Spaces:
Build error
Build error
meg-huggingface
commited on
Commit
•
e8ac901
1
Parent(s):
2981bb2
Merging back dataset statistics
Browse files
data_measurements/dataset_statistics.py
ADDED
@@ -0,0 +1,1313 @@
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1 |
+
# Copyright 2021 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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+
# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
import json
|
16 |
+
import logging
|
17 |
+
import statistics
|
18 |
+
import torch
|
19 |
+
from os import mkdir
|
20 |
+
from os.path import exists, isdir
|
21 |
+
from os.path import join as pjoin
|
22 |
+
|
23 |
+
import nltk
|
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+
import numpy as np
|
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+
import pandas as pd
|
26 |
+
import plotly
|
27 |
+
import plotly.express as px
|
28 |
+
import plotly.figure_factory as ff
|
29 |
+
import plotly.graph_objects as go
|
30 |
+
import pyarrow.feather as feather
|
31 |
+
import matplotlib.pyplot as plt
|
32 |
+
import matplotlib.image as mpimg
|
33 |
+
import seaborn as sns
|
34 |
+
from datasets import load_from_disk
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35 |
+
from nltk.corpus import stopwords
|
36 |
+
from sklearn.feature_extraction.text import CountVectorizer
|
37 |
+
|
38 |
+
from .dataset_utils import (
|
39 |
+
TOT_WORDS,
|
40 |
+
TOT_OPEN_WORDS,
|
41 |
+
CNT,
|
42 |
+
DEDUP_TOT,
|
43 |
+
EMBEDDING_FIELD,
|
44 |
+
LENGTH_FIELD,
|
45 |
+
OUR_LABEL_FIELD,
|
46 |
+
OUR_TEXT_FIELD,
|
47 |
+
PROP,
|
48 |
+
TEXT_NAN_CNT,
|
49 |
+
TOKENIZED_FIELD,
|
50 |
+
TXT_LEN,
|
51 |
+
VOCAB,
|
52 |
+
WORD,
|
53 |
+
extract_field,
|
54 |
+
load_truncated_dataset,
|
55 |
+
)
|
56 |
+
from .embeddings import Embeddings
|
57 |
+
from .npmi import nPMI
|
58 |
+
from .zipf import Zipf
|
59 |
+
|
60 |
+
pd.options.display.float_format = "{:,.3f}".format
|
61 |
+
|
62 |
+
logs = logging.getLogger(__name__)
|
63 |
+
logs.setLevel(logging.WARNING)
|
64 |
+
logs.propagate = False
|
65 |
+
|
66 |
+
if not logs.handlers:
|
67 |
+
|
68 |
+
# Logging info to log file
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+
file = logging.FileHandler("./log_files/dataset_statistics.log")
|
70 |
+
fileformat = logging.Formatter("%(asctime)s:%(message)s")
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+
file.setLevel(logging.INFO)
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+
file.setFormatter(fileformat)
|
73 |
+
|
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+
# Logging debug messages to stream
|
75 |
+
stream = logging.StreamHandler()
|
76 |
+
streamformat = logging.Formatter("[data_measurements_tool] %(message)s")
|
77 |
+
stream.setLevel(logging.WARNING)
|
78 |
+
stream.setFormatter(streamformat)
|
79 |
+
|
80 |
+
logs.addHandler(file)
|
81 |
+
logs.addHandler(stream)
|
82 |
+
|
83 |
+
|
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+
# TODO: Read this in depending on chosen language / expand beyond english
|
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+
nltk.download("stopwords")
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+
_CLOSED_CLASS = (
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+
stopwords.words("english")
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+
+ [
|
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+
"t",
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+
"n",
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+
"ll",
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92 |
+
"d",
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+
"wasn",
|
94 |
+
"weren",
|
95 |
+
"won",
|
96 |
+
"aren",
|
97 |
+
"wouldn",
|
98 |
+
"shouldn",
|
99 |
+
"didn",
|
100 |
+
"don",
|
101 |
+
"hasn",
|
102 |
+
"ain",
|
103 |
+
"couldn",
|
104 |
+
"doesn",
|
105 |
+
"hadn",
|
106 |
+
"haven",
|
107 |
+
"isn",
|
108 |
+
"mightn",
|
109 |
+
"mustn",
|
110 |
+
"needn",
|
111 |
+
"shan",
|
112 |
+
"would",
|
113 |
+
"could",
|
114 |
+
"dont",
|
115 |
+
"u",
|
116 |
+
]
|
117 |
+
+ [str(i) for i in range(0, 21)]
|
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+
)
|
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+
_IDENTITY_TERMS = [
|
120 |
+
"man",
|
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+
"woman",
|
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+
"non-binary",
|
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+
"gay",
|
124 |
+
"lesbian",
|
125 |
+
"queer",
|
126 |
+
"trans",
|
127 |
+
"straight",
|
128 |
+
"cis",
|
129 |
+
"she",
|
130 |
+
"her",
|
131 |
+
"hers",
|
132 |
+
"he",
|
133 |
+
"him",
|
134 |
+
"his",
|
135 |
+
"they",
|
136 |
+
"them",
|
137 |
+
"their",
|
138 |
+
"theirs",
|
139 |
+
"himself",
|
140 |
+
"herself",
|
141 |
+
]
|
142 |
+
# treating inf values as NaN as well
|
143 |
+
pd.set_option("use_inf_as_na", True)
|
144 |
+
|
145 |
+
_MIN_VOCAB_COUNT = 10
|
146 |
+
_TREE_DEPTH = 12
|
147 |
+
_TREE_MIN_NODES = 250
|
148 |
+
# as long as we're using sklearn - already pushing the resources
|
149 |
+
_MAX_CLUSTER_EXAMPLES = 5000
|
150 |
+
_NUM_VOCAB_BATCHES = 2000
|
151 |
+
_TOP_N = 100
|
152 |
+
_CVEC = CountVectorizer(token_pattern="(?u)\\b\\w+\\b", lowercase=True)
|
153 |
+
|
154 |
+
class DatasetStatisticsCacheClass:
|
155 |
+
def __init__(
|
156 |
+
self,
|
157 |
+
cache_dir,
|
158 |
+
dset_name,
|
159 |
+
dset_config,
|
160 |
+
split_name,
|
161 |
+
text_field,
|
162 |
+
label_field,
|
163 |
+
label_names,
|
164 |
+
calculation=None,
|
165 |
+
use_cache=False,
|
166 |
+
):
