code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def score_estimator(estimator, df_test):
"""Score an estimator on the test set."""
y_pred = estimator.predict(df_test)
print(
"MSE: %.3f"
% mean_squared_error(
df_test["Frequency"], y_pred, sample_weight=df_test["Exposure"]
)
)
print(
"MAE: %.3f"
... | Score an estimator on the test set. | score_estimator | python | scikit-learn/scikit-learn | examples/linear_model/plot_poisson_regression_non_normal_loss.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/linear_model/plot_poisson_regression_non_normal_loss.py | BSD-3-Clause |
def _mean_frequency_by_risk_group(y_true, y_pred, sample_weight=None, n_bins=100):
"""Compare predictions and observations for bins ordered by y_pred.
We order the samples by ``y_pred`` and split it in bins.
In each bin the observed mean is compared with the predicted mean.
Parameters
----------
... | Compare predictions and observations for bins ordered by y_pred.
We order the samples by ``y_pred`` and split it in bins.
In each bin the observed mean is compared with the predicted mean.
Parameters
----------
y_true: array-like of shape (n_samples,)
Ground truth (correct) target values.
... | _mean_frequency_by_risk_group | python | scikit-learn/scikit-learn | examples/linear_model/plot_poisson_regression_non_normal_loss.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/linear_model/plot_poisson_regression_non_normal_loss.py | BSD-3-Clause |
def load_mnist(n_samples=None, class_0="0", class_1="8"):
"""Load MNIST, select two classes, shuffle and return only n_samples."""
# Load data from http://openml.org/d/554
mnist = fetch_openml("mnist_784", version=1, as_frame=False)
# take only two classes for binary classification
mask = np.logica... | Load MNIST, select two classes, shuffle and return only n_samples. | load_mnist | python | scikit-learn/scikit-learn | examples/linear_model/plot_sgd_early_stopping.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/linear_model/plot_sgd_early_stopping.py | BSD-3-Clause |
def fit_and_score(estimator, max_iter, X_train, X_test, y_train, y_test):
"""Fit the estimator on the train set and score it on both sets"""
estimator.set_params(max_iter=max_iter)
estimator.set_params(random_state=0)
start = time.time()
estimator.fit(X_train, y_train)
fit_time = time.time() -... | Fit the estimator on the train set and score it on both sets | fit_and_score | python | scikit-learn/scikit-learn | examples/linear_model/plot_sgd_early_stopping.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/linear_model/plot_sgd_early_stopping.py | BSD-3-Clause |
def load_mtpl2(n_samples=None):
"""Fetch the French Motor Third-Party Liability Claims dataset.
Parameters
----------
n_samples: int, default=None
number of samples to select (for faster run time). Full dataset has
678013 samples.
"""
# freMTPL2freq dataset from https://www.openml.o... | Fetch the French Motor Third-Party Liability Claims dataset.
Parameters
----------
n_samples: int, default=None
number of samples to select (for faster run time). Full dataset has
678013 samples.
| load_mtpl2 | python | scikit-learn/scikit-learn | examples/linear_model/plot_tweedie_regression_insurance_claims.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/linear_model/plot_tweedie_regression_insurance_claims.py | BSD-3-Clause |
def plot_obs_pred(
df,
feature,
weight,
observed,
predicted,
y_label=None,
title=None,
ax=None,
fill_legend=False,
):
"""Plot observed and predicted - aggregated per feature level.
Parameters
----------
df : DataFrame
input data
feature: str
a col... | Plot observed and predicted - aggregated per feature level.
Parameters
----------
df : DataFrame
input data
feature: str
a column name of df for the feature to be plotted
weight : str
column name of df with the values of weights or exposure
observed : str
a colum... | plot_obs_pred | python | scikit-learn/scikit-learn | examples/linear_model/plot_tweedie_regression_insurance_claims.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/linear_model/plot_tweedie_regression_insurance_claims.py | BSD-3-Clause |
def make_estimator(name, categorical_columns=None, iforest_kw=None, lof_kw=None):
"""Create an outlier detection estimator based on its name."""
if name == "LOF":
outlier_detector = LocalOutlierFactor(**(lof_kw or {}))
if categorical_columns is None:
preprocessor = RobustScaler()
... | Create an outlier detection estimator based on its name. | make_estimator | python | scikit-learn/scikit-learn | examples/miscellaneous/plot_outlier_detection_bench.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/miscellaneous/plot_outlier_detection_bench.py | BSD-3-Clause |
def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10):
"""Create a sample plot for indices of a cross-validation object."""
use_groups = "Group" in type(cv).__name__
groups = group if use_groups else None
# Generate the training/testing visualizations for each CV split
for ii, (tr, tt) in enumer... | Create a sample plot for indices of a cross-validation object. | plot_cv_indices | python | scikit-learn/scikit-learn | examples/model_selection/plot_cv_indices.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/model_selection/plot_cv_indices.py | BSD-3-Clause |
def refit_strategy(cv_results):
"""Define the strategy to select the best estimator.
The strategy defined here is to filter-out all results below a precision threshold
of 0.98, rank the remaining by recall and keep all models with one standard
deviation of the best by recall. Once these models are sele... | Define the strategy to select the best estimator.
The strategy defined here is to filter-out all results below a precision threshold
of 0.98, rank the remaining by recall and keep all models with one standard
deviation of the best by recall. Once these models are selected, we can select the
fastest mod... | refit_strategy | python | scikit-learn/scikit-learn | examples/model_selection/plot_grid_search_digits.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/model_selection/plot_grid_search_digits.py | BSD-3-Clause |
def lower_bound(cv_results):
"""
Calculate the lower bound within 1 standard deviation
of the best `mean_test_scores`.
Parameters
----------
cv_results : dict of numpy(masked) ndarrays
See attribute cv_results_ of `GridSearchCV`
Returns
-------
float
Lower bound wit... |
Calculate the lower bound within 1 standard deviation
of the best `mean_test_scores`.
