"""Relative Negative Sentiment Bias (RNSB) metric implementation."""
import logging
from typing import Any, Callable, Dict, List, Tuple, Union
import numpy as np
import pandas as pd
from scipy.stats import entropy
from sklearn.base import BaseEstimator
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from wefe.metrics.base_metric import BaseMetric
from wefe.preprocessing import get_embeddings_from_query
from wefe.query import Query
from wefe.word_embedding_model import WordEmbeddingModel
[docs]class RNSB(BaseMetric):
r"""Relative Relative Negative Sentiment Bias (RNSB).
The metric was originally proposed in "A transparent framework for evaluating
unintended demographic bias in word embeddings" [1].
This metric is based on measuring bias through word sentiment.
The main idea is that if there was no bias, all words should be equally negative.
Therefore, its procedure is based on calculating how negative the words in
the target sets are.
For this purpose, RNSB trains a classifier that assigns a probability to each
word of belonging to the negative class (in the original work the classifier is
trained using
:func:`~wefe.datasets.load_bingliu`
of positive and negative words).
Then, it generates a probability distribution with the probabilities calculated in
the previous step and compares them to the uniform distribution
(case where all words have the same probability of being negative) using
KL divergence.
When the negative probability distribution is equal to the uniform one (i.e., there
is no bias), the KL divergence is 0.
The following description of the metric is WEFE's adaptation of what was presented
in the original RNSB work.
RNSB receives as input queries with two attribute sets :math:`A_1` and
:math:`A_2` and two or more target sets, thus has a template (tuple of numbers that
defines the allowed target and attribute sets in the query)
of the form :math:`s=(N,2)` with :math:`N\geq 2`.
Given a query :math:`Q=(\{T_1,T_2,\ldots,T_n\},\{A_1,A_2\})` RNSB is
calculated under the following steps:
1. First constructs a binary classifier :math:`C_{(A_1,A_2)}(\cdot)` using
set :math:`A_1` as training examples for the negative class, and :math:`A_2` as
training examples for the positive class.
2. After the training process, this classifier gives for every word :math:`w` a
probability :math:`C_{(A_1,A_2)}(w)` that can be interpreted as the degree of
association of :math:`w` with respect to :math:`A_2` (value
:math:`1-C_{(A_1,A_2)}(w)` is the degree of association with :math:`A_1`).
3. Then, the metric constructs a probability distribution :math:`P(\cdot)` over all
the words :math:`w` in :math:`T_1\cup \cdots \cup T_n`, by computing
:math:`C_{(A_1,A_2)}(w)` and normalizing it to ensure that
:math:`\sum_w P(w)=1`.
4. Finally RNSB is calculated as the distance between :math:`P(\cdot)` and
the uniform distribution :math:`Y(\cdot)` using the KL-divergence.
The main idea behind RNSB is that the more that :math:`P(\cdot)` resembles a
uniform distribution, the less biased the word embedding model is.
Thus, the optimal value is 0.
You can see the full paper replication in Previous Studies Replication section.
References
----------
| [1]: Chris Sweeney and Maryam Najafian. A transparent framework for evaluating
unintended demographic bias in word embeddings.
| In Proceedings of the 57th Annual Meeting of the Association for
Computational Linguistics, pages 1662–1667, 2019.
| [2]: https://github.com/ChristopherSweeney/AIFairness/blob/master/python_notebooks/Measuring_and_Mitigating_Word_Embedding_Bias.ipynb
""" # noqa: E501
metric_template = ("n", 2)
metric_name = "Relative Negative Sentiment Bias"
metric_short_name = "RNSB"
def _train_classifier(
self,
attribute_embeddings_dict: List[Dict[str, np.ndarray]],
estimator: BaseEstimator,
estimator_params: Dict[str, Any],
random_state: Union[int, None],
holdout: bool,
print_model_evaluation: bool,
) -> Tuple[BaseEstimator, float]:
"""Train the sentiment classifier from the provided attribute embeddings.
