code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def func(*args, **kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only... | Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
| func | python | scikit-learn/scikit-learn | sklearn/utils/_metadata_requests.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_metadata_requests.py | BSD-3-Clause |
def __init_subclass__(cls, **kwargs):
"""Set the ``set_{method}_request`` methods.
This uses PEP-487 [1]_ to set the ``set_{method}_request`` methods. It
looks for the information available in the set default values which are
set using ``__metadata_request__*`` class attributes, or infe... | Set the ``set_{method}_request`` methods.
This uses PEP-487 [1]_ to set the ``set_{method}_request`` methods. It
looks for the information available in the set default values which are
set using ``__metadata_request__*`` class attributes, or inferred
from method signatures.
The... | __init_subclass__ | python | scikit-learn/scikit-learn | sklearn/utils/_metadata_requests.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_metadata_requests.py | BSD-3-Clause |
def _build_request_for_signature(cls, router, method):
"""Build the `MethodMetadataRequest` for a method using its signature.
This method takes all arguments from the method signature and uses
``None`` as their default request value, except ``X``, ``y``, ``Y``,
``Xt``, ``yt``, ``*args``... | Build the `MethodMetadataRequest` for a method using its signature.
This method takes all arguments from the method signature and uses
``None`` as their default request value, except ``X``, ``y``, ``Y``,
``Xt``, ``yt``, ``*args``, and ``**kwargs``.
Parameters
----------
... | _build_request_for_signature | python | scikit-learn/scikit-learn | sklearn/utils/_metadata_requests.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_metadata_requests.py | BSD-3-Clause |
def _get_default_requests(cls):
"""Collect default request values.
This method combines the information present in ``__metadata_request__*``
class attributes, as well as determining request keys from method
signatures.
"""
requests = MetadataRequest(owner=cls.__name__)
... | Collect default request values.
This method combines the information present in ``__metadata_request__*``
class attributes, as well as determining request keys from method
signatures.
| _get_default_requests | python | scikit-learn/scikit-learn | sklearn/utils/_metadata_requests.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_metadata_requests.py | BSD-3-Clause |
def _get_metadata_request(self):
"""Get requested data properties.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
Returns
-------
request : MetadataRequest
A :class:`~sklearn.utils.metadata_routing.MetadataRequest` inst... | Get requested data properties.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
Returns
-------
request : MetadataRequest
A :class:`~sklearn.utils.metadata_routing.MetadataRequest` instance.
| _get_metadata_request | python | scikit-learn/scikit-learn | sklearn/utils/_metadata_requests.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_metadata_requests.py | BSD-3-Clause |
def process_routing(_obj, _method, /, **kwargs):
"""Validate and route input parameters.
This function is used inside a router's method, e.g. :term:`fit`,
to validate the metadata and handle the routing.
Assuming this signature of a router's fit method:
``fit(self, X, y, sample_weight=None, **fit_... | Validate and route input parameters.
This function is used inside a router's method, e.g. :term:`fit`,
to validate the metadata and handle the routing.
Assuming this signature of a router's fit method:
``fit(self, X, y, sample_weight=None, **fit_params)``,
a call to this function would be:
``p... | process_routing | python | scikit-learn/scikit-learn | sklearn/utils/_metadata_requests.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_metadata_requests.py | BSD-3-Clause |
def is_scalar_nan(x):
"""Test if x is NaN.
This function is meant to overcome the issue that np.isnan does not allow
non-numerical types as input, and that np.nan is not float('nan').
Parameters
----------
x : any type
Any scalar value.
Returns
-------
bool
Returns... | Test if x is NaN.
This function is meant to overcome the issue that np.isnan does not allow
non-numerical types as input, and that np.nan is not float('nan').
Parameters
----------
x : any type
Any scalar value.
Returns
-------
bool
Returns true if x is NaN, and false ... | is_scalar_nan | python | scikit-learn/scikit-learn | sklearn/utils/_missing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_missing.py | BSD-3-Clause |
def is_pandas_na(x):
"""Test if x is pandas.NA.
