
"gender": {
"_gen": "weightedOneOf",
"choices": [
{"weight": 49, "value": "male"},
{"weight": 49, "value": "female"},
{"weight": 1, "value": "non-binary"}
]
},
"address": {"_gen": "string", "expr": "#{Address.full_address}"},
"membership": {
"_gen": "oneOf",
"choices": ["bronze", "silver", "gold"]
},
"orderId": {"_gen": "uuid"},
"orderDate": {"_gen": "now"},
"cost": {
"_gen": "number",
"n": {"_gen": "normalDistribution", "mean": 50, "sd": 20}
},
"creditCardNumber": {"_gen": "string", "expr": "#{Finance.credit_card}"}
}
}
],
"connections": {
"pg": {
"kind": "postgres",
"connectionConfigs": {
"host": "localhost",
"port": 5432,
"username": "postgres",
"password": "postgres",
"db": "mydb"
}
}
}
}
----
You need to provide a connection to either Postgres or Kafka, so I had to provide something even though I'm not planning to use either tool.
We can then call ShadowTraffic in Dry-Run mode by passing the `--stdout` parameter and have it generate a single record by passing `--sample 1`.
[source, bash]
----
docker run \
--env-file license.env \
-v ./orders.json:/home/config.json \
shadowtraffic/shadowtraffic:latest \
--config /home/config.json \
-q --stdout --sample 1
----
.Output
[source, json]
----
{
"table" : "orders",
"row" : {
"membership" : "gold",
"gender" : "female",
"customerId" : "2aad62a4-0cd7-4e44-b64f-bb2d0561e8b1",
"cost" : 17.29207782281643,
"name" : "Silas Homenick",
"creditCardNumber" : "6771575378399757",
"address" : "Apt. 604 974 Simon Lakes, New Austinside, PA 21990-2886",
"orderId" : "f147c2ac-0b82-474a-af2d-67c79a3dd0b4",
"orderDate" : 1703266786651
}
}
----
The bit of JSON that we're interested in is under the `row` property, so we're going to filter that using the `jq` tool.
We'll pass in the `-c` flag so that we get the JSON on a single line:
[source, bash]
----
docker run \
--env-file license.env \
-v ./orders.json:/home/config.json \
shadowtraffic/shadowtraffic:latest \
--config /home/config.json \
-q --stdout --sample 1 |
jq -c '.row'
----
.Output
[source, json]
----
{"membership":"gold","gender":"female","customerId":"266422b6-8abd-4b3c-a293-0461c0d45ab8","cost":37.02247112428602,"name":"Tristan Block","creditCardNumber":"3528-7302-3997-0048","address":"Apt. 817 623 Odilia Way, Predovicshire, MD 95620-9365","orderId":"d54ec3e5-5956-4915-b410-dbbe74a98a83","orderDate":1703266858589}
----
If we remove `--sample 1` it will generate infinite JSON messages for us.
I then wrote the following script which reads messages from `stdin` and writes them to S3 every 100,000 messages.
.upload_s3.py
[source, python]
----
import sys
import boto3
from datetime import datetime
def upload_to_s3(file_name, bucket_name, object_name=None):
if object_name is None:
object_name = file_name
s3_client = boto3.client('s3')
try:
print(f"Uploading {file_name} to {bucket_name}")
s3_client.upload_file(file_name, bucket_name, object_name)
print(f"Uploaded {file_name} to {bucket_name}")
except Exception as e:
print(f"Error uploading file: {e}")
def main():
max_entries = 100_000
entries = []
for line in sys.stdin:
entries.append(line)
if len(entries) >= max_entries:
file_name = f"data/batch_{datetime.now().strftime('%Y%m%d%H%M%S')}.json"
with open(file_name, 'w') as file:
for entry in entries:
file.write(entry)
upload_to_s3(file_name, 's3queue.clickhouse.com')
entries = []
if __name__ == "__main__":
main()
----
We can then pipe the messages into `upload_s3.py` by piping the previous command into this script.
[NOTE]
====
Make sure you setup your AWS credentials that have write access to an S3 bucket.
I think the easiest way to do this is with an AWS profile, which you con configure like this:
[source, bash]
----
export AWS_PROFILE="<your-profile>"
----
====
The final code looks like this:
[source, bash]
----
docker run \
--env-file license.env \
-v ./orders.json:/home/config.json \
shadowtraffic/shadowtraffic:latest \
--config /home/config.json \
-q --stdout |
jq -c '.row' |
poetry run python upload_s3.py
----
I found this generated 100,000 messages roughly every 15 seconds, which is pretty neat and more than enough data for my use case. | In this post, we'll learn how to generate sample data in an S3 bucket with shadowtraffic.io. | uploads/2023/12/shadowtraffic-banner.png | [
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false | 2015-03-05 08:52:22 | Python: scikit-learn/lda: Extracting topics from QCon talk abstracts | [
"python"
] | [
"Machine Learning",
"Python"
] | Following on from http://blog.bruggen.com/2015/02/the-qcon-graph.html[Rik van Bruggen's blog post on a QCon graph he's created] ahead of http://qconlondon.com/schedule[this week's conference], I was curious whether we could extract any interesting relationships between talks based on their abstracts.
Talks are already grouped by their hosting track but there's likely to be some overlap in topics even for talks on different tracks. I therefore wanted to extract topics and connect each talk to the topic that describes it best.
My first attempt was following http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html[an example which uses Non-Negative Matrix factorization] which worked very well for extracting topics but didn't seem to provide an obvious way to work out how to link those topics to individual talks.
Instead I ended up looking at the https://pypi.python.org/pypi/lda[lda library] which uses Latent Dirichlet Allocation and allowed me to achieve both goals.
I already had some code to run TF/IDF over each of the talks so I thought I'd be able to feed the matrix output from that into the LDA function. This is what I started with:
[source,python]
----
import csv
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import NMF
from collections import defaultdict
from bs4 import BeautifulSoup, NavigableString
from soupselect import select
def uri_to_file_name(uri):
return uri.replace("/", "-")
sessions = {}
with open("data/sessions.csv", "r") as sessions_file:
reader = csv.reader(sessions_file, delimiter = ",")
reader.next() # header
for row in reader:
session_id = int(row[0])
filename = "data/sessions/" + uri_to_file_name(row[4])
page = open(filename).read()
soup = BeautifulSoup(page)
abstract = select(soup, "div.brenham-main-content p")
if abstract:
sessions[session_id] = {"abstract" : abstract[0].text, "title": row[3] }
else:
abstract = select(soup, "div.pane-content p")
sessions[session_id] = {"abstract" : abstract[0].text, "title": row[3] }
corpus = []
titles = []
for id, session in sorted(sessions.iteritems(), key=lambda t: int(t[0])):
corpus.append(session["abstract"])
titles.append(session["title"])
n_topics = 15
n_top_words = 50
n_features = 6000
vectorizer = TfidfVectorizer(analyzer='word', ngram_range=(1,1), min_df = 0, stop_words = 'english')
matrix = vectorizer.fit_transform(corpus)
feature_names = vectorizer.get_feature_names()
import lda
import numpy as np
vocab = feature_names
model = lda.LDA(n_topics=20, n_iter=500, random_state=1)
model.fit(matrix)
topic_word = model.topic_word_
n_top_words = 20
for i, topic_dist in enumerate(topic_word):
topic_words = np.array(vocab)[np.argsort(topic_dist)][:-n_top_words:-1]
print('Topic {}: {}'.format(i, ' '.join(topic_words)))
----
And if we run it?
[source,bash]
----
Topic 0: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 1: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 2: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 3: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 4: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 5: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 6: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 7: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 8: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 9: 10 faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 10: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 11: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 12: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 13: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 14: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 15: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 16: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 17: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 18: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 19: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
----
As you can see, every topic has the same set of words which isn't what we want. Let's switch out our TF/IDF vectorizer for a simpler count based one:
[source,python]
----
vectorizer = CountVectorizer(analyzer='word', ngram_range=(1,1), min_df = 0, stop_words = 'english')
----
The rest of the code stays the same and these are the topics that get extracted:
[source,bash]
----
Topic 0: time people company did writing real way used let cassandra soundcloud successful know web organization audio lives swift stuck
Topic 1: process development delivery platform developer continuous testing rapidly deployment implementing release demonstrate paas advice hard light predictable radically introduce
Topic 2: way open space kind people change meetings ll lead powerful practice times everyday simple qconlondon organization unconference track extraordinary
Topic 3: apache apis processing open spark distributed leading making environments solr cases brooklyn components existing ingestion contributing data target evolved
Topic 4: management million effective cost halo gameplay player billion ad catastrophic store microsoft final music influence information launch research purchased
Topic 5: product look like use talk problems working analysis projects challenges 2011 functionality useful spread business deep inside happens sensemaker
Topic 6: ll computers started principles free focus face smaller atlas control uses products avoid computing ground billions mean volume consistently
Topic 7: code end users developers just application way line apps mobile features sites hours issues applications write faster game better
Topic 8: ve development teams use things world like time learned lessons think methods multiple story say customer developer experiences organisations
Topic 9: software building docker built challenges monitoring gilt application discuss solution decision talk download source center critical decisions bintray customers
Topic 10: years infrastructure tools language different service lot devops talk adoption scala popular clojure advantages introduced effectively looking wasn includes
Topic 11: high does latency session requirements functional performance real world questions problem second engineering patterns gravity explain discuss expected time
Topic 12: business make build technology technologies help trying developers parts want interfaces small best centres implementations critical moo databases going
Topic 13: need design systems large driven scale software applications slow protocol change needs approach gets new contracts solutions complicated distributed
Topic 14: architecture service micro architectures increasing talk microservices order market value values new present presents services scalable trading practices today
Topic 15: java using fast robovm lmax ios presentation really jvm native best exchange azul hardware started project slowdowns goal bring
Topic 16: data services using traditional create ways support uk large user person complex systems production impact art organizations accessing mirage
Topic 17: agile team experience don work doing processes based key reach extra defined pressure machines nightmare practices learn goals guidance
Topic 18: internet new devices programming things iot big number deliver day connected performing growing got state thing provided times automated
Topic 19: cloud including deploy session api government security culture software type attack techniques environment digital secure microservice better creation interaction
----
Some of the groupings seem to make sense e.g. Topic 11 contains words related to high performance code with low latency; Topic 15 covers Java, the JVM and other related words; but others are more difficult to decipher
e.g. both Topic 14 and Topic 19 talk about micro services but the latter mentions 'government' and 'security' so perhaps the talks linked to that topic come at micro services from a different angle altogether.
Next let's see which topics a talk is most likely to be about. We'll look at the first ten:
[source,python]
----
doc_topic = model.doc_topic_
for i in range(0, 10):
print("{} (top topic: {})".format(titles[i], doc_topic[i].argmax()))
print(doc_topic[i].argsort()[::-1][:3])
To the Moon (top topic: 8)
[ 8 0 11]
Evolutionary Architecture and Micro-Services - A Match Enabled by Continuous Delivery (top topic: 14)
[14 19 16]
How SoundCloud uses Cassandra (top topic: 0)
[0 6 5]
DevOps and the Need for Speed (top topic: 18)
[18 5 16]
Neuro-diversity and agile (top topic: 7)
[17 7 2]
Java 8 in Anger (top topic: 7)
[ 7 15 12]
APIs that Change Lifestyles (top topic: 9)
[ 9 6 19]
Elasticsearch powers the Citizen Advice Bureau (CAB) to monitor trends in society before they become issues (top topic: 16)
[16 12 19]
Architecture Open Space (top topic: 2)
[ 2 19 18]
Don’t let Data Gravity crush your infrastructure (top topic: 11)
[11 16 3]
----
So our third talk on the list 'How SoundCloud uses Cassandra' does end up being tagged with topic 0 which mentions SoundCloud so that's good!
[source,text]
----
Topic 0: time people company did writing real way used let cassandra soundcloud successful know web organization audio lives swift stuck
----
It's next two topics are 5 & 6 which contain the following words\...
[source,text]
----
Topic 5: product look like use talk problems working analysis projects challenges 2011 functionality useful spread business deep inside happens sensemaker
Topic 6: ll computers started principles free focus face smaller atlas control uses products avoid computing ground billions mean volume consistently
----
\...which are not as intuitive. What about Java 8 in Anger? It's been tagged with topics 7, 15 and 12:
[source,text]
----
Topic 7: code end users developers just application way line apps mobile features sites hours issues applications write faster game better
Topic 15: java using fast robovm lmax ios presentation really jvm native best exchange azul hardware started project slowdowns goal bring
Topic 12: business make build technology technologies help trying developers parts want interfaces small best centres implementations critical moo databases going
----
15 makes sense since that mentions Java and perhaps 12 and 7 do as well as they both mention developers.
So while the topics pulled out are not horrendous I don't think they're particularly useful yet either. These are some of the areas I need to do some more research around:
* How do you measure the success of topic modelling? I've been eyeballing the output of the algorithm but I imagine there's an automated way to do that.
* How do you determine the right number of topics? I found http://blog.cigrainger.com/2014/07/lda-number.html#fn:fn-1[an article written by Christophe Grainger] which explains a way of doing that which I need to look at in more detail.
* It feels like I would be able to pull out better topics if I had an ontology of computer science/software words and then ran the words through that to derive topics.
* Another approach suggested by https://twitter.com/mesirii[Michael] is to find the most popular words using the +++<cite>+++CountVectorizer+++</cite>+++ and tag talks with those instead.
If you have any suggestions let me know. The https://github.com/mneedham/neo4j-qcon/blob/master/topics.py[full code is on github] if you want to play around with it. | null | null | [
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] | python-scikit-learnlda-extracting-topics-from-qcon-talk-abstracts | https://markhneedham.com/blog/2015/03/05/python-scikit-learnlda-extracting-topics-from-qcon-talk-abstracts |
false | 2015-03-02 07:48:24 | Python: scikit-learn - Training a classifier with non numeric features | [
"python"
] | [
"Data Science",
"Python"
] | Following on from my http://www.markhneedham.com/blog/2015/02/20/pythonscikit-learn-detecting-which-sentences-in-a-transcript-contain-a-speaker/[previous] http://www.markhneedham.com/blog/2015/02/24/pythonnltk-naive-vs-naive-bayes-vs-decision-tree/[posts] on http://www.markhneedham.com/blog/2015/03/01/python-detecting-the-speaker-in-himym-using-parts-of-speech-pos-tagging/[training a classifier] to pick out the speaker in sentences of http://en.wikipedia.org/wiki/How_I_Met_Your_Mother[HIMYM] transcripts the next thing to do was train a random forest of decision trees to see how that fared.
I've http://www.markhneedham.com/blog/2013/11/09/python-making-scikit-learn-and-pandas-play-nice/[used scikit-learn for this before] so I decided to use that. However, before building a random forest I wanted to check that I could build an equivalent http://scikit-learn.org/stable/modules/tree.html[decision tree].
I initially thought that scikit-learn's DecisionTree classifier would take in data in the same format as nltk's so I started out with the following code:
[source,python]
----
import json
import nltk
import collections
from himymutil.ml import pos_features
from sklearn import tree
from sklearn.cross_validation import train_test_split
with open("data/import/trained_sentences.json", "r") as json_file:
json_data = json.load(json_file)
tagged_sents = []
for sentence in json_data:
tagged_sents.append([(word["word"], word["speaker"]) for word in sentence["words"]])
featuresets = []
for tagged_sent in tagged_sents:
untagged_sent = nltk.tag.untag(tagged_sent)
sentence_pos = nltk.pos_tag(untagged_sent)
for i, (word, tag) in enumerate(tagged_sent):
featuresets.append((pos_features(untagged_sent, sentence_pos, i), tag) )
clf = tree.DecisionTreeClassifier()
train_data, test_data = train_test_split(featuresets, test_size=0.20, train_size=0.80)
>>> train_data[1]
({'word': u'your', 'word-pos': 'PRP$', 'next-word-pos': 'NN', 'prev-word-pos': 'VB', 'prev-word': u'throw', 'next-word': u'body'}, False)
>>> clf.fit([item[0] for item in train_data], [item[1] for item in train_data])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/markneedham/projects/neo4j-himym/himym/lib/python2.7/site-packages/sklearn/tree/tree.py", line 137, in fit
X, = check_arrays(X, dtype=DTYPE, sparse_format="dense")
File "/Users/markneedham/projects/neo4j-himym/himym/lib/python2.7/site-packages/sklearn/utils/validation.py", line 281, in check_arrays
array = np.asarray(array, dtype=dtype)
File "/Users/markneedham/projects/neo4j-himym/himym/lib/python2.7/site-packages/numpy/core/numeric.py", line 460, in asarray
return array(a, dtype, copy=False, order=order)
TypeError: float() argument must be a string or a number
----
In fact, the classifier can only deal with numeric features so we need to http://scikit-learn.org/dev/modules/feature_extraction.html#loading-features-from-dicts[translate our features into that format using DictVectorizer].
[source,python]
----
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer()
X = vec.fit_transform([item[0] for item in featuresets]).toarray()
>>> len(X)
13016
>>> len(X[0])
7302
>>> vec.get_feature_names()[10:15]
['next-word-pos=EX', 'next-word-pos=IN', 'next-word-pos=JJ', 'next-word-pos=JJR', 'next-word-pos=JJS']
----
We end up with one feature for every key/value combination that exists in +++<cite>+++featuresets+++</cite>+++.
I was initially confused about how to split up http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html[training and test data sets] but it's actually fairly easy - +++<cite>+++train_test_split+++</cite>+++ allows us to pass in multiple lists which it splits along the same seam:
[source,python]
----
vec = DictVectorizer()
X = vec.fit_transform([item[0] for item in featuresets]).toarray()
Y = [item[1] for item in featuresets]
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.20, train_size=0.80)
----
Next we want to train the classifier which is a couple of lines of code:
[source,python]
----
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train, Y_train)
----
I wrote the following function to assess the classifier:
[source,python]
----
import collections
import nltk
def assess(text, predictions_actual):
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (prediction, actual) in enumerate(predictions_actual):
refsets[actual].add(i)
testsets[prediction].add(i)
speaker_precision = nltk.metrics.precision(refsets[True], testsets[True])
speaker_recall = nltk.metrics.recall(refsets[True], testsets[True])
non_speaker_precision = nltk.metrics.precision(refsets[False], testsets[False])
non_speaker_recall = nltk.metrics.recall(refsets[False], testsets[False])
return [text, speaker_precision, speaker_recall, non_speaker_precision, non_speaker_recall]
----
We can call it like so:
[source,python]
----
predictions = clf.predict(X_test)
assessment = assess("Decision Tree", zip(predictions, Y_test))
>>> assessment
['Decision Tree', 0.9459459459459459, 0.9210526315789473, 0.9970134395221503, 0.9980069755854509]
----
Those values are in the same ball park as we've seen with the nltk classifier so I'm happy it's all wired up correctly.
The last thing I wanted to do was http://stackoverflow.com/questions/23557545/how-to-explain-the-decision-tree-from-scikit-learn[visualise the decision tree] that had been created and the easiest way to do that is http://scikit-learn.org/stable/modules/generated/sklearn.tree.export_graphviz.html[export the classifier to DOT format] and then use graphviz to create an image:
[source,python]
----
with open("/tmp/decisionTree.dot", 'w') as file:
tree.export_graphviz(clf, out_file = file, feature_names = vec.get_feature_names())
----
[source,bash]
----
dot -Tpng /tmp/decisionTree.dot -o /tmp/decisionTree.png
----
The decision tree is quite a few levels deep so here's part of it:
image::{{<siteurl>}}/uploads/2015/03/decisionTreeSection.png[DecisionTreeSection,600]
The https://github.com/mneedham/neo4j-himym/blob/master/scripts/scikit_dt.py[full script is on github] if you want to play around with it. | null | null | [
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] | python-scikit-learn-training-a-classifier-with-non-numeric-features | https://markhneedham.com/blog/2015/03/02/python-scikit-learn-training-a-classifier-with-non-numeric-features |
false | 2015-03-20 07:30:55 | Badass: Making users awesome - Kathy Sierra: Book Review | [
"books",
"book-review",
"users"
] | [
"Books"
] | I started reading Kathy Sierra's new book 'http://www.amazon.co.uk/Badass-Making-Awesome-Kathy-Sierra/dp/1491919019/ref=sr_1_1?ie=UTF8&qid=1426809815&sr=8-1&keywords=kathy+sierra[Badass: Making users awesome]' a couple of weeks ago and with the gift of flights to/from Stockholm this week I've got through the rest of it.
I really enjoyed the book and have found myself returning to it almost every day to check up exactly what was said on a particular topic.
There were a few things that I've taken away and have been going on about to anyone who will listen.
image::{{<siteurl>}}/uploads/2015/03/2015-03-20_06-52-51.png[2015 03 20 06 52 51,217]
Paraphrasing, '*help users acquire skills, don't throw knowledge at them*.' I found this advice helpful both in my own learning of new things as well as for thinking how to help users of Neo4j get up and running faster.
Whether we're doing a talk, workshop or online training, the goal isn't to teach the user a bunch of information/facts but rather to help them learn skills which they can use to achieve their '*compelling context*'.
Having said that, it's very easy to fall into the information/facts trap as that type of information is much easier to prepare and present. You don't have to spend much time thinking about how the user is going to use, rather you hope that if you throw enough information at them some of it will stick.
A user's compelling context the problem they're trying to solve regardless of the tools they use to solve it. The repeated example of this is a camera - we don't buy a camera because we want to buy a camera, we buy it because we want to *take great photographs*.
image::{{<siteurl>}}/uploads/2015/03/2015-03-17_23-49-25.png[2015 03 17 23 49 25,238]
There's a really interesting section in the middle of the book which talks about expert performance and skill acquisition and how we can achieve this through deliberate practice.
My main take away here is that we have only mastered a skill if we can achieve 95% reliability in repeating the task within 1-3 45-90 minute sessions.
If we can't achieve this then the typical reaction is to either give up or keep trying to achieve the goal for many more hours. Neither of these is considered a useful approach.
Instead we should realise that if we can't do the skill it's probably because there's a small sub skill that we need to master first. So our next step is to break this skill down into its components, master those and then try the original skill again.
Amy Hoy's 'http://files.alexpcoleman.com/newsletter/2014/04/The%2030x500%20Guide%20to%20Doing%20It%20Backwards.pdf[doing it backwards]' guide is very helpful for doing the skill breakdown as it makes you ask the question '*can I do it tomorrow?*' or is there something else that I need to do (learn) first.
I've been trying to apply this approach to my machine learning adventures which most recently has involved various topic modelling attempts on a How I met your mother data set.
I'd heard good things about the http://mallet.cs.umass.edu/[MALLET] open source library but having never used it before sketched out the goals/skills I wanted to achieve: ~~~text Extract topics for HIMYM corpus \-> Train a topic model with mallet \-> Tweak an existing topic model that uses mallet \-> Run an existing topic model that uses mallet \-> Install mallet ~~~
image::{{<siteurl>}}/uploads/2015/03/2015-03-20_00-11-48.png[2015 03 20 00 11 48,300]
The idea is that you then start from the last action and work your way back up the chain - it should also act as a nice deterrent for http://www.markhneedham.com/blog/2011/12/31/yak-shaving-tracking-the-yak-stack/[yak shaving].
While learning about mallet I came across http://mimno.infosci.cornell.edu/topics.html[several] http://dsl.richmond.edu/dispatch/Topics[more] http://www.perseus.tufts.edu/publications/02-jocch-mimno.pdf[articles] that I should read about topic modelling and while these don't directly contribute to learning a skill I think they will give me good background to help pick up some of the intuition behind topic modelling.
My take away about gaining knowledge on a skill is that when we're getting started we should spend more time gaining practical knowledge rather than only reading but once we get more into it we'll naturally become more curious and do the background reading. I often find myself just reading non stop about things but never completely understanding them because I don't go hands on so this was a good reminder.
One of the next things I'm working on is a similar skill break down for people learning Neo4j and then we'll look to apply this to make our http://www.meetup.com/graphdb-london/[meetup sessions] more effective - should be fun!
The other awesome thing about this book is that I've come away with a bunch of other books to read as well:
* http://www.amazon.co.uk/Cambridge-Expertise-Performance-Handbooks-Psychology/dp/0521600812/ref=sr_1_1?ie=UTF8&qid=1426836426&sr=8-1&keywords=the+cambridge+handbook+of+expertise+and+expert+performance[The Cambridge Handbook of Expertise and Expert Performance]
* http://www.amazon.co.uk/Mindset-How-Fulfil-Your-Potential/dp/1780332009/ref=sr_1_1?ie=UTF8&qid=1426836461&sr=8-1&keywords=mindset[Mindset: How you can fulfil your potential]
* http://www.amazon.co.uk/Power-Habit-Why-What-Change/dp/1847946240/ref=sr_1_1?ie=UTF8&qid=1426836485&sr=8-1&keywords=the+power+of+habit[The power of habit]
In summary, if learning is your thing get yourself a copy of the book and read it over a few times - so many great tips, I've only covered a few. | null | null | [
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] | badass-making-users-awesome-kathy-sierra-book-review | https://markhneedham.com/blog/2015/03/20/badass-making-users-awesome-kathy-sierra-book-review |
false | 2015-03-27 06:59:02 | Neo4j: Generating real time recommendations with Cypher | [
"neo4j"
] | [
"neo4j"
] | One of the most common uses of Neo4j is for building real time recommendation engines and a common theme is that they make use of lots of different bits of data to come up with an interesting recommendation.
For example https://vimeo.com/86754045[in this video] https://twitter.com/pandamonial[Amanda] shows how dating websites build real time recommendation engines by starting with social connections and then introducing passions, location and a few other things.
Graph Aware have a https://github.com/graphaware/neo4j-reco#using-graphaware-neo4j-recommendation-engine[neat framework] that helps you to build your own recommendation engine using Java and I was curious what a Cypher version would look like.
This is the sample graph:
[source,cypher]
----
CREATE
(m:Person:Male {name:'Michal', age:30}),
(d:Person:Female {name:'Daniela', age:20}),
(v:Person:Male {name:'Vince', age:40}),
(a:Person:Male {name:'Adam', age:30}),
(l:Person:Female {name:'Luanne', age:25}),
(c:Person:Male {name:'Christophe', age:60}),
(lon:City {name:'London'}),
(mum:City {name:'Mumbai'}),
(m)-[:FRIEND_OF]->(d),
(m)-[:FRIEND_OF]->(l),
(m)-[:FRIEND_OF]->(a),
(m)-[:FRIEND_OF]->(v),
(d)-[:FRIEND_OF]->(v),
(c)-[:FRIEND_OF]->(v),
(d)-[:LIVES_IN]->(lon),
(v)-[:LIVES_IN]->(lon),
(m)-[:LIVES_IN]->(lon),
(l)-[:LIVES_IN]->(mum);
----
We want to recommend some potential friends to 'Adam' so the first layer of our query is to find his friends of friends as there are bound to be some potential friends amongst them:
[source,cypher]
----
MATCH (me:Person {name: "Adam"})
MATCH (me)-[:FRIEND_OF]-()-[:FRIEND_OF]-(potentialFriend)
RETURN me, potentialFriend, COUNT(*) AS friendsInCommon
==> +--------------------------------------------------------------------------------------+
==> | me | potentialFriend | friendsInCommon |
==> +--------------------------------------------------------------------------------------+
==> | Node[1007]{name:"Adam",age:30} | Node[1006]{name:"Vince",age:40} | 1 |
==> | Node[1007]{name:"Adam",age:30} | Node[1005]{name:"Daniela",age:20} | 1 |
==> | Node[1007]{name:"Adam",age:30} | Node[1008]{name:"Luanne",age:25} | 1 |
==> +--------------------------------------------------------------------------------------+
==> 3 rows
----
This query gives us back a list of potential friends and how many friends we have in common.
Now that we've got some potential friends let's start building a ranking for each of them. One indicator which could weigh in favour of a potential friend is if they live in the same location as us so let's add that to our query:
[source,cypher]
----
MATCH (me:Person {name: "Adam"})
MATCH (me)-[:FRIEND_OF]-()-[:FRIEND_OF]-(potentialFriend)
WITH me, potentialFriend, COUNT(*) AS friendsInCommon
RETURN me,
potentialFriend,
SIZE((potentialFriend)-[:LIVES_IN]->()<-[:LIVES_IN]-(me)) AS sameLocation
==> +-----------------------------------------------------------------------------------+
==> | me | potentialFriend | sameLocation |
==> +-----------------------------------------------------------------------------------+
==> | Node[1007]{name:"Adam",age:30} | Node[1006]{name:"Vince",age:40} | 0 |
==> | Node[1007]{name:"Adam",age:30} | Node[1005]{name:"Daniela",age:20} | 0 |
==> | Node[1007]{name:"Adam",age:30} | Node[1008]{name:"Luanne",age:25} | 0 |
==> +-----------------------------------------------------------------------------------+
==> 3 rows
----
Next we'll check whether Adams' potential friends have the same gender as him by comparing the labels each node has. We've got 'Male' and 'Female' labels which indicate gender.
[source,cypher]
----
MATCH (me:Person {name: "Adam"})
MATCH (me)-[:FRIEND_OF]-()-[:FRIEND_OF]-(potentialFriend)
WITH me, potentialFriend, COUNT(*) AS friendsInCommon
RETURN me,
potentialFriend,
SIZE((potentialFriend)-[:LIVES_IN]->()<-[:LIVES_IN]-(me)) AS sameLocation,
LABELS(me) = LABELS(potentialFriend) AS gender
==> +--------------------------------------------------------------------------------------------+
==> | me | potentialFriend | sameLocation | gender |
==> +--------------------------------------------------------------------------------------------+
==> | Node[1007]{name:"Adam",age:30} | Node[1006]{name:"Vince",age:40} | 0 | true |
==> | Node[1007]{name:"Adam",age:30} | Node[1005]{name:"Daniela",age:20} | 0 | false |
==> | Node[1007]{name:"Adam",age:30} | Node[1008]{name:"Luanne",age:25} | 0 | false |
==> +--------------------------------------------------------------------------------------------+
==> 3 rows
----
Next up let's calculate the age different between Adam and his potential friends:
[source,cypher]
----
MATCH (me:Person {name: "Adam"})
MATCH (me)-[:FRIEND_OF]-()-[:FRIEND_OF]-(potentialFriend)
WITH me, potentialFriend, COUNT(*) AS friendsInCommon
RETURN me,
potentialFriend,
SIZE((potentialFriend)-[:LIVES_IN]->()<-[:LIVES_IN]-(me)) AS sameLocation,
abs( me.age - potentialFriend.age) AS ageDifference,
LABELS(me) = LABELS(potentialFriend) AS gender,
friendsInCommon
==> +------------------------------------------------------------------------------------------------------------------------------+
==> | me | potentialFriend | sameLocation | ageDifference | gender | friendsInCommon |
==> +------------------------------------------------------------------------------------------------------------------------------+
==> | Node[1007]{name:"Adam",age:30} | Node[1006]{name:"Vince",age:40} | 0 | 10.0 | true | 1 |
==> | Node[1007]{name:"Adam",age:30} | Node[1005]{name:"Daniela",age:20} | 0 | 10.0 | false | 1 |
==> | Node[1007]{name:"Adam",age:30} | Node[1008]{name:"Luanne",age:25} | 0 | 5.0 | false | 1 |
==> +------------------------------------------------------------------------------------------------------------------------------+
==> 3 rows
----
Now let's do some filtering to get rid of people that Adam is already friends with - there wouldn't be much point in recommending those people!
[source,cypher]
----
MATCH (me:Person {name: "Adam"})
MATCH (me)-[:FRIEND_OF]-()-[:FRIEND_OF]-(potentialFriend)
WITH me, potentialFriend, COUNT(*) AS friendsInCommon
WITH me,
potentialFriend,
SIZE((potentialFriend)-[:LIVES_IN]->()<-[:LIVES_IN]-(me)) AS sameLocation,
abs( me.age - potentialFriend.age) AS ageDifference,
LABELS(me) = LABELS(potentialFriend) AS gender,
friendsInCommon
WHERE NOT (me)-[:FRIEND_OF]-(potentialFriend)
RETURN me,
potentialFriend,
SIZE((potentialFriend)-[:LIVES_IN]->()<-[:LIVES_IN]-(me)) AS sameLocation,
abs( me.age - potentialFriend.age) AS ageDifference,
LABELS(me) = LABELS(potentialFriend) AS gender,
friendsInCommon
==> +------------------------------------------------------------------------------------------------------------------------------+
==> | me | potentialFriend | sameLocation | ageDifference | gender | friendsInCommon |
==> +------------------------------------------------------------------------------------------------------------------------------+
==> | Node[1007]{name:"Adam",age:30} | Node[1006]{name:"Vince",age:40} | 0 | 10.0 | true | 1 |
==> | Node[1007]{name:"Adam",age:30} | Node[1005]{name:"Daniela",age:20} | 0 | 10.0 | false | 1 |
==> | Node[1007]{name:"Adam",age:30} | Node[1008]{name:"Luanne",age:25} | 0 | 5.0 | false | 1 |
==> +------------------------------------------------------------------------------------------------------------------------------+
==> 3 rows
----
In this case we haven't actually filtered anyone out but for some of the other people we would see a reduction in the number of potential friends.
Our final step is to come up with a score for each of the features that we've identified as being important for making a friend suggestion.
We'll assign a score of 10 if the people live in the same location or have the same gender as Adam and 0 if not. For the ageDifference and friendsInCommon we'll apply apply a log curve so that those values don't have a disproportional effect on our final score. We'll use the formula defined in the +++<cite>+++https://github.com/graphaware/neo4j-reco/blob/master/src/main/java/com/graphaware/reco/generic/transform/ParetoScoreTransformer.java#L64[ParetoScoreTransfomer]+++</cite>+++ to do this:
[source,java]
----
public <OUT> float transform(OUT item, float score) {
if (score < minimumThreshold) {
return 0;
}
double alpha = Math.log((double) 5) / eightyPercentLevel;
double exp = Math.exp(-alpha * score);
return new Double(maxScore * (1 - exp)).floatValue();
}
----
And now for our completed recommendation query:
[source,cypher]
----
MATCH (me:Person {name: "Adam"})
MATCH (me)-[:FRIEND_OF]-()-[:FRIEND_OF]-(potentialFriend)
WITH me, potentialFriend, COUNT(*) AS friendsInCommon
WITH me,
potentialFriend,
SIZE((potentialFriend)-[:LIVES_IN]->()<-[:LIVES_IN]-(me)) AS sameLocation,
abs( me.age - potentialFriend.age) AS ageDifference,
LABELS(me) = LABELS(potentialFriend) AS gender,
friendsInCommon
WHERE NOT (me)-[:FRIEND_OF]-(potentialFriend)
WITH potentialFriend,
// 100 -> maxScore, 10 -> eightyPercentLevel, friendsInCommon -> score (from the formula above)
100 * (1 - exp((-1.0 * (log(5.0) / 10)) * friendsInCommon)) AS friendsInCommon,
sameLocation * 10 AS sameLocation,
-1 * (10 * (1 - exp((-1.0 * (log(5.0) / 20)) * ageDifference))) AS ageDifference,
CASE WHEN gender THEN 10 ELSE 0 END as sameGender
RETURN potentialFriend,
{friendsInCommon: friendsInCommon,
sameLocation: sameLocation,
ageDifference:ageDifference,
sameGender: sameGender} AS parts,
friendsInCommon + sameLocation + ageDifference + sameGender AS score
ORDER BY score DESC
==> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | potentialFriend | parts | score |
==> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | Node[1006]{name:"Vince",age:40} | {friendsInCommon -> 14.86600774792154, sameLocation -> 0, ageDifference -> -5.52786404500042, sameGender -> 10} | 19.33814370292112 |
==> | Node[1008]{name:"Luanne",age:25} | {friendsInCommon -> 14.86600774792154, sameLocation -> 0, ageDifference -> -3.312596950235779, sameGender -> 0} | 11.55341079768576 |
==> | Node[1005]{name:"Daniela",age:20} | {friendsInCommon -> 14.86600774792154, sameLocation -> 0, ageDifference -> -5.52786404500042, sameGender -> 0} | 9.33814370292112 |
==> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
----
The final query isn't too bad - the only really complex bit is the log curve calculation. This is where user defined functions will come into their own in the future.
The nice thing about this approach is that we don't have to step outside of cypher so if you're not comfortable with Java you can still do real time recommendations! On the other hand, the different parts of the recommendation engine all get mixed up so it's not as easy to see the whole pipeline as if you use the graph aware framework.
The next step is to apply this to the http://www.markhneedham.com/blog/2015/03/11/pythonneo4j-finding-interesting-computer-sciency-people-to-follow-on-twitter/[Twitter graph] and come up with follower recommendations on there. | null | null | [
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] | neo4j-generating-real-time-recommendations-with-cypher | https://markhneedham.com/blog/2015/03/27/neo4j-generating-real-time-recommendations-with-cypher |
false | 2015-03-11 21:13:26 | Python/Neo4j: Finding interesting computer sciency people to follow on Twitter | [
"neo4j",
"python"
] | [
"neo4j",
"Python"
] | At the beginning of this year I moved from Neo4j's field team to dev team and since the code we write there is much lower level than I'm used to I thought I should find some people to follow on twitter whom I can learn from.
My technique for finding some of those people was to pick a person from the Neo4j kernel team who's very good at systems programming and uses twitter which led me to Mr https://twitter.com/chvest[Chris Vest].
I thought that the people Chris interacts with on twitter are likely to be interested in this type of programming so I manually traversed out to those people and had a look who they interacted with and put them all into a https://twitter.com/markhneedham/lists/comp-science-peoples[list].
This approach has worked well and I've picked up lots of reading material from following these people. It does feel like something that could be automated though and we'll using Python and Neo4j as our tool kit.
We need to find a library to connect to the Twitter API. There are a few to choose from but https://github.com/tweepy/tweepy[tweepy] seems simple enough so I started using that.
The first thing we need to so is fill in our http://docs.tweepy.org/en/latest/getting_started.html#hello-tweepy[Twitter API auth details]. Since the application is just for me I'm not going to bother with setting up OAuth - instead I'll just create an application on https://apps.twitter.com/[apps.twitter.com] and grab the appropriate tokens:
image::{{<siteurl>}}/uploads/2015/03/2015-03-11_01-18-47.png[2015 03 11 01 18 47,492]
Once we've done that we can navigate to that app and get a consumer key, consumer secret, access token and access token secret. They all reside on the 'Keys and Access Tokens' tab:
image::{{<siteurl>}}/uploads/2015/03/2015-03-11_01-20-17.png[2015 03 11 01 20 17,351]
image::{{<siteurl>}}/uploads/2015/03/2015-03-11_01-20-30.png[2015 03 11 01 20 30,438]
Now that we've got ourself all auth'd up let's write some code that starts from Chris and goes out and finds his latest tweets and the people he's interacted with in them. We'll write the appropriate information out to a CSV file so that we can import it into Neo4j later on:
[source,python]
----
import tweepy
import csv
from collections import Counter, deque
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth, wait_on_rate_limit = True, wait_on_rate_limit_notify = True)
counter = Counter()
users_to_process = deque()
USERS_TO_PROCESS = 50
def extract_tweet(tweet):
user_mentions = ",".join([user["screen_name"].encode("utf-8")
for user in tweet.entities["user_mentions"]])
urls = ",".join([url["expanded_url"]
for url in tweet.entities["urls"]])
return [tweet.user.screen_name.encode("utf-8"),
tweet.id,
tweet.text.encode("utf-8"),
user_mentions,
urls]
starting_user = "chvest"
with open("tweets.csv", "a") as tweets:
writer = csv.writer(tweets, delimiter=",", escapechar="\\", doublequote = False)
for tweet in tweepy.Cursor(api.user_timeline, id=starting_user).items(50):
writer.writerow(extract_tweet(tweet))
tweets.flush()
for user in tweet.entities["user_mentions"]:
if not len(users_to_process) > USERS_TO_PROCESS:
users_to_process.append(user["screen_name"])
counter[user["screen_name"]] += 1
else:
break
----
As well as printing out Chris' tweets I'm also capturing other users who he's had interacted and putting them in a queue that we'll drain later on. We're limiting the number of other users that we'll process to 50 for now but it's easy to change.
If we print out the first few lines of 'tweets.csv' this is what we'd see:
[source,bash]
----
$ head -n 5 tweets.csv
userName,tweetId,contents,usersMentioned,urls
chvest,575427045167071233,@shipilev http://t.co/WxqFIsfiSF,shipilev,
chvest,575403105174552576,@AlTobey I often use http://t.co/G7Cdn9Udst for small one-off graph diagrams.,AlTobey,http://www.apcjones.com/arrows/
chvest,575337346687766528,RT @theburningmonk: this is why you need composition over inheritance... :s #CompositionOverInheritance http://t.co/aKRwUaZ0qo,theburningmonk,
chvest,575269402083459072,@chvest except…? “Each library implementation should therefore be identical with respect to the public API”,chvest,
----
We're capturing the user, tweetId, the tweet itself, any users mentioned in the tweet and any URLs shared in the tweet.