|
167 |
+
# This is only used for standalone runs for each kind of measurement.
|
168 |
+
self.calculation = calculation
|
169 |
+
self.our_text_field = OUR_TEXT_FIELD
|
170 |
+
self.our_length_field = LENGTH_FIELD
|
171 |
+
self.our_label_field = OUR_LABEL_FIELD
|
172 |
+
self.our_tokenized_field = TOKENIZED_FIELD
|
173 |
+
self.our_embedding_field = EMBEDDING_FIELD
|
174 |
+
self.cache_dir = cache_dir
|
175 |
+
# Use stored data if there; otherwise calculate afresh
|
176 |
+
self.use_cache = use_cache
|
177 |
+
### What are we analyzing?
|
178 |
+
# name of the Hugging Face dataset
|
179 |
+
self.dset_name = dset_name
|
180 |
+
# name of the dataset config
|
181 |
+
self.dset_config = dset_config
|
182 |
+
# name of the split to analyze
|
183 |
+
self.split_name = split_name
|
184 |
+
# TODO: Chould this be "feature" ?
|
185 |
+
# which text fields are we analysing?
|
186 |
+
self.text_field = text_field
|
187 |
+
# which label fields are we analysing?
|
188 |
+
self.label_field = label_field
|
189 |
+
# what are the names of the classes?
|
190 |
+
self.label_names = label_names
|
191 |
+
## Hugging Face dataset objects
|
192 |
+
self.dset = None # original dataset
|
193 |
+
# HF dataset with all of the self.text_field instances in self.dset
|
194 |
+
self.text_dset = None
|
195 |
+
self.dset_peek = None
|
196 |
+
# HF dataset with text embeddings in the same order as self.text_dset
|
197 |
+
self.embeddings_dset = None
|
198 |
+
# HF dataset with all of the self.label_field instances in self.dset
|
199 |
+
self.label_dset = None
|
200 |
+
## Data frames
|
201 |
+
# Tokenized text
|
202 |
+
self.tokenized_df = None
|
203 |
+
# save sentence length histogram in the class so it doesn't ge re-computed
|
204 |
+
self.length_df = None
|
205 |
+
self.fig_tok_length = None
|
206 |
+
# Data Frame version of self.label_dset
|
207 |
+
self.label_df = None
|
208 |
+
# save label pie chart in the class so it doesn't ge re-computed
|
209 |
+
self.fig_labels = None
|
210 |
+
# Vocabulary with word counts in the dataset
|
211 |
+
self.vocab_counts_df = None
|
212 |
+
# Vocabulary filtered to remove stopwords
|
213 |
+
self.vocab_counts_filtered_df = None
|
214 |
+
self.sorted_top_vocab_df = None
|
215 |
+
## General statistics and duplicates
|
216 |
+
self.total_words = 0
|
217 |
+
self.total_open_words = 0
|
218 |
+
# Number of NaN values (NOT empty strings)
|
219 |
+
self.text_nan_count = 0
|
220 |
+
# Number of text items that appear more than once in the dataset
|
221 |
+
self.dedup_total = 0
|
222 |
+
# Duplicated text items along with their number of occurences ("count")
|
223 |
+
self.dup_counts_df = None
|
224 |
+
self.avg_length = None
|
225 |
+
self.std_length = None
|
226 |
+
self.general_stats_dict = None
|
227 |
+
self.num_uniq_lengths = 0
|
228 |
+
# clustering text by embeddings
|
229 |
+
# the hierarchical clustering tree is represented as a list of nodes,
|
230 |
+
# the first is the root
|
231 |
+
self.node_list = []
|
232 |
+
# save tree figure in the class so it doesn't ge re-computed
|
233 |
+
self.fig_tree = None
|
234 |
+
# keep Embeddings object around to explore clusters
|
235 |
+
self.embeddings = None
|
236 |
+
# nPMI
|
237 |
+
# Holds a nPMIStatisticsCacheClass object
|
238 |
+
self.npmi_stats = None
|
239 |
+
# TODO: Have lowercase be an option for a user to set.
|
240 |
+
self.to_lowercase = True
|
241 |
+
# The minimum amount of times a word should occur to be included in
|
242 |
+
# word-count-based calculations (currently just relevant to nPMI)
|
243 |
+
self.min_vocab_count = _MIN_VOCAB_COUNT
|
244 |
+
# zipf
|
245 |
+
self.z = None
|
246 |
+
self.zipf_fig = None
|
247 |
+
self.cvec = _CVEC
|
248 |
+
# File definitions
|
249 |
+
# path to the directory used for caching
|
250 |
+
if not isinstance(text_field, str):
|
251 |
+
text_field = "-".join(text_field)
|
252 |
+
#if isinstance(label_field, str):
|
253 |
+
# label_field = label_field
|
254 |
+
#else:
|
255 |
+
# label_field = "-".join(label_field)
|
256 |
+
self.cache_path = pjoin(
|
257 |
+
self.cache_dir,
|
258 |
+
f"{dset_name}_{dset_config}_{split_name}_{text_field}", #{label_field},
|
259 |
+
)
|
260 |
+
if not isdir(self.cache_path):
|
261 |
+
logs.warning("Creating cache directory %s." % self.cache_path)
|
262 |
+
mkdir(self.cache_path)
|
263 |
+
|
264 |
+
# Cache files not needed for UI
|
265 |
+
self.dset_fid = pjoin(self.cache_path, "base_dset")
|
266 |
+
self.tokenized_df_fid = pjoin(self.cache_path, "tokenized_df.feather")
|
267 |
+
self.label_dset_fid = pjoin(self.cache_path, "label_dset")
|
268 |
+
|
269 |
+
# Needed for UI -- embeddings
|
270 |
+
self.text_dset_fid = pjoin(self.cache_path, "text_dset")
|
271 |
+
# Needed for UI
|
272 |
+
self.dset_peek_json_fid = pjoin(self.cache_path, "dset_peek.json")
|
273 |
+
|
274 |
+
## Label cache files.
|
275 |
+
# Needed for UI
|
276 |
+
self.fig_labels_json_fid = pjoin(self.cache_path, "fig_labels.json")
|
277 |
+
|
278 |
+
## Length cache files
|
279 |
+
# Needed for UI
|
280 |
+
self.length_df_fid = pjoin(self.cache_path, "length_df.feather")
|
281 |
+
# Needed for UI
|
282 |
+
self.length_stats_json_fid = pjoin(self.cache_path, "length_stats.json")
|
283 |
+
self.vocab_counts_df_fid = pjoin(self.cache_path, "vocab_counts.feather")
|
284 |
+
# Needed for UI
|
285 |
+
self.dup_counts_df_fid = pjoin(self.cache_path, "dup_counts_df.feather")
|
286 |
+
# Needed for UI
|
287 |
+
self.fig_tok_length_fid = pjoin(self.cache_path, "fig_tok_length.json")
|
288 |
+
|
289 |
+
## General text stats
|
290 |
+
# Needed for UI
|
291 |
+
self.general_stats_json_fid = pjoin(self.cache_path, "general_stats_dict.json")
|
292 |
+
# Needed for UI
|
293 |
+
self.sorted_top_vocab_df_fid = pjoin(self.cache_path,
|
294 |
+
"sorted_top_vocab.feather")
|
295 |
+
## Zipf cache files
|
296 |
+
# Needed for UI
|
297 |
+
self.zipf_fid = pjoin(self.cache_path, "zipf_basic_stats.json")
|
298 |
+
# Needed for UI
|
299 |
+
self.zipf_fig_fid = pjoin(self.cache_path, "zipf_fig.json")
|
300 |
+
|
301 |
+
## Embeddings cache files
|
302 |
+
# Needed for UI
|
303 |
+
self.node_list_fid = pjoin(self.cache_path, "node_list.th")
|
304 |
+
# Needed for UI
|
305 |
+
self.fig_tree_json_fid = pjoin(self.cache_path, "fig_tree.json")
|
306 |
+
self.zipf_counts = None
|
307 |
+
|
308 |
+
self.live = False
|
309 |
+
|
310 |
+
def set_deployment(self, live=True):
|
311 |
+
"""
|
312 |
+
Function that we can hit when we deploy, so that cache files are not
|
313 |
+
written out/recalculated, but instead that part of the UI can be punted.
|
314 |
+
"""
|
315 |
+
self.live = live
|
316 |
+
|
317 |
+
def get_base_dataset(self):
|
318 |
+
"""Gets a pointer to the truncated base dataset object."""
|
319 |
+
if not self.dset:
|
320 |
+
self.dset = load_truncated_dataset(
|
321 |
+
self.dset_name,
|
322 |
+
self.dset_config,
|
323 |
+
self.split_name,
|
324 |
+
cache_name=self.dset_fid,
|
325 |
+
use_cache=True,
|
326 |
+
use_streaming=True,
|
327 |
+
)
|
328 |
+
|
329 |
+
def load_or_prepare_general_stats(self, save=True):
|
330 |
+
"""
|
331 |
+
Content for expander_general_stats widget.
|
332 |
+
Provides statistics for total words, total open words,
|
333 |
+
the sorted top vocab, the NaN count, and the duplicate count.
|
334 |
+
Args:
|
335 |
+
|
336 |
+
Returns:
|
337 |
+
|
338 |
+
"""
|
339 |
+
# General statistics
|
340 |
+
if (
|
341 |
+
self.use_cache
|
342 |
+
and exists(self.general_stats_json_fid)
|
343 |
+
and exists(self.dup_counts_df_fid)
|
344 |
+
and exists(self.sorted_top_vocab_df_fid)
|
345 |
+
):
|
346 |
+
logs.info('Loading cached general stats')
|
347 |
+
self.load_general_stats()
|
348 |
+
else:
|
349 |
+
if not self.live:
|
350 |
+
logs.info('Preparing general stats')
|
351 |
+
self.prepare_general_stats()
|
352 |
+
if save:
|
353 |
+
write_df(self.sorted_top_vocab_df, self.sorted_top_vocab_df_fid)
|
354 |
+
write_df(self.dup_counts_df, self.dup_counts_df_fid)
|
355 |
+
write_json(self.general_stats_dict, self.general_stats_json_fid)
|
356 |
+
|
357 |
+
|
358 |
+
def load_or_prepare_text_lengths(self, save=True):
|
359 |
+
"""
|
360 |
+
The text length widget relies on this function, which provides
|
361 |
+
a figure of the text lengths, some text length statistics, and
|
362 |
+
a text length dataframe to peruse.