We intentionally do not use this function to return `True` for `pd.NA` in
`is_scalar_nan`, because estimators that support `pd.NA` are the exception
rather than the rule at the moment. When `pd.NA` is more universally
supported, we may reconsider this... | Test if x is pandas.NA.
We intentionally do not use this function to return `True` for `pd.NA` in
`is_scalar_nan`, because estimators that support `pd.NA` are the exception
rather than the rule at the moment. When `pd.NA` is more universally
supported, we may reconsider this decision.
Parameters
... | is_pandas_na | python | scikit-learn/scikit-learn | sklearn/utils/_missing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_missing.py | BSD-3-Clause |
def _check_X_y(self, X, y=None, should_be_fitted=True):
"""Validate X and y and make extra check.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data set.
`X` is checked only if `check_X` is not `None` (default is None).
y : arr... | Validate X and y and make extra check.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data set.
`X` is checked only if `check_X` is not `None` (default is None).
y : array-like of shape (n_samples), default=None
The correspo... | _check_X_y | python | scikit-learn/scikit-learn | sklearn/utils/_mocking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_mocking.py | BSD-3-Clause |
def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit classifier.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : ar... | Fit classifier.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : array-like of shape (n_samples, n_outputs) or (n_samples,), ... | fit | python | scikit-learn/scikit-learn | sklearn/utils/_mocking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_mocking.py | BSD-3-Clause |
def predict(self, X):
"""Predict the first class seen in `classes_`.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
preds : ndarray of shape (n_samples,)
Predictions of the first clas... | Predict the first class seen in `classes_`.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
preds : ndarray of shape (n_samples,)
Predictions of the first class seen in `classes_`.
| predict | python | scikit-learn/scikit-learn | sklearn/utils/_mocking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_mocking.py | BSD-3-Clause |
def predict_proba(self, X):
"""Predict probabilities for each class.
Here, the dummy classifier will provide a probability of 1 for the
first class of `classes_` and 0 otherwise.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input... | Predict probabilities for each class.
Here, the dummy classifier will provide a probability of 1 for the
first class of `classes_` and 0 otherwise.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------... | predict_proba | python | scikit-learn/scikit-learn | sklearn/utils/_mocking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_mocking.py | BSD-3-Clause |
def decision_function(self, X):
"""Confidence score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
decision : ndarray of shape (n_samples,) if n_classes == 2\
else (n_samples, n_... | Confidence score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
decision : ndarray of shape (n_samples,) if n_classes == 2 else (n_samples, n_classes)
Confidence score.
... | decision_function | python | scikit-learn/scikit-learn | sklearn/utils/_mocking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_mocking.py | BSD-3-Clause |
def score(self, X=None, Y=None):
"""Fake score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data, where `n_samples` is the number of samples and
`n_features` is the number of features.
Y : array-like of shape (n_samples, n... | Fake score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data, where `n_samples` is the number of samples and
`n_features` is the number of features.
Y : array-like of shape (n_samples, n_output) or (n_samples,)
Target ... | score | python | scikit-learn/scikit-learn | sklearn/utils/_mocking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_mocking.py | BSD-3-Clause |
def check_matplotlib_support(caller_name):
"""Raise ImportError with detailed error message if mpl is not installed.
Plot utilities like any of the Display's plotting functions should lazily import
matplotlib and call this helper before any computation.
Parameters
----------
caller_name : str
... | Raise ImportError with detailed error message if mpl is not installed.
Plot utilities like any of the Display's plotting functions should lazily import
matplotlib and call this helper before any computation.
Parameters
----------
caller_name : str
The name of the caller that requires matpl... | check_matplotlib_support | python | scikit-learn/scikit-learn | sklearn/utils/_optional_dependencies.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_optional_dependencies.py | BSD-3-Clause |
def check_pandas_support(caller_name):
"""Raise ImportError with detailed error message if pandas is not installed.