Parameters
----------
cv_results : dict of numpy(masked) ndarrays
See attribute cv_results_ of `GridSearchCV`
Returns
-------
float
Lower bound within 1 standard deviation of the
... | lower_bound | python | scikit-learn/scikit-learn | examples/model_selection/plot_grid_search_refit_callable.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/model_selection/plot_grid_search_refit_callable.py | BSD-3-Clause |
def best_low_complexity(cv_results):
"""
Balance model complexity with cross-validated score.
Parameters
----------
cv_results : dict of numpy(masked) ndarrays
See attribute cv_results_ of `GridSearchCV`.
Return
------
int
Index of a model that has the fewest PCA compon... |
Balance model complexity with cross-validated score.
Parameters
----------
cv_results : dict of numpy(masked) ndarrays
See attribute cv_results_ of `GridSearchCV`.
Return
------
int
Index of a model that has the fewest PCA components
while has its test score within... | best_low_complexity | python | scikit-learn/scikit-learn | examples/model_selection/plot_grid_search_refit_callable.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/model_selection/plot_grid_search_refit_callable.py | BSD-3-Clause |
def corrected_std(differences, n_train, n_test):
"""Corrects standard deviation using Nadeau and Bengio's approach.
Parameters
----------
differences : ndarray of shape (n_samples,)
Vector containing the differences in the score metrics of two models.
n_train : int
Number of samples... | Corrects standard deviation using Nadeau and Bengio's approach.
Parameters
----------
differences : ndarray of shape (n_samples,)
Vector containing the differences in the score metrics of two models.
n_train : int
Number of samples in the training set.
n_test : int
Number of... | corrected_std | python | scikit-learn/scikit-learn | examples/model_selection/plot_grid_search_stats.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/model_selection/plot_grid_search_stats.py | BSD-3-Clause |
def compute_corrected_ttest(differences, df, n_train, n_test):
"""Computes right-tailed paired t-test with corrected variance.
Parameters
----------
differences : array-like of shape (n_samples,)
Vector containing the differences in the score metrics of two models.
df : int
Degrees ... | Computes right-tailed paired t-test with corrected variance.
Parameters
----------
differences : array-like of shape (n_samples,)
Vector containing the differences in the score metrics of two models.
df : int
Degrees of freedom.
n_train : int
Number of samples in the trainin... | compute_corrected_ttest | python | scikit-learn/scikit-learn | examples/model_selection/plot_grid_search_stats.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/model_selection/plot_grid_search_stats.py | BSD-3-Clause |
def load_mnist(n_samples):
"""Load MNIST, shuffle the data, and return only n_samples."""
mnist = fetch_openml("mnist_784", as_frame=False)
X, y = shuffle(mnist.data, mnist.target, random_state=2)
return X[:n_samples] / 255, y[:n_samples] | Load MNIST, shuffle the data, and return only n_samples. | load_mnist | python | scikit-learn/scikit-learn | examples/neighbors/approximate_nearest_neighbors.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/neighbors/approximate_nearest_neighbors.py | BSD-3-Clause |
def construct_grids(batch):
"""Construct the map grid from the batch object
Parameters
----------
batch : Batch object
The object returned by :func:`fetch_species_distributions`
Returns
-------
(xgrid, ygrid) : 1-D arrays
The grid corresponding to the values in batch.covera... | Construct the map grid from the batch object
Parameters
----------
batch : Batch object
The object returned by :func:`fetch_species_distributions`
Returns
-------
(xgrid, ygrid) : 1-D arrays
The grid corresponding to the values in batch.coverages
| construct_grids | python | scikit-learn/scikit-learn | examples/neighbors/plot_species_kde.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/neighbors/plot_species_kde.py | BSD-3-Clause |
def nudge_dataset(X, Y):
"""
This produces a dataset 5 times bigger than the original one,
by moving the 8x8 images in X around by 1px to left, right, down, up
"""
direction_vectors = [
[[0, 1, 0], [0, 0, 0], [0, 0, 0]],
[[0, 0, 0], [1, 0, 0], [0, 0, 0]],
[[0, 0, 0], [0, 0, 1... |
This produces a dataset 5 times bigger than the original one,
by moving the 8x8 images in X around by 1px to left, right, down, up
| nudge_dataset | python | scikit-learn/scikit-learn | examples/neural_networks/plot_rbm_logistic_classification.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/neural_networks/plot_rbm_logistic_classification.py | BSD-3-Clause |
def levenshtein_distance(x, y):
"""Return the Levenshtein distance between two strings."""
if x == "" or y == "":
return max(len(x), len(y))
if x[0] == y[0]:
return levenshtein_distance(x[1:], y[1:])
return 1 + min(
levenshtein_distance(x[1:], y),
levenshtein_distance(x, ... | Return the Levenshtein distance between two strings. | levenshtein_distance | python | scikit-learn/scikit-learn | examples/release_highlights/plot_release_highlights_1_5_0.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/release_highlights/plot_release_highlights_1_5_0.py | BSD-3-Clause |
def plot_decision_function(classifier, sample_weight, axis, title):
"""Plot the synthetic data and the classifier decision function. Points with
larger sample_weight are mapped to larger circles in the scatter plot."""
axis.scatter(
X_plot[:, 0],
X_plot[:, 1],
c=y_plot,
s=100... | Plot the synthetic data and the classifier decision function. Points with
larger sample_weight are mapped to larger circles in the scatter plot. | plot_decision_function | python | scikit-learn/scikit-learn | examples/svm/plot_weighted_samples.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/svm/plot_weighted_samples.py | BSD-3-Clause |
def load_dataset(verbose=False, remove=()):
"""Load and vectorize the 20 newsgroups dataset."""
data_train = fetch_20newsgroups(
subset="train",
categories=categories,
shuffle=True,
random_state=42,
remove=remove,
)
data_test = fetch_20newsgroups(
subset... | Load and vectorize the 20 newsgroups dataset. | load_dataset | python | scikit-learn/scikit-learn | examples/text/plot_document_classification_20newsgroups.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/text/plot_document_classification_20newsgroups.py | BSD-3-Clause |
def token_freqs(doc):
"""Extract a dict mapping tokens from doc to their occurrences."""
freq = defaultdict(int)
for tok in tokenize(doc):
freq[tok] += 1
return freq | Extract a dict mapping tokens from doc to their occurrences. | token_freqs | python | scikit-learn/scikit-learn | examples/text/plot_hashing_vs_dict_vectorizer.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/text/plot_hashing_vs_dict_vectorizer.py | BSD-3-Clause |
def clone(estimator, *, safe=True):
"""Construct a new unfitted estimator with the same parameters.