|
363 |
+
Args:
|
364 |
+
save:
|
365 |
+
Returns:
|
366 |
+
|
367 |
+
"""
|
368 |
+
# Text length figure
|
369 |
+
if (self.use_cache and exists(self.fig_tok_length_fid)):
|
370 |
+
self.fig_tok_length_png = mpimg.imread(self.fig_tok_length_fid)
|
371 |
+
self.fig_tok_length = read_plotly(self.fig_tok_length_fid)
|
372 |
+
else:
|
373 |
+
if not self.live:
|
374 |
+
self.prepare_fig_text_lengths()
|
375 |
+
if save:
|
376 |
+
write_plotly(self.fig_tok_length, self.fig_tok_length_fid)
|
377 |
+
|
378 |
+
# Text length dataframe
|
379 |
+
if self.use_cache and exists(self.length_df_fid):
|
380 |
+
self.length_df = feather.read_feather(self.length_df_fid)
|
381 |
+
else:
|
382 |
+
if not self.live:
|
383 |
+
self.prepare_length_df()
|
384 |
+
if save:
|
385 |
+
write_df(self.length_df, self.length_df_fid)
|
386 |
+
|
387 |
+
# Text length stats.
|
388 |
+
if self.use_cache and exists(self.length_stats_json_fid):
|
389 |
+
with open(self.length_stats_json_fid, "r") as f:
|
390 |
+
self.length_stats_dict = json.load(f)
|
391 |
+
self.avg_length = self.length_stats_dict["avg length"]
|
392 |
+
self.std_length = self.length_stats_dict["std length"]
|
393 |
+
self.num_uniq_lengths = self.length_stats_dict["num lengths"]
|
394 |
+
else:
|
395 |
+
if not self.live:
|
396 |
+
self.prepare_text_length_stats()
|
397 |
+
if save:
|
398 |
+
write_json(self.length_stats_dict, self.length_stats_json_fid)
|
399 |
+
|
400 |
+
def prepare_length_df(self):
|
401 |
+
if not self.live:
|
402 |
+
if self.tokenized_df is None:
|
403 |
+
self.tokenized_df = self.do_tokenization()
|
404 |
+
self.tokenized_df[LENGTH_FIELD] = self.tokenized_df[
|
405 |
+
TOKENIZED_FIELD].apply(len)
|
406 |
+
self.length_df = self.tokenized_df[
|
407 |
+
[LENGTH_FIELD, OUR_TEXT_FIELD]].sort_values(
|
408 |
+
by=[LENGTH_FIELD], ascending=True
|
409 |
+
)
|
410 |
+
|
411 |
+
def prepare_text_length_stats(self):
|
412 |
+
if not self.live:
|
413 |
+
if self.tokenized_df is None or LENGTH_FIELD not in self.tokenized_df.columns or self.length_df is None:
|
414 |
+
self.prepare_length_df()
|
415 |
+
avg_length = sum(self.tokenized_df[LENGTH_FIELD])/len(self.tokenized_df[LENGTH_FIELD])
|
416 |
+
self.avg_length = round(avg_length, 1)
|
417 |
+
std_length = statistics.stdev(self.tokenized_df[LENGTH_FIELD])
|
418 |
+
self.std_length = round(std_length, 1)
|
419 |
+
self.num_uniq_lengths = len(self.length_df["length"].unique())
|
420 |
+
self.length_stats_dict = {"avg length": self.avg_length,
|
421 |
+
"std length": self.std_length,
|
422 |
+
"num lengths": self.num_uniq_lengths}
|
423 |
+
|
424 |
+
def prepare_fig_text_lengths(self):
|
425 |
+
if not self.live:
|
426 |
+
if self.tokenized_df is None or LENGTH_FIELD not in self.tokenized_df.columns:
|
427 |
+
self.prepare_length_df()
|
428 |
+
self.fig_tok_length = make_fig_lengths(self.tokenized_df, LENGTH_FIELD)
|
429 |
+
|
430 |
+
def load_or_prepare_embeddings(self, save=True):
|
431 |
+
if self.use_cache and exists(self.node_list_fid) and exists(self.fig_tree_json_fid):
|
432 |
+
self.node_list = torch.load(self.node_list_fid)
|
433 |
+
self.fig_tree = read_plotly(self.fig_tree_json_fid)
|
434 |
+
elif self.use_cache and exists(self.node_list_fid):
|
435 |
+
self.node_list = torch.load(self.node_list_fid)
|
436 |
+
self.fig_tree = make_tree_plot(self.node_list,
|
437 |
+
self.text_dset)
|
438 |
+
if save:
|
439 |
+
write_plotly(self.fig_tree, self.fig_tree_json_fid)
|
440 |
+
else:
|
441 |
+
self.embeddings = Embeddings(self, use_cache=self.use_cache)
|
442 |
+
self.embeddings.make_hierarchical_clustering()
|
443 |
+
self.node_list = self.embeddings.node_list
|
444 |
+
self.fig_tree = make_tree_plot(self.node_list,
|
445 |
+
self.text_dset)
|
446 |
+
if save:
|
447 |
+
torch.save(self.node_list, self.node_list_fid)
|
448 |
+
write_plotly(self.fig_tree, self.fig_tree_json_fid)
|
449 |
+
|
450 |
+
# get vocab with word counts
|
451 |
+
def load_or_prepare_vocab(self, save=True):
|
452 |
+
"""
|
453 |
+
Calculates the vocabulary count from the tokenized text.
|
454 |
+
The resulting dataframes may be used in nPMI calculations, zipf, etc.
|
455 |
+
:param
|
456 |
+
:return:
|
457 |
+
"""
|
458 |
+
if (
|
459 |
+
self.use_cache
|
460 |
+
and exists(self.vocab_counts_df_fid)
|
461 |
+
):
|
462 |
+
logs.info("Reading vocab from cache")
|
463 |
+
self.load_vocab()
|
464 |
+
self.vocab_counts_filtered_df = filter_vocab(self.vocab_counts_df)
|
465 |
+
else:
|
466 |
+
logs.info("Calculating vocab afresh")
|
467 |
+
if len(self.tokenized_df) == 0:
|
468 |
+
self.tokenized_df = self.do_tokenization()
|
469 |
+
if save:
|
470 |
+
logs.info("Writing out.")
|
471 |
+
write_df(self.tokenized_df, self.tokenized_df_fid)
|
472 |
+
word_count_df = count_vocab_frequencies(self.tokenized_df)
|
473 |
+
logs.info("Making dfs with proportion.")
|
474 |
+
self.vocab_counts_df = calc_p_word(word_count_df)
|
475 |
+
self.vocab_counts_filtered_df = filter_vocab(self.vocab_counts_df)
|
476 |
+
if save:
|
477 |
+
logs.info("Writing out.")
|
478 |
+
write_df(self.vocab_counts_df, self.vocab_counts_df_fid)
|
479 |
+
logs.info("unfiltered vocab")
|
480 |
+
logs.info(self.vocab_counts_df)
|
481 |
+
logs.info("filtered vocab")
|
482 |
+
logs.info(self.vocab_counts_filtered_df)
|
483 |
+
|
484 |
+
def load_vocab(self):
|
485 |
+
with open(self.vocab_counts_df_fid, "rb") as f:
|
486 |
+
self.vocab_counts_df = feather.read_feather(f)
|
487 |
+
# Handling for changes in how the index is saved.
|
488 |
+
self.vocab_counts_df = self._set_idx_col_names(self.vocab_counts_df)
|
489 |
+
|
490 |
+
def load_or_prepare_text_duplicates(self, save=True):
|
491 |
+
if self.use_cache and exists(self.dup_counts_df_fid):
|
492 |
+
with open(self.dup_counts_df_fid, "rb") as f:
|
493 |
+
self.dup_counts_df = feather.read_feather(f)
|
494 |
+
elif self.dup_counts_df is None:
|
495 |
+
if not self.live:
|
496 |
+
self.prepare_text_duplicates()
|
497 |
+
if save:
|
498 |
+
write_df(self.dup_counts_df, self.dup_counts_df_fid)
|
499 |
+
else:
|
500 |
+
if not self.live:
|
501 |
+
# This happens when self.dup_counts_df is already defined;
|
502 |
+
# This happens when general_statistics were calculated first,
|
503 |
+
# since general statistics requires the number of duplicates
|
504 |
+
if save:
|
505 |
+
write_df(self.dup_counts_df, self.dup_counts_df_fid)
|
506 |
+
|
507 |
+
def load_general_stats(self):
|
508 |
+
self.general_stats_dict = json.load(open(self.general_stats_json_fid, encoding="utf-8"))
|
509 |
+
with open(self.sorted_top_vocab_df_fid, "rb") as f:
|
510 |
+
self.sorted_top_vocab_df = feather.read_feather(f)
|
511 |
+
self.text_nan_count = self.general_stats_dict[TEXT_NAN_CNT]
|
512 |
+
self.dedup_total = self.general_stats_dict[DEDUP_TOT]
|
513 |
+
self.total_words = self.general_stats_dict[TOT_WORDS]
|
514 |
+
self.total_open_words = self.general_stats_dict[TOT_OPEN_WORDS]
|
515 |
+
|
516 |
+
def prepare_general_stats(self):
|
517 |
+
if not self.live:
|
518 |
+
if self.tokenized_df is None:
|
519 |
+
logs.warning("Tokenized dataset not yet loaded; doing so.")
|
520 |
+
self.load_or_prepare_dataset()
|
521 |
+
if self.vocab_counts_df is None:
|
522 |
+
logs.warning("Vocab not yet loaded; doing so.")
