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def test_kernel_pca_feature_names_out():
"""Check feature names out for KernelPCA."""
X, *_ = make_blobs(n_samples=100, n_features=4, random_state=0)
kpca = KernelPCA(n_components=2).fit(X)
names = kpca.get_feature_names_out()
assert_array_equal([f"kernelpca{i}" for i in range(2)], names) | Check feature names out for KernelPCA. | test_kernel_pca_feature_names_out | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_kernel_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_kernel_pca.py | BSD-3-Clause |
def test_kernel_pca_inverse_correct_gamma(global_random_seed):
"""Check that gamma is set correctly when not provided.
Non-regression test for #26280
"""
rng = np.random.RandomState(global_random_seed)
X = rng.random_sample((5, 4))
kwargs = {
"n_components": 2,
"random_state": ... | Check that gamma is set correctly when not provided.
Non-regression test for #26280
| test_kernel_pca_inverse_correct_gamma | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_kernel_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_kernel_pca.py | BSD-3-Clause |
def test_kernel_pca_pandas_output():
"""Check that KernelPCA works with pandas output when the solver is arpack.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27579
"""
pytest.importorskip("pandas")
X, _ = load_iris(as_frame=True, return_X_y=True)
with sklearn... | Check that KernelPCA works with pandas output when the solver is arpack.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27579
| test_kernel_pca_pandas_output | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_kernel_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_kernel_pca.py | BSD-3-Clause |
def _beta_divergence_dense(X, W, H, beta):
"""Compute the beta-divergence of X and W.H for dense array only.
Used as a reference for testing nmf._beta_divergence.
"""
WH = np.dot(W, H)
if beta == 2:
return squared_norm(X - WH) / 2
WH_Xnonzero = WH[X != 0]
X_nonzero = X[X != 0]
... | Compute the beta-divergence of X and W.H for dense array only.
Used as a reference for testing nmf._beta_divergence.
| _beta_divergence_dense | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_nmf.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_nmf.py | BSD-3-Clause |
def test_minibatch_nmf_negative_beta_loss(beta_loss):
"""Check that an error is raised if beta_loss < 0 and X contains zeros."""
rng = np.random.RandomState(0)
X = rng.normal(size=(6, 5))
X[X < 0] = 0
nmf = MiniBatchNMF(beta_loss=beta_loss, random_state=0)
msg = "When beta_loss <= 0 and X cont... | Check that an error is raised if beta_loss < 0 and X contains zeros. | test_minibatch_nmf_negative_beta_loss | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_nmf.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_nmf.py | BSD-3-Clause |
def test_feature_names_out():
"""Check feature names out for NMF."""
random_state = np.random.RandomState(0)
X = np.abs(random_state.randn(10, 4))
nmf = NMF(n_components=3).fit(X)
names = nmf.get_feature_names_out()
assert_array_equal([f"nmf{i}" for i in range(3)], names) | Check feature names out for NMF. | test_feature_names_out | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_nmf.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_nmf.py | BSD-3-Clause |
def test_lda_empty_docs(csr_container):
"""Test LDA on empty document (all-zero rows)."""
Z = np.zeros((5, 4))
for X in [Z, csr_container(Z)]:
lda = LatentDirichletAllocation(max_iter=750).fit(X)
assert_almost_equal(
lda.components_.sum(axis=0), np.ones(lda.components_.shape[1])
... | Test LDA on empty document (all-zero rows). | test_lda_empty_docs | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_online_lda.py | BSD-3-Clause |
def test_dirichlet_expectation():
"""Test Cython version of Dirichlet expectation calculation."""
x = np.logspace(-100, 10, 10000)
expectation = np.empty_like(x)
_dirichlet_expectation_1d(x, 0, expectation)
assert_allclose(expectation, np.exp(psi(x) - psi(np.sum(x))), atol=1e-19)
x = x.reshape(... | Test Cython version of Dirichlet expectation calculation. | test_dirichlet_expectation | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_online_lda.py | BSD-3-Clause |
def test_lda_feature_names_out(csr_container):
"""Check feature names out for LatentDirichletAllocation."""
n_components, X = _build_sparse_array(csr_container)
lda = LatentDirichletAllocation(n_components=n_components).fit(X)
names = lda.get_feature_names_out()
assert_array_equal(
[f"laten... | Check feature names out for LatentDirichletAllocation. | test_lda_feature_names_out | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_online_lda.py | BSD-3-Clause |
def test_lda_dtype_match(learning_method, global_dtype):
"""Check data type preservation of fitted attributes."""
rng = np.random.RandomState(0)
X = rng.uniform(size=(20, 10)).astype(global_dtype, copy=False)
lda = LatentDirichletAllocation(
n_components=5, random_state=0, learning_method=learn... | Check data type preservation of fitted attributes. | test_lda_dtype_match | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_online_lda.py | BSD-3-Clause |
def test_lda_numerical_consistency(learning_method, global_random_seed):
"""Check numerical consistency between np.float32 and np.float64."""
rng = np.random.RandomState(global_random_seed)
X64 = rng.uniform(size=(20, 10))
X32 = X64.astype(np.float32)
lda_64 = LatentDirichletAllocation(
n_c... | Check numerical consistency between np.float32 and np.float64. | test_lda_numerical_consistency | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_online_lda.py | BSD-3-Clause |
def test_pca_sparse(
global_random_seed, svd_solver, sparse_container, n_components, density, scale
):
"""Check that the results are the same for sparse and dense input."""