Clone does a deep copy of the model in an estimator
without actually copying attached data. It returns a new estimator
with the same parameters that has not been fitted on any data.
.. versionchange... | Construct a new unfitted estimator with the same parameters.
Clone does a deep copy of the model in an estimator
without actually copying attached data. It returns a new estimator
with the same parameters that has not been fitted on any data.
.. versionchanged:: 1.3
Delegates to `estimator.__s... | clone | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def _clone_parametrized(estimator, *, safe=True):
"""Default implementation of clone. See :func:`sklearn.base.clone` for details."""
estimator_type = type(estimator)
if estimator_type is dict:
return {k: clone(v, safe=safe) for k, v in estimator.items()}
elif estimator_type in (list, tuple, set... | Default implementation of clone. See :func:`sklearn.base.clone` for details. | _clone_parametrized | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def _get_param_names(cls):
"""Get parameter names for the estimator"""
# fetch the constructor or the original constructor before
# deprecation wrapping if any
init = getattr(cls.__init__, "deprecated_original", cls.__init__)
if init is object.__init__:
# No explicit ... | Get parameter names for the estimator | _get_param_names | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def get_params(self, deep=True):
"""
Get parameters for this estimator.
Parameters
----------
deep : bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
... |
Get parameters for this estimator.
Parameters
----------
deep : bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : dict
Parameter n... | get_params | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def _get_params_html(self, deep=True):
"""
Get parameters for this estimator with a specific HTML representation.
Parameters
----------
deep : bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are esti... |
Get parameters for this estimator with a specific HTML representation.
Parameters
----------
deep : bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
p... | _get_params_html | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def is_non_default(param_name, param_value):
"""Finds the parameters that have been set by the user."""
if param_name not in init_default_params:
# happens if k is part of a **kwargs
return True
if init_default_params[param_name] == inspect._empty:
... | Finds the parameters that have been set by the user. | is_non_default | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def set_params(self, **params):
"""Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as :class:`~sklearn.pipeline.Pipeline`). The latter have
parameters of the form ``<component>__<parameter>`` so that it's
possible to... | Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as :class:`~sklearn.pipeline.Pipeline`). The latter have
parameters of the form ``<component>__<parameter>`` so that it's
possible to update each component of a nested object.
... | set_params | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def _validate_params(self):
"""Validate types and values of constructor parameters
The expected type and values must be defined in the `_parameter_constraints`
class attribute, which is a dictionary `param_name: list of constraints`. See
the docstring of `validate_parameter_constraints`... | Validate types and values of constructor parameters
The expected type and values must be defined in the `_parameter_constraints`
class attribute, which is a dictionary `param_name: list of constraints`. See
the docstring of `validate_parameter_constraints` for a description of the
accep... | _validate_params | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def score(self, X, y, sample_weight=None):
"""
Return :ref:`accuracy <accuracy_score>` on provided data and labels.
In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.
... |
Return :ref:`accuracy <accuracy_score>` on provided data and labels.
In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.
Parameters
----------
X : array... | score | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def score(self, X, y, sample_weight=None):
"""Return :ref:`coefficient of determination <r2_score>` on test data.
The coefficient of determination, :math:`R^2`, is defined as
:math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual
sum of squares ``((y_true - y_pred)** 2).sum()`` and... | Return :ref:`coefficient of determination <r2_score>` on test data.
The coefficient of determination, :math:`R^2`, is defined as
:math:`(1 - \frac{u}{v})`, where :math:`u` is the residual
sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`
is the total sum of squares ``((y_tr... | score | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def fit_predict(self, X, y=None, **kwargs):
"""
Perform clustering on `X` and returns cluster labels.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
y : Ignored
Not used, present for API consistency by conventio... |
Perform clustering on `X` and returns cluster labels.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
y : Ignored
Not used, present for API consistency by convention.
**kwargs : dict
Arguments to be... | fit_predict | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def get_indices(self, i):
"""Row and column indices of the `i`'th bicluster.
Only works if ``rows_`` and ``columns_`` attributes exist.
Parameters
----------
i : int
The index of the cluster.
Returns
-------
row_ind : ndarray, dtype=np.intp
... | Row and column indices of the `i`'th bicluster.
Only works if ``rows_`` and ``columns_`` attributes exist.
Parameters
----------
i : int
The index of the cluster.
Returns
-------
row_ind : ndarray, dtype=np.intp
Indices of rows in the da... | get_indices | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def get_shape(self, i):
"""Shape of the `i`'th bicluster.
Parameters
----------
i : int
The index of the cluster.
Returns
-------
n_rows : int
Number of rows in the bicluster.
n_cols : int
Number of columns in the bic... | Shape of the `i`'th bicluster.
Parameters
----------
i : int
The index of the cluster.
Returns
-------
n_rows : int
Number of rows in the bicluster.
n_cols : int
Number of columns in the bicluster.
| get_shape | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def get_submatrix(self, i, data):
"""Return the submatrix corresponding to bicluster `i`.
Parameters
----------
i : int
The index of the cluster.
data : array-like of shape (n_samples, n_features)
The data.