Next we want to get some of the tweets of the people Chris has interacted with ~~~python # Grab the code from here too - https://gist.github.com/mneedham/3188c44b2cceb88c6de0 import tweepy import csv from collections import Counter, deque auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_token_secret) api = tweepy.API(auth, wait_on_rate_limit = True, wait_on_rate_limit_notify = True) counter = Counter() users_to_process = deque() USERS_TO_PROCESS = 50 def extract_tweet(tweet): user_mentions = ",".join([user["screen_name"].encode("utf-8") for user in tweet.entities["user_mentions"]]) urls = ",".join([url["expanded_url"] for url in tweet.entities["urls"]]) return [tweet.user.screen_name.encode("utf-8"), tweet.id, tweet.text.encode("utf-8"), user_mentions, urls] starting_user = "chvest" with open("tweets.csv", "a") as tweets: writer = csv.writer(tweets, delimiter=",", escapechar="\\", doublequote = False) for tweet in tweepy.Cursor(api.user_timeline, id=starting_user).items(50): writer.writerow(extract_tweet(tweet)) tweets.flush() for user in tweet.entities["user_mentions"]: if not len(users_to_process) > USERS_TO_PROCESS: users_to_process.append(user["screen_name"]) counter[user["screen_name"]] += 1 else: break users_processed = set([starting_user]) while True: if len(users_processed) >= USERS_TO_PROCESS: break else: if len(users_to_process) > 0: next_user = users_to_process.popleft() print next_user if next_user in users_processed: "-- user already processed" else: "-- processing user" users_processed.add(next_user) for tweet in tweepy.Cursor(api.user_timeline, id=next_user).items(10): writer.writerow(extract_tweet(tweet)) tweets.flush() for user_mentioned in tweet.entities["user_mentions"]: if not len(users_processed) > 50: users_to_process.append(user_mentioned["screen_name"]) counter[user_mentioned["screen_name"]] += 1 else: break else: break ~~~
Finally let's take a quick look at the users who show up most frequently: ~~~python >>> for user_name, count in counter.most_common(20): print user_name, count neo4j 13 devnexus 12 AlTobey 11 bitprophet 11 hazelcast 10 chvest 9 shipilev 9 AntoineGrondin 8 gvsmirnov 8 GatlingTool 8 lagergren 7 tomsontom 6 dorkitude 5 noctarius2k 5 DanHeidinga 5 chris_mahan 5 coda 4 mccv 4 gAmUssA 4 jmhodges 4 ~~~
A few of the people on that list are in my list which is a good start. We can explore the data set better once it's in Neo4j though so let's write some Cypher import statements to create our own mini Twitter graph: ~~~cypher // add people LOAD CSV WITH HEADERS FROM "file:///Users/markneedham/projects/neo4j-twitter/tweets.csv" AS row MERGE (p:Person {userName: row.userName}); LOAD CSV WITH HEADERS FROM "file:///Users/markneedham/projects/neo4j-twitter/tweets.csv" AS row WITH SPLIT(row.usersMentioned, ",") AS users UNWIND users AS user MERGE (p:Person {userName: user}); // add tweets LOAD CSV WITH HEADERS FROM "file:///Users/markneedham/projects/neo4j-twitter/tweets.csv" AS row MERGE (t:Tweet {id: row.tweetId}) ON CREATE SET t.contents = row.contents; // add urls LOAD CSV WITH HEADERS FROM "file:///Users/markneedham/projects/neo4j-twitter/tweets.csv" AS row WITH SPLIT(row.urls, ",") AS urls UNWIND urls AS url MERGE (:URL {value: url}); // add links LOAD CSV WITH HEADERS FROM "file:///Users/markneedham/projects/neo4j-twitter/tweets.csv" AS row MATCH (p:Person {userName: row.userName}) MATCH (t:Tweet {id: row.tweetId}) MERGE (p)-[:TWEETED]\->(t); LOAD CSV WITH HEADERS FROM "file:///Users/markneedham/projects/neo4j-twitter/tweets.csv" AS row WITH SPLIT(row.usersMentioned, ",") AS users, row UNWIND users AS user MATCH (p:Person {userName: user}) MATCH (t:Tweet {id: row.tweetId}) MERGE (p)-[:MENTIONED_IN]\->(t); LOAD CSV WITH HEADERS FROM "file:///Users/markneedham/projects/neo4j-twitter/tweets.csv" AS row WITH SPLIT(row.urls, ",") AS urls, row UNWIND urls AS url MATCH (u:URL {value: url}) MATCH (t:Tweet {id: row.tweetId}) MERGE (t)-[:CONTAINS_LINK]\->(u); ~~~
We can put all those commands in a file and execute them using neo4j-shell: ~~~bash $ ./neo4j-community-2.2.0-RC01/bin/neo4j-shell --file import.cql ~~~
Now let's write some queries against the graph: ~~~cypher // Find the tweets where Chris mentioned himself MATCH path = (n:Person {userName: "chvest"})-[:TWEETED]\->()\<-[:MENTIONED_IN]-(n) RETURN path ~~~
image::{{<siteurl>}}/uploads/2015/03/graph-5.png[Graph 5] ~~~cypher // Find the most popular links shared in the network MATCH (u:URL)\<-[r:CONTAINS_LINK]\->() RETURN u.value, COUNT(*) AS times ORDER BY times DESC LIMIT 10 +-------------------------------------------------------------------------------------------------+ | u.value | times | +-------------------------------------------------------------------------------------------------+ | "http://www.polyglots.dk/" | 4 | | "http://www.java-forum-nord.de/" | 4 | | "http://hirt.se/blog/?p=646" | 3 | | "http://wp.me/p26jdv-Ja" | 3 | | "https://instagram.com/p/0D4I_hH77t/" | 3 | | "https://blogs.oracle.com/java/entry/new_java_champion_tom_chindl" | 3 | | "http://www.kennybastani.com/2015/03/spark-neo4j-tutorial-docker.html" | 2 | | "https://firstlook.org/theintercept/2015/03/10/ispy-cia-campaign-steal-apples-secrets/" | 2 | | "http://buff.ly/1GzZXlo" | 2 | | "http://buff.ly/1BrgtQd" | 2 | +-------------------------------------------------------------------------------------------------+ 10 rows ~~~
The first link is for a programming language meetup in Copenhagen, the second for a Java conference in Hanovier and the third an announcement about the latest version of Java Mission Control. So far so good!
A next step in this area would be to run the links through Prismatic's https://github.com/Prismatic/interest-graph[interest graph] so we can model topics in our graph as well. For now let's have a look at the interactions between Chris and others in the graph: ~~~cypher // Find the people who Chris interacts with most often MATCH path = (n:Person {userName: "chvest"})-[:TWEETED]\->()\<-[:MENTIONED_IN]-(other) RETURN other.userName, COUNT(*) AS times ORDER BY times DESC LIMIT 5 +------------------------+ | other.userName | times | +------------------------+ | "gvsmirnov" | 7 | | "shipilev" | 5 | | "nitsanw" | 4 | | "DanHeidinga" | 3 | | "AlTobey" | 3 | +------------------------+ 5 rows ~~~
Let's generalise that to find interactions between any pair of people: ~~~cypher // Find the people who interact most often MATCH (n:Person)-[:TWEETED]\->()\<-[:MENTIONED_IN]-(other) WHERE n <> other RETURN n.userName, other.userName, COUNT(*) AS times ORDER BY times DESC LIMIT 5 +------------------------------------------+ | n.userName | other.userName | times | +------------------------------------------+ | "fbogsany" | "AntoineGrondin" | 8 | | "chvest" | "gvsmirnov" | 7 | | "chris_mahan" | "bitprophet" | 6 | | "maxdemarzi" | "neo4j" | 6 | | "chvest" | "shipilev" | 5 | +------------------------------------------+ 5 rows ~~~
Let's combine a couple of these together to come up with a score for each person: ~~~cypher MATCH (n:Person) // number of mentions OPTIONAL MATCH (n)-[mention:MENTIONED_IN]\->() WITH n, COUNT(mention) AS mentions // number of links shared by someone else OPTIONAL MATCH (n)-[:TWEETED]\->()-[:CONTAINS_LINK]\->(link)\<-[:CONTAINS_LINK]-() WITH n, mentions, COUNT(link) AS links RETURN n.userName, mentions + links AS score, mentions, links ORDER BY score DESC LIMIT 10 +------------------------------------------+ | n.userName | score | mentions | links | +------------------------------------------+ | "chvest" | 17 | 10 | 7 | | "hazelcast" | 16 | 10 | 6 | | "neo4j" | 15 | 13 | 2 | | "noctarius2k" | 14 | 4 | 10 | | "devnexus" | 12 | 12 | 0 | | "polyglotsdk" | 11 | 2 | 9 | | "shipilev" | 11 | 10 | 1 | | "AlTobey" | 11 | 10 | 1 | | "bitprophet" | 10 | 9 | 1 | | "GatlingTool" | 10 | 8 | 2 | +------------------------------------------+ 10 rows ~~~
Amusingly Chris is top of his own network but we also see three accounts which aren't people, but rather products - neo4j, hazelcast and GatlingTool. The rest are legit though
That's as far as I've got but to make this more useful I think we need to introduce follower/friend links as well as importing more data.
In the mean time I've got a bunch of links to go and read! | null | null | [
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] | pythonneo4j-finding-interesting-computer-sciency-people-to-follow-on-twitter | https://markhneedham.com/blog/2015/03/11/pythonneo4j-finding-interesting-computer-sciency-people-to-follow-on-twitter |
false | 2015-03-29 00:31:37 | InetAddressImpl#lookupAllHostAddr slow/hangs | [
"software-development"
] | [
"Software Development"
] | Since I upgraded to Yosemite I've noticed that attempts to resolve localhost on my home network have been taking ages (sometimes over a minute) so I thought I'd try and work out why.
This is what my initial +++<cite>+++/etc/hosts+++</cite>+++ file looked like based on the assumption that my machine's hostname was +++<cite>+++teetotal+++</cite>+++:
[source,bash]
----
$ cat /etc/hosts
##
# Host Database
#
# localhost is used to configure the loopback interface
# when the system is booting. Do not change this entry.
##
127.0.0.1 localhost
255.255.255.255 broadcasthost
::1 localhost
#fe80::1%lo0 localhost
127.0.0.1 wuqour.local
127.0.0.1 teetotal
----
I setup a little test which replicated the problem:
[source,java]
----
import java.net.InetAddress;
import java.net.UnknownHostException;
public class LocalhostResolution
{
public static void main( String[] args ) throws UnknownHostException
{
long start = System.currentTimeMillis();
InetAddress localHost = InetAddress.getLocalHost();
System.out.println(localHost);
System.out.println(System.currentTimeMillis() - start);
}
}
----
which has the following output:
[source,text]
----
Exception in thread "main" java.net.UnknownHostException: teetotal-2: teetotal-2: nodename nor servname provided, or not known
at java.net.InetAddress.getLocalHost(InetAddress.java:1473)
at LocalhostResolution.main(LocalhostResolution.java:9)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:134)
Caused by: java.net.UnknownHostException: teetotal-2: nodename nor servname provided, or not known
at java.net.Inet6AddressImpl.lookupAllHostAddr(Native Method)
at java.net.InetAddress$1.lookupAllHostAddr(InetAddress.java:901)
at java.net.InetAddress.getAddressesFromNameService(InetAddress.java:1293)
at java.net.InetAddress.getLocalHost(InetAddress.java:1469)
... 6 more
----
Somehow my hostname has changed to +++<cite>+++teetotal-2+++</cite>+++ so I added the following entry to +++<cite>+++/etc/hosts+++</cite>+++:
[source,text]
----
127.0.0.1 teetotal-2
----
Now if we run the program we see this output instead:
[source,text]
----
teetotal-2/127.0.0.1
5011
----
It's still taking 5 seconds to resolve which is much longer than I'd expect. After break pointing through the code it seems like it's trying to do an IPv6 resolution rather than IPv4 so I added an +++<cite>+++/etc/hosts+++</cite>+++ entry for that too:
[source,text]
----
::1 teetotal-2
----
Now resolution is much quicker:
[source,text]
----
teetotal-2/127.0.0.1 6
----
Happy days! | null | null | [
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] | inetaddressimpllookupallhostaddr-slowhangs | https://markhneedham.com/blog/2015/03/29/inetaddressimpllookupallhostaddr-slowhangs |
false | 2015-03-17 22:46:13 | Neo4j: Detecting potential typos using EXPLAIN | [
"neo4j"
] | [
"neo4j"
] | I've been running a few intro to Neo4j training sessions recently using Neo4j 2.2.0 RC1 and at some stage in every session somebody will make a typo when writing out of the example queries.
For example one of the queries that we do about half way finds the actors and directors who have worked together and aggregates the movies they were in.
This is the correct query:
[source,cypher]
----
MATCH (actor:Person)-[:ACTED_IN]->(movie)<-[:DIRECTED]-(director)
RETURN actor.name, director.name, COLLECT(movie.title) AS movies
ORDER BY LENGTH(movies) DESC
LIMIT 5
----
which should yield the following results:
[source,bash]
----
==> +-----------------------------------------------------------------------------------------------------------------------+
==> | actor.name | director.name | movies |
==> +-----------------------------------------------------------------------------------------------------------------------+
==> | "Hugo Weaving" | "Andy Wachowski" | ["Cloud Atlas","The Matrix Revolutions","The Matrix Reloaded","The Matrix"] |
==> | "Hugo Weaving" | "Lana Wachowski" | ["Cloud Atlas","The Matrix Revolutions","The Matrix Reloaded","The Matrix"] |
==> | "Laurence Fishburne" | "Lana Wachowski" | ["The Matrix Revolutions","The Matrix Reloaded","The Matrix"] |
==> | "Keanu Reeves" | "Lana Wachowski" | ["The Matrix Revolutions","The Matrix Reloaded","The Matrix"] |
==> | "Carrie-Anne Moss" | "Lana Wachowski" | ["The Matrix Revolutions","The Matrix Reloaded","The Matrix"] |
==> +-----------------------------------------------------------------------------------------------------------------------+
----
However, a common typo is to write 'DIRECTED_IN' instead of 'DIRECTED' in which case we'll see no results:
[source,cypher]
----
MATCH (actor:Person)-[:ACTED_IN]->(movie)<-[:DIRECTED_IN]-(director)
RETURN actor.name, director.name, COLLECT(movie.title) AS movies
ORDER BY LENGTH(movies) DESC
LIMIT 5
==> +-------------------------------------+
==> | actor.name | director.name | movies |
==> +-------------------------------------+
==> +-------------------------------------+
==> 0 row
----
It's not immediately obvious why we aren't seeing any results which can be quite frustrating.
However, in Neo4j 2.2 the 'EXPLAIN' keyword has been introduced and we can use this to see what the query planner thinks of the query we want to execute without actually executing it.
Instead the planner makes use of knowledge that it has about our schema to come up with a plan that it would run and how much of the graph it thinks that plan would touch: ~~~cypher EXPLAIN MATCH (actor:Person)-[:ACTED_IN]\->(movie)\<-[:DIRECTED_IN]-(director) RETURN actor.name, director.name, COLLECT(movie.title) AS movies ORDER BY LENGTH(movies) DESC LIMIT 5 ~~~
image::{{<siteurl>}}/uploads/2015/03/2015-03-17_23-39-55.png[2015 03 17 23 39 55]
The first row of the query plan describes an all nodes scan which tells us that the query will start from the 'director' but it's the second row that's interesting.
The estimated rows when expanding the 'DIRECTED_IN' relationship is 0 when we'd expect it to at least be a positive value if there were some instances of that relationship in the database.
If we compare this to the plan generated when using the proper 'DIRECTED' relationship we can see the difference:
image::{{<siteurl>}}/uploads/2015/03/2015-03-17_23-43-11.png[2015 03 17 23 43 11]
Here we see an estimated 44 rows from expanding the 'DIRECTED' relationship so we know there are at least some nodes connected by that relationship type.
In summary if you find your query not returning anything when you expect it to, prefix an 'EXPLAIN' and make sure you're not seeing the dreaded '0 expected rows'. | null | null | [
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] | neo4j-detecting-potential-typos-using-explain | https://markhneedham.com/blog/2015/03/17/neo4j-detecting-potential-typos-using-explain |
false | 2015-03-17 01:32:18 | One month of mini habits | [
"software-development"
] | [
"Software Development"
] | I recently read a book in the 'getting things done' genre written by Stephen Guise titled 'http://www.amazon.co.uk/Mini-Habits-Smaller-Bigger-Results-ebook/dp/B00HGKNBDK[Mini Habits]' and although I generally don't like those types of books I quite enjoyed this one and decided to give his system a try.
The underlying idea is that there are two parts of actually doing stuff:
* Planning what to do
* Doing it
We often get stuck in between the first and second steps because what we've planned to do is too big and overwhelming.
Guise's approach for overcoming this inaction is to shrink the amount of work to do until it's *small enough that we don't feel any resistance to getting started*.
It should be something that you can do in 1 or 2 minutes - stupidly small - something that you can do even on your worst day when you have no time/energy.
I'm extremely good at procrastinating so I thought I'd give it a try and see if it helped. Guise suggests starting with one or two habits but I had four things that I want to do so I've ignored that advice for now.
My attempted habits are the following:
* Read one page of a data science related paper/article a day
* Read one page of a computer science related paper/article a day
* Write one line of data science related code a day
* Write 50 words on blog a day
== Sooooo\....has it helped?
In terms of doing each of the habits I've been successful so far - today is the 35th day in a row that I've managed to do each of them. Having said that, there have been some times when I've got back home at 11pm and realised that I haven't done 2 of the habits and need to quickly do the minimum to 'tick them off'.
The habit I've enjoyed doing the most is writing one line of data science related code a day.
My initial intention was that this was going to only involved writing machine learning code but at the moment I've made it a bit more generic so it can include things like the http://www.markhneedham.com/blog/2015/03/11/pythonneo4j-finding-interesting-computer-sciency-people-to-follow-on-twitter/[Twitter Graph] or other bits and pieces that I want to get started on.
The main problem I've had with making progress on mini projects like that is that I imagine its end state and it feels too daunting to start on. Committing to just one line of code a day has been liberating in some way.
One tweak I have made to all the habits is to have some rough goal of where all the daily habits are leading as I noticed that the stuff I was doing each day was becoming very random. https://twitter.com/mesirii[Michael] pointed me at Amy Hoy's 'http://files.alexpcoleman.com/newsletter/2014/04/The%2030x500%20Guide%20to%20Doing%20It%20Backwards.pdf[Guide to doing it backwards]' which describes a neat technique for working back from a goal and determining the small steps required to achieve it.
Writing at least 50 words a day has been beneficial for getting blog posts written. Before the last month I've found myself writing most of my posts at the end of month but I have a more regular cadence now which feels better.
Computer science wise I've been picking up papers which have some sort of link to databases to try and learn more of the low level detail there. e.g. I've read the http://www.cs.iit.edu/~yee/classes/cs525aut04/oneil93lruk.pdf[LRU-K cache] paper which Neo4j 2.2's page cache is based on and have been flicking through the original https://hal.archives-ouvertes.fr/inria-00555588/document[CRDTs] paper over the last few days.
I also recently came across the Papers We Love repository so I'll probably work through some of the https://github.com/papers-we-love/papers-we-love/tree/master/distributed_systems[distributed systems papers] they've collated next.
== Other observations
I've found that if I do stuff early in the morning it feels better as you know it's out of the way and doesn't linger over you for the rest of the day.
I sometimes find myself wanting to just tick off the habits for the day even when it might be interesting to spend more time on one of the habits. I'm not sure what to make of this really - perhaps I should reduce the number of habits to the ones I'm really interested in?
With the writing it does sometimes feel like I'm just writing for the sake of it but it is a good habit to get into as it forces me to explain what I'm working on and get ideas from other people so I'm going to keep doing it.
I've enjoyed my experience with 'mini habits' so far although I think I'd be better off focusing on fewer habits so that there's still enough time in the day to read/learn random spontaneous stuff that doesn't fit into these habits. | null | null | [
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] | one-month-of-mini-habits | https://markhneedham.com/blog/2015/03/17/one-month-of-mini-habits |
false | 2015-03-26 00:02:54 | Python: matplotlib hangs and shows nothing (Mac OS X) | [
"python",
"matplotlib"
] | [
"Python"
] | I've been playing around with some of the http://matplotlib.org/[matplotlib] demos recently and discovered that simply copying one of the examples didn't actually work for me.
I was following the http://matplotlib.org/examples/api/barchart_demo.html[bar chart example] and had the following code:
[source,python]
----
import numpy as np
import matplotlib.pyplot as plt
N = 5
ind = np.arange(N)
fig, ax = plt.subplots()
menMeans = (20, 35, 30, 35, 27)
menStd = (2, 3, 4, 1, 2)
width = 0.35 # the width of the bars
rects1 = ax.bar(ind, menMeans, width, color='r', yerr=menStd)
plt.show()
----
When I execute this script from the command line it just hangs and I don't see anything at all.
Via a http://stackoverflow.com/questions/21129055/no-plot-window-shows-up-with-matplotlib-pyplot-run-with-enthought-canopy-python[combination of] http://stackoverflow.com/questions/7534453/matplotlib-does-not-show-my-drawings-although-i-call-pyplot-show[different] http://stackoverflow.com/questions/2512225/matplotlib-not-showing-up-in-mac-osx[blog posts] (which all suggested different things!) I ended up with the following variation of imports which seems to do the job:
[source,python]
----
import numpy as np
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
N = 5
ind = np.arange(N)
fig, ax = plt.subplots()
menMeans = (20, 35, 30, 35, 27)
menStd = (2, 3, 4, 1, 2)
width = 0.35 # the width of the bars
rects1 = ax.bar(ind, menMeans, width, color='r', yerr=menStd)
plt.show()
----
If I run this script a Python window pops up and contains the following image which is what I expected to happen in the first place!
image::{{<siteurl>}}/uploads/2015/03/2015-03-25_23-56-08.png[2015 03 25 23 56 08]
The thing to notice is that we've had to change the http://matplotlib.org/faq/usage_faq.html#what-is-a-backend[backend] in order to use matplotlib from the shell:
____
With the TkAgg backend, which uses the Tkinter user interface toolkit, you can use matplotlib from an arbitrary non-gui python shell.
____
Current state: Wishing for ggplot! | null | null | [
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] | python-matplotlib-hangs-and-shows-nothing-mac-os-x | https://markhneedham.com/blog/2015/03/26/python-matplotlib-hangs-and-shows-nothing-mac-os-x |
false | 2015-03-09 23:00:56 | Python: Streaming/Appending to a file | [
"python"
] | [
"Python"
] | I've been playing around with Twitter's API (via the https://github.com/tweepy/tweepy[tweepy] library) and due to the rate limiting it imposes I wanted to stream results to a CSV file rather than waiting until my whole program had finished.
I wrote the following program to simulate what I was trying to do:
[source,python]
----
import csv
import time
with open("rows.csv", "a") as file:
writer = csv.writer(file, delimiter = ",")
end = time.time() + 10
while True:
if time.time() > end:
break
else:
writer.writerow(["mark", "123"])
time.sleep(1)
----
The program will run for 10 seconds and append one line to 'rows.csv' once a second. Although I have used the 'a' flag in my call to 'open' if I poll that file before the 10 seconds is up it's empty:
[source,bash]
----
$ date && wc -l rows.csv
Mon 9 Mar 2015 22:54:27 GMT
0 rows.csv
$ date && wc -l rows.csv
Mon 9 Mar 2015 22:54:31 GMT
0 rows.csv
$ date && wc -l rows.csv
Mon 9 Mar 2015 22:54:34 GMT
0 rows.csv
$ date && wc -l rows.csv
Mon 9 Mar 2015 22:54:43 GMT
10 rows.csv
----
I thought the flushing of the file was completely controlled by the with block but lucky for me there's actually a +++<cite>+++https://docs.python.org/2/library/io.html#io.IOBase.flush[flush()]+++</cite>+++ function which allows me to force writes to the file whenever I want.
Here's the new and improved sample program:
[source,python]
----
import csv
import time
with open("rows.csv", "a") as file:
writer = csv.writer(file, delimiter = ",")
end = time.time() + 10
while True:
if time.time() > end:
break
else:
writer.writerow(["mark", "123"])
time.sleep(1)
file.flush()
----
And if we poll the file while the program's running:
[source,python]
----
$ date && wc -l rows.csv
Mon 9 Mar 2015 22:57:36 GMT
14 rows.csv
$ date && wc -l rows.csv
Mon 9 Mar 2015 22:57:37 GMT
15 rows.csv
$ date && wc -l rows.csv
Mon 9 Mar 2015 22:57:40 GMT
18 rows.csv
$ date && wc -l rows.csv
Mon 9 Mar 2015 22:57:45 GMT
20 rows.csv
----
Much easier than I expected - I ♥ python! | null | null | [
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] | python-streamingappending-to-a-file | https://markhneedham.com/blog/2015/03/09/python-streamingappending-to-a-file |
false | 2015-03-30 22:28:23 | Python: Creating a skewed random discrete distribution | [
"python"
] | [
"Python"
] | I'm planning to write a variant of the http://en.wikipedia.org/wiki/Tf%E2%80%93idf[TF/IDF] algorithm over the HIMYM corpus which weights in favour of term that appear in a medium number of documents and as a prerequisite needed a function that when given a number of documents would return a weighting.
It should return a higher value when a term appears in a medium number of documents i.e. if I pass in 10 I should get back a higher value than 200 as a term that appears in 10 episodes is likely to be more interesting than one which appears in almost every episode.
I went through a few different scipy distributions but none of them did exactly what I want so I ended up writing a sampling function which http://stackoverflow.com/questions/24854965/create-random-numbers-with-left-skewed-probability-distribution[chooses random numbers in a range with different probabilities]:
[source,python]
----
import math
import numpy as np
values = range(1, 209)
probs = [1.0 / 208] * 208
for idx, prob in enumerate(probs):
if idx > 3 and idx < 20:
probs[idx] = probs[idx] * (1 + math.log(idx + 1))
if idx > 20 and idx < 40:
probs[idx] = probs[idx] * (1 + math.log((40 - idx) + 1))
probs = [p / sum(probs) for p in probs]
sample = np.random.choice(values, 1000, p=probs)
>>> print sample[:10]
[ 33 9 22 126 54 4 20 17 45 56]
----
Now let's visualise the distribution of this sample by plotting a histogram:
[source,python]
----
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
binwidth = 2
plt.hist(sample, bins=np.arange(min(sample), max(sample) + binwidth, binwidth))
plt.xlim([0, max(sample)])
plt.show()
----
image::{{<siteurl>}}/uploads/2015/03/2015-03-30_23-25-05.png[2015 03 30 23 25 05]
It's a bit hacky but it does seem to work in terms of weighting the values correctly. It would be nice if it I could smooth it off a bit better but I'm not sure how at the moment.
One final thing we can do is get the count of any one of the values by using the http://docs.scipy.org/doc/numpy/reference/generated/numpy.bincount.html[bincount] function:
[source,python]
----
>>> print np.bincount(sample)[1]
4
>>> print np.bincount(sample)[10]
16
>>> print np.bincount(sample)[206]
3
----
Now I need to plug this into the rest of my code and see if it actually works! | null | null | [
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] | python-creating-a-skewed-random-discrete-distribution | https://markhneedham.com/blog/2015/03/30/python-creating-a-skewed-random-discrete-distribution |
false | 2015-03-08 13:24:19 | Neo4j: TF/IDF (and variants) with cypher | [
"neo4j"
] | [
"neo4j"
] | A few weeks ago I wrote a blog post on http://www.markhneedham.com/blog/2015/02/15/pythonscikit-learn-calculating-tfidf-on-how-i-met-your-mother-transcripts/[running TF/IDF over HIMYM transcripts using scikit-learn] to find the most important phrases by episode and afterwards I was curious how difficult it'd be to do in Neo4j.
I started by translating one of http://en.wikipedia.org/wiki/Tf%E2%80%93idf#Example_of_tf.E2.80.93idf[wikipedia's TF/IDF examples] to cypher to see what the algorithm would look like:
[source,cypher]
----
WITH 3 as termFrequency, 2 AS numberOfDocuments, 1 as numberOfDocumentsWithTerm
WITH termFrequency, log10(numberOfDocuments / numberOfDocumentsWithTerm) AS inverseDocumentFrequency
return termFrequency * inverseDocumentFrequency
0.9030899869919435
----
Next I needed to go over the HIMYM episode transcripts and extract phrases and their corresponding counts in each episode. I used scikit-learn's +++<cite>+++http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html[CountVectorizer]+++</cite>+++ to do this and wrote the results into a CSV file.
Here's a preview of that file:
[source,bash]
----
$ head -n 10 data/import/words_scikit.csv
EpisodeId,Phrase,Count
1,2005,1
1,2005 seven,1
1,2005 seven just,1
1,2030,3
1,2030 kids,1
1,2030 kids intently,1
1,2030 narrator,1
1,2030 narrator kids,1
1,2030 son,1
----
Now let's import that into Neo4j using the LOAD CSV tool:
[source,cypher]
----
// phrases
USING PERIODIC COMMIT 1000
LOAD CSV WITH HEADERS FROM "file:///Users/markneedham/projects/neo4j-himym/data/import/words_scikit.csv" AS row
MERGE (phrase:Phrase {value: row.Phrase});
----
[source,cypher]
----
// episode -> phrase
USING PERIODIC COMMIT 1000
LOAD CSV WITH HEADERS FROM "file:///Users/markneedham/projects/neo4j-himym/data/import/words_scikit.csv" AS row
MATCH (phrase:Phrase {value: row.Phrase})
MATCH (episode:Episode {id: TOINT(row.EpisodeId)})
MERGE (episode)-[:CONTAINED_PHRASE {times:TOINT(row.Count)}]->(phrase);
----
Now that all the data's in we can translate the TF/IDF query to make use of our graph. We'll start with episode 1:
[source,cypher]
----
match (e:Episode)
WITH COUNT(e) AS numberOfDocuments
match (p:Phrase)<-[r:CONTAINED_PHRASE]-(e:Episode {id: 1})
WITH numberOfDocuments, p, r.times AS termFrequency
MATCH (p)<-[:CONTAINED_PHRASE]->(otherEpisode)
WITH p, COUNT(otherEpisode) AS numberOfDocumentsWithTerm, numberOfDocuments, termFrequency
WITH p, numberOfDocumentsWithTerm, log10(numberOfDocuments / numberOfDocumentsWithTerm) AS inverseDocumentFrequency, termFrequency, numberOfDocuments
RETURN p.value, termFrequency, numberOfDocumentsWithTerm, inverseDocumentFrequency, termFrequency * inverseDocumentFrequency AS score
ORDER BY score DESC
LIMIT 10
==> +--------------------------------------------------------------------------------------------------------------------+
==> | p.value | termFrequency | numberOfDocumentsWithTerm | inverseDocumentFrequency | score |
==> +--------------------------------------------------------------------------------------------------------------------+
==> | "olives" | 18 | 2 | 2.0170333392987803 | 36.306600107378046 |
==> | "yasmine" | 13 | 1 | 2.3180633349627615 | 30.1348233545159 |
==> | "signal" | 11 | 5 | 1.6127838567197355 | 17.740622423917088 |
==> | "goanna" | 10 | 4 | 1.7160033436347992 | 17.16003343634799 |
==> | "flashback date" | 6 | 1 | 2.3180633349627615 | 13.908380009776568 |
==> | "scene" | 17 | 37 | 0.6989700043360189 | 11.88249007371232 |
==> | "flashback date robin" | 5 | 1 | 2.3180633349627615 | 11.590316674813808 |
==> | "ted yasmine" | 5 | 1 | 2.3180633349627615 | 11.590316674813808 |
==> | "smurf pen1s" | 5 | 2 | 2.0170333392987803 | 10.085166696493902 |
==> | "eye patch" | 5 | 2 | 2.0170333392987803 | 10.085166696493902 |
==> +--------------------------------------------------------------------------------------------------------------------+
==> 10 rows
----
The score we've calculated is different to scikit-learn's but the relative order seems fine so that's good. The neat thing about calculating this in Neo4j is that we can now vary the 'inverse document' part of the equation e.g. to find out the most important phrases in a season rather than an episode:
[source,cypher]
----
match (:Season)
WITH COUNT(*) AS numberOfDocuments
match (p:Phrase)<-[r:CONTAINED_PHRASE]-(:Episode)-[:IN_SEASON]->(s:Season {number: "1"})
WITH p, SUM(r.times) AS termFrequency, numberOfDocuments
MATCH (p)<-[:CONTAINED_PHRASE]->(otherEpisode)-[:IN_SEASON]->(s:Season)
WITH p, COUNT(DISTINCT s) AS numberOfDocumentsWithTerm, termFrequency, numberOfDocuments
WITH p, numberOfDocumentsWithTerm, log10(numberOfDocuments / numberOfDocumentsWithTerm) AS inverseDocumentFrequency, termFrequency, numberOfDocuments
RETURN p.value, termFrequency, numberOfDocumentsWithTerm, inverseDocumentFrequency, termFrequency * inverseDocumentFrequency AS score
ORDER BY score DESC
LIMIT 10
==> +-------------------------------------------------------------------------------------------------------------+
==> | p.value | termFrequency | numberOfDocumentsWithTerm | inverseDocumentFrequency | score |
==> +-------------------------------------------------------------------------------------------------------------+
==> | "moby" | 46 | 1 | 0.9542425094393249 | 43.895155434208945 |
==> | "int" | 71 | 3 | 0.47712125471966244 | 33.87560908509603 |
==> | "ellen" | 53 | 2 | 0.6020599913279624 | 31.909179540382006 |
==> | "claudia" | 104 | 4 | 0.3010299956639812 | 31.307119549054043 |
==> | "ericksen" | 59 | 3 | 0.47712125471966244 | 28.150154028460083 |
==> | "party number" | 29 | 1 | 0.9542425094393249 | 27.67303277374042 |
==> | "subtitle" | 27 | 1 | 0.9542425094393249 | 25.76454775486177 |
==> | "vo" | 47 | 3 | 0.47712125471966244 | 22.424698971824135 |
==> | "ted vo" | 47 | 3 | 0.47712125471966244 | 22.424698971824135 |
==> | "future ted vo" | 45 | 3 | 0.47712125471966244 | 21.47045646238481 |
==> +-------------------------------------------------------------------------------------------------------------+
==> 10 rows
----
From this query we learn that 'Moby' was only mentioned once in the whole series and actually all of those mentions were in the http://en.wikipedia.org/wiki/The_Limo_%28How_I_Met_Your_Mother%29[same episode]. The occurrence of 'int' seems to be more of a data issue - in some episodes the transcript describes location but in many it doesn't:
[source,bash]
----
$ ack -iw "int" data/import/sentences.csv
2361,8,1,8,"INT. LIVING ROOM, YEAR 2030"
2377,8,1,8,INT. CHINESE RESTAURANT
2395,8,1,8,INT. APARTMENT
2412,8,1,8,INT. APARTMENT
2419,8,1,8,INT. BAR
2472,8,1,8,INT. APARTMENT
2489,8,1,8,INT. BAR
2495,8,1,8,INT. APARTMENT
2506,8,1,8,INT. BAR
2584,8,1,8,INT. APARTMENT
2629,8,1,8,INT. RESTAURANT
2654,8,1,8,INT. APARTMENT
2682,8,1,8,INT. RESTAURANT
2689,8,1,8,(Robin gets up and leaves restaurant) INT. HOSPITAL WAITING AREA
----
'vo' stands for voice over which should probably be stripped out in the stop words as it doesn't add much value. It shows up here because the transcripts aren't consistent in the way they represent Future Ted speaking.
Let's have a look at the final season to see how that fares:
[source,cypher]
----
match (:Season)
WITH COUNT(*) AS numberOfDocuments
match (p:Phrase)<-[r:CONTAINED_PHRASE]-(:Episode)-[:IN_SEASON]->(s:Season {number: "9"})
WITH p, SUM(r.times) AS termFrequency, numberOfDocuments
MATCH (p)<-[:CONTAINED_PHRASE]->(otherEpisode:Episode)-[:IN_SEASON]->(s:Season)
WITH p, COUNT(DISTINCT s) AS numberOfDocumentsWithTerm, termFrequency, numberOfDocuments
WITH p, numberOfDocumentsWithTerm, log10(numberOfDocuments / numberOfDocumentsWithTerm) AS inverseDocumentFrequency, termFrequency, numberOfDocuments
RETURN p.value, termFrequency, numberOfDocumentsWithTerm, inverseDocumentFrequency, termFrequency * inverseDocumentFrequency AS score
ORDER BY score DESC
LIMIT 10
==> +------------------------------------------------------------------------------------------------------------------+
==> | p.value | termFrequency | numberOfDocumentsWithTerm | inverseDocumentFrequency | score |
==> +------------------------------------------------------------------------------------------------------------------+
==> | "ring bear" | 28 | 1 | 0.9542425094393249 | 26.718790264301095 |
==> | "click options" | 26 | 1 | 0.9542425094393249 | 24.810305245422448 |
==> | "thank linus" | 26 | 1 | 0.9542425094393249 | 24.810305245422448 |
==> | "vow" | 39 | 2 | 0.6020599913279624 | 23.480339661790534 |
==> | "just click" | 24 | 1 | 0.9542425094393249 | 22.901820226543798 |
==> | "rehearsal dinner" | 23 | 1 | 0.9542425094393249 | 21.947577717104473 |
==> | "linus" | 36 | 2 | 0.6020599913279624 | 21.674159687806647 |
==> | "just click options" | 22 | 1 | 0.9542425094393249 | 20.993335207665147 |
==> | "locket" | 32 | 2 | 0.6020599913279624 | 19.265919722494797 |
==> | "cassie" | 19 | 1 | 0.9542425094393249 | 18.13060767934717 |
==> +------------------------------------------------------------------------------------------------------------------+
----
There are several phrases which are specific to Barney & Robin's wedding ('vow', 'ring bear', 'rehearsal dinner') so it makes sense that those come out top. The 'linus' here mostly refers to the server at the bar who interacts with Lily although a quick search over the transcripts reveals that she also had an Uncle Linus!
[source,bash]
----
$ ack -iw "linus" data/import/sentences.csv | head -n 5
18649,61,3,17,Lily: Why don't we just call Duluth Mental Hospital and say my Uncle Linus can live with us?
59822,185,9,1,Linus.
59826,185,9,1,"Are you my guy, Linus?"
59832,185,9,1,Thank you Linus.
59985,185,9,1,"Thank you, Linus."
...
----
From doing this exercise I think TF/IDF is an interesting way of exploring unstructured data but for a phrase to be really interesting to us it should appear across multiple episodes/seasons.
One way to achieve that would be to weight those features more so I'll try that out next.
https://github.com/mneedham/neo4j-himym[All the code in this post is on github] if you want to take a look and improve it. | null | null | [
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] | neo4j-tfidf-and-variants-with-cypher | https://markhneedham.com/blog/2015/03/08/neo4j-tfidf-and-variants-with-cypher |
false | 2015-03-01 02:36:06 | Python: Detecting the speaker in HIMYM using Parts of Speech (POS) tagging | [
"python"
] | [
"Python"
] | Over the last couple of weeks I've been experimenting with http://www.markhneedham.com/blog/2015/02/20/pythonscikit-learn-detecting-which-sentences-in-a-transcript-contain-a-speaker/[different] http://www.markhneedham.com/blog/2015/02/24/pythonnltk-naive-vs-naive-bayes-vs-decision-tree/[classifiers] to detect speakers in HIMYM transcripts and in all my attempts so far the only features I've used have been words.
This led to classifiers that were overfitted to the training data so I wanted to generalise them by introducing parts of speech of the words in sentences which are more generic.
First I changed the function which generates the features for each word to also contain the parts of speech of the previous and next words as well as the word itself:
[source,python]
----
def pos_features(sentence, sentence_pos, i):
features = {}
features["word"] = sentence[i]
features["word-pos"] = sentence_pos[i][1]
if i == 0:
features["prev-word"] = "<START>"
features["prev-word-pos"] = "<START>"
else:
features["prev-word"] = sentence[i-1]
features["prev-word-pos"] = sentence_pos[i-1][1]
if i == len(sentence) - 1:
features["next-word"] = "<END>"
features["next-word-pos"] = "<END>"
else:
features["next-word"] = sentence[i+1]
features["next-word-pos"] = sentence_pos[i+1][1]
return features
----
Next we need to tweak our calling code to calculate the parts of speech tags for each sentence and pass it in:
[source,python]
----
featuresets = []
for tagged_sent in tagged_sents:
untagged_sent = nltk.tag.untag(tagged_sent)
sentence_pos = nltk.pos_tag(untagged_sent)
for i, (word, tag) in enumerate(tagged_sent):
featuresets.append((pos_features(untagged_sent, sentence_pos, i), tag) )
----
I'm using nltk to do this and although it's https://honnibal.wordpress.com/2013/09/11/a-good-part-of-speechpos-tagger-in-about-200-lines-of-python/[slower than some alternatives], the data set is small enough that it's not an issue.
Now it's time to train a Decision Tree with the new features. I created three variants - one with both words and POS; one with only words; one with only POS.
I took a deep copy of the training/test data sets and then removed the appropriate keys:
[source,python]
----
def get_rid_of(entry, *keys):
for key in keys:
del entry[key]
import copy
# Word based classifier
tmp_train_data = copy.deepcopy(train_data)
for entry, tag in tmp_train_data:
get_rid_of(entry, 'prev-word-pos', 'word-pos', 'next-word-pos')
tmp_test_data = copy.deepcopy(test_data)
for entry, tag in tmp_test_data:
get_rid_of(entry, 'prev-word-pos', 'word-pos', 'next-word-pos')
c = nltk.DecisionTreeClassifier.train(tmp_train_data)
c.classify(tmp_test_data)
# POS based classifier
tmp_train_data = copy.deepcopy(train_data)
for entry, tag in tmp_train_data:
get_rid_of(entry, 'prev-word', 'word', 'next-word')
tmp_test_data = copy.deepcopy(test_data)
for entry, tag in tmp_test_data:
get_rid_of(entry, 'prev-word', 'word', 'next-word')
c = nltk.DecisionTreeClassifier.train(tmp_train_data)
c.classify(tmp_test_data)
----
The https://github.com/mneedham/neo4j-himym/blob/bae87fb6cea228bcd4ee8b4b406bf18c605c6834/scripts/detect_speaker.py[full code is on my github] but these were the results I saw:
[source,bash]
----
$ time python scripts/detect_speaker.py
Classifier speaker precision speaker recall non-speaker precision non-speaker recall
-------------------- ------------------- ---------------- ----------------------- --------------------
Decision Tree All In 0.911765 0.939394 0.997602 0.996407
Decision Tree Words 0.911765 0.939394 0.997602 0.996407
Decision Tree POS 0.90099 0.919192 0.996804 0.996008
----
There's still not much in it - the POS one has slightly more false positives and false positives when classifying speakers but on other runs it performed better.