|
523 |
+
self.load_or_prepare_vocab()
|
524 |
+
self.sorted_top_vocab_df = self.vocab_counts_filtered_df.sort_values(
|
525 |
+
"count", ascending=False
|
526 |
+
).head(_TOP_N)
|
527 |
+
self.total_words = len(self.vocab_counts_df)
|
528 |
+
self.total_open_words = len(self.vocab_counts_filtered_df)
|
529 |
+
self.text_nan_count = int(self.tokenized_df.isnull().sum().sum())
|
530 |
+
self.prepare_text_duplicates()
|
531 |
+
self.dedup_total = sum(self.dup_counts_df[CNT])
|
532 |
+
self.general_stats_dict = {
|
533 |
+
TOT_WORDS: self.total_words,
|
534 |
+
TOT_OPEN_WORDS: self.total_open_words,
|
535 |
+
TEXT_NAN_CNT: self.text_nan_count,
|
536 |
+
DEDUP_TOT: self.dedup_total,
|
537 |
+
}
|
538 |
+
|
539 |
+
def prepare_text_duplicates(self):
|
540 |
+
if not self.live:
|
541 |
+
if self.tokenized_df is None:
|
542 |
+
self.load_or_prepare_tokenized_df()
|
543 |
+
dup_df = self.tokenized_df[
|
544 |
+
self.tokenized_df.duplicated([OUR_TEXT_FIELD])]
|
545 |
+
self.dup_counts_df = pd.DataFrame(
|
546 |
+
dup_df.pivot_table(
|
547 |
+
columns=[OUR_TEXT_FIELD], aggfunc="size"
|
548 |
+
).sort_values(ascending=False),
|
549 |
+
columns=[CNT],
|
550 |
+
)
|
551 |
+
self.dup_counts_df[OUR_TEXT_FIELD] = self.dup_counts_df.index.copy()
|
552 |
+
|
553 |
+
def load_or_prepare_dataset(self, save=True):
|
554 |
+
"""
|
555 |
+
Prepares the HF datasets and data frames containing the untokenized and
|
556 |
+
tokenized text as well as the label values.
|
557 |
+
self.tokenized_df is used further for calculating text lengths,
|
558 |
+
word counts, etc.
|
559 |
+
Args:
|
560 |
+
save: Store the calculated data to disk.
|
561 |
+
|
562 |
+
Returns:
|
563 |
+
|
564 |
+
"""
|
565 |
+
logs.info("Doing text dset.")
|
566 |
+
self.load_or_prepare_text_dset(save)
|
567 |
+
logs.info("Doing tokenized dataframe")
|
568 |
+
self.load_or_prepare_tokenized_df(save)
|
569 |
+
logs.info("Doing dataset peek")
|
570 |
+
self.load_or_prepare_dset_peek(save)
|
571 |
+
|
572 |
+
def load_or_prepare_dset_peek(self, save=True):
|
573 |
+
if self.use_cache and exists(self.dset_peek_json_fid):
|
574 |
+
with open(self.dset_peek_json_fid, "r") as f:
|
575 |
+
self.dset_peek = json.load(f)["dset peek"]
|
576 |
+
else:
|
577 |
+
if self.dset is None:
|
578 |
+
self.get_base_dataset()
|
579 |
+
self.dset_peek = self.dset[:100]
|
580 |
+
if save:
|
581 |
+
write_json({"dset peek": self.dset_peek}, self.dset_peek_json_fid)
|
582 |
+
|
583 |
+
def load_or_prepare_tokenized_df(self, save=True):
|
584 |
+
if (self.use_cache and exists(self.tokenized_df_fid)):
|
585 |
+
self.tokenized_df = feather.read_feather(self.tokenized_df_fid)
|
586 |
+
else:
|
587 |
+
if not self.live:
|
588 |
+
# tokenize all text instances
|
589 |
+
self.tokenized_df = self.do_tokenization()
|
590 |
+
if save:
|
591 |
+
logs.warning("Saving tokenized dataset to disk")
|
592 |
+
# save tokenized text
|
593 |
+
write_df(self.tokenized_df, self.tokenized_df_fid)
|
594 |
+
|
595 |
+
def load_or_prepare_text_dset(self, save=True):
|
596 |
+
if (self.use_cache and exists(self.text_dset_fid)):
|
597 |
+
# load extracted text
|
598 |
+
self.text_dset = load_from_disk(self.text_dset_fid)
|
599 |
+
logs.warning("Loaded dataset from disk")
|
600 |
+
logs.info(self.text_dset)
|
601 |
+
# ...Or load it from the server and store it anew
|
602 |
+
else:
|
603 |
+
if not self.live:
|
604 |
+
self.prepare_text_dset()
|
605 |
+
if save:
|
606 |
+
# save extracted text instances
|
607 |
+
logs.warning("Saving dataset to disk")
|
608 |
+
self.text_dset.save_to_disk(self.text_dset_fid)
|
609 |
+
|
610 |
+
def prepare_text_dset(self):
|
611 |
+
if not self.live:
|
612 |
+
self.get_base_dataset()
|
613 |
+
# extract all text instances
|
614 |
+
self.text_dset = self.dset.map(
|
615 |
+
lambda examples: extract_field(
|
616 |
+
examples, self.text_field, OUR_TEXT_FIELD
|
617 |
+
),
|
618 |
+
batched=True,
|
619 |
+
remove_columns=list(self.dset.features),
|
620 |
+
)
|
621 |
+
|
622 |
+
def do_tokenization(self):
|
623 |
+
"""
|
624 |
+
Tokenizes the dataset
|
625 |
+
:return:
|
626 |
+
"""
|
627 |
+
if self.text_dset is None:
|
628 |
+
self.load_or_prepare_text_dset()
|
629 |
+
sent_tokenizer = self.cvec.build_tokenizer()
|
630 |
+
|
631 |
+
def tokenize_batch(examples):
|
632 |
+
# TODO: lowercase should be an option
|
633 |
+
res = {
|
634 |
+
TOKENIZED_FIELD: [
|
635 |
+
tuple(sent_tokenizer(text.lower()))
|
636 |
+
for text in examples[OUR_TEXT_FIELD]
|
637 |
+
]
|
638 |
+
}
|
639 |
+
res[LENGTH_FIELD] = [len(tok_text) for tok_text in res[TOKENIZED_FIELD]]
|
640 |
+
return res
|
641 |
+
|
642 |
+
tokenized_dset = self.text_dset.map(
|
643 |
+
tokenize_batch,
|
644 |
+
batched=True,
|
645 |
+
# remove_columns=[OUR_TEXT_FIELD], keep around to print
|
646 |
+
)
|
647 |
+
tokenized_df = pd.DataFrame(tokenized_dset)
|
648 |
+
return tokenized_df
|
649 |
+
|
650 |
+
def set_label_field(self, label_field="label"):
|
651 |
+
"""
|
652 |
+
Setter for label_field. Used in the CLI when a user asks for information
|
653 |
+
about labels, but does not specify the field;
|
654 |
+
'label' is assumed as a default.
|
655 |
+
"""
|
656 |
+
self.label_field = label_field
|
657 |
+
|
658 |
+
def load_or_prepare_labels(self, save=True):
|
659 |
+
# TODO: This is in a transitory state for creating fig cache.
|
660 |
+
# Clean up to be caching and reading everything correctly.
|
661 |
+
"""
|
662 |
+
Extracts labels from the Dataset
|
663 |
+
:return:
|
664 |
+
"""
|
665 |
+
# extracted labels
|
666 |
+
if len(self.label_field) > 0:
|
667 |
+
if self.use_cache and exists(self.fig_labels_json_fid):
|
668 |
+
self.fig_labels = read_plotly(self.fig_labels_json_fid)
|
669 |
+
elif self.use_cache and exists(self.label_dset_fid):
|
670 |
+
# load extracted labels
|
671 |
+
self.label_dset = load_from_disk(self.label_dset_fid)
|
672 |
+
self.label_df = self.label_dset.to_pandas()
|
673 |
+
self.fig_labels = make_fig_labels(
|
674 |
+
self.label_df, self.label_names, OUR_LABEL_FIELD
|
675 |
+
)
|
676 |
+
if save:
|
677 |
+
write_plotly(self.fig_labels, self.fig_labels_json_fid)
|
678 |
+
else:
|
679 |
+
if not self.live:
|
680 |
+
self.prepare_labels()
|
681 |
+
if save:
|
682 |
+
# save extracted label instances
|
683 |
+
self.label_dset.save_to_disk(self.label_dset_fid)
|
684 |
+
write_plotly(self.fig_labels, self.fig_labels_json_fid)
|
685 |
+
|
686 |
+
def prepare_labels(self):
|
687 |
+
if not self.live:
|
688 |
+
self.get_base_dataset()
|
689 |
+
self.label_dset = self.dset.map(
|
690 |
+
lambda examples: extract_field(
|
691 |
+
examples, self.label_field, OUR_LABEL_FIELD
|
692 |
+
),
|
693 |
+
batched=True,
|
694 |
+
remove_columns=list(self.dset.features),
|
695 |
+
)
|
696 |
+
self.label_df = self.label_dset.to_pandas()
|
697 |
+
self.fig_labels = make_fig_labels(
|
698 |
+
self.label_df, self.label_names, OUR_LABEL_FIELD
|
699 |
+
)
|
700 |
+
|
701 |
+
def load_or_prepare_npmi(self):
|
702 |
+
self.npmi_stats = nPMIStatisticsCacheClass(self, use_cache=self.use_cache)
|
703 |
+
self.npmi_stats.load_or_prepare_npmi_terms()
|
704 |
+
|
705 |
+
def load_or_prepare_zipf(self, save=True):
|
706 |
+
# TODO: Current UI only uses the fig, meaning the self.z here is irrelevant
|
707 |
+
# when only reading from cache. Either the UI should use it, or it should
|
708 |
+
# be removed when reading in cache
|
709 |
+
if self.use_cache and exists(self.zipf_fig_fid) and exists(self.zipf_fid):
|
710 |
+
with open(self.zipf_fid, "r") as f:
|
711 |
+
zipf_dict = json.load(f)
|
712 |
+
self.z = Zipf()
|
713 |
+
self.z.load(zipf_dict)
