def _fdf(self):
if self.fdf is not None:
return self.fdf
f = Fairness()
self.fdf = f.get_group_value_fairness(self._bdf())
return self.fdf
def audit(df, configs, model_id=1, preprocessed=False):
"""
:param df:
:param ref_groups_method:
:param model_id:
:param configs:
:param report:
:param preprocessed:
:return:
"""
if not preprocessed:
df, attr_cols_input = preprocess_input_df(df)
if not configs.attr_cols:
configs.attr_cols = attr_cols_input
g = Group()
print('Welcome to Aequitas-Audit')
print('Fairness measures requested:',
','.join(configs.fair_measures_requested))
groups_model, attr_cols = g.get_crosstabs(
df,
score_thresholds=configs.score_thresholds,
model_id=model_id,
attr_cols=configs.attr_cols)
print('audit: df shape from the crosstabs:', groups_model.shape)
b = Bias()
# todo move this to the new configs object / the attr_cols now are passed through the configs object...
ref_groups_method = configs.ref_groups_method
if ref_groups_method == 'predefined' and configs.ref_groups:
bias_df = b.get_disparity_predefined_groups(groups_model,
configs.ref_groups)
elif ref_groups_method == 'majority':
bias_df = b.get_disparity_major_group(groups_model)
else:
bias_df = b.get_disparity_min_metric(groups_model)
print('Any NaN?: ', bias_df.isnull().values.any())
print('bias_df shape:', bias_df.shape)
f = Fairness(tau=configs.fairness_threshold)
print('Fairness Threshold:', configs.fairness_threshold)
print('Fairness Measures:', configs.fair_measures_requested)
group_value_df = f.get_group_value_fairness(
bias_df, fair_measures_requested=configs.fair_measures_requested)
group_attribute_df = f.get_group_attribute_fairness(
group_value_df,
fair_measures_requested=configs.fair_measures_requested)
fair_results = f.get_overall_fairness(group_attribute_df)
print(fair_results)
report = None
if configs.report is True:
report = audit_report_markdown(configs, group_value_df,
f.fair_measures_depend, fair_results)
return group_value_df, report
def get_model_fairness(self, level='model'):
g = Group()
xtab, _ = g.get_crosstabs(self.df)
b = Bias()
majority_bdf = b.get_disparity_major_group(xtab,
original_df=self.df,
mask_significance=True)
f = Fairness()
fdf = f.get_group_value_fairness(majority_bdf)
f_res = fdf
if level == 'model':
f_res = f.get_overall_fairness(fdf)
elif level == 'attribute':
f_res = f.get_group_attribute_fairness(fdf)
return f_res
def tabla_medidas_equidad(data, attr_ref, tau=0.8):
# Calculamos las métricas de grupo
g = Group()
xtab, _ = g.get_crosstabs(data)
# Calculamos las metricas de sesgo
b = Bias()
# Establecemos los atributos de referencia
bdf = b.get_disparity_predefined_groups(xtab,
original_df=data,
ref_groups_dict=attr_ref,
alpha=0.05,
mask_significance=True)
# Definimos las medidas de equidad a partir de la tabla de metricas de sesgo
f = Fairness()
# Establecemos el valor del umbral con la variable tau
fdf = f.get_group_value_fairness(bdf, tau=tau)
# Tabla con si se cumplen las medidas de equidad para cada atributo
tabla_equidad = f.get_group_attribute_fairness(fdf)
return tabla_equidad
def fairness(df):
"""
Genera todo el módulo de Equidad
"""
print("Módulo de Equidad")
print("-"*30)
f = Fairness()
bias_ = Bias()
g = Group()
xtab, atts = g.get_crosstabs(df, attr_cols=["delegacion"])
absolute_metrics = g.list_absolute_metrics(xtab)
bdf = bias_.get_disparity_predefined_groups(xtab, original_df = df,
ref_groups_dict = {'delegacion': 'IZTAPALAPA'},
alpha=0.05)
fdf = f.get_group_value_fairness(bdf)
parity_determinations = f.list_parities(fdf)
print("Imprimiendo tabla de métricas (conteos en frecuencias):")
print(fdf[['attribute_name', 'attribute_value'] + absolute_metrics + bias_.list_disparities(fdf) + parity_determinations].round(2))
print("Impriendo métricas generales")
gof = f.get_overall_fairness(fdf)
print(gof)
print("Aequitas analysis completed.")
