def test_isolationforest():
# Load data
X, _ = make_blobs(n_samples=400, centers=[[0, 0], [0, 0]], cluster_std=0.5, n_features=2, random_state=42)
X_outlier = np.random.RandomState(42).uniform(low=-6, high=6, size=(50, 2))
# Create and fit model
model = IsolationForest(random_state=42)
model.fit(X)
# Compute counterfactuals
x = X[0,:]
y_target = -1
assert model.predict([x]) == 1
x_cf, y_cf, _ = generate_counterfactual(model, x, y_target=y_target, return_as_dict=False)
assert y_cf == y_target
assert model.predict(np.array([x_cf])) == y_target
x = X_outlier[1,:]
y_target = 1
assert model.predict([x]) == -1
x_cf, y_cf, _ = generate_counterfactual(model, x, y_target=y_target, return_as_dict=False)
assert y_cf == y_target
assert model.predict(np.array([x_cf])) == y_target
def test_pipeline_pca_linearregression():
# Load data
X, y = load_boston(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 >= 25 and y_orig_pred < 26
# Compute counterfactual
y_target = 20.
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])))
def test_isolationforest():
# Load data
X, _ = make_blobs(n_samples=400,
centers=[[0, 0], [0, 0]],
cluster_std=0.5,
n_features=2,
random_state=42)
X_outlier = np.random.RandomState(42).uniform(low=-6, high=6, size=(50, 2))
# Create and fit model
model = IsolationForest(random_state=42)
model.fit(X)
# Compute counterfactuals
x = X[0, :]
y_target = -1
assert model.predict([x]) == 1
x_cf, y_cf, _ = generate_counterfactual(model,
x,
y_target=y_target,
return_as_dict=False)
assert y_cf == y_target
assert model.predict(np.array([x_cf])) == y_target
x = X_outlier[1, :]
y_target = 1
assert model.predict([x]) == -1
x_cf, y_cf, _ = generate_counterfactual(model,
x,
y_target=y_target,
return_as_dict=False)
assert y_cf == y_target
assert model.predict(np.array([x_cf])) == y_target
cf = generate_counterfactual(model,
x,
y_target=y_target,
return_as_dict=True)
assert cf["y_cf"] == y_target
assert model.predict(np.array([cf["x_cf"]])) == y_target
# Other stuff
from ceml.sklearn import IsolationForest as IsolationForestCf
model_cf = IsolationForestCf(model)
assert model.predict([x]) == model_cf.predict(x)
with pytest.raises(TypeError):
IsolationForestCf(sklearn.linear_model.LogisticRegression())
def test_decisiontree_regressor():
# Load data
X, y = load_boston(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
model = DecisionTreeRegressor(max_depth=3, random_state=42)
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 >= 19. and y_orig_pred < 21.
# Compute counterfactual
y_target = 25.
y_target_done = lambda z: np.abs(z - y_target) < 1.
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
y_target_done,
features_whitelist=features_whitelist,
regularization="l1",
return_as_dict=False)
assert y_target_done(y_cf)
assert y_target_done(model.predict(np.array([x_cf])))
features_whitelist = [0, 2, 4, 5, 7, 8, 9, 12]
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
y_target_done,
features_whitelist=features_whitelist,
regularization="l1",
return_as_dict=False)
assert y_target_done(y_cf)
assert y_target_done(model.predict(np.array([x_cf])))
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
def test_decisiontree_classifier():
# 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
model = DecisionTreeClassifier(max_depth=3, random_state=42)
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
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization="l1",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
features_whitelist = [0, 2]
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization="l1",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[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 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 test_randomforest_classifier():
# Load data
X, y = load_iris(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
model = RandomForestClassifier(n_estimators=10, random_state=42)
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
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization="l1",
C=0.01,
optimizer="nelder-mead",
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,
regularization=None,
optimizer="nelder-mead",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
features_whitelist = [0, 2]
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization="l1",
C=1.0,
optimizer="nelder-mead",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization=None,
optimizer="nelder-mead",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
def test_glvq():
# 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
model = GlvqModel(prototypes_per_class=3, max_iter=100, random_state=4242)
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
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization="l1",
optimizer="mp",
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,
regularization="l2",
optimizer="mp",
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,
regularization="l1",
C=1.0,
optimizer="bfgs",
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,
regularization="l1",
C=1.0,
optimizer="nelder-mead",
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,
regularization=None,
optimizer="bfgs",
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,
regularization=None,
optimizer="nelder-mead",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
features_whitelist = [0, 1, 2, 3]
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization="l1",