Returns
-------
submatr... | Return the submatrix corresponding to bicluster `i`.
Parameters
----------
i : int
The index of the cluster.
data : array-like of shape (n_samples, n_features)
The data.
Returns
-------
submatrix : ndarray of shape (n_rows, n_cols)
... | get_submatrix | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def fit_transform(self, X, y=None, **fit_params):
"""
Fit to data, then transform it.
Fits transformer to `X` and `y` with optional parameters `fit_params`
and returns a transformed version of `X`.
Parameters
----------
X : array-like of shape (n_samples, n_feat... |
Fit to data, then transform it.
Fits transformer to `X` and `y` with optional parameters `fit_params`
and returns a transformed version of `X`.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input samples.
y : array-like of ... | fit_transform | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.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.
- If `input_features` is `None`, then `feature_names_in_` is
... | Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Input features.
- If `input_features` is `None`, then `feature_names_in_` is
used as feature names in. If `feature_names_in_` is not... | get_feature_names_out | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def get_feature_names_out(self, input_features=None):
"""Get output feature names for transformation.
The feature names out will prefixed by the lowercased class name. For
example, if the transformer outputs 3 features, then the feature names
out are: `["class_name0", "class_name1", "cl... | Get output feature names for transformation.
The feature names out will prefixed by the lowercased class name. For
example, if the transformer outputs 3 features, then the feature names
out are: `["class_name0", "class_name1", "class_name2"]`.
Parameters
----------
inpu... | get_feature_names_out | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def fit_predict(self, X, y=None, **kwargs):
"""Perform fit on X and returns labels for X.
Returns -1 for outliers and 1 for inliers.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples.
y : Ignored
... | Perform fit on X and returns labels for X.
Returns -1 for outliers and 1 for inliers.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples.
y : Ignored
Not used, present for API consistency by conventi... | fit_predict | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def is_classifier(estimator):
"""Return True if the given estimator is (probably) a classifier.
Parameters
----------
estimator : object
Estimator object to test.
Returns
-------
out : bool
True if estimator is a classifier and False otherwise.
Examples
--------
... | Return True if the given estimator is (probably) a classifier.
Parameters
----------
estimator : object
Estimator object to test.
Returns
-------
out : bool
True if estimator is a classifier and False otherwise.
Examples
--------
>>> from sklearn.base import is_cla... | is_classifier | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def is_regressor(estimator):
"""Return True if the given estimator is (probably) a regressor.
Parameters
----------
estimator : estimator instance
Estimator object to test.
Returns
-------
out : bool
True if estimator is a regressor and False otherwise.
Examples
--... | Return True if the given estimator is (probably) a regressor.
Parameters
----------
estimator : estimator instance
Estimator object to test.
Returns
-------
out : bool
True if estimator is a regressor and False otherwise.
Examples
--------
>>> from sklearn.base imp... | is_regressor | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def is_clusterer(estimator):
"""Return True if the given estimator is (probably) a clusterer.
.. versionadded:: 1.6
Parameters
----------
estimator : object
Estimator object to test.
Returns
-------
out : bool
True if estimator is a clusterer and False otherwise.
... | Return True if the given estimator is (probably) a clusterer.
.. versionadded:: 1.6
Parameters
----------
estimator : object
Estimator object to test.
Returns
-------
out : bool
True if estimator is a clusterer and False otherwise.
Examples
--------
>>> from s... | is_clusterer | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def is_outlier_detector(estimator):
"""Return True if the given estimator is (probably) an outlier detector.
Parameters
----------
estimator : estimator instance
Estimator object to test.
Returns
-------
out : bool
True if estimator is an outlier detector and False otherwis... | Return True if the given estimator is (probably) an outlier detector.
Parameters
----------
estimator : estimator instance
Estimator object to test.
Returns
-------
out : bool
True if estimator is an outlier detector and False otherwise.
| is_outlier_detector | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def _fit_context(*, prefer_skip_nested_validation):
"""Decorator to run the fit methods of estimators within context managers.
Parameters
----------
prefer_skip_nested_validation : bool
If True, the validation of parameters of inner estimators or functions
called during fit will be skip... | Decorator to run the fit methods of estimators within context managers.
Parameters
----------
prefer_skip_nested_validation : bool
If True, the validation of parameters of inner estimators or functions
called during fit will be skipped.
This is useful to avoid validating many times... | _fit_context | python | scikit-learn/scikit-learn | sklearn/base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py | BSD-3-Clause |
def _get_estimator(self):
"""Resolve which estimator to return (default is LinearSVC)"""
if self.estimator is None:
# we want all classifiers that don't expose a random_state
# to be deterministic (and we don't want to expose this one).
estimator = LinearSVC(random_st... | Resolve which estimator to return (default is LinearSVC) | _get_estimator | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit the calibrated model.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,)
Target values.
sample_weight : array-li... | Fit the calibrated model.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,)
Target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.... | fit | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def predict_proba(self, X):
"""Calibrated probabilities of classification.
This function returns calibrated probabilities of classification
according to each class on an array of test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
... | Calibrated probabilities of classification.
This function returns calibrated probabilities of classification
according to each class on an array of test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The samples, as accepted by `est... | predict_proba | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def predict(self, X):
"""Predict the target of new samples.
The predicted class is the class that has the highest probability,
and can thus be different from the prediction of the uncalibrated classifier.
Parameters
----------
X : array-like of shape (n_samples, n_featu... | Predict the target of new samples.
The predicted class is the class that has the highest probability,
and can thus be different from the prediction of the uncalibrated classifier.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The samples, as ... | predict | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def get_metadata_routing(self):
"""Get metadata routing of this object.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
Returns
-------
routing : MetadataRouter
A :class:`~sklearn.utils.metadata_routing.MetadataRouter` e... | Get metadata routing of this object.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
Returns
-------
routing : MetadataRouter
A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
routing informatio... | get_metadata_routing | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def _fit_classifier_calibrator_pair(
estimator,
X,
y,
train,
test,
method,
classes,
sample_weight=None,
fit_params=None,
):
"""Fit a classifier/calibration pair on a given train/test split.
