fr/fr_env/lib/python3.8/site-packages/sklearn/tests/test_dummy.py

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2021-03-02 18:34:59 +05:30
import pytest
import numpy as np
import scipy.sparse as sp
from sklearn.base import clone
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_raises
from sklearn.utils._testing import assert_warns_message
from sklearn.utils._testing import ignore_warnings
from sklearn.utils.stats import _weighted_percentile
from sklearn.dummy import DummyClassifier, DummyRegressor
from sklearn.exceptions import NotFittedError
@ignore_warnings
def _check_predict_proba(clf, X, y):
proba = clf.predict_proba(X)
# We know that we can have division by zero
log_proba = clf.predict_log_proba(X)
y = np.atleast_1d(y)
if y.ndim == 1:
y = np.reshape(y, (-1, 1))
n_outputs = y.shape[1]
n_samples = len(X)
if n_outputs == 1:
proba = [proba]
log_proba = [log_proba]
for k in range(n_outputs):
assert proba[k].shape[0] == n_samples
assert proba[k].shape[1] == len(np.unique(y[:, k]))
assert_array_almost_equal(proba[k].sum(axis=1), np.ones(len(X)))
# We know that we can have division by zero
assert_array_almost_equal(np.log(proba[k]), log_proba[k])
def _check_behavior_2d(clf):
# 1d case
X = np.array([[0], [0], [0], [0]]) # ignored
y = np.array([1, 2, 1, 1])
est = clone(clf)
est.fit(X, y)
y_pred = est.predict(X)
assert y.shape == y_pred.shape
# 2d case
y = np.array([[1, 0],
[2, 0],
[1, 0],
[1, 3]])
est = clone(clf)
est.fit(X, y)
y_pred = est.predict(X)
assert y.shape == y_pred.shape
def _check_behavior_2d_for_constant(clf):
# 2d case only
X = np.array([[0], [0], [0], [0]]) # ignored
y = np.array([[1, 0, 5, 4, 3],
[2, 0, 1, 2, 5],
[1, 0, 4, 5, 2],
[1, 3, 3, 2, 0]])
est = clone(clf)
est.fit(X, y)
y_pred = est.predict(X)
assert y.shape == y_pred.shape
def _check_equality_regressor(statistic, y_learn, y_pred_learn,
y_test, y_pred_test):
assert_array_almost_equal(np.tile(statistic, (y_learn.shape[0], 1)),
y_pred_learn)
assert_array_almost_equal(np.tile(statistic, (y_test.shape[0], 1)),
y_pred_test)
def test_most_frequent_and_prior_strategy():
X = [[0], [0], [0], [0]] # ignored
y = [1, 2, 1, 1]
for strategy in ("most_frequent", "prior"):
clf = DummyClassifier(strategy=strategy, random_state=0)
clf.fit(X, y)
assert_array_equal(clf.predict(X), np.ones(len(X)))
_check_predict_proba(clf, X, y)
if strategy == "prior":
assert_array_almost_equal(clf.predict_proba([X[0]]),
clf.class_prior_.reshape((1, -1)))
else:
assert_array_almost_equal(clf.predict_proba([X[0]]),
clf.class_prior_.reshape((1, -1)) > 0.5)
def test_most_frequent_and_prior_strategy_with_2d_column_y():
# non-regression test added in
# https://github.com/scikit-learn/scikit-learn/pull/13545
X = [[0], [0], [0], [0]]
y_1d = [1, 2, 1, 1]
y_2d = [[1], [2], [1], [1]]
for strategy in ("most_frequent", "prior"):
clf_1d = DummyClassifier(strategy=strategy, random_state=0)
clf_2d = DummyClassifier(strategy=strategy, random_state=0)
clf_1d.fit(X, y_1d)
clf_2d.fit(X, y_2d)
assert_array_equal(clf_1d.predict(X), clf_2d.predict(X))
def test_most_frequent_and_prior_strategy_multioutput():
X = [[0], [0], [0], [0]] # ignored
y = np.array([[1, 0],
[2, 0],
[1, 0],
[1, 3]])
n_samples = len(X)
for strategy in ("prior", "most_frequent"):
clf = DummyClassifier(strategy=strategy, random_state=0)