|
714 |
+
# TODO: Should this be cached?
|
715 |
+
self.zipf_counts = self.z.calc_zipf_counts(self.vocab_counts_df)
|
716 |
+
self.zipf_fig = read_plotly(self.zipf_fig_fid)
|
717 |
+
elif self.use_cache and exists(self.zipf_fid):
|
718 |
+
# TODO: Read zipf data so that the vocab is there.
|
719 |
+
with open(self.zipf_fid, "r") as f:
|
720 |
+
zipf_dict = json.load(f)
|
721 |
+
self.z = Zipf()
|
722 |
+
self.z.load(zipf_dict)
|
723 |
+
self.zipf_fig = make_zipf_fig(self.vocab_counts_df, self.z)
|
724 |
+
if save:
|
725 |
+
write_plotly(self.zipf_fig, self.zipf_fig_fid)
|
726 |
+
else:
|
727 |
+
self.z = Zipf(self.vocab_counts_df)
|
728 |
+
self.zipf_fig = make_zipf_fig(self.vocab_counts_df, self.z)
|
729 |
+
if save:
|
730 |
+
write_zipf_data(self.z, self.zipf_fid)
|
731 |
+
write_plotly(self.zipf_fig, self.zipf_fig_fid)
|
732 |
+
|
733 |
+
def _set_idx_col_names(self, input_vocab_df):
|
734 |
+
if input_vocab_df.index.name != VOCAB and VOCAB in input_vocab_df.columns:
|
735 |
+
input_vocab_df = input_vocab_df.set_index([VOCAB])
|
736 |
+
input_vocab_df[VOCAB] = input_vocab_df.index
|
737 |
+
return input_vocab_df
|
738 |
+
|
739 |
+
|
740 |
+
class nPMIStatisticsCacheClass:
|
741 |
+
""" "Class to interface between the app and the nPMI class
|
742 |
+
by calling the nPMI class with the user's selections."""
|
743 |
+
|
744 |
+
def __init__(self, dataset_stats, use_cache=False):
|
745 |
+
self.live = dataset_stats.live
|
746 |
+
self.dstats = dataset_stats
|
747 |
+
self.pmi_cache_path = pjoin(self.dstats.cache_path, "pmi_files")
|
748 |
+
if not isdir(self.pmi_cache_path):
|
749 |
+
logs.warning("Creating pmi cache directory %s." % self.pmi_cache_path)
|
750 |
+
# We need to preprocess everything.
|
751 |
+
mkdir(self.pmi_cache_path)
|
752 |
+
self.joint_npmi_df_dict = {}
|
753 |
+
# TODO: Users ideally can type in whatever words they want.
|
754 |
+
self.termlist = _IDENTITY_TERMS
|
755 |
+
# termlist terms that are available more than _MIN_VOCAB_COUNT times
|
756 |
+
self.available_terms = _IDENTITY_TERMS
|
757 |
+
logs.info(self.termlist)
|
758 |
+
self.use_cache = use_cache
|
759 |
+
# TODO: Let users specify
|
760 |
+
self.open_class_only = True
|
761 |
+
self.min_vocab_count = self.dstats.min_vocab_count
|
762 |
+
self.subgroup_files = {}
|
763 |
+
self.npmi_terms_fid = pjoin(self.dstats.cache_path, "npmi_terms.json")
|
764 |
+
|
765 |
+
def load_or_prepare_npmi_terms(self):
|
766 |
+
"""
|
767 |
+
Figures out what identity terms the user can select, based on whether
|
768 |
+
they occur more than self.min_vocab_count times
|
769 |
+
:return: Identity terms occurring at least self.min_vocab_count times.
|
770 |
+
"""
|
771 |
+
# TODO: Add the user's ability to select subgroups.
|
772 |
+
# TODO: Make min_vocab_count here value selectable by the user.
|
773 |
+
if (
|
774 |
+
self.use_cache
|
775 |
+
and exists(self.npmi_terms_fid)
|
776 |
+
and json.load(open(self.npmi_terms_fid))["available terms"] != []
|
777 |
+
):
|
778 |
+
self.available_terms = json.load(open(self.npmi_terms_fid))["available terms"]
|
779 |
+
else:
|
780 |
+
if not self.live:
|
781 |
+
if self.dstats.vocab_counts_df is None:
|
782 |
+
self.dstats.load_or_prepare_vocab()
|
783 |
+
|
784 |
+
true_false = [
|
785 |
+
term in self.dstats.vocab_counts_df.index for term in self.termlist
|
786 |
+
]
|
787 |
+
word_list_tmp = [x for x, y in zip(self.termlist, true_false) if y]
|
788 |
+
true_false_counts = [
|
789 |
+
self.dstats.vocab_counts_df.loc[word, CNT] >= self.min_vocab_count
|
790 |
+
for word in word_list_tmp
|
791 |
+
]
|
792 |
+
available_terms = [
|
793 |
+
word for word, y in zip(word_list_tmp, true_false_counts) if y
|
794 |
+
]
|
795 |
+
logs.info(available_terms)
|
796 |
+
with open(self.npmi_terms_fid, "w+") as f:
|
797 |
+
json.dump({"available terms": available_terms}, f)
|
798 |
+
self.available_terms = available_terms
|
799 |
+
return self.available_terms
|
800 |
+
|
801 |
+
def load_or_prepare_joint_npmi(self, subgroup_pair, save=True):
|
802 |
+
"""
|
803 |
+
Run on-the fly, while the app is already open,
|
804 |
+
as it depends on the subgroup terms that the user chooses
|
805 |
+
:param subgroup_pair:
|
806 |
+
:return:
|
807 |
+
"""
|
808 |
+
# Canonical ordering for subgroup_list
|
809 |
+
subgroup_pair = sorted(subgroup_pair)
|
810 |
+
subgroup1 = subgroup_pair[0]
|
811 |
+
subgroup2 = subgroup_pair[1]
|
812 |
+
subgroups_str = "-".join(subgroup_pair)
|
813 |
+
if not isdir(self.pmi_cache_path):
|
814 |
+
logs.warning("Creating cache")
|
815 |
+
# We need to preprocess everything.
|
816 |
+
# This should eventually all go into a prepare_dataset CLI
|
817 |
+
mkdir(self.pmi_cache_path)
|
818 |
+
joint_npmi_fid = pjoin(self.pmi_cache_path, subgroups_str + "_npmi.csv")
|
819 |
+
subgroup_files = define_subgroup_files(subgroup_pair, self.pmi_cache_path)
|
820 |
+
# Defines the filenames for the cache files from the selected subgroups.
|
821 |
+
# Get as much precomputed data as we can.
|
822 |
+
if self.use_cache and exists(joint_npmi_fid):
|
823 |
+
# When everything is already computed for the selected subgroups.
|
824 |
+
logs.info("Loading cached joint npmi")
|
825 |
+
joint_npmi_df = self.load_joint_npmi_df(joint_npmi_fid)
|
826 |
+
npmi_display_cols = ['npmi-bias', subgroup1 + '-npmi', subgroup2 + '-npmi', subgroup1 + '-count', subgroup2 + '-count']
|
827 |
+
joint_npmi_df = joint_npmi_df[npmi_display_cols]
|
828 |
+
# When maybe some things have been computed for the selected subgroups.
|
829 |
+
else:
|
830 |
+
if not self.live:
|
831 |
+
logs.info("Preparing new joint npmi")
|
832 |
+
joint_npmi_df, subgroup_dict = self.prepare_joint_npmi_df(
|
833 |
+
subgroup_pair, subgroup_files
|
834 |
+
)
|
835 |
+
if save:
|
836 |
+
if joint_npmi_df is not None:
|
837 |
+
# Cache new results
|
838 |
+
logs.info("Writing out.")