print("-"*30)
def fairnessf(bdf, absolute_metrics, bias):
"""
args:
df (dataframe): Recibe el data frame que tiene los features sobre los que queremos medir la equidad.
returns:
-
"""
fair = Fairness()
fdf = fair.get_group_value_fairness(bdf)
parity_determinations = fair.list_parities(fdf)
fairness = fdf[['attribute_name', 'attribute_value'] + absolute_metrics +
bias.list_disparities(fdf) + parity_determinations].round(2)
## Storing metadata
aq_metadata["v_group"] = str(fdf.loc[0, "attribute_value"])
aq_metadata["FOR_p"] = str(fdf.loc[0, "FOR Parity"])
aq_metadata["FNR_p"] = str(fdf.loc[0, "FNR Parity"])
#return df_aeq
gaf = fair.get_group_attribute_fairness(fdf)
gof = fair.get_overall_fairness(fdf)
return fairness, gaf, gof
def _write_audit_to_db(self, model_id, protected_df, predictions_proba,
labels, tie_breaker, subset_hash, matrix_type,
evaluation_start_time, evaluation_end_time,
matrix_uuid):
"""
Runs the bias audit and saves the result in the bias table.
Args:
model_id (int) primary key of the model
protected_df (pandas.DataFrame) A dataframe with protected group attributes:
predictions_proba (np.array) List of prediction probabilities
labels (pandas.Series): List of labels
tie_breaker: 'best' or 'worst' case tiebreaking rule that the predictions and labels were sorted by
subset_hash (str) the hash of the subset, if any, that the
evaluation is made on
matrix_type (triage.component.catwalk.storage.MatrixType)
The type of matrix used
evaluation_start_time (pandas._libs.tslibs.timestamps.Timestamp)
first as_of_date included in the evaluation period
evaluation_end_time (pandas._libs.tslibs.timestamps.Timestamp) last
as_of_date included in the evaluation period
matrix_uuid: the uuid of the matrix
Returns:
"""
if protected_df.empty:
return
# to preprocess aequitas requires the following columns:
# score, label value, model_id, protected attributes
# fill out the protected_df, which just has protected attributes at this point
protected_df = protected_df.copy()
protected_df['model_id'] = model_id
protected_df['score'] = predictions_proba
protected_df['label_value'] = labels
aequitas_df, attr_cols_input = preprocess_input_df(protected_df)
# create group crosstabs
g = Group()
score_thresholds = {}
score_thresholds['rank_abs'] = self.bias_config['thresholds'].get(
'top_n', [])
# convert 0-100 percentile to 0-1 that Aequitas expects
score_thresholds['rank_pct'] = [
value / 100.0
for value in self.bias_config['thresholds'].get('percentiles', [])
]
groups_model, attr_cols = g.get_crosstabs(
aequitas_df,
score_thresholds=score_thresholds,
attr_cols=attr_cols_input)
# analyze bias from reference groups
bias = Bias()
ref_groups_method = self.bias_config.get('ref_groups_method', None)
if ref_groups_method == 'predefined' and self.bias_config['ref_groups']:
bias_df = bias.get_disparity_predefined_groups(
groups_model, aequitas_df, self.bias_config['ref_groups'])
elif ref_groups_method == 'majority':
bias_df = bias.get_disparity_major_group(groups_model, aequitas_df)
else:
bias_df = bias.get_disparity_min_metric(groups_model, aequitas_df)
# analyze fairness for each group
f = Fairness(tau=0.8) # the default fairness threshold is 0.8
group_value_df = f.get_group_value_fairness(bias_df)
group_value_df['subset_hash'] = subset_hash
group_value_df['tie_breaker'] = tie_breaker
group_value_df['evaluation_start_time'] = evaluation_start_time
group_value_df['evaluation_end_time'] = evaluation_end_time
group_value_df['matrix_uuid'] = matrix_uuid
group_value_df = group_value_df.rename(
index=str, columns={"score_threshold": "parameter"})
if group_value_df.empty:
raise ValueError(f"""
Bias audit: aequitas_audit() failed.
