File size: 20,596 Bytes
0e929cb |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 |
# global
import string
from typing import List, Tuple
import numpy as np
import pandas as pd
import re
import nltk
from sklearn.utils import resample
from gensim.models.doc2vec import Doc2Vec, TaggedDocument
from nltk.tokenize import RegexpTokenizer
import tensorflow as tf
from keras.layers import TextVectorization
from keras.preprocessing.text import Tokenizer
from keras.utils import pad_sequences
# local
from utils import Doc2VecModel
punct = string.punctuation
stemmer = nltk.stem.PorterStemmer()
eng_stopwords = nltk.corpus.stopwords.words("english")
class Preprocessor:
"""Responsible for preprocessing case facts."""
def __init__(self) -> None:
pass
def _nltk_tokenizer(self, text: str) -> List[str]:
"""
Tokenize a given `text` using the RegexpTokenizer from the nltk library.
Parameters:
-----------
- text : str
A string containing the text to be tokenized.
Returns:
--------
- tokens : List[str]
A list of tokens generated by the tokenizer.
"""
tokenizer = RegexpTokenizer(r"\w+")
tokens = tokenizer.tokenize(text)
return tokens
def _tokenize_text(self, text_column: pd.Series) -> pd.Series:
"""Splitting `text_column` into tokens.
Parameters:
------------
- text_column : pd.Series
Contains text that needs to be tokenized.
Returns:
--------
- tokenized_text : pd.Series
Contains tokenized version of `text_column`.
"""
tokenized_text = text_column.apply(self._nltk_tokenizer)
return tokenized_text
def _convert_to_tagged_document(
self, text_column: pd.Series
) -> Tuple[List[str], List[TaggedDocument]]:
"""
Convert `text_column` of specific to TaggedDocuments.
Parameters:
------------
- column : pd.Series
Contains the list of tokens of each fact.
Returns:
--------
A tuble containing the following items:
- tokens_list : list[str]
Contains all tokens of each case in the `text_column`.
- tagged_docs : list[TaggedDocument]
Contains TaggedDocument object for each case.
"""
tokens_list = text_column.to_list()
tagged_docs = [TaggedDocument(t, [str(i)])
for i, t in enumerate(tokens_list)]
return tokens_list, tagged_docs
def _vectorize_text(
self, doc2vec_model: Doc2Vec, df: pd.Series, tokens_list: List[str]
) -> pd.DataFrame:
"""
Convert values of `tokens_list` to a vector.
Parameters:
-----------
- doc2vec_model : Doc2Vev
Trained Doc2Vec model.
- df : pd.Series
This will use only to get its indicies for the new generated dataframe.
- tokens_list : List[str]
Contains all tokens of each case.
Returns:
--------
- text_vectors_df : pd.DataFrame
Contains the vector representaion for each case.
"""
text_vectors = [doc2vec_model.infer_vector(doc) for doc in tokens_list]
text_vectors_df = pd.DataFrame(text_vectors, index=df.index)
return text_vectors_df
def _anonymize_case_facts(
self, first_party_name: str, second_party_name: str, facts: str
) -> str:
"""
Anonymize case facts by replacing its party names with "_PARTY_" tag.
Parameters:
------------
- first_party_name : str
Represents first party name or petitioner name.
- second_party_name : str
Represents second party name or respondent name.
- facts : str
Represents case facts.
Returns:
--------
- anonymized_facts : str
An anonymized version of `facts`.
"""
# remove any commas and any non alphabet characters
first_party_name = re.sub(r"[\,+]", " ", first_party_name)
first_party_name = re.sub(r"[^a-zA-Z]", " ", first_party_name)
second_party_name = re.sub(r"[\,+]", " ", second_party_name)
second_party_name = re.sub(r"[^a-zA-Z]", " ", second_party_name)
for name in first_party_name.split():
facts = re.sub(name, " _PARTY_ ", facts)
for name in second_party_name.split():
facts = re.sub(name, " _PARTY_ ", facts)
# replace any consecutive _PARTY_ tags with only one _PARTY_ tag.
regex_continous_tags = r"(_PARTY_\s+){2,}"
anonymized_facts = re.sub(regex_continous_tags, " _PARTY_ ", facts)
# remove ant consecutive spaces
anonymized_facts = re.sub(r"\s+", " ", anonymized_facts)
return anonymized_facts
def _preprocess_text(self, text: str) -> str:
"""
Preprocessing & cleaning `text` including:
- lowercasing
- removing quotation marks
- removing digits
- removing punctuation
- removing brackets, braces, and paranthesis
- removeing stopwords
- stemming tokens
Parameters:
------------
- text : str
Text need to be processed (cleaned).