Parameters
----------
attribute_embeddings_dict : dict[str, np.ndarray]
A dict with the attributes keys and embeddings
estimator : BaseEstimator, optional
A scikit-learn classifier class that implements predict_proba function,
by default LogisticRegression,
estimator_params : dict, optional
Parameters that will use the classifier, by default { 'max_iter': 10000, }
random_state : Union[int, None], optional
A seed that allows making the execution of the query reproducible, by
default None
holdout: bool, optional
True indicates that a holdout (split attributes in train/test sets) will
be executed before running the model training.
This option allows for evaluating the performance of the classifier
(can be printed using print_model_evaluation=True) at the cost of training
the classifier with fewer examples. False disables this functionality.
Note that holdout divides into 80% train and 20% test, performs a shuffle
and tries to maintain the original ratio of the classes via stratify param,
by default True.
print_model_evaluation : bool, optional
Indicates whether the classifier evaluation is printed after the
training process is completed, by default False
Returns
-------
Tuple[BaseEstimator, float]
The trained classifier and the accuracy obtained by the model.
"""
# when random_state is not none, set it on classifier params.
if random_state is not None:
estimator_params["random_state"] = random_state
# the attribute 0 words are treated as positive words.
positive_embeddings = np.array(list(attribute_embeddings_dict[0].values()))
# the attribute 1 words are treated as negative words.
negative_embeddings = np.array(list(attribute_embeddings_dict[1].values()))
# generate the labels (1, -1) for each embedding
positive_labels = np.ones(positive_embeddings.shape[0])
negative_labels = -np.ones(negative_embeddings.shape[0])
attributes_embeddings = np.concatenate(
(positive_embeddings, negative_embeddings)
)
attributes_labels = np.concatenate((positive_labels, negative_labels))
if holdout:
split = train_test_split(
attributes_embeddings,
attributes_labels,
shuffle=True,
random_state=random_state,
test_size=0.2,
stratify=attributes_labels,
)
X_embeddings_train, X_embeddings_test, y_train, y_test = split
num_train_negative_examples = np.count_nonzero((y_train == -1))
num_train_positive_examples = np.count_nonzero((y_train == 1))
# Check the number of train and test examples.
if num_train_positive_examples == 1:
raise Exception(
"After splitting the dataset using train_test_split "
"(with test_size=0.1), the first attribute remained with 0 "
"training examples."
)
if num_train_negative_examples < 1:
raise Exception(
"After splitting the dataset using train_test_split "
"(with test_size=0.1), the second attribute remained with 0 "
"training examples."
)
estimator = estimator(**estimator_params)
estimator.fit(X_embeddings_train, y_train)
# evaluate
y_pred = estimator.predict(X_embeddings_test)
score = estimator.score(X_embeddings_test, y_test)
if print_model_evaluation:
print(
"Classification Report:\n{}".format(
classification_report(y_test, y_pred, labels=estimator.classes_)
)
)
else:
estimator = estimator(**estimator_params)
estimator.fit(attributes_embeddings, attributes_labels)
score = estimator.score(attributes_embeddings, attributes_labels)
if print_model_evaluation:
print("Holdout is disabled. No evaluation was performed.")
return estimator, score
def _calc_rnsb(
self,
target_embeddings_dict: List[Dict[str, np.ndarray]],
classifier: BaseEstimator,
) -> Tuple[np.float_, dict]:
"""Calculate the RNSB metric.
Parameters
----------
target_embeddings_dict : Dict[str, np.ndarray]
dict with the target words and their embeddings.
classifier : BaseEstimator
Trained scikit-learn classifier in the previous step.
Returns
-------
Tuple[np.float_, dict]
return the calculated kl_divergence and
negative_sentiment_probabilities in that order.
