def test_shape_values_linear_many_features():
import numpy as np
import shap_domino
from sklearn.linear_model import Ridge
np.random.seed(0)
coef = np.array([1, 2]).T
# generate linear data
X = np.random.normal(1, 10, size=(1000, len(coef)))
y = np.dot(X, coef) + 1 + np.random.normal(scale=0.1, size=1000)
# train linear model
model = Ridge(0.1)
model.fit(X, y)
# explain the model's predictions using SHAP values
explainer = shap_domino.LinearExplainer(model, X)
values = explainer.shap_values(X)
assert values.shape == (1000, 2)
expected = (X - X.mean(0)) * coef
np.testing.assert_allclose(expected - values, 0, atol=0.01)
def test_sparse():
""" Validate running LinearExplainer on scipy sparse data
"""
import sklearn.linear_model
from sklearn.datasets import make_multilabel_classification
from scipy.special import expit
import numpy as np
import shap_domino
np.random.seed(0)
n_features = 20
X, y = make_multilabel_classification(n_samples=100,
sparse=True,
n_features=n_features,
n_classes=1,
n_labels=2)
# train linear model
model = sklearn.linear_model.LogisticRegression()
model.fit(X, y)
# explain the model's predictions using SHAP values
explainer = shap_domino.LinearExplainer(model, X)
shap_values = explainer.shap_values(X)
assert np.max(np.abs(expit(explainer.expected_value + shap_values.sum(1)) - model.predict_proba(X)[:, 1])) < 1e-6
def test_tied_pair():
import numpy as np
import shap_domino
np.random.seed(0)
beta = np.array([1, 0, 0])
mu = np.zeros(3)
Sigma = np.array([[1, 0.999999, 0], [0.999999, 1, 0], [0, 0, 1]])
X = np.ones((1,3))
explainer = shap_domino.LinearExplainer((beta, 0), (mu, Sigma), feature_dependence="correlation")
assert np.abs(explainer.shap_values(X) - np.array([0.5, 0.5, 0])).max() < 0.05
def test_tied_triple():
import numpy as np
import shap_domino
np.random.seed(0)
beta = np.array([0, 1, 0, 0])
mu = 1*np.ones(4)
Sigma = np.array([[1, 0.999999, 0.999999, 0], [0.999999, 1, 0.999999, 0], [0.999999, 0.999999, 1, 0], [0, 0, 0, 1]])
X = 2*np.ones((1,4))
explainer = shap_domino.LinearExplainer((beta, 0), (mu, Sigma), feature_dependence="correlation")
assert explainer.expected_value == 1
assert np.abs(explainer.shap_values(X) - np.array([0.33333, 0.33333, 0.33333, 0])).max() < 0.05
def test_perfect_colinear():
import shap_domino
from sklearn.linear_model import LinearRegression
import numpy as np
X,y = shap_domino.datasets.boston()
X.iloc[:,0] = X.iloc[:,4] # test duplicated features
X.iloc[:,5] = X.iloc[:,6] - X.iloc[:,6] # test multiple colinear features
X.iloc[:,3] = 0 # test null features
model = LinearRegression()
model.fit(X, y)
explainer = shap_domino.LinearExplainer(model, X, feature_dependence="correlation")
shap_values = explainer.shap_values(X)
assert np.abs(shap_values.sum(1) - model.predict(X) + model.predict(X).mean()).sum() < 1e-7
def test_sklearn_linear():
import numpy as np
import shap_domino
np.random.seed(0)
from sklearn.linear_model import Ridge
import shap_domino
# train linear model
X,y = shap_domino.datasets.boston()
model = Ridge(0.1)
model.fit(X, y)
# explain the model's predictions using SHAP values
explainer = shap_domino.LinearExplainer(model, X)
assert np.abs(explainer.expected_value - model.predict(X).mean()) < 1e-6
explainer.shap_values(X)
def test_sklearn_multiclass_no_intercept():
import numpy as np
import shap_domino
np.random.seed(0)
from sklearn.linear_model import Ridge
import shap_domino
# train linear model
X,y = shap_domino.datasets.boston()
# make y multiclass
multiclass_y = np.expand_dims(y, axis=-1)
model = Ridge(fit_intercept=False)
model.fit(X, multiclass_y)
# explain the model's predictions using SHAP values
explainer = shap_domino.LinearExplainer(model, X)
assert np.abs(explainer.expected_value - model.predict(X).mean()) < 1e-6
explainer.shap_values(X)
def test_single_feature():
""" Make sure things work with a univariate linear regression.
"""
import sklearn.linear_model
import numpy as np
import shap_domino
np.random.seed(0)
# generate linear data
X = np.random.normal(1, 10, size=(1000, 1))
y = 2 * X[:, 0] + 1 + np.random.normal(scale=0.1, size=1000)
# train linear model
model = sklearn.linear_model.Ridge(0.1)
model.fit(X, y)
# explain the model's predictions using SHAP values
explainer = shap_domino.LinearExplainer(model, X)
shap_values = explainer.shap_values(X)
assert np.abs(explainer.expected_value - model.predict(X).mean()) < 1e-6
assert np.max(np.abs(explainer.expected_value + shap_values.sum(1) - model.predict(X))) < 1e-6