Returned empty dataframe for model_id = {model_id}, and subset_hash = {subset_hash}
and matrix_type = {matrix_type}""")
with scoped_session(self.db_engine) as session:
for index, row in group_value_df.iterrows():
session.query(matrix_type.aequitas_obj).filter_by(
model_id=row['model_id'],
evaluation_start_time=row['evaluation_start_time'],
evaluation_end_time=row['evaluation_end_time'],
subset_hash=row['subset_hash'],
parameter=row['parameter'],
tie_breaker=row['tie_breaker'],
matrix_uuid=row['matrix_uuid'],
attribute_name=row['attribute_name'],
attribute_value=row['attribute_value']).delete()
session.bulk_insert_mappings(
matrix_type.aequitas_obj,
group_value_df.to_dict(orient="records"))
def execeute(self):
model = self.download_model()
tabla_3 = pd.read_sql_table('centers', self.engine, schema="transformed")
tabla_4 = pd.read_sql_table('inspections', self.engine, schema="transformed")
centros = tabla_3.copy()
centros.rename(columns={"dc_id":"center_id"}, inplace=True)
inspecciones = tabla_4.copy()
last_inspections = inspecciones.sort_values(by="inspectiondate").drop_duplicates(subset=["center_id"], keep="last")
centros = centros.drop(['centername', 'legalname', 'building', 'street', 'zipcode', 'phone', 'permitnumber', 'permitexp', 'status', 'agerange', 'childcaretype', 'bin', 'url', 'datepermitted', 'actual','violationratepercent','violationavgratepercent', 'publichealthhazardviolationrate','averagepublichealthhazardiolationrate','criticalviolationrate','avgcriticalviolationrate'], axis=1)
centros = centros.reset_index(drop=True)
tabla_5 = pd.merge(last_inspections, centros)
tabla_5.sort_values(['inspectiondate'], ascending=[False], inplace=True)
tabla_5['maximumcapacity'] = tabla_5['maximumcapacity'].astype(int)
tabla_5['totaleducationalworkers'] = tabla_5['totaleducationalworkers'].astype(int)
tabla_5['totaleducationalworkers'] = tabla_5['totaleducationalworkers'].astype(int)
tabla_5['averagetotaleducationalworkers'] = tabla_5['averagetotaleducationalworkers'].astype(float)
tabla_5 = tabla_5.drop(['regulationsummary', 'healthcodesubsection', 'violationstatus', 'borough', 'reason', 'inspectiondate', 'violationcategory_nan'], axis=1)
tabla_5 = tabla_5.set_index(['center_id'])
tabla_5 = tabla_5.fillna(0)
for col in tabla_5.select_dtypes(object):
tabla_5[col] = tabla_5[col].astype(float)
tabla_5 = tabla_5.fillna(0)
prds = model.predict(tabla_5.drop(['violationcategory_public_health_hazard'],axis=1))
probas = model.predict_proba(tabla_5.drop(['violationcategory_public_health_hazard'],axis=1))
res = pd.DataFrame({
"center":tabla_5.index,
"etiqueta":prds,
"proba_0":probas[:,0],
"proba_1":probas[:,1]
})
res.loc[res['proba_0'] > res['proba_1'], 'score'] = res['proba_0']
res.loc[res['proba_0'] < res['proba_1'], 'score'] = res['proba_1']
categorias_1 = ["programtype_all_age_camp","programtype_infant_toddler","programtype_preschool", "programtype_preschool_camp", "programtype_school_age_camp"]
programtype = pd.get_dummies(centros[categorias_1]).idxmax(1)
categorias_2 = ["borough_bronx","borough_brooklyn","borough_manhattan", "borough_queens", "borough_staten_island"]