optimizer="mp",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization="l2",
optimizer="mp",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
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])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization="l1",
C=1.0,
optimizer="nelder-mead",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
features_whitelist = [0, 2]
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization=None,
optimizer="nelder-mead",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
# Other stuff
with pytest.raises(TypeError):
LvqCf(sklearn.linear_model.LogisticRegression())
def test_softmaxregression():
# Load data
X, y = load_iris(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
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
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
# Create weighted manhattan distance cost function
md = np.median(X_train, axis=0)
mad = np.median(np.abs(X_train - md), axis=0)
regularization_mad = LMadCost(x_orig, mad)
# Compute counterfactual
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
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])) == 0
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization="l1",
C=1.0,
optimizer=MyOptimizer(),
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,
regularization=regularization_mad,
C=1.0,
optimizer="bfgs",
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,
regularization="l1",
C=1.0,
optimizer="cg",
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,
regularization="l1",
C=1.0,
optimizer="nelder-mead",
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,
regularization=None,
optimizer="bfgs",
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,
regularization=None,
optimizer="nelder-mead",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
features_whitelist = [1, 2]
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
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])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization="l1",
C=1.0,
optimizer="nelder-mead",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
features_whitelist = [0, 1, 2]
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization=None,
optimizer="bfgs",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization=None,
optimizer="nelder-mead",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
import cvxpy as cp
from ceml.sklearn import generate_counterfactual
#.......
#model = ......
#x_orig = .....
#y_target = .....
# Change optimization parameters
#opt = ....
opt_args = {
"epsilon": 10.e-4,
"solver": cp.SCS,
"solver_verbosity": False,
"max_iter": 200
}
# Compute counterfactual explanations
x_cf, y_cf, delta = generate_counterfactual(model,
x_orig,
y_target,
features_whitelist=None,
C=0.1,
regularization="l1",
optimizer=opt,
optimizer_args=opt_args,
return_as_dict=False)
self.f_grad = f_grad
self.x0 = x0
self.tol = tol
self.max_iter = max_iter
def is_grad_based(self):
return True
def __call__(self):
optimum = minimize(fun=self.f, x0=self.x0, jac=self.f_grad, tol=self.tol, options={'maxiter': self.max_iter}, method="BFGS")
return np.array(optimum["x"])
if __name__ == "__main__":
# 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
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model.fit(X_train, y_train)
# Select data point for explaining its prediction
x = X_test[1,:]
print("Prediction on x: {0}".format(model.predict([x])))
# Compute counterfactual by using our custom optimizer 'MyOptimizer'
print("\nCompute counterfactual ....")
print(generate_counterfactual(model, x, y_target=0, optimizer=MyOptimizer(), features_whitelist=None, regularization="l1", C=0.5))
def test_softmaxregression():
# Load data
X, y = load_iris(return_X_y=True)
# Binary classification problem
idx = y > 1 # Convert data into a binary problem
X_, y_ = X[idx,:], y[idx]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242) # Split data into training and test set
model = LogisticRegression(solver='lbfgs', multi_class='multinomial') # Create and fit model
model.fit(X_train, y_train)
x_orig = X_test[1:4][0,:] # Select data point for explaining its prediction
assert model.predict([x_orig]) == 2
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, return_as_dict=False) # Compute counterfactual explanation
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
cf = generate_counterfactual(model, x_orig, 0, return_as_dict=True) # Compute counterfactual explanation
assert cf["y_cf"]== 0
assert model.predict(np.array([cf["x_cf"]])) == 0
# Multiclass classification problem
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
# Create and fit model
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
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
# Create weighted manhattan distance cost function
md = np.median(X_train, axis=0)
mad = np.median(np.abs(X_train - md), axis=0)
regularization_mad = LMadCost(x_orig, mad)
# Compute counterfactual
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
cf = generate_counterfactual(model, x_orig, 0, return_as_dict=True)
assert cf["y_cf"] == 0
assert model.predict(np.array([cf["x_cf"]])) == 0
