Spaces:
Runtime error
Runtime error
File size: 11,801 Bytes
f760ec3 cb76d50 08090c3 cb76d50 08090c3 cb76d50 08090c3 cb76d50 295dbc0 cb76d50 af6a1d7 cb76d50 8dd2bc6 cb76d50 8dd2bc6 af6a1d7 cb76d50 58f2ab9 cb76d50 58f2ab9 8dd2bc6 af6a1d7 8dd2bc6 08090c3 cb76d50 58f2ab9 cb76d50 f760ec3 3d8ee64 cb76d50 c72f38a cb76d50 08090c3 39f8f41 08090c3 cb76d50 08090c3 cb76d50 8dd2bc6 295dbc0 eba677f 08090c3 cb76d50 23efaf2 8dd2bc6 cb76d50 3d8ee64 cb76d50 3d8ee64 cb76d50 3d8ee64 cb76d50 0154388 08090c3 0154388 8a7fbed 0154388 08090c3 cb76d50 8dd2bc6 8a7fbed cb76d50 f760ec3 8dd2bc6 76e2fde f760ec3 0154388 ca8e444 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 |
## LIBRARIES ###
## Data
import numpy as np
import pandas as pd
import torch
import json
from tqdm import tqdm
from math import floor
from datasets import load_dataset
from collections import defaultdict
from transformers import AutoTokenizer
pd.options.display.float_format = '${:,.2f}'.format
# Analysis
# from gensim.models.doc2vec import Doc2Vec
# from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
import nltk
from nltk.cluster import KMeansClusterer
import scipy.spatial.distance as sdist
from scipy.spatial import distance_matrix
# nltk.download('punkt') #make sure that punkt is downloaded
# App & Visualization
import streamlit as st
import altair as alt
import plotly.graph_objects as go
from streamlit_vega_lite import altair_component
# utils
from random import sample
from error_analysis import utils as ut
def down_samp(embedding):
"""Down sample a data frame for altiar visualization """
# total number of positive and negative sentiments in the class
#embedding = embedding.groupby('slice').apply(lambda x: x.sample(frac=0.3))
total_size = embedding.groupby(['slice','label'], as_index=False).count()
user_data = 0
# if 'Your Sentences' in str(total_size['slice']):
# tmp = embedding.groupby(['slice'], as_index=False).count()
# val = int(tmp[tmp['slice'] == "Your Sentences"]['source'])
# user_data = val
max_sample = total_size.groupby('slice').max()['content']
# # down sample to meeting altair's max values
# # but keep the proportional representation of groups
down_samp = 1/(sum(max_sample.astype(float))/(1000-user_data))
max_samp = max_sample.apply(lambda x: floor(x*down_samp)).astype(int).to_dict()
max_samp['Your Sentences'] = user_data
# # sample down for each group in the data frame
embedding = embedding.groupby('slice').apply(lambda x: x.sample(n=max_samp.get(x.name))).reset_index(drop=True)
# # order the embedding
return(embedding)
def data_comparison(df):
selection = alt.selection_multi(fields=['cluster:N','label:O'])
color = alt.condition(alt.datum.slice == 'high-loss', alt.Color('cluster:N', scale = alt.Scale(domain=df.cluster.unique().tolist())), alt.value("lightgray"))
opacity = alt.condition(selection, alt.value(0.7), alt.value(0.25))
# basic chart
scatter = alt.Chart(df).mark_point(size=100, filled=True).encode(
x=alt.X('x:Q', axis=None),
y=alt.Y('y:Q', axis=None),
color=color,
shape=alt.Shape('label:O', scale=alt.Scale(range=['circle', 'diamond'])),
tooltip=['cluster:N','slice:N','content:N','label:O','pred:O'],
opacity=opacity
).properties(
width=1000,
height=800
).interactive()
legend = alt.Chart(df).mark_point(size=100, filled=True).encode(
x=alt.X("label:O"),
y=alt.Y('cluster:N', axis=alt.Axis(orient='right'), title=""),
shape=alt.Shape('label:O', scale=alt.Scale(
range=['circle', 'diamond']), legend=None),
color=color,
).add_selection(
selection
)
layered = scatter | legend
layered = layered.configure_axis(
grid=False
).configure_view(
strokeOpacity=0
)
return layered
def quant_panel(embedding_df):
""" Quantitative Panel Layout"""
all_metrics = {}
st.warning("**Error slice visualization**")
with st.expander("How to read this chart:"):
st.markdown("* Each **point** is an input example.")