"""
# join the embeddings and the word sets in their respective arrays
target_embeddings_sets = [
list(target_dict.values()) for target_dict in target_embeddings_dict
]
target_words_sets = [
list(target_dict.keys()) for target_dict in target_embeddings_dict
]
# get the probabilities associated with each target word vector
probabilities = [
classifier.predict_proba(target_embeddings)
for target_embeddings in target_embeddings_sets
]
# row where the negative probabilities are located
negative_class_clf_row = np.where(classifier.classes_ == -1)[0][0]
# extract only the negative sentiment probability for each word
negative_probabilities = np.concatenate(
[
probability[:, negative_class_clf_row].flatten()
for probability in probabilities
]
)
# normalization of the probabilities
normalized_negative_probabilities = np.array(
negative_probabilities / np.sum(negative_probabilities)
)
# get the uniform dist
uniform_dist = (
np.ones(normalized_negative_probabilities.shape[0])
* 1
/ normalized_negative_probabilities.shape[0]
)
# calc the kl divergence
kl_divergence = entropy(normalized_negative_probabilities, uniform_dist)
flatten_target_words = [
item for sublist in target_words_sets for item in sublist
]
# set the probabilities for each word in a dict.
negative_sentiment_probabilities = {
word: prob
for word, prob in zip(flatten_target_words, negative_probabilities)
}
return kl_divergence, negative_sentiment_probabilities
[docs] def run_query(
self,
query: Query,
model: WordEmbeddingModel,
estimator: BaseEstimator = LogisticRegression,
estimator_params: Dict[str, Any] = {"solver": "liblinear", "max_iter": 10000},
n_iterations: int = 1,
random_state: Union[int, None] = None,
holdout: bool = True,
print_model_evaluation: bool = False,
lost_vocabulary_threshold: float = 0.2,
preprocessors: List[Dict[str, Union[str, bool, Callable]]] = [{}],
strategy: str = "first",
normalize: bool = False,
warn_not_found_words: bool = False,
*args: Any,
**kwargs: Any
) -> Dict[str, Any]:
"""Calculate the RNSB metric over the provided parameters.
Note if you want to use with Bing Liu dataset, you have to pass
the positive and negative words in the first and second place of
attribute set array respectively.
Scores on this metric vary with each run due to different instances
of classifier training. For this reason, the robustness of these scores
can be improved by repeating the test several times and returning the
average of the scores obtained. This can be indicated in the
n_iterations parameter.
Parameters
----------
query : Query
A Query object that contains the target and attribute word sets to
be tested.
model : WordEmbeddingModel
A word embedding model.
estimator : BaseEstimator
A scikit-learn classifier class that implements predict_proba function,
by default None,
estimator_params : dict
Parameters that will use the classifier, by default { 'solver': 'liblinear',
'max_iter': 10000, }
n_iterations : int, optional
When provided, it tells the metric to run the specified number of times
and then average its results. This functionality is indicated to
strengthen the results obtained.
Note that you cannot specify random_state next to n_iterations as this
would always produce the same results,
by default 1.
random_state : Union[int, None], optional
Seed that allows making the execution of the query reproducible.
Warning: if a random_state other than None is provided along with
n_iterations, each iteration will split the dataset and train a
classifier associated to the same seed, so the results of each iteration
will always be the same, by default None.
holdout: bool, optional
True indicates that a holdout (split attributes in train/test sets) will
be executed before running the model training.
This option allows for evaluating the performance of the classifier
(can be printed using print_model_evaluation=True) at the cost of training
the classifier with fewer examples. False disables this functionality.
Note that holdout divides into 80% train and 20% test, performs a shuffle
and tries to maintain the original ratio of the classes via stratify param,
by default True.
print_model_evaluation : bool, optional
Indicates whether the classifier evaluation is printed after the
training process is completed, by default False
preprocessors : List[Dict[str, Union[str, bool, Callable]]]
A list with preprocessor options.
A ``preprocessor`` is a dictionary that specifies what processing(s) are
performed on each word before it is looked up in the model vocabulary.
For example, the ``preprocessor``
``{'lowecase': True, 'strip_accents': True}`` allows you to lowercase
and remove the accent from each word before searching for them in the
model vocabulary. Note that an empty dictionary ``{}`` indicates that no
preprocessing is done.
The possible options for a preprocessor are:
* ``lowercase``: ``bool``. Indicates that the words are transformed to
lowercase.
* ``uppercase``: ``bool``. Indicates that the words are transformed to
uppercase.
* ``titlecase``: ``bool``. Indicates that the words are transformed to
titlecase.