Plot utilities like :func:`fetch_openml` should lazily import
pandas and call this helper before any computation.
Parameters
----------
caller_name : str
The name of the ... | Raise ImportError with detailed error message if pandas is not installed.
Plot utilities like :func:`fetch_openml` should lazily import
pandas and call this helper before any computation.
Parameters
----------
caller_name : str
The name of the caller that requires pandas.
Returns
... | check_pandas_support | python | scikit-learn/scikit-learn | sklearn/utils/_optional_dependencies.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_optional_dependencies.py | BSD-3-Clause |
def validate_parameter_constraints(parameter_constraints, params, caller_name):
"""Validate types and values of given parameters.
Parameters
----------
parameter_constraints : dict or {"no_validation"}
If "no_validation", validation is skipped for this parameter.
If a dict, it must be ... | Validate types and values of given parameters.
Parameters
----------
parameter_constraints : dict or {"no_validation"}
If "no_validation", validation is skipped for this parameter.
If a dict, it must be a dictionary `param_name: list of constraints`.
A parameter is valid if it sati... | validate_parameter_constraints | python | scikit-learn/scikit-learn | sklearn/utils/_param_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_param_validation.py | BSD-3-Clause |
def make_constraint(constraint):
"""Convert the constraint into the appropriate Constraint object.
Parameters
----------
constraint : object
The constraint to convert.
Returns
-------
constraint : instance of _Constraint
The converted constraint.
"""
if isinstance(c... | Convert the constraint into the appropriate Constraint object.
Parameters
----------
constraint : object
The constraint to convert.
Returns
-------
constraint : instance of _Constraint
The converted constraint.
| make_constraint | python | scikit-learn/scikit-learn | sklearn/utils/_param_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_param_validation.py | BSD-3-Clause |
def validate_params(parameter_constraints, *, prefer_skip_nested_validation):
"""Decorator to validate types and values of functions and methods.
Parameters
----------
parameter_constraints : dict
A dictionary `param_name: list of constraints`. See the docstring of
`validate_parameter_c... | Decorator to validate types and values of functions and methods.
Parameters
----------
parameter_constraints : dict
A dictionary `param_name: list of constraints`. See the docstring of
`validate_parameter_constraints` for a description of the accepted constraints.
Note that the *ar... | validate_params | python | scikit-learn/scikit-learn | sklearn/utils/_param_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_param_validation.py | BSD-3-Clause |
def _type_name(t):
"""Convert type into human readable string."""
module = t.__module__
qualname = t.__qualname__
if module == "builtins":
return qualname
elif t == Real:
return "float"
elif t == Integral:
return "int"
return f"{module}.{qualname}" | Convert type into human readable string. | _type_name | python | scikit-learn/scikit-learn | sklearn/utils/_param_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_param_validation.py | BSD-3-Clause |
def is_satisfied_by(self, val):
"""Whether or not a value satisfies the constraint.
Parameters
----------
val : object
The value to check.
Returns
-------
is_satisfied : bool
Whether or not the constraint is satisfied by this value.
... | Whether or not a value satisfies the constraint.
Parameters
----------
val : object
The value to check.
Returns
-------
is_satisfied : bool
Whether or not the constraint is satisfied by this value.
| is_satisfied_by | python | scikit-learn/scikit-learn | sklearn/utils/_param_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_param_validation.py | BSD-3-Clause |
def _mark_if_deprecated(self, option):
"""Add a deprecated mark to an option if needed."""
option_str = f"{option!r}"
if option in self.deprecated:
option_str = f"{option_str} (deprecated)"
return option_str | Add a deprecated mark to an option if needed. | _mark_if_deprecated | python | scikit-learn/scikit-learn | sklearn/utils/_param_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_param_validation.py | BSD-3-Clause |
def generate_invalid_param_val(constraint):
"""Return a value that does not satisfy the constraint.
Raises a NotImplementedError if there exists no invalid value for this constraint.
This is only useful for testing purpose.