st.markdown("* Gray points have low-loss and the colored have high-loss. High-loss instances are clustered using **kmeans** and each color represents a cluster.")
st.markdown("* The **shape** of each point reflects the label category -- positive (diamond) or negative sentiment (circle).")
st.altair_chart(data_comparison(down_samp(embedding_df)), use_container_width=True)
def frequent_tokens(data, tokenizer, loss_quantile=0.95, top_k=200, smoothing=0.005):
unique_tokens = []
tokens = []
for row in tqdm(data['content']):
tokenized = tokenizer(row,padding=True, return_tensors='pt')
tokens.append(tokenized['input_ids'].flatten())
unique_tokens.append(torch.unique(tokenized['input_ids']))
losses = data['loss'].astype(float)
high_loss = losses.quantile(loss_quantile)
loss_weights = (losses > high_loss)
loss_weights = loss_weights / loss_weights.sum()
token_frequencies = defaultdict(float)
token_frequencies_error = defaultdict(float)
weights_uniform = np.full_like(loss_weights, 1 / len(loss_weights))
num_examples = len(data)
for i in tqdm(range(num_examples)):
for token in unique_tokens[i]:
token_frequencies[token.item()] += weights_uniform[i]
token_frequencies_error[token.item()] += loss_weights[i]
token_lrs = {k: (smoothing+token_frequencies_error[k]) / (smoothing+token_frequencies[k]) for k in token_frequencies}
tokens_sorted = list(map(lambda x: x[0], sorted(token_lrs.items(), key=lambda x: x[1])[::-1]))
top_tokens = []
for i, (token) in enumerate(tokens_sorted[:top_k]):
top_tokens.append(['%10s' % (tokenizer.decode(token)), '%.4f' % (token_frequencies[token]), '%.4f' % (
token_frequencies_error[token]), '%4.2f' % (token_lrs[token])])
return pd.DataFrame(top_tokens, columns=['Token', 'Freq', 'Freq error slice', 'lrs'])
@st.cache(ttl=600)
def get_data(spotlight, emb):
preds = spotlight.outputs.numpy()
losses = spotlight.losses.numpy()
embeddings = pd.DataFrame(emb, columns=['x', 'y'])
num_examples = len(losses)
# dataset_labels = [dataset[i]['label'] for i in range(num_examples)]
return pd.concat([pd.DataFrame(np.transpose(np.vstack([dataset[:num_examples]['content'],
dataset[:num_examples]['label'], preds, losses])), columns=['content', 'label', 'pred', 'loss']), embeddings], axis=1)
@st.cache(ttl=600)
def clustering(data,num_clusters):
X = np.array(data['embedding'].tolist())
kclusterer = KMeansClusterer(
num_clusters, distance=nltk.cluster.util.cosine_distance,
repeats=25,avoid_empty_clusters=True)
assigned_clusters = kclusterer.cluster(X, assign_clusters=True)
data['cluster'] = pd.Series(assigned_clusters, index=data.index).astype('int')
data['centroid'] = data['cluster'].apply(lambda x: kclusterer.means()[x])
return data, assigned_clusters
@st.cache(ttl=600)
def kmeans(df, num_clusters=3):
data_hl = df.loc[df['slice'] == 'high-loss']
data_kmeans,clusters = clustering(data_hl,num_clusters)
merged = pd.merge(df, data_kmeans, left_index=True, right_index=True, how='outer', suffixes=('', '_y'))
merged.drop(merged.filter(regex='_y$').columns.tolist(),axis=1,inplace=True)
merged['cluster'] = merged['cluster'].fillna(num_clusters).astype('int')
return merged
@st.cache(ttl=600)
def distance_from_centroid(row):
return sdist.norm(row['embedding'] - row['centroid'].tolist())
@st.cache(ttl=600)
def topic_distribution(weights, smoothing=0.01):
topic_frequencies = defaultdict(float)
topic_frequencies_spotlight = defaultdict(float)
weights_uniform = np.full_like(weights, 1 / len(weights))
num_examples = len(weights)
for i in range(num_examples):
example = dataset[i]
category = example['title']
topic_frequencies[category] += weights_uniform[i]
topic_frequencies_spotlight[category] += weights[i]
topic_ratios = {c: (smoothing + topic_frequencies_spotlight[c]) / (