borough = pd.get_dummies(centros[categorias_2]).idxmax(1)
ambas = pd.concat([borough, programtype], axis=1,)
ambas = ambas.rename(columns={0:'borough', 1:'programtype'})
tabla_1 = pd.concat([centros, ambas], axis=1)
tabla_2 = pd.merge(res, tabla_1, left_on='center', right_on='center_id')
for i in list(tabla_2.index):
if str(tabla_2.iloc[i].borough_bronx) == "1":
tabla_2.loc[tabla_2.index == i ,"borough"] = "bronx"
elif str(tabla_2.iloc[i].borough_brooklyn) == "1":
tabla_2.loc[tabla_2.index == i ,"borough"] = "brooklyn"
elif str(tabla_2.iloc[i].borough_manhattan) == "1":
tabla_2.loc[tabla_2.index == i ,"borough"] = "manhattan"
elif str(tabla_2.iloc[i].borough_queens) == "1":
tabla_2.loc[tabla_2.index == i ,"borough"] = "queens"
elif str(tabla_2.iloc[i].borough_staten_island) == "1":
tabla_2.loc[tabla_2.index == i ,"borough"] = "staten_island"
tabla_2.drop(categorias_2, axis=1, inplace=True)
for i in list(tabla_2.index):
if str(tabla_2.iloc[i].programtype_all_age_camp) == "1":
tabla_2.loc[tabla_2.index == i ,"programtype"] = "all_age_camp"
elif str(tabla_2.iloc[i].programtype_infant_toddler) == "1":
tabla_2.loc[tabla_2.index == i ,"programtype"] = "infant_toddler"
elif str(tabla_2.iloc[i].programtype_preschool) == "1":
tabla_2.loc[tabla_2.index == i ,"programtype"] = "preschool"
elif str(tabla_2.iloc[i].programtype_preschool_camp) == "1":
tabla_2.loc[tabla_2.index == i ,"programtype"] = "preschool_camp"
elif str(tabla_2.iloc[i].programtype_school_age_camp) == "1":
tabla_2.loc[tabla_2.index == i ,"programtype"] = "school_age_camp"
tabla_2.drop(categorias_1, axis=1, inplace=True)
tabla_6 = tabla_2.loc[:, ['center', 'etiqueta', 'score', 'borough', 'programtype']]
tabla_6 = tabla_6.rename(columns = {'etiqueta':'label_value'})
tabla_6.set_index('center', inplace=True)
g = Group()
xtab, _ = g.get_crosstabs(tabla_6)
b = Bias()
bdf = b.get_disparity_predefined_groups(xtab, original_df=tabla_6, ref_groups_dict={'borough':'brooklyn', 'programtype':'preschool'}, alpha=0.05, mask_significance=True)
f = Fairness()
fdf = f.get_group_value_fairness(bdf)
fdf['model_id'] = self.model_id
fdf['date'] = self.date_param
self.output_table = fdf
return [tuple(x) for x in fdf.to_numpy()], [(c.replace("for", "forr").replace(" ", "_"), 'VARCHAR') for c in list(fdf.columns)]
alpha=0.05,
mask_significance=True)
calculated_disparities = b.list_disparities(bdf)
disparity_significance = b.list_significance(bdf)
# Mostramos la tabla de metricas de sesgo
print(bdf[['attribute_name', 'attribute_value'] + calculated_disparities +
disparity_significance])
# Plots de disparidad
#aqp.plot_disparity(bdf, group_metric='fpr_disparity', attribute_name='race', significance_alpha=0.05)
#j = aqp.plot_disparity_all(bdf, metrics=['precision_disparity', 'fpr_disparity'], attributes=['age_cat'], significance_alpha=0.05)
# Definimos las medidas de equidad a partir de la tabla de metricas de sesgo
f = Fairness()
# Establecemos el valor del umbral con la variable tau
fdf = f.get_group_value_fairness(bdf, tau=0.8)