Parameters
----------
constraint : _Constraint instance
The c... | Return a value that does not satisfy the constraint.
Raises a NotImplementedError if there exists no invalid value for this constraint.
This is only useful for testing purpose.
Parameters
----------
constraint : _Constraint instance
The constraint to generate a value for.
Returns
... | generate_invalid_param_val | python | scikit-learn/scikit-learn | sklearn/utils/_param_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_param_validation.py | BSD-3-Clause |
def generate_valid_param(constraint):
"""Return a value that does satisfy a constraint.
This is only useful for testing purpose.
Parameters
----------
constraint : Constraint instance
The constraint to generate a value for.
Returns
-------
val : object
A value that doe... | Return a value that does satisfy a constraint.
This is only useful for testing purpose.
Parameters
----------
constraint : Constraint instance
The constraint to generate a value for.
Returns
-------
val : object
A value that does satisfy the constraint.
| generate_valid_param | python | scikit-learn/scikit-learn | sklearn/utils/_param_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_param_validation.py | BSD-3-Clause |
def _get_legend_label(curve_legend_metric, curve_name, legend_metric_name):
"""Helper to get legend label using `name` and `legend_metric`"""
if curve_legend_metric is not None and curve_name is not None:
label = f"{curve_name} ({legend_metric_name} = {curve_legend_metric:0.2f})"
eli... | Helper to get legend label using `name` and `legend_metric` | _get_legend_label | python | scikit-learn/scikit-learn | sklearn/utils/_plotting.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_plotting.py | BSD-3-Clause |
def _validate_curve_kwargs(
n_curves,
name,
legend_metric,
legend_metric_name,
curve_kwargs,
**kwargs,
):
"""Get validated line kwargs for each curve.
Parameters
----------
n_curves : int
Number of curves.
name : l... | Get validated line kwargs for each curve.
Parameters
----------
n_curves : int
Number of curves.
name : list of str or None
Name for labeling legend entries.
legend_metric : dict
Dictionary with "mean" and "std" keys, or "metric" key of metr... | _validate_curve_kwargs | python | scikit-learn/scikit-learn | sklearn/utils/_plotting.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_plotting.py | BSD-3-Clause |
def _validate_score_name(score_name, scoring, negate_score):
"""Validate the `score_name` parameter.
If `score_name` is provided, we just return it as-is.
If `score_name` is `None`, we use `Score` if `negate_score` is `False` and
`Negative score` otherwise.
If `score_name` is a string or a callable... | Validate the `score_name` parameter.
If `score_name` is provided, we just return it as-is.
If `score_name` is `None`, we use `Score` if `negate_score` is `False` and
`Negative score` otherwise.
If `score_name` is a string or a callable, we infer the name. We replace `_` by
spaces and capitalize the... | _validate_score_name | python | scikit-learn/scikit-learn | sklearn/utils/_plotting.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_plotting.py | BSD-3-Clause |
def _interval_max_min_ratio(data):
"""Compute the ratio between the largest and smallest inter-point distances.
A value larger than 5 typically indicates that the parameter range would
better be displayed with a log scale while a linear scale would be more
suitable otherwise.
"""
diff = np.diff... | Compute the ratio between the largest and smallest inter-point distances.
A value larger than 5 typically indicates that the parameter range would
better be displayed with a log scale while a linear scale would be more
suitable otherwise.
| _interval_max_min_ratio | python | scikit-learn/scikit-learn | sklearn/utils/_plotting.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_plotting.py | BSD-3-Clause |
def _validate_style_kwargs(default_style_kwargs, user_style_kwargs):
"""Create valid style kwargs by avoiding Matplotlib alias errors.
Matplotlib raises an error when, for example, 'color' and 'c', or 'linestyle' and
'ls', are specified together. To avoid this, we automatically keep only the one
specif... | Create valid style kwargs by avoiding Matplotlib alias errors.
Matplotlib raises an error when, for example, 'color' and 'c', or 'linestyle' and
'ls', are specified together. To avoid this, we automatically keep only the one
specified by the user and raise an error if the user specifies both.
