def test_baseline_binary_mode(data_type, make_data_type):
X = pd.DataFrame({
'one': [1, 2, 3, 4],
'two': [2, 3, 4, 5],
'three': [1, 2, 3, 4]
})
y = pd.Series([10, 11, 10, 10])
X = make_data_type(data_type, X)
y = make_data_type(data_type, y)
clf = BaselineClassifier(strategy="mode")
fitted = clf.fit(X, y)
assert isinstance(fitted, BaselineClassifier)
assert clf.classes_ == [10, 11]
expected_predictions = pd.Series(np.array([10] * X.shape[0]),
dtype="Int64")
predictions = clf.predict(X)
assert_series_equal(expected_predictions, predictions.to_series())
predicted_proba = clf.predict_proba(X)
assert predicted_proba.shape == (X.shape[0], 2)
expected_predictions_proba = pd.DataFrame({
10: [1., 1., 1., 1.],
11: [0., 0., 0., 0.]
})
assert_frame_equal(expected_predictions_proba,
predicted_proba.to_dataframe())
np.testing.assert_allclose(clf.feature_importance,
np.array([0.0] * X.shape[1]))
def test_baseline_binary_random_weighted(X_y_binary):
X, y = X_y_binary
values, counts = np.unique(y, return_counts=True)
percent_freq = counts.astype(float) / len(y)
assert percent_freq.sum() == 1.0
clf = BaselineClassifier(strategy="random_weighted", random_state=0)
clf.fit(X, y)
assert clf.classes_ == [0, 1]
expected_predictions = pd.Series(get_random_state(0).choice(
np.unique(y), len(X), p=percent_freq),
dtype="Int64")
predictions = clf.predict(X)
assert_series_equal(expected_predictions, predictions.to_series())
predicted_proba = clf.predict_proba(X)
assert predicted_proba.shape == (len(X), 2)
expected_predictions_proba = pd.DataFrame(
np.array([[percent_freq[i] for i in range(len(values))]] * len(X)))
assert_frame_equal(expected_predictions_proba,
predicted_proba.to_dataframe())
np.testing.assert_allclose(clf.feature_importance,
np.array([0.0] * X.shape[1]))
def test_baseline_multiclass_mode():
X = pd.DataFrame({
'one': [1, 2, 3, 4],
'two': [2, 3, 4, 5],
'three': [1, 2, 3, 4]
})
y = pd.Series([10, 12, 11, 11])
clf = BaselineClassifier(strategy="mode")
clf.fit(X, y)
assert clf.classes_ == [10, 11, 12]
predictions = clf.predict(X)
expected_predictions = pd.Series([11] * len(X), dtype="Int64")
assert_series_equal(expected_predictions, predictions.to_series())
predicted_proba = clf.predict_proba(X)
assert predicted_proba.shape == (len(X), 3)
expected_predictions_proba = pd.DataFrame({
10: [0., 0., 0., 0.],
11: [1., 1., 1., 1.],
12: [0., 0., 0., 0.]