#parity_detrminations = f.list_parities(fdf)
# Tabla con si se cumplen las medidas de equidad para cada atributo
gaf = f.get_group_attribute_fairness(fdf)
#print(gaf['Equalized Odds'])
# Metricas de grupo y de sesgo una vez aplicados los umbrales de equidad
fg = aqp.plot_fairness_group_all(
fdf, ncols=2, metrics=['ppr', 'pprev', 'fdr', 'fpr', 'for', 'fnr'])
fg.savefig('./figures/LAW_DATA/disparity_group_law.png')
m = aqp.plot_fairness_disparity_all(fdf,
metrics=['for', 'fnr'],
attributes=['race'])
m.savefig('./figures/LAW_DATA/disparity_law_race.png')
m = aqp.plot_fairness_disparity_all(fdf,
metrics=['for', 'fnr'],
hbdf = b.get_disparity_predefined_groups(xtab,
original_df=df,
ref_groups_dict={
'race': 'WHITE',
'gender': 'MALE'
},
alpha=0.5,
mask_significance=False)
majority_bdf = b.get_disparity_major_group(xtab,
original_df=df,
mask_significance=False)
majority_bdf[['attribute_name', 'attribute_value'] + calculated_disparities +
disparity_significance]
tm_capped = aqp.plot_disparity_all(hbdf,
attributes=['gender', 'race'],
metrics='all',
significance_alpha=0.05)
f = Fairness()
fdf = f.get_group_value_fairness(bdf)
parity_detrminations = f.list_parities(fdf)
fdf[['attribute_name', 'attribute_value'] + absolute_metrics +
calculated_disparities + parity_detrminations].style
fg = aqp.plot_fairness_group_all(fdf, ncols=5, metrics="all")
n_tm = aqp.plot_fairness_disparity_all(fdf,
attributes=['race', 'gender'],
significance_alpha=0.05)
def run_aequitas(predictions_data_path):
'''
Check for False negative rate, chances of certain group missing out on assistance using aequitas toolkit
The functions transform the data to make it aequitas complaint and checks for series of bias and fairness metrics
Input: model prediction path for the selected model (unzip the selected file to run)
Output: plots saved in charts folder
'''
best_model_pred = pd.read_csv(predictions_data_path)
# Transform data for aquetias module compliance
aqc = [
'Other', 'White', 'African American', 'Asian', 'Hispanic',
'American Indian'
]
aqcol = [
'White alone_scale', 'Black/AfAmer alone_scale',
'AmInd/Alaskn alone_scale', 'Asian alone_scale', 'HI alone_scale',
'Some other race alone_scale', 'Hispanic or Latino_scale'
]
display(aqcol)
aqcol_label = [
'no_renew_nextpd', 'pred_class_10%',
'Median household income (1999 dollars)_scale'
] + aqcol
aqus = best_model_pred[aqcol_label]
print('Creating classes for racial and income distribution', '\n')
# Convert to binary
bin_var = [
'no_renew_nextpd',
'pred_class_10%',
]
for var in bin_var:
aqus[var] = np.where(aqus[var] == True, 1, 0)
# Rename
aqus.rename(columns={
'no_renew_nextpd': 'label_value',
'pred_class_10%': 'score'
},
inplace=True)
print('Define majority rule defined on relative proportion of the class',
'\n')
aqus['race'] = aqus[aqcol].idxmax(axis=1)
# Use quantile income distribution
aqus['income'] = pd.qcut(
aqus['Median household income (1999 dollars)_scale'],
3,
labels=["rich", "median", "poor"])
# Final form
aqus.drop(aqcol, axis=1, inplace=True)
aqus.drop(['Median household income (1999 dollars)_scale'],