Fit the classifier on the train set, compute its predictions on the test
set and ... | Fit a classifier/calibration pair on a given train/test split.
Fit the classifier on the train set, compute its predictions on the test
set and use the predictions as input to fit the calibrator along with the
test labels.
Parameters
----------
estimator : estimator instance
Cloned bas... | _fit_classifier_calibrator_pair | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def _fit_calibrator(clf, predictions, y, classes, method, sample_weight=None):
"""Fit calibrator(s) and return a `_CalibratedClassifier`
instance.
`n_classes` (i.e. `len(clf.classes_)`) calibrators are fitted.
However, if `n_classes` equals 2, one calibrator is fitted.
Parameters
----------
... | Fit calibrator(s) and return a `_CalibratedClassifier`
instance.
`n_classes` (i.e. `len(clf.classes_)`) calibrators are fitted.
However, if `n_classes` equals 2, one calibrator is fitted.
Parameters
----------
clf : estimator instance
Fitted classifier.
predictions : array-like, s... | _fit_calibrator | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def predict_proba(self, X):
"""Calculate calibrated probabilities.
Calculates classification calibrated probabilities
for each class, in a one-vs-all manner, for `X`.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
The sample data.
... | Calculate calibrated probabilities.
Calculates classification calibrated probabilities
for each class, in a one-vs-all manner, for `X`.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
The sample data.
Returns
-------
proba... | predict_proba | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def _sigmoid_calibration(
predictions, y, sample_weight=None, max_abs_prediction_threshold=30
):
"""Probability Calibration with sigmoid method (Platt 2000)
Parameters
----------
predictions : ndarray of shape (n_samples,)
The decision function or predict proba for the samples.
y : nda... | Probability Calibration with sigmoid method (Platt 2000)
Parameters
----------
predictions : ndarray of shape (n_samples,)
The decision function or predict proba for the samples.
y : ndarray of shape (n_samples,)
The targets.
sample_weight : array-like of shape (n_samples,), defau... | _sigmoid_calibration | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def fit(self, X, y, sample_weight=None):
"""Fit the model using X, y as training data.
Parameters
----------
X : array-like of shape (n_samples,)
Training data.
y : array-like of shape (n_samples,)
Training target.
sample_weight : array-like of ... | Fit the model using X, y as training data.
Parameters
----------
X : array-like of shape (n_samples,)
Training data.
y : array-like of shape (n_samples,)
Training target.
sample_weight : array-like of shape (n_samples,), default=None
Sample ... | fit | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def predict(self, T):
"""Predict new data by linear interpolation.
Parameters
----------
T : array-like of shape (n_samples,)
Data to predict from.
Returns
-------
T_ : ndarray of shape (n_samples,)
The predicted data.
"""
... | Predict new data by linear interpolation.
Parameters
----------
T : array-like of shape (n_samples,)
Data to predict from.
Returns
-------
T_ : ndarray of shape (n_samples,)
The predicted data.
| predict | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def calibration_curve(
y_true,
y_prob,
*,
pos_label=None,
n_bins=5,
strategy="uniform",
):
"""Compute true and predicted probabilities for a calibration curve.
The method assumes the inputs come from a binary classifier, and
discretize the [0, 1] interval into bins.
Calibration... | Compute true and predicted probabilities for a calibration curve.
The method assumes the inputs come from a binary classifier, and
discretize the [0, 1] interval into bins.
Calibration curves may also be referred to as reliability diagrams.
Read more in the :ref:`User Guide <calibration>`.
Param... | calibration_curve | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
"""Plot visualization.
Extra keyword arguments will be passed to
:func:`matplotlib.pyplot.plot`.
Parameters
----------
ax : Matplotlib Axes, default=None
Axes object to plot on. If `None`, a new... | Plot visualization.
Extra keyword arguments will be passed to
:func:`matplotlib.pyplot.plot`.
Parameters
----------
ax : Matplotlib Axes, default=None
Axes object to plot on. If `None`, a new figure and axes is
created.
name : str, default=None
... | plot | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def from_estimator(
cls,
estimator,
X,
y,
*,
n_bins=5,
strategy="uniform",
pos_label=None,
name=None,
ax=None,
ref_line=True,
**kwargs,
):
"""Plot calibration curve using a binary classifier and data.
A ... | Plot calibration curve using a binary classifier and data.
A calibration curve, also known as a reliability diagram, uses inputs
from a binary classifier and plots the average predicted probability
for each bin against the fraction of positive classes, on the
y-axis.
Extra keyw... | from_estimator | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def from_predictions(
cls,
y_true,
y_prob,
*,
n_bins=5,
strategy="uniform",
pos_label=None,
name=None,
ax=None,
ref_line=True,
**kwargs,
):
"""Plot calibration curve using true labels and predicted probabilities.
... | Plot calibration curve using true labels and predicted probabilities.
Calibration curve, also known as reliability diagram, uses inputs
from a binary classifier and plots the average predicted probability
for each bin against the fraction of positive classes, on the
y-axis.
Ext... | from_predictions | python | scikit-learn/scikit-learn | sklearn/calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/calibration.py | BSD-3-Clause |
def _fetch_fixture(f):
"""Fetch dataset (download if missing and requested by environment)."""
download_if_missing = environ.get("SKLEARN_SKIP_NETWORK_TESTS", "1") == "0"
@wraps(f)
def wrapped(*args, **kwargs):
kwargs["download_if_missing"] = download_if_missing
try:
return ... | Fetch dataset (download if missing and requested by environment). | _fetch_fixture | python | scikit-learn/scikit-learn | sklearn/conftest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/conftest.py | BSD-3-Clause |
def pytest_collection_modifyitems(config, items):
"""Called after collect is completed.