# Set atol in addition of the default rtol to account for the very wide range of
# result values (1e-8 to 1e0).
atol = 1e-12
... | Check that the results are the same for sparse and dense input. | test_pca_sparse | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_pca.py | BSD-3-Clause |
def test_sparse_pca_auto_arpack_singluar_values_consistency(
global_random_seed, sparse_container
):
"""Check that "auto" and "arpack" solvers are equivalent for sparse inputs."""
random_state = np.random.RandomState(global_random_seed)
X = sparse_container(
sp.sparse.random(
SPARSE_... | Check that "auto" and "arpack" solvers are equivalent for sparse inputs. | test_sparse_pca_auto_arpack_singluar_values_consistency | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_pca.py | BSD-3-Clause |
def test_pca_randomized_svd_n_oversamples():
"""Check that exposing and setting `n_oversamples` will provide accurate results
even when `X` as a large number of features.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/20589
"""
rng = np.random.RandomState(0)
n_... | Check that exposing and setting `n_oversamples` will provide accurate results
even when `X` as a large number of features.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/20589
| test_pca_randomized_svd_n_oversamples | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_pca.py | BSD-3-Clause |
def test_feature_names_out():
"""Check feature names out for PCA."""
pca = PCA(n_components=2).fit(iris.data)
names = pca.get_feature_names_out()
assert_array_equal([f"pca{i}" for i in range(2)], names) | Check feature names out for PCA. | test_feature_names_out | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_pca.py | BSD-3-Clause |
def test_variance_correctness(copy):
"""Check the accuracy of PCA's internal variance calculation"""
rng = np.random.RandomState(0)
X = rng.randn(1000, 200)
pca = PCA().fit(X)
pca_var = pca.explained_variance_ / pca.explained_variance_ratio_
true_var = np.var(X, ddof=1, axis=0).sum()
np.test... | Check the accuracy of PCA's internal variance calculation | test_variance_correctness | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_pca.py | BSD-3-Clause |
def test_spca_feature_names_out(SPCA):
"""Check feature names out for *SparsePCA."""
rng = np.random.RandomState(0)
n_samples, n_features = 12, 10
X = rng.randn(n_samples, n_features)
model = SPCA(n_components=4).fit(X)
names = model.get_feature_names_out()
estimator_name = SPCA.__name__.l... | Check feature names out for *SparsePCA. | test_spca_feature_names_out | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_sparse_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_sparse_pca.py | BSD-3-Clause |
def test_spca_early_stopping(global_random_seed):
"""Check that `tol` and `max_no_improvement` act as early stopping."""
rng = np.random.RandomState(global_random_seed)
n_samples, n_features = 50, 10
X = rng.randn(n_samples, n_features)
# vary the tolerance to force the early stopping of one of the... | Check that `tol` and `max_no_improvement` act as early stopping. | test_spca_early_stopping | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_sparse_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_sparse_pca.py | BSD-3-Clause |
def test_equivalence_components_pca_spca(global_random_seed):
"""Check the equivalence of the components found by PCA and SparsePCA.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/23932
"""
rng = np.random.RandomState(global_random_seed)
X = rng.randn(50, 4)
n... | Check the equivalence of the components found by PCA and SparsePCA.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/23932
| test_equivalence_components_pca_spca | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_sparse_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_sparse_pca.py | BSD-3-Clause |
def test_sparse_pca_inverse_transform(global_random_seed):
"""Check that `inverse_transform` in `SparsePCA` and `PCA` are similar."""
rng = np.random.RandomState(global_random_seed)
n_samples, n_features = 10, 5
X = rng.randn(n_samples, n_features)
n_components = 2
spca = SparsePCA(
n_c... | Check that `inverse_transform` in `SparsePCA` and `PCA` are similar. | test_sparse_pca_inverse_transform | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_sparse_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_sparse_pca.py | BSD-3-Clause |
def test_transform_inverse_transform_round_trip(SPCA, global_random_seed):
"""Check the `transform` and `inverse_transform` round trip with no loss of
information.
"""
rng = np.random.RandomState(global_random_seed)
n_samples, n_features = 10, 5
X = rng.randn(n_samples, n_features)
n_compon... | Check the `transform` and `inverse_transform` round trip with no loss of
information.
| test_transform_inverse_transform_round_trip | python | scikit-learn/scikit-learn | sklearn/decomposition/tests/test_sparse_pca.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/tests/test_sparse_pca.py | BSD-3-Clause |
def _generate_bagging_indices(
random_state,
bootstrap_features,
bootstrap_samples,
n_features,
n_samples,
max_features,
max_samples,
):
"""Randomly draw feature and sample indices."""