clf.fit(X, y)
assert_array_equal(clf.predict(X),
np.hstack([np.ones((n_samples, 1)),
np.zeros((n_samples, 1))]))
_check_predict_proba(clf, X, y)
_check_behavior_2d(clf)
def test_stratified_strategy():
X = [[0]] * 5 # ignored
y = [1, 2, 1, 1, 2]
clf = DummyClassifier(strategy="stratified", random_state=0)
clf.fit(X, y)
X = [[0]] * 500
y_pred = clf.predict(X)
p = np.bincount(y_pred) / float(len(X))
assert_almost_equal(p[1], 3. / 5, decimal=1)
assert_almost_equal(p[2], 2. / 5, decimal=1)
_check_predict_proba(clf, X, y)
def test_stratified_strategy_multioutput():
X = [[0]] * 5 # ignored
y = np.array([[2, 1],
[2, 2],
[1, 1],
[1, 2],
[1, 1]])
clf = DummyClassifier(strategy="stratified", random_state=0)
clf.fit(X, y)
X = [[0]] * 500
y_pred = clf.predict(X)
for k in range(y.shape[1]):
p = np.bincount(y_pred[:, k]) / float(len(X))
assert_almost_equal(p[1], 3. / 5, decimal=1)
assert_almost_equal(p[2], 2. / 5, decimal=1)
_check_predict_proba(clf, X, y)
_check_behavior_2d(clf)
def test_uniform_strategy():
X = [[0]] * 4 # ignored
y = [1, 2, 1, 1]
clf = DummyClassifier(strategy="uniform", random_state=0)
clf.fit(X, y)
X = [[0]] * 500
y_pred = clf.predict(X)
p = np.bincount(y_pred) / float(len(X))
assert_almost_equal(p[1], 0.5, decimal=1)
assert_almost_equal(p[2], 0.5, decimal=1)
_check_predict_proba(clf, X, y)
def test_uniform_strategy_multioutput():
X = [[0]] * 4 # ignored
y = np.array([[2, 1],
[2, 2],
[1, 2],
[1, 1]])
clf = DummyClassifier(strategy="uniform", random_state=0)
clf.fit(X, y)
X = [[0]] * 500
y_pred = clf.predict(X)
for k in range(y.shape[1]):
p = np.bincount(y_pred[:, k]) / float(len(X))
assert_almost_equal(p[1], 0.5, decimal=1)
assert_almost_equal(p[2], 0.5, decimal=1)
_check_predict_proba(clf, X, y)
_check_behavior_2d(clf)
def test_string_labels():
X = [[0]] * 5
y = ["paris", "paris", "tokyo", "amsterdam", "berlin"]
clf = DummyClassifier(strategy="most_frequent")
clf.fit(X, y)
assert_array_equal(clf.predict(X), ["paris"] * 5)
@pytest.mark.parametrize("y,y_test", [
([2, 1, 1, 1], [2, 2, 1, 1]),
(np.array([[2, 2],
[1, 1],
[1, 1],
[1, 1]]),
np.array([[2, 2],
[2, 2],
[1, 1],
[1, 1]]))
])
def test_classifier_score_with_None(y, y_test):
clf = DummyClassifier(strategy="most_frequent")
clf.fit(None, y)
assert clf.score(None, y_test) == 0.5
@pytest.mark.parametrize("strategy", [
"stratified",
"most_frequent",
"prior",
"uniform",
"constant"
])
def test_classifier_prediction_independent_of_X(strategy):
y = [0, 2, 1, 1]
X1 = [[0]] * 4
clf1 = DummyClassifier(strategy=strategy, random_state=0, constant=0)
clf1.fit(X1, y)
predictions1 = clf1.predict(X1)
X2 = [[1]] * 4
clf2 = DummyClassifier(strategy=strategy, random_state=0, constant=0)
clf2.fit(X2, y)
predictions2 = clf2.predict(X2)
assert_array_equal(predictions1, predictions2)
def test_classifier_exceptions():
clf = DummyClassifier(strategy="unknown")
assert_raises(ValueError, clf.fit, [], [])
assert_raises(NotFittedError, clf.predict, [])
assert_raises(NotFittedError, clf.predict_proba, [])
def test_mean_strategy_regressor():
random_state = np.random.RandomState(seed=1)
X = [[0]] * 4 # ignored
y = random_state.randn(4)
reg = DummyRegressor()
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.mean(y)] * len(X))
def test_mean_strategy_multioutput_regressor():