|
839 |
+
for subgroup in subgroup_pair:
|
840 |
+
write_subgroup_npmi_data(subgroup, subgroup_dict, subgroup_files)
|
841 |
+
with open(joint_npmi_fid, "w+") as f:
|
842 |
+
joint_npmi_df.to_csv(f)
|
843 |
+
else:
|
844 |
+
joint_npmi_df = pd.DataFrame()
|
845 |
+
logs.info("The joint npmi df is")
|
846 |
+
logs.info(joint_npmi_df)
|
847 |
+
return joint_npmi_df
|
848 |
+
|
849 |
+
def load_joint_npmi_df(self, joint_npmi_fid):
|
850 |
+
"""
|
851 |
+
Reads in a saved dataframe with all of the paired results.
|
852 |
+
:param joint_npmi_fid:
|
853 |
+
:return: paired results
|
854 |
+
"""
|
855 |
+
with open(joint_npmi_fid, "rb") as f:
|
856 |
+
joint_npmi_df = pd.read_csv(f)
|
857 |
+
joint_npmi_df = self._set_idx_cols_from_cache(joint_npmi_df)
|
858 |
+
return joint_npmi_df.dropna()
|
859 |
+
|
860 |
+
def prepare_joint_npmi_df(self, subgroup_pair, subgroup_files):
|
861 |
+
"""
|
862 |
+
Computs the npmi bias based on the given subgroups.
|
863 |
+
Handles cases where some of the selected subgroups have cached nPMI
|
864 |
+
computations, but other's don't, computing everything afresh if there
|
865 |
+
are not cached files.
|
866 |
+
:param subgroup_pair:
|
867 |
+
:return: Dataframe with nPMI for the words, nPMI bias between the words.
|
868 |
+
"""
|
869 |
+
subgroup_dict = {}
|
870 |
+
# When npmi is computed for some (but not all) of subgroup_list
|
871 |
+
for subgroup in subgroup_pair:
|
872 |
+
logs.info("Load or failing...")
|
873 |
+
# When subgroup npmi has been computed in a prior session.
|
874 |
+
cached_results = self.load_or_fail_cached_npmi_scores(
|
875 |
+
subgroup, subgroup_files[subgroup]
|
876 |
+
)
|
877 |
+
# If the function did not return False and we did find it, use.
|
878 |
+
if cached_results:
|
879 |
+
# FYI: subgroup_cooc_df, subgroup_pmi_df, subgroup_npmi_df = cached_results
|
880 |
+
# Holds the previous sessions' data for use in this session.
|
881 |
+
subgroup_dict[subgroup] = cached_results
|
882 |
+
logs.info("Calculating for subgroup list")
|
883 |
+
joint_npmi_df, subgroup_dict = self.do_npmi(subgroup_pair, subgroup_dict)
|
884 |
+
return joint_npmi_df, subgroup_dict
|
885 |
+
|
886 |
+
# TODO: Update pairwise assumption
|
887 |
+
def do_npmi(self, subgroup_pair, subgroup_dict):
|
888 |
+
"""
|
889 |
+
Calculates nPMI for given identity terms and the nPMI bias between.
|
890 |
+
:param subgroup_pair: List of identity terms to calculate the bias for
|
891 |
+
:return: Subset of data for the UI
|
892 |
+
:return: Selected identity term's co-occurrence counts with
|
893 |
+
other words, pmi per word, and nPMI per word.
|
894 |
+
"""
|
895 |
+
no_results = False
|
896 |
+
logs.info("Initializing npmi class")
|
897 |
+
npmi_obj = self.set_npmi_obj()
|
898 |
+
# Canonical ordering used
|
899 |
+
subgroup_pair = tuple(sorted(subgroup_pair))
|
900 |
+
# Calculating nPMI statistics
|
901 |
+
for subgroup in subgroup_pair:
|
902 |
+
# If the subgroup data is already computed, grab it.
|
903 |
+
# TODO: Should we set idx and column names similarly to
|
904 |
+
# how we set them for cached files?
|
905 |
+
if subgroup not in subgroup_dict:
|
906 |
+
logs.info("Calculating statistics for %s" % subgroup)
|
907 |
+
vocab_cooc_df, pmi_df, npmi_df = npmi_obj.calc_metrics(subgroup)
|
908 |
+
if vocab_cooc_df is None:
|
909 |
+
no_results = True
|
910 |
+
else:
|
911 |
+
# Store the nPMI information for the current subgroups
|
912 |
+
subgroup_dict[subgroup] = (vocab_cooc_df, pmi_df, npmi_df)
|
913 |
+
if no_results:
|
914 |
+
logs.warning("Couldn't grap the npmi files -- Under construction")
|
915 |
+
return None, None
|
916 |
+
else:
|
917 |
+
# Pair the subgroups together, indexed by all words that
|
918 |
+
# co-occur between them.
|
919 |
+
logs.info("Computing pairwise npmi bias")
|
920 |
+
paired_results = npmi_obj.calc_paired_metrics(subgroup_pair, subgroup_dict)
|
921 |
+
UI_results = make_npmi_fig(paired_results, subgroup_pair)
|
922 |
+
return UI_results.dropna(), subgroup_dict
|
923 |
+
|
924 |
+
def set_npmi_obj(self):
|
925 |
+
"""
|
926 |
+
Initializes the nPMI class with the given words and tokenized sentences.
|
927 |
+
:return:
|
928 |
+
"""
|
929 |
+
npmi_obj = nPMI(self.dstats.vocab_counts_df, self.dstats.tokenized_df)
|
930 |
+
return npmi_obj
|
931 |
+
|
932 |
+
def load_or_fail_cached_npmi_scores(self, subgroup, subgroup_fids):
|
933 |
+
"""
|
934 |
+
Reads cached scores from the specified subgroup files
|
935 |
+
:param subgroup: string of the selected identity term
|
936 |
+
:return:
|
937 |
+
"""
|
938 |
+
# TODO: Ordering of npmi, pmi, vocab triple should be consistent
|
939 |
+
subgroup_npmi_fid, subgroup_pmi_fid, subgroup_cooc_fid = subgroup_fids
|
940 |
+
if (
|
941 |
+
exists(subgroup_npmi_fid)
|
942 |
+
and exists(subgroup_pmi_fid)
|
943 |
+
and exists(subgroup_cooc_fid)
|
944 |
+
):
|
945 |
+
logs.info("Reading in pmi data....")
|
946 |
+
with open(subgroup_cooc_fid, "rb") as f:
|
947 |
+
subgroup_cooc_df = pd.read_csv(f)
|
948 |
+
logs.info("pmi")
|
949 |
+
with open(subgroup_pmi_fid, "rb") as f:
|
950 |
+
subgroup_pmi_df = pd.read_csv(f)
|
951 |
+
logs.info("npmi")
|
952 |
+
with open(subgroup_npmi_fid, "rb") as f:
|
953 |
+
subgroup_npmi_df = pd.read_csv(f)
|
954 |
+
subgroup_cooc_df = self._set_idx_cols_from_cache(
|
955 |
+
subgroup_cooc_df, subgroup, "count"
|
956 |
+
)
|
957 |
+
subgroup_pmi_df = self._set_idx_cols_from_cache(
|
958 |
+
subgroup_pmi_df, subgroup, "pmi"
|
959 |
+
)
|
960 |
+
subgroup_npmi_df = self._set_idx_cols_from_cache(
|
961 |
+
subgroup_npmi_df, subgroup, "npmi"
|
962 |
+
)
|
963 |
+
return subgroup_cooc_df, subgroup_pmi_df, subgroup_npmi_df
|
964 |
+
return False
|
965 |
+
|
966 |
+
def _set_idx_cols_from_cache(self, csv_df, subgroup=None, calc_str=None):
|
967 |
+
"""
|
968 |
+
Helps make sure all of the read-in files can be accessed within code
|
969 |
+
via standardized indices and column names.
|
970 |
+
:param csv_df:
|
971 |
+
:param subgroup:
|
972 |
+
:param calc_str:
|
973 |
+
:return:
|
974 |
+
"""
|
975 |
+
# The csv saves with this column instead of the index, so that's weird.
|
976 |
+
if "Unnamed: 0" in csv_df.columns:
|
977 |
+
csv_df = csv_df.set_index("Unnamed: 0")
|
978 |
+
csv_df.index.name = WORD
|
979 |
+
elif WORD in csv_df.columns:
|
980 |
+
csv_df = csv_df.set_index(WORD)
|
981 |
+
csv_df.index.name = WORD
|
982 |
+
elif VOCAB in csv_df.columns:
|
983 |
+
csv_df = csv_df.set_index(VOCAB)
|
984 |
+
csv_df.index.name = WORD
|
985 |
+
if subgroup and calc_str:
|
986 |
+
csv_df.columns = [subgroup + "-" + calc_str]
|
987 |
+
elif subgroup:
|
988 |
+
csv_df.columns = [subgroup]
|
989 |
+
elif calc_str:
|
990 |
+
csv_df.columns = [calc_str]
|
991 |
+
return csv_df
|
992 |
+
|
993 |
+
def get_available_terms(self):
|
994 |
+
return self.load_or_prepare_npmi_terms()
|
995 |
+
|
996 |
+
def dummy(doc):
|
997 |
+
return doc
|
998 |
+
|
999 |
+
def count_vocab_frequencies(tokenized_df):
|
1000 |
+
"""
|
1001 |
+
Based on an input pandas DataFrame with a 'text' column,
|
1002 |
+
this function will count the occurrences of all words.