Parameters
----------
config : pytest config
items : list of collected items
"""
run_network_tests = environ.get("SKLEARN_SKIP_NETWORK_TESTS", "1") == "0"
skip_network = pytest.mark.skip(
rea... | Called after collect is completed.
Parameters
----------
config : pytest config
items : list of collected items
| pytest_collection_modifyitems | python | scikit-learn/scikit-learn | sklearn/conftest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/conftest.py | BSD-3-Clause |
def pyplot():
"""Setup and teardown fixture for matplotlib.
This fixture checks if we can import matplotlib. If not, the tests will be
skipped. Otherwise, we close the figures before and after running the
functions.
Returns
-------
pyplot : module
The ``matplotlib.pyplot`` module.
... | Setup and teardown fixture for matplotlib.
This fixture checks if we can import matplotlib. If not, the tests will be
skipped. Otherwise, we close the figures before and after running the
functions.
Returns
-------
pyplot : module
The ``matplotlib.pyplot`` module.
| pyplot | python | scikit-learn/scikit-learn | sklearn/conftest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/conftest.py | BSD-3-Clause |
def pytest_generate_tests(metafunc):
"""Parametrization of global_random_seed fixture
based on the SKLEARN_TESTS_GLOBAL_RANDOM_SEED environment variable.
The goal of this fixture is to prevent tests that use it to be sensitive
to a specific seed value while still being deterministic by default.
S... | Parametrization of global_random_seed fixture
based on the SKLEARN_TESTS_GLOBAL_RANDOM_SEED environment variable.
The goal of this fixture is to prevent tests that use it to be sensitive
to a specific seed value while still being deterministic by default.
See the documentation for the SKLEARN_TESTS_G... | pytest_generate_tests | python | scikit-learn/scikit-learn | sklearn/conftest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/conftest.py | BSD-3-Clause |
def print_changed_only_false():
"""Set `print_changed_only` to False for the duration of the test."""
set_config(print_changed_only=False)
yield
set_config(print_changed_only=True) # reset to default | Set `print_changed_only` to False for the duration of the test. | print_changed_only_false | python | scikit-learn/scikit-learn | sklearn/conftest.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/conftest.py | BSD-3-Clause |
def _cov(X, shrinkage=None, covariance_estimator=None):
"""Estimate covariance matrix (using optional covariance_estimator).
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
shrinkage : {'empirical', 'auto'} or float, default=None
Shrinkage parameter... | Estimate covariance matrix (using optional covariance_estimator).
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
shrinkage : {'empirical', 'auto'} or float, default=None
Shrinkage parameter, possible values:
- None or 'empirical': no shrinkag... | _cov | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def _class_means(X, y):
"""Compute class means.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
Returns
-------
means : array-like of shape (n_classes, n_fea... | Compute class means.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
Returns
-------
means : array-like of shape (n_classes, n_features)
Class means.
... | _class_means | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def _class_cov(X, y, priors, shrinkage=None, covariance_estimator=None):
"""Compute weighted within-class covariance matrix.
The per-class covariance are weighted by the class priors.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
y : array-like of s... | Compute weighted within-class covariance matrix.
The per-class covariance are weighted by the class priors.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
priors :... | _class_cov | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def decision_function(self, X):
"""Apply decision function to an array of samples.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Array of samples (test vectors).
Returns
-------
y_scores : ndarray of shape (n_... | Apply decision function to an array of samples.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Array of samples (test vectors).
Returns
-------
y_scores : ndarray of shape (n_samples,) or (n_samples, n_classes)
... | decision_function | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def predict_log_proba(self, X):
"""Estimate log class probabilities.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data.
Returns
-------
y_log_proba : ndarray of shape (n_samples, n_classes)
... | Estimate log class probabilities.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data.
Returns
-------
y_log_proba : ndarray of shape (n_samples, n_classes)
Estimated log probabilities.
| predict_log_proba | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def _solve_lstsq(self, X, y, shrinkage, covariance_estimator):
"""Least squares solver.
The least squares solver computes a straightforward solution of the
optimal decision rule based directly on the discriminant functions. It
can only be used for classification (with any covariance est... | Least squares solver.
The least squares solver computes a straightforward solution of the
optimal decision rule based directly on the discriminant functions. It
can only be used for classification (with any covariance estimator),
because
estimation of eigenvectors is not perform... | _solve_lstsq | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def _solve_eigen(self, X, y, shrinkage, covariance_estimator):
"""Eigenvalue solver.
The eigenvalue solver computes the optimal solution of the Rayleigh
coefficient (basically the ratio of between class scatter to within
class scatter). This solver supports both classification and
... | Eigenvalue solver.
The eigenvalue solver computes the optimal solution of the Rayleigh
coefficient (basically the ratio of between class scatter to within
class scatter). This solver supports both classification and
dimensionality reduction (with any covariance estimator).
Para... | _solve_eigen | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def _solve_svd(self, X, y):
"""SVD solver.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
"""
xp, is_array_api_compliant =... | SVD solver.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
| _solve_svd | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def fit(self, X, y):
"""Fit the Linear Discriminant Analysis model.
.. versionchanged:: 0.19
`store_covariance` and `tol` has been moved to main constructor.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y ... | Fit the Linear Discriminant Analysis model.
.. versionchanged:: 0.19
`store_covariance` and `tol` has been moved to main constructor.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples... | fit | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def transform(self, X):
"""Project data to maximize class separation.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
Returns
-------
X_new : ndarray of shape (n_samples, n_components) or \
(n_samples, mi... | Project data to maximize class separation.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
Returns
-------
X_new : ndarray of shape (n_samples, n_components) or (n_samples, min(rank, n_components))
Tr... | transform | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def predict_proba(self, X):
"""Estimate probability.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
Returns
-------
C : ndarray of shape (n_samples, n_classes)
Estimated probabilities.
