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from genbit.genbit_metrics import GenBitMetrics
import pandas as pd
from utils.read_config import get_args
from utils.load_csv import load_sample
def cal_metrics(dataset):
# Create a GenBit object with the desired settings:
genbit_metrics_object = GenBitMetrics(language_code="en", context_window=5, distance_weight=0.95, percentile_cutoff=80)
# Let's say you want to use GenBit with a test sentence, you can add the sentence to GenBit:
#dataset = ["I think she does not like cats. I think he does not like cats.", "He is a dog person."]
genbit_metrics_object.add_data(dataset, tokenized=False)
# To generate the gender bias metrics, we run `get_metrics` by setting `output_statistics` and `output_word_lists` to false, we can reduce the number of metrics created.
metrics = genbit_metrics_object.get_metrics(output_statistics=True, output_word_list=True)
return metrics
# Function to extract genbit metrics
def extract_genbit_metris(stats):
metrics = {}
metrics["genbit_score"] = str(stats["genbit_score"])
metrics["percentage_of_female_gender_definition_words"] = str(stats["percentage_of_female_gender_definition_words"])
metrics["percentage_of_male_gender_definition_words"] = str(stats["percentage_of_male_gender_definition_words"])
metrics["percentage_of_non_binary_gender_definition_words"] = str(stats["percentage_of_non_binary_gender_definition_words"])
metrics["percentage_of_trans_gender_definition_words"] = str(stats["percentage_of_trans_gender_definition_words"])
metrics["percentage_of_cis_gender_definition_words"] = str(stats["percentage_of_cis_gender_definition_words"])
metrics["num_words_considered"] = str(stats["statistics"]["num_words_considered"])
return metrics
def load_dataset_and_get_genbit_metrics(df, sample_method, col_name, num_sample_records):
sample_df = load_sample(num_sample_records, sample_method, df, col_name)
# Turn into a list of text.
sample_text = sample_df[col_name].tolist()
# Call cal_metrics function
stats = cal_metrics(sample_text)
metrics = extract_genbit_metris(stats)
return metrics
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