Spaces:
Runtime error
Runtime error
File size: 4,686 Bytes
69a2a60 |
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 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
from datetime import datetime
import requests
import os
import joblib
import pandas as pd
import json
def decode_features(df, feature_view):
"""Decodes features in the input DataFrame using corresponding Hopsworks Feature Store transformation functions"""
df_res = df.copy()
import inspect
td_transformation_functions = feature_view._batch_scoring_server._transformation_functions
res = {}
for feature_name in td_transformation_functions:
if feature_name in df_res.columns:
td_transformation_function = td_transformation_functions[feature_name]
sig, foobar_locals = inspect.signature(td_transformation_function.transformation_fn), locals()
param_dict = dict([(param.name, param.default) for param in sig.parameters.values() if param.default != inspect._empty])
if td_transformation_function.name == "min_max_scaler":
df_res[feature_name] = df_res[feature_name].map(
lambda x: x * (param_dict["max_value"] - param_dict["min_value"]) + param_dict["min_value"])
elif td_transformation_function.name == "standard_scaler":
df_res[feature_name] = df_res[feature_name].map(
lambda x: x * param_dict['std_dev'] + param_dict["mean"])
elif td_transformation_function.name == "label_encoder":
dictionary = param_dict['value_to_index']
dictionary_ = {v: k for k, v in dictionary.items()}
df_res[feature_name] = df_res[feature_name].map(
lambda x: dictionary_[x])
return df_res
def get_model(project, model_name, evaluation_metric, sort_metrics_by):
"""Retrieve desired model or download it from the Hopsworks Model Registry.
In second case, it will be physically downloaded to this directory"""
TARGET_FILE = "model_temp.pkl"
list_of_files = [os.path.join(dirpath,filename) for dirpath, _, filenames \
in os.walk('.') for filename in filenames if filename == TARGET_FILE]
if list_of_files:
model_path = list_of_files[0]
model = joblib.load(model_path)
else:
if not os.path.exists(TARGET_FILE):
mr = project.get_model_registry()
# get best model based on custom metrics
model = mr.get_best_model(model_name,
evaluation_metric,
sort_metrics_by)
model_dir = model.download()
model = joblib.load(model_dir + "/model_temp.pkl")
return model
def get_weather_json(date, WEATHER_API_KEY):
return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/helsinki/{date}?unitGroup=metric&include=days&key={WEATHER_API_KEY}&contentType=json').json()
def get_weather_data(date):
WEATHER_API_KEY = os.getenv('WEATHER_API_KEY')
json = get_weather_json(date, WEATHER_API_KEY)
data = json['days'][0]
return [
json['address'].capitalize(),
data['datetime'],
data['tempmax'],
data['tempmin'],
data['temp'],
data['feelslikemax'],
data['feelslikemin'],
data['feelslike'],
data['dew'],
data['humidity'],
data['precip'],
data['precipprob'],
data['precipcover'],
data['snow'],
data['snowdepth'],
data['windgust'],
data['windspeed'],
data['winddir'],
data['pressure'],
data['cloudcover'],
data['visibility'],
data['solarradiation'],
data['solarenergy'],
data['uvindex'],
data['conditions']
]
def get_weather_df(data):
col_names = [
'city',
'date',
'tempmax',
'tempmin',
'temp',
'feelslikemax',
'feelslikemin',
'feelslike',
'dew',
'humidity',
'precip',
'precipprob',
'precipcover',
'snow',
'snowdepth',
'windgust',
'windspeed',
'winddir',
'pressure',
'cloudcover',
'visibility',
'solarradiation',
'solarenergy',
'uvindex',
'conditions'
]
new_data = pd.DataFrame(
data,
columns=col_names
)
new_data.date = new_data.date.apply(timestamp_2_time1)
return new_data
def timestamp_2_time1(x):
dt_obj = datetime.strptime(str(x), '%Y-%m-%d')
dt_obj = dt_obj.timestamp() * 1000
return int(dt_obj)
def timestamp_2_time(x):
dt_obj = datetime.strptime(str(x), '%m/%d/%Y')
dt_obj = dt_obj.timestamp() * 1000
return int(dt_obj) |