"""
... | Estimate probability.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
Returns
-------
C : ndarray of shape (n_samples, n_classes)
Estimated probabilities.
| predict_proba | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def predict_log_proba(self, X):
"""Estimate log probability.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
Returns
-------
C : ndarray of shape (n_samples, n_classes)
Estimated log probabilities.
... | Estimate log probability.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
Returns
-------
C : ndarray of shape (n_samples, n_classes)
Estimated log probabilities.
| predict_log_proba | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def fit(self, X, y):
"""Fit the model according to the given training data and parameters.
.. versionchanged:: 0.19
``store_covariances`` has been moved to main constructor as
``store_covariance``.
.. versionchanged:: 0.19
``tol`` has been moved to main cons... | Fit the model according to the given training data and parameters.
.. versionchanged:: 0.19
``store_covariances`` has been moved to main constructor as
``store_covariance``.
.. versionchanged:: 0.19
``tol`` has been moved to main constructor.
Parameters
... | fit | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def predict_proba(self, X):
"""Return posterior probabilities of classification.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Array of samples/test vectors.
Returns
-------
C : ndarray of shape (n_samples, n_classes)
... | Return posterior probabilities of classification.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Array of samples/test vectors.
Returns
-------
C : ndarray of shape (n_samples, n_classes)
Posterior probabilities of classifi... | predict_proba | python | scikit-learn/scikit-learn | sklearn/discriminant_analysis.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py | BSD-3-Clause |
def fit(self, X, y, sample_weight=None):
"""Fit the baseline classifier.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
Target values.
sample_we... | Fit the baseline classifier.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
Target values.
sample_weight : array-like of shape (n_samples,), default=Non... | fit | python | scikit-learn/scikit-learn | sklearn/dummy.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/dummy.py | BSD-3-Clause |
def predict(self, X):
"""Perform classification on test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Test data.
Returns
-------
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
Predicted t... | Perform classification on test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Test data.
Returns
-------
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
Predicted target values for X.
| predict | python | scikit-learn/scikit-learn | sklearn/dummy.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/dummy.py | BSD-3-Clause |
def predict_proba(self, X):
"""
Return probability estimates for the test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Test data.
Returns
-------
P : ndarray of shape (n_samples, n_classes) or list of such ... |
Return probability estimates for the test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Test data.
Returns
-------
P : ndarray of shape (n_samples, n_classes) or list of such arrays
Returns the probabil... | predict_proba | python | scikit-learn/scikit-learn | sklearn/dummy.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/dummy.py | BSD-3-Clause |
def predict_log_proba(self, X):
"""
Return log probability estimates for the test vectors X.
Parameters
----------
X : {array-like, object with finite length or shape}
Training data.
Returns
-------
P : ndarray of shape (n_samples, n_classes)... |
Return log probability estimates for the test vectors X.
Parameters
----------
X : {array-like, object with finite length or shape}
Training data.
Returns
-------
P : ndarray of shape (n_samples, n_classes) or list of such arrays
Returns... | predict_log_proba | python | scikit-learn/scikit-learn | sklearn/dummy.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/dummy.py | BSD-3-Clause |
def score(self, X, y, sample_weight=None):
"""Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.
Parameters
... | Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.
Parameters
----------
X : None or array-like of s... | score | python | scikit-learn/scikit-learn | sklearn/dummy.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/dummy.py | BSD-3-Clause |
def fit(self, X, y, sample_weight=None):
"""Fit the baseline regressor.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
Target values.
sample_wei... | Fit the baseline regressor.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
Target values.
sample_weight : array-like of shape (n_samples,), default=None... | fit | python | scikit-learn/scikit-learn | sklearn/dummy.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/dummy.py | BSD-3-Clause |
def predict(self, X, return_std=False):
"""Perform classification on test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Test data.
return_std : bool, default=False
Whether to return the standard deviation of posterior p... | Perform classification on test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Test data.
return_std : bool, default=False
Whether to return the standard deviation of posterior prediction.
All zeros in this case.
... | predict | python | scikit-learn/scikit-learn | sklearn/dummy.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/dummy.py | BSD-3-Clause |
def score(self, X, y, sample_weight=None):
"""Return the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as `(1 - u/v)`, where `u` is the
residual sum of squares `((y_true - y_pred) ** 2).sum()` and `v` is the
total sum of squares `((y_true - y_true.me... | Return the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as `(1 - u/v)`, where `u` is the
residual sum of squares `((y_true - y_pred) ** 2).sum()` and `v` is the
total sum of squares `((y_true - y_true.mean()) ** 2).sum()`. The best
possible score is... | score | python | scikit-learn/scikit-learn | sklearn/dummy.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/dummy.py | BSD-3-Clause |
def check_increasing(x, y):
"""Determine whether y is monotonically correlated with x.
y is found increasing or decreasing with respect to x based on a Spearman
correlation test.
Parameters
----------
x : array-like of shape (n_samples,)
Training data.
y : array-like of shape ... | Determine whether y is monotonically correlated with x.
y is found increasing or decreasing with respect to x based on a Spearman
correlation test.
Parameters
----------
x : array-like of shape (n_samples,)
Training data.
y : array-like of shape (n_samples,)
Training targe... | check_increasing | python | scikit-learn/scikit-learn | sklearn/isotonic.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/isotonic.py | BSD-3-Clause |
def isotonic_regression(
y, *, sample_weight=None, y_min=None, y_max=None, increasing=True
):
"""Solve the isotonic regression model.
Read more in the :ref:`User Guide <isotonic>`.
Parameters
----------
y : array-like of shape (n_samples,)
The data.
sample_weight : array-like of s... | Solve the isotonic regression model.
Read more in the :ref:`User Guide <isotonic>`.
Parameters
----------
y : array-like of shape (n_samples,)
The data.
sample_weight : array-like of shape (n_samples,), default=None
Weights on each point of the regression.