|
1003 |
+
:return: [num_words x num_sentences] DataFrame with the rows corresponding to the
|
1004 |
+
different vocabulary words and the column to the presence (0 or 1) of that word.
|
1005 |
+
"""
|
1006 |
+
|
1007 |
+
cvec = CountVectorizer(
|
1008 |
+
tokenizer=dummy,
|
1009 |
+
preprocessor=dummy,
|
1010 |
+
)
|
1011 |
+
# We do this to calculate per-word statistics
|
1012 |
+
# Fast calculation of single word counts
|
1013 |
+
logs.info("Fitting dummy tokenization to make matrix using the previous tokenization")
|
1014 |
+
cvec.fit(tokenized_df[TOKENIZED_FIELD])
|
1015 |
+
document_matrix = cvec.transform(tokenized_df[TOKENIZED_FIELD])
|
1016 |
+
batches = np.linspace(0, tokenized_df.shape[0], _NUM_VOCAB_BATCHES).astype(int)
|
1017 |
+
i = 0
|
1018 |
+
tf = []
|
1019 |
+
while i < len(batches) - 1:
|
1020 |
+
logs.info("%s of %s vocab batches" % (str(i), str(len(batches))))
|
1021 |
+
batch_result = np.sum(
|
1022 |
+
document_matrix[batches[i] : batches[i + 1]].toarray(), axis=0
|
1023 |
+
)
|
1024 |
+
tf.append(batch_result)
|
1025 |
+
i += 1
|
1026 |
+
word_count_df = pd.DataFrame(
|
1027 |
+
[np.sum(tf, axis=0)], columns=cvec.get_feature_names()
|
1028 |
+
).transpose()
|
1029 |
+
# Now organize everything into the dataframes
|
1030 |
+
word_count_df.columns = [CNT]
|
1031 |
+
word_count_df.index.name = WORD
|
1032 |
+
return word_count_df
|
1033 |
+
|
1034 |
+
def calc_p_word(word_count_df):
|
1035 |
+
# p(word)
|
1036 |
+
word_count_df[PROP] = word_count_df[CNT] / float(sum(word_count_df[CNT]))
|
1037 |
+
vocab_counts_df = pd.DataFrame(word_count_df.sort_values(by=CNT, ascending=False))
|
1038 |
+
vocab_counts_df[VOCAB] = vocab_counts_df.index
|
1039 |
+
return vocab_counts_df
|
1040 |
+
|
1041 |
+
|
1042 |
+
def filter_vocab(vocab_counts_df):
|
1043 |
+
# TODO: Add warnings (which words are missing) to log file?
|
1044 |
+
filtered_vocab_counts_df = vocab_counts_df.drop(_CLOSED_CLASS,
|
1045 |
+
errors="ignore")
|
1046 |
+
filtered_count = filtered_vocab_counts_df[CNT]
|
1047 |
+
filtered_count_denom = float(sum(filtered_vocab_counts_df[CNT]))
|
1048 |
+
filtered_vocab_counts_df[PROP] = filtered_count / filtered_count_denom
|
1049 |
+
return filtered_vocab_counts_df
|
1050 |
+
|
1051 |
+
|
1052 |
+
## Figures ##
|
1053 |
+
|
1054 |
+
def write_plotly(fig, fid):
|
1055 |
+
write_json(plotly.io.to_json(fig), fid)
|
1056 |
+
|
1057 |
+
def read_plotly(fid):
|
1058 |
+
fig = plotly.io.from_json(json.load(open(fid, encoding="utf-8")))
|
1059 |
+
return fig
|
1060 |
+
|
1061 |
+
def make_fig_lengths(tokenized_df, length_field):
|
1062 |
+
fig_tok_length = px.histogram(
|
1063 |
+
tokenized_df, x=length_field, marginal="rug", hover_data=[length_field]
|
1064 |
+
)
|
1065 |
+
return fig_tok_length
|
1066 |
+
|
1067 |
+
def make_fig_labels(label_df, label_names, label_field):
|
1068 |
+
labels = label_df[label_field].unique()
|
1069 |
+
label_sums = [len(label_df[label_df[label_field] == label]) for label in labels]
|
1070 |
+
fig_labels = px.pie(label_df, values=label_sums, names=label_names)
|
1071 |
+
return fig_labels
|
1072 |
+
|
1073 |
+
|
1074 |
+
def make_zipf_fig_ranked_word_list(vocab_df, unique_counts, unique_ranks):
|
1075 |
+
ranked_words = {}
|
1076 |
+
for count, rank in zip(unique_counts, unique_ranks):
|
1077 |
+
vocab_df[vocab_df[CNT] == count]["rank"] = rank
|
1078 |
+
ranked_words[rank] = ",".join(
|
1079 |
+
vocab_df[vocab_df[CNT] == count].index.astype(str)
|
1080 |
+
) # Use the hovertext kw argument for hover text
|
1081 |
+
ranked_words_list = [wrds for rank, wrds in sorted(ranked_words.items())]
|
1082 |
+
return ranked_words_list
|
1083 |
+
|
1084 |
+
|
1085 |
+
def make_npmi_fig(paired_results, subgroup_pair):
|
1086 |
+
subgroup1, subgroup2 = subgroup_pair
|
1087 |
+
UI_results = pd.DataFrame()
|
1088 |
+
if "npmi-bias" in paired_results:
|
1089 |
+
UI_results["npmi-bias"] = paired_results["npmi-bias"].astype(float)
|
1090 |
+
UI_results[subgroup1 + "-npmi"] = paired_results["npmi"][
|
1091 |
+
subgroup1 + "-npmi"
|
1092 |
+
].astype(float)
|
1093 |
+
UI_results[subgroup1 + "-count"] = paired_results["count"][
|
1094 |
+
subgroup1 + "-count"
|
1095 |
+
].astype(int)
|
1096 |
+
if subgroup1 != subgroup2:
|
1097 |
+
UI_results[subgroup2 + "-npmi"] = paired_results["npmi"][
|
1098 |
+
subgroup2 + "-npmi"
|
1099 |
+
].astype(float)
|
1100 |
+
UI_results[subgroup2 + "-count"] = paired_results["count"][
|
1101 |
+
subgroup2 + "-count"
|
1102 |
+
].astype(int)
|
1103 |
+
return UI_results.sort_values(by="npmi-bias", ascending=True)
|
1104 |
+
|
1105 |
+
|
1106 |
+
def make_zipf_fig(vocab_counts_df, z):
|
1107 |
+
zipf_counts = z.calc_zipf_counts(vocab_counts_df)
|
1108 |
+
unique_counts = z.uniq_counts
|
1109 |
+
unique_ranks = z.uniq_ranks
|
1110 |
+
ranked_words_list = make_zipf_fig_ranked_word_list(
|
1111 |
+
vocab_counts_df, unique_counts, unique_ranks
|
1112 |
+
)
|
1113 |
+
zmin = z.get_xmin()
|
1114 |
+
logs.info("zipf counts is")
|
1115 |
+
logs.info(zipf_counts)
|
1116 |
+
layout = go.Layout(xaxis=dict(range=[0, 100]))
|
1117 |
+
fig = go.Figure(
|
1118 |
+
data=[
|
1119 |
+
go.Bar(
|
1120 |
+
x=z.uniq_ranks,
|
1121 |
+
y=z.uniq_counts,
|
1122 |
+
hovertext=ranked_words_list,
|
1123 |
+
name="Word Rank Frequency",
|
1124 |
+
)
|
1125 |
+
],
|
1126 |
+
layout=layout,
|
1127 |
+
)
|
1128 |
+
fig.add_trace(
|
1129 |
+
go.Scatter(
|
1130 |
+
x=z.uniq_ranks[zmin : len(z.uniq_ranks)],
|
1131 |
+
y=zipf_counts[zmin : len(z.uniq_ranks)],
|
1132 |
+
hovertext=ranked_words_list[zmin : len(z.uniq_ranks)],
|
1133 |
+
line=go.scatter.Line(color="crimson", width=3),
|
1134 |
+
name="Zipf Predicted Frequency",
|
1135 |
+
)
|
1136 |
+
)
|
1137 |
+
# Customize aspect
|
1138 |
+
# fig.update_traces(marker_color='limegreen',
|
1139 |
+
# marker_line_width=1.5, opacity=0.6)
|
1140 |
+
fig.update_layout(title_text="Word Counts, Observed and Predicted by Zipf")
|
1141 |
+
fig.update_layout(xaxis_title="Word Rank")
|
1142 |
+