axis=1,
inplace=True)
aq = aqus.reset_index()
aq.rename(columns={'index': 'entity_id'}, inplace=True)
aq['race'] = aq['race'].replace({
'Some other race alone_scale':
'Other',
'White alone_scale':
'White',
'Black/AfAmer alone_scale':
'African American',
'Asian alone_scale':
'Asian',
'HI alone_scale':
'Hispanic',
'AmInd/Alaskn alone_scale':
'American Indian'
})
# Consolidate types
aq['income'] = aq['income'].astype(object)
aq['entity_id'] = aq['entity_id'].astype(object)
aq['score'] = aq['score'].astype(object)
aq['label_value'] = aq['label_value'].astype(object)
# Distribuion of categories
aq_palette = sns.diverging_palette(225, 35, n=2)
by_race = sns.countplot(x="race", data=aq[aq.race.isin(aqc)])
by_race.set_xticklabels(by_race.get_xticklabels(), rotation=40, ha="right")
plt.savefig('charts/Racial distribution in data.png')
# Primary distribuion against score
aq_palette = sns.diverging_palette(225, 35, n=2)
by_race = sns.countplot(x="race",
hue="score",
data=aq[aq.race.isin(aqc)],
palette=aq_palette)
by_race.set_xticklabels(by_race.get_xticklabels(), rotation=40, ha="right")
# Race
plt.savefig('charts/race_score.png')
# Income
by_inc = sns.countplot(x="income",
hue="score",
data=aq,
palette=aq_palette)
plt.savefig('charts/income_score.png')
# Set Group
g = Group()
xtab, _ = g.get_crosstabs(aq)
# False Negative Rates
aqp = Plot()
fnr = aqp.plot_group_metric(xtab, 'fnr', min_group_size=0.05)
p = aqp.plot_group_metric_all(xtab,
metrics=['ppr', 'pprev', 'fnr', 'fpr'],
ncols=4)
p.savefig('charts/eth_metrics.png')
# Bias with respect to white rich category
b = Bias()
bdf = b.get_disparity_predefined_groups(xtab,
original_df=aq,
ref_groups_dict={
'race': 'White',
'income': 'rich'
},
alpha=0.05,
mask_significance=True)
bdf.style
calculated_disparities = b.list_disparities(bdf)
disparity_significance = b.list_significance(bdf)
aqp.plot_disparity(bdf,
group_metric='fpr_disparity',
attribute_name='race',
significance_alpha=0.05)
plt.savefig('charts/disparity.png')
# Fairness
hbdf = b.get_disparity_predefined_groups(xtab,
original_df=aq,
ref_groups_dict={
'race': 'African American',
'income': 'poor'
},
alpha=0.05,
mask_significance=False)
majority_bdf = b.get_disparity_major_group(xtab,
original_df=aq,
mask_significance=True)
min_metric_bdf = b.get_disparity_min_metric(df=xtab, original_df=aq)
f = Fairness()
fdf = f.get_group_value_fairness(bdf)
parity_detrminations = f.list_parities(fdf)
gaf = f.get_group_attribute_fairness(fdf)
gof = f.get_overall_fairness(fdf)
z = aqp.plot_fairness_group(fdf, group_metric='ppr')
plt.savefig('charts/fairness_overall.png')
# Checking for False Omission Rate and False Negative Rates
fg = aqp.plot_fairness_group_all(fdf, metrics=['for', 'fnr'], ncols=2)
fg.savefig('charts/fairness_metrics.png')
return None
def fun_bias_fair(a_zip, a_type, fea_eng, model):
X = fea_eng.drop([
'aka_name', 'facility_type', 'address', 'inspection_date',
'inspection_type', 'violations', 'results', 'pass'
],
axis=1)
y_pred = model.predict(X)
xt = pd.DataFrame([
fea_eng['zip'].astype(float), fea_eng['facility_type'],