# Get valid random state
random_state = check_random_state(random_state)
# Draw indices
... | Randomly draw feature and sample indices. | _generate_bagging_indices | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def _parallel_build_estimators(
n_estimators,
ensemble,
X,
y,
seeds,
total_n_estimators,
verbose,
check_input,
fit_params,
):
"""Private function used to build a batch of estimators within a job."""
# Retrieve settings
n_samples, n_features = X.shape
max_features = en... | Private function used to build a batch of estimators within a job. | _parallel_build_estimators | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def _parallel_predict_proba(
estimators,
estimators_features,
X,
n_classes,
predict_params=None,
predict_proba_params=None,
):
"""Private function used to compute (proba-)predictions within a job."""
n_samples = X.shape[0]
proba = np.zeros((n_samples, n_classes))
for estimator, ... | Private function used to compute (proba-)predictions within a job. | _parallel_predict_proba | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def _parallel_predict_log_proba(estimators, estimators_features, X, n_classes, params):
"""Private function used to compute log probabilities within a job."""
n_samples = X.shape[0]
log_proba = np.empty((n_samples, n_classes))
log_proba.fill(-np.inf)
all_classes = np.arange(n_classes, dtype=int)
... | Private function used to compute log probabilities within a job. | _parallel_predict_log_proba | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def _parallel_decision_function(estimators, estimators_features, X, params):
"""Private function used to compute decisions within a job."""
return sum(
estimator.decision_function(X[:, features], **params)
for estimator, features in zip(estimators, estimators_features)
) | Private function used to compute decisions within a job. | _parallel_decision_function | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def _parallel_predict_regression(estimators, estimators_features, X, params):
"""Private function used to compute predictions within a job."""
return sum(
estimator.predict(X[:, features], **params)
for estimator, features in zip(estimators, estimators_features)
) | Private function used to compute predictions within a job. | _parallel_predict_regression | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def fit(self, X, y, sample_weight=None, **fit_params):
"""Build a Bagging ensemble of estimators from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only... | Build a Bagging ensemble of estimators from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.
... | fit | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def _fit(
self,
X,
y,
max_samples=None,
max_depth=None,
check_input=True,
sample_weight=None,
**fit_params,
):
"""Build a Bagging ensemble of estimators from the training
set (X, y).
Parameters
----------
X :... | Build a Bagging ensemble of estimators from the training
set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimato... | _fit | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def _get_estimator(self):
"""Resolve which estimator to return (default is DecisionTreeClassifier)"""
if self.estimator is None:
return DecisionTreeClassifier()
return self.estimator | Resolve which estimator to return (default is DecisionTreeClassifier) | _get_estimator | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def predict(self, X, **params):
"""Predict class for X.
The predicted class of an input sample is computed as the class with
the highest mean predicted probability. If base estimators do not
implement a ``predict_proba`` method, then it resorts to voting.
Parameters
---... | Predict class for X.
The predicted class of an input sample is computed as the class with
the highest mean predicted probability. If base estimators do not
implement a ``predict_proba`` method, then it resorts to voting.
Parameters
----------
X : {array-like, sparse mat... | predict | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def predict_proba(self, X, **params):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the mean predicted class probabilities of the base estimators in the
ensemble. If base estimators do not implement a ``predict_proba``
... | Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the mean predicted class probabilities of the base estimators in the
ensemble. If base estimators do not implement a ``predict_proba``
method, then it resorts to voting and the predict... | predict_proba | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def predict_log_proba(self, X, **params):
"""Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the base
estimators in the ensemble.
Parameters
----------
... | Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the base
estimators in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_sam... | predict_log_proba | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def decision_function(self, X, **params):
"""Average of the decision functions of the base classifiers.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if
they ar... | Average of the decision functions of the base classifiers.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.
**params ... | decision_function | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def predict(self, X, **params):
"""Predict regression target for X.
The predicted regression target of an input sample is computed as the
mean predicted regression targets of the estimators in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape ... | Predict regression target for X.
The predicted regression target of an input sample is computed as the
mean predicted regression targets of the estimators in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The tra... | predict | python | scikit-learn/scikit-learn | sklearn/ensemble/_bagging.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py | BSD-3-Clause |
def _fit_single_estimator(
estimator, X, y, fit_params, message_clsname=None, message=None
):
"""Private function used to fit an estimator within a job."""
# TODO(SLEP6): remove if-condition for unrouted sample_weight when metadata
# routing can't be disabled.
if not _routing_enabled() and "sample_w... | Private function used to fit an estimator within a job. | _fit_single_estimator | python | scikit-learn/scikit-learn | sklearn/ensemble/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_base.py | BSD-3-Clause |
def _set_random_states(estimator, random_state=None):
"""Set fixed random_state parameters for an estimator.