If we take a look at the decision tree that's been built for the POS one we can see that it's all about POS now as you'd expect:
[source,python]
----
>>> print(c.pseudocode(depth=2))
if next-word-pos == '$': return False
if next-word-pos == "''": return False
if next-word-pos == ',': return False
if next-word-pos == '-NONE-': return False
if next-word-pos == '.': return False
if next-word-pos == ':':
if prev-word-pos == ',': return False
if prev-word-pos == '.': return False
if prev-word-pos == ':': return False
if prev-word-pos == '<START>': return True
if prev-word-pos == 'CC': return False
if prev-word-pos == 'CD': return False
if prev-word-pos == 'DT': return False
if prev-word-pos == 'IN': return False
if prev-word-pos == 'JJ': return False
if prev-word-pos == 'JJS': return False
if prev-word-pos == 'MD': return False
if prev-word-pos == 'NN': return False
if prev-word-pos == 'NNP': return False
if prev-word-pos == 'NNS': return False
if prev-word-pos == 'POS': return False
if prev-word-pos == 'PRP': return False
if prev-word-pos == 'PRP$': return False
if prev-word-pos == 'RB': return False
if prev-word-pos == 'RP': return False
if prev-word-pos == 'TO': return False
if prev-word-pos == 'VB': return False
if prev-word-pos == 'VBD': return False
if prev-word-pos == 'VBG': return False
if prev-word-pos == 'VBN': return True
if prev-word-pos == 'VBP': return False
if prev-word-pos == 'VBZ': return False
if next-word-pos == '<END>': return False
if next-word-pos == 'CC': return False
if next-word-pos == 'CD':
if word-pos == '$': return False
if word-pos == ',': return False
if word-pos == ':': return True
if word-pos == 'CD': return True
if word-pos == 'DT': return False
if word-pos == 'IN': return False
if word-pos == 'JJ': return False
if word-pos == 'JJR': return False
if word-pos == 'JJS': return False
if word-pos == 'NN': return False
if word-pos == 'NNP': return False
if word-pos == 'PRP$': return False
if word-pos == 'RB': return False
if word-pos == 'VB': return False
if word-pos == 'VBD': return False
if word-pos == 'VBG': return False
if word-pos == 'VBN': return False
if word-pos == 'VBP': return False
if word-pos == 'VBZ': return False
if word-pos == 'WDT': return False
if word-pos == '``': return False
if next-word-pos == 'DT': return False
if next-word-pos == 'EX': return False
if next-word-pos == 'IN': return False
if next-word-pos == 'JJ': return False
if next-word-pos == 'JJR': return False
if next-word-pos == 'JJS': return False
if next-word-pos == 'MD': return False
if next-word-pos == 'NN': return False
if next-word-pos == 'NNP': return False
if next-word-pos == 'NNPS': return False
if next-word-pos == 'NNS': return False
if next-word-pos == 'PDT': return False
if next-word-pos == 'POS': return False
if next-word-pos == 'PRP': return False
if next-word-pos == 'PRP$': return False
if next-word-pos == 'RB': return False
if next-word-pos == 'RBR': return False
if next-word-pos == 'RBS': return False
if next-word-pos == 'RP': return False
if next-word-pos == 'TO': return False
if next-word-pos == 'UH': return False
if next-word-pos == 'VB': return False
if next-word-pos == 'VBD': return False
if next-word-pos == 'VBG': return False
if next-word-pos == 'VBN': return False
if next-word-pos == 'VBP': return False
if next-word-pos == 'VBZ': return False
if next-word-pos == 'WDT': return False
if next-word-pos == 'WP': return False
if next-word-pos == 'WRB': return False
if next-word-pos == '``': return False
----
I like that it's identified the '+++<speaker>+++:+++<sentence>+++' pattern:</p> ~~~python if next-word-pos == ':': \... if prev-word-pos == '+++<START>+++': return True ~~~
Next I need to drill into the types of sentence structures that it's failing on and work out some features that can handle those. I still need to see how well a random forest of decision trees would as well.+++</START>++++++</sentence>++++++</speaker>+++ | null | null | [
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] | python-detecting-the-speaker-in-himym-using-parts-of-speech-pos-tagging | https://markhneedham.com/blog/2015/03/01/python-detecting-the-speaker-in-himym-using-parts-of-speech-pos-tagging |
false | 2015-03-24 22:33:42 | Topic Modelling: Working out the optimal number of topics | [
"python"
] | [
"Machine Learning",
"Python"
] | In my continued exploration of topic modelling I came across The Programming Historian blog and a post showing http://programminghistorian.org/lessons/topic-modeling-and-mallet[how to derive topics from a corpus] using the Java library http://mallet.cs.umass.edu/[mallet].
The instructions on the blog make it very easy to get up and running but as with other libraries I've used, you have to specify how many topics the corpus consists of. I'm never sure what value to select but the authors make the following suggestion:
____
How do you know the number of topics to search for? Is there a natural number of topics? What we have found is that one has to run the train-topics with varying numbers of topics to see how the composition file breaks down. If we end up with the majority of our original texts all in a very limited number of topics, then we take that as a signal that we need to increase the number of topics; the settings were too coarse. There are computational ways of searching for this, including using MALLETs hlda command, but for the reader of this tutorial, it is probably just quicker to cycle through a number of iterations (but for more see Griffiths, T. L., & Steyvers, M. (2004). http://psiexp.ss.uci.edu/research/papers/sciencetopics.pdf[Finding scientific topics]. Proceedings of the National Academy of Science, 101, 5228-5235).
____
Since I haven't yet had the time to dive into the paper or explore how to use the appropriate option in mallet I thought I'd do some variations on the stop words and number of topics and see how that panned out.
As I understand it, the idea is to try and get a uniform spread of topics \-> documents i.e. we don't want all the documents to have the same topic otherwise any topic similarity calculations we run won't be that interesting.
</p>
I tried running mallet with 10,15,20 and 30 topics and also varied the stop words used. I had one version which just stripped out the main characters and the word 'narrator' & another where I stripped out the top 20% of words by occurrence and any words that appeared less than 10 times.
The reason for doing this was that it should identify interesting phrases across episodes better than TF/IDF can while not just selecting the most popular words across the whole corpus.
I used mallet from the command line and ran it in two parts.
. Generate the model
. Work out the allocation of topics and documents based on hyper parameters
I wrote a script to help me out:
[source,bash]
----
#!/bin/sh
train_model() {
./mallet-2.0.7/bin/mallet import-dir \
--input mallet-2.0.7/sample-data/himym \
--output ${2} \
--keep-sequence \
--remove-stopwords \
--extra-stopwords ${1}
}
extract_topics() {
./mallet-2.0.7/bin/mallet train-topics \
--input ${2} --num-topics ${1} \
--optimize-interval 20 \
--output-state himym-topic-state.gz \
--output-topic-keys output/himym_${1}_${3}_keys.txt \
--output-doc-topics output/himym_${1}_${3}_composition.txt
}
train_model "stop_words.txt" "output/himym.mallet"
train_model "main-words-stop.txt" "output/himym.main.words.stop.mallet"
extract_topics 10 "output/himym.mallet" "all.stop.words"
extract_topics 15 "output/himym.mallet" "all.stop.words"
extract_topics 20 "output/himym.mallet" "all.stop.words"
extract_topics 30 "output/himym.mallet" "all.stop.words"
extract_topics 10 "output/himym.main.words.stop.mallet" "main.stop.words"
extract_topics 15 "output/himym.main.words.stop.mallet" "main.stop.words"
extract_topics 20 "output/himym.main.words.stop.mallet" "main.stop.words"
extract_topics 30 "output/himym.main.words.stop.mallet" "main.stop.words"
----
As you can see, this script first generates a bunch of models from text files in 'mallet-2.0.7/sample-data/himym' - there is one file per episode of HIMYM. We then use that model to generate differently sized topic models.
The output is two files; one containing a list of topics and another describing what percentage of the words in each document come from each topic.
[source,bash]
----
$ cat output/himym_10_all.stop.words_keys.txt
0 0.08929 back brad natalie loretta monkey show call classroom mitch put brunch betty give shelly tyler interview cigarette mc laren
1 0.05256 zoey jerry arthur back randy arcadian gael simon blauman blitz call boats becky appartment amy gary made steve boat
2 0.06338 back claudia trudy doug int abby call carl stuart voix rachel stacy jenkins cindy vo katie waitress holly front
3 0.06792 tony wendy royce back jersey jed waitress bluntly lucy made subtitle film curt mosley put laura baggage officer bell
4 0.21609 back give patrice put find show made bilson nick call sam shannon appartment fire robots top basketball wrestlers jinx
5 0.07385 blah bob back thanksgiving ericksen maggie judy pj valentine amanda made call mickey marcus give put dishes juice int
6 0.04638 druthers karen back jen punchy jeanette lewis show jim give pr dah made cougar call jessica sparkles find glitter
7 0.05751 nora mike pete scooter back magazine tiffany cootes garrison kevin halloween henrietta pumpkin slutty made call bottles gruber give
8 0.07321 ranjit back sandy mary burger call find mall moby heather give goat truck made put duck found stangel penelope
9 0.31692 back give call made find put move found quinn part ten original side ellen chicago italy locket mine show
----
[source,bash]
----
$ head -n 10 output/himym_10_all.stop.words_composition.txt
#doc name topic proportion ...
0 file:/Users/markneedham/projects/mallet/mallet-2.0.7/sample-data/himym/1.txt 0 0.70961794636687 9 0.1294699168584466 8 0.07950442338871108 2 0.07192178481473664 4 0.008360809510263838 5 2.7862560133367015E-4 3 2.562409242784946E-4 7 2.1697378721335337E-4 1 1.982849604752168E-4 6 1.749937876710496E-4
1 file:/Users/markneedham/projects/mallet/mallet-2.0.7/sample-data/himym/10.txt 2 0.9811551470820473 9 0.016716882136209997 4 6.794128563082893E-4 0 2.807350575301132E-4 5 2.3219634098530471E-4 8 2.3018997315244256E-4 3 2.1354177341696056E-4 7 1.8081798384467614E-4 1 1.6524340216541808E-4 6 1.4583339433951297E-4
2 file:/Users/markneedham/projects/mallet/mallet-2.0.7/sample-data/himym/100.txt 2 0.724061485807234 4 0.13624729774423758 0 0.13546964196228636 9 0.0019436342339785994 5 4.5291919356563914E-4 8 4.490055982996677E-4 3 4.1653183421485213E-4 7 3.5270123154213927E-4 1 3.2232165301666123E-4 6 2.8446074162457316E-4
3 file:/Users/markneedham/projects/mallet/mallet-2.0.7/sample-data/himym/101.txt 2 0.7815231689893246 0 0.14798271520316794 9 0.023582384458063092 8 0.022251052243582908 1 0.022138209217973336 4 0.0011804626661380394 5 4.0343527385745457E-4 3 3.7102343418895774E-4 7 3.1416667687862693E-4 6 2.533818368250992E-
4 file:/Users/markneedham/projects/mallet/mallet-2.0.7/sample-data/himym/102.txt 6 0.6448245189567259 4 0.18612146979166502 3 0.16624873439661025 9 0.0012233726722317548 0 3.4467218590717303E-4 5 2.850788252495599E-4 8 2.8261550915084904E-4 2 2.446611421432842E-4 7 2.2199909869250053E-4 1 2.028774216237081E-
5 file:/Users/markneedham/projects/mallet/mallet-2.0.7/sample-data/himym/103.txt 8 0.7531586740033047 5 0.17839539108961253 0 0.06512376460651902 9 0.001282794040111701 4 8.746645156304241E-4 3 2.749100345664577E-4 2 2.5654476523149865E-4 7 2.327819863700214E-4 1 2.1273153572848481E-4 6 1.8774342292520802E-4
6 file:/Users/markneedham/projects/mallet/mallet-2.0.7/sample-data/himym/104.txt 7 0.9489502365148181 8 0.030091466847852504 4 0.017936457663121977 9 0.0013482824985091328 0 3.7986419553884905E-4 5 3.141861834124008E-4 3 2.889445824352445E-4 2 2.6964174000656E-4 1 2.2359178288566958E-4 6 1.9732799141958482E-4
7 file:/Users/markneedham/projects/mallet/mallet-2.0.7/sample-data/himym/105.txt 8 0.7339694064061175 7 0.1237041841318045 9 0.11889696041555338 0 0.02005288536233353 4 0.0014026751618923005 5 4.793786828705149E-4 3 4.408655780020889E-4 2 4.1141370625324785E-4 1 3.411516484151411E-4 6 3.0107890675777946E-4
8 file:/Users/markneedham/projects/mallet/mallet-2.0.7/sample-data/himym/106.txt 5 0.37064909999661005 9 0.3613559917055785 0 0.14857567731040344 6 0.09545466082502917 4 0.022300625744661403 8 3.8725629469313333E-4 3 3.592484711785775E-4 2 3.3524900189121E-4 7 3.041961449432886E-4 1 2.779945050112539E-4
----
The output is a bit tricky to understand on its own so I did a bit of post processing using pandas and then ran the results of that through matplotlib to see the distribution of documents for different topics sizes with different stop words. You can https://github.com/mneedham/topic-modelling-mallet/blob/master/results.py[see the script here].
I ended up with the following chart:
image::{{<siteurl>}}/uploads/2015/03/2015-03-24_22-08-481.png[2015 03 24 22 08 48,598]
On the left hand side we're using more stop words and on the right just the main ones. For most of the variations there are one or two topics which most documents belong to but interestingly the most uniform distribution seems to be when we have few topics.
These are the main words for the most popular topics on the left hand side:
_15 topics_
[source,text]
----
8 0.50732 back give call made put find found part move show side ten mine top abby front fire full fianc
----
_20 topics_
[source,text]
----
12 0.61545 back give call made put find show found part move side mine top front ten full cry fire fianc
----
_30 topics_
[source,text]
----
22 0.713 back call give made put find show part found side move front ten full top mine fire cry bottom~~~
<p>All contain more or less the same words which at first glance seem like quite generic words so I'm surprised they weren't excluded.</p>
<p>On the right hand side we haven't removed many words so we'd expect common words in the English language to dominate. Let's see if they do:</p>
<em>10 topics</em>
~~~text
1 3.79451 don yeah ll hey ve back time guys good gonna love god night wait uh thing guy great make
----
_15 topics_
[source,text]
----
5 2.81543 good time love ll great man guy ve night make girl day back wait god life yeah years thing
10 1.52295 don yeah hey gonna uh guys didn back ve ll um kids give wow doesn thing totally god fine
----
_20 topics_
[source,text]
----
1 3.06732 good time love wait great man make day back ve god life years thought big give apartment people work
13 1.68795 don yeah hey gonna ll uh guys night didn back ve girl um kids wow guy kind thing baby
----
_30 topics_
[source,text]
----
14 1.42509 don yeah hey gonna uh guys didn back ve um thing ll kids wow time doesn totally kind wasn
24 2.19053 guy love man girl wait god ll back great yeah day call night people guys years home room phone
29 1.84685 good make ve ll stop time made nice put feel love friends big long talk baby thought things happy
----
Again we have similar words across each run and as expected they are all quite generic words.
My take away from this exploration is that I should vary the stop word percentages as well and see if that leads to an improved distribution.
Taking out very common words like we do with the left hand side charts seems to make sense although I need to work out why there's a single outlier in each group.
The authors suggest that having the majority of our texts in a small number of topics means we need to create more of them so I will investigate that too.
The code is https://github.com/mneedham/topic-modelling-mallet[all on github] along with the transcripts so give it a try and let me know what you think. | null | null | [
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] | topic-modelling-working-out-the-optimal-number-of-topics | https://markhneedham.com/blog/2015/03/24/topic-modelling-working-out-the-optimal-number-of-topics |
false | 2015-03-23 00:45:00 | Python: Equivalent to flatMap for flattening an array of arrays | [
"python"
] | [
"Python"
] | I found myself wanting to flatten an array of arrays while writing some Python code earlier this afternoon and being lazy my first attempt involved building the flattened array manually:
[source,python]
----
episodes = [
{"id": 1, "topics": [1,2,3]},
{"id": 2, "topics": [4,5,6]}
]
flattened_episodes = []
for episode in episodes:
for topic in episode["topics"]:
flattened_episodes.append({"id": episode["id"], "topic": topic})
for episode in flattened_episodes:
print episode
----
If we run that we'll see this output:
[source,bash]
----
$ python flatten.py
{'topic': 1, 'id': 1}
{'topic': 2, 'id': 1}
{'topic': 3, 'id': 1}
{'topic': 4, 'id': 2}
{'topic': 5, 'id': 2}
{'topic': 6, 'id': 2}
----
What I was really looking for was the Python equivalent to the http://www.markhneedham.com/blog/2011/07/03/clojure-equivalent-to-scalas-flatmapcs-selectmany/[flatmap function] which I learnt can be achieved in Python http://stackoverflow.com/questions/1077015/python-list-comprehensions-compressing-a-list-of-lists[with a list comprehension] like so:
[source,python]
----
flattened_episodes = [{"id": episode["id"], "topic": topic}
for episode in episodes
for topic in episode["topics"]]
for episode in flattened_episodes:
print episode
----
We could also choose to http://naiquevin.github.io/a-look-at-some-of-pythons-useful-itertools.html[use itertools] in which case we'd have the following code:
[source,python]
----
from itertools import chain, imap
flattened_episodes = chain.from_iterable(
imap(lambda episode: [{"id": episode["id"], "topic": topic}
for topic in episode["topics"]],
episodes))
for episode in flattened_episodes:
print episode
----
We can then simplify this approach a little by wrapping it up in a 'flatmap' function:
[source,python]
----
def flatmap(f, items):
return chain.from_iterable(imap(f, items))
flattened_episodes = flatmap(
lambda episode: [{"id": episode["id"], "topic": topic} for topic in episode["topics"]], episodes)
for episode in flattened_episodes:
print episode
----
I think the list comprehensions approach still works but I need to look into itertools more - it looks like it could work well for other list operations. | null | null | [
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] | python-equivalent-to-flatmap-for-flattening-an-array-of-arrays | https://markhneedham.com/blog/2015/03/23/python-equivalent-to-flatmap-for-flattening-an-array-of-arrays |
false | 2015-03-15 22:43:17 | Python: Transforming Twitter datetime string to timestamp (z' is a bad directive in format) | [
"python"
] | [
"Python"
] | I've been playing around with importing Twitter data into Neo4j and since Neo4j can't store dates natively just yet I needed to convert a date string to timestamp.
I started with the following which unfortunately throws an exception:
[source,python]
----
from datetime import datetime
date = "Sat Mar 14 18:43:19 +0000 2015"
>>> datetime.strptime(date, "%a %b %d %H:%M:%S %z %Y")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/_strptime.py", line 317, in _strptime
(bad_directive, format))
ValueError: 'z' is a bad directive in format '%a %b %d %H:%M:%S %z %Y'
----
https://docs.python.org/2/library/datetime.html#strftime-strptime-behavior[%z is actually a valid option] used to extract the timezone but my googling suggests it not working is one of the idiosyncrasies of strptime.
I eventually came across the +++<cite>+++python-dateutil+++</cite>+++ library, as http://stackoverflow.com/questions/3305413/python-strptime-and-timezones[recommended by Joe Shaw on StackOverflow].
Using that library the problem is suddenly much simpler:
[source,bash]
----
$ pip install python-dateutil
----
[source,python]
----
from dateutil import parser
parsed_date = parser.parse(date)
>>> parsed_date
datetime.datetime(2015, 3, 14, 18, 43, 19, tzinfo=tzutc())
----
To get to a timestamp we can use calendar as I've http://www.markhneedham.com/blog/2014/10/20/python-converting-a-date-string-to-timestamp/[described before]:
[source,python]
----
import calendar
timestamp = calendar.timegm(parser.parse(date).timetuple())
>>> timestamp
1426358599
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] | python-transforming-twitter-datetime-string-to-timestamp-z-is-a-bad-directive-in-format | https://markhneedham.com/blog/2015/03/15/python-transforming-twitter-datetime-string-to-timestamp-z-is-a-bad-directive-in-format |
false | 2015-03-14 02:52:02 | Python: Checking any value in a list exists in a line of text | [
"python"
] | [
"Python"
] | I've been doing some log file analysis to see what cypher queries were being run on a Neo4j instance and I wanted to narrow down the lines I looked at to only contain ones which had mutating operations i.e. those containing the words MERGE, DELETE, SET or CREATE
Here's an example of the text file I was parsing:
[source,bash]
----
$ cat blog.txt
MATCH n RETURN n
MERGE (n:Person {name: "Mark"}) RETURN n
MATCH (n:Person {name: "Mark"}) ON MATCH SET n.counter = 1 RETURN n
----
So I only want lines 2 & 3 to be returned as the first one only returns data and doesn't execute any updates on the graph.
I started off with a very crude way of doing this;
[source,python]
----
with open("blog.txt", "r") as ins:
for line in ins:
if "MERGE" in line or "DELETE" in line or "SET" in line or "CREATE" in line:
print line.strip()
----
A better way of doing this is to use the +++<cite>+++https://docs.python.org/2/library/functions.html#any[any]+++</cite>+++ command and make sure at least one of the words exists in the line:
[source,python]
----
mutating_commands = ["SET", "DELETE", "MERGE", "CREATE"]
with open("blog.txt", "r") as ins:
for line in ins:
if any(command in line for command in mutating_commands):
print line.strip()
----
I thought I might be able to simplify the code even further by using https://docs.python.org/2/library/itertools.html[itertools] but my best attempt so far is less legible than the above:
[source,python]
----
import itertools
commands = ["SET", "CREATE", "MERGE", "DELETE"]
with open("blog.txt", "r") as ins:
for line in ins:
if len(list(itertools.ifilter(lambda x: x in line, mutating_commands))) > 0:
print line.strip()
----
I think I'll go with the 2nd approach for now but if I'm doing something wrong with itertools and it's much easier to use than the example I've shown do correct me! | null | null | [
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] | python-checking-any-value-in-a-list-exists-in-a-line-of-text | https://markhneedham.com/blog/2015/03/14/python-checking-any-value-in-a-list-exists-in-a-line-of-text |
false | 2015-03-22 01:51:52 | Python: Simplifying the creation of a stop word list with defaultdict | [
"python"
] | [
"Python"
] | I've been playing around with topics models again and recently read a http://www.perseus.tufts.edu/publications/02-jocch-mimno.pdf[paper by David Mimno] which suggested the following heuristic for working out which words should go onto the stop list:
____
A good heuristic for identifying such words is to remove those that occur in more than 5-10% of documents (most common) and those that occur fewer than 5-10 times in the entire corpus (least common).
____
I decided to try this out on the https://github.com/mneedham/neo4j-himym/blob/master/data/import/sentences.csv[HIMYM dataset] that I've been working on over the last couple of months.
I started out with the following code to build a dictionary of words, their total occurrences and the episodes they'd been used in:
[source,python]
----
import csv
from sklearn.feature_extraction.text import CountVectorizer
from collections import defaultdict
episodes = defaultdict(str)
with open("sentences.csv", "r") as file:
reader = csv.reader(file, delimiter = ",")
reader.next()
for row in reader:
episodes[row[1]] += row[4]
vectorizer = CountVectorizer(analyzer='word', min_df = 0, stop_words = 'english')
matrix = vectorizer.fit_transform(episodes.values())
features = vectorizer.get_feature_names()
words = {}
for doc_id, doc in enumerate(matrix.todense()):
for word_id, score in enumerate(doc.tolist()[0]):
word = features[word_id]
if not words.get(word):
words[word] = {}
if not words[word].get("score"):
words[word]["score"] = 0
words[word]["score"] += score
if not words[word].get("episodes"):
words[word]["episodes"] = set()
if score > 0:
words[word]["episodes"].add(doc_id)
----
This works fine but the code inside the last for block is ugly and most of it is handling the case when parts of a dictionary aren't yet initialised which is defaultdict territory. You'll notice I am using defaultdict in the first part of the code but not yet the second as I'd struggled to get it working.
This was my first attempt to make the 'words' variable based on it:
[source,python]
----
>>> words = defaultdict({})
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: first argument must be callable
----
We can see why this doesn't work if we try to evaluate '{}' as a function which is what defaultdict does internally:
[source,python]
----
>>> {}()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'dict' object is not callable
----
Instead what we need is to pass in 'dict':
[source,python]
----
>>> dict()
{}
>>> words = defaultdict(dict)
>>> words
defaultdict(<type 'dict'>, {})
----
That simplifies the first bit of the loop:
[source,python]
----
words = defaultdict(dict)
for doc_id, doc in enumerate(matrix.todense()):
for word_id, score in enumerate(doc.tolist()[0]):
word = features[word_id]
if not words[word].get("score"):
words[word]["score"] = 0
words[word]["score"] += score
if not words[word].get("episodes"):
words[word]["episodes"] = set()
if score > 0:
words[word]["episodes"].add(doc_id)
----
We've still got a couple of other places to simplify though which we can do by defining a http://pymotw.com/2/collections/defaultdict.html[custom function] and passing that into defaultdict:
[source,python]
----
def default_dict_function():
return {"score": 0, "episodes": set()}
>>> words = defaultdict(default_dict_function)
>>> words
defaultdict(<function default_dict_function at 0x10963fcf8>, {})
----
And here's the final product:
[source,python]
----
def default_dict_function():
return {"score": 0, "episodes": set()}
words = defaultdict(default_dict_function)
for doc_id, doc in enumerate(matrix.todense()):
for word_id, score in enumerate(doc.tolist()[0]):
word = features[word_id]
words[word]["score"] += score
if score > 0:
words[word]["episodes"].add(doc_id)
----
After this we can write out the words to our stop list:
[source,python]
----
with open("stop_words.txt", "w") as file:
writer = csv.writer(file, delimiter = ",")
for word, value in words.iteritems():
# appears in > 10% of episodes
if len(value["episodes"]) > int(len(episodes) / 10):
writer.writerow([word.encode('utf-8')])
# less than 10 occurences
if value["score"] < 10:
writer.writerow([word.encode('utf-8')])
---- | null | null | [
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] | python-simplifying-the-creation-of-a-stop-word-list-with-defaultdict | https://markhneedham.com/blog/2015/03/22/python-simplifying-the-creation-of-a-stop-word-list-with-defaultdict |
false | 2015-03-22 01:28:33 | Python: Forgetting to use enumerate | [
"python"
] | [
"Python"
] | Earlier this evening I found myself writing the equivalent of the following Python code while building a stop list for a topic model\...
[source,python]
----
words = ["mark", "neo4j", "michael"]
word_position = 0
for word in words:
print word_position, word
word_position +=1
----
\...which is very foolish given that there's https://docs.python.org/2/library/functions.html#enumerate[already a function] that makes it really easy to grab the position of an item in a list:
[source,python]
----
for word_position, word in enumerate(words):
print word_position, word
----
Python does make things extremely easy at times - you're welcome future Mark! | null | null | [
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] | python-forgetting-to-use-enumerate | https://markhneedham.com/blog/2015/03/22/python-forgetting-to-use-enumerate |
false | 2015-04-03 23:31:33 | How I met your mother: Story arcs | [
"machine-learning-2"
] | [
"Machine Learning"
] | After weeks of playing around with various algorithms to extract story arcs in How I met your mother I've come to the conclusion that I don't yet have the skills to completely automate this process so I'm going to change my approach.
The new plan is to treat the outputs of the algorithms as suggestions for possible themes but then have a manual step where I extract what I think are interesting themes in the series.
A theme can consist of a single word or a phrase and the idea is that once a story arc is identified we'll search over the corpus and find the episodes where that phrase occurs.
We can then generate a CSV file of (story arc) \-> (episodeId), store that into our HIMYM graph and use the story arc as another factor for episode similarity.
I ended up with the following script to work out https://raw.githubusercontent.com/mneedham/neo4j-himym/master/data/import/sentences.csv[which episodes contained a story arc]:
[source,bash]
----
#!/bin/bash
find_term() {
arc=${1}
searchTerm=${2}
episodes=$(grep --color -iE "${searchTerm}" data/import/sentences.csv | awk -F"," '{ print $2 }' | sort | uniq)
for episode in ${episodes}; do
echo ${arc},${episode}
done
}
find_term "Bro Code" "bro code"
find_term "Legendary" "legen(.*)ary"
find_term "Slutty Pumpkin" "slutty pumpkin"
find_term "Magician's Code" "magician's code"
find_term "Thanksgiving" "thanksgiving"
find_term "The Playbook" "playbook"
find_term "Slap Bet" "slap bet"
find_term "Wrestlers and Robots" "wrestlers"
find_term "Robin Sparkles" "sparkles"
find_term "Blue French Horn" "blue french horn"
find_term "Olive Theory" "olive"
find_term "Thank You Linus" "thank you, linus"
find_term "Have you met...?" "have you met"
find_term "Laser Tag" "laser tag"
find_term "Goliath National Bank" "goliath national bank"
find_term "Challenge Accepted" "challenge accepted"
find_term "Best Man" "best man"
----
If we run this script we'll see something like the following:
[source,bash]
----
$ ./scripts/arcs.sh
Bro Code,14
Bro Code,155
Bro Code,170
Bro Code,188
Bro Code,201
Bro Code,61
Bro Code,64
Legendary,117
Legendary,120
Legendary,122
Legendary,136
Legendary,137
Legendary,15
Legendary,152
Legendary,157
Legendary,162
Legendary,171
...
Best Man,208
Best Man,30
Best Man,32
Best Man,41
Best Man,42
----
I pulled out these themes by eyeballing the output of the following scripts:
* https://github.com/mneedham/neo4j-himym/blob/master/scripts/scikit_ngram.py[TF/IDF] - calculates TF/IDF scores for ngrams. This helps find important themes in the context of a single episode. I then did some manual searching to see how many of those themes existed in other episodes
* https://github.com/mneedham/neo4j-himym/blob/master/scripts/tfidf_special.py[Weighted Term Frequency] - this returns a weighted term frequency for ngrams of different lengths. The weights are determined by the http://www.markhneedham.com/blog/2015/03/30/python-creating-a-skewed-random-discrete-distribution/[skewed random discrete distribution I wrote about earlier in the week]. I ran it with different skews and ngram lengths.
* https://github.com/mneedham/topic-modelling-mallet/blob/master/ner.py[Named entity extraction] - this pulls out any phrases that are named entities. It mostly pulled out names of people (which I used as a stop word list in some other algorithms) but also revealed a couple of themes.
* https://github.com/mneedham/topic-modelling-mallet/blob/master/train_himym.sh[Topic modelling] - I used http://mallet.cs.umass.edu/[mallet] to extract topics across the corpus. Most of them didn't make much sense to me but there were a few which identified themes that I recognised.
I can't remember off the top of my head if any obvious themes have been missed so if you know HIMYM better than me let me know and I'll try and work out why those didn't surface.
Next I want to see how these scripts fare against some other TV shows and see how quickly I can extract themes for those. It'd also be cool if I can make the whole process a bit more automated. | null | null | [
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] | how-i-met-your-mother-story-arcs | https://markhneedham.com/blog/2015/04/03/how-i-met-your-mother-story-arcs |
false | 2015-04-05 10:07:12 | R: Markov Chain Wikipedia Example | [
"r-2"
] | [
"R"
] | Over the weekend I've been https://github.com/gigasquid/howistart/blob/master/clojure/1/index.md[reading] http://freakonometrics.blog.free.fr/index.php?post/2011/12/20/Basic-on-Markov-Chain-(for-parents)[about] http://en.wikipedia.org/wiki/Markov_chain[Markov Chains] and I thought it'd be an interesting exercise for me to translate Wikipedia's example into R code.
But first a definition:
____
A Markov chain is a random process that undergoes transitions from one state to another on a state space. It is required to possess a property that is usually characterized as "memoryless": the probability distribution of the next state depends only on the current state and not on the sequence of events that preceded it.
____
that 'random process' could be moves in a Monopoly game, the next word in a sentence or, as in Wikipedia's example, the next state of the Stock Market.
The diagram below shows the probabilities of transitioning between the various states:
image::{{<siteurl>}}/uploads/2015/04/800px-Finance_Markov_chain_example_state_space.svg_.png[800px Finance Markov chain example state space svg,399]
e.g. if we're in a Bull Market the probability of the state of the market next week being a Bull Market is 0.9, a Bear Market is 0.075 and a Stagnant Market is 0.025.
We can model the various transition probabilities as a matrix:
[source,r]
----
M = matrix(c(0.9, 0.075, 0.025, 0.15, 0.8, 0.05, 0.25, 0.25, 0.5),
nrow = 3,
ncol = 3,
byrow = TRUE)
> M
[,1] [,2] [,3]
[1,] 0.90 0.075 0.025
[2,] 0.15 0.800 0.050
[3,] 0.25 0.250 0.500
----
Rows/Cols 1-3 are Bull, Bear, Stagnant respectively.
Now let's say we start with a Bear market and want to find the probability of each state in 3 weeks time.
We can do this is by multiplying our probability/transition matrix by itself 3 times and then multiplying the result by a vector representing the initial Bear market state.
[source,R]
----
threeIterations = (M %*% M %*% M)
> threeIterations
> threeIterations
[,1] [,2] [,3]
[1,] 0.7745 0.17875 0.04675
[2,] 0.3575 0.56825 0.07425
[3,] 0.4675 0.37125 0.16125
> c(0,1,0) %*% threeIterations
[,1] [,2] [,3]
[1,] 0.3575 0.56825 0.07425
----
So we have a 56.825% chance of still being in a Bear Market, 35.75% chance that we're now in a Bull Market and only a 7.425% chance of being in a stagnant market.
I found it a bit annoying having to type '%*% M' multiple times but luckily the expm library allows us to apply a Matrix power operation:
[source,r]
----
install.packages("expm")
library(expm)
> M %^% 3
[,1] [,2] [,3]
[1,] 0.7745 0.17875 0.04675
[2,] 0.3575 0.56825 0.07425
[3,] 0.4675 0.37125 0.16125
----
The nice thing about this function is that we can now easily see where the stock market will trend towards over a large number of weeks:
[source,r]
----
> M %^% 100
[,1] [,2] [,3]
[1,] 0.625 0.3125 0.0625
[2,] 0.625 0.3125 0.0625
[3,] 0.625 0.3125 0.0625
----
i.e. 62.5% of weeks we will be in a bull market, 31.25% of weeks will be in a bear market and 6.25% of weeks will be stagnant, | null | null | [
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] | r-markov-chain-wikipedia-example | https://markhneedham.com/blog/2015/04/05/r-markov-chain-wikipedia-example |
false | 2015-04-18 23:53:20 | R: Removing for loops | [
"r-2"
] | [
"R"
] | In my last blog post I showed http://www.markhneedham.com/blog/2015/04/16/r-think-bayes-more-posterior-probability-calculations/[the translation of a likelihood function from Think Bayes into R] and in my first attempt at this function I used a couple of nested for loops.
[source,r]
----
likelihoods = function(names, mixes, observations) {
scores = rep(1, length(names))
names(scores) = names
for(name in names) {
for(observation in observations) {
scores[name] = scores[name] * mixes[[name]][observation]
}
}
return(scores)
}
----
[source,r]
----
Names = c("Bowl 1", "Bowl 2")
bowl1Mix = c(0.75, 0.25)
names(bowl1Mix) = c("vanilla", "chocolate")
bowl2Mix = c(0.5, 0.5)
names(bowl2Mix) = c("vanilla", "chocolate")
Mixes = list("Bowl 1" = bowl1Mix, "Bowl 2" = bowl2Mix)
Mixes
Observations = c("vanilla", "vanilla", "vanilla", "chocolate")
l = likelihoods(Names, Mixes, Observations)
> l / sum(l)
Bowl 1 Bowl 2
0.627907 0.372093
----
We pass in a vector of bowls, a nested dictionary describing the mixes of cookies in each bowl and the observations that we've made. The function tells us that there's an almost 2/3 probability of the cookies coming from Bowl 1 and just over 1/3 of being Bowl 2.
In this case there probably won't be much of a performance improvement by getting rid of the loops but we should be able to write something that's more concise and hopefully idiomatic.
Let's start by getting rid of the inner for loop. That can be replace by a call to the +++<cite>+++http://adv-r.had.co.nz/Functionals.html[Reduce]+++</cite>+++ function like so:
[source,r]
----
likelihoods2 = function(names, mixes, observations) {
scores = rep(0, length(names))
names(scores) = names
for(name in names) {
scores[name] = Reduce(function(acc, observation) acc * mixes[[name]][observation], Observations, 1)
}
return(scores)
}
----
[source,r]
----
l2 = likelihoods2(Names, Mixes, Observations)
> l2 / sum(l2)
Bowl 1 Bowl 2
0.627907 0.372093
----
So that's good, we've still got the same probabilities as before. Now to get rid of the outer for loop. The +++<cite>+++http://adv-r.had.co.nz/Functionals.html[Map]+++</cite>+++ function helps us out here:
[source,r]
----
likelihoods3 = function(names, mixes, observations) {
scores = rep(0, length(names))
names(scores) = names
scores = Map(function(name)
Reduce(function(acc, observation) acc * mixes[[name]][observation], Observations, 1),
names)
return(scores)
}
l3 = likelihoods3(Names, Mixes, Observations)
> l3
$`Bowl 1`
vanilla
0.1054688
$`Bowl 2`
vanilla
0.0625
----
We end up with a list instead of a vector which we need to fix by using the +++<cite>+++https://stat.ethz.ch/R-manual/R-devel/library/base/html/unlist.html[unlist]+++</cite>+++ function:
[source,r]
----
likelihoods3 = function(names, mixes, observations) {
scores = rep(0, length(names))
names(scores) = names
scores = Map(function(name)
Reduce(function(acc, observation) acc * mixes[[name]][observation], Observations, 1),
names)
return(unlist(scores))
}
l3 = likelihoods3(Names, Mixes, Observations)
> l3 / sum(l3)
Bowl 1.vanilla Bowl 2.vanilla
0.627907 0.372093
----
Now we just have this annoying 'vanilla' in the name. That's fixed easily enough:
[source,r]
----
likelihoods3 = function(names, mixes, observations) {
scores = rep(0, length(names))
names(scores) = names
scores = Map(function(name)
Reduce(function(acc, observation) acc * mixes[[name]][observation], Observations, 1),
names)
result = unlist(scores)
names(result) = names
return(result)
}
l3 = likelihoods3(Names, Mixes, Observations)
> l3 / sum(l3)
Bowl 1 Bowl 2
0.627907 0.372093
----
A slightly cleaner alternative makes use of the +++<cite>+++https://stat.ethz.ch/R-manual/R-devel/library/base/html/lapply.html[sapply]+++</cite>+++ function:
[source,r]
----
likelihoods3 = function(names, mixes, observations) {
scores = rep(0, length(names))
names(scores) = names
scores = sapply(names, function(name)
Reduce(function(acc, observation) acc * mixes[[name]][observation], Observations, 1))
names(scores) = names
return(scores)
}
l3 = likelihoods3(Names, Mixes, Observations)
> l3 / sum(l3)
Bowl 1 Bowl 2
0.627907 0.372093
----
That's the best I've got for now but I wonder if we could write a version of this using matrix operations some how - but that's for next time! | null | null | [
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] | r-removing-for-loops | https://markhneedham.com/blog/2015/04/18/r-removing-for-loops |
false | 2015-04-27 22:34:43 | R: dplyr - Error in (list: invalid subscript type 'double' | [
"r-2",
"rstats"
] | [
"R"
] | In my continued playing around with R I wanted to find the minimum value for a specified percentile given a data frame representing a cumulative distribution function (CDF).
e.g. imagine we have the following CDF represented in a data frame:
[source,r]
----
library(dplyr)
df = data.frame(score = c(5,7,8,10,12,20), percentile = c(0.05,0.1,0.15,0.20,0.25,0.5))
----
and we want to find the minimum value for the 0.05 percentile. We can use the +++<cite>+++filter+++</cite>+++ function to do so:
[source,r]
----
> (df %>% filter(percentile > 0.05) %>% slice(1))$score
[1] 7
----
Things become more tricky if we want to return multiple percentiles in one go.
My first thought was to create a data frame with one row for each target percentile and then pull in the appropriate row from our original data frame:
[source,r]
----
targetPercentiles = c(0.05, 0.2)
percentilesDf = data.frame(targetPercentile = targetPercentiles)
> percentilesDf %>%
group_by(targetPercentile) %>%
mutate(x = (df %>% filter(percentile > targetPercentile) %>% slice(1))$score)
Error in (list(score = c(5, 7, 8, 10, 12, 20), percentile = c(0.05, 0.1, :
invalid subscript type 'double'
----
Unfortunately this didn't quite work as I expected - https://twitter.com/tonkouts[Antonios] pointed out that this is probably because we're mixing up two pipelines and dplyr can't figure out what we want to do.