Returns:
--------
- processed_text : str
A preprocessed version of `text`.
"""
text = text.lower()
# remove quotation marks
text = re.sub(r"\'", "", text)
# remove digits
text = re.sub(r"\d+", "", text)
# remove punctuation but with keeping '_' letter
text = "".join([ch for ch in text if (ch == "_") or (ch not in punct)])
# remove brackets, braces, and parantheses
text = re.sub(r"[\[\]\(\)\{\}]+", " ", text)
tokens = nltk.word_tokenize(text)
# remove stopwords and stemming tokens
tokens = [stemmer.stem(token)
for token in tokens if token not in eng_stopwords]
# convert tokens back to string
processed_text = " ".join(tokens)
return processed_text
def convert_text_to_vectors_doc2vec(
self,
text_column: pd.Series,
train: bool = True,
embeddings_doc2vec: Doc2Vec = None,
) -> Tuple[Doc2Vec, pd.DataFrame] | pd.DataFrame:
"""
Converting `text_column` to vectors using `Doc2Vec` model
Parameters:
------------
- text_column : pd.Series
Contains the case facts.
- train : bool, optional
Defines whether the model will be trained or not. (if True, Doc2Vec will be trained |
else, Doc2Vec will used the passed `embeddings_Doc2Vec`). (Default is True).
- embeddings_doc2vec : Doc2Vec, optional
Trained Doc2Vec model will be used for generating embeddings of `text_column` if
`train` is False. (Default is None).
Returns:
--------
1. A tuple contains the following:
- embeddings_doc2vec : Doc2Vec
Trained Doc2Vec model.
- text_vectors_df : pd.DataFrame
A DataFrame contains `text_column` vectors if `train` is True.
2. text_vectors_df : pd.DataFrame
A DataFrame contains `text_column` vectors if `train` is False.
Raises:
-------
- AssertionError
If train is False and `embeddings_doc2vec` is None.
- AssertionError
If train is False and `embedding_doc2vec` is not an instance of Doc2Vec
"""
tokenized_text = self._tokenize_text(text_column)
tokens_list, tagged_docs = self._convert_to_tagged_document(
tokenized_text)
if train:
doc2vec_model = Doc2VecModel()
embeddings_doc2vec = doc2vec_model.train_doc2vec_embeddings_model(
tagged_docs
)
text_vectors_df = self._vectorize_text(
embeddings_doc2vec, text_column, tokens_list
)
return embeddings_doc2vec, text_vectors_df
assert (
embeddings_doc2vec is not None
), "`embedding_doc2vec` argument must be not None."
assert isinstance(
embeddings_doc2vec, Doc2Vec
), "`embedding_doc2vec` argument must be an instance of Doc2Vec to infer vectors."
text_vectors_df = self._vectorize_text(
embeddings_doc2vec, text_column, tokens_list
)
return text_vectors_df
def convert_text_to_vectors_tf_idf(
self,
text_column: pd.Series,
ngrams: int = 2,
max_tokens: int = 10000,
output_mode: str = "tf-idf",
train: bool = True,
text_vectorizer: TextVectorization = None,
) -> Tuple[TextVectorization, tf.Tensor] | tf.Tensor:
"""
Converting `text_column` to vectors using `TextVectorization` layer.
Parameters:
------------
- text_column : pd.Series
Contains the case facts.
- ngrams : int, optional
Defines the number of n-gram (Default is 2).
- max_tokens : int, optional
Defines the number of max_tokens of `text_vectorizer` (Default is 10,000).
- output_mode : str, optional
Represents the output vectors type whether it is "tfi-df" or "binary" or "count"
(Default is "tf-idf").
- train : bool, optional
Defines whether the model will be trained or not. (if True, TextVectorization
will be trained, else, TextVectorization will used the passed `text_vectorizer`).
(Default is True).
- text_vectorizer : TextVectorization, optional
Trained TextVectorization layer will be used for generating embeddings of
`text_column` if `train` is False. (Default is None).