* ``strip_accents``: ``bool``, ``{'ascii', 'unicode'}``: Specifies that
the accents of the words are eliminated. The stripping type can be
specified. True uses 'unicode' by default.
* ``preprocessor``: ``Callable``. It receives a function that operates
on each word. In the case of specifying a function, it overrides the
default preprocessor (i.e., the previous options stop working).
A list of preprocessor options allows you to search for several
variants of the words into the model. For example, the preprocessors
``[{}, {"lowercase": True, "strip_accents": True}]``
``{}`` allows searching first for the original words in the vocabulary of
the model. In case some of them are not found,
``{"lowercase": True, "strip_accents": True}`` is executed on these words
and then they are searched in the model vocabulary.
strategy : str, optional
The strategy indicates how it will use the preprocessed words: 'first' will
include only the first transformed word found. 'all' will include all
transformed words found, by default "first".
normalize : bool, optional
True indicates that embeddings will be normalized, by default False
warn_not_found_words : bool, optional
Specifies if the function will warn (in the logger)
the words that were not found in the model's vocabulary, by default False.
Returns
-------
Dict[str, Any]
A dictionary with the query name, the calculated kl-divergence,
the negative probabilities for all tested target words and
the normalized distribution of probabilities.
Examples
--------
The following example shows how to run a query that measures gender
bias using RNSB.
Note that by default the RNSB score is returned plus the negative class
probabilities for each word and its distribution (the above probabilities
normalized to 1).
>>> from wefe.query import Query
>>> from wefe.utils import load_test_model
>>> from wefe.metrics import RNSB
>>>
>>> # define the query
>>> query = Query(
... target_sets=[
... ["female", "woman", "girl", "sister", "she", "her", "hers",
... "daughter"],
... ["male", "man", "boy", "brother", "he", "him", "his", "son"],
... ],
... attribute_sets=[
... ["home", "parents", "children", "family", "cousins", "marriage",
... "wedding", "relatives",],
... ["executive", "management", "professional", "corporation", "salary",
... "office", "business", "career", ],
... ],
... target_sets_names=["Female terms", "Male Terms"],
... attribute_sets_names=["Family", "Careers"],
... )
>>>
>>> # load the model (in this case, the test model included in wefe)
>>> model = load_test_model()
>>>
>>> # instance the metric and run the query
>>> RNSB().run_query(query, model)
{
"query_name": "Female terms and Male Terms wrt Family and Careers",
"result": 0.10769060995617141,
"rnsb": 0.10769060995617141,
"negative_sentiment_probabilities": {
"female": 0.5742192708509877,
"woman": 0.32330898567978306,
"girl": 0.17573260129841273,
"sister": 0.15229835340332343,
"she": 0.3761328719677399,
"her": 0.35739995104539557,
"hers": 0.2911542159275662,
"daughter": 0.11714195753410628,
"male": 0.4550779245077232,
"man": 0.39826729589696475,
"boy": 0.17445392462199483,
"brother": 0.16517694979156405,
"he": 0.5044892468050808,
"him": 0.45426103796811057,
"his": 0.5013980699813614,
"son": 0.1509265229834842,
},
"negative_sentiment_distribution": {
"female": 0.11103664779476699,
"woman": 0.0625181838962096,
"girl": 0.033981372529543176,
"sister": 0.02944989742595416,
"she": 0.07273272658861042,
"her": 0.06911034599602082,
"hers": 0.0563004234950174,
"daughter": 0.022651713275710483,
