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test_sklearn_pipeline.py
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test_sklearn_pipeline.py
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# -*- coding: utf-8 -*-
import sys
sys.path.insert(0,'..')
import numpy as np
np.random.seed(42)
import sklearn
from sklearn.datasets import load_iris, load_diabetes
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression, Lasso
from sklearn.preprocessing import StandardScaler, RobustScaler, PolynomialFeatures, Normalizer, MinMaxScaler, MaxAbsScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline
from ceml.sklearn import generate_counterfactual
def compute_counterfactuals(model, x, y):
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l1", C=1.0, optimizer="bfgs", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == y
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l1", C=1.0, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization=None, optimizer="bfgs", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization=None, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
features_whitelist = [1, 2]
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l1", C=1.0, optimizer="bfgs", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l1", C=1.0, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
features_whitelist = [0, 1, 2]
x_cf, y_cf, delta = generate_counterfactual(model, x, 0, features_whitelist=features_whitelist, regularization=None, optimizer="bfgs", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x, 0, features_whitelist=features_whitelist, regularization=None, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
def compute_counterfactuals_poly(model, x, y):
features_whitelist = None
#x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l1", C=1.0, optimizer="bfgs", return_as_dict=False)
#assert y_cf == y
#assert model.predict(np.array([x_cf])) == y
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l1", C=1.0, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization=None, optimizer="bfgs", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization=None, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
features_whitelist = [0, 1, 2]
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l1", C=1.0, optimizer="bfgs", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l1", C=1.0, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization=None, optimizer="bfgs", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x, 0, features_whitelist=features_whitelist, regularization=None, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
def compute_counterfactuals_2(model, x, y):
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization=None, optimizer="bfgs", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization=None, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization=None, optimizer="powell", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l1", C=0.001, optimizer="bfgs", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l1", C=0.001, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l1", C=0.001, optimizer="powell", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
features_whitelist = [0, 1, 2]
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization=None, optimizer="bfgs", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization=None, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization=None, optimizer="powell", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l2", C=0.001, optimizer="bfgs", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l2", C=0.001, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x, y, features_whitelist=features_whitelist, regularization="l2", C=0.001, optimizer="powell", return_as_dict=False)
assert y_cf == y
assert model.predict(np.array([x_cf])) == y
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x.shape[0])])
def test_pipeline_scaler_softmaxregression():
# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
# Create and fit model
scaler = StandardScaler()
pca = PCA(n_components=2)
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model = make_pipeline(scaler, model)
model.fit(X_train, y_train)
# Select data point for explaining its prediction
x_orig = X_test[1:4][0,:]
assert model.predict([x_orig]) == 2
# Compute counterfactual
compute_counterfactuals(model, x_orig, 0)
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=None, optimizer="mp", regularization=None, return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=None, optimizer="mp", regularization="l1", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
features_whitelist = [0, 1, 2]
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, optimizer="mp", regularization=None, return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, optimizer="mp", regularization="l1", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
# More than one preprocessing
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model = make_pipeline(pca, scaler, model)
model.fit(X_train, y_train)
assert model.predict([x_orig]) == 2
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=None, optimizer="mp", regularization=None, return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=None, optimizer="mp", regularization="l1", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
def test_pipeline_robustscaler_softmaxregression():
# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
# Create and fit model
scaler = RobustScaler()
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model = make_pipeline(scaler, model)
model.fit(X_train, y_train)
# Select data point for explaining its prediction
x_orig = X_test[1:4][0,:]
assert model.predict([x_orig]) == 2
# Compute counterfactual
compute_counterfactuals(model, x_orig, 0)
def test_pipeline_maxabsscaler_softmaxregression():
# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
# Create and fit model
scaler = MaxAbsScaler()
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model = make_pipeline(scaler, model)
model.fit(X_train, y_train)
# Select data point for explaining its prediction
x_orig = X_test[1:4][0,:]
assert model.predict([x_orig]) == 2
# Compute counterfactual
compute_counterfactuals_2(model, x_orig, 0)
def test_pipeline_minmaxscaler_softmaxregression():
# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
# Create and fit model
scaler = MinMaxScaler()
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model = make_pipeline(scaler, model)
model.fit(X_train, y_train)
# Select data point for explaining its prediction
x_orig = X_test[1:4][0,:]
assert model.predict([x_orig]) == 2
# Compute counterfactual
compute_counterfactuals_2(model, x_orig, 0)
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=None, optimizer="mp", regularization=None, return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=None, optimizer="mp", regularization="l1", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
features_whitelist = [0, 1, 2]
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, optimizer="mp", regularization=None, return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, optimizer="mp", regularization="l1", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
def test_pipeline_normalizer_softmaxregression():
# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
# Create and fit model
scaler = Normalizer()
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model = make_pipeline(scaler, model)
model.fit(X_train, y_train)
# Select data point for explaining its prediction
x_orig = X_test[1:4][0,:]
assert model.predict([x_orig]) == 2
# Compute counterfactual
compute_counterfactuals_2(model, x_orig, 0)
def test_pipeline_poly_softmaxregression():
# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
# Create and fit model
poly = PolynomialFeatures(degree=2)
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model = make_pipeline(poly, model)
model.fit(X_train, y_train)
# Select data point for explaining its prediction
x_orig = X_test[1:4][0,:]
assert model.predict([x_orig]) == 2
# Compute counterfactual
compute_counterfactuals_poly(model, x_orig, 0)
def test_pipeline_scaler_poly_softmaxregression():
# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
# Create and fit model
poly = PolynomialFeatures(degree=2)
scaler = StandardScaler()
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model = make_pipeline(poly, scaler, model)
model.fit(X_train, y_train)
# Select data point for explaining its prediction
x_orig = X_test[1:4][0,:]
assert model.predict([x_orig]) == 2
# Compute counterfactual
compute_counterfactuals_poly(model, x_orig, 0)
def test_pipeline_pca_linearregression():
# Load data
X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
# Create and fit model
pca = PCA(n_components=4)
model = Lasso()
model = make_pipeline(pca, model)
model.fit(X_train, y_train)
# Select data point for explaining its prediction
x_orig = X_test[1:4][0,:]
y_orig_pred = model.predict([x_orig])
assert y_orig_pred >= 100 and y_orig_pred < 150
# Compute counterfactual
y_target = 80.
y_target_done = lambda z: np.abs(z - y_target) < 3.
x_cf, y_cf, _ = generate_counterfactual(model, x_orig, y_target=y_target, done=y_target_done, regularization="l1", C=0.1, features_whitelist=None, optimizer="bfgs", return_as_dict=False)
assert y_target_done(y_cf)
assert y_target_done(model.predict(np.array([x_cf])))
x_cf, y_cf, _ = generate_counterfactual(model, x_orig, y_target=y_target, done=y_target_done, regularization="l1", features_whitelist=None, optimizer="mp", return_as_dict=False)
assert y_target_done(y_cf)
assert y_target_done(model.predict(np.array([x_cf])))
x_cf, y_cf, _ = generate_counterfactual(model, x_orig, y_target=y_target, done=y_target_done, regularization="l2", features_whitelist=None, optimizer="mp", return_as_dict=False)
assert y_target_done(y_cf)
assert y_target_done(model.predict(np.array([x_cf])))