Finds all parameters ending ``random_state`` and sets them to integers
derived from ``random_state``.
Parameters
----------
estimator : estimator supporting get/set_params
Estim... | Set fixed random_state parameters for an estimator.
Finds all parameters ending ``random_state`` and sets them to integers
derived from ``random_state``.
Parameters
----------
estimator : estimator supporting get/set_params
Estimator with potential randomness managed by random_state
... | _set_random_states | python | scikit-learn/scikit-learn | sklearn/ensemble/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_base.py | BSD-3-Clause |
def _validate_estimator(self, default=None):
"""Check the base estimator.
Sets the `estimator_` attributes.
"""
if self.estimator is not None:
self.estimator_ = self.estimator
else:
self.estimator_ = default | Check the base estimator.
Sets the `estimator_` attributes.
| _validate_estimator | python | scikit-learn/scikit-learn | sklearn/ensemble/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_base.py | BSD-3-Clause |
def _make_estimator(self, append=True, random_state=None):
"""Make and configure a copy of the `estimator_` attribute.
Warning: This method should be used to properly instantiate new
sub-estimators.
"""
estimator = clone(self.estimator_)
estimator.set_params(**{p: getatt... | Make and configure a copy of the `estimator_` attribute.
Warning: This method should be used to properly instantiate new
sub-estimators.
| _make_estimator | python | scikit-learn/scikit-learn | sklearn/ensemble/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_base.py | BSD-3-Clause |
def _partition_estimators(n_estimators, n_jobs):
"""Private function used to partition estimators between jobs."""
# Compute the number of jobs
n_jobs = min(effective_n_jobs(n_jobs), n_estimators)
# Partition estimators between jobs
n_estimators_per_job = np.full(n_jobs, n_estimators // n_jobs, dty... | Private function used to partition estimators between jobs. | _partition_estimators | python | scikit-learn/scikit-learn | sklearn/ensemble/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_base.py | BSD-3-Clause |
def _get_n_samples_bootstrap(n_samples, max_samples):
"""
Get the number of samples in a bootstrap sample.
Parameters
----------
n_samples : int
Number of samples in the dataset.
max_samples : int or float
The maximum number of samples to draw from the total available:
... |
Get the number of samples in a bootstrap sample.
Parameters
----------
n_samples : int
Number of samples in the dataset.
max_samples : int or float
The maximum number of samples to draw from the total available:
- if float, this indicates a fraction of the total and sho... | _get_n_samples_bootstrap | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def _generate_unsampled_indices(random_state, n_samples, n_samples_bootstrap):
"""
Private function used to forest._set_oob_score function."""
sample_indices = _generate_sample_indices(
random_state, n_samples, n_samples_bootstrap
)
sample_counts = np.bincount(sample_indices, minlength=n_sam... |
Private function used to forest._set_oob_score function. | _generate_unsampled_indices | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def _parallel_build_trees(
tree,
bootstrap,
X,
y,
sample_weight,
tree_idx,
n_trees,
verbose=0,
class_weight=None,
n_samples_bootstrap=None,
missing_values_in_feature_mask=None,
):
"""
Private function used to fit a single tree in parallel."""
if verbose > 1:
... |
Private function used to fit a single tree in parallel. | _parallel_build_trees | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def apply(self, X):
"""
Apply trees in the forest to X, return leaf indices.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sp... |
Apply trees in the forest to X, return leaf indices.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it wil... | apply | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def decision_path(self, X):
"""
Return the decision path in the forest.
.. versionadded:: 0.18
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``... |
Return the decision path in the forest.
.. versionadded:: 0.18
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix ... | decision_path | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def fit(self, X, y, sample_weight=None):
"""
Build a forest of trees from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, its dtype will be converted
t... |
Build a forest of trees from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, its dtype will be converted
to ``dtype=np.float32``. If a sparse matrix is provid... | fit | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def _set_oob_score_and_attributes(self, X, y, scoring_function=None):
"""Compute and set the OOB score and attributes.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
y : ndarray of shape (n_samples, n_outputs)
The t... | Compute and set the OOB score and attributes.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
y : ndarray of shape (n_samples, n_outputs)
The target matrix.
scoring_function : callable, default=None
Scori... | _set_oob_score_and_attributes | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def _compute_oob_predictions(self, X, y):
"""Compute and set the OOB score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
y : ndarray of shape (n_samples, n_outputs)
The target matrix.
Returns
----... | Compute and set the OOB score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
y : ndarray of shape (n_samples, n_outputs)
The target matrix.
Returns
-------
oob_pred : ndarray of shape (n_samples, n... | _compute_oob_predictions | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def _validate_X_predict(self, X):
"""
Validate X whenever one tries to predict, apply, predict_proba."""
check_is_fitted(self)
if self.estimators_[0]._support_missing_values(X):
ensure_all_finite = "allow-nan"
else:
ensure_all_finite = True
X = va... |
Validate X whenever one tries to predict, apply, predict_proba. | _validate_X_predict | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def feature_importances_(self):
"""
The impurity-based feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini imp... |
The impurity-based feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance.