"male": 0.08799831316676666,
"man": 0.07701285503210042,
"boy": 0.033734115115920706,
"brother": 0.031940228635376634,
"he": 0.09755296914839981,
"him": 0.08784035200525282,
"his": 0.09695522899987082,
"son": 0.029184626894479145,
},
}
If you want to perform a holdout to evaluate (defualt option) the model
and print the evaluation, use the params ``holdout=True`` and
``print_model_evaluation=True``
>>> RNSB().run_query(
... query,
... model,
... holdout=True,
... print_model_evaluation=True)
"Classification Report:"
" precision recall f1-score support"
" "
" -1.0 1.00 1.00 1.00 2"
" 1.0 1.00 1.00 1.00 2"
" "
" accuracy 1.00 4"
" macro avg 1.00 1.00 1.00 4"
"weighted avg 1.00 1.00 1.00 4"
{
"query_name": "Female terms and Male Terms wrt Family and Careers",
"result": 0.09400726375514418,
"rnsb": 0.09400726375514418,
"negative_sentiment_probabilities": {
"female": 0.5583010801302075,
"woman": 0.3159147912504866,
"girl": 0.20753840501109977,
"sister": 0.16020059726421976,
"she": 0.4266765171984158,
"her": 0.4066467259229203,
"hers": 0.32435655424005905,
"daughter": 0.13318012193912765,
"male": 0.44129601598998147,
"man": 0.42681869843678866,
"boy": 0.21830517614567535,
"brother": 0.2037443178553553,
"he": 0.5655603842644314,
"him": 0.512466010254818,
"his": 0.5689713390373838,
"son": 0.18364286185769785,
},
"negative_sentiment_distribution": {
"female": 0.09875108690481095,
"woman": 0.05587832464521873,
"girl": 0.0367089439708001,
"sister": 0.02833593497428311,
"she": 0.07546961904547295,
"her": 0.07192679290859644,
"hers": 0.05737148541506475,
"daughter": 0.023556611770367462,
"male": 0.0780554843555203,
"man": 0.07549476775523993,
"boy": 0.038613347150078775,
"brother": 0.03603785404499525,
"he": 0.1000350969111323,
"him": 0.09064387893111858,
"his": 0.10063841921015712,
"son": 0.032482352007143285,
},
}
If you want to disable the holdout, use the param ``holdout=False``.
>>> # instance the metric and run the query
>>> RNSB().run_query(
... query,
... model,
... holdout=False,
... print_model_evaluation=True) # doctest: +SKIP
"Holdout is disabled. No evaluation was performed."
{
"query_name": "Female terms and Male Terms wrt Family and Careers",
"result": 0.12921977967420623,
"rnsb": 0.12921977967420623,
"negative_sentiment_probabilities": {
"female": 0.5815344717945401,
"woman": 0.28951392462851366,
"girl": 0.16744925298532254,
"sister": 0.1365690846140981,
"she": 0.40677635339222296,
"her": 0.3861243765483825,
"hers": 0.2794312043966708,
"daughter": 0.1035870893754135,
"male": 0.45492464330345805,
"man": 0.4143325974603802,
"boy": 0.18150440132198242,
"brother": 0.1607078872193466,
"he": 0.5656269380025241,
"him": 0.5025663479575841,
"his": 0.5657745694122852,
"son": 0.14803033326417403,
},
"negative_sentiment_distribution": {
"female": 0.10881083995606983,
"woman": 0.05417091306830872,
"girl": 0.03133140811261611,
"sister": 0.025553423794525788,
"she": 0.0761118709786697,
"her": 0.07224768225706711,
"hers": 0.05228433658715302,
"daughter": 0.019382166922557734,
"male": 0.08512089128923325,
"man": 0.07752571883094242,
"boy": 0.03396126510372403,
"brother": 0.030070032034284336,
"he": 0.10583438336150637,
"him": 0.09403512449772648,
"his": 0.1058620066555313,
"son": 0.027697936550083888,
},
}
Since each run of RNSB may give a different result due to the random
``train_test_split`` and random initializations, RNSB can be requested to
run many times and returns the average of all runs through the
parameter ``n_iterations``.
This makes it potentially more stable and robust to outlier runs.