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l1", optimizer="mp", 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, regularization="l2", optimizer="mp", 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, regularization="l1", C=0.1, optimizer="bfgs", 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, regularization="l1", C=0.1, optimizer=MyOptimizer(), 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, regularization=regularization_mad, C=0.1, optimizer="bfgs", 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, regularization="l1", C=0.1, optimizer="cg", 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, regularization="l1", C=1.0, optimizer="nelder-mead", 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, regularization=None, optimizer="bfgs", 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, regularization=None, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
features_whitelist = [1, 2]
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l1", optimizer="mp", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([True if i in features_whitelist else delta[i] <= 1e-5 for i in range(x_orig.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l2", optimizer="mp", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([True if i in features_whitelist else delta[i] <= 1e-5 for i in range(x_orig.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, 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])) == 0
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l1", C=1.0, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
features_whitelist = [0, 1, 2]
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization=None, optimizer="bfgs", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization=None, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
# Test binary case
X, y = load_iris(return_X_y=True)
idx = y != 2
X, y = X[idx, :], y[idx]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model.fit(X_train, y_train)
x_orig = X_test[1:4][0,:]
assert model.predict([x_orig]) == 0
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 1, features_whitelist=features_whitelist, regularization="l1", optimizer="mp", return_as_dict=False)
assert y_cf == 1
assert model.predict(np.array([x_cf])) == 1
x_orig = X_test[0,:]
assert model.predict([x_orig]) == 1
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l1", optimizer="mp", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
# Other stuff
from ceml.sklearn import SoftmaxCounterfactual
with pytest.raises(TypeError):
SoftmaxCounterfactual(sklearn.linear_model.LinearRegression())
with pytest.raises(ValueError):
SoftmaxCounterfactual(LogisticRegression(multi_class="ovr"))
def test_knn_regressor():
# Load data
X, y = load_boston(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
model = KNeighborsRegressor(n_neighbors=3)
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 >= 20. and y_orig_pred <= 21.
# Compute counterfactual
y_target = 25.
y_target_done = lambda z: np.abs(z - y_target) < 2.
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
y_target,
done=y_target_done,
features_whitelist=features_whitelist,
regularization="l1",
C=1.0,
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, delta = generate_counterfactual(
model,
x_orig,
y_target,
done=y_target_done,
features_whitelist=features_whitelist,
regularization="l1",
C=1.0,
optimizer="nelder-mead",
return_as_dict=False)
assert y_target_done(y_cf)
assert y_target_done(model.predict(np.array([x_cf])))
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
y_target,
done=y_target_done,
features_whitelist=features_whitelist,
regularization=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, delta = generate_counterfactual(
model,
x_orig,
y_target,
done=y_target_done,
features_whitelist=features_whitelist,
regularization=None,
optimizer="nelder-mead",
return_as_dict=False)
assert y_target_done(y_cf)
assert y_target_done(model.predict(np.array([x_cf])))
features_whitelist = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
y_target,
done=y_target_done,
features_whitelist=features_whitelist,
regularization="l1",
C=1.0,
optimizer="bfgs",
return_as_dict=False)
assert y_target_done(y_cf)
assert y_target_done(model.predict(np.array([x_cf])))
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
y_target,
done=y_target_done,
features_whitelist=features_whitelist,
regularization="l1",
C=1.0,
optimizer="nelder-mead",
return_as_dict=False)
assert y_target_done(y_cf)
assert y_target_done(model.predict(np.array([x_cf])))
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
y_target,
done=y_target_done,
features_whitelist=features_whitelist,
regularization=None,
optimizer="bfgs",
return_as_dict=False)
assert y_target_done(y_cf)
assert y_target_done(model.predict(np.array([x_cf])))
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
y_target,
done=y_target_done,
features_whitelist=features_whitelist,
regularization=None,
optimizer="nelder-mead",
return_as_dict=False)
assert y_target_done(y_cf)
assert y_target_done(model.predict(np.array([x_cf])))
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
if __name__ == "__main__":
# 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)
# Whitelist of features - list of features we can change/use when computing a counterfactual
features_whitelist = None # All features can be used.
# Create and fit the model
model = GaussianNB() # Note that ceml requires: multi_class='multinomial'
model.fit(X_train, y_train)
# Select data point for explaining its prediction
x = X_test[1, :]
print("Prediction on x: {0}".format(model.predict([x])))
# Create custom regularization function
regularization = MyRegularization(x)
# Compute counterfactual
print("\nCompute counterfactual ....")