Instead he suggested the following variant which uses the +++<cite>+++https://github.com/hadley/dplyr#do[do]+++</cite>+++ function
[source,r]
----
df = data.frame(score = c(5,7,8,10,12,20), percentile = c(0.05,0.1,0.15,0.20,0.25,0.5))
targetPercentiles = c(0.05, 0.2)
> data.frame(targetPercentile = targetPercentiles) %>%
group_by(targetPercentile) %>%
do(df) %>%
filter(percentile > targetPercentile) %>%
slice(1) %>%
select(targetPercentile, score)
Source: local data frame [2 x 2]
Groups: targetPercentile
targetPercentile score
1 0.05 7
2 0.20 12
----
We can then wrap this up in a function:
[source,r]
----
percentiles = function(df, targetPercentiles) {
# make sure the percentiles are in order
df = df %>% arrange(percentile)
data.frame(targetPercentile = targetPercentiles) %>%
group_by(targetPercentile) %>%
do(df) %>%
filter(percentile > targetPercentile) %>%
slice(1) %>%
select(targetPercentile, score)
}
----
which we call like this:
[source,r]
----
df = data.frame(score = c(5,7,8,10,12,20), percentile = c(0.05,0.1,0.15,0.20,0.25,0.5))
> percentiles(df, c(0.08, 0.10, 0.50, 0.80))
Source: local data frame [2 x 2]
Groups: targetPercentile
targetPercentile score
1 0.08 7
2 0.10 8
----
Note that we don't actually get any rows back for 0.50 or 0.80 since we don't have any entries greater than those percentiles. With a proper CDF we would though so the function does its job. | null | null | [
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] | r-dplyr-error-in-list-invalid-subscript-type-double | https://markhneedham.com/blog/2015/04/27/r-dplyr-error-in-list-invalid-subscript-type-double |
false | 2015-04-16 20:57:20 | R: Think Bayes - More posterior probability calculations | [
"r-2"
] | [
"R"
] | As I mentioned in a post last week I've been reading through http://thinkbayes.com[Think Bayes] and http://www.markhneedham.com/blog/2015/04/12/r-creating-an-object-with-functions-to-calculate-conditional-probability/[translating some of the examples form Python to R].
After my first post https://twitter.com/tonkouts[Antonios] suggested a more idiomatic way of writing the function in R so I thought I'd give it a try to calculate the probability that combinations of cookies had come from each bowl.
In the simplest case we have this function which takes in the names of the bowls and the likelihood scores:
[source,r]
----
f = function(names,likelihoods) {
# Assume each option has an equal prior
priors = rep(1, length(names)) / length(names)
# create a data frame with all info you have
dt = data.frame(names,priors,likelihoods)
# calculate posterior probabilities
dt$post = dt$priors*dt$likelihoods / sum(dt$priors*dt$likelihoods)
# specify what you want the function to return
list(names=dt$names, priors=dt$priors, likelihoods=dt$likelihoods, posteriors=dt$post)
}
----
We assume a prior probability of 0.5 for each bowl.
Given the following probabilities of of different cookies being in each bowl\...
[source,python]
----
mixes = {
'Bowl 1':dict(vanilla=0.75, chocolate=0.25),
'Bowl 2':dict(vanilla=0.5, chocolate=0.5),
}
----
\...we can simulate taking one vanilla cookie with the following parameters:
[source,r]
----
Likelihoods = c(0.75,0.5)
Names = c("Bowl 1", "Bowl 2")
res=f(Names,Likelihoods)
> res$posteriors[res$name == "Bowl 1"]
[1] 0.6
> res$posteriors[res$name == "Bowl 2"]
[1] 0.4
----
If we want to simulate taking 3 vanilla cookies and 1 chocolate one we'd have the following:
[source,r]
----
Likelihoods = c((0.75 ** 3) * (0.25 ** 1), (0.5 ** 3) * (0.5 ** 1))
Names = c("Bowl 1", "Bowl 2")
res=f(Names,Likelihoods)
> res$posteriors[res$name == "Bowl 1"]
[1] 0.627907
> res$posteriors[res$name == "Bowl 2"]
[1] 0.372093
----
That's a bit clunky and the intent of '3 vanilla cookies and 1 chocolate' has been lost. I decided to refactor the code to take in a vector of cookies and calculate the likelihoods internally.
First we need to create a data structure to store the mixes of cookies in each bowl that we defined above. It turns out we can do this using a http://stackoverflow.com/questions/22097029/nested-dictionary-in-r[nested list]:
[source,r]
----
bowl1Mix = c(0.75, 0.25)
names(bowl1Mix) = c("vanilla", "chocolate")
bowl2Mix = c(0.5, 0.5)
names(bowl2Mix) = c("vanilla", "chocolate")
Mixes = list("Bowl 1" = bowl1Mix, "Bowl 2" = bowl2Mix)
> Mixes
$`Bowl 1`
vanilla chocolate
0.75 0.25
$`Bowl 2`
vanilla chocolate
0.5 0.5
----
Now let's tweak our function to take in observations rather than likelihoods and then calculate those likelihoods internally:
~~~r likelihoods = function(names, observations) { scores = c(1,1) names(scores) = names for(name in names) { for(observation in observations) { scores[name] = scores[name] * mixes[[name]][observation] } } return(scores) } f = function(names,mixes,observations) { # Assume each option has an equal prior priors = rep(1, length(names)) / length(names) # create a data frame with all info you have dt = data.frame(names,priors) dt$likelihoods = likelihoods(Names, Observations) # calculate posterior probabilities dt$post = dt$priors*dt$likelihoods / sum(dt$priors*dt$likelihoods) # specify what you want the function to return list(names=dt$names, priors=dt$priors, likelihoods=dt$likelihoods, posteriors=dt$post) } ~~~
And if we call that function: ~~~r Names = c("Bowl 1", "Bowl 2") bowl1Mix = c(0.75, 0.25) names(bowl1Mix) = c("vanilla", "chocolate") bowl2Mix = c(0.5, 0.5) names(bowl2Mix) = c("vanilla", "chocolate") Mixes = list("Bowl 1" = bowl1Mix, "Bowl 2" = bowl2Mix) Mixes Observations = c("vanilla", "vanilla", "vanilla", "chocolate") res=f(Names,Mixes,Observations) > res$posteriors[res$names == "Bowl 1"] [1] 0.627907 > res$posteriors[res$names == "Bowl 2"] [1] 0.372093 ~~~
Exactly the same result as before! #win | null | null | [
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] | r-think-bayes-more-posterior-probability-calculations | https://markhneedham.com/blog/2015/04/16/r-think-bayes-more-posterior-probability-calculations |
false | 2015-04-19 23:08:45 | R: non-numeric argument to binary operator | [
"r-2"
] | [
"R"
] | When debugging R code, given my Java background, I often find myself trying to print out the state of variables along with an appropriate piece of text like this:
[source,r]
----
names = c(1,2,3,4,5,6)
> print("names: " + names)
Error in "names: " + names : non-numeric argument to binary operator
----
We might try this next:
[source,r]
----
> print("names: ", names)
[1] "names: "
----
which doesn't actually print the +++<cite>+++names+++</cite>+++ variable - only the first argument to the print function is printed.
We'll find more success with the +++<cite>+++https://stat.ethz.ch/R-manual/R-devel/library/base/html/paste.html[paste]+++</cite>+++ function:
[source,r]
----
> print(paste("names: ", names))
[1] "names: 1" "names: 2" "names: 3" "names: 4" "names: 5" "names: 6"
----
This is an improvement but it repeats the 'names:' prefix multiple times which isn't what we want. Introducing the +++<cite>+++https://stat.ethz.ch/R-manual/R-devel/library/base/html/toString.html[toString]+++</cite>+++ function gets us over the line:
[source,r]
----
> print(paste("names: ", toString(names)))
[1] "names: 1, 2, 3, 4, 5, 6"
---- | null | null | [
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] | r-non-numeric-argument-to-binary-operator | https://markhneedham.com/blog/2015/04/19/r-non-numeric-argument-to-binary-operator |
false | 2015-04-21 05:59:24 | R: Numeric keys in the nested list/dictionary | [
"r-2"
] | [
"R"
] | Last week I described how I've been http://www.markhneedham.com/blog/2015/04/16/r-think-bayes-more-posterior-probability-calculations/[creating fake dictionaries in R using lists] and I found myself using the same structure while solving the http://www.greenteapress.com/thinkbayes/dice.py[dice problem] in http://www.greenteapress.com/thinkbayes/[Think Bayes].
The dice problem is described as follows:
____
Suppose I have a box of dice that contains a 4-sided die, a 6-sided die, an 8-sided die, a 12-sided die, and a 20-sided die. If you have ever played Dungeons & Dragons, you know what I am talking about. Suppose I select a die from the box at random, roll it, and get a 6. What is the probability that I rolled each die?
____
Here's a simple example of the nested list that I started with:
[source,r]
----
dice = c(4,6,8,12,20)
priors = rep(1.0 / length(dice), length(dice))
names(priors) = dice
> priors
4 6 8 12 20
0.2 0.2 0.2 0.2 0.2
----
I wanted to retrieve the prior for the 8 dice which I tried to do like this:
[source,r]
----
> priors[8]
<NA>
NA
----
That comes back with 'NA' because we're actually looking for the numeric index 8 rather than the item in that column.
As far as I understand if we want to look up values by name we have to use a string so I tweaked the code to store names as strings:
[source,r]
----
dice = c(4,6,8,12,20)
priors = rep(1.0 / length(dice), length(dice))
names(priors) = sapply(dice, paste)
> priors["8"]
8
0.2
----
That works much better but with some experimentation I realised I didn't even need to run 'dice' through the https://stat.ethz.ch/R-manual/R-devel/library/base/html/lapply.html[+++<cite>+++sapply+++</cite>+++] function, it already works the way it was:
[source,r]
----
dice = c(4,6,8,12,20)
priors = rep(1.0 / length(dice), length(dice))
names(priors) = dice
> priors["8"]
8
0.2
----
Now that we've got that working we can write a likelihood function which takes in observed dice rolls and tells us how likely it was that we rolled each type of dice. We start simple by copying the above code into a function:
[source,r]
----
likelihoods = function(names, observations) {
scores = rep(1.0 / length(names), length(names))
names(scores) = names
return(scores)
}
dice = c(4,6,8,12,20)
l1 = likelihoods(dice, c(6))
> l1
4 6 8 12 20
0.2 0.2 0.2 0.2 0.2
----
Next we'll update the score for a particular dice to 0 if one of the observed rolls is greater than the dice's maximum score:
[source,r]
----
likelihoods = function(names, observations) {
scores = rep(1.0 / length(names), length(names))
names(scores) = names
for(name in names) {
for(observation in observations) {
if(name < observation) {
scores[paste(name)] = 0
}
}
}
return(scores)
}
dice = c(4,6,8,12,20)
l1 = likelihoods(dice, c(6))
> l1
4 6 8 12 20
0.0 0.2 0.2 0.2 0.2
----
The 4 dice has been ruled out since we've rolled a 6! Now let's put in the else condition which updates our score by the probability that we got the observed roll with each of valid dice. i.e. we have a 1/20 chance of rolling any number with the 20 side dice, a 1/8 chance with the 8 sided dice etc.
[source,r]
----
likelihoods = function(names, observations) {
scores = rep(1.0 / length(names), length(names))
names(scores) = names
for(name in names) {
for(observation in observations) {
if(name < observation) {
scores[paste(name)] = 0
} else {
scores[paste(name)] = scores[paste(name)] * (1.0 / name)
}
}
}
return(scores)
}
dice = c(4,6,8,12,20)
l1 = likelihoods(dice, c(6))
> l1
4 6 8 12 20
0.00000000 0.03333333 0.02500000 0.01666667 0.01000000
----
And finally let's normalise those scores so they're a bit more readable:
[source,r]
----
> l1 / sum(l1)
4 6 8 12 20
0.0000000 0.3921569 0.2941176 0.1960784 0.1176471
---- | null | null | [
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] | r-numeric-keys-in-the-nested-listdictionary | https://markhneedham.com/blog/2015/04/21/r-numeric-keys-in-the-nested-listdictionary |
false | 2015-04-07 20:59:49 | Neo4j: The learning to cycle dependency graph | [
"neo4j"
] | [
"neo4j"
] | Over the past couple of weeks I've been reading about skill building and the break down of skills into more manageable chunks, and recently had a chance to break down the skills required to learn to cycle.
I initially sketched out the skill progression but quickly realised I had drawn a dependency graph and thought that putting it into Neo4j would simplify things.
I started out with the overall goal for cycling which was to 'Be able to cycle through a public park':
[source,cypher]
----
MERGE (:Goal:Task {name: "Be able to cycle through a public park"})
----
This goal is easy for someone who's already learnt to cycle but if we're starting from scratch it's a bit daunting so we need to break it down into a simpler skill that we can practice.
The mini algorithm that we're going to employ for task breakdown is this:
. Can we do the given task now?
. Break the task down into something simpler and return to 1.
One of the things to keep in mind is that we won't get the break down perfect the first time so we may need to change it. For a diagram drawn on a piece of paper this would be annoying but in Neo4j it's just a simpler refactoring.
Going back to cycling. Since the goal isn't yet achievable we need to break that down into something a bit easier. Let's start with something really simple:
[source,cypher]
----
MERGE (task:Task {name: "Take a few steps forward while standing over the bike"})
WITH task
MATCH (goal:Goal:Task {name: "Be able to cycle through a public park"})
MERGE (goal)-[:DEPENDS_ON]->(task)
----
In the first line we create our new task and then we connect it to our goal which we created earlier.
image::{{<siteurl>}}/uploads/2015/04/graph-9.png[Graph 9,300]
After we've got the hang of walking with the bike we want to get comfortable with cycling forward a few rotations while sitting on the bike but to do that we need to be able to get the bike moving from a standing start. We might also have another step where we cycle forward while standing on the bike as that might be slightly easier.
Let's update our graph:
[source,cypher]
----
// First let's get rid of the relationship between our initial task and the goal
MATCH (initialTask:Task {name: "Take a few steps forward while standing over the bike"})
MATCH (goal:Goal {name: "Be able to cycle through a public park"})
MATCH (goal)-[rel:DEPENDS_ON]->(initialTask)
DELETE rel
WITH initialTask, goal, ["Get bike moving from standing start", "Cycle forward while standing", "Cycle forward while sitting"] AS newTasks
// Create some nodes for our new tasks
UNWIND newTasks AS newTask
MERGE (t:Task {name: newTask})
WITH initialTask, goal, COLLECT(t) AS newTasks
WITH initialTask, goal, newTasks, newTasks[0] AS firstTask, newTasks[-1] AS lastTask
// Connect the last task to the goal
MERGE (goal)-[:DEPENDS_ON]->(lastTask)
// And the first task to our initial task
MERGE (firstTask)-[:DEPENDS_ON]->(initialTask)
// And all the tasks to each other
FOREACH(i in RANGE(0, length(newTasks) - 2) |
FOREACH(t1 in [newTasks[i]] | FOREACH(t2 in [newTasks[i+1]] |
MERGE (t2)-[:DEPENDS_ON]->(t1)
)))
----
image::{{<siteurl>}}/uploads/2015/04/graph-10.png[Graph 10,600]
We don't strictly need to learn how to cycle while standing up - we could just go straight from getting the bike moving to cycling forward while sitting. Let's update the graph to reflect that:
[source,cypher]
----
MATCH (sitting:Task {name: "Cycle forward while sitting"})
MATCH (moving:Task {name: "Get bike moving from standing start"})
MERGE (sitting)-[:DEPENDS_ON]->(moving)
----
image::{{<siteurl>}}/uploads/2015/04/graph-11.png[Graph 11,600]
Once we've got the hang of those tasks let's add in a few more to get us closer to our goal:
[source,cypher]
----
WITH [
{skill: "Controlled stop using brakes/feet", dependsOn: "Cycle forward while sitting"},
{skill: "Steer around stationary objects", dependsOn: "Controlled stop using brakes/feet"},
{skill: "Steer around people", dependsOn: "Steer around stationary objects"},
{skill: "Navigate a small circular circuit", dependsOn: "Steer around stationary objects"},
{skill: "Navigate a loop of a section of the park", dependsOn: "Navigate a small circular circuit"},
{skill: "Navigate a loop of a section of the park", dependsOn: "Steer around people"},
{skill: "Be able to cycle through a public park", dependsOn: "Navigate a loop of a section of the park"}
] AS newTasks
FOREACH(newTask in newTasks |
MERGE (t1:Task {name: newTask.skill})
MERGE (t2:Task {name: newTask.dependsOn})
MERGE (t1)-[:DEPENDS_ON]->(t2)
)
----
Finally let's get rid of the relationship from our goal to 'Cycle forward while sitting' since we've replaced that with some intermediate steps:
[source,cypher]
----
MATCH (task:Task {name: "Cycle forward while sitting"})
WITH task
MATCH (goal:Goal:Task {name: "Be able to cycle through a public park"})
MERGE (goal)-[rel:DEPENDS_ON]->(task)
DELETE rel
----
And here's what the final dependency graph looks like:
image::{{<siteurl>}}/uploads/2015/04/graph-13.png[Graph 13,600]
Although I put this into Neo4j in order to visualise the dependencies we can now query the data as well. For example, let's say I know how to cycle forward while sitting on the bike. What steps are there between me and being able to cycle around a park?
[source,cypher]
----
MATCH (t:Task {name: "Cycle forward while sitting"}),
(g:Goal {name: "Be able to cycle through a public park"}),
path = shortestpath((g)-[:DEPENDS_ON*]->(t))
RETURN path
----
image::{{<siteurl>}}/uploads/2015/04/graph-14.png[Graph 14,400]
Or if we want a list of the tasks we need to do next we could restructure the query slightly:
[source,cypher]
----
MATCH (t:Task {name: "Cycle forward while sitting"}),
(g:Goal {name: "Be able to cycle through a public park"}),
path = shortestpath((t)<-[:DEPENDS_ON*]->(g))
WITH [n in nodes(path) | n.name] AS tasks
UNWIND tasks AS task
RETURN task
==> +--------------------------------------------+
==> | task |
==> +--------------------------------------------+
==> | "Cycle forward while sitting" |
==> | "Controlled stop using brakes/feet" |
==> | "Steer around stationary objects" |
==> | "Steer around people" |
==> | "Navigate a loop of a section of the park" |
==> | "Be able to cycle through a public park" |
==> +--------------------------------------------+
==> 6 rows
----
That's all for now but I think this is an interesting way of tracking how you'd learn a skill. I'm trying a similar approach for some statistics topics I'm learning about but I've found the order of tasks isn't so linear there - interestingly much more a graph than a tree. | null | null | [
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] | neo4j-the-learning-to-cycle-dependency-graph | https://markhneedham.com/blog/2015/04/07/neo4j-the-learning-to-cycle-dependency-graph |
false | 2015-04-09 22:02:18 | R: Snakes and ladders markov chain | [
"r-2"
] | [
"R"
] | A few days ago I read a really cool blog post http://freakonometrics.blog.free.fr/index.php?post/2011/12/20/Basic-on-Markov-Chain-(for-parents)[explaining how Markov chains can be used to model the possible state transitions in a game of snakes and ladders], a use of Markov chains I hadn't even thought of!
While the example is very helpful for understanding the concept, my understanding of the code is that it works off the assumption that any roll of the dice that puts you on a score > 100 is a winning roll.
In the version of the game that I know you have to land exactly on 100 to win. e.g if you're on square 98 and roll a 6 you would go forward 2 spaces to 100 and then bounce back 4 spaces to 96.
I thought it'd be a good exercise to tweak the code to cater for this:
[source,r]
----
n=100
# We have 6 extra columns because we want to represent throwing of the dice which results in a final square > 100
M=matrix(0,n+1,n+1+6)
rownames(M)=0:n
colnames(M)=0:(n+6)
# set probabilities of landing on each square assuming that there aren't any snakes or ladders
for(i in 1:6){
diag(M[,(i+1):(i+1+n)])=1/6
}
# account for 'bounce back' if a dice roll leads to a final score > 100
for(i in 96:100) {
for(c in 102:107) {
idx = 101 - (c - 101)
M[i, idx] = M[i, idx] + M[i, c]
}
}
----
We can inspect the last few rows to check that if the transition matrix is accurate:
[source,r]
----
> M[95:100,95:101]
94 95 96 97 98 99 100
94 0 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667
95 0 0.0000000 0.1666667 0.1666667 0.1666667 0.3333333 0.1666667
96 0 0.0000000 0.0000000 0.1666667 0.3333333 0.3333333 0.1666667
97 0 0.0000000 0.0000000 0.1666667 0.3333333 0.3333333 0.1666667
98 0 0.0000000 0.1666667 0.1666667 0.1666667 0.3333333 0.1666667
99 0 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667
----
If we're on the 99th square (the last row) we could roll a 1 and end up on 100, a 2 and end up on 99 (1 forward, 1 back), a 3 and end up on 98 (1 forward, 2 back), a 4 and end up on 97 (1 forward, 3 back), a 5 and end up on 96 (1 forward, 4 back) or a 6 and end up on 95 (1 forward, 5 back). i.e. we can land on 95, 96, 97, 98, 99 or 100 with 1/6 probability.
If we're on the 96th square (the 3rd row) we could roll a 1 and end up on 97, a 2 and end up on 98, a 3 and end up on 99, a 4 and end up on 100, a 5 and end up on 99 (4 forward, 1 back) or a 6 and end up on 98 (4 forward, 2 back). i.e. we can land on 97 with 1/6 probability, 98 with 2/6 probability, 99 with 2/6 probability or 100 with 1/6 probability.
We could do a similar analysis for the other squares but it seems like the probabilities are being calculated correctly.
Next we can update the matrix with the snakes and ladders. That code stays the same:
[source,r]
----
# get rid of the extra columns, we don't need them anymore
M=M[,1:(n+1)]
# add in the snakes and ladders
starting = c(4,9,17,20,28,40,51,54,62,64,63,71,93,95,92)
ending = c(14,31,7,38,84,59,67,34,19,60,81,91,73,75,78)
for(i in 1:length(starting)) {
# Retrieve current probabilities of landing on the starting square
v=M[,starting[i]+1]
ind=which(v>0)
# Set no probability of falling on the starting squares
M[ind,starting[i]+1]=0
# Move all existing probabilities to the ending squares
M[ind,ending[i]+1]=M[ind,ending[i]+1]+v[ind]
}
----
We can also simplify the +++<cite>+++powermat+++</cite>+++ function which is used to simulate what the board would look like after a certain number of dice rolls:
[source,r]
----
# original
powermat=function(P,h){
Ph=P
if(h>1) {
for(k in 2:h) {
Ph=Ph%*%P
}
}
return(Ph)
}
#new
library(expm)
powermat = function(P,h) {
return (P %^% h)
}
----
[source,r]
----
initial=c(1,rep(0,n))
h = 1
> (initial%*%powermat(M,h))[1:15]
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
[1,] 0 0.1666667 0.1666667 0.1666667 0 0.1666667 0.1666667 0 0 0 0 0 0 0 0.1666667 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
[1,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
----
One interesting thing I noticed is that it now seems to take way more turns on average to finish the game than when you didn't need to score exactly 100 to win:
[source,r]
----
> sum(1 - game)
[1] 999
----
[source,r]
----
distrib=initial%*%M
game=rep(NA,1000)
for(h in 1:length(game)){
game[h]=distrib[n+1]
distrib=distrib%*%M}
plot(1-game[1:200],type="l",lwd=2,col="red",
ylab="Probability to be still playing")
----
image::{{<siteurl>}}/uploads/2015/04/2015-04-09_22-48-24.png[2015 04 09 22 48 24,574]
I expected it to take longer to finish the game but not this long! I think I've probably made a mistake but I'm not sure where\...
== Update
https://twitter.com/tonkouts[Antonios] found the mistake I'd made - when on the 100th square we should have a 1 as the probability of getting to the 100th square. i.e. we need to update M like so:
[source,r]
----
M[101,101] = 1
----
Now if we visualise he probability that we're still playing we get a more accurate curve:
[source,r]
----
distrib=initial%*%M
game=rep(NA,1000)
for(h in 1:length(game)){
game[h]=distrib[n+1]
distrib=distrib%*%M}
plot(1-game[1:200],type="l",lwd=2,col="red",
ylab="Probability to be still playing")
----
image::{{<siteurl>}}/uploads/2015/04/2015-04-10_23-49-21.png[2015 04 10 23 49 21,570] | null | null | [
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] | r-snakes-and-ladders-markov-chain | https://markhneedham.com/blog/2015/04/09/r-snakes-and-ladders-markov-chain |
false | 2015-04-30 07:48:38 | Deliberate Practice: Building confidence vs practicing | [
"software-development"
] | [
"Software Development",
"Deliberate Practice"
] | A few weeks ago I wrote about the http://www.markhneedham.com/blog/2015/04/07/neo4j-the-learning-to-cycle-dependency-graph/[learning to cycle dependency graph] which described some of the skills required to become proficient at riding a bike.
image::{{<siteurl>}}/uploads/2015/04/IMG_20150430_073120.jpg[IMG 20150430 073120,300]
While we've been practicing various skills/sub skills I've often found myself saying the following:
____
if it's not hard you're not practicing me, April 2015
____
i.e. you should find the skill you're currently practicing difficult otherwise you're not stretching yourself and therefore aren't getting better.
For example, in cycling you could be very comfortable riding with both hands on the handle bars and find using one hand a struggle. However, if you don't practice that you won't be able to indicate and turn corners.
This ties in with all my reading about deliberate practice which suggests that the type of exercises you do while deliberately practicing aren't intended to be fun and are meant to http://www.markhneedham.com/blog/2015/04/25/deliberate-practice-watching-yourself-fail/[expose your lack of knowledge].
In an ideal world we would spend all our time practicing these challenging skills but in reality there's some part of us that wants to feel that we're actually improving by spending some of the time doing things that we're good at. Doing things you're not good at is a bit of a slog as well so we might find that we have less motivation for this type of thing.
We therefore need to find *a balance between doing challenging exercises and having fun building something* or writing code that we already know how to do. I've found the best way to do this is to combine the two types of work into mini projects which contain some tasks that we're already good at and some that require us to struggle.
For me this might involved cleaning up and importing a data set into Neo4j, which I'm comfortable with, and combining that with something else that I want to learn.
For example in the middle of last year I did some http://www.markhneedham.com/blog/2014/05/31/neo4jr-analysing-london-nosql-meetup-membership/[meetup analysis] which involved creating a Neo4j graph of London's NoSQL meetups and learning a bit about R, dplyr and linear regression along the way.
In January I built a http://www.markhneedham.com/blog/2015/02/19/pythons-pandas-vs-neo4js-cypher-exploring-popular-phrases-in-how-i-met-your-mother-transcripts/[How I met your mother graph] and then spent a few weeks learning various algorithms for extracting topics from free text to give even more ways to explore the dataset.
Most recently I've been practicing exercises from http://www.greenteapress.com/thinkbayes/[Think Bayes] and while it's good practice I think I'd probably spend more time doing it if I linked it into a mini project with something I'm already comfortable with.
I'll go off and have a think what that should be! | null | null | [
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false | 2015-04-01 11:54:03 | Neo4j: Cypher - Building the query for a movie's profile page | [
"neo4j"
] | [
"neo4j"
] | image::{{<siteurl>}}/uploads/2015/04/2015-04-01_12-11-51.png[2015 04 01 12 11 51,256]
Yesterday I spent the day in Berlin delivering a workshop as part of the http://datascienceretreat.com/[Data Science Retreat] and one of the exercises we did was write a query that would pull back all the information you'd need to create the http://www.imdb.com/title/tt0133093/?ref_=fn_al_tt_1[IMDB page for a movie].
Scanning the page we can see that need to get some basic meta data including the title. Next we'll need to pull in the actors, directors, producers and finally a recommendation for some other movies the viewer might like to see.
I'd struggle to be able to write this all in one go - it's non trivial. However, if we break it down there are actually 5 simpler queries that we probably can write. Our final step is then to work out how to glue them all together.
Let's get started.
_If you want to follow along open up your Neo4j browser and type +++<cite>+++:play movies+++</cite>+++ and import the built in data set._
We're going to create the query for The Matrix home page so the first step is to find the node representing that movie in the database:
[source,cypher]
----
match (movie:Movie {title: "The Matrix"})
return movie.title
==> +--------------+
==> | movie.title |
==> +--------------+
==> | "The Matrix" |
==> +--------------+
==> 1 row
----
Easy enough. Now let's get back the producers:
[source,cypher]
----
match (movie:Movie {title: "The Matrix"})
optional match (producer)-[:PRODUCED]->(movie)
RETURN movie.title, COLLECT(producer.name) AS producers
==> +--------------------------------+
==> | movie.title | producers |
==> +--------------------------------+
==> | "The Matrix" | ["Joel Silver"] |
==> +--------------------------------+
==> 1 row
----
We've introduced the http://neo4j.com/docs/milestone/query-aggregation.html#aggregation-collect[COLLECT] function here as we want to ensure that our final result only has one row regardless of how many producers there are. COLLECT applies an implicit group by 'movie.title' and collects the producers for each movie (in this case just The Matrix) into an array.
We've used OPTIONAL MATCH *LINK* because we still want to return a row for the query even if it has no producers. In the case that there are no producers we'd hope to see an empty array.
Now let's write the same query to pull back the directors of the movie:
[source,cypher]
----
match (movie:Movie {title: "The Matrix"})
optional match (director)-[:DIRECTED]->(movie)
RETURN movie.title, COLLECT(director.name) AS directors
==> +----------------------------------------------------+
==> | movie.title | directors |
==> +----------------------------------------------------+
==> | "The Matrix" | ["Lana Wachowski","Andy Wachowski"] |
==> +----------------------------------------------------+
==> 1 row
----
We really want to do both of these in one query so we get back a single result with 3 columns. To do that we're going to introduce the http://neo4j.com/docs/stable/query-with.html[WITH] clause which allows us combine the results of traversals together.
In this case we'll first do a traversal to get the producers, collect those into an array and then traverse out again to get the directors and collect those. This is what the query looks like:
[source,cypher]
----
match (movie:Movie {title: "The Matrix"})
optional match (producer)-[:PRODUCED]->(movie)
with movie, COLLECT(producer.name) AS producers
optional match (director)-[:DIRECTED]->(movie)
RETURN movie.title, producers, COLLECT(director.name) AS directors
==> +----------------------------------------------------------------------+
==> | movie.title | producers | directors |
==> +----------------------------------------------------------------------+
==> | "The Matrix" | ["Joel Silver"] | ["Lana Wachowski","Andy Wachowski"] |
==> +----------------------------------------------------------------------+
==> 1 row
----
We can follow the same pattern to return the actors:
[source,cypher]
----
match (movie:Movie {title: "The Matrix"})
optional match (producer)-[:PRODUCED]->(movie)
with movie, COLLECT(producer.name) AS producers
optional match (director)-[:DIRECTED]->(movie)
with movie, producers, COLLECT(director.name) AS directors
optional match (actor)-[:ACTED_IN]->(movie)
RETURN movie.title, COLLECT(actor.name) AS actors, producers, directors
==> +--------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | movie.title | actors | producers | directors |
==> +--------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | "The Matrix" | ["Hugo Weaving","Laurence Fishburne","Carrie-Anne Moss","Keanu Reeves","Emil Eifrem"] | ["Joel Silver"] | ["Lana Wachowski","Andy Wachowski"] |
==> +--------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> 1 row
----
So far, so good. We've got everything except the other movies recommendation which is a bit trickier so we'll write it on its own first:
[source,cypher]
----
match (movie:Movie {title: "The Matrix"})<-[:ACTED_IN]-(actor)-[:ACTED_IN]->(otherMovie)
RETURN otherMovie, COUNT(*) AS score
ORDER BY score DESC
==> +---------------------------------------------------------------------------------------------------------------------------+
==> | otherMovie | score |
==> +---------------------------------------------------------------------------------------------------------------------------+
==> | Node[348]{title:"The Matrix Revolutions",released:2003,tagline:"Everything that has a beginning has an end"} | 4 |
==> | Node[347]{title:"The Matrix Reloaded",released:2003,tagline:"Free your mind"} | 4 |
==> | Node[490]{title:"Something's Gotta Give",released:2003} | 1 |
==> | Node[349]{title:"The Devil's Advocate",released:1997,tagline:"Evil has its winning ways"} | 1 |
==> | Node[438]{title:"Johnny Mnemonic",released:1995,tagline:"The hottest data on earth. In the coolest head in town"} | 1 |
==> | Node[443]{title:"Cloud Atlas",released:2012,tagline:"Everything is connected"} | 1 |
==> | Node[452]{title:"V for Vendetta",released:2006,tagline:"Freedom! Forever!"} | 1 |
==> | Node[425]{title:"The Replacements",released:2000,tagline:"Pain heals, Chicks dig scars... Glory lasts forever"} | 1 |
==> +---------------------------------------------------------------------------------------------------------------------------+
==> 8 rows
----
Our recommendation query finds all the actors in The Matrix and then traverses out to find other movies they've acted in and orders those movies based on how many of our actors appeared in them. Not surprisingly the other Matrix movies come out top.
In order to plug this into the rest of the query we need a single row to be returned i.e. our other movie suggestions need to be returned as an array rather than individual rows. Let's do that:
[source,cypher]
----
match (movie:Movie {title: "The Matrix"})<-[:ACTED_IN]-(actor)-[:ACTED_IN]->(otherMovie)
WITH otherMovie, COUNT(*) AS score
ORDER BY score DESC
RETURN COLLECT({movie: otherMovie.title, score: score}) AS otherMovies
==> +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | recommended |
==> +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | [{movie -> "The Matrix Revolutions", score -> 4},{movie -> "The Matrix Reloaded", score -> 4},{movie -> "Something's Gotta Give", score -> 1},{movie -> "The Devil's Advocate", score -> 1},{movie -> "Johnny Mnemonic", score -> 1},{movie -> "Cloud Atlas", score -> 1},{movie -> "V for Vendetta", score -> 1},{movie -> "The Replacements", score -> 1}] |
==> +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
----
We've introduced a WITH clause for two reasons:
. To ensure the order of the movies based on highest score
. Because we can't do an aggregation within an aggregation i.e. COLLECT(COUNT(\...)) would be an illegal operation in Cypher.
Now we're ready to plug this recommendation query into our main one:
[source,cypher]
----
match (movie:Movie {title: "The Matrix"})
optional match (producer)-[:PRODUCED]->(movie)
with movie, COLLECT(producer.name) AS producers
optional match (director)-[:DIRECTED]->(movie)
with movie, producers, COLLECT(director.name) AS directors
optional match (actor)-[:ACTED_IN]->(movie)
WITH movie, COLLECT(actor.name) AS actors, producers, directors
optional match (movie)<-[:ACTED_IN]-(actor)-[:ACTED_IN]->(otherMovie)
WITH movie, actors, producers, directors, otherMovie, COUNT(*) AS score
ORDER BY score DESC
RETURN movie, actors, producers, directors,
COLLECT({movie: otherMovie.title, score: score}) AS recommended
==> +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | movie | actors | producers | directors | recommended |
==> +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | Node[338]{title:"The Matrix",released:1999,tagline:"Welcome to the Real World"} | ["Hugo Weaving","Laurence Fishburne","Carrie-Anne Moss","Keanu Reeves","Emil Eifrem"] | ["Joel Silver"] | ["Lana Wachowski","Andy Wachowski"] | [{movie -> "The Matrix Revolutions", score -> 4},{movie -> "The Matrix Reloaded", score -> 4},{movie -> "Johnny Mnemonic", score -> 1},{movie -> "The Replacements", score -> 1},{movie -> "Cloud Atlas", score -> 1},{movie -> "V for Vendetta", score -> 1},{movie -> "Something's Gotta Give", score -> 1},{movie -> "The Devil's Advocate", score -> 1}] |
==> +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> 1 row
----
Voila! 4 different types of data gathered and just one query to do it all.
For the eagle eyed cypher specialists (Hi Michael!), you'll have noticed a bit of duplication in how we traverse out to the actors twice, once to retrieve them and once to make the movie recommendation.
We could optimise this by collecting the actors once and then using the http://neo4j.com/docs/stable/query-unwind.html[UNWIND] clause but that's an optimisation which I think slightly obscures the intent of the query so I've left it like this for now. | null | null | [
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] | neo4j-cypher-building-the-query-for-a-movies-profile-page | https://markhneedham.com/blog/2015/04/01/neo4j-cypher-building-the-query-for-a-movies-profile-page |
false | 2015-04-24 23:53:12 | R: Think Bayes Locomotive Problem - Posterior probabilities for different priors | [
"r-2"
] | [
"R"
] | In my continued reading of Think Bayes the next problem to tackle is the http://www.greenteapress.com/thinkbayes/train2.py[Locomotive problem] which is defined thus:
____
A railroad numbers its locomotives in order 1..N. One day you see a locomotive with the number 60. Estimate how many loco- motives the railroad has.
____
The interesting thing about this question is that it initially seems that we don't have enough information to come up with any sort of answer. However, we can get an estimate if we come up with a prior to work with.
The simplest prior is to assume that there's one railroad operator with between say 1 and 1000 railroads with an equal probability of each size.
We can then write similar code as with the http://www.markhneedham.com/blog/2015/04/22/r-replacing-for-loops-with-data-frames/[dice problem] to update the prior based on the trains we've seen.
First we'll create a data frame which captures the product of 'number of locomotives' and the observations of locomotives that we've seen (in this case we've only seen one locomotive with number '60':)
[source,r]
----
library(dplyr)
possibleValues = 1:1000
observations = c(60)
l = list(value = possibleValues, observation = observations)
df = expand.grid(l)
> df %>% head()
value observation
1 1 60
2 2 60
3 3 60
4 4 60
5 5 60
6 6 60
----
Next we want to add a column which represents the probability that the observed locomotive could have come from a particular fleet. If the number of railroads is less than 60 then we have a 0 probability, otherwise we have 1 / numberOfRailroadsInFleet:
[source,r]
----
prior = 1 / length(possibleValues)
df = df %>% mutate(score = ifelse(value < observation, 0, 1/value))
> df %>% sample_n(10)
value observation score
179 179 60 0.005586592
1001 1001 60 0.000999001
400 400 60 0.002500000
438 438 60 0.002283105
667 667 60 0.001499250
661 661 60 0.001512859
284 284 60 0.003521127
233 233 60 0.004291845
917 917 60 0.001090513
173 173 60 0.005780347
----
To find the probability of each fleet size we write the following code:
[source,r]
----
weightedDf = df %>%
group_by(value) %>%
summarise(aggScore = prior * prod(score)) %>%
ungroup() %>%
mutate(weighted = aggScore / sum(aggScore))
> weightedDf %>% sample_n(10)
Source: local data frame [10 x 3]
value aggScore weighted
1 906 1.102650e-06 0.0003909489
2 262 3.812981e-06 0.0013519072
3 994 1.005031e-06 0.0003563377
4 669 1.493275e-06 0.0005294465
5 806 1.239455e-06 0.0004394537
6 673 1.484400e-06 0.0005262997
7 416 2.401445e-06 0.0008514416
8 624 1.600963e-06 0.0005676277
9 40 0.000000e+00 0.0000000000
10 248 4.028230e-06 0.0014282246
----
Let's plot the data frame to see how the probability varies for each fleet size:
[source,r]
----
library(ggplot2)
ggplot(aes(x = value, y = weighted), data = weightedDf) +
geom_line(color="dark blue")
----
image::{{<siteurl>}}/uploads/2015/04/2015-04-25_00-25-47.png[2015 04 25 00 25 47,500]
The most likely choice is a fleet size of 60 based on this diagram but an alternative would be to find the mean of the posterior which we can do like so:
[source,r]
----
> weightedDf %>% mutate(mean = value * weighted) %>% select(mean) %>% sum()
[1] 333.6561
----
Now let's create a function with all that code in so we can play around with some different priors and observations:
[source,R]
----
meanOfPosterior = function(values, observations) {
l = list(value = values, observation = observations)
df = expand.grid(l) %>% mutate(score = ifelse(value < observation, 0, 1/value))
prior = 1 / length(possibleValues)
weightedDf = df %>%
group_by(value) %>%
summarise(aggScore = prior * prod(score)) %>%
ungroup() %>%
mutate(weighted = aggScore / sum(aggScore))
return (weightedDf %>% mutate(mean = value * weighted) %>% select(mean) %>% sum())
}
----
If we update our observed railroads to have numbers 60, 30 and 90 we'd get the following means of posteriors assuming different priors:
[source,r]
----
> meanOfPosterior(1:500, c(60, 30, 90))
[1] 151.8496
> meanOfPosterior(1:1000, c(60, 30, 90))
[1] 164.3056
> meanOfPosterior(1:2000, c(60, 30, 90))
[1] 171.3382
----
At the moment the function assumes that we always want to have a uniform prior i.e. every option has an equal opportunity of being chosen, but we might want to vary the prior to see how different assumptions influence the posterior.
We can refactor the function to take in values & priors instead of calculating the priors in the function:
[source,R]
----
meanOfPosterior = function(values, priors, observations) {
priorDf = data.frame(value = values, prior = priors)
l = list(value = priorDf$value, observation = observations)
df = merge(expand.grid(l), priorDf, by.x = "value", by.y = "value") %>%
mutate(score = ifelse(value < observation, 0, 1 / value))
df %>%
group_by(value) %>%
summarise(aggScore = max(prior) * prod(score)) %>%
ungroup() %>%
mutate(weighted = aggScore / sum(aggScore)) %>%
mutate(mean = value * weighted) %>%
select(mean) %>%
sum()
}
----
Now let's check we get the same posterior means for the uniform priors:
[source,r]
----
> meanOfPosterior(1:500, 1/length(1:500), c(60, 30, 90))
[1] 151.8496
> meanOfPosterior(1:1000, 1/length(1:1000), c(60, 30, 90))
[1] 164.3056
> meanOfPosterior(1:2000, 1/length(1:2000), c(60, 30, 90))
[1] 171.3382
----
Now if instead of a uniform prior let's use a power law one where the assumption is that smaller fleets are more likely:
[source,r]
----
> meanOfPosterior(1:500, sapply(1:500, function(x) x ** -1), c(60, 30, 90))
[1] 130.7085
> meanOfPosterior(1:1000, sapply(1:1000, function(x) x ** -1), c(60, 30, 90))
[1] 133.2752
> meanOfPosterior(1:2000, sapply(1:2000, function(x) x ** -1), c(60, 30, 90))
[1] 133.9975
> meanOfPosterior(1:5000, sapply(1:5000, function(x) x ** -1), c(60, 30, 90))
[1] 134.212
> meanOfPosterior(1:10000, sapply(1:10000, function(x) x ** -1), c(60, 30, 90))
[1] 134.2435
----
Now we get very similar posterior means which converge on 134 and so that's our best prediction. | null | null | [
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] | r-think-bayes-locomotive-problem-posterior-probabilities-for-different-priors | https://markhneedham.com/blog/2015/04/24/r-think-bayes-locomotive-problem-posterior-probabilities-for-different-priors |
false | 2015-04-12 07:55:29 | R: Creating an object with functions to calculate conditional probability | [
"r-2",
"rstats"
] | [
"R"
] | I've been working through Alan Downey's http://www.greenteapress.com/thinkbayes/[Thinking Bayes] and I thought it'd be an interesting exercise to translate some of the code from Python to R.