random_state = np.random.RandomState(seed=1)
X_learn = random_state.randn(10, 10)
y_learn = random_state.randn(10, 5)
mean = np.mean(y_learn, axis=0).reshape((1, -1))
X_test = random_state.randn(20, 10)
y_test = random_state.randn(20, 5)
# Correctness oracle
est = DummyRegressor()
est.fit(X_learn, y_learn)
y_pred_learn = est.predict(X_learn)
y_pred_test = est.predict(X_test)
_check_equality_regressor(mean, y_learn, y_pred_learn, y_test, y_pred_test)
_check_behavior_2d(est)
def test_regressor_exceptions():
reg = DummyRegressor()
assert_raises(NotFittedError, reg.predict, [])
def test_median_strategy_regressor():
random_state = np.random.RandomState(seed=1)
X = [[0]] * 5 # ignored
y = random_state.randn(5)
reg = DummyRegressor(strategy="median")
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.median(y)] * len(X))
def test_median_strategy_multioutput_regressor():
random_state = np.random.RandomState(seed=1)
X_learn = random_state.randn(10, 10)
y_learn = random_state.randn(10, 5)
median = np.median(y_learn, axis=0).reshape((1, -1))
X_test = random_state.randn(20, 10)
y_test = random_state.randn(20, 5)
# Correctness oracle
est = DummyRegressor(strategy="median")
est.fit(X_learn, y_learn)
y_pred_learn = est.predict(X_learn)
y_pred_test = est.predict(X_test)
_check_equality_regressor(
median, y_learn, y_pred_learn, y_test, y_pred_test)
_check_behavior_2d(est)
def test_quantile_strategy_regressor():
random_state = np.random.RandomState(seed=1)
X = [[0]] * 5 # ignored
y = random_state.randn(5)
reg = DummyRegressor(strategy="quantile", quantile=0.5)
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.median(y)] * len(X))
reg = DummyRegressor(strategy="quantile", quantile=0)
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.min(y)] * len(X))
reg = DummyRegressor(strategy="quantile", quantile=1)
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.max(y)] * len(X))
reg = DummyRegressor(strategy="quantile", quantile=0.3)
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.percentile(y, q=30)] * len(X))
def test_quantile_strategy_multioutput_regressor():
random_state = np.random.RandomState(seed=1)
X_learn = random_state.randn(10, 10)
y_learn = random_state.randn(10, 5)
median = np.median(y_learn, axis=0).reshape((1, -1))
quantile_values = np.percentile(y_learn, axis=0, q=80).reshape((1, -1))
X_test = random_state.randn(20, 10)
y_test = random_state.randn(20, 5)
# Correctness oracle
est = DummyRegressor(strategy="quantile", quantile=0.5)
est.fit(X_learn, y_learn)
y_pred_learn = est.predict(X_learn)
y_pred_test = est.predict(X_test)
_check_equality_regressor(
median, y_learn, y_pred_learn, y_test, y_pred_test)
_check_behavior_2d(est)
# Correctness oracle
est = DummyRegressor(strategy="quantile", quantile=0.8)
est.fit(X_learn, y_learn)
y_pred_learn = est.predict(X_learn)
y_pred_test = est.predict(X_test)
_check_equality_regressor(
quantile_values, y_learn, y_pred_learn, y_test, y_pred_test)
_check_behavior_2d(est)
def test_quantile_invalid():
X = [[0]] * 5 # ignored
y = [0] * 5 # ignored
est = DummyRegressor(strategy="quantile")
assert_raises(ValueError, est.fit, X, y)
est = DummyRegressor(strategy="quantile", quantile=None)
assert_raises(ValueError, est.fit, X, y)