Paramete... | _validate_style_kwargs | python | scikit-learn/scikit-learn | sklearn/utils/_plotting.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_plotting.py | BSD-3-Clause |
def _despine(ax):
"""Remove the top and right spines of the plot.
Parameters
----------
ax : matplotlib.axes.Axes
The axes of the plot to despine.
"""
for s in ["top", "right"]:
ax.spines[s].set_visible(False)
for s in ["bottom", "left"]:
ax.spines[s].set_bounds(0, 1... | Remove the top and right spines of the plot.
Parameters
----------
ax : matplotlib.axes.Axes
The axes of the plot to despine.
| _despine | python | scikit-learn/scikit-learn | sklearn/utils/_plotting.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_plotting.py | BSD-3-Clause |
def _convert_to_list_leaving_none(param):
"""Convert parameters to a list, leaving `None` as is."""
if param is None:
return None
if isinstance(param, list):
return param
return [param] | Convert parameters to a list, leaving `None` as is. | _convert_to_list_leaving_none | python | scikit-learn/scikit-learn | sklearn/utils/_plotting.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_plotting.py | BSD-3-Clause |
def _check_param_lengths(required, optional, class_name):
"""Check required and optional parameters are of the same length."""
optional_provided = {}
for name, param in optional.items():
if isinstance(param, list):
optional_provided[name] = param
all_params = {**required, **optional... | Check required and optional parameters are of the same length. | _check_param_lengths | python | scikit-learn/scikit-learn | sklearn/utils/_plotting.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_plotting.py | BSD-3-Clause |
def _changed_params(estimator):
"""Return dict (param_name: value) of parameters that were given to
estimator with non-default values."""
params = estimator.get_params(deep=False)
init_func = getattr(estimator.__init__, "deprecated_original", estimator.__init__)
init_params = inspect.signature(init... | Return dict (param_name: value) of parameters that were given to
estimator with non-default values. | _changed_params | python | scikit-learn/scikit-learn | sklearn/utils/_pprint.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_pprint.py | BSD-3-Clause |
def _format_params_or_dict_items(
self, object, stream, indent, allowance, context, level, is_dict
):
"""Format dict items or parameters respecting the compact=True
parameter. For some reason, the builtin rendering of dict items doesn't
respect compact=True and will use one line per ... | Format dict items or parameters respecting the compact=True
parameter. For some reason, the builtin rendering of dict items doesn't
respect compact=True and will use one line per key-value if all cannot
fit in a single line.
Dict items will be rendered as <'key': value> while params will... | _format_params_or_dict_items | python | scikit-learn/scikit-learn | sklearn/utils/_pprint.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_pprint.py | BSD-3-Clause |
def _format_items(self, items, stream, indent, allowance, context, level):
"""Format the items of an iterable (list, tuple...). Same as the
built-in _format_items, with support for ellipsis if the number of
elements is greater than self.n_max_elements_to_show.
"""
write = stream.... | Format the items of an iterable (list, tuple...). Same as the
built-in _format_items, with support for ellipsis if the number of
elements is greater than self.n_max_elements_to_show.
| _format_items | python | scikit-learn/scikit-learn | sklearn/utils/_pprint.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_pprint.py | BSD-3-Clause |
def _pprint_key_val_tuple(self, object, stream, indent, allowance, context, level):
"""Pretty printing for key-value tuples from dict or parameters."""
k, v = object
rep = self._repr(k, context, level)
if isinstance(object, KeyValTupleParam):
rep = rep.strip("'")
... | Pretty printing for key-value tuples from dict or parameters. | _pprint_key_val_tuple | python | scikit-learn/scikit-learn | sklearn/utils/_pprint.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_pprint.py | BSD-3-Clause |
def _safe_repr(object, context, maxlevels, level, changed_only=False):
"""Same as the builtin _safe_repr, with added support for Estimator
objects."""
typ = type(object)
if typ in pprint._builtin_scalars:
return repr(object), True, False
r = getattr(typ, "__repr__", None)
if issubclass... | Same as the builtin _safe_repr, with added support for Estimator
objects. | _safe_repr | python | scikit-learn/scikit-learn | sklearn/utils/_pprint.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_pprint.py | BSD-3-Clause |
def _process_predict_proba(*, y_pred, target_type, classes, pos_label):
"""Get the response values when the response method is `predict_proba`.
