Python package to generate counterfactuals using Monte Carlo sampling of realistic counterfactual explanations.
For other examples (e.g., how to use MCCE with the CARLA package), see examples/.
This example uses the US adult census dataset in the Data/ repository. Although there are some predefined data and model classes (see examples/Example_notebook.ipynb), in this Readme, we define the data/model classes from scratch.
Load data using pandas and specify column names, feature types, response name, and a list of immutable features,.
import pandas as pd
feature_order = ['age', 'workclass', 'fnlwgt', 'education-num', 'marital-status', 'occupation',
'relationship', 'race', 'sex', 'hours-per-week']
dtypes = {"age": "float",
"workclass": "category",
"fnlwgt": "float",
"education-num": "float",
"marital-status": "category",
"occupation": "category",
"relationship": "category",
"race": "category",
"sex": "category",
"hours-per-week": "float",
"income": "category"}
categorical = ['workclass', 'marital-status', 'occupation', 'relationship', 'race', 'sex']
continuous = ['age', 'fnlwgt', 'education-num', 'hours-per-week']
immutable_features = ['age', 'sex']
target = ['income']
features = categorical + continuous
path = '../Data/adult_data.csv'
df = pd.read_csv(path)
df = df[features + target]Using sklearn, scale the continuous features between 0 and 1 and encode the categorical features using one-hot encoding (drop the first level).
from sklearn import preprocessing
encoder = preprocessing.OneHotEncoder(drop="first", sparse=False).fit(df[categorical])
df_encoded = encoder.transform(df[categorical])
scaler = preprocessing.MinMaxScaler().fit(df[continuous])
df_scaled = scaler.transform(df[continuous])
categorical_encoded = encoder.get_feature_names(categorical).tolist()
df_scaled = pd.DataFrame(df_scaled, columns=continuous)
df_encoded = pd.DataFrame(df_encoded, columns=categorical_encoded)
df = pd.concat([df_scaled, df_encoded, df[target]], axis=1)Define an inverse function to go easily back to non scaled/encoded version of the features.
def inverse_transform(df,
scaler,
encoder,
continuous,
categorical,
categorical_encoded,
):
df_categorical = pd.DataFrame(encoder.inverse_transform(df[categorical_encoded]), columns=categorical)
df_continuous = pd.DataFrame(scaler.inverse_transform(df[continuous]), columns=continuous)
return pd.concat([df_categorical, df_continuous], axis=1)Since the feature "sex" is a categorical feature with two levels, the encoded version of the data set now has a new feature name. Below, we find its new "encoded name":
immutable_features_encoded = []
for immutable in immutable_features:
if immutable in categorical:
for new_col in categorical_encoded:
match = re.search(immutable, new_col)
if match:
immutable_features_encoded.append(new_col)
else:
immutable_features_encoded.append(immutable)Create data object to pass to MCCE method
class Dataset():
def __init__(self,
immutable_features,
target,
categorical,
immutable_features_encoded,
continuous,
features,
encoder,
scaler,
inverse_transform,
):
self.immutable_features = immutable_features
self.target = target
self.feature_order = feature_order
self.dtypes = dtypes
self.categorical = categorical
self.continuous = continuous
self.features = self.categorical + self.continuous
self.cols = self.features + [self.target]
self.immutable_features_encoded = immutable_features_encoded
self.encoder = encoder
self.scaler = scaler
self.inverse_transform = inverse_transform
dataset = Dataset(immutable_features,
target,
categorical,
immutable_features_encoded,
continuous,
features,
encoder,
scaler,
inverse_transform)
dtypes = dict([(x, "float") for x in continuous])
for x in categorical_encoded:
dtypes[x] = "category"
df = (df).astype(dtypes)Train a random forest to predict Income>=50000
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
y = df[target]
X = df.drop(target, axis=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
clf = RandomForestClassifier(max_depth=None, random_state=0)
ml_model = clf.fit(X_train, y_train)
pred_train = ml_model.predict(X_train)
pred_test = ml_model.predict(X_test)
fpr, tpr, _ = metrics.roc_curve(y_train, pred_train, pos_label=1)
train_auc = metrics.auc(fpr, tpr)
fpr, tpr, _ = metrics.roc_curve(y_test, pred_test, pos_label=1)
test_auc = metrics.auc(fpr, tpr)
model_prediction = clf.predict(X)Decide which observations to generate counterfactual explanations for.
import numpy as np
preds = ml_model.predict_proba(dataset.df)[:,1]
factual_id = np.where(preds < 0.5)
factuals = dataset.df.loc[factual_id]
test_factual = factuals.iloc[:5]Use MCCE to generate counterfactual explanations
from mcce import MCCE
mcce = MCCE(dataset, ml_model)
mcce.fit(df.drop(target, axis=1), dtypes) # remove the response from training data set
cfs = mcce.generate(test_factual.drop(target, axis=1), k=100) # sample 100 times per node
mcce.postprocess(cfs, test_factual, cutoff=0.5) # predicted >= 0.5 is considered positive; < 0.5 is negativePrint original feature values for the five test observations
cfs = mcce.results_sparse
cfs['income'] = test_factual['income']
# invert the features to their original form
decoded_factuals = dataset.inverse_transform(test_factual,
scaler,
encoder,
continuous,
categorical,
categorical_encoded)[feature_order]
print(decoded_factuals) age workclass fnlwgt education-num marital-status occupation \
0 39.0 3 77516.0 13.0 1 3
2 38.0 0 215646.0 9.0 2 3
3 53.0 0 234721.0 7.0 0 3
4 28.0 0 338409.0 13.0 0 0
5 37.0 0 284582.0 14.0 0 2
relationship race sex
0 1 0 0
2 1 0 0
3 0 1 0
4 3 1 1
5 3 0 1 Print counterfactual explanations values for five test observations
decoded_cfs = dataset.inverse_transform(cfs,
scaler,
encoder,
continuous,
categorical,
categorical_encoded)[feature_order]
print(decoded_cfs) age workclass fnlwgt education-num marital-status occupation \
0 39.0 0 175232.0 13.0 1 0
1 38.0 0 86643.0 16.0 2 0
2 53.0 0 184176.0 9.0 0 3
3 28.0 0 132686.0 14.0 0 0
4 37.0 0 174150.0 14.0 0 2
relationship race sex hours-per-week
0 1 0 0 40.0
1 1 0 0 45.0
2 0 0 0 40.0
3 0 0 1 40.0
4 0 0 1 40.0