The first example is a simple one about conditional probablity and the author creates a class 'PMF' (Probability Mass Function) to solve the following problem:
____
Suppose there are two bowls of cookies. Bowl 1 contains 30 vanilla cookies and 10 chocolate cookies. Bowl 2 contains 20 of each. Now suppose you choose one of the bowls at random and, without looking, select a cookie at random. The cookie is vanilla. What is the probability that it came from Bowl 1?
____
In Python the code looks like this:
[source,python]
----
pmf = Pmf()
pmf.Set('Bowl 1', 0.5)
pmf.Set('Bowl 2', 0.5)
pmf.Mult('Bowl 1', 0.75)
pmf.Mult('Bowl 2', 0.5)
pmf.Normalize()
print pmf.Prob('Bowl 1')
----
The 'PMF' class is defined http://www.greenteapress.com/thinkbayes/thinkbayes.py[here].
* 'Set' defines the prior probability of picking a cookie from either bowl i.e. in our case it's random.
* 'Mult' defines the likelihood of picking a vanilla biscuit from either bowl
* 'Normalize' applies a normalisation so that our posterior probabilities add up to 1.
We want to create something similar in R and the actual calculation is stragiht forward:
[source,r]
----
pBowl1 = 0.5
pBowl2 = 0.5
pVanillaGivenBowl1 = 0.75
pVanillaGivenBowl2 = 0.5
> (pBowl1 * pVanillaGivenBowl1) / ((pBowl1 * pVanillaGivenBowl1) + (PBowl2 * pVanillaGivenBowl2))
0.6
> (pBowl2 * pVanillaGivenBowl2) / ((pBowl1 * pVanillaGivenBowl1) + (pBowl2 * pVanillaGivenBowl2))
0.4
----
The problem is we have quite a bit of duplication and it doesn't read as cleanly as the Python version.
I'm not sure of the idiomatic way of handling this type of problem in R with mutable state in R but it seems like we can achieve this http://cran.r-project.org/doc/manuals/r-release/R-intro.html#Writing-your-own-functions[using functions].
I ended up writing the following function which returns a list of other functions to call.
[source,r]
----
create.pmf = function() {
priors <<- c()
likelihoods <<- c()
list(
prior = function(option, probability) {
l = c(probability)
names(l) = c(option)
priors <<- c(priors, l)
},
likelihood = function(option, probability) {
l = c(probability)
names(l) = c(option)
likelihoods <<- c(likelihoods, l)
},
posterior = function(option) {
names = names(priors)
normalised = 0.0
for(name in names) {
normalised = normalised + (priors[name] * likelihoods[name])
}
(priors[option] * likelihoods[option]) / normalised
}
)
}
----
I couldn't work out how to get 'priors' and 'likelihoods' to be lexically scoped so I've currently got those defined as global variables. I'm using a http://stackoverflow.com/questions/2858014/working-with-dictionaries-lists-in-r[list as a kind of dictionary following a suggestion on Stack Overflow].
The code doesn't handle the unhappy path very well but it seems to work for the example from the book:
[source,r]
----
pmf = create.pmf()
pmf$prior("Bowl 1", 0.5)
pmf$prior("Bowl 2", 0.5)
pmf$likelihood("Bowl 1", 0.75)
pmf$likelihood("Bowl 2", 0.5)
> pmf$posterior("Bowl 1")
Bowl 1
0.6
> pmf$posterior("Bowl 2")
Bowl 2
0.4
----
How would you solve this type of problem? Is there a cleaner/better way? | null | null | [
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] | r-creating-an-object-with-functions-to-calculate-conditional-probability | https://markhneedham.com/blog/2015/04/12/r-creating-an-object-with-functions-to-calculate-conditional-probability |
false | 2015-04-14 22:56:35 | Spark: Generating CSV files to import into Neo4j | [
"neo4j",
"spark-2"
] | [
"Spark"
] | About a year ago http://twitter.com/iansrobinson[Ian] pointed me at a https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2[Chicago Crime] data set which seemed like a good fit for Neo4j and after much procrastination I've finally got around to importing it.
The data set covers crimes committed from 2001 until now. It contains around 4 million crimes and meta data around those crimes such as the location, type of crime and year to name a few.
The contents of the file follow this structure:
[source,bash]
----
$ head -n 10 ~/Downloads/Crimes_-_2001_to_present.csv
ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
9464711,HX114160,01/14/2014 05:00:00 AM,028XX E 80TH ST,0560,ASSAULT,SIMPLE,APARTMENT,false,true,0422,004,7,46,08A,1196652,1852516,2014,01/20/2014 12:40:05 AM,41.75017626412204,-87.55494559131228,"(41.75017626412204, -87.55494559131228)"
9460704,HX113741,01/14/2014 04:55:00 AM,091XX S JEFFERY AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0413,004,8,48,03,1191060,1844959,2014,01/18/2014 12:39:56 AM,41.729576153145636,-87.57568059471686,"(41.729576153145636, -87.57568059471686)"
9460339,HX113740,01/14/2014 04:44:00 AM,040XX W MAYPOLE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,1114,011,28,26,14,1149075,1901099,2014,01/16/2014 12:40:00 AM,41.884543798701515,-87.72803579358926,"(41.884543798701515, -87.72803579358926)"
9461467,HX114463,01/14/2014 04:43:00 AM,059XX S CICERO AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0813,008,13,64,06,1145661,1865031,2014,01/16/2014 12:40:00 AM,41.785633535413176,-87.74148516669783,"(41.785633535413176, -87.74148516669783)"
9460355,HX113738,01/14/2014 04:21:00 AM,070XX S PEORIA ST,0820,THEFT,$500 AND UNDER,STREET,true,false,0733,007,17,68,06,1171480,1858195,2014,01/16/2014 12:40:00 AM,41.766348042591375,-87.64702037047671,"(41.766348042591375, -87.64702037047671)"
9461140,HX113909,01/14/2014 03:17:00 AM,016XX W HUBBARD ST,0610,BURGLARY,FORCIBLE ENTRY,COMMERCIAL / BUSINESS OFFICE,false,false,1215,012,27,24,05,1165029,1903111,2014,01/16/2014 12:40:00 AM,41.889741146006095,-87.66939334853973,"(41.889741146006095, -87.66939334853973)"
9460361,HX113731,01/14/2014 03:12:00 AM,022XX S WENTWORTH AVE,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,0914,009,25,34,06,1175363,1889525,2014,01/20/2014 12:40:05 AM,41.85223460427207,-87.63185047834335,"(41.85223460427207, -87.63185047834335)"
9461691,HX114506,01/14/2014 03:00:00 AM,087XX S COLFAX AVE,0650,BURGLARY,HOME INVASION,RESIDENCE,false,false,0423,004,7,46,05,1195052,1847362,2014,01/17/2014 12:40:17 AM,41.73607283858007,-87.56097809501115,"(41.73607283858007, -87.56097809501115)"
9461792,HX114824,01/14/2014 03:00:00 AM,012XX S CALIFORNIA BLVD,0810,THEFT,OVER $500,STREET,false,false,1023,010,28,29,06,1157929,1894034,2014,01/17/2014 12:40:17 AM,41.86498077118534,-87.69571529596696,"(41.86498077118534, -87.69571529596696)"
----
Since I wanted to import this into Neo4j I needed to do some massaging of the data since the http://neo4j.com/docs/2.2.0/import-tool.html[neo4j-import tool] expects to receive CSV files containing the nodes and relationships we want to create.
image::{{<siteurl>}}/uploads/2015/04/Spark-logo-192x100px.png[Spark logo 192x100px,96]
I'd been looking at https://spark.apache.org/[Spark] towards the end of last year and the pre-processing of the big initial file into smaller CSV files containing nodes and relationships seemed like a good fit.
I therefore needed to create a Spark job to do this. We'll then pass this job to a Spark executor running locally and it will spit out CSV files.
image::{{<siteurl>}}/uploads/2015/04/2015-04-15_00-51-42.png[2015 04 15 00 51 42,400]
We start by creating a Scala object with a main method that will contain our processing code. Inside that main method we'll instantiate a Spark context:
[source,scala]
----
import org.apache.spark.{SparkConf, SparkContext}
object GenerateCSVFiles {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("Chicago Crime Dataset")
val sc = new SparkContext(conf)
}
}
----
Easy enough. Next we'll read in the CSV file. I found the easiest way to reference this was with an environment variable but perhaps there's a more idiomatic way:
[source,scala]
----
import java.io.File
import org.apache.spark.{SparkConf, SparkContext}
object GenerateCSVFiles {
def main(args: Array[String]) {
var crimeFile = System.getenv("CSV_FILE")
if(crimeFile == null || !new File(crimeFile).exists()) {
throw new RuntimeException("Cannot find CSV file [" + crimeFile + "]")
}
println("Using %s".format(crimeFile))
val conf = new SparkConf().setAppName("Chicago Crime Dataset")
val sc = new SparkContext(conf)
val crimeData = sc.textFile(crimeFile).cache()
}
----
The type of +++<cite>+++crimeData+++</cite>+++ is +++<cite>+++RDD[String]+++</cite>+++ - Spark's way of representing the (lazily evaluated) lines of the CSV file. This also includes the header of the file so let's write a function to get rid of that since we'll be generating our own headers for the different files:
[source,scala]
----
import org.apache.spark.rdd.RDD
// http://mail-archives.apache.org/mod_mbox/spark-user/201404.mbox/%3CCAEYYnxYuEaie518ODdn-fR7VvD39d71=CgB_Dxw_4COVXgmYYQ@mail.gmail.com%3E
def dropHeader(data: RDD[String]): RDD[String] = {
data.mapPartitionsWithIndex((idx, lines) => {
if (idx == 0) {
lines.drop(1)
}
lines
})
}
----
Now we're ready to start generating our new CSV files so we'll write a function which parses each line and extracts the appropriate columns. I'm using Open CSV for this:
[source,scala]
----
import au.com.bytecode.opencsv.CSVParser
def generateFile(file: String, withoutHeader: RDD[String], fn: Array[String] => Array[String], header: String , distinct:Boolean = true, separator: String = ",") = {
FileUtil.fullyDelete(new File(file))
val tmpFile = "/tmp/" + System.currentTimeMillis() + "-" + file
val rows: RDD[String] = withoutHeader.mapPartitions(lines => {
val parser = new CSVParser(',')
lines.map(line => {
val columns = parser.parseLine(line)
fn(columns).mkString(separator)
})
})
if (distinct) rows.distinct() saveAsTextFile tmpFile else rows.saveAsTextFile(tmpFile)
}
----
We then call this function like this:
[source,scala]
----
generateFile("/tmp/crimes.csv", withoutHeader, columns => Array(columns(0),"Crime", columns(2), columns(6)), "id:ID(Crime),:LABEL,date,description", false)
----
The output into 'tmpFile' is actually 32 'part files' but I wanted to be able to http://www.markhneedham.com/blog/2014/11/30/spark-write-to-csv-file-with-header-using-saveasfile/[merge those together into individual CSV files] that were easier to work with.
I won't paste the the full job here but if you want to take a look https://github.com/mneedham/neo4j-spark-chicago/blob/master/src/main/scala/GenerateCSVFiles.scala[it's on github].
Now we need to submit the job to Spark. I've wrapped this in a https://github.com/mneedham/neo4j-spark-chicago/blob/master/create_files.sh[script] if you want to follow along but these are the contents:
[source,bash]
----
./spark-1.1.0-bin-hadoop1/bin/spark-submit \
--driver-memory 5g \
--class GenerateCSVFiles \
--master local[8] \
target/scala-2.10/playground_2.10-1.0.jar \
$@
----
If we execute that we'll see the following output..."
[source,bash]
----
Spark assembly has been built with Hive, including Datanucleus jars on classpath
Using Crimes_-_2001_to_present.csv
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
15/04/15 00:31:44 INFO SparkContext: Running Spark version 1.3.0
...
15/04/15 00:47:26 INFO TaskSchedulerImpl: Removed TaskSet 8.0, whose tasks have all completed, from pool
15/04/15 00:47:26 INFO DAGScheduler: Stage 8 (saveAsTextFile at GenerateCSVFiles.scala:51) finished in 2.702 s
15/04/15 00:47:26 INFO DAGScheduler: Job 4 finished: saveAsTextFile at GenerateCSVFiles.scala:51, took 8.715588 s
real 0m44.935s
user 4m2.259s
sys 0m14.159s
----
and these CSV files will be generated:
[source,bash]
----
$ ls -alh /tmp/*.csv
-rwxrwxrwx 1 markneedham wheel 3.0K 14 Apr 07:37 /tmp/beats.csv
-rwxrwxrwx 1 markneedham wheel 217M 14 Apr 07:37 /tmp/crimes.csv
-rwxrwxrwx 1 markneedham wheel 84M 14 Apr 07:37 /tmp/crimesBeats.csv
-rwxrwxrwx 1 markneedham wheel 120M 14 Apr 07:37 /tmp/crimesPrimaryTypes.csv
-rwxrwxrwx 1 markneedham wheel 912B 14 Apr 07:37 /tmp/primaryTypes.csv
----
Let's have a quick check what they contain:
[source,bash]
----
$ head -n 10 /tmp/beats.csv
id:ID(Beat),:LABEL
1135,Beat
1421,Beat
2312,Beat
1113,Beat
1014,Beat
2411,Beat
1333,Beat
2521,Beat
1652,Beat
----
[source,bash]
----
$ head -n 10 /tmp/crimes.csv
id:ID(Crime),:LABEL,date,description
9464711,Crime,01/14/2014 05:00:00 AM,SIMPLE
9460704,Crime,01/14/2014 04:55:00 AM,ARMED: HANDGUN
9460339,Crime,01/14/2014 04:44:00 AM,TO PROPERTY
9461467,Crime,01/14/2014 04:43:00 AM,$500 AND UNDER
9460355,Crime,01/14/2014 04:21:00 AM,$500 AND UNDER
9461140,Crime,01/14/2014 03:17:00 AM,FORCIBLE ENTRY
9460361,Crime,01/14/2014 03:12:00 AM,$500 AND UNDER
9461691,Crime,01/14/2014 03:00:00 AM,HOME INVASION
9461792,Crime,01/14/2014 03:00:00 AM,OVER $500
----
[source,bash]
----
$ head -n 10 /tmp/crimesBeats.csv
:START_ID(Crime),:END_ID(Beat),:TYPE
5896915,0733,ON_BEAT
9208776,2232,ON_BEAT
8237555,0111,ON_BEAT
6464775,0322,ON_BEAT
6468868,0411,ON_BEAT
4189649,0524,ON_BEAT
7620897,0421,ON_BEAT
7720402,0321,ON_BEAT
5053025,1115,ON_BEAT
----
Looking good. Let's get them imported into Neo4j:
[source,bash]
----
$ ./neo4j-community-2.2.0/bin/neo4j-import --into /tmp/my-neo --nodes /tmp/crimes.csv --nodes /tmp/beats.csv --nodes /tmp/primaryTypes.csv --relationships /tmp/crimesBeats.csv --relationships /tmp/crimesPrimaryTypes.csv
Nodes
[*>:45.76 MB/s----------------------------------|PROPERTIES(2)=============|NODE:3|v:118.05 MB/] 4M
Done in 5s 605ms
Prepare node index
[*RESOLVE:64.85 MB-----------------------------------------------------------------------------] 4M
Done in 4s 930ms
Calculate dense nodes
[>:42.33 MB/s-------------------|*PREPARE(7)===================================|CALCULATOR-----] 8M
Done in 5s 417ms
Relationships
[>:42.33 MB/s-------------|*PREPARE(7)==========================|RELATIONSHIP------------|v:44.] 8M
Done in 6s 62ms
Node --> Relationship
[*>:??-----------------------------------------------------------------------------------------] 4M
Done in 324ms
Relationship --> Relationship
[*LINK-----------------------------------------------------------------------------------------] 8M
Done in 1s 984ms
Node counts
[*>:??-----------------------------------------------------------------------------------------] 4M
Done in 360ms
Relationship counts
[*>:??-----------------------------------------------------------------------------------------] 8M
Done in 653ms
IMPORT DONE in 26s 517ms
----
Next I updated +++<cite>+++conf/neo4j-server.properties+++</cite>+++ to point to my new database:
[source,text]
----
#***************************************************************
# Server configuration
#***************************************************************
# location of the database directory
#org.neo4j.server.database.location=data/graph.db
org.neo4j.server.database.location=/tmp/my-neo
----
Now I can start up Neo and start exploring the data:
[source,bash]
----
$ ./neo4j-community-2.2.0/bin/neo4j start
----
[source,cypher]
----
MATCH (:Crime)-[r:CRIME_TYPE]->()
RETURN r
LIMIT 10
----
image::{{<siteurl>}}/uploads/2015/04/graph-15.png[Graph 15,600]
There's lots more relationships and entities that we could pull out of this data set - what I've done is just a start. So if you're up for some more Chicago crime exploration the https://github.com/mneedham/neo4j-spark-chicago[code and instructions explaining how to run it are on github]. | null | null | [
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] | spark-generating-csv-files-to-import-into-neo4j | https://markhneedham.com/blog/2015/04/14/spark-generating-csv-files-to-import-into-neo4j |
false | 2015-04-22 22:18:00 | R: Replacing for loops with data frames | [
"r-2"
] | [
"R"
] | In my last blog post I showed how to http://www.markhneedham.com/blog/2015/04/21/r-numeric-keys-in-the-nested-listdictionary/[derive posterior probabilities for the Think Bayes dice problem]:
____
Suppose I have a box of dice that contains a 4-sided die, a 6-sided die, an 8-sided die, a 12-sided die, and a 20-sided die. If you have ever played Dungeons & Dragons, you know what I am talking about. Suppose I select a die from the box at random, roll it, and get a 6.
What is the probability that I rolled each die?
____
To recap, this was my final solution:
[source,R]
----
likelihoods = function(names, observations) {
scores = rep(1.0 / length(names), length(names))
names(scores) = names
for(name in names) {
for(observation in observations) {
if(name < observation) {
scores[paste(name)] = 0
} else {
scores[paste(name)] = scores[paste(name)] * (1.0 / name)
}
}
}
return(scores)
}
dice = c(4,6,8,12,20)
l1 = likelihoods(dice, c(6))
> l1 / sum(l1)
4 6 8 12 20
0.0000000 0.3921569 0.2941176 0.1960784 0.1176471
----
Although it works we have nested for loops which aren't very idiomatic R so let's try and get rid of them.
The first thing we want to do is return a data frame rather than a vector so we tweak the first two lines to read like this:
[source,r]
----
scores = rep(1.0 / length(names), length(names))
df = data.frame(score = scores, name = names)
----
Next we can get rid of the inner for loop and replace it with a call to ifelse wrapped inside a dplyr mutate call:
[source,r]
----
library(dplyr)
likelihoods2 = function(names, observations) {
scores = rep(1.0 / length(names), length(names))
df = data.frame(score = scores, name = names)
for(observation in observations) {
df = df %>% mutate(score = ifelse(name < observation, 0, score * (1.0 / name)) )
}
return(df)
}
dice = c(4,6,8,12,20)
l1 = likelihoods2(dice, c(6))
> l1
score name
1 0.00000000 4
2 0.03333333 6
3 0.02500000 8
4 0.01666667 12
5 0.01000000 20
----
Finally we'll tidy up the scores so they're relatively weighted against each other:
[source,r]
----
likelihoods2 = function(names, observations) {
scores = rep(1.0 / length(names), length(names))
df = data.frame(score = scores, name = names)
for(observation in observations) {
df = df %>% mutate(score = ifelse(name < observation, 0, score * (1.0 / name)) )
}
return(df %>% mutate(weighted = score / sum(score)) %>% select(name, weighted))
}
dice = c(4,6,8,12,20)
l1 = likelihoods2(dice, c(6))
> l1
name weighted
1 4 0.0000000
2 6 0.3921569
3 8 0.2941176
4 12 0.1960784
5 20 0.1176471
----
Now we're down to just the one for loop. Getting rid of that one is a bit trickier. First we'll create a data frame which contains a row for every (observation, dice) pair, simulating the nested for loops:
[source,r]
----
likelihoods3 = function(names, observations) {
l = list(observation = observations, roll = names)
obsDf = do.call(expand.grid,l) %>%
mutate(likelihood = 1.0 / roll,
score = ifelse(roll < observation, 0, likelihood))
return(obsDf)
}
dice = c(4,6,8,12,20)
l1 = likelihoods3(dice, c(6))
> l1
observation roll likelihood score
1 6 4 0.25000000 0.00000000
2 6 6 0.16666667 0.16666667
3 6 8 0.12500000 0.12500000
4 6 12 0.08333333 0.08333333
5 6 20 0.05000000 0.05000000
l2 = likelihoods3(dice, c(6, 4, 8, 7, 7, 2))
> l2
observation roll likelihood score
1 6 4 0.25000000 0.00000000
2 4 4 0.25000000 0.25000000
3 8 4 0.25000000 0.00000000
4 7 4 0.25000000 0.00000000
5 7 4 0.25000000 0.00000000
6 2 4 0.25000000 0.25000000
7 6 6 0.16666667 0.16666667
8 4 6 0.16666667 0.16666667
9 8 6 0.16666667 0.00000000
10 7 6 0.16666667 0.00000000
11 7 6 0.16666667 0.00000000
12 2 6 0.16666667 0.16666667
13 6 8 0.12500000 0.12500000
14 4 8 0.12500000 0.12500000
15 8 8 0.12500000 0.12500000
16 7 8 0.12500000 0.12500000
17 7 8 0.12500000 0.12500000
18 2 8 0.12500000 0.12500000
19 6 12 0.08333333 0.08333333
20 4 12 0.08333333 0.08333333
21 8 12 0.08333333 0.08333333
22 7 12 0.08333333 0.08333333
23 7 12 0.08333333 0.08333333
24 2 12 0.08333333 0.08333333
25 6 20 0.05000000 0.05000000
26 4 20 0.05000000 0.05000000
27 8 20 0.05000000 0.05000000
28 7 20 0.05000000 0.05000000
29 7 20 0.05000000 0.05000000
30 2 20 0.05000000 0.05000000
----
Now we need to iterate over the data frame, grouping by 'roll' so that we end up with one row for each one.
We'll add a new column which stores the posterior probability for each dice. This will be calculated by multiplying the prior probability by the product of the 'score' entries.
This is what our new likelihood function looks like:
[source,r]
----
likelihoods3 = function(names, observations) {
l = list(observation = observations, roll = names)
obsDf = do.call(expand.grid,l) %>%
mutate(likelihood = 1.0 / roll,
score = ifelse(roll < observation, 0, likelihood))
return (obsDf %>%
group_by(roll) %>%
summarise(s = (1.0/length(names)) * prod(score) ) %>%
ungroup() %>%
mutate(weighted = s / sum(s)) %>%
select(roll, weighted))
}
l1 = likelihoods3(dice, c(6))
> l1
Source: local data frame [5 x 2]
roll weighted
1 4 0.0000000
2 6 0.3921569
3 8 0.2941176
4 12 0.1960784
5 20 0.1176471
l2 = likelihoods3(dice, c(6, 4, 8, 7, 7, 2))
> l2
Source: local data frame [5 x 2]
roll weighted
1 4 0.000000000
2 6 0.000000000
3 8 0.915845272
4 12 0.080403426
5 20 0.003751302
----
We've now got the same result as we did with our nested for loops so I think the refactoring has been a success. | null | null | [
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] | r-replacing-for-loops-with-data-frames | https://markhneedham.com/blog/2015/04/22/r-replacing-for-loops-with-data-frames |
false | 2015-04-25 22:26:51 | Deliberate Practice: Watching yourself fail | [
"software-development"
] | [
"Software Development",
"Deliberate Practice"
] | image::{{<siteurl>}}/uploads/2015/04/think_bayes_cover_medium.png[Think bayes cover medium,125]
I've recently been reading the literature written by K. Anders Eriksson and co on https://www.scribd.com/doc/54636778/2009-Ericsson-K-a-Discovering-Deliberate-Practice-Activities-That-Overcome-Plateaus-and-Limits-on-Improvement-of-Performance[Deliberate] https://www.scribd.com/doc/194207680/The-Rigorous-Aplication-of-Deliberate-Practice[Practice] and one of the suggestions for increasing our competence at a skill is to put ourselves in a situation where we can fail.
I've been reading http://greenteapress.com/thinkbayes/[Think Bayes] - an introductory text on Bayesian statistics, something I know nothing about - and each chapter concludes with a set of exercises to practice, a potentially perfect exercise in failure!
I've been going through the exercises and https://www.techsmith.com/camtasia.html[capturing my screen] while I do so, an idea I picked up from one of the papers:
____
our most important breakthrough was developing a relatively inexpensive and efficient way for students to record their exercises on video and to review and analyze their own performances against well-defined criteria
____
Ideally I'd get a coach to review the video but that seems too much of an ask of someone. https://twitter.com/tonkouts[Antonios] has taken a look at some of my answers, however, and made suggestions for how he'd solve them which has been really helpful.
After each exercise I watch the video and look for areas where I get stuck or don't make progress so that I can go and practice more in that area. I also try to find inefficiencies in how I solve a problem as well as the types of approaches I'm taking.
These are some of the observations from watching myself back over the last week or so:
* I was most successful when I had some *idea of what I was going to try first*. Most of the time the first code I wrote didn't end up being correct but it moved me closer to the answer or ruled out an approach. It's much easier to see the error in approach if there is an approach! On one occasion where I hadn't planned out an approach I ended up staring at the question for 10 minutes and didn't make any progress at all.
* I could either solve the problems within 20 minutes or I wasn't going to solve them and needed to chunk down to a simpler problem and then try the original exercise again. e.g. one exercise was to calculate the 5th percentile of a posterior distribution which I flailed around with for 15 minutes before giving up. Watching back on the video it was obvious that I hadn't completely understood what a http://en.wikipedia.org/wiki/Probability_mass_function[probability mass function] was. I read the Wikipedia entry and retried the exercise and this time got the answer.
* Knowing that you're going to watch the video back stops you from getting distracted by email, twitter, Facebook etc.
* It's a painful experience watching yourself struggle - you can see exactly which functions you don't know or things you need to look up on Google.
* I deliberately don't copy/paste any code while doing these exercises. I want to see how well I can do the exercises from scratch so that would defeat the point.
One of the suggestions that Eriksson makes for practice sessions is to focus on 'technique' during practice sessions rather than only on outcome but I haven't yet been able to translate what exactly that would involved in a programming context.
If you have any ideas or thoughts on this approach do let me know in the comments. | null | null | [
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] | deliberate-practice-watching-yourself-fail | https://markhneedham.com/blog/2015/04/25/deliberate-practice-watching-yourself-fail |
false | 2015-05-03 00:19:51 | Coding: Visualising a bitmap | [
"coding"
] | [
"Coding",
"Java"
] | Over the last month or so I've spent some time each day reading a new part of the Neo4j code base to get more familiar with it, and one of my favourite classes is the https://github.com/neo4j/neo4j/blob/2.3/community/kernel/src/main/java/org/neo4j/kernel/impl/util/Bits.java[Bits] class which does all things low level on the wire and to disk.
In particular I like its toString method which returns a binary representation of the values that we're storing in bytes, ints and longs.
I thought it'd be a fun exercise to try and write my own function which takes in a 32 bit map and returns a string containing a 1 or 0 depending if a bit is set or not.
The key insight is that we need to iterate down from the highest order bit and then create a bit mask of that value and do a bitwise and with the full bitmap. If the result of that calculation is 0 then the bit isn't set, otherwise it is.
For example, to check if the highest order bit (index 31) was set our bit mask would have the 32nd bit set and all of the others 0'd out.
[source,java]
----
java> (1 << 31) & 0x80000000
java.lang.Integer res5 = -2147483648
----
If we wanted to check if lowest order bit was set then we'd run this computation instead:
[source,java]
----
java> (1 << 0) & 0x00000001
java.lang.Integer res7 = 0
java> (1 << 0) & 0x00000001
java.lang.Integer res8 = 1
----
Now let's put that into a function which checks all 32 bits of the bitmap rather than just the ones we define:
[source,java]
----
private String asString( int bitmap )
{
StringBuilder sb = new StringBuilder();
sb.append( "[" );
for ( int i = Integer.SIZE - 1; i >= 0; i-- )
{
int bitMask = 1 << i;
boolean bitIsSet = (bitmap & bitMask) != 0;
sb.append( bitIsSet ? "1" : "0" );
if ( i > 0 && i % 8 == 0 )
{
sb.append( "," );
}
}
sb.append( "]" );
return sb.toString();
}
----
And a quick test to check it works:
[source,java]
----
@Test
public void shouldInspectBits()
{
System.out.println(asString( 0x00000001 ));
// [00000000,00000000,00000000,00000001]
System.out.println(asString( 0x80000000 ));
// [10000000,00000000,00000000,00000000]
System.out.println(asString( 0xA0 ));
// [00000000,00000000,00000000,10100000]
System.out.println(asString( 0xFFFFFFFF ));
// [11111111,11111111,11111111,11111111]
}
----
Neat! | null | null | [
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] | coding-visualising-a-bitmap | https://markhneedham.com/blog/2015/05/03/coding-visualising-a-bitmap |
false | 2015-05-04 09:56:22 | Neo4j: LOAD CSV - java.io.InputStreamReader there's a field starting with a quote and whereas it ends that quote there seems to be character in that field after that ending quote. That isn't supported. | [
"neo4j",
"cypher"
] | [
"neo4j"
] | I recently came across the http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/lastfm-360K.html[last.fm dataset] via http://www.benfrederickson.com/distance-metrics/[Ben Frederickson's blog] and thought it'd be an interesting one to load into Neo4j and explore.
I started with a simple query to parse the CSV file and count the number of rows:
[source,cypher]
----
LOAD CSV FROM "file:///Users/markneedham/projects/neo4j-recommendations/lastfm-dataset-360K/usersha1-artmbid-artname-plays.tsv"
AS row FIELDTERMINATOR "\t"
return COUNT(*)
At java.io.InputStreamReader@4d307fda:6484 there's a field starting with a quote and whereas it ends that quote there seems to be character in that field after that ending quote. That isn't supported. This is what I read: 'weird al"'
----
This blows up because (as the message says) we've got a field which uses double quotes but then has other characters either side of the quotes.
A quick search through the file reveals one of the troublesome lines:
[source,bash]
----
$ grep "\"weird" lastfm-dataset-360K/usersha1-artmbid-artname-plays.tsv | head -n 1
0015371426d2cbef354b2f680340de38d0ebd2f0 7746d775-9550-4360-b8d5-c37bd448ce01 "weird al" yankovic 4099
----
I ran a file containing only that line through http://csvlint.io/validation/55473958637376058f000000[CSV Lint] to see what it thought and indeed it is invalid:
image::{{<siteurl>}}/uploads/2015/05/2015-05-04_10-50-43.png[2015 05 04 10 50 43,597]
Let's clean up our file to use single quotes instead of double quotes and try the query again:
[source,bash]
----
$ tr "\"" "'" < lastfm-dataset-360K/usersha1-artmbid-artname-plays.tsv > lastfm-dataset-360K/clean.tsv
----
[source,cypher]
----
LOAD CSV FROM "file:///Users/markneedham/projects/neo4j-recommendations/lastfm-dataset-360K/clean.tsv" as row FIELDTERMINATOR "\t"
return COUNT(*)
17559530
----
And we're back in business! Interestingly Python's CSV reader chooses to strip out the double quotes rather than throw an exception:
[source,python]
----
import csv
with open("smallWeird.tsv", "r") as file:
reader = csv.reader(file, delimiter="\t")
for row in reader:
print row
----
[source,bash]
----
$ python explore.py
['0015371426d2cbef354b2f680340de38d0ebd2f0', '7746d775-9550-4360-b8d5-c37bd448ce01', 'weird al yankovic', '4099']
----
I prefer LOAD CSV's approach but it's an interesting trade off I hadn't considred before. | null | null | [
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] | neo4j-load-csv-java-io-inputstreamreader-theres-a-field-starting-with-a-quote-and-whereas-it-ends-that-quote-there-seems-to-be-character-in-that-field-after-that-ending-quote-that-isnt-suppor | https://markhneedham.com/blog/2015/05/04/neo4j-load-csv-java-io-inputstreamreader-theres-a-field-starting-with-a-quote-and-whereas-it-ends-that-quote-there-seems-to-be-character-in-that-field-after-that-ending-quote-that-isnt-suppor |
false | 2015-05-05 21:39:24 | Python: Selecting certain indexes in an array | [
"python"
] | [
"Python"
] | A couple of days ago I was scrapping the http://en.wikipedia.org/wiki/List_of_United_Kingdom_Parliament_constituencies[UK parliament constituencies] from Wikipedia in preparation for the http://www.meetup.com/graphdb-london/events/221364888/[Graph Connect hackathon] and had got to the point where I had an array with one entry per column in the table.
image::{{<siteurl>}}/uploads/2015/05/2015-05-05_22-22-57.png[2015 05 05 22 22 57,600]
[source,python]
----
import requests
from bs4 import BeautifulSoup
from soupselect import select
page = open("constituencies.html", 'r')
soup = BeautifulSoup(page.read())
for row in select(soup, "table.wikitable tr"):
if select(row, "th"):
print [cell.text for cell in select(row, "th")]
if select(row, "td"):
print [cell.text for cell in select(row, "td")]
----
[source,bash]
----
$ python blog.py
[u'Constituency', u'Electorate (2000)', u'Electorate (2010)', u'Largest Local Authority', u'Country of the UK']
[u'Aldershot', u'66,499', u'71,908', u'Hampshire', u'England']
[u'Aldridge-Brownhills', u'58,695', u'59,506', u'West Midlands', u'England']
[u'Altrincham and Sale West', u'69,605', u'72,008', u'Greater Manchester', u'England']
[u'Amber Valley', u'66,406', u'69,538', u'Derbyshire', u'England']
[u'Arundel and South Downs', u'71,203', u'76,697', u'West Sussex', u'England']
[u'Ashfield', u'74,674', u'77,049', u'Nottinghamshire', u'England']
[u'Ashford', u'72,501', u'81,947', u'Kent', u'England']
[u'Ashton-under-Lyne', u'67,334', u'68,553', u'Greater Manchester', u'England']
[u'Aylesbury', u'72,023', u'78,750', u'Buckinghamshire', u'England']
...
----
I wanted to get rid of the 2nd and 3rd columns (containing the electorates) from the array since those aren't interesting to me as I have another source where I've got that data from.
I was struggling to do this but http://stackoverflow.com/questions/3179106/python-select-subset-from-list-based-on-index-set[two] http://stackoverflow.com/questions/522563/accessing-the-index-in-python-for-loops[different] Stack Overflow questions came to the rescue with suggestions to use enumerate to get the index of each column and then add to the list comprehension to filter appropriately.
First we'll look at the filtering on a simple example. Imagine we have a list of 5 people:
[source,python]
----
people = ["mark", "michael", "brian", "alistair", "jim"]
----
And we only want to keep the 1st, 4th and 5th people. We therefore only want to keep the values that exist in index positions 0,3 and 4 which we can do like this: ~~~python >>> [x[1] for x in enumerate(people) if x[0] in [0,3,4]] ['mark', 'alistair', 'jim'] ~~~
Now let's apply the same approach to our constituencies data set: ~~~python import requests from bs4 import BeautifulSoup from soupselect import select page = open("constituencies.html", 'r') soup = BeautifulSoup(page.read()) for row in select(soup, "table.wikitable tr"): if select(row, "th"): print [entry[1].text for entry in enumerate(select(row, "th")) if entry[0] in [0,3,4]] if select(row, "td"): print [entry[1].text for entry in enumerate(select(row, "td")) if entry[0] in [0,3,4]] ~~~ ~~~bash $ python blog.py [u'Constituency', u'Largest Local Authority', u'Country of the UK'] [u'Aldershot', u'Hampshire', u'England'] [u'Aldridge-Brownhills', u'West Midlands', u'England'] [u'Altrincham and Sale West', u'Greater Manchester', u'England'] [u'Amber Valley', u'Derbyshire', u'England'] [u'Arundel and South Downs', u'West Sussex', u'England'] [u'Ashfield', u'Nottinghamshire', u'England'] [u'Ashford', u'Kent', u'England'] [u'Ashton-under-Lyne', u'Greater Manchester', u'England'] [u'Aylesbury', u'Buckinghamshire', u'England'] ~~~ | null | null | [
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] | python-selecting-certain-indexes-in-an-array | https://markhneedham.com/blog/2015/05/05/python-selecting-certain-indexes-in-an-array |
false | 2015-05-11 23:16:07 | R: Cohort heatmap of Neo4j London meetup | [
"r-2",
"rstats"
] | [
"R"
] | A few months ago I had a go at doing some http://www.markhneedham.com/blog/2015/02/24/r-cohort-analysis-of-neo4j-meetup-members/[cohort analysis of the Neo4j London meetup group] which was an interesting experiment but unfortunately resulted in a chart that was completely illegible.
image::{{<siteurl>}}/uploads/2015/02/unnamed-chunk-5-3.png[]
I wasn't sure how to progress from there but a few days ago I came across the http://www.r-bloggers.com/cohort-analysis-with-heatmap/[cohort heatmap] which seemed like a better way of visualising things over time.
The underlying idea is still the same - we've comparing different cohorts of users against each other to see whether a change or intervention we did at a certain time had any impact.
However, the way we display the cohorts changes and I think for the better.
To recap, we start with the following data frame:
[source,r]
----
df = read.csv("/tmp/df.csv")
> df %>% sample_n(5)
rsvp.time person.id time date
255 1.354277e+12 12228948 2012-11-30 12:05:08 2012-11
2475 1.407342e+12 19057581 2014-08-06 16:26:04 2014-08
3988 1.421769e+12 66122172 2015-01-20 15:58:02 2015-01
4411 1.419377e+12 165750262 2014-12-23 23:27:44 2014-12
1010 1.383057e+12 74602292 2013-10-29 14:24:32 2013-10
----
And we need to transform this into a data frame which is grouped by cohort (members who attended their first meetup in a particular month). The following code gets us there:
[source,r]
----
firstMeetup = df %>%
group_by(person.id) %>%
summarise(firstEvent = min(time), count = n()) %>%
arrange(desc(count))
firstMeetup$date = format(as.Date(firstMeetup$firstEvent), "%Y-%m")
countsForCohort = function(df, firstMeetup, cohort) {
members = (firstMeetup %>% filter(date == cohort))$person.id
attendance = df %>%
filter(person.id %in% members) %>%
count(person.id, date) %>%
ungroup() %>%
count(date)
allCohorts = df %>% select(date) %>% unique
cohortAttendance = merge(allCohorts, attendance, by = "date", all.x = TRUE)
cohortAttendance[is.na(cohortAttendance) & cohortAttendance$date > cohort] = 0
cohortAttendance %>% mutate(cohort = cohort, retention = n / length(members), members = n)
}
cohorts = collect(df %>% select(date) %>% unique())[,1]
cohortAttendance = data.frame()
for(cohort in cohorts) {
cohortAttendance = rbind(cohortAttendance,countsForCohort(df, firstMeetup, cohort))
}
monthNumber = function(cohort, date) {
cohortAsDate = as.yearmon(cohort)
dateAsDate = as.yearmon(date)
if(cohortAsDate > dateAsDate) {
"NA"
} else {
paste(round((dateAsDate - cohortAsDate) * 12), sep="")
}
}
cohortAttendanceWithMonthNumber = cohortAttendance %>%
group_by(row_number()) %>%
mutate(monthNumber = monthNumber(cohort, date)) %>%
filter(monthNumber != "NA") %>%
filter(!is.na(members)) %>%
mutate(monthNumber = as.numeric(monthNumber)) %>%
arrange(monthNumber)
> cohortAttendanceWithMonthNumber %>% head(10)
Source: local data frame [10 x 7]
Groups: row_number()
date n cohort retention members row_number() monthNumber
1 2011-06 4 2011-06 1.00 4 1 0
2 2011-07 1 2011-06 0.25 1 2 1
3 2011-08 1 2011-06 0.25 1 3 2
4 2011-09 2 2011-06 0.50 2 4 3
5 2011-10 1 2011-06 0.25 1 5 4
6 2011-11 1 2011-06 0.25 1 6 5
7 2012-01 1 2011-06 0.25 1 7 7
8 2012-04 2 2011-06 0.50 2 8 10
9 2012-05 1 2011-06 0.25 1 9 11
10 2012-06 1 2011-06 0.25 1 10 12
----
Now let's create our first heatmap.
[source,r]
----
t <- max(cohortAttendanceWithMonthNumber$members)
cols <- c("#e7f0fa", "#c9e2f6", "#95cbee", "#0099dc", "#4ab04a", "#ffd73e", "#eec73a", "#e29421", "#e29421", "#f05336", "#ce472e")
ggplot(cohortAttendanceWithMonthNumber, aes(y=cohort, x=date, fill=members)) +
theme_minimal() +
geom_tile(colour="white", linewidth=2, width=.9, height=.9) +
scale_fill_gradientn(colours=cols, limits=c(0, t),
breaks=seq(0, t, by=t/4),
labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)),
guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) +
theme(legend.position='bottom',
legend.direction="horizontal",
plot.title = element_text(size=20, face="bold", vjust=2),
axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) +
ggtitle("Cohort Activity Heatmap (number of members who attended event)")
----
image::{{<siteurl>}}/uploads/2015/05/2015-05-11_23-55-56.png[2015 05 11 23 55 56,598]
't' is the maximum number of members within a cohort who attended a meetup in a given month. This makes it easy to see which cohorts started with the most members but makes it difficult to compare their retention over time.