fea_eng['pass'], y_pred
]).transpose()
a_zip['zip'] = a_zip['zip'].astype(float)
compas = pd.merge(left=xt,
right=a_zip,
how='left',
left_on='zip',
right_on='zip')
compas = pd.merge(left=compas,
right=a_type,
how='left',
left_on='facility_type',
right_on='facility_type')
compas = compas.rename(columns={
'Unnamed 0': 'score',
'pass': '******'
})
compas.pop('zip')
compas.pop('facility_type')
compas['zone'] = compas['zone'].astype(str)
compas['score'] = compas['score'].astype(int)
compas['label_value'] = compas['label_value'].astype(int)
from aequitas.group import Group
from aequitas.bias import Bias
from aequitas.fairness import Fairness
#Group
g = Group()
xtab, attrbs = g.get_crosstabs(compas)
absolute_metrics = g.list_absolute_metrics(xtab)
xtab[[col for col in xtab.columns if col not in absolute_metrics]]
group_df = xtab[['attribute_name', 'attribute_value'] +
[col for col in xtab.columns
if col in absolute_metrics]].round(4)
abs_gpo = xtab[['attribute_name', 'attribute_value'] +
[col for col in xtab.columns
if col in absolute_metrics]].round(4)
#Bias
bias = Bias()
bdf = bias.get_disparity_predefined_groups(xtab,
original_df=compas,
ref_groups_dict={
'zone': 'West',
'facility_group': 'grocery'
},
alpha=0.05)
# View disparity metrics added to dataframe
bias_bdf = bdf[['attribute_name', 'attribute_value'] +
bias.list_disparities(bdf)].round(2)
majority_bdf = bias.get_disparity_major_group(xtab, original_df=compas)
bias_maj_bdf = majority_bdf[['attribute_name', 'attribute_value'] +
bias.list_disparities(majority_bdf)].round(2)
min_bdf = bias.get_disparity_min_metric(xtab, original_df=compas)
bias_min_bdf = min_bdf[['attribute_name', 'attribute_value'] +
bias.list_disparities(min_bdf)].round(2)
min_bdf[['attribute_name', 'attribute_value'] +
bias.list_disparities(min_bdf)].round(2)
#Fairness
fair = Fairness()
fdf = fair.get_group_value_fairness(bdf)
parity_determinations = fair.list_parities(fdf)
fair_fdf = fdf[['attribute_name', 'attribute_value'] + absolute_metrics +
bias.list_disparities(fdf) + parity_determinations].round(2)
gaf = fair.get_group_attribute_fairness(fdf)
fairness_df = fdf.copy()
gof = fair.get_overall_fairness(fdf)
tab_bias_fair = fair_fdf[[
'attribute_name', 'attribute_value', 'for', 'fnr', 'for_disparity',
'fnr_disparity', 'FOR Parity', 'FNR Parity'
]]
tab_bias_fair.rename(columns={
'attribute_value': 'group_name',
'FOR Parity': 'for_parity',
'FNR Parity': 'fnr_parity',
'for': 'for_'
},
inplace=True)
print(tab_bias_fair)
return tab_bias_fair
def audit(df, configs, preprocessed=False):
"""
:param df:
:param configs:
:param preprocessed:
:return:
"""
if not preprocessed:
df, attr_cols_input = preprocess_input_df(df)
if not configs.attr_cols:
configs.attr_cols = attr_cols_input
g = Group()
print('Welcome to Aequitas-Audit')
print('Fairness measures requested:',
','.join(configs.fair_measures_requested))
groups_model, attr_cols = g.get_crosstabs(
df,
score_thresholds=configs.score_thresholds,
attr_cols=configs.attr_cols)
print('audit: df shape from the crosstabs:', groups_model.shape)
b = Bias()