Warning: impurity-based f... | feature_importances_ | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def _accumulate_prediction(predict, X, out, lock):
"""
This is a utility function for joblib's Parallel.
It can't go locally in ForestClassifier or ForestRegressor, because joblib
complains that it cannot pickle it when placed there.
"""
prediction = predict(X, check_input=False)
with lock:... |
This is a utility function for joblib's Parallel.
It can't go locally in ForestClassifier or ForestRegressor, because joblib
complains that it cannot pickle it when placed there.
| _accumulate_prediction | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def _get_oob_predictions(tree, X):
"""Compute the OOB predictions for an individual tree.
Parameters
----------
tree : DecisionTreeClassifier object
A single decision tree classifier.
X : ndarray of shape (n_samples, n_features)
The OOB samples.
... | Compute the OOB predictions for an individual tree.
Parameters
----------
tree : DecisionTreeClassifier object
A single decision tree classifier.
X : ndarray of shape (n_samples, n_features)
The OOB samples.
Returns
-------
y_pred : ndarr... | _get_oob_predictions | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def _set_oob_score_and_attributes(self, X, y, scoring_function=None):
"""Compute and set the OOB score and attributes.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
y : ndarray of shape (n_samples, n_outputs)
The t... | Compute and set the OOB score and attributes.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
y : ndarray of shape (n_samples, n_outputs)
The target matrix.
scoring_function : callable, default=None
Scori... | _set_oob_score_and_attributes | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def predict(self, X):
"""
Predict class for X.
The predicted class of an input sample is a vote by the trees in
the forest, weighted by their probability estimates. That is,
the predicted class is the one with highest mean probability
estimate across the trees.
... |
Predict class for X.
The predicted class of an input sample is a vote by the trees in
the forest, weighted by their probability estimates. That is,
the predicted class is the one with highest mean probability
estimate across the trees.
Parameters
----------
... | predict | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def predict_proba(self, X):
"""
Predict class probabilities for X.
The predicted class probabilities of an input sample are computed as
the mean predicted class probabilities of the trees in the forest.
The class probability of a single tree is the fraction of samples of
... |
Predict class probabilities for X.
The predicted class probabilities of an input sample are computed as
the mean predicted class probabilities of the trees in the forest.
The class probability of a single tree is the fraction of samples of
the same class in a leaf.
Par... | predict_proba | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def predict_log_proba(self, X):
"""
Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the trees in the
forest.
Parameters
----------
X : {ar... |
Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the trees in the
forest.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_sample... | predict_log_proba | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def predict(self, X):
"""
Predict regression target for X.
The predicted regression target of an input sample is computed as the
mean predicted regression targets of the trees in the forest.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_sampl... |
Predict regression target for X.
The predicted regression target of an input sample is computed as the
mean predicted regression targets of the trees in the forest.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The i... | predict | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def _get_oob_predictions(tree, X):
"""Compute the OOB predictions for an individual tree.
Parameters
----------
tree : DecisionTreeRegressor object
A single decision tree regressor.
X : ndarray of shape (n_samples, n_features)
The OOB samples.
Re... | Compute the OOB predictions for an individual tree.
Parameters
----------
tree : DecisionTreeRegressor object
A single decision tree regressor.
X : ndarray of shape (n_samples, n_features)
The OOB samples.
Returns
-------
y_pred : ndarray... | _get_oob_predictions | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def _set_oob_score_and_attributes(self, X, y, scoring_function=None):
"""Compute and set the OOB score and attributes.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
y : ndarray of shape (n_samples, n_outputs)
The t... | Compute and set the OOB score and attributes.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
y : ndarray of shape (n_samples, n_outputs)
The target matrix.
scoring_function : callable, default=None
Scori... | _set_oob_score_and_attributes | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def _compute_partial_dependence_recursion(self, grid, target_features):
"""Fast partial dependence computation.
Parameters
----------
grid : ndarray of shape (n_samples, n_target_features), dtype=DTYPE
The grid points on which the partial dependence should be
eva... | Fast partial dependence computation.
Parameters
----------
grid : ndarray of shape (n_samples, n_target_features), dtype=DTYPE
The grid points on which the partial dependence should be
evaluated.
target_features : ndarray of shape (n_target_features), dtype=np.in... | _compute_partial_dependence_recursion | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def fit(self, X, y=None, sample_weight=None):
"""
Fit estimator.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Use ``dtype=np.float32`` for maximum
efficiency. Sparse matrices are also supported,... |
Fit estimator.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Use ``dtype=np.float32`` for maximum
efficiency. Sparse matrices are also supported, use sparse
``csc_matrix`` for maximum effici... | fit | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def fit_transform(self, X, y=None, sample_weight=None):
"""
Fit estimator and transform dataset.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data used to build forests. Use ``dtype=np.float32`` for
maximum ... |
Fit estimator and transform dataset.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data used to build forests. Use ``dtype=np.float32`` for
maximum efficiency.
y : Ignored
Not used, present for ... | fit_transform | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def get_feature_names_out(self, input_features=None):
"""Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Only used to validate feature names with the names seen in :meth:`fit`.