est = DummyRegressor(strategy="quantile", quantile=[0])
assert_raises(ValueError, est.fit, X, y)
est = DummyRegressor(strategy="quantile", quantile=-0.1)
assert_raises(ValueError, est.fit, X, y)
est = DummyRegressor(strategy="quantile", quantile=1.1)
assert_raises(ValueError, est.fit, X, y)
est = DummyRegressor(strategy="quantile", quantile='abc')
assert_raises(TypeError, est.fit, X, y)
def test_quantile_strategy_empty_train():
est = DummyRegressor(strategy="quantile", quantile=0.4)
assert_raises(ValueError, est.fit, [], [])
def test_constant_strategy_regressor():
random_state = np.random.RandomState(seed=1)
X = [[0]] * 5 # ignored
y = random_state.randn(5)
reg = DummyRegressor(strategy="constant", constant=[43])
reg.fit(X, y)
assert_array_equal(reg.predict(X), [43] * len(X))
reg = DummyRegressor(strategy="constant", constant=43)
reg.fit(X, y)
assert_array_equal(reg.predict(X), [43] * len(X))
def test_constant_strategy_multioutput_regressor():
random_state = np.random.RandomState(seed=1)
X_learn = random_state.randn(10, 10)
y_learn = random_state.randn(10, 5)
# test with 2d array
constants = random_state.randn(5)
X_test = random_state.randn(20, 10)
y_test = random_state.randn(20, 5)
# Correctness oracle
est = DummyRegressor(strategy="constant", constant=constants)
est.fit(X_learn, y_learn)
y_pred_learn = est.predict(X_learn)
y_pred_test = est.predict(X_test)
_check_equality_regressor(
constants, y_learn, y_pred_learn, y_test, y_pred_test)
_check_behavior_2d_for_constant(est)
def test_y_mean_attribute_regressor():
X = [[0]] * 5
y = [1, 2, 4, 6, 8]
# when strategy = 'mean'
est = DummyRegressor(strategy='mean')
est.fit(X, y)
assert est.constant_ == np.mean(y)
def test_unknown_strategey_regressor():
X = [[0]] * 5
y = [1, 2, 4, 6, 8]
est = DummyRegressor(strategy='gona')
assert_raises(ValueError, est.fit, X, y)
def test_constants_not_specified_regressor():
X = [[0]] * 5
y = [1, 2, 4, 6, 8]
est = DummyRegressor(strategy='constant')
assert_raises(TypeError, est.fit, X, y)
def test_constant_size_multioutput_regressor():
random_state = np.random.RandomState(seed=1)
X = random_state.randn(10, 10)
y = random_state.randn(10, 5)
est = DummyRegressor(strategy='constant', constant=[1, 2, 3, 4])
assert_raises(ValueError, est.fit, X, y)
def test_constant_strategy():
X = [[0], [0], [0], [0]] # ignored
y = [2, 1, 2, 2]
clf = DummyClassifier(strategy="constant", random_state=0, constant=1)
clf.fit(X, y)
assert_array_equal(clf.predict(X), np.ones(len(X)))
_check_predict_proba(clf, X, y)
X = [[0], [0], [0], [0]] # ignored
y = ['two', 'one', 'two', 'two']
clf = DummyClassifier(strategy="constant", random_state=0, constant='one')
clf.fit(X, y)
assert_array_equal(clf.predict(X), np.array(['one'] * 4))
_check_predict_proba(clf, X, y)
def test_constant_strategy_multioutput():
X = [[0], [0], [0], [0]] # ignored
y = np.array([[2, 3],
[1, 3],
[2, 3],
[2, 0]])
n_samples = len(X)
clf = DummyClassifier(strategy="constant", random_state=0,
constant=[1, 0])
clf.fit(X, y)
assert_array_equal(clf.predict(X),
np.hstack([np.ones((n_samples, 1)),
np.zeros((n_samples, 1))]))
_check_predict_proba(clf, X, y)
@pytest.mark.parametrize('y, params, err_msg', [
([2, 1, 2, 2],
{'random_state': 0},
"Constant.*has to be specified"),
([2, 1, 2, 2],
{'constant': [2, 0]},