We can fix that by showing the percentage of members in the cohort who attend each month rather than using absolute values. To do that we must first add an extra column containing the percentage values:
[source,r]
----
cohortAttendanceWithMonthNumber$retentionPercentage = ifelse(!is.na(cohortAttendanceWithMonthNumber$retention), cohortAttendanceWithMonthNumber$retention * 100, 0)
t <- max(cohortAttendanceWithMonthNumber$retentionPercentage)
cols <- c("#e7f0fa", "#c9e2f6", "#95cbee", "#0099dc", "#4ab04a", "#ffd73e", "#eec73a", "#e29421", "#e29421", "#f05336", "#ce472e")
ggplot(cohortAttendanceWithMonthNumber, aes(y=cohort, x=date, fill=retentionPercentage)) +
theme_minimal() +
geom_tile(colour="white", linewidth=2, width=.9, height=.9) +
scale_fill_gradientn(colours=cols, limits=c(0, t),
breaks=seq(0, t, by=t/4),
labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)),
guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) +
theme(legend.position='bottom',
legend.direction="horizontal",
plot.title = element_text(size=20, face="bold", vjust=2),
axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) +
ggtitle("Cohort Activity Heatmap (number of members who attended event)")
----
image::{{<siteurl>}}/uploads/2015/05/2015-05-12_00-01-55.png[2015 05 12 00 01 55,599]
This version allows us to compare cohorts against each other but now we don't have the exact numbers which means earlier cohorts will look better since there are less people in them. We can get the best of both worlds by keeping this visualisation but showing the actual values inside each box:
[source,r]
----
t <- max(cohortAttendanceWithMonthNumber$retentionPercentage)
cols <- c("#e7f0fa", "#c9e2f6", "#95cbee", "#0099dc", "#4ab04a", "#ffd73e", "#eec73a", "#e29421", "#e29421", "#f05336", "#ce472e")
ggplot(cohortAttendanceWithMonthNumber, aes(y=cohort, x=date, fill=retentionPercentage)) +
theme_minimal() +
geom_tile(colour="white", linewidth=2, width=.9, height=.9) +
scale_fill_gradientn(colours=cols, limits=c(0, t),
breaks=seq(0, t, by=t/4),
labels=c("0", round(t/4*1, 1), round(t/4*2, 1), round(t/4*3, 1), round(t/4*4, 1)),
guide=guide_colourbar(ticks=T, nbin=50, barheight=.5, label=T, barwidth=10)) +
theme(legend.position='bottom',
legend.direction="horizontal",
plot.title = element_text(size=20, face="bold", vjust=2),
axis.text.x=element_text(size=8, angle=90, hjust=.5, vjust=.5, face="plain")) +
ggtitle("Cohort Activity Heatmap (number of members who attended event)") +
geom_text(aes(label=members),size=3)
----
image::{{<siteurl>}}/uploads/2015/05/2015-05-12_00-04-31.png[2015 05 12 00 04 31,599]
What we can learn overall is that the majority of people seem to have a passing interest and then we have a smaller percentage who will continue to come to events.
It seems like we did a better job at retaining attendees in the middle of last year - one hypothesis is that the events we ran around then were more compelling but I need to do more analysis.
Next I'm going to drill further into some of the recent events and see what cohorts the attendees came from. | null | null | [
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] | r-cohort-heatmap-of-neo4j-london-meetup | https://markhneedham.com/blog/2015/05/11/r-cohort-heatmap-of-neo4j-london-meetup |
false | 2015-05-16 21:13:01 | Neo4j: BBC football live text fouls graph | [
"neo4j"
] | [
"neo4j"
] | I recently came across the http://www.partiallyderivative.com/[Partially Derivative podcast] and in http://www.partiallyderivative.com/news/2015/3/20/episode-17-get-back-to-work-you-slackers[episode 17] they describe how Kirk Goldsberry scraped a bunch of data about shots in basketball matches then ran some analysis on that data.
It got me thinking that we might be able to do something similar for football matches and although event based data for football matches only comes from Opta, the BBC does expose some of them in http://www.bbc.co.uk/sport/0/football/32683310[live text feeds].
We'll start with the Champions League match between Barcelona and Bayern Munich from last Tuesday.
image::{{<siteurl>}}/uploads/2015/05/2015-05-16_23-10-43.png[2015 05 16 23 10 43,500]
Our first task is to extract the events that happened in the match along with the players involved. After we've got that we'll generate a Neo4j graph and see if we can find some interesting insights.
I find the feedback cycle with this type of work is dramatically improved if we have the source data available locally so the first step was to get the BBC web page downloaded:
[source,bash]
----
$ wget http://www.bbc.co.uk/sport/0/football/32683310
----
Next we need to write a scraper which will extract all the events. We want to get an array containing one entry for each event, where the following is an example of an event:
image::{{<siteurl>}}/uploads/2015/05/2015-05-16_22-19-00.png[2015 05 16 22 19 00,400]
HTML-wise it looks like this:
image::{{<siteurl>}}/uploads/2015/05/2015-05-16_22-20-28.png[2015 05 16 22 20 28,400]
image:{{<siteurl>}}/uploads/2015/05/4709393221_bddd85c64e_z.jpg[4709393221 bddd85c64e z,250]
I do most of my scraping work in Python so I used the http://www.crummy.com/software/BeautifulSoup/[Beautiful Soup library] with the http://code.google.com/p/soupselect/[soupselect wrapper] to get the data into CSV format ready to import into Neo4j.
It was mostly a straight forward job of finding the appropriate CSS tag and pulling out the values although the way fouls are described in the page is a bit strange - sometimes the person fouled comes first row and the fouler comes on the next line and sometimes vice versa.
Luckily the two parts of the foul can be joined together by matching the time which made life easier.
The https://github.com/mneedham/neo4j-bbc/blob/master/extract_events.py[full code for the scrapper is on github] if you want to play with it.
This is what the resulting CSV file looks like:
[source,bash]
----
$ head -n 10 data/events.csv
matchId,foulId,freeKickId,time,foulLocation,fouledPlayer,fouledPlayerTeam,foulingPlayer,foulingPlayerTeam
32683310,3,2,90:00 +0:40,in the defensive half.,Xabi Alonso,FC Bayern München,Pedro,Barcelona
32683310,9,8,84:38,on the right wing.,Rafinha,FC Bayern München,Pedro,Barcelona
32683310,12,13,83:17,in the attacking half.,Lionel Messi,Barcelona,Sebastian Rode,FC Bayern München
32683310,15,14,82:43,in the defensive half.,Sebastian Rode,FC Bayern München,Neymar,Barcelona
32683310,17,18,80:41,in the attacking half.,Pedro,Barcelona,Xabi Alonso,FC Bayern München
32683310,22,23,76:31,in the defensive half.,Neymar,Barcelona,Rafinha,FC Bayern München
32683310,25,26,75:03,in the attacking half.,Lionel Messi,Barcelona,Xabi Alonso,FC Bayern München
32683310,31,30,69:37,in the attacking half.,Bastian Schweinsteiger,FC Bayern München,Dani Alves,Barcelona
32683310,36,35,63:27,in the attacking half.,Robert Lewandowski,FC Bayern München,Ivan Rakitic,Barcelona
----
Now it's time to create a graph. We'll aim to massage the data into this model:
image::{{<siteurl>}}/uploads/2015/05/2015-05-16_22-50-32.png[2015 05 16 22 50 32,465]
Next we need to write some Cypher code to get the CSV data into the graph. The https://github.com/mneedham/neo4j-bbc/blob/master/import.cql[full script is here], a sample of which is below:
[source,cypher]
----
// match
LOAD CSV WITH HEADERS FROM "file:///Users/markhneedham/projects/neo4j-bbc/data/events.csv" AS row
MERGE (:Match {id: row.matchId});
// teams
LOAD CSV WITH HEADERS FROM "file:///Users/markhneedham/projects/neo4j-bbc/data/events.csv" AS row
MERGE (:Team {name: row.foulingPlayerTeam});
LOAD CSV WITH HEADERS FROM "file:///Users/markhneedham/projects/neo4j-bbc/data/events.csv" AS row
MERGE (:Team {name: row.fouledPlayerTeam});
// players
LOAD CSV WITH HEADERS FROM "file:///Users/markhneedham/projects/neo4j-bbc/data/events.csv" AS row
MERGE (player:Player {id: row.foulingPlayer + "_" + row.foulingPlayerTeam})
ON CREATE SET player.name = row.foulingPlayer;
// appearances
LOAD CSV WITH HEADERS FROM "file:///Users/markhneedham/projects/neo4j-bbc/data/events.csv" AS row
MATCH (match:Match {id: row.matchId})
MATCH (player:Player {id: row.foulingPlayer + "_" + row.foulingPlayerTeam})
MATCH (team:Team {name: row.foulingPlayerTeam})
MERGE (appearance:Appearance {id: player.id + " in " + row.matchId})
MERGE (player)-[:MADE_APPEARANCE]->(appearance)
MERGE (appearance)-[:IN_MATCH]->(match)
MERGE (appearance)-[:FOR_TEAM]->(team);
// fouls
LOAD CSV WITH HEADERS FROM "file:///Users/markhneedham/projects/neo4j-bbc/data/events.csv" AS row
MATCH (foulingPlayer:Player {id:row.foulingPlayer + "_" + row.foulingPlayerTeam })
MATCH (fouledPlayer:Player {id:row.fouledPlayer + "_" + row.fouledPlayerTeam })
MATCH (match:Match {id: row.matchId})
MERGE (foul:Foul {eventId: row.foulId})
ON CREATE SET foul.time = row.time, foul.location = row.foulLocation
MERGE (foul)<-[:COMMITTED_FOUL]-(foulingPlayer)
MERGE (foul)-[:COMMITTED_AGAINST]->(fouledPlayer)
MERGE (foul)-[:COMMITTED_IN_MATCH]->(match);
----
We'll use neo4j-shell to execute the script:
[source,bash]
----
$ ./neo4j-community-2.2.1/bin/neo4j-shell --file import.cql
----
Now that we've got the data into Neo4j we need to come up with some questions to ask of it. I came up with the following but perhaps you can think of some others!
* Where do the fouls happen on the pitch?
* Who made the most fouls?
* Who was fouled the most?
* Who fouled who the most?
* Which team fouled the most?
* Who's the worst fouler in each team?
* Who's the most fouled in each team?
== Where do the fouls happen?
[source,cypher]
----
match (match:Match)<-[:COMMITTED_IN_MATCH]-(foul)
RETURN foul.location AS location, COUNT(*) as fouls
ORDER BY fouls DESC;
+----------------------------------+
| location | fouls |
+----------------------------------+
| "in the defensive half." | 12 |
| "in the attacking half." | 12 |
| "on the right wing." | 3 |
| "on the left wing." | 3 |
+----------------------------------+
4 rows
----
== Who fouls the most?
[source,cypher]
----
match (match:Match)<-[:COMMITTED_IN_MATCH]-(foul)<-[:COMMITTED_FOUL]-(fouler)
RETURN fouler.name AS fouler, COUNT(*) as fouls
ORDER BY fouls DESC
LIMIT 10;
+------------------------------+
| fouler | fouls |
+------------------------------+
| "Rafinha" | 4 |
| "Pedro" | 3 |
| "Medhi Benatia" | 3 |
| "Dani Alves" | 3 |
| "Xabi Alonso" | 3 |
| "Javier Mascherano" | 2 |
| "Thiago Alcántara" | 2 |
| "Robert Lewandowski" | 2 |
| "Sebastian Rode" | 1 |
| "Sergio Busquets" | 1 |
+------------------------------+
10 rows
----
== Who was fouled the most?
[source,cypher]
----
// who was fouled the most
match (match:Match)<-[:COMMITTED_IN_MATCH]-(foul)-[:COMMITTED_AGAINST]->(fouled)
RETURN fouled.name AS fouled, COUNT(*) as fouls
ORDER BY fouls DESC
LIMIT 10;
+----------------------------------+
| fouled | fouls |
+----------------------------------+
| "Robert Lewandowski" | 4 |
| "Lionel Messi" | 4 |
| "Neymar" | 3 |
| "Pedro" | 2 |
| "Xabi Alonso" | 2 |
| "Andrés Iniesta" | 2 |
| "Rafinha" | 2 |
| "Bastian Schweinsteiger" | 2 |
| "Sebastian Rode" | 1 |
| "Sergio Busquets" | 1 |
+----------------------------------+
10 rows
----
== Who fouled who the most?
[source,cypher]
----
match (match:Match)<-[:COMMITTED_IN_MATCH]-(foul)-[:COMMITTED_AGAINST]->(fouled),
(foul)<-[:COMMITTED_FOUL]-(fouler)
RETURN fouler.name AS fouler, fouled.name AS fouled, COUNT(*) as fouls
ORDER BY fouls DESC
LIMIT 10;
+--------------------------------------------------------+
| fouler | fouled | fouls |
+--------------------------------------------------------+
| "Javier Mascherano" | "Robert Lewandowski" | 2 |
| "Dani Alves" | "Bastian Schweinsteiger" | 2 |
| "Xabi Alonso" | "Lionel Messi" | 2 |
| "Rafinha" | "Neymar" | 2 |
| "Rafinha" | "Andrés Iniesta" | 2 |
| "Dani Alves" | "Xabi Alonso" | 1 |
| "Thiago Alcántara" | "Javier Mascherano" | 1 |
| "Pedro" | "Juan Bernat" | 1 |
| "Medhi Benatia" | "Pedro" | 1 |
| "Neymar" | "Sebastian Rode" | 1 |
+--------------------------------------------------------+
10 rows
----
== Which team fouled the most?
[source,cypher]
----
match (match:Match)<-[:COMMITTED_IN_MATCH]-(foul)<-[:COMMITTED_FOUL]-(fouler),
(fouler)-[:MADE_APPEARANCE]-(app)-[:IN_MATCH]-(match),
(app)-[:FOR_TEAM]->(team)
RETURN team.name, COUNT(*) as fouls
ORDER BY fouls DESC
LIMIT 10;
+-----------------------------+
| team.name | fouls |
+-----------------------------+
| "FC Bayern München" | 18 |
| "Barcelona" | 12 |
+-----------------------------+
2 rows
----
== Worst fouler for each team?
[source,cypher]
----
match (match:Match)<-[:COMMITTED_IN_MATCH]-(foul)<-[:COMMITTED_FOUL]-(fouler),
(fouler)-[:MADE_APPEARANCE]-(app)-[:IN_MATCH]-(match),
(app)-[:FOR_TEAM]->(team)
WITH team, fouler, COUNT(*) AS fouls
ORDER BY team.name, fouls DESC
WITH team, COLLECT({fouler:fouler, fouls:fouls})[0] AS topFouler
RETURN team.name, topFouler.fouler.name, topFouler.fouls;
+---------------------------------------------------------------+
| team.name | topFouler.fouler.name | topFouler.fouls |
+---------------------------------------------------------------+
| "FC Bayern München" | "Rafinha" | 4 |
| "Barcelona" | "Pedro" | 3 |
+---------------------------------------------------------------+
2 rows
----
== Most fouled against for each team
[source,cypher]
----
match (match:Match)<-[:COMMITTED_IN_MATCH]-(foul)-[:COMMITTED_AGAINST]-(fouled),
(fouled)-[:MADE_APPEARANCE]-(app)-[:IN_MATCH]-(match),
(app)-[:FOR_TEAM]->(team)
WITH team, fouled, COUNT(*) AS fouls
ORDER BY team.name, fouls DESC
WITH team, COLLECT({fouled:fouled, fouls:fouls})[0] AS topFouled
RETURN team.name, topFouled.fouled.name, topFouled.fouls;
+---------------------------------------------------------------+
| team.name | topFouled.fouled.name | topFouled.fouls |
+---------------------------------------------------------------+
| "FC Bayern München" | "Robert Lewandowski" | 4 |
| "Barcelona" | "Lionel Messi" | 4 |
+---------------------------------------------------------------+
2 rows
----
So Bayern fouled a bit more than Barca, the main forwards for each team (Messi/Lewandowski) were the most fouled players on the pitch and the fouling was mostly in the middle of the pitch.
I expect this graph will become much more interesting to query with more matches and with the other event types as well but I haven't got those scraped yet. The https://github.com/mneedham/neo4j-bbc[code is on github]if you want to play around with it and perhaps get the other events into the graph. | null | null | [
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] | neo4j-bbc-football-live-text-fouls-graph | https://markhneedham.com/blog/2015/05/16/neo4j-bbc-football-live-text-fouls-graph |
false | 2015-05-28 20:56:08 | Python: Look ahead multiple elements in an iterator/generator | [
"python"
] | [
"Python"
] | As part of the BBC live text scraping code I've been working on I needed to take an iterator of raw events created by a generator and transform this into an iterator of cards shown in a match.
The structure of the raw events I'm interested in is as follows:
* Line 1: Player booked
* Line 2: Player fouled
* Line 3: Information about the foul
e.g.
[source,text]
----
events = [
{'event': u'Booking Pedro (Barcelona) is shown the yellow card for a bad foul.', 'sortable_time': 5083, 'match_id': '32683310', 'formatted_time': u'84:43'},
{'event': u'Rafinha (FC Bayern M\xfcnchen) wins a free kick on the right wing.', 'sortable_time': 5078, 'match_id': '32683310', 'formatted_time': u'84:38'},
{'event': u'Foul by Pedro (Barcelona).', 'sortable_time': 5078, 'match_id': '32683310', 'formatted_time': u'84:38'}
]
----
We want to take these 3 raw events and create one 'booking event'. We therefore need to have access to all 3 lines at the same time.
I started with the following function:
[source,python]
----
def cards(events):
events = iter(events)
item = events.next()
next = events.next()
event_id = 0
for next_next in events:
event = item["event"]
booking = re.findall("Booking.*", event)
if booking:
player = re.findall("Booking([^(]*)", event)[0].strip()
team = re.findall("Booking([^(]*) \((.*)\)", event)[0][1]
associated_foul = [x for x in [(next, event_id+1), (next_next, event_id+2)]
if re.findall("Foul by.*", x[0]["event"])]
if associated_foul:
associated_foul = associated_foul[0]
yield event_id, associated_foul[1], player, team, item, "yellow"
else:
yield event_id, "", player, team, item, "yellow"
item = next
next = next_next
event_id += 1
----
If we run the sample events through it we'd see this output:
[source,python]
----
>>> for card_id, associated_foul, player, team, item, card_type in cards(iter(events)):
print card_id, associated_foul, player, team, item, card_type
0 2 Pedro Barcelona {'match_id': '32683310', 'event': u'Booking Pedro (Barcelona) is shown the yellow card for a bad foul.', 'formatted_time': u'84:43', 'sortable_time': 5083} yellow
----
In retrospect, it's a bit of a hacky way of moving a window of 3 items over the iterator of raw events and yielding a 'booking' event if the regex matches.
I thought there was probably a better way of doing this using https://docs.python.org/2/library/itertools.html[itertools] and http://stackoverflow.com/questions/6822725/rolling-or-sliding-window-iterator-in-python[indeed there is]!
First we need to create a window function which will return us an iterator of tuples containing consecutive events:
[source,python]
----
from itertools import tee, izip
def window(iterable, size):
iters = tee(iterable, size)
for i in xrange(1, size):
for each in iters[i:]:
next(each, None)
return izip(*iters)
----
Now let's have a look at how it works using a simple example:
[source,python]
----
>>> numbers = iter(range(0,10))
>>> for triple in window(numbers, 3):
print triple
(0, 1, 2)
(1, 2, 3)
(2, 3, 4)
(3, 4, 5)
(4, 5, 6)
(5, 6, 7)
(6, 7, 8)
(7, 8, 9)
----
Exactly what we need. Now let's plug the windows function into our cards function:
[source,python]
----
def cards(events):
events = iter(events)
for event_id, triple in enumerate(window(events, 3)):
item = triple[0]
event = triple[0]["event"]
booking = re.findall("Booking.*", event)
if booking:
player = re.findall("Booking([^(]*)", event)[0].strip()
team = re.findall("Booking([^(]*) \((.*)\)", event)[0][1]
associated_foul = [x for x in [(triple[1], event_id+1), (triple[2], event_id+2)]
if re.findall("Foul by.*", x[0]["event"])]
if associated_foul:
associated_foul = associated_foul[0]
yield event_id, associated_foul[1], player, team, item, "yellow"
else:
yield event_id, "", player, team, item, "yellow"
----
And finally check it still processes events correctly:
[source,python]
----
>>> for card_id, associated_foul, player, team, item, card_type in cards(iter(events)):
print card_id, associated_foul, player, team, item, card_type
0 2 Pedro Barcelona {'match_id': '32683310', 'event': u'Booking Pedro (Barcelona) is shown the yellow card for a bad foul.', 'formatted_time': u'84:43', 'sortable_time': 5083} yellow
---- | null | null | [
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] | python-look-ahead-multiple-elements-in-an-iteratorgenerator | https://markhneedham.com/blog/2015/05/28/python-look-ahead-multiple-elements-in-an-iteratorgenerator |
false | 2015-05-17 11:04:59 | Neo4j: Refactoring the BBC football live text fouls graph | [
"neo4j"
] | [
"neo4j"
] | Yesterday I wrote about http://www.markhneedham.com/blog/2015/05/16/neo4j-bbc-football-live-text-fouls-graph/[a Neo4j graph I've started building] which contains all the fouls committed in the Champions League game between Barcelona & Bayern Munich and surrounding meta data.
While adding other events into the graph I realised that I'd added some duplication in the model and the model could do with some refactoring to make it easier to use.
To recap, this is the model that we designed in the previous blog post:
image::{{<siteurl>}}/uploads/2015/05/2015-05-16_22-50-32.png[,465]
The duplication is on the left hand side of the model - we model a foul as being committed by one player against another and then hook the foul back into the match. By doing that we're not using the 'appearance' concept which links a player and a match together.
We can make the 'COMMITTED_IN_MATCH' relationship redundant by connecting the foul to appearance rather than to player. The match the foul was committed in can then be found by navigating through the appearance node.
This is what we want the graph to look like:
image::{{<siteurl>}}/uploads/2015/05/2015-05-17_10-40-44.png[2015 05 17 10 40 44,465]
We'll move towards this new model in 3 steps:
* Introduce the new structure alongside the existing one
* Rewrite our queries to use the new structure
* Remove the old structure
== Introducing the new structure
First up let's write a query to introduce the new structure.
[source,cypher]
----
match (foul:Foul)-[:COMMITTED_AGAINST]->(fouledPlayer),
(foul)<-[:COMMITTED_FOUL]-(foulingPlayer),
(foul)-[:COMMITTED_IN_MATCH]->(match:Match {id: "32683310"}),
(foulingPlayer)-[:MADE_APPEARANCE]-(foulingPlayerApp)-[:IN_MATCH]->(match),
(fouledPlayer)-[:MADE_APPEARANCE]-(fouledPlayerApp)-[:IN_MATCH]->(match)
MERGE (foul)<-[:COMMITTED_FOUL]-(foulingPlayerApp)
MERGE (foul)-[:COMMITTED_AGAINST]->(fouledPlayerApp)
----
Remember we're not going to delete the old structure yet so that's why there aren't any delete statements in here.
== Rewriting our queries
Now we need to update our queries to work against the new graph structure:
== Where do the fouls happen?
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:COMMITTED_IN_MATCH]-(foul)
RETURN foul.location AS location, COUNT(*) as fouls
ORDER BY fouls DESC
----
becomes
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:IN_MATCH]-()<-[]-(foul:Foul)
RETURN foul.location AS location, COUNT(*) as fouls
ORDER BY fouls DESC
----
== Who fouls the most?
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:COMMITTED_IN_MATCH]-(foul:Foul)<-[:COMMITTED_FOUL]-(fouler:Player)
RETURN fouler.name AS fouler, COUNT(*) as fouls
ORDER BY fouls DESC
LIMIT 10;
----
becomes
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:IN_MATCH]-(appearance)-[:COMMITTED_FOUL]->(foul:Foul),
(appearance)<-[:MADE_APPEARANCE]-(fouler)
RETURN fouler.name AS fouler, COUNT(*) as fouls
ORDER BY fouls DESC
LIMIT 10
----
== Who was fouled the most?
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:IN_MATCH]-(appearance)-[r:COMMITTED_FOUL]->(foul:Foul),
(appearance)<-[:MADE_APPEARANCE]-(fouler)
RETURN fouler.name AS fouler, COUNT(*) as fouls
ORDER BY fouls DESC
LIMIT 10
----
becomes
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:IN_MATCH]-(appearance)<-[:COMMITTED_AGAINST]->(foul:Foul),
(appearance)<-[:MADE_APPEARANCE]-(fouled)
RETURN fouled.name AS fouled, COUNT(*) as fouls
ORDER BY fouls DESC
LIMIT 10
----
== Who fouled who the most?
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:COMMITTED_IN_MATCH]-(foul:Foul)-[:COMMITTED_AGAINST]->(fouled:Player),
(foul)<-[:COMMITTED_FOUL]-(fouler:Player)
RETURN fouler.name AS fouler, fouled.name AS fouled, COUNT(*) as fouls
ORDER BY fouls DESC
LIMIT 10
----
becomes
[source,cypher]
----
match (match:Match {id: "32683310"}),
(match)<-[:IN_MATCH]-(fouledApp)<-[:COMMITTED_AGAINST]->(foul:Foul)<-[:COMMITTED_FOUL]-(foulerApp)-[:IN_MATCH]->(match),
(fouledApp)<-[:MADE_APPEARANCE]-(fouled),
(foulerApp)<-[:MADE_APPEARANCE]-(fouler)
RETURN fouler.name AS fouler, fouled.name AS fouled, COUNT(*) as fouls
ORDER BY fouls DESC
LIMIT 10;
----
== Which team fouled most?
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:COMMITTED_IN_MATCH]-()<-[:COMMITTED_FOUL]-(fouler),
(fouler)-[:MADE_APPEARANCE]-(app)-[:IN_MATCH]-(match),
(app)-[:FOR_TEAM]->(team)
RETURN team.name, COUNT(*) as fouls
ORDER BY fouls DESC
----
becomes
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:IN_MATCH]-(app:Appearance)-[:COMMITTED_FOUL]->(),
(app)-[:FOR_TEAM]->(team)
RETURN team.name, COUNT(*) as fouls
ORDER BY fouls DESC
----
== Worst fouler for each team
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:COMMITTED_IN_MATCH]-(foul)<-[:COMMITTED_FOUL]-(fouler),
(fouler)-[:MADE_APPEARANCE]-(app)-[:IN_MATCH]-(match),
(app)-[:FOR_TEAM]->(team)
WITH team, fouler, COUNT(*) AS fouls
ORDER BY team.name, fouls DESC
WITH team, COLLECT({fouler:fouler, fouls:fouls})[0] AS topFouler
RETURN team.name, topFouler.fouler.name, topFouler.fouls;
----
becomes
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:IN_MATCH]-(app:Appearance)-[:COMMITTED_FOUL]->(),
(app)-[:FOR_TEAM]->(team),
(fouler)-[:MADE_APPEARANCE]->(app)
WITH team, fouler, COUNT(*) AS fouls
ORDER BY team.name, fouls DESC
WITH team, COLLECT({fouler:fouler, fouls:fouls})[0] AS topFouler
RETURN team.name, topFouler.fouler.name, topFouler.fouls;
----
== Most fouled against for each team
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:COMMITTED_IN_MATCH]-(foul)<-[:COMMITTED_FOUL]-(fouler),
(fouler)-[:MADE_APPEARANCE]-(app)-[:IN_MATCH]-(match),
(app)-[:FOR_TEAM]->(team)
WITH team, fouler, COUNT(*) AS fouls
ORDER BY team.name, fouls DESC
WITH team, COLLECT({fouler:fouler, fouls:fouls})[0] AS topFouler
RETURN team.name, topFouler.fouler.name, topFouler.fouls
----
becomes
[source,cypher]
----
match (match:Match {id: "32683310"})<-[:IN_MATCH]-(app:Appearance)<-[:COMMITTED_AGAINST]->(),
(app)-[:FOR_TEAM]->(team),
(fouled)-[:MADE_APPEARANCE]->(app)
WITH team, fouled, COUNT(*) AS fouls
ORDER BY team.name, fouls DESC
WITH team, COLLECT({fouled:fouled, fouls:fouls})[0] AS topFouled
RETURN team.name, topFouled.fouled.name, topFouled.fouls
----
The early queries are made more complicated by the refactoring but the latter ones are slightly simpler. I think we need to hook some more events onto the appearance node to see whether this refactoring is worthwhile or not.
== Removing the old structure
Holding judgement for now, let's look at how we'd remove the old structure - the final step in this refactoring:
[source,cypher]
----
match (match:Match {id: "32683310"})<-[oldRel:COMMITTED_IN_MATCH]-(foul:Foul)
DELETE oldRel
----
[source,cypher]
----
match (player:Player)<-[oldRel:COMMITTED_AGAINST]-(foul:Foul)
DELETE oldRel
----
[source,cypher]
----
match (player:Player)-[oldRel:COMMITTED_FOUL]->(foul:Foul)
DELETE oldRel
----
Hopefully you can see how you'd go about refactoring your own graph if you realise the model isn't quite what you want.
Any questions/thoughts/suggestions let me know! | null | null | [
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] | neo4j-refactoring-the-bbc-football-live-text-fouls-graph | https://markhneedham.com/blog/2015/05/17/neo4j-refactoring-the-bbc-football-live-text-fouls-graph |
false | 2015-05-19 22:45:48 | Neo4j: Finding all shortest paths | [
"neo4j",
"cypher"
] | [
"neo4j"
] | One of the Cypher language features we show in Neo4j training courses is the http://neo4j.com/docs/milestone/query-match.html#_shortest_path[shortest path function] which allows you to find the shortest path in terms of number of relationships between two nodes.
Using the movie graph, which you can import via the ':play movies' command in the browser, we'll first create a 'KNOWS' relationship between any people that have appeared in the same movie:
[source,cypher]
----
MATCH (p1:Person)-[:ACTED_IN]->()<-[:ACTED_IN]-(p2:Person)
MERGE (p1)-[:KNOWS]-(p2)
----
Now that we've got that relationship we can easily find the shortest path between two people, say Tom Cruise and Tom Hanks:
[source,cypher]
----
MATCH (p1:Person {name: "Tom Hanks"}), (p2:Person {name: "Tom Cruise"}),
path = shortestpath((p1)-[:KNOWS*]-(p2))
RETURN path
----
image::{{<siteurl>}}/uploads/2015/05/graph-18.png[Graph 18,300]
That works pretty well but what if we want to find the longest shortest path between any two people in the graph? We can calculate it like this:
[source,cypher]
----
MATCH (p1:Person), (p2:Person),
path = shortestpath((p1)-[:KNOWS*]-(p2))
RETURN path
ORDER BY LENGTH(path) DESC
LIMIT 1
----
image::{{<siteurl>}}/uploads/2015/05/graph-19.png[Graph 19,500]
So that's 6 hops which is actually the http://en.wikipedia.org/wiki/Six_Degrees_of_Kevin_Bacon[Bacon number] - I expect we'd probably see a smaller maximum value if we imported all the movies.
And to round off the post what if we want to find the longest shortest path between the 10 people who acted in the most movies? We might start out with the following query which seems like it should do the job:
[source,cypher]
----
MATCH (p1:Person)-[:ACTED_IN]->()
WITH p1, COUNT(*) AS appearances
ORDER BY appearances DESC
LIMIT 10
WITH p1 AS p1, p1 AS p2
MATCH path = shortestpath((p1)-[:KNOWS*]-(p2))
RETURN path
ORDER BY LENGTH(path) DESC
LIMIT 1
----
*Unfortunately if we run that query we get no rows returned because 'p1' and 'p2' always refer to the same node.*
Instead we can calculate the shortest path between our hardest working people by creating a cross product using COLLECT and UNWIND:
[source,cypher]
----
MATCH (p1:Person)-[:ACTED_IN]->()
WITH p1, COUNT(*) AS appearances
ORDER BY appearances DESC
LIMIT 10
WITH COLLECT(p1) AS ps
UNWIND ps AS p1 UNWIND ps AS p2
MATCH path = shortestpath((p1)-[:KNOWS*]-(p2))
RETURN path
ORDER BY LENGTH(path) DESC
LIMIT 1
----
image::{{<siteurl>}}/uploads/2015/05/graph-20.png[Graph 20,400]
That's all for now! | null | null | [
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] | neo4j-finding-all-shortest-paths | https://markhneedham.com/blog/2015/05/19/neo4j-finding-all-shortest-paths |
false | 2015-05-26 07:03:36 | Neo4j: The foul revenge graph | [
"neo4j"
] | [
"neo4j"
] | Last week I was showing the http://www.markhneedham.com/blog/2015/05/16/neo4j-bbc-football-live-text-fouls-graph/[foul graph] to my colleague http://www.apcjones.com/[Alistair] who came up with the idea of running a 'foul revenge' query to find out which players gained revenge for a foul with one of their own later in them match.
Queries like this are very path centric and therefore work well in a graph. To recap, this is what the foul graph looks like:
image::{{<siteurl>}}/uploads/2015/05/2015-05-26_07-35-33.png[2015 05 26 07 35 33,598]
The first thing that we need to do is connect the fouls in a linked list based on time so that we can query their order more easily.
We can do this with the following query:
[source,cypher]
----
MATCH (foul:Foul)-[:COMMITTED_IN_MATCH]->(match)
WITH foul,match
ORDER BY match.id, foul.sortableTime
WITH match, COLLECT(foul) AS fouls
FOREACH(i in range(0, length(fouls) -2) |
FOREACH(foul1 in [fouls[i]] | FOREACH (foul2 in [fouls[i+1]] |
MERGE (foul1)-[:NEXT]->(foul2)
)));
----
This query collects fouls grouped by match and then adds a 'NEXT' relationship between adjacent fouls. The graph now looks like this:
image::{{<siteurl>}}/uploads/2015/05/2015-05-26_07-43-28.png[2015 05 26 07 43 28,598]
Now let's find the revenge foulers in the Bayern Munich vs Barcelona match. We're looking for the following pattern:
image::{{<siteurl>}}/uploads/2015/05/2015-05-26_07-55-45.png[2015 05 26 07 55 45,324]
This translates to the following cypher query:
[source,cypher]
----
match (foul1:Foul)-[:COMMITTED_AGAINST]->(app1)-[:COMMITTED_FOUL]->(foul2)-[:COMMITTED_AGAINST]->(app2)-[:COMMITTED_FOUL]->(foul1),
(player1)-[:MADE_APPEARANCE]->(app1), (player2)-[:MADE_APPEARANCE]->(app2),
(foul1)-[:COMMITTED_IN_MATCH]->(match:Match {id: "32683310"})<-[:COMMITTED_IN_MATCH]-(foul2)
WHERE (foul1)-[:NEXT*]->(foul2)
RETURN player2.name AS firstFouler, player1.name AS revengeFouler, foul1.time, foul1.location, foul2.time, foul2.location
----
I've added in a few extra parts to the pattern to pull out the players involved and to find the revenge foulers in a specific match - the Bayern Munich vs Barcelona Semi Final 2nd leg.
We end up with the following revenge fouls:
image::{{<siteurl>}}/uploads/2015/05/2015-05-26_00-05-48.png[2015 05 26 00 05 48,469]
We can see here that Dani Alves actually gains revenge on Bastian Schweinsteiger twice for a foul he made in the 10th minute.
If we tweak the query to the following we can get a visual representation of the revenge fouls as well:
[source,cypher]
----
match (foul1:Foul)-[:COMMITTED_AGAINST]->(app1)-[:COMMITTED_FOUL]->(foul2)-[:COMMITTED_AGAINST]->(app2)-[:COMMITTED_FOUL]->(foul1),
(player1)-[:MADE_APPEARANCE]->(app1), (player2)-[:MADE_APPEARANCE]->(app2),
(foul1)-[:COMMITTED_IN_MATCH]->(match:Match {id: "32683310"})<-[:COMMITTED_IN_MATCH]-(foul2),
(foul1)-[:NEXT*]->(foul2)
RETURN *
----
image::{{<siteurl>}}/uploads/2015/05/2015-05-23_15-23-22.png[2015 05 23 15 23 22,599]
At the moment I've restricted the revenge concept to single matches but I wonder whether it'd be more interesting to create a linked list of fouls which crosses matches between teams in the same season.
The https://github.com/mneedham/neo4j-bbc[code for all of this is on github] - the README is a bit sketchy at the moment but I'll be fixing that up soon. | null | null | [
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] | neo4j-the-foul-revenge-graph | https://markhneedham.com/blog/2015/05/26/neo4j-the-foul-revenge-graph |
false | 2015-05-21 06:14:32 | Python: UnicodeEncodeError: 'ascii' codec can't encode character u'\xfc' in position 11: ordinal not in range(128) | [
"python"
] | [
"Python"
] | I've been trying to write some Python code to extract the players and the team they represented in the Bayern Munich/Barcelona match into a CSV file and had much more difficulty than I expected.
I have some scraping code (which is beyond the scope of this article) which gives me a list of (player, team) pairs that I want to write to disk. The contents of the list is as follows:
[source,python]
----
$ python extract_players.py
(u'Sergio Busquets', u'Barcelona')
(u'Javier Mascherano', u'Barcelona')
(u'Jordi Alba', u'Barcelona')
(u'Bastian Schweinsteiger', u'FC Bayern M\xfcnchen')
(u'Dani Alves', u'Barcelona')
----
I started with the following script:
[source,python]
----
with open("data/players.csv", "w") as file:
writer = csv.writer(file, delimiter=",")
writer.writerow(["player", "team"])
for player, team in players:
print player, team, type(player), type(team)
writer.writerow([player, team])
----
And if I run that I'll see this error:
[source,bash]
----
$ python extract_players.py
...
Bastian Schweinsteiger FC Bayern München <type 'unicode'> <type 'unicode'>
Traceback (most recent call last):
File "extract_players.py", line 67, in <module>
writer.writerow([player, team])
UnicodeEncodeError: 'ascii' codec can't encode character u'\xfc' in position 11: ordinal not in range(128)
----
So it looks like the 'ü' in 'FC Bayern München' is causing us issues. Let's try and encode the teams to avoid this:
[source,python]
----
with open("data/players.csv", "w") as file:
writer = csv.writer(file, delimiter=",")
writer.writerow(["player", "team"])
for player, team in players:
print player, team, type(player), type(team)
writer.writerow([player, team.encode("utf-8")])
----
[source,bash]
----
$ python extract_players.py
...
Thomas Müller FC Bayern München <type 'unicode'> <type 'unicode'>
Traceback (most recent call last):
File "extract_players.py", line 70, in <module>
writer.writerow([player, team.encode("utf-8")])
UnicodeEncodeError: 'ascii' codec can't encode character u'\xfc' in position 8: ordinal not in range(128)
----
Now we've got the same issue with the 'ü' in Müller so let's encode the players too:
[source,python]
----
with open("data/players.csv", "w") as file:
writer = csv.writer(file, delimiter=",")
writer.writerow(["player", "team"])
for player, team in players:
print player, team, type(player), type(team)
writer.writerow([player.encode("utf-8"), team.encode("utf-8")])
----
[source,bash]
----
$ python extract_players.py
...
Gerard Piqué Barcelona <type 'str'> <type 'unicode'>
Traceback (most recent call last):
File "extract_players.py", line 70, in <module>
writer.writerow([player.encode("utf-8"), team.encode("utf-8")])
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 11: ordinal not in range(128)
----
Now we've got a problem with Gerard Piqué because that value has type string rather than unicode. Let's fix that:
[source,python]
----
with open("data/players.csv", "w") as file:
writer = csv.writer(file, delimiter=",")
writer.writerow(["player", "team"])
for player, team in players:
if isinstance(player, str):
player = unicode(player, "utf-8")
print player, team, type(player), type(team)
writer.writerow([player.encode("utf-8"), team.encode("utf-8")])
----
Et voila! All the players are now successfully written to the file.
An alternative approach is to http://stackoverflow.com/questions/21129020/how-to-fix-unicodedecodeerror-ascii-codec-cant-decode-byte[change the default encoding of the whole script] to be 'UTF-8', like so:
[source,python]
----
# encoding=utf8
import sys
reload(sys)
sys.setdefaultencoding('utf8')
with open("data/players.csv", "w") as file:
writer = csv.writer(file, delimiter=",")
writer.writerow(["player", "team"])
for player, team in players:
print player, team, type(player), type(team)
writer.writerow([player, team])
----
It took me a while to figure it out but finally the players are ready to go! | null | null | [
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] | python-unicodeencodeerror-ascii-codec-cant-encode-character-uxfc-in-position-11-ordinal-not-in-range128 | https://markhneedham.com/blog/2015/05/21/python-unicodeencodeerror-ascii-codec-cant-encode-character-uxfc-in-position-11-ordinal-not-in-range128 |
false | 2015-05-09 22:33:05 | R: Neo4j London meetup group - How many events do people come to? | [
"r-2",
"rstats"
] | [
"R"
] | Earlier this week the number of members in the http://www.meetup.com/graphdb-london/[Neo4j London meetup group] creeped over the 2,000 mark and I thought it'd be fun to re-explore the data that I previously imported into https://github.com/mneedham/neo4j-meetup[Neo4j].
How often do people come to meetups?
