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# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import warnings
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.preprocessing import MultiLabelBinarizer
# Might be nice to print to log instead? Happens when we drop closed class.
warnings.filterwarnings(action="ignore", category=UserWarning)
# When we divide by 0 in log
np.seterr(divide="ignore")
# treating inf values as NaN as well
pd.set_option("use_inf_as_na", True)
logs = logging.getLogger(__name__)
logs.setLevel(logging.INFO)
logs.propagate = False
if not logs.handlers:
Path("./log_files").mkdir(exist_ok=True)
# Logging info to log file
file = logging.FileHandler("./log_files/npmi.log")
fileformat = logging.Formatter("%(asctime)s:%(message)s")
file.setLevel(logging.INFO)
file.setFormatter(fileformat)
# Logging debug messages to stream
stream = logging.StreamHandler()
streamformat = logging.Formatter("[data_measurements_tool] %(message)s")
stream.setLevel(logging.WARNING)
stream.setFormatter(streamformat)
logs.addHandler(file)
logs.addHandler(stream)
_NUM_BATCHES = 500
class nPMI:
# TODO: Expand beyond pairwise
def __init__(
self,
vocab_counts_df,
tokenized_df,
tokenized_col_name="tokenized_text",
num_batches=_NUM_BATCHES,
):
logs.info("Initiating npmi class.")
logs.info("vocab is")
logs.info(vocab_counts_df)
self.vocab_counts_df = vocab_counts_df
logs.info("tokenized is")
self.tokenized_df = tokenized_df
logs.info(self.tokenized_df)
self.tokenized_col_name = tokenized_col_name
# self.mlb_list holds num batches x num_sentences
self.mlb_list = []
def binarize_words_in_sentence(self):
logs.info("Creating co-occurrence matrix for PMI calculations.")
batches = np.linspace(0, self.tokenized_df.shape[0], _NUM_BATCHES).astype(int)
i = 0
# Creates list of size (# batches x # sentences)
while i < len(batches) - 1:
# Makes a sparse matrix (shape: # sentences x # words),
# with the occurrence of each word per sentence.
mlb = MultiLabelBinarizer(classes=self.vocab_counts_df.index)
logs.info(
"%s of %s sentence binarize batches." % (str(i), str(len(batches)))
)
# Returns series: batch size x num_words
mlb_series = mlb.fit_transform(
self.tokenized_df[self.tokenized_col_name][batches[i] : batches[i + 1]]
)
i += 1
self.mlb_list.append(mlb_series)
def calc_cooccurrences(self, subgroup, subgroup_idx):
initialize = True
coo_df = None
# Big computation here! Should only happen once.
logs.info(
"Approaching big computation! Here, we binarize all words in the sentences, making a sparse matrix of sentences."
)
if not self.mlb_list:
self.binarize_words_in_sentence()
for batch_id in range(len(self.mlb_list)):
logs.info(
"%s of %s co-occurrence count batches"
% (str(batch_id), str(len(self.mlb_list)))
)
# List of all the sentences (list of vocab) in that batch
batch_sentence_row = self.mlb_list[batch_id]
# Dataframe of # sentences in batch x vocabulary size
sent_batch_df = pd.DataFrame(batch_sentence_row)
# logs.info('sent batch df is')
# logs.info(sent_batch_df)
# Subgroup counts per-sentence for the given batch
subgroup_df = sent_batch_df[subgroup_idx]
subgroup_df.columns = [subgroup]
# Remove the sentences where the count of the subgroup is 0.
# This way we have less computation & resources needs.
subgroup_df = subgroup_df[subgroup_df > 0]
logs.info("Removing 0 counts, subgroup_df is")
logs.info(subgroup_df)
mlb_subgroup_only = sent_batch_df[sent_batch_df[subgroup_idx] > 0]
logs.info("mlb subgroup only is")
logs.info(mlb_subgroup_only)
# Create cooccurrence matrix for the given subgroup and all words.
logs.info("Now we do the T.dot approach for co-occurrences")
batch_coo_df = pd.DataFrame(mlb_subgroup_only.T.dot(subgroup_df))
# Creates a batch-sized dataframe of co-occurrence counts.
# Note these could just be summed rather than be batch size.
if initialize:
coo_df = batch_coo_df
else:
coo_df = coo_df.add(batch_coo_df, fill_value=0)
logs.info("coo_df is")
logs.info(coo_df)
initialize = False
logs.info("Returning co-occurrence matrix")
logs.info(coo_df)
return pd.DataFrame(coo_df)
def calc_paired_metrics(self, subgroup_pair, subgroup_npmi_dict):
"""
Calculates nPMI metrics between paired subgroups.
Special handling for a subgroup paired with itself.