>>> RNSB().run_query(query, model, n_iterations=1000)
{
"query_name": "Female terms and Male Terms wrt Family and Careers",
"result": 0.09649701346914233,
"rnsb": 0.09649701346914233,
"negative_sentiment_probabilities": {
"female": 0.5618993210534083,
"woman": 0.31188456697468364,
"girl": 0.1968846981458747,
"sister": 0.1666990161087616,
"she": 0.4120315698794307,
"her": 0.3956786125532543,
"hers": 0.3031550094192968,
"daughter": 0.13259627603249385,
"male": 0.45579890258209677,
"man": 0.4210218238530363,
"boy": 0.2104231329680286,
"brother": 0.18879207133574177,
"he": 0.5473770682025214,
"him": 0.4924664455586234,
"his": 0.5479209229372095,
"son": 0.1770764765027373,
},
"negative_sentiment_distribution": {
"female": 0.10176190651838826,
"woman": 0.056483371593163176,
"girl": 0.035656498409823205,
"sister": 0.030189767202716926,
"she": 0.07462033949087124,
"her": 0.07165876247453706,
"hers": 0.05490241858857015,
"daughter": 0.024013643264435475,
"male": 0.08254675451251275,
"man": 0.07624850551663455,
"boy": 0.03810835568595322,
"brother": 0.034190895761652934,
"he": 0.09913187640146649,
"him": 0.08918737310881396,
"his": 0.09923037037111067,
"son": 0.03206916109934998,
},
}
If you want the embeddings to be normalized before calculating the metrics
use the ``normalize=True`` before executing the query.
>>> RNSB().run_query(query, model, normalize=True)
{
"query_name": "Female terms and Male Terms wrt Family and Careers",
"result": 0.00957187793390364,
"rnsb": 0.00957187793390364,
"negative_sentiment_probabilities": {
"female": 0.5078372178028085,
"woman": 0.4334357574118245,
"girl": 0.3764103216054252,
"sister": 0.35256834229924383,
"she": 0.4454357087596428,
"her": 0.4390986149718311,
"hers": 0.41329577968574494,
"daughter": 0.33427930165282493,
"male": 0.470250420503012,
"man": 0.4577545228416623,
"boy": 0.3698438702135818,
"brother": 0.35380575403374315,
"he": 0.49962008627445753,
"him": 0.47052126448152776,
"his": 0.49505591114138436,
"son": 0.34192683607526553,
},
"negative_sentiment_distribution": {
"female": 0.07511118533317328,
"woman": 0.06410690741777263,
"girl": 0.05567261405091151,
"sister": 0.05214628855999085,
"she": 0.06588174891831232,
"her": 0.0649444670309599,
"hers": 0.061128122983393235,
"daughter": 0.04944126523085684,
"male": 0.0695519454840733,
"man": 0.06770375151119586,
"boy": 0.054701409243182085,
"brother": 0.05232930677698083,
"he": 0.07389583823474007,
"him": 0.06959200440758956,
"his": 0.07322077821098569,
"son": 0.050572366605882095,
},
}
RNSB accepts more than 2 sets of target words.
This example shows how to measure words representing different nationalities
with respect to positive and negative words.
Note this is one of the tests that was proposed in the RNSB paper. You can
see the full paper replication in Previous Studies Replication.
>>> import gensim.downloader as api
>>>
>>> from wefe.word_embedding_model import WordEmbeddingModel
>>> from wefe.query import Query
>>> from wefe.datasets import load_bingliu
>>> from wefe.metrics import RNSB
>>>
>>> # Load the model
>>> model = WordEmbeddingModel(
... api.load('glove-wiki-gigaword-300'), 'Glove wiki'
... )
>>>
>>> RNSB_words = [
... ["swedish"],
... ["irish"],
... ["mexican"],
... ["chinese"],
... ["filipino"],
... ["german"],
... ["english"],
... ["french"],
... ["norwegian"],
... ["american"],
... ["indian"],
... ["dutch"],
... ["russian"],
... ["scottish"],
... ["italian"],
... ]
>>>
>>> bing_liu = load_bingliu()
>>>
>>> # Create the query
>>> query = Query(
... RNSB_words,
... [bing_liu["positive_words"], bing_liu["negative_words"]],
... attribute_sets_names=["Positive words", "Negative words"],
... )
>>>
>>> results = RNSB().run_query(
... query,
... model,
... preprocessors=[{"lowercase": True}],
... holdout=True,
... print_model_evaluation=True,
... n_iterations=500,
... )
>>> results
{
"query_name": (
"Target set 0, Target set 1, Target set 2, Target set 3, "
"Target set 4, Target set 5, Target set 6, Target set 7, "