[source,r]
----
library(RNeo4j)
library(dplyr)
graph = startGraph("http://localhost:7474/db/data/")
query = "MATCH (g:Group {name: 'Neo4j - London User Group'})-[:HOSTED_EVENT]->(event)<-[:TO]-({response: 'yes'})<-[:RSVPD]-(profile)-[:HAS_MEMBERSHIP]->(membership)-[:OF_GROUP]->(g)
WHERE (event.time + event.utc_offset) < timestamp()
RETURN event.id, event.time + event.utc_offset AS eventTime, profile.id, membership.joined"
df = cypher(graph, query)
> df %>% head()
event.id eventTime profile.id membership.joined
1 20616111 1.309372e+12 6436797 1.307285e+12
2 20616111 1.309372e+12 12964956 1.307275e+12
3 20616111 1.309372e+12 14533478 1.307290e+12
4 20616111 1.309372e+12 10793775 1.307705e+12
5 24528711 1.311793e+12 10793775 1.307705e+12
6 29953071 1.314815e+12 10595297 1.308154e+12
----
[source,R]
----
byEventsAttended = df %>% count(profile.id)
> byEventsAttended %>% sample_n(10)
Source: local data frame [10 x 2]
profile.id n
1 128137932 2
2 126947632 1
3 98733862 2
4 20468901 11
5 48293132 5
6 144764532 1
7 95259802 1
8 14524524 3
9 80611852 2
10 134907492 2
----
Now let's visualise the number of people that have attended certain number of events:
[source,R]
----
ggplot(aes(x = n), data = byEventsAttended) +
geom_bar(binwidth = 1, fill = "Dark Blue") +
scale_y_continuous(breaks = seq(0,750,by = 50))
----
image::{{<siteurl>}}/uploads/2015/05/2015-05-09_01-15-02.png[2015 05 09 01 15 02,400]
Most people come to one meetup and then there's a long tail after that with fewer and fewer people coming to lots of meetups.
The chart has lots of blank space due to the sparseness of people on the right hand side. If we exclude any people who've attended more than 20 events we might get a more interesting visualisation:
[source,r]
----
ggplot(aes(x = n), data = byEventsAttended %>% filter(n <= 20)) +
geom_bar(binwidth = 1, fill = "Dark Blue") +
scale_y_continuous(breaks = seq(0,750,by = 50))
----
image::{{<siteurl>}}/uploads/2015/05/2015-05-09_01-15-36.png[2015 05 09 01 15 36,400]
https://twitter.com/_nicolemargaret[Nicole] suggested a more interesting visualisation would be a box plot so I decided to try that next:
[source,R]
----
ggplot(aes(x = "Attendees", y = n), data = byEventsAttended) +
geom_boxplot(fill = "grey80", colour = "Dark Blue") +
coord_flip()
----
image::{{<siteurl>}}/uploads/2015/05/2015-05-09_22-31-20.png[2015 05 09 22 31 20,400]
This visualisation really emphasises that the majority are between 1 and 3 and it's much less obvious how many values there are at the higher end. A quick check of the data with the +++<cite>+++summary+++</cite>+++ function reveals as much:
[source,R]
----
> summary(byEventsAttended$n)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 1.000 2.000 2.837 3.000 69.000
----
Now to figure out how to move that box plot a bit to the right :) | null | null | [
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] | r-neo4j-london-meetup-group-how-many-events-do-people-come-to | https://markhneedham.com/blog/2015/05/09/r-neo4j-london-meetup-group-how-many-events-do-people-come-to |
false | 2015-05-31 22:33:54 | Python: CSV writing - TypeError: 'builtin_function_or_method' object has no attribute '*getitem*' | [
"python"
] | [
"Python"
] | When I'm working in Python I often find myself writing to CSV files using the https://docs.python.org/2/library/csv.html[in built library] and every now and then make a mistake when calling writerow:
[source,python]
----
import csv
writer = csv.writer(file, delimiter=",")
writer.writerow["player", "team"]
----
This results in the following error message:
[source,text]
----
TypeError: 'builtin_function_or_method' object has no attribute '__getitem__'
----
The error message is a bit weird at first but it's basically saying that I've tried to do an http://stackoverflow.com/questions/13075632/typeerror-builtin-function-or-method-object-has-no-attribute-getitem[associative lookup on an object which doesn't support that operation].
The resolution is simply to include the appropriate parentheses instead of leaving them out!
[source,python]
----
writer.writerow(["player", "team"])
----
This one's for future Mark. | null | null | [
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] | python-csv-writing-typeerror-builtin_function_or_method-object-has-no-attribute-__getitem__ | https://markhneedham.com/blog/2015/05/31/python-csv-writing-typeerror-builtin_function_or_method-object-has-no-attribute-__getitem__ |
false | 2015-05-31 23:11:50 | R: Think Bayes Euro Problem | [
"r-2",
"rstats"
] | [
"R"
] | I've got back to working my way through http://www.greenteapress.com/thinkbayes/[Think Bayes] after a month's break and started out with the one euro coin problem in Chapter 4:
____
A statistical statement appeared in "`The Guardian" on Friday January 4, 2002: When spun on edge 250 times, a Belgian one-euro coin came up heads 140 times and tails 110. '`It looks very suspicious to me,`' said Barry Blight, a statistics lecturer at the London School of Economics. '`If the coin were unbiased, the chance of getting a result as extreme as that would be less than 7%.`' But do these data give evidence that the coin is biased rather than fair?
____
We're going to create a data frame with each row representing the probability that heads shows up that often. We need one row for each value between 0 (no heads) and 100 (all heads) and we'll start with the assumption that each value can be chosen equally (a uniform prior):
[source,r]
----
library(dplyr)
values = seq(0, 100)
scores = rep(1.0 / length(values), length(values))
df = data.frame(score = scores, value = values)
> df %>% sample_n(10)
score value
60 0.00990099 59
101 0.00990099 100
10 0.00990099 9
41 0.00990099 40
2 0.00990099 1
83 0.00990099 82
44 0.00990099 43
97 0.00990099 96
100 0.00990099 99
12 0.00990099 11
----
Now we need to feed in our observations. We need to create a vector containing 140 heads and 110 tails. The 'rep' function comes in handy here:
[source,r]
----
observations = c(rep("T", times = 110), rep("H", times = 140))
> observations
[1] "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T"
[29] "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T"
[57] "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T"
[85] "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "H" "H"
[113] "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H"
[141] "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H"
[169] "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H"
[197] "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H"
[225] "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H"
----
Now we need to iterate over each of the observations and update our data frame appropriately.
[source,r]
----
for(observation in observations) {
if(observation == "H") {
df = df %>% mutate(score = score * (value / 100.0))
} else {
df = df %>% mutate(score = score * (1.0 - (value / 100.0)))
}
}
df = df %>% mutate(weighted = score / sum(score))
----
Now that we've done that we can calculate the maximum likelihood, mean, median and credible interval. We'll create a 'percentile' function to help us out:
[source,r]
----
percentile = function(df, p) {
df %>% filter(cumsum(weighted) > p) %>% head(1) %>% select(value) %>% as.numeric
}
----
And now let's calculate the values:
[source,r]
----
# Maximum likelihood
> df %>% filter(weighted == max(weighted)) %>% select(value) %>% as.numeric
[1] 56
# Mean
> df %>% mutate(mean = value * weighted) %>% select(mean) %>% sum
[1] 55.95238
# Median
> percentile(df, 0.5)
[1] 56
# Credible Interval
percentage = 90
prob = (1 - percentage / 100.0) / 2
# lower
> percentile(df, prob)
[1] 51
# upper
> percentile(df, 1 - prob)
[1] 61
----
This all wraps up nicely into a function:
[source,r]
----
euro = function(values, priors, observations) {
df = data.frame(score = priors, value = values)
for(observation in observations) {
if(observation == "H") {
df = df %>% mutate(score = score * (value / 100.0))
} else {
df = df %>% mutate(score = score * (1.0 - (value / 100.0)))
}
}
return(df %>% mutate(weighted = score / sum(score)))
}
----
which we can call like so:
[source,r]
----
values = seq(0,100)
priors = rep(1.0 / length(values), length(values))
observations = c(rep("T", times = 110), rep("H", times = 140))
df = euro(values, priors, observations)
----
The next part of the problem requires us to change the prior distribution to be more weighted to values close to 50%. We can tweak the parameters we pass into the function accordingly:
[source,r]
----
values = seq(0,100)
priors = sapply(values, function(x) ifelse(x < 50, x, 100 - x))
priors = priors / sum(priors)
observations = c(rep("T", times = 110), rep("H", times = 140))
df = euro(values, priors, observations)
----
In fact even with the adjusted priors we still end up with the same posterior distribution:
[source,r]
----
> df %>% filter(weighted == max(weighted)) %>% select(value) %>% as.numeric
[1] 56
> df %>% mutate(mean = value * weighted) %>% select(mean) %>% sum
[1] 55.7435
> percentile(df, 0.5)
[1] 56
> percentile(df, 0.05)
[1] 51
> percentile(df, 0.95)
[1] 61
----
The book describes this phenemenom as follows:
____
This is an example of swamping the priors: with enough data, people who start with different priors will tend to converge on the same posterior.
____ | null | null | [
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] | r-think-bayes-euro-problem | https://markhneedham.com/blog/2015/05/31/r-think-bayes-euro-problem |
false | 2015-05-30 21:45:07 | Neo4j: The BBC Champions League graph | [
"neo4j"
] | [
"neo4j"
] | A couple of weekends ago I started scraping the BBC live text feed of the http://www.bbc.co.uk/sport/0/football/32683310[Bayern Munich/Barcelona match], initially starting out with just the fouls and building the foul graph.
I've spent a bit more time on it since then and have managed to model several other events as well including attempts, goals, cards and free kicks.
I started doing this just for the Bayern Munich/Barcelona match but realised it wasn't particularly difficult to extend this out and graph the events for every match in the Champions League 2014/2015.
To do this we first need to download the pages for each of the matches. I downloaded http://www.bbc.co.uk/sport/football/champions-league/results[this page] and wrote a simple Python script to get the appropriate URIs:
[source,python]
----
from bs4 import BeautifulSoup
from soupselect import select
import bs4
soup = BeautifulSoup(open("data/results", "r"))
matches = select(soup, "a.report")
for match in matches:
print "http://www.bbc.co.uk/%s" %(match.get("href"))
----
I then piped the output of running this script into wget:
[source,bash]
----
find_all_matches.py | xargs wget -O data/raw
----
It was relatively simple to update the scraping and import code to handle multiple matches. The whole process from end to end looks like this:
image::{{<siteurl>}}/uploads/2015/05/2015-05-29_23-27-56.png[2015 05 29 23 27 56,500]
Most of the code is in the 'scraping magic' phase where I go through all the events and pull out the appropriate elements that we can link together in the graph.
e.g. a freekick and foul event are typically adjacent so we'd look to pull out the two players involved, the type of card issues, the time of the incident and the match the event occurred in.
I used Python's http://www.crummy.com/software/BeautifulSoup/[Beautiful Soup library] for this task but there's no reason you couldn't use another set of tools.
The https://github.com/mneedham/neo4j-bbc[README page] shows how to create your own version of the graph but here's an overview of what the graph looks like using https://twitter.com/rvanbruggen[Rik's] http://neo4j.com/blog/rvb-2-2-meta-graph/[meta graph query]:
image::{{<siteurl>}}/uploads/2015/05/graph-21.png[Graph 21,600]
Here's a few of my favourite queries so far:
== Which player with more than 10 shots has the best conversion rate?
[source,cypher]
----
match (a:Attempt)<-[:HAD_ATTEMPT]-(app)<-[:MADE_APPEARANCE]-(player),
(app)-[:FOR_TEAM]-(team)
WITH player, COUNT(*) as times, COLLECT(a) AS attempts, team
WITH player, times, LENGTH([a in attempts WHERE a:Goal]) AS goals, team
WHERE times > 10
RETURN player.name, team.name, goals, times, (goals * 1.0 / times) AS conversionRate
ORDER BY conversionRate DESC
LIMIT 10
==> +------------------------------------------------------------------------------------+
==> | player.name | team.name | goals | times | conversionRate |
==> +------------------------------------------------------------------------------------+
==> | "Luiz Adriano" | "Shakhtar Donetsk" | 9 | 14 | 0.6428571428571429 |
==> | "Yacine Brahimi" | "FC Porto" | 5 | 13 | 0.38461538461538464 |
==> | "Mario Mandzukic" | "Atlético de Madrid" | 5 | 14 | 0.35714285714285715 |
==> | "Sergio Agüero" | "Manchester City" | 6 | 18 | 0.3333333333333333 |
==> | "Karim Benzema" | "Real Madrid" | 6 | 19 | 0.3157894736842105 |
==> | "Klaas-Jan Huntelaar" | "FC Schalke 04" | 5 | 16 | 0.3125 |
==> | "Neymar" | "Barcelona" | 9 | 29 | 0.3103448275862069 |
==> | "Thomas Müller" | "FC Bayern München" | 7 | 24 | 0.2916666666666667 |
==> | "Jackson Martínez" | "FC Porto" | 7 | 24 | 0.2916666666666667 |
==> | "Callum McGregor" | "Celtic" | 3 | 11 | 0.2727272727272727 |
==> +------------------------------------------------------------------------------------+
----
== Which players gained immediate revenge for a foul?
[source,cypher]
----
match (firstFoul:Foul)-[:COMMITTED_AGAINST]->(app1)<-[:MADE_APPEARANCE]-(revengeFouler),
(app1)-[:IN_MATCH]->(match), (firstFoulerApp)-[:COMMITTED_FOUL]->(firstFoul),
(app1)-[:COMMITTED_FOUL]->(revengeFoul)-[:COMMITTED_AGAINST]->(firstFoulerApp),
(firstFouler)-[:MADE_APPEARANCE]->(firstFoulerApp)
WHERE (firstFoul)-[:NEXT]->(revengeFoul)
RETURN firstFouler.name AS firstFouler, revengeFouler.name AS revengeFouler, firstFoul.time, revengeFoul.time, match.home + " vs " + match.away
==> +---------------------------------------------------------------------------------------------------------------------------------+
==> | firstFouler | revengeFouler | firstFoul.time | revengeFoul.time | match.home + " vs " + match.away |
==> +---------------------------------------------------------------------------------------------------------------------------------+
==> | "Derk Boerrigter" | "Jean Philippe Mendy" | "88:48" | "89:42" | "Celtic vs NK Maribor" |
==> | "Mario Suárez" | "Pajtim Kasami" | "27:17" | "32:38" | "Olympiakos vs Atlético de Madrid" |
==> | "Aleksandr Volodko" | "Casemiro" | "39:27" | "44:32" | "FC Porto vs BATE Borisov" |
==> | "Thomas Müller" | "Mario Fernandes" | "87:22" | "88:31" | "CSKA Moscow vs FC Bayern München" |
==> | "Vinicius" | "Marco Verratti" | "56:36" | "58:00" | "APOEL Nicosia vs Paris Saint Germain" |
==> | "Lasse Schöne" | "Dani Alves" | "84:08" | "86:18" | "Barcelona vs Ajax" |
==> | "Nick Viergever" | "Dani Alves" | "57:22" | "60:37" | "Barcelona vs Ajax" |
==> | "Nani" | "Atsuto Uchida" | "6:10" | "8:40" | "FC Schalke 04 vs Sporting Lisbon" |
==> | "Andreas Samaris" | "Yannick Ferreira-Carrasco" | "89:21" | "90:00 +4:21" | "Monaco vs Benfica" |
==> | "Simon Kroon" | "Guillherme Siqueira" | "84:05" | "90:00 +0:29" | "Atlético de Madrid vs Malmö FF" |
==> | "Mario Suárez" | "Isaac Thelin" | "32:02" | "38:47" | "Atlético de Madrid vs Malmö FF" |
==> | "Hakan Balta" | "Henrikh Mkhitaryan" | "62:09" | "64:14" | "Borussia Dortmund vs Galatasaray" |
==> | "Marco Reus" | "Selcuk Inan" | "36:17" | "44:03" | "Borussia Dortmund vs Galatasaray" |
==> | "Hakan Balta" | "Sven Bender" | "10:57" | "12:51" | "Borussia Dortmund vs Galatasaray" |
==> | "Vinicius" | "Edinson Cavani" | "87:56" | "90:00 +1:25" | "Paris Saint Germain vs APOEL Nicosia" |
==> | "Jackson Martínez" | "Carlos Gurpegi" | "64:55" | "66:17" | "Athletic Club vs FC Porto" |
==> | "Nani" | "Chinedu Obasi" | "1:30" | "4:47" | "Sporting Lisbon vs FC Schalke 04" |
==> | "Vitali Rodionov" | "Bruno Martins Indi" | "52:16" | "60:08" | "BATE Borisov vs FC Porto" |
==> | "Raheem Sterling" | "Behrang Safari" | "29:00" | "33:27" | "Liverpool vs FC Basel" |
==> | "Derlis González" | "Fábio Coentrão" | "52:55" | "57:59" | "FC Basel vs Real Madrid" |
==> | "Josip Drmic" | "Lisandro López" | "15:04" | "17:35" | "Benfica vs Bayer 04 Leverkusen" |
==> | "Fred" | "Bastian Schweinsteiger" | "6:04" | "9:28" | "Shakhtar Donetsk vs FC Bayern München" |
==> | "Alex Sandro" | "Derlis González" | "4:07" | "7:28" | "FC Basel vs FC Porto" |
==> | "Luca Zuffi" | "Ruben Neves" | "73:49" | "84:44" | "FC Porto vs FC Basel" |
==> | "Marco Verratti" | "Oscar" | "28:49" | "34:04" | "Chelsea vs Paris Saint Germain" |
==> | "Cristiano Ronaldo" | "Jesús Gámez" | "20:59" | "25:37" | "Real Madrid vs Atlético de Madrid" |
==> | "Bernardo Silva" | "Álvaro Morata" | "49:20" | "62:31" | "Monaco vs Juventus" |
==> | "Arturo Vidal" | "Fabinho" | "38:19" | "45:00" | "Monaco vs Juventus" |
==> +---------------------------------------------------------------------------------------------------------------------------------+
----
== Which players took the longest to gain revenge for a foul?
[source,cypher]
----
match (foul1:Foul)-[:COMMITTED_AGAINST]->(app1)-[:COMMITTED_FOUL]->(foul2)-[:COMMITTED_AGAINST]->(app2)-[:COMMITTED_FOUL]->(foul1),
(player1)-[:MADE_APPEARANCE]->(app1), (player2)-[:MADE_APPEARANCE]->(app2),
(foul1)-[:COMMITTED_IN_MATCH]->(match:Match)<-[:COMMITTED_IN_MATCH]-(foul2)
WHERE (foul1)-[:NEXT*]->(foul2)
WITH match, foul1, player1, player2, foul2 ORDER BY foul1.sortableTime, foul2.sortableTime
WITH match, foul1, player1, player2, COLLECT(foul2) AS revenge
WITH match, foul1, player1,player2, revenge[0] AS revengeFoul
RETURN player1.name, player2.name, foul1.time, revengeFoul.time, revengeFoul.sortableTime - foul1.sortableTime AS secondsWaited, match.home + " vs " + match.away AS match
ORDER BY secondsWaited DESC
LIMIT 5
==> +---------------------------------------------------------------------------------------------------------------------------+
==> | player1.name | player2.name | foul1.time | revengeFoul.time | secondsWaited | match |
==> +---------------------------------------------------------------------------------------------------------------------------+
==> | "Stefan Johansen" | "Ondrej Duda" | "1:30" | "82:11" | 4841 | "Legia Warsaw vs Celtic" |
==> | "Neymar" | "Vinicius" | "2:35" | "80:08" | 4653 | "Barcelona vs APOEL Nicosia" |
==> | "Jérémy Toulalan" | "Stefan Kießling" | "9:19" | "86:37" | 4638 | "Monaco vs Bayer 04 Leverkusen" |
==> | "Nabil Dirar" | "Domenico Criscito" | "6:32" | "82:39" | 4567 | "Zenit St Petersburg vs Monaco" |
==> | "Nabil Dirar" | "Eliseu" | "7:20" | "81:30" | 4450 | "Monaco vs Benfica" |
==> +---------------------------------------------------------------------------------------------------------------------------+
----
== Who's had the most shots?
[source,cypher]
----
match (team)<-[:FOR_TEAM]-(app)<-[appRel:MADE_APPEARANCE]-(player:Player)
optional match (a:Attempt)<-[att:HAD_ATTEMPT]-(app)
WITH player, COUNT( DISTINCT appRel) AS apps, COUNT(att) as times, COLLECT(a) AS attempts, team
WITH player,apps, times, LENGTH([a in attempts WHERE a:Goal]) AS goals, team
WHERE times > 10
RETURN player.name, team.name, apps, goals, times, (goals * 1.0 / times) AS conversionRate
ORDER BY times DESC
LIMIT 10
==> +-------------------------------------------------------------------------------------------+
==> | player.name | team.name | apps | goals | times | conversionRate |
==> +-------------------------------------------------------------------------------------------+
==> | "Cristiano Ronaldo" | "Real Madrid" | 12 | 10 | 69 | 0.14492753623188406 |
==> | "Lionel Messi" | "Barcelona" | 12 | 10 | 51 | 0.19607843137254902 |
==> | "Robert Lewandowski" | "FC Bayern München" | 12 | 6 | 43 | 0.13953488372093023 |
==> | "Carlos Tévez" | "Juventus" | 12 | 7 | 34 | 0.20588235294117646 |
==> | "Gareth Bale" | "Real Madrid" | 10 | 2 | 32 | 0.0625 |
==> | "Luis Suárez" | "Barcelona" | 9 | 6 | 30 | 0.2 |
==> | "Neymar" | "Barcelona" | 11 | 9 | 29 | 0.3103448275862069 |
==> | "Hakan Calhanoglu" | "Bayer 04 Leverkusen" | 8 | 2 | 29 | 0.06896551724137931 |
==> | "Edinson Cavani" | "Paris Saint Germain" | 8 | 6 | 27 | 0.2222222222222222 |
==> | "Alexis Sánchez" | "Arsenal" | 9 | 4 | 25 | 0.16 |
==> +-------------------------------------------------------------------------------------------+
----
Maybe you can think of some cooler ones? I'd love to see them. Grab the https://github.com/mneedham/neo4j-bbc[code from github] and give it a try. | null | null | [
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] | neo4j-the-bbc-champions-league-graph | https://markhneedham.com/blog/2015/05/30/neo4j-the-bbc-champions-league-graph |
false | 2015-05-24 23:51:25 | Python: Joining multiple generators/iterators | [
"python"
] | [
"Python"
] | In my previous blog post I described how I'd http://www.markhneedham.com/blog/2015/05/23/python-refactoring-to-iterator/[refactored some scraping code I've been working on to use iterators] and ended up with a function which returned a generator containing all the events for one BBC live text match:
[source,python]
----
match_id = "32683310"
events = extract_events("data/raw/%s" % (match_id))
>>> print type(events)
<type 'generator'>
----
The next thing I wanted to do is get the events for multiple matches which meant I needed to glue together multiple generators into one big generator.
itertools' +++<cite>+++https://docs.python.org/2/library/itertools.html#itertools.chain[chain]+++</cite>+++ function http://stackoverflow.com/questions/3211041/how-to-join-two-generators-in-python[does exactly what we want]:
____
itertools.chain(*iterables) Make an iterator that returns elements from the first iterable until it is exhausted, then proceeds to the next iterable, until all of the iterables are exhausted. Used for treating consecutive sequences as a single sequence.
____
First let's try it out on a collection of range generators:
[source,python]
----
import itertools
gens = [(n*2 for n in range(0, 3)), (n*2 for n in range(4,7))]
>>> gens
[<generator object <genexpr> at 0x10ff3b140>, <generator object <genexpr> at 0x10ff7d870>]
output = itertools.chain()
for gen in gens:
output = itertools.chain(output, gen)
----
Now if we iterate through 'output' we'd expect to see the multiples of 2 up to and including 12:
[source,python]
----
>>> for item in output:
... print item
...
0
2
4
8
10
12
----
Exactly as we expected! Our scraping code looks like this once we plug the chaining in:
[source,python]
----
matches = ["32683310", "32683303", "32384894", "31816155"]
raw_events = itertools.chain()
for match_id in matches:
raw_events = itertools.chain(raw_events, extract_events("data/raw/%s" % (match_id)))
----
'raw_events' now contains a single generator that we can iterate through and process the events for all matches. | null | null | [
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] | python-joining-multiple-generatorsiterators | https://markhneedham.com/blog/2015/05/24/python-joining-multiple-generatorsiterators |
false | 2015-05-23 10:14:38 | Python: Refactoring to iterator | [
"python"
] | [
"Python"
] | Over the last week I've been building a set of scripts to scrape the events from the http://www.bbc.co.uk/sport/0/football/32683310[Bayern Munich/Barcelona game] and I've ended up with a few hundred lines of nested for statements, if statements and mutated lists. I thought it was about time I did a bit of refactoring.
The following is a function which takes in a match file and spits out a collection of maps containing times & events.
[source,python]
----
import bs4
import re
from bs4 import BeautifulSoup
from soupselect import select
def extract_events(file):
match = open(file, 'r')
soup = BeautifulSoup(match.read())
all_events = []
for event in select(soup, 'div#live-text-commentary-wrapper div#live-text'):
for child in event.children:
if type(child) is bs4.element.Tag:
all_events.append(child.getText().strip())
for event in select(soup, 'div#live-text-commentary-wrapper div#more-live-text'):
for child in event.children:
if type(child) is bs4.element.Tag:
all_events.append(child.getText().strip())
timed_events = []
for i in range(0, len(all_events)):
event = all_events[i]
time = re.findall("\d{1,2}:\d{2}", event)
formatted_time = " +".join(time)
if time:
timed_events.append({'time': formatted_time, 'event': all_events[i+1]})
return timed_events
----
We call it like this:
[source,python]
----
match_id = "32683310"
for event in extract_events("data/%s" % (match_id))[:10]:
print event
----
The file we're loading is the http://www.bbc.co.uk/sport/0/football/32683310[Bayern Munich vs Barcelona match] HTML file which I have saved locally. After we've got that read into beautiful soup we locate the two divs on the page which contain the match events.
We then iterate over that list and create a new list containing (time, event) pairs which we return.
I think we should be able to get to our resulting collection without persisting an intermediate list, but first things first - let's remove the duplicated for loops:
[source,python]
----
def extract_events(file):
match = open(file, 'r')
soup = BeautifulSoup(match.read())
all_events = []
events = select(soup, 'div#live-text-commentary-wrapper div#live-text')
more_events = select(soup, 'div#live-text-commentary-wrapper div#more-live-text')
for event in events + more_events:
for child in event.children:
if type(child) is bs4.element.Tag:
all_events.append(child.getText().strip())
timed_events = []
for i in range(0, len(all_events)):
event = all_events[i]
time = re.findall("\d{1,2}:\d{2}", event)
formatted_time = " +".join(time)
if time:
timed_events.append({'time': formatted_time, 'event': all_events[i+1]})
return timed_events
----
The next step is to refactor towards using an iterator. After http://anandology.com/python-practice-book/iterators.html#generators[a bit of reading] I realised a generator would make life even easier.
I created a function which returned an iterator of the raw events and plugged that into the original function:
[source,python]
----
def raw_events(file):
match = open(file, 'r')
soup = BeautifulSoup(match.read())
events = select(soup, 'div#live-text-commentary-wrapper div#live-text')
more_events = select(soup, 'div#live-text-commentary-wrapper div#more-live-text')
for event in events + more_events:
for child in event.children:
if type(child) is bs4.element.Tag:
yield child.getText().strip()
def extract_events(file):
all_events = list(raw_events(file))
timed_events = []
for i in range(0, len(all_events)):
event = all_events[i]
time = re.findall("\d{1,2}:\d{2}", event)
formatted_time = " +".join(time)
if time:
timed_events.append({'time': formatted_time, 'event': all_events[i+1]})
return timed_events
----
If we run that function we still get the same output as before which is good. Now we need to work out how to clean up the second bit of the code which groups the appropriate rows together.
The goal is that 'extract_events' returns an iterator rather than a list - we need to figure out how to iterate over the output of 'raw_events' in such a way that when we find a 'time row' we can yield that and the row immediately after.
Luckily I found http://stackoverflow.com/questions/16789776/iterating-over-two-values-of-a-list-at-a-time-in-python[a Stack Overflow post] explaining that you can use the 'next' function inside an iterator to achieve this:
[source,python]
----
def extract_events(file):
events = raw_events(file)
for event in events:
time = re.findall("\d{1,2}:\d{2}", event)
formatted_time = " +".join(time)
if time:
yield {'time': formatted_time, 'event': next(events)}
----
It's not that much less code than the original function but I think it's an improvement. Any thoughts/tips to simplify it further are always welcome. | null | null | [
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] | python-refactoring-to-iterator | https://markhneedham.com/blog/2015/05/23/python-refactoring-to-iterator |
false | 2015-05-14 00:17:02 | R: ggplot - Displaying multiple charts with a for loop | [
"r-2",
"rstats",
"ggplot"
] | [
"neo4j",
"R"
] | Continuing with my analysis of the http://www.meetup.com/graphdb-london/[Neo4j London user group] I wanted to drill into some individual meetups and see the makeup of the people attending those meetups with respect to the cohort they belong to.
I started by writing a function which would take in an event ID and output a bar chart showing the number of people who attended that event from each cohort. <?p>
We can work out the cohort that a member belongs to by querying for the first event they attended.
Our query for the most recent http://www.meetup.com/graphdb-london/events/220750415/[Intro to graphs] session looks like this: ~~~r library(RNeo4j) graph = startGraph("http://127.0.0.1:7474/db/data/") eventId = "220750415" query = "match (g:Group {name: 'Neo4j - London User Group'})-[:HOSTED_EVENT]\-> (e {id: \{id}})\<-[:TO]-(rsvp {response: 'yes'})\<-[:RSVPD]-(person) WITH rsvp, person MATCH (person)-[:RSVPD]\->(otherRSVP) WITH person, rsvp, otherRSVP ORDER BY person.id, otherRSVP.time WITH person, rsvp, COLLECT(otherRSVP)[0] AS earliestRSVP return rsvp.time, earliestRSVP.time, person.id" df = cypher(graph, query, id= eventId) > df %>% sample_n(10) rsvp.time earliestRSVP.time person.id 18 1.430819e+12 1.392726e+12 130976662 95 1.430069e+12 1.430069e+12 10286388 79 1.429035e+12 1.429035e+12 38344282 64 1.428108e+12 1.412935e+12 153473172 73 1.429513e+12 1.398236e+12 143322942 19 1.430389e+12 1.430389e+12 129261842 37 1.429643e+12 1.327603e+12 9750821 49 1.429618e+12 1.429618e+12 184325696 69 1.430781e+12 1.404554e+12 67485912 1 1.430929e+12 1.430146e+12 185405773 ~~~
We're not doing anything too clever here, just using a couple of WITH clauses to order RSVPs so we can get the earlier one for each person.
Once we've done that we'll tidy up the data frame so that it contains columns containing the cohort in which the member attended their first event: ~~~r timestampToDate \<- function(x) as.POSIXct(x / 1000, origin="1970-01-01", tz = "GMT") df$time = timestampToDate(df$rsvp.time) df$date = format(as.Date(df$time), "%Y-%m") df$earliestTime = timestampToDate(df$earliestRSVP.time) df$earliestDate = format(as.Date(df$earliestTime), "%Y-%m") > df %>% sample_n(10) rsvp.time earliestRSVP.time person.id time date earliestTime earliestDate 47 1.430697e+12 1.430697e+12 186893861 2015-05-03 23:47:11 2015-05 2015-05-03 23:47:11 2015-05 44 1.430924e+12 1.430924e+12 186998186 2015-05-06 14:49:44 2015-05 2015-05-06 14:49:44 2015-05 85 1.429611e+12 1.422378e+12 53761842 2015-04-21 10:13:46 2015-04 2015-01-27 16:56:02 2015-01 14 1.430125e+12 1.412690e+12 7994846 2015-04-27 09:01:58 2015-04 2014-10-07 13:57:09 2014-10 29 1.430035e+12 1.430035e+12 37719672 2015-04-26 07:59:03 2015-04 2015-04-26 07:59:03 2015-04 12 1.430855e+12 1.430855e+12 186968869 2015-05-05 19:38:10 2015-05 2015-05-05 19:38:10 2015-05 41 1.428917e+12 1.422459e+12 133623562 2015-04-13 09:20:07 2015-04 2015-01-28 15:37:40 2015-01 87 1.430927e+12 1.430927e+12 185155627 2015-05-06 15:46:59 2015-05 2015-05-06 15:46:59 2015-05 62 1.430849e+12 1.430849e+12 186965212 2015-05-05 17:56:23 2015-05 2015-05-05 17:56:23 2015-05 8 1.430237e+12 1.425567e+12 184979500 2015-04-28 15:58:23 2015-04 2015-03-05 14:45:40 2015-03 ~~~ Now that we've got that we can group by the earliestDate cohort and then create a bar chart: ~~~r byCohort = df %>% count(earliestDate) ggplot(aes(x= earliestDate, y = n), data = byCohort) + geom_bar(stat="identity", fill = "dark blue") + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=1)) ~~~
image::{{<siteurl>}}/uploads/2015/05/2015-05-13_00-30-59.png[2015 05 13 00 30 59,400]
This is good and gives us the insight that most of the members attending this version of intro to graphs just joined the group. The event was on 7th April and most people joined in March which makes sense.
Let's see if that trend continues over the previous two years. To do this we need to create a for loop which goes over all the Intro to Graphs events and then outputs a chart for each one.
First I pulled out the code above into a function: ~~~r plotEvent = function(eventId) { query = "match (g:Group {name: 'Neo4j - London User Group'})-[:HOSTED_EVENT]\-> (e {id: \{id}})\<-[:TO]-(rsvp {response: 'yes'})\<-[:RSVPD]-(person) WITH rsvp, person MATCH (person)-[:RSVPD]\->(otherRSVP) WITH person, rsvp, otherRSVP ORDER BY person.id, otherRSVP.time WITH person, rsvp, COLLECT(otherRSVP)[0] AS earliestRSVP return rsvp.time, earliestRSVP.time, person.id" df = cypher(graph, query, id= eventId) df$time = timestampToDate(df$rsvp.time) df$date = format(as.Date(df$time), "%Y-%m") df$earliestTime = timestampToDate(df$earliestRSVP.time) df$earliestDate = format(as.Date(df$earliestTime), "%Y-%m") byCohort = df %>% count(earliestDate) ggplot(aes(x= earliestDate, y = n), data = byCohort) + geom_bar(stat="identity", fill = "dark blue") + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=1)) } ~~~
We'd call it like this for the Intro to graphs meetup: ~~~r > plotEvent("220750415") ~~~
Next I tweaked the code to look up all Into to graphs events and then loop through and output a chart for each event: ~~~r events = cypher(graph, "match (e:Event {name: 'Intro to Graphs'}) RETURN e.id ORDER BY e.time") for(event in events$n) { plotEvent(as.character(event)) } ~~~
Unfortunately that doesn't print anything at all which we can fix by http://stackoverflow.com/questions/9315611/grid-of-multiple-ggplot2-plots-which-have-been-made-in-a-for-loop[storing our plots in a list] and then displaying it afterwards: ~~~R library(gridExtra) p = list() for(i in 1:count(events)$n) { event = events[i, 1] p[[i]] = plotEvent(as.character(event)) } do.call(grid.arrange,p) ~~~
image::{{<siteurl>}}/uploads/2015/05/2015-05-14_00-57-10.png[2015 05 14 00 57 10,599]
This visualisation is probably better without any of the axis so let's update the function to scrap those. We'll also add the date of the event at the top of each chart which will require a slight tweak of the query: ~~~R plotEvent = function(eventId) { query = "match (g:Group {name: 'Neo4j - London User Group'})-[:HOSTED_EVENT]\-> (e {id: \{id}})\<-[:TO]-(rsvp {response: 'yes'})\<-[:RSVPD]-(person) WITH e,rsvp, person MATCH (person)-[:RSVPD]\->(otherRSVP) WITH e,person, rsvp, otherRSVP ORDER BY person.id, otherRSVP.time WITH e, person, rsvp, COLLECT(otherRSVP)[0] AS earliestRSVP return rsvp.time, earliestRSVP.time, person.id, e.time" df = cypher(graph, query, id= eventId) df$time = timestampToDate(df$rsvp.time) df$eventTime = timestampToDate(df$e.time) df$date = format(as.Date(df$time), "%Y-%m") df$earliestTime = timestampToDate(df$earliestRSVP.time) df$earliestDate = format(as.Date(df$earliestTime), "%Y-%m") byCohort = df %>% count(earliestDate) ggplot(aes(x= earliestDate, y = n), data = byCohort) + geom_bar(stat="identity", fill = "dark blue") + theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) + labs(title = df$eventTime[1]) } ~~~
image::{{<siteurl>}}/uploads/2015/05/2015-05-14_01-08-54.png[2015 05 14 01 08 54,599]
I think this makes it a bit easier to read although I've made the mistake of not having all the charts representing the same scale - one to fix for next time.
We started doing the intro to graphs sessions less frequently towards the end of last year so my hypothesis was that we'd see a range of people from different cohorts RSVPing for them but that doesn't seem to be the case. Instead it's very dominated by people signing up close to the event. | null | null | [
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] | r-ggplot-displaying-multiple-charts-with-a-for-loop | https://markhneedham.com/blog/2015/05/14/r-ggplot-displaying-multiple-charts-with-a-for-loop |
false | 2015-02-20 22:42:59 | Python/scikit-learn: Detecting which sentences in a transcript contain a speaker | [
"python",
"scikit-learn"
] | [
"Python"
] | Over the past couple of months I've been playing around with https://github.com/mneedham/neo4j-himym/blob/master/data/import/speakers.csv[How I met your mother transcripts] and the most recent thing I've been working on is how to extract the speaker for a particular sentence.
This initially seemed like a really simple problem as most of the initial sentences I looked at weere structured like this:
[source,text]
----
<speaker>: <sentence>
----
If there were all in that format then we could write a simple regular expression and then move on but unfortunately they aren't. We could probably write a more complex regex to pull out the speaker but I thought it'd be fun to see if I could train a model to work it out instead.
The approach I've taken is derived from http://www.nltk.org/book/ch06.html[an example in the NLTK book].
The first problem with this approach was that I didn't have any labelled data to work with so I wrote a little web application that made it easy for me to train chunks of sentences at a time:
image::{{<siteurl>}}/uploads/2015/02/2015-02-20_00-44-38.png[2015 02 20 00 44 38,600]
I stored the https://github.com/mneedham/neo4j-himym/blob/master/data/import/trained_sentences.json[trained words in a JSON file]. Each entry looks like this:
[source,python]
----
import json
with open("data/import/trained_sentences.json", "r") as json_file:
json_data = json.load(json_file)
>>> json_data[0]
{u'words': [{u'word': u'You', u'speaker': False}, {u'word': u'ca', u'speaker': False}, {u'word': u"n't", u'speaker': False}, {u'word': u'be', u'speaker': False}, {u'word': u'friends', u'speaker': False}, {u'word': u'with', u'speaker': False}, {u'word': u'Robin', u'speaker': False}, {u'word': u'.', u'speaker': False}]}
>>> json_data[1]
{u'words': [{u'word': u'Robin', u'speaker': True}, {u'word': u':', u'speaker': False}, {u'word': u'Well', u'speaker': False}, {u'word': u'...', u'speaker': False}, {u'word': u'it', u'speaker': False}, {u'word': u"'s", u'speaker': False}, {u'word': u'a', u'speaker': False}, {u'word': u'bit', u'speaker': False}, {u'word': u'early', u'speaker': False}, {u'word': u'...', u'speaker': False}, {u'word': u'but', u'speaker': False}, {u'word': u'...', u'speaker': False}, {u'word': u'of', u'speaker': False}, {u'word': u'course', u'speaker': False}, {u'word': u',', u'speaker': False}, {u'word': u'I', u'speaker': False}, {u'word': u'might', u'speaker': False}, {u'word': u'consider', u'speaker': False}, {u'word': u'...', u'speaker': False}, {u'word': u'I', u'speaker': False}, {u'word': u'moved', u'speaker': False}, {u'word': u'here', u'speaker': False}, {u'word': u',', u'speaker': False}, {u'word': u'let', u'speaker': False}, {u'word': u'me', u'speaker': False}, {u'word': u'think', u'speaker': False}, {u'word': u'.', u'speaker': False}]}
----
Each word in the sentence is represented by a JSON object which also indicates if that word was a speaker in the sentence.
== Feature selection
Now that I've got some trained data to work with I needed to choose which features I'd use to train my model.
One of the most obvious indicators that a word is the speaker in the sentence is that the next word is ':' so 'next word' can be a feature. I also went with 'previous word' and the word itself for my first cut.