This function process the `y_pred` array in the binary and multi-label cases.
In the binary case, it selects the column corresponding to the positive
class. In... | Get the response values when the response method is `predict_proba`.
This function process the `y_pred` array in the binary and multi-label cases.
In the binary case, it selects the column corresponding to the positive
class. In the multi-label case, it stacks the predictions if they are not
in the "co... | _process_predict_proba | python | scikit-learn/scikit-learn | sklearn/utils/_response.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_response.py | BSD-3-Clause |
def _process_decision_function(*, y_pred, target_type, classes, pos_label):
"""Get the response values when the response method is `decision_function`.
This function process the `y_pred` array in the binary and multi-label cases.
In the binary case, it inverts the sign of the score if the positive label
... | Get the response values when the response method is `decision_function`.
This function process the `y_pred` array in the binary and multi-label cases.
In the binary case, it inverts the sign of the score if the positive label
is not `classes[1]`. In the multi-label case, it stacks the predictions if
th... | _process_decision_function | python | scikit-learn/scikit-learn | sklearn/utils/_response.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_response.py | BSD-3-Clause |
def _get_response_values(
estimator,
X,
response_method,
pos_label=None,
return_response_method_used=False,
):
"""Compute the response values of a classifier, an outlier detector, or a regressor.
The response values are predictions such that it follows the following shape:
- for binary... | Compute the response values of a classifier, an outlier detector, or a regressor.
The response values are predictions such that it follows the following shape:
- for binary classification, it is a 1d array of shape `(n_samples,)`;
- for multiclass classification, it is a 2d array of shape `(n_samples, n_c... | _get_response_values | python | scikit-learn/scikit-learn | sklearn/utils/_response.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_response.py | BSD-3-Clause |
def _get_response_values_binary(
estimator, X, response_method, pos_label=None, return_response_method_used=False
):
"""Compute the response values of a binary classifier.
Parameters
----------
estimator : estimator instance
Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
... | Compute the response values of a binary classifier.
Parameters
----------
estimator : estimator instance
Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
in which the last estimator is a binary classifier.
X : {array-like, sparse matrix} of shape (n_samples, n_features... | _get_response_values_binary | python | scikit-learn/scikit-learn | sklearn/utils/_response.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_response.py | BSD-3-Clause |
def create_container(self, X_output, X_original, columns, inplace=False):
"""Create container from `X_output` with additional metadata.
Parameters
----------
X_output : {ndarray, dataframe}
Data to wrap.
X_original : {ndarray, dataframe}
Original input d... | Create container from `X_output` with additional metadata.
Parameters
----------
X_output : {ndarray, dataframe}
Data to wrap.
X_original : {ndarray, dataframe}
Original input dataframe. This is used to extract the metadata that should
be passed to `... | create_container | python | scikit-learn/scikit-learn | sklearn/utils/_set_output.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_set_output.py | BSD-3-Clause |
def is_supported_container(self, X):
"""Return True if X is a supported container.
Parameters
----------
Xs: container
Containers to be checked.
Returns
-------
is_supported_container : bool
True if X is a supported container.
""" | Return True if X is a supported container.
Parameters
----------
Xs: container
Containers to be checked.
Returns
-------
is_supported_container : bool
True if X is a supported container.
| is_supported_container | python | scikit-learn/scikit-learn | sklearn/utils/_set_output.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_set_output.py | BSD-3-Clause |
def rename_columns(self, X, columns):
"""Rename columns in `X`.
Parameters
----------
X : container
Container which columns is updated.
columns : ndarray of str
Columns to update the `X`'s columns with.