# todo move this to the new configs object / the attr_cols now are passed through the configs object...
ref_groups_method = configs.ref_groups_method
if ref_groups_method == 'predefined' and configs.ref_groups:
bias_df = b.get_disparity_predefined_groups(
groups_model,
df,
configs.ref_groups,
check_significance=configs.check_significance,
alpha=configs.alpha,
selected_significance=configs.selected_significance,
mask_significance=configs.mask_significance)
elif ref_groups_method == 'majority':
bias_df = b.get_disparity_major_group(
groups_model,
df,
check_significance=configs.check_significance,
alpha=configs.alpha,
selected_significance=configs.selected_significance,
mask_significance=configs.mask_significance)
else:
bias_df = b.get_disparity_min_metric(
df=groups_model,
original_df=df,
check_significance=configs.check_significance,
alpha=configs.alpha,
label_score_ref='fpr',
selected_significance=configs.selected_significance,
mask_significance=configs.mask_significance)
print('Any NaN?: ', bias_df.isnull().values.any())
print('bias_df shape:', bias_df.shape)
aqp = Plot()
if configs.plot_bias_metrics:
if len(configs.plot_bias_metrics) == 1:
fig1 = aqp.plot_group_metric(
bias_df, group_metric=configs.plot_bias_metrics[0])
elif len(configs.plot_bias_metrics) > 1:
fig1 = aqp.plot_group_metric_all(
bias_df, metrics=configs.plot_disparity_attributes)
if (len(configs.plot_bias_disparities) == 1) and (len(
configs.plot_disparity_attributes) == 1):
fig2 = aqp.plot_disparity(
bias_df,
group_metric=configs.plot_bias_disparities[0],
attribute_name=configs.plot_disparity_attributes[0])
elif (len(configs.plot_bias_disparities) > 1) or (len(
configs.plot_disparity_attributes) > 1):
fig2 = aqp.plot_disparity_all(
bias_df,
metrics=configs.plot_bias_disparities,
attributes=configs.plot_disparity_attributes)
f = Fairness(tau=configs.fairness_threshold)
print('Fairness Threshold:', configs.fairness_threshold)
print('Fairness Measures:', configs.fair_measures_requested)
group_value_df = f.get_group_value_fairness(
bias_df, fair_measures_requested=configs.fair_measures_requested)
group_attribute_df = f.get_group_attribute_fairness(
group_value_df,
fair_measures_requested=configs.fair_measures_requested)
fair_results = f.get_overall_fairness(group_attribute_df)
if configs.plot_bias_metrics:
if len(configs.plot_bias_metrics) == 1:
fig3 = aqp.plot_fairness_group(
group_value_df, group_metric=configs.plot_bias_metrics[0])
elif len(configs.plot_bias_metrics) > 1:
fig3 = aqp.plot_fairness_group_all(
group_value_df, metrics=configs.plot_bias_metrics)
if (len(configs.plot_bias_disparities) == 1) and (len(
configs.plot_disparity_attributes) == 1):
fig4 = aqp.plot_fairness_disparity(
group_value_df,
group_metric=configs.plot_bias_disparities[0],
attribute_name=configs.plot_disparity_attributes[0])
elif (len(configs.plot_bias_disparities) > 1) or (len(
configs.plot_disparity_attributes) > 1):
fig4 = aqp.plot_fairness_disparity_all(
group_value_df,
metrics=configs.plot_bias_disparities,
attributes=configs.plot_disparity_attributes)
print(fair_results)
report = None
if configs.report is True:
report = audit_report_markdown(configs, group_value_df,
f.fair_measures_depend, fair_results)
return group_value_df, report
#Filtrar los datos de aequitas para calculo de FNR
datos_aequitas = datos_aequitas[['score', 'label_value', 'day_sem']]
datos_aequitas.head()
#Instalación de Aequitas
#pip install aequitas
g = Group()
xtab, _ = g.get_crosstabs(datos_aequitas)
#xtab contiene calculos de todas la métricas de FP, FN, TP, TN
#Calculo de Bias
b = Bias()
bdf = b.get_disparity_predefined_groups(
xtab,
original_df=datos_aequitas,
ref_groups_dict={'day_sem': 'e:viernes'},
alpha=0.05,
check_significance=False)
#Calculo de Fairness
f = Fairness()
fdf = f.get_group_value_fairness(bdf) #Mismo grupo de referencia
#Exportar archivos
ruta = "bdf.csv"
bdf.to_csv(ruta)
ruta = "fdf.csv"
fdf.to_csv(ruta)