print(
generate_counterfactual(model,
x,
y_target=0,
features_whitelist=features_whitelist,
regularization=regularization,
optimizer="bfgs"))
def test_qda():
# 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
model = QuadraticDiscriminantAnalysis(store_covariance=True)
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
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l1", optimizer="mp", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
cf = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l1", optimizer="mp", return_as_dict=True)
assert cf["y_cf"] == 0
assert model.predict(np.array([cf["x_cf"]])) == 0
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l2", optimizer="mp", 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, regularization="l1", C=1.0, optimizer="bfgs", 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, regularization="l1", C=1.0, optimizer="nelder-mead", 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, regularization=None, optimizer="bfgs", 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, regularization=None, optimizer="nelder-mead", 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, regularization="l1", optimizer="mp", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([True if i in features_whitelist else delta[i] <= 1e-5 for i in range(x_orig.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l2", optimizer="mp", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([True if i in features_whitelist else delta[i] <= 1e-3 for i in range(x_orig.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l1", C=0.1, optimizer="bfgs", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l1", C=1.0, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization=None, optimizer="bfgs", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization=None, optimizer="nelder-mead", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
# Test binary case
X, y = load_iris(return_X_y=True)
idx = y != 2
X, y = X[idx, :], y[idx]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
model = QuadraticDiscriminantAnalysis(store_covariance=True)
model.fit(X_train, y_train)
x_orig = X_test[1:4][0,:]
assert model.predict([x_orig]) == 0
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, y_target=1, features_whitelist=features_whitelist, optimizer="mp", return_as_dict=False)
assert y_cf == 1
assert model.predict(np.array([x_cf])) == 1
cf = generate_counterfactual(model, x_orig, y_target=1, features_whitelist=features_whitelist, optimizer="mp", return_as_dict=True)
assert cf["y_cf"] == 1
assert model.predict(np.array([cf["x_cf"]])) == 1
x_orig = X_test[0,:]
assert model.predict([x_orig]) == 1
x_cf, y_cf, delta = generate_counterfactual(model, x_orig, y_target=0, features_whitelist=features_whitelist, optimizer="mp", return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
# Other stuff
from ceml.sklearn import QdaCounterfactual
with pytest.raises(TypeError):
QdaCounterfactual(sklearn.linear_model.LogisticRegression())
model = QuadraticDiscriminantAnalysis()
model.fit(X_train, y_train)
with pytest.raises(AttributeError):
QdaCounterfactual(model)
with pytest.raises(AttributeError):
generate_counterfactual(model, x_orig, 0)
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from ceml.sklearn import generate_counterfactual
if __name__ == "__main__":
# Load data
X, y = load_iris(True)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=4242)
# Whitelist of features - list of features we can change/use when computing a counterfactual
features_whitelist = None # We can use all features
# Create and fit model
model = DecisionTreeClassifier(max_depth=3)
model.fit(X_train, y_train)
# Select data point for explaining its prediction
x = X_test[1, :]
print("Prediction on x: {0}".format(model.predict([x])))
# Compute counterfactual
print("\nCompute counterfactual ....")
print(
generate_counterfactual(model,
x,
y_target=0,
features_whitelist=features_whitelist))
def test_lgmlvq_classwise():
# Load data
X, y = load_iris(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
model = LgmlvqModel(prototypes_per_class=3,
classwise=True,
max_iter=100,
random_state=4242)
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
features_whitelist = None
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
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])) == 0
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization="l1",
C=1.0,
optimizer="nelder-mead",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
features_whitelist = [0, 1, 2, 3]
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
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])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
x_cf, y_cf, delta = generate_counterfactual(
model,
x_orig,
0,
features_whitelist=features_whitelist,
regularization="l1",
C=1.0,
optimizer="nelder-mead",
return_as_dict=False)
assert y_cf == 0
assert model.predict(np.array([x_cf])) == 0
assert all([
True if i in features_whitelist else delta[i] == 0.
for i in range(x_orig.shape[0])
])
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=4242)
# Whitelist of features - list of features we can change/use when computing a counterfactual
features_whitelist = [0, 1, 2, 3, 4] # Use the first five features only
# Create and fit model
model = Ridge()
model.fit(X_train, y_train)
# Select data point for explaining its prediction
x = X_test[1, :]
print("Prediction on x: {0}".format(model.predict([x])))
# Compute counterfactual
print("\nCompute counterfactual ....")
y_target = 25.0
done = lambda z: np.abs(
y_target - z
) <= 0.5 # Since we might not be able to achieve `y_target` exactly, we tell ceml that we are happy if we do not deviate more than 0.5 from it.
print(
generate_counterfactual(model,
x,
y_target=y_target,
features_whitelist=features_whitelist,
C=1.0,
regularization="l2",
optimizer="bfgs",
done=done))