Returns:
--------
- if `train` == True:
A tuple contains the following:
- text_vectorizer : TextVectorization
Trained TextVectorization layer.
- text_vectors : tf.Tensor
A Tensor contains `text_column` training vectors.
- otherwise:
text_vectors : tf.Tensor
A Tensor contains `text_column` testing vectors.
Raises:
-------
- AssertionError
If train is False and `text_vectorizer` is None.
- AssertionError
If train is False and `text_vectorizer` is not an instance of TextVectorization.
"""
if train:
text_vectorizer = TextVectorization(
ngrams=ngrams, max_tokens=max_tokens, output_mode=output_mode
)
text_vectorizer.adapt(text_column)
text_vectors = text_vectorizer(text_column)
return text_vectorizer, text_vectors
assert (
text_vectorizer is not None
), "`text_vectorizer` argument must be not None."
assert isinstance(
text_vectorizer, TextVectorization
), "`text_vectorizer` argument must be an instance of TextVectorization to infer vectors."
text_vectors = text_vectorizer(text_column)
return text_vectors
def convert_text_to_vectors_cnn(
self,
text_column: pd.Series,
max_tokens: int = 2000,
output_sequence_length: int = 500,
output_mode: str = "int",
train: bool = True,
text_vectorizer: TextVectorization = None,
) -> Tuple[TextVectorization, tf.Tensor] | tf.Tensor:
"""
Converting `text_column` to vectors using `TextVectorization` layer.
Parameters:
------------
- text_column : pd.Series
Contains the case facts.
- max_tokens : int, optional
Defines the number of max_tokens of `text_vectorizer` (Default is 2000).
- output_sequence_length : int, optional
Represents the dimensions of the output vector (Default is 500).
- output_mode : str, optional
Represents the output vectors type whether it is "int" or "binary" or "tfi-df".
- train : bool, optional
Defines whether the model will be trained or not. (if True,
TextVectorization will be trained | else, TextVectorization will used the
passed `text_vectorizer`). (Default is True).
- text_vectorizer : TextVectorization, optional
Trained TextVectorization layer will be used for generating embeddings of
`text_column` if `train` is False. (Default is None).
Returns:
--------
- if `train` == True:
A tuple contains the following:
- text_vectorizer : TextVectorization
Trained TextVectorization layer.
- text_vectors : tf.Tensor
A Tensor contains `text_column` training vectors.
- otherwise:
text_vectors : tf.Tensor
A Tensor contains `text_column` testing vectors.
Raises:
-------
- AssertionError
If train is False and `text_vectorizer` is None.
- AssertionError
If train is False and `text_vectorizer` is not an instance of TextVectorization.
"""
if train:
text_vectorizer = TextVectorization(
max_tokens=max_tokens,
output_mode=output_mode,
output_sequence_length=output_sequence_length,
)
text_vectorizer.adapt(text_column)
text_vectors = text_vectorizer(text_column)
return text_vectorizer, text_vectors
assert (
text_vectorizer is not None
), "`text_vectorizer` argument must be not None."
assert isinstance(
text_vectorizer, TextVectorization
), "`text_vectorizer` argument must be an instance of TextVectorization to infer vectors."
text_vectors = text_vectorizer(text_column)
return text_vectors
def convert_text_to_vectors_glove(
self,
text_column: pd.Series,
train: bool = True,
glove_tokenizer: Tokenizer = None,
vocab_size: int = 1000,
oov_token: str = "<OOV>",
max_length: int = 50,
padding_type: str = "post",
truncation_type: str = "post",
) -> Tuple[Tokenizer, np.ndarray] | np.ndarray:
"""
Converting `text_column` to vectors using `glove_tokenizer`.
Parameters:
------------
- text_column : pd.Series
Contains the case facts.
- train : bool, optional
Defines whether the model will be trained or not. (if True,
Tokenizer will be trained | else, Tokenizer will used the
passed `glove_tokenizer`). (Default is True).
- glove_tokenizer : Tokenizer, optional
Trained Tokenizer layer will be used for generating embeddings of
`text_column` if `train` is False. (Default is None).
- vocab_size : int, optional
Represents the number of supported vocabulary of the Tokenizer,
any token not in this vocabulary will be treated as an out-of-vocabulary
token(OOV). (Default is 1000).