"Target set 8, Target set 9, Target set 10, Target set 11, "
"Target set 12, Target set 13 and Target set 14 "
"wrt Positive words and Negative words"
),
"result": 0.6313118439654091,
"rnsb": 0.6313118439654091,
"negative_sentiment_probabilities": {
"swedish": 0.03865446713798508,
"irish": 0.12266387930214015,
"mexican": 0.5038405165657709,
"chinese": 0.01913990969357335,
"filipino": 0.08074140612507152,
"german": 0.0498762435972975,
"english": 0.058042779461913364,
"french": 0.08030917713203162,
"norwegian": 0.12177903128690087,
"american": 0.22908203952254658,
"indian": 0.7836948288757486,
"dutch": 0.22748838866881654,
"russian": 0.4877408793080844,
"scottish": 0.027805085889223837,
"italian": 0.007885923500742055,
},
"negative_sentiment_distribution": {
"swedish": 0.01361674725376778,
"irish": 0.04321060837966024,
"mexican": 0.17748709213331876,
"chinese": 0.006742385345191273,
"filipino": 0.02844264587050847,
"german": 0.017569824481278706,
"english": 0.020446636995867174,
"french": 0.028290385255119174,
"norwegian": 0.04289890438595362,
"american": 0.08069836330742804,
"indian": 0.27607092269031136,
"dutch": 0.08013697047258364,
"russian": 0.17181569869170976,
"scottish": 0.009794853090881381,
"italian": 0.0027779616464207076,
},
}
"""
# check the types of the provided arguments (only the defaults).
self._check_input(query, model, locals())
if n_iterations > 1 and random_state is not None:
raise ValueError(
"It is not possible to specify random_state together with n_iterations"
" > 1 since all iterations would produce the same results."
)
# transform query word sets into embeddings
embeddings = get_embeddings_from_query(
model=model,
query=query,
lost_vocabulary_threshold=lost_vocabulary_threshold,
preprocessors=preprocessors,
strategy=strategy,
normalize=normalize,
warn_not_found_words=warn_not_found_words,
)
# if there is any/some set has less words than the allowed limit,
# return the default value (nan)
if embeddings is None:
return {
"query_name": query.query_name,
"result": np.nan,
"rnsb": np.nan,
"negative_sentiment_probabilities": {},
"negative_sentiment_distribution": {},
}
# get the targets and attribute sets transformed into embeddings.
target_sets, attribute_sets = embeddings
# get only the embeddings of the sets.
target_embeddings = list(target_sets.values())
attribute_embeddings = list(attribute_sets.values())
# create the arrays that will contain the scores for each iteration
calculated_divergences = []
calculated_negative_sentiment_probabilities = []
scores = []
# calculate the scores for each iteration
for i in range(n_iterations):
try:
if print_model_evaluation and (i > 0 and i < 2):
print(
"When n_iterations > 1, only the first evaluation is printed."
)
print_model_evaluation = False
# train the logit with the train data.
trained_classifier, score = self._train_classifier(
attribute_embeddings_dict=attribute_embeddings,
random_state=random_state,
estimator=estimator,
estimator_params=estimator_params,
holdout=holdout,
print_model_evaluation=print_model_evaluation,
)
scores.append(score)
# get the scores
divergence, negative_sentiment_probabilities = self._calc_rnsb(
target_embeddings, trained_classifier
)
calculated_divergences.append(divergence)
calculated_negative_sentiment_probabilities.append(
negative_sentiment_probabilities
)
except Exception as e:
logging.exception("RNSB Iteration omitted: " + str(e))
# aggregate results
divergence = np.mean(np.array(calculated_divergences))
negative_sentiment_probabilities = dict(
pd.DataFrame(calculated_negative_sentiment_probabilities).mean()
)
sum_of_prob = np.sum(list(negative_sentiment_probabilities.values()))
negative_sentiment_distribution = {
word: prob / sum_of_prob
for word, prob in negative_sentiment_probabilities.items()
}
return {
"query_name": query.query_name,
"result": divergence,
"rnsb": divergence,
"negative_sentiment_probabilities": negative_sentiment_probabilities,
"negative_sentiment_distribution": negative_sentiment_distribution,
}