})
assert_frame_equal(expected_predictions_proba,
predicted_proba.to_dataframe())
np.testing.assert_allclose(clf.feature_importance,
np.array([0.0] * X.shape[1]))
def test_baseline_no_mode():
X = pd.DataFrame([[1, 2, 3, 0, 1]])
y = pd.Series([1, 0, 2, 0, 1])
clf = BaselineClassifier()
clf.fit(X, y)
assert clf.classes_ == [0, 1, 2]
np.testing.assert_allclose(clf.predict(X), np.array([0] * len(X)))
predicted_proba = clf.predict_proba(X)
assert predicted_proba.shape == (len(X), 3)
np.testing.assert_allclose(
predicted_proba,
np.array([[1.0 if i == 0 else 0.0 for i in range(3)]] * len(X)))
np.testing.assert_allclose(clf.feature_importance,
np.array([0.0] * X.shape[1]))
def test_baseline_multiclass_random(X_y_multi):
X, y = X_y_multi
values = np.unique(y)
clf = BaselineClassifier(strategy="random", random_seed=0)
clf.fit(X, y)
assert clf.classes_ == [0, 1, 2]
expected_predictions = pd.Series(get_random_state(0).choice(np.unique(y), len(X)), dtype="Int64")
predictions = clf.predict(X)
assert_series_equal(expected_predictions, predictions.to_series())
predicted_proba = clf.predict_proba(X)
assert predicted_proba.shape == (len(X), 3)
assert_frame_equal(pd.DataFrame(np.array([[1. / 3 for i in range(len(values))]] * len(X))), predicted_proba.to_dataframe())
np.testing.assert_allclose(clf.feature_importance, np.array([0.0] * X.shape[1]))
def test_baseline_no_mode():
X = pd.DataFrame([[1, 2, 3, 0, 1]])
y = pd.Series([1, 0, 2, 0, 1])
clf = BaselineClassifier()
clf.fit(X, y)
assert clf.classes_ == [0, 1, 2]
expected_predictions = pd.Series([0] * len(X), dtype="Int64")
predictions = clf.predict(X)
assert_series_equal(expected_predictions, predictions.to_series())
predicted_proba = clf.predict_proba(X)
assert predicted_proba.shape == (len(X), 3)
assert_frame_equal(pd.DataFrame(np.array([[1.0 if i == 0 else 0.0 for i in range(3)]] * len(X))), predicted_proba.to_dataframe())
np.testing.assert_allclose(clf.feature_importance, np.array([0.0] * X.shape[1]))
def test_baseline_binary_random(X_y_binary):
X, y = X_y_binary
values = np.unique(y)
clf = BaselineClassifier(strategy="random", random_state=0)
clf.fit(X, y)
assert clf.classes_ == [0, 1]
np.testing.assert_allclose(
clf.predict(X),
get_random_state(0).choice(np.unique(y), len(X)))
predicted_proba = clf.predict_proba(X)
assert predicted_proba.shape == (len(X), 2)
np.testing.assert_allclose(
predicted_proba,
np.array([[0.5 for i in range(len(values))]] * len(X)))
np.testing.assert_allclose(clf.feature_importance,
np.array([0.0] * X.shape[1]))
def test_baseline_binary_random_weighted(X_y_binary):
X, y = X_y_binary
values, counts = np.unique(y, return_counts=True)
percent_freq = counts.astype(float) / len(y)
assert percent_freq.sum() == 1.0
clf = BaselineClassifier(strategy="random_weighted", random_state=0)
clf.fit(X, y)
assert clf.classes_ == [0, 1]
np.testing.assert_allclose(
clf.predict(X),
get_random_state(0).choice(np.unique(y), len(X), p=percent_freq))
predicted_proba = clf.predict_proba(X)
assert predicted_proba.shape == (len(X), 2)
np.testing.assert_allclose(
predicted_proba,
np.array([[percent_freq[i] for i in range(len(values))]] * len(X)))
np.testing.assert_allclose(clf.feature_importance,
np.array([0.0] * X.shape[1]))
def test_baseline_binary_mode(data_type, X_y_binary):
X = pd.DataFrame({
'one': [1, 2, 3, 4],
'two': [2, 3, 4, 5],
'three': [1, 2, 3, 4]
})
y = pd.Series([10, 11, 10, 10])
if data_type == 'ww':
X = ww.DataTable(X)
y = ww.DataColumn(y)
clf = BaselineClassifier(strategy="mode")
fitted = clf.fit(X, y)
assert isinstance(fitted, BaselineClassifier)
assert clf.classes_ == [10, 11]
np.testing.assert_allclose(clf.predict(X), np.array([10] * X.shape[0]))
predicted_proba = clf.predict_proba(X)
assert predicted_proba.shape == (X.shape[0], 2)
expected_predicted_proba = pd.DataFrame({
10: [1., 1., 1., 1.],
11: [0., 0., 0., 0.]
})
pd.testing.assert_frame_equal(expected_predicted_proba, predicted_proba)
np.testing.assert_allclose(clf.feature_importance,
np.array([0.0] * X.shape[1]))