def test_function_sampler_func(X, y):
def func(X, y):
return X[:10], y[:10]
sampler = FunctionSampler(func=func)
X_res, y_res = sampler.fit_sample(X, y)
assert_allclose_dense_sparse(X_res, X[:10])
assert_array_equal(y_res, y[:10])
def test_function_sampler_func(X, y):
def func(X, y):
return X[:10], y[:10]
sampler = FunctionSampler(func=func)
X_res, y_res = sampler.fit_sample(X, y)
assert_allclose_dense_sparse(X_res, X[:10])
assert_array_equal(y_res, y[:10])
def test_function_sampler_func_kwargs(X, y):
def func(X, y, ratio, random_state):
rus = RandomUnderSampler(ratio=ratio, random_state=random_state)
return rus.fit_sample(X, y)
sampler = FunctionSampler(func=func, kw_args={'ratio': 'auto',
'random_state': 0})
X_res, y_res = sampler.fit_sample(X, y)
X_res_2, y_res_2 = RandomUnderSampler(random_state=0).fit_sample(X, y)
assert_allclose_dense_sparse(X_res, X_res_2)
assert_array_equal(y_res, y_res_2)
def test_function_sampler_identity(X, y):
sampler = FunctionSampler()
X_res, y_res = sampler.fit_sample(X, y)
assert_allclose_dense_sparse(X_res, X)
assert_array_equal(y_res, y)
X_test = np.vstack([moons, blobs])
y_test = np.hstack([np.ones(moons.shape[0], dtype=np.int8),
np.zeros(blobs.shape[0], dtype=np.int8)])
plot_scatter(X_test, y_test, 'Testing dataset')
def outlier_rejection(X, y):
model = IsolationForest(max_samples=100,
contamination=0.4,
random_state=rng)
model.fit(X)
y_pred = model.predict(X)
return X[y_pred == 1], y[y_pred == 1]
reject_sampler = FunctionSampler(func=outlier_rejection)
X_inliers, y_inliers = reject_sampler.fit_sample(X_train, y_train)
plot_scatter(X_inliers, y_inliers, 'Training data without outliers')
pipe = make_pipeline(FunctionSampler(func=outlier_rejection),
LogisticRegression(random_state=rng))
y_pred = pipe.fit(X_train, y_train).predict(X_test)
print(classification_report(y_test, y_pred))
clf = LogisticRegression(random_state=rng)
y_pred = clf.fit(X_train, y_train).predict(X_test)
print(classification_report(y_test, y_pred))
plt.show()
def test_function_sampler_identity(X, y):
sampler = FunctionSampler()
X_res, y_res = sampler.fit_sample(X, y)
assert_allclose_dense_sparse(X_res, X)
assert_array_equal(y_res, y)