"Constant.*should have shape"),
(np.transpose([[2, 1, 2, 2], [2, 1, 2, 2]]),
{'constant': 2},
"Constant.*should have shape"),
([2, 1, 2, 2],
{'constant': 'my-constant'},
"constant=my-constant.*Possible values.*\\[1, 2]"),
(np.transpose([[2, 1, 2, 2], [2, 1, 2, 2]]),
{'constant': [2, 'unknown']},
"constant=\\[2, 'unknown'].*Possible values.*\\[1, 2]")],
ids=["no-constant", "too-many-constant", "not-enough-output",
"single-output", "multi-output"]
)
def test_constant_strategy_exceptions(y, params, err_msg):
X = [[0], [0], [0], [0]]
clf = DummyClassifier(strategy="constant", **params)
with pytest.raises(ValueError, match=err_msg):
clf.fit(X, y)
def test_classification_sample_weight():
X = [[0], [0], [1]]
y = [0, 1, 0]
sample_weight = [0.1, 1., 0.1]
clf = DummyClassifier(strategy="stratified").fit(X, y, sample_weight)
assert_array_almost_equal(clf.class_prior_, [0.2 / 1.2, 1. / 1.2])
def test_constant_strategy_sparse_target():
X = [[0]] * 5 # ignored
y = sp.csc_matrix(np.array([[0, 1],
[4, 0],
[1, 1],
[1, 4],
[1, 1]]))
n_samples = len(X)
clf = DummyClassifier(strategy="constant", random_state=0, constant=[1, 0])
clf.fit(X, y)
y_pred = clf.predict(X)
assert sp.issparse(y_pred)
assert_array_equal(y_pred.toarray(), np.hstack([np.ones((n_samples, 1)),
np.zeros((n_samples, 1))]))
def test_uniform_strategy_sparse_target_warning():
X = [[0]] * 5 # ignored
y = sp.csc_matrix(np.array([[2, 1],
[2, 2],
[1, 4],
[4, 2],
[1, 1]]))
clf = DummyClassifier(strategy="uniform", random_state=0)
assert_warns_message(UserWarning,
"the uniform strategy would not save memory",
clf.fit, X, y)
X = [[0]] * 500
y_pred = clf.predict(X)
for k in range(y.shape[1]):
p = np.bincount(y_pred[:, k]) / float(len(X))
assert_almost_equal(p[1], 1 / 3, decimal=1)
assert_almost_equal(p[2], 1 / 3, decimal=1)
assert_almost_equal(p[4], 1 / 3, decimal=1)
def test_stratified_strategy_sparse_target():
X = [[0]] * 5 # ignored
y = sp.csc_matrix(np.array([[4, 1],
[0, 0],
[1, 1],
[1, 4],
[1, 1]]))
clf = DummyClassifier(strategy="stratified", random_state=0)
clf.fit(X, y)
X = [[0]] * 500
y_pred = clf.predict(X)
assert sp.issparse(y_pred)
y_pred = y_pred.toarray()
for k in range(y.shape[1]):
p = np.bincount(y_pred[:, k]) / float(len(X))
assert_almost_equal(p[1], 3. / 5, decimal=1)
assert_almost_equal(p[0], 1. / 5, decimal=1)
assert_almost_equal(p[4], 1. / 5, decimal=1)
def test_most_frequent_and_prior_strategy_sparse_target():
X = [[0]] * 5 # ignored
y = sp.csc_matrix(np.array([[1, 0],
[1, 3],
[4, 0],
[0, 1],
[1, 0]]))
n_samples = len(X)
y_expected = np.hstack([np.ones((n_samples, 1)), np.zeros((n_samples, 1))])
for strategy in ("most_frequent", "prior"):
clf = DummyClassifier(strategy=strategy, random_state=0)
clf.fit(X, y)
y_pred = clf.predict(X)
assert sp.issparse(y_pred)
assert_array_equal(y_pred.toarray(), y_expected)
def test_dummy_regressor_sample_weight(n_samples=10):
random_state = np.random.RandomState(seed=1)
X = [[0]] * n_samples
y = random_state.rand(n_samples)
sample_weight = random_state.rand(n_samples)
est = DummyRegressor(strategy="mean").fit(X, y, sample_weight)
assert est.constant_ == np.average(y, weights=sample_weight)
est = DummyRegressor(strategy="median").fit(X, y, sample_weight)
assert est.constant_ == _weighted_percentile(y, sample_weight, 50.)