If None, weight ... | isotonic_regression | python | scikit-learn/scikit-learn | sklearn/isotonic.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/isotonic.py | BSD-3-Clause |
def fit(self, X, y, sample_weight=None):
"""Fit the model using X, y as training data.
Parameters
----------
X : array-like of shape (n_samples,) or (n_samples, 1)
Training data.
.. versionchanged:: 0.24
Also accepts 2d array with 1 feature.
... | Fit the model using X, y as training data.
Parameters
----------
X : array-like of shape (n_samples,) or (n_samples, 1)
Training data.
.. versionchanged:: 0.24
Also accepts 2d array with 1 feature.
y : array-like of shape (n_samples,)
... | fit | python | scikit-learn/scikit-learn | sklearn/isotonic.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/isotonic.py | BSD-3-Clause |
def _transform(self, T):
"""`_transform` is called by both `transform` and `predict` methods.
Since `transform` is wrapped to output arrays of specific types (e.g.
NumPy arrays, pandas DataFrame), we cannot make `predict` call `transform`
directly.
The above behaviour could be ... | `_transform` is called by both `transform` and `predict` methods.
Since `transform` is wrapped to output arrays of specific types (e.g.
NumPy arrays, pandas DataFrame), we cannot make `predict` call `transform`
directly.
The above behaviour could be changed in the future, if we decide ... | _transform | python | scikit-learn/scikit-learn | sklearn/isotonic.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/isotonic.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
Ignored.
Returns
-------
feature_names_out : ndarray of str objects
... | Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Ignored.
Returns
-------
feature_names_out : ndarray of str objects
An ndarray with one string i.e. ["isotonicregression0"... | get_feature_names_out | python | scikit-learn/scikit-learn | sklearn/isotonic.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/isotonic.py | BSD-3-Clause |
def __getstate__(self):
"""Pickle-protocol - return state of the estimator."""
state = super().__getstate__()
# remove interpolation method
state.pop("f_", None)
return state | Pickle-protocol - return state of the estimator. | __getstate__ | python | scikit-learn/scikit-learn | sklearn/isotonic.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/isotonic.py | BSD-3-Clause |
def __setstate__(self, state):
"""Pickle-protocol - set state of the estimator.
We need to rebuild the interpolation function.
"""
super().__setstate__(state)
if hasattr(self, "X_thresholds_") and hasattr(self, "y_thresholds_"):
self._build_f(self.X_thresholds_, self... | Pickle-protocol - set state of the estimator.
We need to rebuild the interpolation function.
| __setstate__ | python | scikit-learn/scikit-learn | sklearn/isotonic.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/isotonic.py | BSD-3-Clause |
def fit(self, X, y=None):
"""Fit the model with X.
Initializes the internal variables. The method needs no information
about the distribution of data, so we only care about n_features in X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_fea... | Fit the model with X.
Initializes the internal variables. The method needs no information
about the distribution of data, so we only care about n_features in X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data, whe... | fit | python | scikit-learn/scikit-learn | sklearn/kernel_approximation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/kernel_approximation.py | BSD-3-Clause |
def transform(self, X):
"""Generate the feature map approximation for X.
Parameters
----------
X : {array-like}, shape (n_samples, n_features)
New data, where `n_samples` is the number of samples
and `n_features` is the number of features.
Returns
... | Generate the feature map approximation for X.
Parameters
----------
X : {array-like}, shape (n_samples, n_features)
New data, where `n_samples` is the number of samples
and `n_features` is the number of features.
Returns
-------
X_new : array-lik... | transform | python | scikit-learn/scikit-learn | sklearn/kernel_approximation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/kernel_approximation.py | BSD-3-Clause |
def fit(self, X, y=None):
"""Fit the model with X.
Samples random projection according to n_features.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_fe... | Fit the model with X.
Samples random projection according to n_features.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
... | fit | python | scikit-learn/scikit-learn | sklearn/kernel_approximation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/kernel_approximation.py | BSD-3-Clause |
def transform(self, X):
"""Apply the approximate feature map to X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
New data, where `n_samples` is the number of samples
and `n_features` is the number of features.
Retur... | Apply the approximate feature map to X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
New data, where `n_samples` is the number of samples
and `n_features` is the number of features.
Returns
-------
X_new : ... | transform | python | scikit-learn/scikit-learn | sklearn/kernel_approximation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/kernel_approximation.py | BSD-3-Clause |
def fit(self, X, y=None):
"""Fit the model with X.
Samples random projection according to n_features.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the nu... | Fit the model with X.
Samples random projection according to n_features.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
y : array-... | fit | python | scikit-learn/scikit-learn | sklearn/kernel_approximation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/kernel_approximation.py | BSD-3-Clause |
def transform(self, X):
"""Apply the approximate feature map to X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
New data, where `n_samples` is the number of samples
and `n_features` is the number of features. All values of X must be
... | Apply the approximate feature map to X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
New data, where `n_samples` is the number of samples
and `n_features` is the number of features. All values of X must be
strictly greater than "-skewed... | transform | python | scikit-learn/scikit-learn | sklearn/kernel_approximation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/kernel_approximation.py | BSD-3-Clause |
def fit(self, X, y=None):
"""Only validates estimator's parameters.
This method allows to: (i) validate the estimator's parameters and
(ii) be consistent with the scikit-learn transformer API.
Parameters
----------
X : array-like, shape (n_samples, n_features)
... | Only validates estimator's parameters.
This method allows to: (i) validate the estimator's parameters and
(ii) be consistent with the scikit-learn transformer API.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where `n_samples` i... | fit | python | scikit-learn/scikit-learn | sklearn/kernel_approximation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/kernel_approximation.py | BSD-3-Clause |
def transform(self, X):
"""Apply approximate feature map to X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
Retu... | Apply approximate feature map to X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
Returns
-------
X_new :... | transform | python | scikit-learn/scikit-learn | sklearn/kernel_approximation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/kernel_approximation.py | BSD-3-Clause |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.