- oov_tokens : str, optional
Represents the token of an out-of-vocabulary token (Default is "<OOV>").
- max_length : int, optional
Defins the output vector's dimension. (Default is 50).
- padding_type : str, optional
Defines the padding type of the vectors, if the vector size is less than
`max_length`, the rest of the `max_length` will be padded with 0 (Default is "post").
- truncation_type : str, optional
Defines the truncation type of the vectors, if the vector size is more than
`max_length`, the extra of the `max_length` will be truncated (Default is "post").
Returns:
--------
- if `train` == True:
A tuple contains the following:
- glove_tokenizer : Tokenizer
Trained Tokenizer layer.
- text_padded : np.ndarray
An array contains `text_column` vectors.
- otherwise:
text_padded : np.ndarray
An array contains `text_column` vectors.
Raises:
-------
- AssertionError
If train is False and `glove_tokenizer` is None.
- AssertionError
If train is False and `glove_tokenizer` is not instance of Tokenizer.
"""
if train:
glove_tokenizer = Tokenizer(
num_words=vocab_size, oov_token=oov_token)
glove_tokenizer.fit_on_texts(text_column)
text_sequences = glove_tokenizer.texts_to_sequences(text_column)
text_padded = pad_sequences(
text_sequences,
maxlen=max_length,
padding=padding_type,
truncating=truncation_type,
)
return glove_tokenizer, text_padded
assert (
glove_tokenizer is not None
), "`glove_tokenizer` argument must be not None."
assert isinstance(
glove_tokenizer, Tokenizer
), "`glove_tokenizer` argument must be an instance of Tokenizer."
text_sequences = glove_tokenizer.texts_to_sequences(text_column)
text_padded = pad_sequences(
text_sequences,
maxlen=max_length,
padding=padding_type,
truncating=truncation_type,
)
return text_padded
def balance_data(self, X_train: pd.Series, y_train: pd.Series) -> pd.DataFrame:
"""
Balancing `X_train` and `y_train` to distribute the targets in `y_train` equally.
Parameters:
------------
- text_column : pd.Series
Contains the case facts.
- y_train : pd.Series
Contains the training targets.
Returns:
--------
- shuffled_balanced_df : pd.DataFrame
Contains the new balanced dataframe with shuffling indicies.
"""
df = pd.concat([X_train, y_train], axis=1)
first_party = df[df["winner_index"] == 0]
second_party = df[df["winner_index"] == 1]
upsample_second_party = resample(
second_party, replace=True, n_samples=len(first_party), random_state=42
)
upsample_df = pd.concat([upsample_second_party, first_party])
shuffled_indices = np.arange(upsample_df.shape[0])
np.random.shuffle(shuffled_indices)
shuffled_balanced_df = upsample_df.iloc[shuffled_indices, :]
return shuffled_balanced_df
def anonymize_data(
self,
first_party_names: pd.Series,
second_party_names: pd.Series,
text_column: pd.Series,
) -> pd.Series:
"""
Anonymize `text_column` by replacing `first_party_names` and
`second_party_names` wit "_PARTY_" tag.
Parameters:
------------
- first_party_names : pd.Series
Contains all first party names needed to be anonymized.
- second_party_names : pd.Series
Contains all second party names needed to be anonymized.
- text_column : pd.Series
Contains all texts needed to be anonymized.
Returns:
--------
- all_anonyimzed_facts : pd.Series
Contains anonymized version of `text_column`.
"""
all_anonymized_facts = []
for i in range(text_column.shape[0]):
facts = text_column.iloc[i]
first_party_name = first_party_names.iloc[i]
second_party_name = second_party_names.iloc[i]
anonymized_facts = self._anonymize_case_facts(
first_party_name, second_party_name, facts
)
all_anonymized_facts.append(anonymized_facts)
return pd.Series(all_anonymized_facts)
def preprocess_data(self, text_column: pd.Series) -> pd.Series:
"""
Preprocessing & cleaning all texts in `text_column`.
Parameters:
------------
- text_column : pd.Series
Contains all case facts.
Returns:
--------
- preprocessed_text : pd.Series
Contains all texts after being processed.
"""
preprocessed_text = text_column.apply(self._preprocess_text)
return preprocessed_text
|