Returns
... | Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Only used to validate feature names with the names seen in :meth:`fit`.
Returns
-------
feature_names_out : ndarray of str objects
... | get_feature_names_out | python | scikit-learn/scikit-learn | sklearn/ensemble/_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py | BSD-3-Clause |
def _safe_divide(numerator, denominator):
"""Prevents overflow and division by zero."""
# This is used for classifiers where the denominator might become zero exactly.
# For instance for log loss, HalfBinomialLoss, if proba=0 or proba=1 exactly, then
# denominator = hessian = 0, and we should set the no... | Prevents overflow and division by zero. | _safe_divide | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def _init_raw_predictions(X, estimator, loss, use_predict_proba):
"""Return the initial raw predictions.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
The data array.
estimator : object
The estimator to use to compute the predictions.
loss : BaseLoss
... | Return the initial raw predictions.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
The data array.
estimator : object
The estimator to use to compute the predictions.
loss : BaseLoss
An instance of a loss function class.
use_predict_proba : bool
... | _init_raw_predictions | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def _update_terminal_regions(
loss,
tree,
X,
y,
neg_gradient,
raw_prediction,
sample_weight,
sample_mask,
learning_rate=0.1,
k=0,
):
"""Update the leaf values to be predicted by the tree and raw_prediction.
The current raw predictions of the model (of this stage) are upd... | Update the leaf values to be predicted by the tree and raw_prediction.
The current raw predictions of the model (of this stage) are updated.
Additionally, the terminal regions (=leaves) of the given tree are updated as well.
This corresponds to the line search step in "Greedy Function Approximation" by
... | _update_terminal_regions | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def set_huber_delta(loss, y_true, raw_prediction, sample_weight=None):
"""Calculate and set self.closs.delta based on self.quantile."""
abserr = np.abs(y_true - raw_prediction.squeeze())
# sample_weight is always a ndarray, never None.
delta = _weighted_percentile(abserr, sample_weight, 100 * loss.quant... | Calculate and set self.closs.delta based on self.quantile. | set_huber_delta | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def init(self, est, begin_at_stage=0):
"""Initialize reporter
Parameters
----------
est : Estimator
The estimator
begin_at_stage : int, default=0
stage at which to begin reporting
"""
# header fields and line format str
header_fie... | Initialize reporter
Parameters
----------
est : Estimator
The estimator
begin_at_stage : int, default=0
stage at which to begin reporting
| init | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def update(self, j, est):
"""Update reporter with new iteration.
Parameters
----------
j : int
The new iteration.
est : Estimator
The estimator.
"""
do_oob = est.subsample < 1
# we need to take into account if we fit additional est... | Update reporter with new iteration.
Parameters
----------
j : int
The new iteration.
est : Estimator
The estimator.
| update | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def _init_state(self):
"""Initialize model state and allocate model state data structures."""
self.init_ = self.init
if self.init_ is None:
if is_classifier(self):
self.init_ = DummyClassifier(strategy="prior")
elif isinstance(self._loss, (AbsoluteError, ... | Initialize model state and allocate model state data structures. | _init_state | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def _clear_state(self):
"""Clear the state of the gradient boosting model."""
if hasattr(self, "estimators_"):
self.estimators_ = np.empty((0, 0), dtype=object)
if hasattr(self, "train_score_"):
del self.train_score_
if hasattr(self, "oob_improvement_"):
... | Clear the state of the gradient boosting model. | _clear_state | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def _resize_state(self):
"""Add additional ``n_estimators`` entries to all attributes."""
# self.n_estimators is the number of additional est to fit
total_n_estimators = self.n_estimators
if total_n_estimators < self.estimators_.shape[0]:
raise ValueError(
"re... | Add additional ``n_estimators`` entries to all attributes. | _resize_state | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def fit(self, X, y, sample_weight=None, monitor=None):
"""Fit the gradient boosting model.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a ... | Fit the gradient boosting model.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
... | fit | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def _fit_stages(
self,
X,
y,
raw_predictions,
sample_weight,
random_state,
X_val,
y_val,
sample_weight_val,
begin_at_stage=0,
monitor=None,
):
"""Iteratively fits the stages.
For each stage it computes the progr... | Iteratively fits the stages.
For each stage it computes the progress (OOB, train score)
and delegates to ``_fit_stage``.