:param subgroup_npmi_dict:
:return:
"""
paired_results_dict = {"npmi": {}, "pmi": {}, "count": {}}
# Canonical ordering. This is done previously, but just in case...
subgroup1, subgroup2 = sorted(subgroup_pair)
vocab_cooc_df1, pmi_df1, npmi_df1 = subgroup_npmi_dict[subgroup1]
logs.info("vocab cooc")
logs.info(vocab_cooc_df1)
if subgroup1 == subgroup2:
shared_npmi_df = npmi_df1
shared_pmi_df = pmi_df1
shared_vocab_cooc_df = vocab_cooc_df1
else:
vocab_cooc_df2, pmi_df2, npmi_df2 = subgroup_npmi_dict[subgroup2]
logs.info("vocab cooc2")
logs.info(vocab_cooc_df2)
# Note that lsuffix and rsuffix should not come into play.
shared_npmi_df = npmi_df1.join(
npmi_df2, how="inner", lsuffix="1", rsuffix="2"
)
shared_pmi_df = pmi_df1.join(pmi_df2, how="inner", lsuffix="1", rsuffix="2")
shared_vocab_cooc_df = vocab_cooc_df1.join(
vocab_cooc_df2, how="inner", lsuffix="1", rsuffix="2"
)
shared_vocab_cooc_df = shared_vocab_cooc_df.dropna()
shared_vocab_cooc_df = shared_vocab_cooc_df[
shared_vocab_cooc_df.index.notnull()
]
logs.info("shared npmi df")
logs.info(shared_npmi_df)
logs.info("shared vocab df")
logs.info(shared_vocab_cooc_df)
npmi_bias = (
shared_npmi_df[subgroup1 + "-npmi"] - shared_npmi_df[subgroup2 + "-npmi"]
)
paired_results_dict["npmi-bias"] = npmi_bias.dropna()
paired_results_dict["npmi"] = shared_npmi_df.dropna()
paired_results_dict["pmi"] = shared_pmi_df.dropna()
paired_results_dict["count"] = shared_vocab_cooc_df.dropna()
return paired_results_dict
def calc_metrics(self, subgroup):
# Index of the subgroup word in the sparse vector
subgroup_idx = self.vocab_counts_df.index.get_loc(subgroup)
logs.info("Calculating co-occurrences...")
df_coo = self.calc_cooccurrences(subgroup, subgroup_idx)
vocab_cooc_df = self.set_idx_cols(df_coo, subgroup)
logs.info(vocab_cooc_df)
logs.info("Calculating PMI...")
pmi_df = self.calc_PMI(vocab_cooc_df, subgroup)
logs.info(pmi_df)
logs.info("Calculating nPMI...")
npmi_df = self.calc_nPMI(pmi_df, vocab_cooc_df, subgroup)
logs.info(npmi_df)
return vocab_cooc_df, pmi_df, npmi_df
def set_idx_cols(self, df_coo, subgroup):
"""
:param df_coo: Co-occurrence counts for subgroup, length is num_words
:return:
"""
count_df = df_coo.set_index(self.vocab_counts_df.index)
count_df.columns = [subgroup + "-count"]
count_df[subgroup + "-count"] = count_df[subgroup + "-count"].astype(int)
return count_df
def calc_PMI(self, vocab_cooc_df, subgroup):
"""
# PMI(x;y) = h(y) - h(y|x)
# = h(subgroup) - h(subgroup|word)
# = log (p(subgroup|word) / p(subgroup))
# nPMI additionally divides by -log(p(x,y)) = -log(p(x|y)p(y))
"""
# Calculation of p(subgroup)
subgroup_prob = self.vocab_counts_df.loc[subgroup]["proportion"]
# Calculation of p(subgroup|word) = count(subgroup,word) / count(word)
# Because the inidices match (the vocab words),
# this division doesn't need to specify the index (I think?!)
p_subgroup_g_word = (
vocab_cooc_df[subgroup + "-count"] / self.vocab_counts_df["count"]
)
logs.info("p_subgroup_g_word is")
logs.info(p_subgroup_g_word)
pmi_df = pd.DataFrame()
pmi_df[subgroup + "-pmi"] = np.log(p_subgroup_g_word / subgroup_prob)
# Note: A potentially faster solution for adding count, npmi,
# can be based on this zip idea:
# df_test['size_kb'], df_test['size_mb'], df_test['size_gb'] =
# zip(*df_test['size'].apply(sizes))
return pmi_df.dropna()
def calc_nPMI(self, pmi_df, vocab_cooc_df, subgroup):
"""
# nPMI additionally divides by -log(p(x,y)) = -log(p(x|y)p(y))
# = -log(p(word|subgroup)p(word))
"""
p_word_g_subgroup = vocab_cooc_df[subgroup + "-count"] / sum(
vocab_cooc_df[subgroup + "-count"]
)
p_word = pmi_df.apply(
lambda x: self.vocab_counts_df.loc[x.name]["proportion"], axis=1
)
normalize_pmi = -np.log(p_word_g_subgroup * p_word)
npmi_df = pd.DataFrame()
npmi_df[subgroup + "-npmi"] = pmi_df[subgroup + "-pmi"] / normalize_pmi
return npmi_df.dropna()
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