In [1]:

import sys
import json
from IPython import display
import pandas as pd
import numpy as np
import category_encoders as ce
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import ConfusionMatrixDisplay, RocCurveDisplay, precision_score, recall_score
from sklearn.pipeline import Pipeline

# parent directory to work with dev
sys.path.insert(0, '..')
import verifyml.model_card_toolkit as mctlib
from verifyml.model_card_toolkit import model_card_pb2, ModelCard
from verifyml.model_tests.utils import plot_to_str
from verifyml.model_tests.FEAT import (
    SubgroupDisparity,
    MinMaxMetricThreshold,
    Perturbation,
    SHAPFeatureImportance,
    FeatureImportance,
    DataShift
)

import sys import json from IPython import display import pandas as pd import numpy as np import category_encoders as ce from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import ConfusionMatrixDisplay, RocCurveDisplay, precision_score, recall_score from sklearn.pipeline import Pipeline # parent directory to work with dev sys.path.insert(0, '..') import verifyml.model_card_toolkit as mctlib from verifyml.model_card_toolkit import model_card_pb2, ModelCard from verifyml.model_tests.utils import plot_to_str from verifyml.model_tests.FEAT import ( SubgroupDisparity, MinMaxMetricThreshold, Perturbation, SHAPFeatureImportance, FeatureImportance, DataShift )

In [2]:

# Credit card fraud Dataset
df = pd.read_csv("../data/fraud.csv")
x = df.drop("is_fraud", axis=1)
y = df["is_fraud"]


# Train-Test data Split
x_train, x_test, y_train, y_test = train_test_split(
    x, y, test_size=0.5, random_state=50
)


## Build ML model with protected attributes as model features

# Apply one hot encoding to categorical columns (auto-detect object columns) and random forest model in the pipeline
estimator = Pipeline(steps=[('onehot', ce.OneHotEncoder(use_cat_names=True)),
                      ('classifier', RandomForestClassifier(n_estimators=4, max_features="sqrt", random_state = 882))])


# Fit, predict and compute performance metrics
estimator.fit(x_train, y_train)

output = x_test.copy() # x_test df with output columns, to be appended later
y_pred = estimator.predict(x_test)
y_probas = estimator.predict_proba(x_test)[::, 1]

precision_train = round(precision_score(y_train, estimator.predict(x_train)),3)
recall_train = round(recall_score(y_train, estimator.predict(x_train)), 3)
precision_test = round(precision_score(y_test, y_pred),3)
recall_test = round(recall_score(y_test, y_pred), 3)


# Add output columns to this dataframe, to be used as a input for feat tests
output["truth"] = y_test
output["prediction"] = y_pred
output["prediction_probas"] = y_probas


# Dataframe with categorical features encoded
x_train_encoded = estimator[0].transform(x_train)
x_test_encoded = estimator[0].transform(x_test)


# Get feature importance values
df_importance = pd.DataFrame(
    {"features": x_test_encoded.columns, "value": estimator[-1].feature_importances_}
)

# Credit card fraud Dataset df = pd.read_csv("../data/fraud.csv") x = df.drop("is_fraud", axis=1) y = df["is_fraud"] # Train-Test data Split x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.5, random_state=50 ) ## Build ML model with protected attributes as model features # Apply one hot encoding to categorical columns (auto-detect object columns) and random forest model in the pipeline estimator = Pipeline(steps=[('onehot', ce.OneHotEncoder(use_cat_names=True)), ('classifier', RandomForestClassifier(n_estimators=4, max_features="sqrt", random_state = 882))]) # Fit, predict and compute performance metrics estimator.fit(x_train, y_train) output = x_test.copy() # x_test df with output columns, to be appended later y_pred = estimator.predict(x_test) y_probas = estimator.predict_proba(x_test)[::, 1] precision_train = round(precision_score(y_train, estimator.predict(x_train)),3) recall_train = round(recall_score(y_train, estimator.predict(x_train)), 3) precision_test = round(precision_score(y_test, y_pred),3) recall_test = round(recall_score(y_test, y_pred), 3) # Add output columns to this dataframe, to be used as a input for feat tests output["truth"] = y_test output["prediction"] = y_pred output["prediction_probas"] = y_probas # Dataframe with categorical features encoded x_train_encoded = estimator[0].transform(x_train) x_test_encoded = estimator[0].transform(x_test) # Get feature importance values df_importance = pd.DataFrame( {"features": x_test_encoded.columns, "value": estimator[-1].feature_importances_} )

is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead

Get confusion matrix and ROC curve on train/test set¶

In [3]:

# Train set
ConfusionMatrixDisplay.from_estimator(estimator, x_train, y_train)
confusion_matrix_train = plot_to_str()
RocCurveDisplay.from_estimator(estimator, x_train, y_train)
roc_curve_train = plot_to_str()

# Test set
ConfusionMatrixDisplay.from_estimator(estimator, x_test, y_test)
confusion_matrix_test = plot_to_str()
RocCurveDisplay.from_estimator(estimator, x_test, y_test)
roc_curve_test = plot_to_str()

# Train set ConfusionMatrixDisplay.from_estimator(estimator, x_train, y_train) confusion_matrix_train = plot_to_str() RocCurveDisplay.from_estimator(estimator, x_train, y_train) roc_curve_train = plot_to_str() # Test set ConfusionMatrixDisplay.from_estimator(estimator, x_test, y_test) confusion_matrix_test = plot_to_str() RocCurveDisplay.from_estimator(estimator, x_test, y_test) roc_curve_test = plot_to_str()

Run some FEAT Tests on the data¶

In [4]:

# ROC/Min Max Threshold Test

smt_test = MinMaxMetricThreshold(
    #test_name="",        # Default test name and description will be used accordingly if not specified
    #test_desc="",
    attr="gender",
    metric="fpr",
    threshold=0.025,
    #proba_threshold = 0.6 # Outcome probability threshold, default at 0.5
)
smt_test.run(df_test_with_output=output)
smt_test.plot()

smt_test2 = MinMaxMetricThreshold(
    attr="age",
    metric="fpr",
    threshold=0.025,
)
smt_test2.run(df_test_with_output=output)
smt_test2.plot()

# ROC/Min Max Threshold Test smt_test = MinMaxMetricThreshold( #test_name="", # Default test name and description will be used accordingly if not specified #test_desc="", attr="gender", metric="fpr", threshold=0.025, #proba_threshold = 0.6 # Outcome probability threshold, default at 0.5 ) smt_test.run(df_test_with_output=output) smt_test.plot() smt_test2 = MinMaxMetricThreshold( attr="age", metric="fpr", threshold=0.025, ) smt_test2.run(df_test_with_output=output) smt_test2.plot()

In [5]:

# Subgroup Disparity Test

sgd_test = SubgroupDisparity(
    attr='age',
    metric='fpr',
    method='ratio',
    threshold=1.5,
)
sgd_test.run(output)
sgd_test.plot(alpha=0.05)     # default alpha argument shows 95% C.I bands

sgd_test2 = SubgroupDisparity(
    attr='gender',
    metric='fpr',
    method='ratio',
    threshold=1.5,
)
sgd_test2.run(output)
sgd_test2.plot(alpha=0.05)    # default alpha argument shows 95% C.I bands

# Subgroup Disparity Test sgd_test = SubgroupDisparity( attr='age', metric='fpr', method='ratio', threshold=1.5, ) sgd_test.run(output) sgd_test.plot(alpha=0.05) # default alpha argument shows 95% C.I bands sgd_test2 = SubgroupDisparity( attr='gender', metric='fpr', method='ratio', threshold=1.5, ) sgd_test2.run(output) sgd_test2.plot(alpha=0.05) # default alpha argument shows 95% C.I bands

In [6]:

# Subgroup Perturbation Test

np.random.seed(123)
pmt = Perturbation(
    attr='age',
    metric='fpr',
    method='ratio',
    threshold=1.5,
    #proba_threshold=0.6,  # Outcome probability threshold, default at 0.5
)

pmt.run(
    x_test=x_test,
    y_test=y_test,
    encoder=estimator[0],
    model=estimator[-1]
)

pmt.plot(alpha=0.05)   # default alpha argument shows 95% C.I bands

# Subgroup Perturbation Test np.random.seed(123) pmt = Perturbation( attr='age', metric='fpr', method='ratio', threshold=1.5, #proba_threshold=0.6, # Outcome probability threshold, default at 0.5 ) pmt.run( x_test=x_test, y_test=y_test, encoder=estimator[0], model=estimator[-1] ) pmt.plot(alpha=0.05) # default alpha argument shows 95% C.I bands

In [7]:

pmt.result

pmt.result

Out[7]:

	fpr of original data	fpr of perturbed data	ratio	passed
age_26-39	0.021	0.019	1.140	True
age_40-64	0.024	0.019	1.268	True
age_<=25	0.012	0.024	0.482	True
age_>=65	0.011	0.012	0.959	True

In [8]:

# Shapely Importance Features Test

shap_test = SHAPFeatureImportance(
    attrs=['gender','age'],
    threshold=10
)

shap_test.run(
    model=estimator[-1],
    model_type='trees',
    x_train_encoded=x_train_encoded,
    x_test_encoded=x_test_encoded,
)
shap_test.shap_summary_plot(x_test_encoded)
shap_test.shap_dependence_plot(x_test_encoded, show_all=False) # Show only dependence plots of attributes that failed the test

# Shapely Importance Features Test shap_test = SHAPFeatureImportance( attrs=['gender','age'], threshold=10 ) shap_test.run( model=estimator[-1], model_type='trees', x_train_encoded=x_train_encoded, x_test_encoded=x_test_encoded, ) shap_test.shap_summary_plot(x_test_encoded) shap_test.shap_dependence_plot(x_test_encoded, show_all=False) # Show only dependence plots of attributes that failed the test

model_output = "margin" has been renamed to model_output = "raw"

In [9]:

# User inputted Feature importance test

imp_test = FeatureImportance(
    attrs=['gender','age'],
    threshold=10
)

imp_test.run(df_importance)
imp_test.plot(df_importance, show_n=10)   # Show top 10 most important features

# User inputted Feature importance test imp_test = FeatureImportance( attrs=['gender','age'], threshold=10 ) imp_test.run(df_importance) imp_test.plot(df_importance, show_n=10) # Show top 10 most important features

In [10]:

# Data distribution Shift Test

shift_test = DataShift(
    protected_attr = ['gender','age'],
    method = 'chi2',
    threshold = 0.05
)

shift_test.run(x_train = x_train, x_test = x_test)
shift_test.plot(alpha=0.05)   # default alpha argument shows 95% C.I bands

# Data distribution Shift Test shift_test = DataShift( protected_attr = ['gender','age'], method = 'chi2', threshold = 0.05 ) shift_test.run(x_train = x_train, x_test = x_test) shift_test.plot(alpha=0.05) # default alpha argument shows 95% C.I bands

Bootstrap model card from VerifyML model card editor and scaffold assets¶

We can add the quantitative analysis, explainability analysis and fairness analysis sections to a bootstrap model card for convenience. In this example, we use an existing model card which we created from the VerifyML model card editor. This is meant only as an example - the dataset and risk evaluation in the model card is a fictional use case.

In [11]:

# Initialize the mct and scaffold using the existing protobuf
mct = mctlib.ModelCardToolkit(output_dir = "model_card_output", file_name="credit_card_fraud_example")
mc = mct.scaffold_assets(path="initial_model_card.proto")

# Initialize the mct and scaffold using the existing protobuf mct = mctlib.ModelCardToolkit(output_dir = "model_card_output", file_name="credit_card_fraud_example") mc = mct.scaffold_assets(path="initial_model_card.proto")

Convert test objects to a model-card-compatible format¶

In [12]:

# init model card test objects
mc_smt_test = mctlib.Test()
mc_smt_test2 = mctlib.Test()
mc_sgd_test = mctlib.Test()
mc_sgd_test2 = mctlib.Test()
mc_pmt_test = mctlib.Test()
mc_shap_test = mctlib.Test()
mc_imp_test = mctlib.Test()
mc_shift_test = mctlib.Test()

# assign tests to them
mc_smt_test.read_model_test(smt_test)
mc_smt_test2.read_model_test(smt_test2)
mc_sgd_test.read_model_test(sgd_test)
mc_sgd_test2.read_model_test(sgd_test2)
mc_pmt_test.read_model_test(pmt)
mc_imp_test.read_model_test(imp_test)
mc_shap_test.read_model_test(shap_test)
mc_shift_test.read_model_test(shift_test)

# init model card test objects mc_smt_test = mctlib.Test() mc_smt_test2 = mctlib.Test() mc_sgd_test = mctlib.Test() mc_sgd_test2 = mctlib.Test() mc_pmt_test = mctlib.Test() mc_shap_test = mctlib.Test() mc_imp_test = mctlib.Test() mc_shift_test = mctlib.Test() # assign tests to them mc_smt_test.read_model_test(smt_test) mc_smt_test2.read_model_test(smt_test2) mc_sgd_test.read_model_test(sgd_test) mc_sgd_test2.read_model_test(sgd_test2) mc_pmt_test.read_model_test(pmt) mc_imp_test.read_model_test(imp_test) mc_shap_test.read_model_test(shap_test) mc_shift_test.read_model_test(shift_test)

In [13]:

# Add quantitative analysis

# Create 4 PerformanceMetric to store our results
mc.quantitative_analysis.performance_metrics = [mctlib.PerformanceMetric() for i in range(0, 4)]
mc.quantitative_analysis.performance_metrics[0].type = "Recall"
mc.quantitative_analysis.performance_metrics[0].value = str(recall_train)
mc.quantitative_analysis.performance_metrics[0].slice = "Training Set"

mc.quantitative_analysis.performance_metrics[1].type = "Precision"
mc.quantitative_analysis.performance_metrics[1].value = str(precision_train)
mc.quantitative_analysis.performance_metrics[1].slice = "Training Set"
mc.quantitative_analysis.performance_metrics[1].graphics.description = (
  'Confusion matrix and ROC Curve')
mc.quantitative_analysis.performance_metrics[1].graphics.collection = [
    mctlib.Graphic(image=confusion_matrix_train), mctlib.Graphic(image=roc_curve_train)
]

mc.quantitative_analysis.performance_metrics[2].type = "Recall"
mc.quantitative_analysis.performance_metrics[2].value = str(recall_test)
mc.quantitative_analysis.performance_metrics[2].slice = "Test Set"

mc.quantitative_analysis.performance_metrics[3].type = "Precision"
mc.quantitative_analysis.performance_metrics[3].value = str(precision_test)
mc.quantitative_analysis.performance_metrics[3].slice = "Test Set"
mc.quantitative_analysis.performance_metrics[3].graphics.description = (
  'Confusion matrix and ROC Curve')
mc.quantitative_analysis.performance_metrics[3].graphics.collection = [
    mctlib.Graphic(image=confusion_matrix_test), mctlib.Graphic(image=roc_curve_test)
]

# Add quantitative analysis # Create 4 PerformanceMetric to store our results mc.quantitative_analysis.performance_metrics = [mctlib.PerformanceMetric() for i in range(0, 4)] mc.quantitative_analysis.performance_metrics[0].type = "Recall" mc.quantitative_analysis.performance_metrics[0].value = str(recall_train) mc.quantitative_analysis.performance_metrics[0].slice = "Training Set" mc.quantitative_analysis.performance_metrics[1].type = "Precision" mc.quantitative_analysis.performance_metrics[1].value = str(precision_train) mc.quantitative_analysis.performance_metrics[1].slice = "Training Set" mc.quantitative_analysis.performance_metrics[1].graphics.description = ( 'Confusion matrix and ROC Curve') mc.quantitative_analysis.performance_metrics[1].graphics.collection = [ mctlib.Graphic(image=confusion_matrix_train), mctlib.Graphic(image=roc_curve_train) ] mc.quantitative_analysis.performance_metrics[2].type = "Recall" mc.quantitative_analysis.performance_metrics[2].value = str(recall_test) mc.quantitative_analysis.performance_metrics[2].slice = "Test Set" mc.quantitative_analysis.performance_metrics[3].type = "Precision" mc.quantitative_analysis.performance_metrics[3].value = str(precision_test) mc.quantitative_analysis.performance_metrics[3].slice = "Test Set" mc.quantitative_analysis.performance_metrics[3].graphics.description = ( 'Confusion matrix and ROC Curve') mc.quantitative_analysis.performance_metrics[3].graphics.collection = [ mctlib.Graphic(image=confusion_matrix_test), mctlib.Graphic(image=roc_curve_test) ]

In [14]:

# You can add the components of a test (e.g. on explainability) in a report
mc.explainability_analysis.explainability_reports = [
    mctlib.ExplainabilityReport(
        type="Top 10 most important features", graphics=mctlib.GraphicsCollection(
            collection = [mctlib.Graphic(name=n, image=i) for n, i in imp_test.plots.items()]
        )
    )
]

# Or you can add it as a test directly
mc.explainability_analysis.explainability_reports.append(
    mctlib.ExplainabilityReport(type="Protected Attributes should not be model's top important features", tests=[mc_shap_test])
)

# You can add the components of a test (e.g. on explainability) in a report mc.explainability_analysis.explainability_reports = [ mctlib.ExplainabilityReport( type="Top 10 most important features", graphics=mctlib.GraphicsCollection( collection = [mctlib.Graphic(name=n, image=i) for n, i in imp_test.plots.items()] ) ) ] # Or you can add it as a test directly mc.explainability_analysis.explainability_reports.append( mctlib.ExplainabilityReport(type="Protected Attributes should not be model's top important features", tests=[mc_shap_test]) )

In [15]:

# The bootstrap template comes with two requirements on fairness analysis:
# Minimum acceptable service and Equal false positive rate
# We add the relevant tests associated with it
mc.fairness_analysis.fairness_reports[0].tests = [mc_smt_test,mc_smt_test2]
mc.fairness_analysis.fairness_reports[1].tests = [mc_sgd_test,mc_sgd_test2]

# We