Returns the number of stages fit; might differ from ``n_estimators``
due to early stopping.
| _fit_stages | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def _raw_predict_init(self, X):
"""Check input and compute raw predictions of the init estimator."""
self._check_initialized()
X = self.estimators_[0, 0]._validate_X_predict(X, check_input=True)
if self.init_ == "zero":
raw_predictions = np.zeros(
shape=(X.sha... | Check input and compute raw predictions of the init estimator. | _raw_predict_init | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def _raw_predict(self, X):
"""Return the sum of the trees raw predictions (+ init estimator)."""
check_is_fitted(self)
raw_predictions = self._raw_predict_init(X)
predict_stages(self.estimators_, X, self.learning_rate, raw_predictions)
return raw_predictions | Return the sum of the trees raw predictions (+ init estimator). | _raw_predict | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def _staged_raw_predict(self, X, check_input=True):
"""Compute raw predictions of ``X`` for each iteration.
This method allows monitoring (i.e. determine error on testing set)
after each stage.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n... | Compute raw predictions of ``X`` for each iteration.
This method allows monitoring (i.e. determine error on testing set)
after each stage.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will b... | _staged_raw_predict | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def feature_importances_(self):
"""The impurity-based feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance.
... | The impurity-based feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance.
Warning: impurity-based feature im... | feature_importances_ | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def _compute_partial_dependence_recursion(self, grid, target_features):
"""Fast partial dependence computation.
Parameters
----------
grid : ndarray of shape (n_samples, n_target_features), dtype=np.float32
The grid points on which the partial dependence should be
... | Fast partial dependence computation.
Parameters
----------
grid : ndarray of shape (n_samples, n_target_features), dtype=np.float32
The grid points on which the partial dependence should be
evaluated.
target_features : ndarray of shape (n_target_features,), dtype... | _compute_partial_dependence_recursion | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def apply(self, X):
"""Apply trees in the ensemble to X, return leaf indices.
.. versionadded:: 0.17
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dt... | Apply trees in the ensemble to X, return leaf indices.
.. versionadded:: 0.17
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse m... | apply | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def decision_function(self, X):
"""Compute the decision function of ``X``.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is... | Compute the decision function of ``X``.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_mat... | decision_function | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def predict(self, X):
"""Predict class for X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sp... | Predict class for X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
Ret... | predict | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def staged_predict(self, X):
"""Predict class at each stage for X.
This method allows monitoring (i.e. determine error on testing set)
after each stage.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples.... | Predict class at each stage for X.
This method allows monitoring (i.e. determine error on testing set)
after each stage.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
... | staged_predict | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def staged_predict_proba(self, X):
"""Predict class probabilities at each stage for X.
This method allows monitoring (i.e. determine error on testing set)
after each stage.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
... | Predict class probabilities at each stage for X.
This method allows monitoring (i.e. determine error on testing set)
after each stage.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be co... | staged_predict_proba | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def predict(self, X):
"""Predict regression target for X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
... | Predict regression target for X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
... | predict | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def apply(self, X):
"""Apply trees in the ensemble to X, return leaf indices.
.. versionadded:: 0.17
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dt... | Apply trees in the ensemble to X, return leaf indices.
.. versionadded:: 0.17
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse m... | apply | python | scikit-learn/scikit-learn | sklearn/ensemble/_gb.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py | BSD-3-Clause |
def _parallel_compute_tree_depths(
tree,
X,
features,
tree_decision_path_lengths,
tree_avg_path_lengths,
depths,
lock,
):
"""Parallel computation of isolation tree depth."""
if features is None:
X_subset = X
else:
X_subset = X[:, features]
leaves_index = tree... | Parallel computation of isolation tree depth. | _parallel_compute_tree_depths | python | scikit-learn/scikit-learn | sklearn/ensemble/_iforest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_iforest.py | BSD-3-Clause |
def fit(self, X, y=None, sample_weight=None):
"""
Fit estimator.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Use ``dtype=np.float32`` for maximum
efficiency. Sparse matrices are also supported,... |
Fit estimator.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Use ``dtype=np.float32`` for maximum
efficiency. Sparse matrices are also supported, use sparse
``csc_matrix`` for maximum effici... | fit | python | scikit-learn/scikit-learn | sklearn/ensemble/_iforest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_iforest.py | BSD-3-Clause |
def predict(self, X):
"""
Predict if a particular sample is an outlier or not.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a spar... |
Predict if a particular sample is an outlier or not.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
... | predict | python | scikit-learn/scikit-learn | sklearn/ensemble/_iforest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_iforest.py | BSD-3-Clause |
def decision_function(self, X):
"""
Average anomaly score of X of the base classifiers.
The anomaly score of an input sample is computed as
the mean anomaly score of the trees in the forest.
The measure of normality of an observation given a tree is the depth
of the lea... |
Average anomaly score of X of the base classifiers.
The anomaly score of an input sample is computed as
the mean anomaly score of the trees in the forest.
The measure of normality of an observation given a tree is the depth
of the leaf containing this observation, which is equ... | decision_function | python | scikit-learn/scikit-learn | sklearn/ensemble/_iforest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_iforest.py | BSD-3-Clause |
def score_samples(self, X):
"""
Opposite of the anomaly score defined in the original paper.