Returns
-------
updated_co... | Rename columns in `X`.
Parameters
----------
X : container
Container which columns is updated.
columns : ndarray of str
Columns to update the `X`'s columns with.
Returns
-------
updated_container : container
Container with ne... | rename_columns | python | scikit-learn/scikit-learn | sklearn/utils/_set_output.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_set_output.py | BSD-3-Clause |
def hstack(self, Xs):
"""Stack containers horizontally (column-wise).
Parameters
----------
Xs : list of containers
List of containers to stack.
Returns
-------
stacked_Xs : container
Stacked containers.
""" | Stack containers horizontally (column-wise).
Parameters
----------
Xs : list of containers
List of containers to stack.
Returns
-------
stacked_Xs : container
Stacked containers.
| hstack | python | scikit-learn/scikit-learn | sklearn/utils/_set_output.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_set_output.py | BSD-3-Clause |
def _get_adapter_from_container(container):
"""Get the adapter that knows how to handle such container.
See :class:`sklearn.utils._set_output.ContainerAdapterProtocol` for more
details.
"""
module_name = container.__class__.__module__.split(".")[0]
try:
return ADAPTERS_MANAGER.adapters[... | Get the adapter that knows how to handle such container.
See :class:`sklearn.utils._set_output.ContainerAdapterProtocol` for more
details.
| _get_adapter_from_container | python | scikit-learn/scikit-learn | sklearn/utils/_set_output.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_set_output.py | BSD-3-Clause |
def _get_output_config(method, estimator=None):
"""Get output config based on estimator and global configuration.
Parameters
----------
method : {"transform"}
Estimator's method for which the output container is looked up.
estimator : estimator instance or None
Estimator to get the... | Get output config based on estimator and global configuration.
Parameters
----------
method : {"transform"}
Estimator's method for which the output container is looked up.
estimator : estimator instance or None
Estimator to get the output configuration from. If `None`, check global
... | _get_output_config | python | scikit-learn/scikit-learn | sklearn/utils/_set_output.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_set_output.py | BSD-3-Clause |
def _wrap_data_with_container(method, data_to_wrap, original_input, estimator):
"""Wrap output with container based on an estimator's or global config.
Parameters
----------
method : {"transform"}
Estimator's method to get container output for.
data_to_wrap : {ndarray, dataframe}
D... | Wrap output with container based on an estimator's or global config.
Parameters
----------
method : {"transform"}
Estimator's method to get container output for.
data_to_wrap : {ndarray, dataframe}
Data to wrap with container.
original_input : {ndarray, dataframe}
Original... | _wrap_data_with_container | python | scikit-learn/scikit-learn | sklearn/utils/_set_output.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_set_output.py | BSD-3-Clause |
def _wrap_method_output(f, method):
"""Wrapper used by `_SetOutputMixin` to automatically wrap methods."""
@wraps(f)
def wrapped(self, X, *args, **kwargs):
data_to_wrap = f(self, X, *args, **kwargs)
if isinstance(data_to_wrap, tuple):
# only wrap the first output for cross decom... | Wrapper used by `_SetOutputMixin` to automatically wrap methods. | _wrap_method_output | python | scikit-learn/scikit-learn | sklearn/utils/_set_output.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_set_output.py | BSD-3-Clause |
def _auto_wrap_is_configured(estimator):
"""Return True if estimator is configured for auto-wrapping the transform method.
`_SetOutputMixin` sets `_sklearn_auto_wrap_output_keys` to `set()` if auto wrapping
is manually disabled.
"""
auto_wrap_output_keys = getattr(estimator, "_sklearn_auto_wrap_out... | Return True if estimator is configured for auto-wrapping the transform method.
`_SetOutputMixin` sets `_sklearn_auto_wrap_output_keys` to `set()` if auto wrapping
is manually disabled.
| _auto_wrap_is_configured | python | scikit-learn/scikit-learn | sklearn/utils/_set_output.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_set_output.py | BSD-3-Clause |
def set_output(self, *, transform=None):
"""Set output container.