This is the function I wrote to convert a word in a sentence into a set of features:
[source,python]
----
def pos_features(sentence, i):
features = {}
features["word"] = sentence[i]
if i == 0:
features["prev-word"] = "<START>"
else:
features["prev-word"] = sentence[i-1]
if i == len(sentence) - 1:
features["next-word"] = "<END>"
else:
features["next-word"] = sentence[i+1]
return features
----
Let's try a couple of examples:
[source,python]
----
import nltk
>>> pos_features(nltk.word_tokenize("Robin: Hi Ted, how are you?"), 0)
{'prev-word': '<START>', 'word': 'Robin', 'next-word': ':'}
>>> pos_features(nltk.word_tokenize("Robin: Hi Ted, how are you?"), 5)
{'prev-word': ',', 'word': 'how', 'next-word': 'are'}
----
Now let's run that function over our full set of labelled data:
[source,python]
----
with open("data/import/trained_sentences.json", "r") as json_file:
json_data = json.load(json_file)
tagged_sents = []
for sentence in json_data:
tagged_sents.append([(word["word"], word["speaker"]) for word in sentence["words"]])
featuresets = []
for tagged_sent in tagged_sents:
untagged_sent = nltk.tag.untag(tagged_sent)
for i, (word, tag) in enumerate(tagged_sent):
featuresets.append( (pos_features(untagged_sent, i), tag) )
----
Here's a sample of the contents of +++<cite>+++featuresets+++</cite>+++:
[source,python]
----
>>> featuresets[:5]
[({'prev-word': '<START>', 'word': u'You', 'next-word': u'ca'}, False), ({'prev-word': u'You', 'word': u'ca', 'next-word': u"n't"}, False), ({'prev-word': u'ca', 'word': u"n't", 'next-word': u'be'}, False), ({'prev-word': u"n't", 'word': u'be', 'next-word': u'friends'}, False), ({'prev-word': u'be', 'word': u'friends', 'next-word': u'with'}, False)]
----
It's nearly time to train our model, but first we need to split out labelled data into training and test sets so we can see how well our model performs on data it hasn't seen before. sci-kit learn has http://stackoverflow.com/questions/3674409/numpy-how-to-split-partition-a-dataset-array-into-training-and-test-datasets[a function that does this for us]:
[source,python]
----
from sklearn.cross_validation import train_test_split
train_data,test_data = train_test_split(featuresets, test_size=0.20, train_size=0.80)
>>> len(train_data)
9480
>>> len(test_data)
2370
----
Now let's train our model. I decided to try out Naive Bayes and Decision tree models to see how they got on:
[source,python]
----
>>> classifier = nltk.NaiveBayesClassifier.train(train_data)
>>> print nltk.classify.accuracy(classifier, test_data)
0.977215189873
>>> classifier = nltk.DecisionTreeClassifier.train(train_data)
>>> print nltk.classify.accuracy(classifier, test_data)
0.997046413502
----
It looks like both are doing a good job here with the decision tree doing slightly better. One thing to keep in mind is that most of the sentences we've trained at in the form '+++<speaker>+++:+++<sentence>+++' and we can get those correct with a simple regex so we should expect the accuracy to be very high. </p>
If we explore the internals of the decision tree we'll see that it's massively overfitting which makes sense given our small training data set and the repetitiveness of the data: ~~~python >>> print(classifier.pseudocode(depth=2)) if next-word == u'!': return False if next-word == u'$': return False \... if next-word == u"'s": return False if next-word == u"'ve": return False if next-word == u'(': if word == u'!': return False \... if next-word == u'*': return False if next-word == u'*****': return False if next-word == u',': if word == u"''": return False \... if next-word == u'--': return False if next-word == u'.': return False if next-word == u'\...': \... if word == u'who': return False if word == u'you': return False if next-word == u'/i': return False if next-word == u'1': return True \... if next-word == u':': if prev-word == u"'s": return True if prev-word == u',': return False if prev-word == u'\...': return False if prev-word == u'2030': return True if prev-word == '+++<START>+++': return True if prev-word == u'?': return False \... if next-word == u'\u266a\u266a': return False ~~~
One update I may make to the features is to include the part of speech of the word rather than its actual value to see if that makes the model a bit more general. Another option is to train a bunch of decision trees against a subset of the data and build an ensemble/random forest of those trees.
Once I've got a working 'speaker detector' I want to then go and *work out who the likely speaker is* for the sentences which don't contain a speaker. The plan is to calculate the word distributions of the speakers from sentences I do have and then calculate the probability that they spoke the unlabelled sentences.
This might not work perfectly as there could be new characters in those episodes but hopefully we can come up with something decent.
The https://github.com/mneedham/neo4j-himym/blob/master/scripts/detect_speaker.py[full code for this example is on github] if you want to have a play with it.
Any suggestions for improvements are always welcome in the comments.+++</START>++++++</sentence>++++++</speaker>+++ | null | null | [
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] | pythonscikit-learn-detecting-which-sentences-in-a-transcript-contain-a-speaker | https://markhneedham.com/blog/2015/02/20/pythonscikit-learn-detecting-which-sentences-in-a-transcript-contain-a-speaker |
false | 2015-02-27 00:49:54 | R/ggplot: Controlling X axis order | [
"r-2",
"ggplot"
] | [
"R"
] | As part of a talk I gave at the Neo4j London meetup earlier this week I wanted to show how you could build a simple chart showing the number of friends that different actors had using the ggplot library.
I started out with the following code:
[source,r]
----
df = read.csv("/tmp/friends.csv")
top = df %>% head(20)
ggplot(aes(x = p.name, y = colleagues), data = top) +
geom_bar(fill = "dark blue", stat = "identity")
----
The https://gist.github.com/mneedham/070d82a1e2aa031f42e6[friends CSV file is available as a gist] if you want to reproduce the chart. This is how the chart renders:
image::{{<siteurl>}}/uploads/2015/02/2015-02-27_00-41-08.png[2015 02 27 00 41 08,600]
It's in a fairly arbitrary order when it would be quite cool if we could get the most popular people to show from left to right.
I had the people's names in the correct order in the data frame but annoyingly it was then sorting them into alphabetical order. Luckily I came across the http://stackoverflow.com/questions/3253641/how-to-change-the-order-of-a-discrete-x-scale-in-ggplot[by using the +++<cite>+++scale_x_discrete+++</cite>+++ function] which does exactly what I needed.
If we pass in the list of names to that function we get the chart we desire:
[source,r]
----
ggplot(aes(x = p.name, y = colleagues), data = top) +
geom_bar(fill = "dark blue", stat = "identity") +
scale_x_discrete(limits= top$p.name)
----
image::{{<siteurl>}}/uploads/2015/02/2015-02-27_00-45-01.png[2015 02 27 00 45 01,600] | null | null | [
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] | rggplot-controlling-x-axis-order | https://markhneedham.com/blog/2015/02/27/rggplot-controlling-x-axis-order |
false | 2015-02-11 07:09:25 | R: Weather vs attendance at NoSQL meetups | [
"r-2",
"rstats"
] | [
"R"
] | A few weeks ago I came across a tweet by https://twitter.com/seanjtaylor[Sean Taylor] asking for a weather data set with a few years worth of recording and I was surprised to learn that R already has such a thing - the http://cran.r-project.org/web/packages/weatherData/index.html[weatherData] package.
+++
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Winner is: <a href="https://twitter.com/UTVilla?ref_src=twsrc%5Etfw">@UTVilla</a>! <br>library(weatherData)<br>df <- getWeatherForYear("SFO", 2013)<br>ggplot(df, aes(x=Date, y = Mean_TemperatureF)) + geom_line()</p>— Sean J. Taylor (@seanjtaylor) <a href="https://twitter.com/seanjtaylor/status/558073466419941378?ref_src=twsrc%5Etfw">January 22, 2015</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
+++
weatherData provides a thin veneer around the http://www.wunderground.com/[wunderground] API and was exactly what I'd been looking for to compare meetup at London's NoSQL against weather conditions that day.
The first step was to download the appropriate weather recordings and save them to a CSV file so I wouldn't have to keep calling the API.
I thought I may as well download all the recordings available to me and wrote the following code to make that happen:
[source,r]
----
library(weatherData)
# London City Airport
getDetailedWeatherForYear = function(year) {
getWeatherForDate("LCY",
start_date= paste(sep="", year, "-01-01"),
end_date = paste(sep="", year, "-12-31"),
opt_detailed = FALSE,
opt_all_columns = TRUE)
}
df = rbind(getDetailedWeatherForYear(2011),
getDetailedWeatherForYear(2012),
getDetailedWeatherForYear(2013),
getDetailedWeatherForYear(2014),
getWeatherForDate("LCY", start_date="2015-01-01",
end_date = "2015-01-25",
opt_detailed = FALSE,
opt_all_columns = TRUE))
----
I then saved that to a CSV file:
[source,r]
----
write.csv(df, 'weather/temp_data.csv', row.names = FALSE)
----
[source,bash]
----
"Date","GMT","Max_TemperatureC","Mean_TemperatureC","Min_TemperatureC","Dew_PointC","MeanDew_PointC","Min_DewpointC","Max_Humidity","Mean_Humidity","Min_Humidity","Max_Sea_Level_PressurehPa","Mean_Sea_Level_PressurehPa","Min_Sea_Level_PressurehPa","Max_VisibilityKm","Mean_VisibilityKm","Min_VisibilitykM","Max_Wind_SpeedKm_h","Mean_Wind_SpeedKm_h","Max_Gust_SpeedKm_h","Precipitationmm","CloudCover","Events","WindDirDegrees"
2011-01-01,"2011-1-1",7,6,4,5,3,1,93,85,76,1027,1025,1023,10,9,3,14,10,NA,0,7,"Rain",312
2011-01-02,"2011-1-2",4,3,2,1,0,-1,87,81,75,1029,1028,1027,10,10,10,11,8,NA,0,7,"",321
2011-01-03,"2011-1-3",4,2,1,0,-2,-5,87,74,56,1028,1024,1019,10,10,10,8,5,NA,0,6,"Rain-Snow",249
2011-01-04,"2011-1-4",6,3,1,3,1,-1,93,83,65,1019,1013,1008,10,10,10,21,6,NA,0,5,"Rain",224
2011-01-05,"2011-1-5",8,7,5,6,3,0,93,80,61,1008,1000,994,10,9,4,26,16,45,0,4,"Rain",200
2011-01-06,"2011-1-6",7,4,3,6,3,1,93,90,87,1002,996,993,10,9,5,13,6,NA,0,5,"Rain",281
2011-01-07,"2011-1-7",11,6,2,9,5,2,100,91,82,1003,999,996,10,7,2,24,11,NA,0,5,"Rain-Snow",124
2011-01-08,"2011-1-8",11,7,4,8,4,-1,87,77,65,1004,997,987,10,10,5,39,23,50,0,5,"Rain",230
2011-01-09,"2011-1-9",7,4,3,1,0,-1,87,74,57,1018,1012,1004,10,10,10,24,16,NA,0,NA,"",242
----
If we want to read that back in future we can do so with the following code:
[source,R]
----
weather = read.csv("weather/temp_data.csv")
weather$Date = as.POSIXct(weather$Date)
> weather %>% sample_n(10) %>% select(Date, Min_TemperatureC, Mean_TemperatureC, Max_TemperatureC)
Date Min_TemperatureC Mean_TemperatureC Max_TemperatureC
1471 2015-01-10 5 9 14
802 2013-03-12 -2 1 4
1274 2014-06-27 14 18 22
848 2013-04-27 5 8 10
832 2013-04-11 6 8 10
717 2012-12-17 6 7 9
1463 2015-01-02 6 9 13
1090 2013-12-25 4 6 7
560 2012-07-13 15 18 20
1230 2014-05-14 9 14 19
----
The next step was to bring the weather data together with the meetup attendance data that I already had.
For simplicity's sake I've got those saved in a CSV file as we can just read those in as well:
[source,r]
----
timestampToDate <- function(x) as.POSIXct(x / 1000, origin="1970-01-01", tz = "GMT")
events = read.csv("events.csv")
events$eventTime = timestampToDate(events$eventTime)
> events %>% sample_n(10) %>% select(event.name, rsvps, eventTime)
event.name rsvps eventTime
36 London Office Hours - Old Street 10 2012-01-18 17:00:00
137 Enterprise Search London 34 2011-05-23 18:15:00
256 MarkLogic User Group London: Jim Fuller 40 2014-04-29 18:30:00
117 Neural Networks and Data Science 171 2013-03-28 18:30:00
210 London Office Hours - Old Street 3 2011-09-15 17:00:00
443 July social! 12 2014-07-14 19:00:00
322 Intro to Graphs 39 2014-09-03 18:30:00
203 Vendor focus: Amazon CloudSearch 24 2013-05-16 17:30:00
17 Neo4J Tales from the Trenches: A Recommendation Engine Case Study 12 2012-04-25 18:30:00
55 London Office Hours 10 2013-09-18 17:00:00
----
Now that we've got our two datasets ready we can plot a simple chart of the average attendance and temperature grouped by month:
[source,r]
----
byMonth = events %>%
mutate(month = factor(format(eventTime, "%B"), levels=month.name)) %>%
group_by(month) %>%
summarise(events = n(),
count = sum(rsvps)) %>%
mutate(ave = count / events) %>%
arrange(desc(ave))
averageTemperatureByMonth = weather %>%
mutate(month = factor(format(Date, "%B"), levels=month.name)) %>%
group_by(month) %>%
summarise(aveTemperature = mean(Mean_TemperatureC))
g1 = ggplot(aes(x = month, y = aveTemperature, group=1), data = averageTemperatureByMonth) +
geom_line( ) +
ggtitle("Temperature by month")
g2 = ggplot(aes(x = month, y = count, group=1), data = byMonth) +
geom_bar(stat="identity", fill="dark blue") +
ggtitle("Attendance by month")
library(gridExtra)
grid.arrange(g1,g2, ncol = 1)
----
image::{{<siteurl>}}/uploads/2015/02/2015-02-09_20-32-50.png[2015 02 09 20 32 50,600]
We can see a rough inverse correlation between the temperature and attendance, particularly between April and August - as the temperature increases, total attendance decreases.
But what about if we compare at a finer level of granularity such as a specific date? We can do that by adding a 'day' column to our events data frame and merging it with the weather one:
[source,r]
----
byDay = events %>%
mutate(day = as.Date(as.POSIXct(eventTime))) %>%
group_by(day) %>%
summarise(events = n(),
count = sum(rsvps)) %>%
mutate(ave = count / events) %>%
arrange(desc(ave))
weather = weather %>% mutate(day = Date)
merged = merge(weather, byDay, by = "day")
----
Now we can plot the attendance vs the mean temperature for individual days:
[source,R]
----
ggplot(aes(x =count, y = Mean_TemperatureC,group = day), data = merged) +
geom_point()
----
image::{{<siteurl>}}/uploads/2015/02/2015-02-10_07-21-24.png[2015 02 10 07 21 24,600]
Interestingly there now doesn't seem to be any correlation between the temperature and attendance. We can confirm our suspicions by running a correlation:
[source,r]
----
> cor(merged$count, merged$Mean_TemperatureC)
[1] 0.008516294
----
Not even 1% correlation between the values! One way we could confirm that non correlation is to plot the average temperature against the average attendance rather than total attendance:
[source,r]
----
g1 = ggplot(aes(x = month, y = aveTemperature, group=1), data = averageTemperatureByMonth) +
geom_line( ) +
ggtitle("Temperature by month")
g2 = ggplot(aes(x = month, y = ave, group=1), data = byMonth) +
geom_bar(stat="identity", fill="dark blue") +
ggtitle("Attendance by month")
grid.arrange(g1,g2, ncol = 1)
----
image::{{<siteurl>}}/uploads/2015/02/2015-02-11_06-48-05.png[2015 02 11 06 48 05,600]
Now we can see there's not really that much of a correlation between temperature and month - in fact 9 of the months have a very similar average attendance. It's only July, December and especially August where there's a noticeable dip.
This could suggest there's another variable other than temperature which is influencing attendance in these months. My hypothesis is that we'd see lower attendance in the weeks of school holidays - the main ones happen in July/August, December and March/April (which interestingly don't show the dip!)
Another interesting thing to look into is whether the reason for the dip in attendance isn't through lack of will from attendees but rather because there aren't actually any events to go to. Let's plot the number of events being hosted each month against the temperature:
[source,r]
----
g1 = ggplot(aes(x = month, y = aveTemperature, group=1), data = averageTemperatureByMonth) +
geom_line( ) +
ggtitle("Temperature by month")
g2 = ggplot(aes(x = month, y = events, group=1), data = byMonth) +
geom_bar(stat="identity", fill="dark blue") +
ggtitle("Events by month")
grid.arrange(g1,g2, ncol = 1)
----
image::{{<siteurl>}}/uploads/2015/02/2015-02-11_06-57-16.png[2015 02 11 06 57 16,600]
Here we notice there's a big dip in events in December - organisers are hosting less events and we know from our earlier plot that on average less people are attending those events. Lots of events are hosted in the Autumn, slightly fewer in the Spring and fewer in January, March and August in particular.
Again there's no particular correlation between temperature and the number of events being hosted on a particular day:
[source,r]
----
ggplot(aes(x = events, y = Mean_TemperatureC,group = day), data = merged) +
geom_point()
----
image::{{<siteurl>}}/uploads/2015/02/2015-02-11_07-05-48.png[2015 02 11 07 05 48,600]
There's not any obvious correlation from looking at this plot although I find it difficult to interpret plots where we have the values all grouped around very few points (often factor variables) on one axis and spread out (continuous variable) on the other. Let's confirm our suspicion by calculating the correlation between these two variables:
[source,r]
----
> cor(merged$events, merged$Mean_TemperatureC)
[1] 0.0251698
----
Back to the drawing board for my attendance prediction model then!
If you have any suggestions for doing this analysis more effectively or I've made any mistakes please let me know in the comments, I'm still learning how to investigate what data is actually telling us. | null | null | [
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] | r-weather-vs-attendance-at-nosql-meetups | https://markhneedham.com/blog/2015/02/11/r-weather-vs-attendance-at-nosql-meetups |
false | 2015-02-16 21:39:16 | Python/pandas: Column value in list (ValueError: The truth value of a Series is ambiguous.) | [
"python"
] | [
"Python"
] | I've been using Python's http://pandas.pydata.org/[pandas] library while exploring some CSV files and although for the most part I've found it intuitive to use, I had trouble filtering a data frame based on checking whether a column value was in a list.
A subset of one of the CSV files I've been working with looks like this:
[source,python]
----
$ cat foo.csv
"Foo"
1
2
3
4
5
6
7
8
9
10
----
Loading it into a pandas data frame is reasonably simple:
[source,python]
----
import pandas as pd
df = pd.read_csv('foo.csv', index_col=False, header=0)
>>> df
Foo
0 1
1 2
2 3
3 4
4 5
5 6
6 7
7 8
8 9
9 10
----
If we want to find the rows which have a value of 1 we'd write the following:
[source,python]
----
>>> df[df["Foo"] == 1]
Foo
0 1
----
Finding the rows with a value less than 7 is as you'd expect too:
[source,python]
----
>>> df[df["Foo"] < 7]
Foo
0 1
1 2
2 3
3 4
4 5
5 6
----
Next I wanted to filter out the rows containing odd numbers which I initially tried to do like this:
[source,python]
----
odds = [i for i in range(1,10) if i % 2 <> 0]
>>> odds
[1, 3, 5, 7, 9]
>>> df[df["Foo"] in odds]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/markneedham/projects/neo4j-himym/himym/lib/python2.7/site-packages/pandas/core/generic.py", line 698, in __nonzero__
.format(self.__class__.__name__))
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
----
Unfortunately that doesn't work and I couldn't get any of the suggestions from the error message to work either. Luckily pandas has a special +++<cite>+++http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.isin.html[isin]+++</cite>+++ function for this use case which we can call like this:
[source,python]
----
>>> df[df["Foo"].isin(odds)]
Foo
0 1
2 3
4 5
6 7
8 9
----
Much better! | null | null | [
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] | pythonpandas-column-value-in-list-valueerror-the-truth-value-of-a-series-is-ambiguous | https://markhneedham.com/blog/2015/02/16/pythonpandas-column-value-in-list-valueerror-the-truth-value-of-a-series-is-ambiguous |
false | 2015-02-19 00:52:10 | Python's pandas vs Neo4j's cypher: Exploring popular phrases in How I met your mother transcripts | [
"neo4j",
"python"
] | [
"neo4j"
] | I've previously written about http://www.markhneedham.com/blog/2015/02/15/pythonscikit-learn-calculating-tfidf-on-how-i-met-your-mother-transcripts/[extracting TF/IDF scores for phrases in documents using scikit-learn] and the final step in that post involved writing the words into a CSV file for analysis later on.
I wasn't sure what the most appropriate tool of choice for that analysis was so I decided to explore the data using Python's pandas library and load it into Neo4j and write some Cypher queries.
To do anything with Neo4j we need to first load the CSV file into the database. The easiest way to do that is with Cypher's LOAD CSV command.
First we'll load the phrases in and then we'll connect them to the episodes which were previously loaded:
[source,cypher]
----
USING PERIODIC COMMIT 1000
LOAD CSV WITH HEADERS FROM "file:///Users/markneedham/projects/neo4j-himym/data/import/tfidf_scikit.csv" AS row
MERGE (phrase:Phrase {value: row.Phrase});
----
[source,cypher]
----
USING PERIODIC COMMIT 1000
LOAD CSV WITH HEADERS FROM "file:///Users/markneedham/projects/neo4j-himym/data/import/tfidf_scikit.csv" AS row
MATCH (phrase:Phrase {value: row.Phrase})
MATCH (episode:Episode {id: TOINT(row.EpisodeId)})
MERGE (phrase)-[:USED_IN_EPISODE {tfidfScore: TOFLOAT(row.Score)}]->(episode);
----
Now we're ready to start writing some queries. To start with we'll write a simple query to find the top 3 phrases for each episode.
In pandas this is quite easy - we just need to group by the appropriate field and then take the top 3 records in that grouping:
[source,python]
----
top_words_by_episode = df \
.sort(["EpisodeId", "Score"], ascending = [True, False]) \
.groupby(["EpisodeId"], sort = False) \
.head(3)
>>> print(top_words_by_episode.to_string())
EpisodeId Phrase Score
3976 1 ted 0.262518
2912 1 olives 0.195714
2441 1 marshall 0.155515
8143 2 ted 0.292184
5197 2 carlos 0.227454
7482 2 robin 0.195150
12551 3 ted 0.232662
9040 3 barney 0.187255
11254 3 mcneil 0.170619
15641 4 natalie 0.562485
16763 4 ted 0.191873
16234 4 robin 0.102671
20715 5 subtitle 0.310866
18121 5 coat check 0.181682
20861 5 ted 0.169973
...
----
The cypher version looks quite similar, the main difference being that we use the +++<cite>+++COLLECT+++</cite>+++ to generate an array of phrases by episode and then take the top 3:
[source,cypher]
----
MATCH (e:Episode)<-[rel:USED_IN_EPISODE]-(phrase)
WITH e, rel, phrase
ORDER BY e.id, rel.tfidfScore DESC
RETURN e.id, e.title, COLLECT({phrase: phrase.value, score: rel.tfidfScore})[..3]
ORDER BY e.id
==> +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | e.id | e.title | COLLECT({phrase: phrase.value, score: rel.tfidfScore})[..3] |
==> +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | 1 | "Pilot" | [{phrase -> "ted", score -> 0.2625177493269755},{phrase -> "olives", score -> 0.19571419072701732},{phrase -> "marshall", score -> 0.15551468983363487}] |
==> | 2 | "Purple Giraffe" | [{phrase -> "ted", score -> 0.292184496766088},{phrase -> "carlos", score -> 0.22745438090499026},{phrase -> "robin", score -> 0.19514993122773566}] |
==> | 3 | "Sweet Taste of Liberty" | [{phrase -> "ted", score -> 0.23266190616714866},{phrase -> "barney", score -> 0.18725456678444408},{phrase -> "officer mcneil", score -> 0.17061872221616137}] |
==> | 4 | "Return of the Shirt" | [{phrase -> "natalie", score -> 0.5624848345525686},{phrase -> "ted", score -> 0.19187323894701674},{phrase -> "robin", score -> 0.10267067360622682}] |
==> | 5 | "Okay Awesome" | [{phrase -> "subtitle", score -> 0.310865508347106},{phrase -> "coat check", score -> 0.18168178787561182},{phrase -> "ted", score -> 0.16997258596683185}] |
==> | 6 | "Slutty Pumpkin" | [{phrase -> "mike", score -> 0.2966610054610693},{phrase -> "ted", score -> 0.19333276951599407},{phrase -> "robin", score -> 0.1656172994411056}] |
==> | 7 | "Matchmaker" | [{phrase -> "ellen", score -> 0.4947912795578686},{phrase -> "sarah", score -> 0.24462913913669443},{phrase -> "ted", score -> 0.23728319597607636}] |
==> | 8 | "The Duel" | [{phrase -> "ted", score -> 0.26713931416222847},{phrase -> "marshall", score -> 0.22816702335751904},{phrase -> "swords", score -> 0.17841675237702592}] |
==> | 9 | "Belly Full of Turkey" | [{phrase -> "ericksen", score -> 0.43145756691027665},{phrase -> "mrs ericksen", score -> 0.1939318283559959},{phrase -> "kendall", score -> 0.1846969793866628}] |
==> | 10 | "The Pineapple Incident" | [{phrase -> "ted", score -> 0.439756993033922},{phrase -> "trudy", score -> 0.36367907631894536},{phrase -> "carl", score -> 0.16413071244131686}] |
==> | 11 | "The Limo" | [{phrase -> "moby", score -> 0.48314164479037003},{phrase -> "party number", score -> 0.30458929780262456},{phrase -> "ranjit", score -> 0.1991061739767796}] |
...
==> +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
----
In the cypher version we get one row per episode whereas with the Python version we get 3 rows. It might be possible to achieve this effect with pandas too but I wasn't sure how to do so.
Next let's find the top phrases for a single episode - the type of query that might be part of an episode page on a How I met your mother wiki:
[source,python]
----
top_words = df[(df["EpisodeId"] == 1)] \
.sort(["Score"], ascending = False) \
.head(20)
>>> print(top_words.to_string())
EpisodeId Phrase Score
3976 1 ted 0.262518
2912 1 olives 0.195714
2441 1 marshall 0.155515
4732 1 yasmine 0.152279
3347 1 robin 0.130418
209 1 barney 0.124412
2146 1 lily 0.122925
3637 1 signal 0.103793
1366 1 goanna 0.098138
3524 1 scene 0.095342
710 1 cut 0.091734
2720 1 narrator 0.086462
1147 1 flashback 0.078296
1148 1 flashback date 0.070283
3224 1 ranjit 0.069393
4178 1 ted yasmine 0.058569
1149 1 flashback date robin 0.058569
525 1 carl 0.058210
3714 1 smurf pen1s 0.054365
2048 1 lebanese 0.054365
----
[source,cypher]
----
MATCH (e:Episode {title: "Pilot"})<-[rel:USED_IN_EPISODE]-(phrase)
WITH phrase, rel
ORDER BY rel.tfidfScore DESC
RETURN phrase.value AS phrase, rel.tfidfScore AS score
LIMIT 20
==> +-----------------------------------------------+
==> | phrase | score |
==> +-----------------------------------------------+
==> | "ted" | 0.2625177493269755 |
==> | "olives" | 0.19571419072701732 |
==> | "marshall" | 0.15551468983363487 |
==> | "yasmine" | 0.15227880637176266 |
==> | "robin" | 0.1304175242341549 |
==> | "barney" | 0.12441175186690791 |
==> | "lily" | 0.12292497785945679 |
==> | "signal" | 0.1037932464656365 |
==> | "goanna" | 0.09813798750091524 |
==> | "scene" | 0.09534236041231685 |
==> | "cut" | 0.09173366535740156 |
==> | "narrator" | 0.08646229819848741 |
==> | "flashback" | 0.07829592155397117 |
==> | "flashback date" | 0.07028252601773662 |
==> | "ranjit" | 0.06939276915589167 |
==> | "ted yasmine" | 0.05856877168144719 |
==> | "flashback date robin" | 0.05856877168144719 |
==> | "carl" | 0.058210117288760355 |
==> | "smurf pen1s" | 0.05436505297972703 |
==> | "lebanese" | 0.05436505297972703 |
==> +-----------------------------------------------+
----
Our next query is a negation - find the episodes which don't mention the phrase 'robin'. In python we can do some simple set operations to work this out:
[source,python]
----
all_episodes = set(range(1, 209))
robin_episodes = set(df[(df["Phrase"] == "robin")]["EpisodeId"])
>>> print(set(all_episodes) - set(robin_episodes))
set([145, 198, 143])
----
In cypher land a query will suffice:
[source,cypher]
----
MATCH (episode:Episode), (phrase:Phrase {value: "robin"})
WHERE NOT (episode)<-[:USED_IN_EPISODE]-(phrase)
RETURN episode.id AS id, episode.season AS season, episode.number AS episode
----
And finally a mini recommendation engine type query - how many of the top phrases in Episode 1 were used in other episodes:
First python:
[source,python]
----
phrases_used = set(df[(df["EpisodeId"] == 1)] \
.sort(["Score"], ascending = False) \
.head(10)["Phrase"])
phrases = df[df["Phrase"].isin(phrases_used)]
print (phrases[phrases["EpisodeId"] != 1] \
.groupby(["Phrase"]) \
.size() \
.order(ascending = False))
----
Here we've pulled it out into a few steps - first we identify the top phrases, then we find out where they occur across the whole data set and finally we filter out the occurrences in the first episode and count the other occurrences.
[source,python]
----
Phrase
marshall 207
barney 207
ted 206
lily 206
robin 204
scene 36
signal 4
goanna 3
olives 1
----
In cypher we can write a query to do this as well:
[source,cypher]
----
MATCH (episode:Episode {title: "Pilot"})<-[rel:USED_IN_EPISODE]-(phrase)
WITH phrase, rel, episode
ORDER BY rel.tfidfScore DESC
LIMIT 10
MATCH (phrase)-[:USED_IN_EPISODE]->(otherEpisode)
WHERE otherEpisode <> episode
RETURN phrase.value AS phrase, COUNT(*) AS numberOfOtherEpisodes
ORDER BY numberOfOtherEpisodes DESC
==> +------------------------------------+
==> | phrase | numberOfOtherEpisodes |
==> +------------------------------------+
==> | "barney" | 207 |
==> | "marshall" | 207 |
==> | "ted" | 206 |
==> | "lily" | 206 |
==> | "robin" | 204 |
==> | "scene" | 36 |
==> | "signal" | 4 |
==> | "goanna" | 3 |
==> | "olives" | 1 |
==> +------------------------------------+
----
Overall there's not much in it - for some of the queries I found it easier in cypher and for others easier with pandas. It's always useful to have multiple tools in the toolbox! | null | null | [
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] | pythons-pandas-vs-neo4js-cypher-exploring-popular-phrases-in-how-i-met-your-mother-transcripts | https://markhneedham.com/blog/2015/02/19/pythons-pandas-vs-neo4js-cypher-exploring-popular-phrases-in-how-i-met-your-mother-transcripts |
false | 2015-02-26 00:45:42 | R: Conditionally updating rows of a data frame | [
"r-2"
] | [
"R"
] | In a blog post I wrote a couple of days ago about http://www.markhneedham.com/blog/2015/02/24/r-cohort-analysis-of-neo4j-meetup-members/[cohort analysis] I had to assign a monthNumber to each row in a data frame and started out with the following code:
[source,r]
----
library(zoo)
library(dplyr)
monthNumber = function(cohort, date) {
cohortAsDate = as.yearmon(cohort)
dateAsDate = as.yearmon(date)
if(cohortAsDate > dateAsDate) {
"NA"
} else {
paste(round((dateAsDate - cohortAsDate) * 12), sep="")
}
}
cohortAttendance %>%
group_by(row_number()) %>%
mutate(monthNumber = monthNumber(cohort, date)) %>%
filter(monthNumber != "NA") %>%
filter(monthNumber != "0") %>%
mutate(monthNumber = as.numeric(monthNumber)) %>%
arrange(monthNumber)
----
If we time this function using +++<cite>+++system.time+++</cite>+++ we'll see that it's not very snappy:
[source,r]
----
system.time(cohortAttendance %>%
group_by(row_number()) %>%
mutate(monthNumber = monthNumber(cohort, date)) %>%
filter(monthNumber != "NA") %>%
filter(monthNumber != "0") %>%
mutate(monthNumber = as.numeric(monthNumber)) %>%
arrange(monthNumber))
user system elapsed
1.968 0.019 2.016
----
The reason for the poor performance is that we process each row of the data table individually due to the call to +++<cite>+++group_by+++</cite>+++ on the second line. One way we can refactor the code is to use the +++<cite>+++ifelse+++</cite>+++ which can process multiple rows at a time:
[source,r]
----
system.time(
cohortAttendance %>%
mutate(monthNumber = ifelse(as.yearmon(cohort) > as.yearmon(date),
paste((round(as.yearmon(date) - as.yearmon(cohort))*12), sep=""),
NA)))
user system elapsed
0.026 0.000 0.026
----
https://twitter.com/tonkouts[Antonios] suggested another approach which involves first setting every row to 'NA' and then http://stackoverflow.com/questions/8214303/conditional-replacement-of-values-in-a-data-frame[selectively updating the appropriate rows]. I ended up with the following code:
[source,r]
----
cohortAttendance$monthNumber = NA
cohortAttendance$monthNumber[as.yearmon(cohortAttendance$cohort) > as.yearmon(cohortAttendance$date)] = paste((round(as.yearmon(cohortAttendance$date) - as.yearmon(cohortAttendance$cohort))*12), sep="")
----
Let's measure that:
[source,r]
----
system.time(paste((round(as.yearmon(cohortAttendance$date) - as.yearmon(cohortAttendance$cohort))*12), sep=""))
user system elapsed
0.013 0.000 0.013
----
Both approaches are much quicker than my original version although this one seems to be marginally quicker than the ifelse approach.
Note to future Mark: try to avoid grouping by row number - there's usually a better and faster solution! | null | null | [
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] | r-conditionally-updating-rows-of-a-data-frame | https://markhneedham.com/blog/2015/02/26/r-conditionally-updating-rows-of-a-data-frame |
false | 2015-02-24 01:19:26 | R: Cohort analysis of Neo4j meetup members | [
"r-2",
"rstats"
] | [
"R"
] | A few weeks ago I came across a blog post explaining how to apply http://analyzecore.com/2014/07/03/cohort-analysis-in-r-retention-charts/[cohort analysis] to customer retention using R and I thought it'd be a fun exercise to calculate something similar for meetup attendees.
In the customer retention example we track customer purchases on a month by month basis and each customer is put into a cohort or bucket based on the first month they made a purchase in.
We then calculate how many of them made purchases in subsequent months and compare that with the behaviour of people in other cohorts.
In our case we aren't selling anything so our equivalent will be a person attending a meetup. We'll put people into cohorts based on the month of the first meetup they attended.
This can act as a proxy for when people become interested in a technology and could perhaps allow us to see how the behaviour of innovators, early adopters and the early majority differs, if at all.
The first thing we need to do is get the data showing the events that people RSVP'd 'yes' to. I've already got the data in Neo4j so we'll write a query to extract it as a data frame:
[source,r]
----
library(RNeo4j)
graph = startGraph("http://127.0.0.1:7474/db/data/")
query = "MATCH (g:Group {name: \"Neo4j - London User Group\"})-[:HOSTED_EVENT]->(e),
(e)<-[:TO]-(rsvp {response: \"yes\"})<-[:RSVPD]-(person)
RETURN rsvp.time, person.id"
timestampToDate <- function(x) as.POSIXct(x / 1000, origin="1970-01-01", tz = "GMT")
df = cypher(graph, query)
df$time = timestampToDate(df$rsvp.time)
df$date = format(as.Date(df$time), "%Y-%m")
----
[source,r]
----
> df %>% head()
## rsvp.time person.id time date
## 612 1.404857e+12 23362191 2014-07-08 22:00:29 2014-07
## 1765 1.380049e+12 112623332 2013-09-24 18:58:00 2013-09
## 1248 1.390563e+12 9746061 2014-01-24 11:24:35 2014-01
## 1541 1.390920e+12 7881615 2014-01-28 14:40:35 2014-01
## 3056 1.420670e+12 12810159 2015-01-07 22:31:04 2015-01
## 607 1.406025e+12 14329387 2014-07-22 10:34:51 2014-07
## 1634 1.391445e+12 91330472 2014-02-03 16:33:58 2014-02
## 2137 1.371453e+12 68874702 2013-06-17 07:17:10 2013-06
## 430 1.407835e+12 150265192 2014-08-12 09:15:31 2014-08
## 2957 1.417190e+12 182752269 2014-11-28 15:45:18 2014-11
----
Next we need to find the first meetup that a person attended - this will determine the cohort that the person is assigned to:
[source,r]
----
firstMeetup = df %>%
group_by(person.id) %>%
summarise(firstEvent = min(time), count = n()) %>%
arrange(desc(count))
> firstMeetup
## Source: local data frame [10 x 3]
##
## person.id firstEvent count
## 1 13526622 2013-01-24 20:25:19 2
## 2 119400912 2014-10-03 13:09:09 2
## 3 122524352 2014-08-14 14:09:44 1
## 4 37201052 2012-05-21 10:26:24 3
## 5 137112712 2014-07-31 09:32:12 1
## 6 152448642 2014-06-20 08:32:50 17
## 7 98563682 2014-11-05 17:27:57 1
## 8 146976492 2014-05-17 00:04:42 4
## 9 12318409 2014-11-03 05:25:26 2
## 10 41280492 2014-10-16 19:02:03 5
----
Let's assign each person to a cohort (month/year) and see how many people belong to each one:
[source,r]
----
firstMeetup$date = format(as.Date(firstMeetup$firstEvent), "%Y-%m")
byMonthYear = firstMeetup %>% count(date) %>% arrange(date)
ggplot(aes(x=date, y = n), data = byMonthYear) +
geom_bar(stat="identity", fill = "dark blue") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
----
image::{{<siteurl>}}/uploads/2015/02/unnamed-chunk-4-1.png[Unnamed chunk 4 1,600]
Next we need to track a cohort over time to see whether people keep coming to events. I wrote the following function to work it out:
[source,r]
----
countsForCohort = function(df, firstMeetup, cohort) {
members = (firstMeetup %>% filter(date == cohort))$person.id
attendance = df %>%
filter(person.id %in% members) %>%
count(person.id, date) %>%
ungroup() %>%
count(date)
allCohorts = df %>% select(date) %>% unique
cohortAttendance = merge(allCohorts, attendance, by = "date", all = TRUE)
cohortAttendance[is.na(cohortAttendance) & cohortAttendance$date > cohort] = 0
cohortAttendance %>% mutate(cohort = cohort, retention = n / length(members))
}
----
On the first line we get the ids of all the people in the cohort so that we can filter the data frame to only include RSVPs by these people. The first call to 'count' makes sure that we only have one entry per person per month and the second call gives us a count of how many people attended an event in a given month.
Next we do the equivalent of a left join using the merge function to ensure we have a row representing each month even if noone from the cohort attended. This will lead to NA entries if there's no matching row in the 'attendance' data frame - we'll replace those with a 0 if the cohort is in the future. If not we'll leave it as it is.
Finally we calculate the retention rate for each month for that cohort. e.g. these are some of the rows for the '2011-06' cohort:
[source,r]
----
> countsForCohort(df, firstMeetup, "2011-06") %>% sample_n(10)
date n cohort retention
16 2013-01 1 2011-06 0.25
5 2011-10 1 2011-06 0.25
30 2014-03 0 2011-06 0.00
29 2014-02 0 2011-06 0.00
40 2015-01 0 2011-06 0.00
31 2014-04 0 2011-06 0.00
8 2012-04 2 2011-06 0.50
39 2014-12 0 2011-06 0.00
2 2011-07 1 2011-06 0.25
19 2013-04 1 2011-06 0.25
----
We could then choose to plot that cohort:
[source,r]
----
ggplot(aes(x=date, y = retention, colour = cohort), data = countsForCohort(df, firstMeetup, "2011-06")) +
geom_line(aes(group = cohort)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
----
image::{{<siteurl>}}/uploads/2015/02/unnamed-chunk-5-1.png[Unnamed chunk 5 1,600]
From this chart we can see that none of the people who first attended a Neo4j meetup in June 2011 have attended any events for the last two years.
Next we want to be able to plot multiple cohorts on the same chart which we can easily do by constructing one big data frame and passing it to ggplot:
[source,r]
----
cohorts = collect(df %>% select(date) %>% unique())[,1]
cohortAttendance = data.frame()
for(cohort in cohorts) {
cohortAttendance = rbind(cohortAttendance,countsForCohort(df, firstMeetup, cohort))
}
ggplot(aes(x=date, y = retention, colour = cohort), data = cohortAttendance) +
geom_line(aes(group = cohort)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
----
image::{{<siteurl>}}/uploads/2015/02/unnamed-chunk-5-2.png[Unnamed chunk 5 2,600]
This all looks a bit of a mess and at the moment we can't easily compare cohorts as they start at different places on the x axis. We can fix that by adding a 'monthNumber' column to the data frame which we calculate with the following function:
[source,r]
----
monthNumber = function(cohort, date) {
cohortAsDate = as.yearmon(cohort)
dateAsDate = as.yearmon(date)
if(cohortAsDate > dateAsDate) {
"NA"
} else {
paste(round((dateAsDate - cohortAsDate) * 12), sep="")
}
}
----
Now let's create a new data frame with the month field added:
[source,r]
----
cohortAttendanceWithMonthNumber = cohortAttendance %>%
group_by(row_number()) %>%
mutate(monthNumber = monthNumber(cohort, date)) %>%
filter(monthNumber != "NA") %>%
filter(monthNumber != "0") %>%
mutate(monthNumber = as.numeric(monthNumber)) %>%
arrange(monthNumber)
----
We're also filtering out any 'NA' columns which would represent row entries for months from before the cohort started. We don't want to plot those.
finally let's plot a chart containing all cohorts normalised by month number:
[source,r]
----
ggplot(aes(x=monthNumber, y = retention, colour = cohort), data = cohortAttendanceWithMonthNumber) +
geom_line(aes(group = cohort)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1), panel.background = element_blank())
----
image::{{<siteurl>}}/uploads/2015/02/unnamed-chunk-5-3.png[Unnamed chunk 5 3,600]
It's still a bit of a mess but what stands out is that when the number of people in a cohort is small the fluctuation in the retention value can be quite pronounced.
The next step is to make the cohorts a bit more coarse grained to see if it reveals some insights. I think I'll start out with a cohort covering a 3 month period and see how that works out. | null | null | [
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] | r-cohort-analysis-of-neo4j-meetup-members | https://markhneedham.com/blog/2015/02/24/r-cohort-analysis-of-neo4j-meetup-members |