The anomaly score of an input sample is computed as
the mean anomaly score of the trees in the forest.
The measure of normality of an observation given a tree is the depth
of th... |
Opposite of the anomaly score defined in the original paper.
The anomaly score of an input sample is computed as
the mean anomaly score of the trees in the forest.
The measure of normality of an observation given a tree is the depth
of the leaf containing this observation, whi... | score_samples | python | scikit-learn/scikit-learn | sklearn/ensemble/_iforest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_iforest.py | BSD-3-Clause |
def _score_samples(self, X):
"""Private version of score_samples without input validation.
Input validation would remove feature names, so we disable it.
"""
# Code structure from ForestClassifier/predict_proba
check_is_fitted(self)
# Take the opposite of the scores as... | Private version of score_samples without input validation.
Input validation would remove feature names, so we disable it.
| _score_samples | python | scikit-learn/scikit-learn | sklearn/ensemble/_iforest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_iforest.py | BSD-3-Clause |
def _compute_score_samples(self, X, subsample_features):
"""
Compute the score of each samples in X going through the extra trees.
Parameters
----------
X : array-like or sparse matrix
Data matrix.
subsample_features : bool
Whether features shoul... |
Compute the score of each samples in X going through the extra trees.
Parameters
----------
X : array-like or sparse matrix
Data matrix.
subsample_features : bool
Whether features should be subsampled.
Returns
-------
scores : n... | _compute_score_samples | python | scikit-learn/scikit-learn | sklearn/ensemble/_iforest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_iforest.py | BSD-3-Clause |
def _average_path_length(n_samples_leaf):
"""
The average path length in a n_samples iTree, which is equal to
the average path length of an unsuccessful BST search since the
latter has the same structure as an isolation tree.
Parameters
----------
n_samples_leaf : array-like of shape (n_samp... |
The average path length in a n_samples iTree, which is equal to
the average path length of an unsuccessful BST search since the
latter has the same structure as an isolation tree.
Parameters
----------
n_samples_leaf : array-like of shape (n_samples,)
The number of training samples in e... | _average_path_length | python | scikit-learn/scikit-learn | sklearn/ensemble/_iforest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_iforest.py | BSD-3-Clause |
def _concatenate_predictions(self, X, predictions):
"""Concatenate the predictions of each first layer learner and
possibly the input dataset `X`.
If `X` is sparse and `self.passthrough` is False, the output of
`transform` will be dense (the predictions). If `X` is sparse
and `s... | Concatenate the predictions of each first layer learner and
possibly the input dataset `X`.
If `X` is sparse and `self.passthrough` is False, the output of
`transform` will be dense (the predictions). If `X` is sparse
and `self.passthrough` is True, the output of `transform` will
... | _concatenate_predictions | python | scikit-learn/scikit-learn | sklearn/ensemble/_stacking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_stacking.py | BSD-3-Clause |
def fit(self, X, y, **fit_params):
"""Fit the estimators.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : ... | Fit the estimators.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : array-like of shape (n_samples,)
T... | fit | python | scikit-learn/scikit-learn | sklearn/ensemble/_stacking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_stacking.py | BSD-3-Clause |
def _transform(self, X):
"""Concatenate and return the predictions of the estimators."""
check_is_fitted(self)
predictions = [
getattr(est, meth)(X)
for est, meth in zip(self.estimators_, self.stack_method_)
if est != "drop"
]
return self._conc... | Concatenate and return the predictions of the estimators. | _transform | python | scikit-learn/scikit-learn | sklearn/ensemble/_stacking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_stacking.py | BSD-3-Clause |
def get_feature_names_out(self, input_features=None):
"""Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Input features. The input feature names are only used when `passthrough` is
`True`... | Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Input features. The input feature names are only used when `passthrough` is
`True`.
- If `input_features` is `None`, then `feature_nam... | get_feature_names_out | python | scikit-learn/scikit-learn | sklearn/ensemble/_stacking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_stacking.py | BSD-3-Clause |
def predict(self, X, **predict_params):
"""Predict target for X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.
... | Predict target for X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.
**predict_params : dict of str -> obj
... | predict | python | scikit-learn/scikit-learn | sklearn/ensemble/_stacking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_stacking.py | BSD-3-Clause |
def _validate_estimators(self):
"""Overload the method of `_BaseHeterogeneousEnsemble` to be more
lenient towards the type of `estimators`.
Regressors can be accepted for some cases such as ordinal regression.
"""
if len(self.estimators) == 0:
raise ValueError(
... | Overload the method of `_BaseHeterogeneousEnsemble` to be more
lenient towards the type of `estimators`.
Regressors can be accepted for some cases such as ordinal regression.
| _validate_estimators | python | scikit-learn/scikit-learn | sklearn/ensemble/_stacking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_stacking.py | BSD-3-Clause |
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