est = DummyRegressor(strategy="quantile", quantile=.95).fit(X, y,
sample_weight)
assert est.constant_ == _weighted_percentile(y, sample_weight, 95.)
def test_dummy_regressor_on_3D_array():
X = np.array([[['foo']], [['bar']], [['baz']]])
y = np.array([2, 2, 2])
y_expected = np.array([2, 2, 2])
cls = DummyRegressor()
cls.fit(X, y)
y_pred = cls.predict(X)
assert_array_equal(y_pred, y_expected)
def test_dummy_classifier_on_3D_array():
X = np.array([[['foo']], [['bar']], [['baz']]])
y = [2, 2, 2]
y_expected = [2, 2, 2]
y_proba_expected = [[1], [1], [1]]
cls = DummyClassifier(strategy="stratified")
cls.fit(X, y)
y_pred = cls.predict(X)
y_pred_proba = cls.predict_proba(X)
assert_array_equal(y_pred, y_expected)
assert_array_equal(y_pred_proba, y_proba_expected)
def test_dummy_regressor_return_std():
X = [[0]] * 3 # ignored
y = np.array([2, 2, 2])
y_std_expected = np.array([0, 0, 0])
cls = DummyRegressor()
cls.fit(X, y)
y_pred_list = cls.predict(X, return_std=True)
# there should be two elements when return_std is True
assert len(y_pred_list) == 2
# the second element should be all zeros
assert_array_equal(y_pred_list[1], y_std_expected)
@pytest.mark.parametrize("y,y_test", [
([1, 1, 1, 2], [1.25] * 4),
(np.array([[2, 2],
[1, 1],
[1, 1],
[1, 1]]),
[[1.25, 1.25]] * 4)
])
def test_regressor_score_with_None(y, y_test):
reg = DummyRegressor()
reg.fit(None, y)
assert reg.score(None, y_test) == 1.0
@pytest.mark.parametrize("strategy", [
"mean",
"median",
"quantile",
"constant"
])
def test_regressor_prediction_independent_of_X(strategy):
y = [0, 2, 1, 1]
X1 = [[0]] * 4
reg1 = DummyRegressor(strategy=strategy, constant=0, quantile=0.7)
reg1.fit(X1, y)
predictions1 = reg1.predict(X1)
X2 = [[1]] * 4
reg2 = DummyRegressor(strategy=strategy, constant=0, quantile=0.7)
reg2.fit(X2, y)
predictions2 = reg2.predict(X2)
assert_array_equal(predictions1, predictions2)
@pytest.mark.parametrize(
"strategy", ["stratified", "most_frequent", "prior", "uniform", "constant"]
)
def test_dtype_of_classifier_probas(strategy):
y = [0, 2, 1, 1]
X = np.zeros(4)
model = DummyClassifier(strategy=strategy, random_state=0, constant=0)
probas = model.fit(X, y).predict_proba(X)
assert probas.dtype == np.float64
@pytest.mark.parametrize('Dummy', (DummyRegressor, DummyClassifier))
def test_n_features_in_(Dummy):
X = [[1, 2]]
y = [0]
d = Dummy()
assert not hasattr(d, 'n_features_in_')
d.fit(X, y)
assert d.n_features_in_ is None