smoothing + topic_frequencies[c]) for c in topic_frequencies}
categories_sorted = map(lambda x: x[0], sorted(
topic_ratios.items(), key=lambda x: x[1], reverse=True))
topic_distr = []
for category in categories_sorted:
topic_distr.append(['%.3f' % topic_frequencies[category], '%.3f' %
topic_frequencies_spotlight[category], '%.2f' % topic_ratios[category], '%s' % category])
return pd.DataFrame(topic_distr, columns=['Overall frequency', 'Error frequency', 'Ratio', 'Category'])
# for category in categories_sorted:
# return(topic_frequencies[category], topic_frequencies_spotlight[category], topic_ratios[category], category)
if __name__ == "__main__":
### STREAMLIT APP CONGFIG ###
st.set_page_config(layout="wide", page_title="Interactive Error Analysis")
ut.init_style()
lcol, rcol = st.columns([2, 2])
# ******* loading the mode and the data
#st.sidebar.mardown("<h4>Interactive Error Analysis</h4>", unsafe_allow_html=True)
dataset = st.sidebar.selectbox(
"Dataset",
["amazon_polarity", "yelp_polarity"],
index = 1
)
model = st.sidebar.selectbox(
"Model",
["distilbert-base-uncased-finetuned-sst-2-english",
"albert-base-v2-yelp-polarity"],
)
### LOAD DATA AND SESSION VARIABLES ###
data_df = pd.read_parquet('./assets/data/'+dataset+ '_'+ model+'.parquet')
if model == 'albert-base-v2-yelp-polarity':
tokenizer = AutoTokenizer.from_pretrained('textattack/'+model)
else:
tokenizer = AutoTokenizer.from_pretrained(model)
if "user_data" not in st.session_state:
st.session_state["user_data"] = data_df
if "selected_slice" not in st.session_state:
st.session_state["selected_slice"] = None
loss_quantile = st.sidebar.slider(
"Loss Quantile", min_value=0.5, max_value=1.0,step=0.01,value=0.95
)
data_df['loss'] = data_df['loss'].astype(float)
losses = data_df['loss']
high_loss = losses.quantile(loss_quantile)
data_df['slice'] = 'high-loss'
data_df['slice'] = data_df['slice'].where(data_df['loss'] > high_loss, 'low-loss')
with rcol:
with st.spinner(text='loading...'):
st.markdown('<h3>Word Distribution in Error Slice</h3>', unsafe_allow_html=True)
#uncomment the next two lines to run dynamically and not from file
#commontokens = frequent_tokens(data_df, tokenizer, loss_quantile=loss_quantile)
commontokens = pd.read_parquet('./assets/data/'+dataset+ '_'+ model+'_commontokens.parquet')
with st.expander("How to read the table:"):
st.markdown("* The table displays the most frequent tokens in error slices, relative to their frequencies in the val set.")
st.write(commontokens)
run_kmeans = st.sidebar.radio("Cluster error slice?", ('True', 'False'), index=0)
num_clusters = st.sidebar.slider("# clusters", min_value=1, max_value=20, step=1, value=3)
if run_kmeans == 'True':
merged = kmeans(data_df,num_clusters=num_clusters)
with lcol:
st.markdown('<h3>Error Slices</h3>',unsafe_allow_html=True)
dataframe=pd.read_parquet('./assets/data/'+dataset+ '_'+ model+'_error-slices.parquet')
#uncomment the next next line to run dynamically and not from file
# dataframe = merged[['content', 'label', 'pred', 'loss', 'cluster']].sort_values(
# by=['loss'], ascending=False)
# table_html = dataframe.to_html(
# columns=['content', 'label', 'pred', 'loss', 'cluster'], max_rows=50)
# table_html = table_html.replace("<th>", '<th align="left">') # left-align the headers
with st.expander("How to read the table:"):
st.markdown("* *Error slice* refers to the subset of evaluation dataset the model performs poorly on.")
st.markdown("* The table displays model error slices on the evaluation dataset, sorted by loss.")
st.markdown("* Each row is an input example that includes the label, model pred, loss, and error cluster.")
st.write(dataframe,width=900, height=300)
quant_panel(merged) |