fig.update_layout(yaxis_title="Frequency")
|
1143 |
+
fig.update_layout(legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.10))
|
1144 |
+
return fig
|
1145 |
+
|
1146 |
+
|
1147 |
+
def make_tree_plot(node_list, text_dset):
|
1148 |
+
nid_map = dict([(node["nid"], nid) for nid, node in enumerate(node_list)])
|
1149 |
+
|
1150 |
+
for nid, node in enumerate(node_list):
|
1151 |
+
node["label"] = node.get(
|
1152 |
+
"label",
|
1153 |
+
f"{nid:2d} - {node['weight']:5d} items <br>"
|
1154 |
+
+ "<br>".join(
|
1155 |
+
[
|
1156 |
+
"> " + txt[:64] + ("..." if len(txt) >= 63 else "")
|
1157 |
+
for txt in list(
|
1158 |
+
set(text_dset.select(node["example_ids"])[OUR_TEXT_FIELD])
|
1159 |
+
)[:5]
|
1160 |
+
]
|
1161 |
+
),
|
1162 |
+
)
|
1163 |
+
|
1164 |
+
# make plot nodes
|
1165 |
+
# TODO: something more efficient than set to remove duplicates
|
1166 |
+
labels = [node["label"] for node in node_list]
|
1167 |
+
|
1168 |
+
root = node_list[0]
|
1169 |
+
root["X"] = 0
|
1170 |
+
root["Y"] = 0
|
1171 |
+
|
1172 |
+
def rec_make_coordinates(node):
|
1173 |
+
total_weight = 0
|
1174 |
+
add_weight = len(node["example_ids"]) - sum(
|
1175 |
+
[child["weight"] for child in node["children"]]
|
1176 |
+
)
|
1177 |
+
for child in node["children"]:
|
1178 |
+
child["X"] = node["X"] + total_weight
|
1179 |
+
child["Y"] = node["Y"] - 1
|
1180 |
+
total_weight += child["weight"] + add_weight / len(node["children"])
|
1181 |
+
rec_make_coordinates(child)
|
1182 |
+
|
1183 |
+
rec_make_coordinates(root)
|
1184 |
+
|
1185 |
+
E = [] # list of edges
|
1186 |
+
Xn = []
|
1187 |
+
Yn = []
|
1188 |
+
Xe = []
|
1189 |
+
Ye = []
|
1190 |
+
for nid, node in enumerate(node_list):
|
1191 |
+
Xn += [node["X"]]
|
1192 |
+
Yn += [node["Y"]]
|
1193 |
+
for child in node["children"]:
|
1194 |
+
E += [(nid, nid_map[child["nid"]])]
|
1195 |
+
Xe += [node["X"], child["X"], None]
|
1196 |
+
Ye += [node["Y"], child["Y"], None]
|
1197 |
+
|
1198 |
+
# make figure
|
1199 |
+
fig = go.Figure()
|
1200 |
+
fig.add_trace(
|
1201 |
+
go.Scatter(
|
1202 |
+
x=Xe,
|
1203 |
+
y=Ye,
|
1204 |
+
mode="lines",
|
1205 |
+
line=dict(color="rgb(210,210,210)", width=1),
|
1206 |
+
hoverinfo="none",
|
1207 |
+
)
|
1208 |
+
)
|
1209 |
+
fig.add_trace(
|
1210 |
+
go.Scatter(
|
1211 |
+
x=Xn,
|
1212 |
+
y=Yn,
|
1213 |
+
mode="markers",
|
1214 |
+
name="nodes",
|
1215 |
+
marker=dict(
|
1216 |
+
symbol="circle-dot",
|
1217 |
+
size=18,
|
1218 |
+
color="#6175c1",
|
1219 |
+
line=dict(color="rgb(50,50,50)", width=1)
|
1220 |
+
# '#DB4551',
|
1221 |
+
),
|
1222 |
+
text=labels,
|
1223 |
+
hoverinfo="text",
|
1224 |
+
opacity=0.8,
|
1225 |
+
)
|
1226 |
+
)
|
1227 |
+
return fig
|
1228 |
+
|
1229 |
+
|
1230 |
+
## Input/Output ###
|
1231 |
+
|
1232 |
+
|
1233 |
+
def define_subgroup_files(subgroup_list, pmi_cache_path):
|
1234 |
+
"""
|
1235 |
+
Sets the file ids for the input identity terms
|
1236 |
+
:param subgroup_list: List of identity terms
|
1237 |
+
:return:
|
1238 |
+
"""
|
1239 |
+
subgroup_files = {}
|
1240 |
+
for subgroup in subgroup_list:
|
1241 |
+
# TODO: Should the pmi, npmi, and count just be one file?
|
1242 |
+
subgroup_npmi_fid = pjoin(pmi_cache_path, subgroup + "_npmi.csv")
|
1243 |
+
subgroup_pmi_fid = pjoin(pmi_cache_path, subgroup + "_pmi.csv")
|
1244 |
+
subgroup_cooc_fid = pjoin(pmi_cache_path, subgroup + "_vocab_cooc.csv")
|
1245 |
+
subgroup_files[subgroup] = (
|
1246 |
+
subgroup_npmi_fid,
|
1247 |
+
subgroup_pmi_fid,
|
1248 |
+
subgroup_cooc_fid,
|
1249 |
+
)
|
1250 |
+
return subgroup_files
|
1251 |
+
|
1252 |
+
|
1253 |
+
## Input/Output ##
|
1254 |
+
|
1255 |
+
|
1256 |
+
def intersect_dfs(df_dict):
|
1257 |
+
started = 0
|
1258 |
+
new_df = None
|
1259 |
+
for key, df in df_dict.items():
|
1260 |
+
if df is None:
|
1261 |
+
continue
|
1262 |
+
for key2, df2 in df_dict.items():
|
1263 |
+
if df2 is None:
|
1264 |
+
continue
|
1265 |
+
if key == key2:
|
1266 |
+
continue
|
1267 |
+
if started:
|
1268 |
+
new_df = new_df.join(df2, how="inner", lsuffix="1", rsuffix="2")
|
1269 |
+
else:
|
1270 |
+
new_df = df.join(df2, how="inner", lsuffix="1", rsuffix="2")
|
1271 |
+
started = 1
|
1272 |
+
return new_df.copy()
|
1273 |
+
|
1274 |
+
|
1275 |
+
def write_df(df, df_fid):
|
1276 |
+
feather.write_feather(df, df_fid)
|
1277 |
+
|
1278 |
+
|
1279 |
+
def write_json(json_dict, json_fid):
|
1280 |
+
with open(json_fid, "w", encoding="utf-8") as f:
|
1281 |
+
json.dump(json_dict, f)
|
1282 |
+
|
1283 |
+
def write_subgroup_npmi_data(subgroup, subgroup_dict, subgroup_files):
|
1284 |
+
"""
|
1285 |
+
Saves the calculated nPMI statistics to their output files.
|
1286 |
+
Includes the npmi scores for each identity term, the pmi scores, and the
|
1287 |
+
co-occurrence counts of the identity term with all the other words
|
1288 |
+
:param subgroup: Identity term
|
1289 |
+
:return:
|
1290 |
+
"""
|
1291 |
+
subgroup_fids = subgroup_files[subgroup]
|
1292 |
+
subgroup_npmi_fid, subgroup_pmi_fid, subgroup_cooc_fid = subgroup_fids
|
1293 |
+
subgroup_dfs = subgroup_dict[subgroup]
|
1294 |
+
subgroup_cooc_df, subgroup_pmi_df, subgroup_npmi_df = subgroup_dfs
|
1295 |
+
with open(subgroup_npmi_fid, "w+") as f:
|
1296 |
+
subgroup_npmi_df.to_csv(f)
|
1297 |
+
with open(subgroup_pmi_fid, "w+") as f:
|
1298 |
+
subgroup_pmi_df.to_csv(f)
|
1299 |
+
with open(subgroup_cooc_fid, "w+") as f:
|
1300 |
+
subgroup_cooc_df.to_csv(f)
|
1301 |
+
|
1302 |
+
def write_zipf_data(z, zipf_fid):
|
1303 |
+
zipf_dict = {}
|
1304 |
+
zipf_dict["xmin"] = int(z.xmin)
|
1305 |
+
zipf_dict["xmax"] = int(z.xmax)
|
1306 |
+
zipf_dict["alpha"] = float(z.alpha)
|
1307 |
+
zipf_dict["ks_distance"] = float(z.distance)
|
1308 |
+
zipf_dict["p-value"] = float(z.ks_test.pvalue)
|
1309 |
+
zipf_dict["uniq_counts"] = [int(count) for count in z.uniq_counts]
|
1310 |
+
zipf_dict["uniq_ranks"] = [int(rank) for rank in z.uniq_ranks]
|
1311 |
+
with open(zipf_fid, "w+", encoding="utf-8") as f:
|
1312 |
+
json.dump(zipf_dict, f)
|
1313 |
+
|