See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
for an example on how to use the API.
Parameters
----------
transform : {"default", "pandas", "polars"}, default=None
Configu... | Set output container.
See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
for an example on how to use the API.
Parameters
----------
transform : {"default", "pandas", "polars"}, default=None
Configure output of `transform` and `fit_transform`.
... | set_output | python | scikit-learn/scikit-learn | sklearn/utils/_set_output.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_set_output.py | BSD-3-Clause |
def _safe_set_output(estimator, *, transform=None):
"""Safely call estimator.set_output and error if it not available.
This is used by meta-estimators to set the output for child estimators.
Parameters
----------
estimator : estimator instance
Estimator instance.
transform : {"default... | Safely call estimator.set_output and error if it not available.
This is used by meta-estimators to set the output for child estimators.
Parameters
----------
estimator : estimator instance
Estimator instance.
transform : {"default", "pandas", "polars"}, default=None
Configure outp... | _safe_set_output | python | scikit-learn/scikit-learn | sklearn/utils/_set_output.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_set_output.py | BSD-3-Clause |
def _get_sys_info():
"""System information
Returns
-------
sys_info : dict
system and Python version information
"""
python = sys.version.replace("\n", " ")
blob = [
("python", python),
("executable", sys.executable),
("machine", platform.platform()),
]... | System information
Returns
-------
sys_info : dict
system and Python version information
| _get_sys_info | python | scikit-learn/scikit-learn | sklearn/utils/_show_versions.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_show_versions.py | BSD-3-Clause |
def _get_deps_info():
"""Overview of the installed version of main dependencies
This function does not import the modules to collect the version numbers
but instead relies on standard Python package metadata.
Returns
-------
deps_info: dict
version information on relevant Python librar... | Overview of the installed version of main dependencies
This function does not import the modules to collect the version numbers
but instead relies on standard Python package metadata.
Returns
-------
deps_info: dict
version information on relevant Python libraries
| _get_deps_info | python | scikit-learn/scikit-learn | sklearn/utils/_show_versions.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_show_versions.py | BSD-3-Clause |
def show_versions():
"""Print useful debugging information"
.. versionadded:: 0.20
Examples
--------
>>> from sklearn import show_versions
>>> show_versions() # doctest: +SKIP
"""
sys_info = _get_sys_info()
deps_info = _get_deps_info()
print("\nSystem:")
for k, stat in s... | Print useful debugging information"
.. versionadded:: 0.20
Examples
--------
>>> from sklearn import show_versions
>>> show_versions() # doctest: +SKIP
| show_versions | python | scikit-learn/scikit-learn | sklearn/utils/_show_versions.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_show_versions.py | BSD-3-Clause |
def default_tags(estimator) -> Tags:
"""Get the default tags for an estimator.
This ignores any ``__sklearn_tags__`` method that the estimator may have.
If the estimator is a classifier or a regressor, ``target_tags.required``
will be set to ``True``, otherwise it will be set to ``False``.
``tran... | Get the default tags for an estimator.
This ignores any ``__sklearn_tags__`` method that the estimator may have.
If the estimator is a classifier or a regressor, ``target_tags.required``
will be set to ``True``, otherwise it will be set to ``False``.
``transformer_tags`` will be set to :class:`~.skle... | default_tags | python | scikit-learn/scikit-learn | sklearn/utils/_tags.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_tags.py | BSD-3-Clause |
def get_tags(estimator) -> Tags:
"""Get estimator tags.
:class:`~sklearn.BaseEstimator` provides the estimator tags machinery.
However, if an estimator does not inherit from this base class, we should
fall-back to the default tags.
For scikit-learn built-in estimators, we should still rely on
... | Get estimator tags.
:class:`~sklearn.BaseEstimator` provides the estimator tags machinery.
However, if an estimator does not inherit from this base class, we should
fall-back to the default tags.
For scikit-learn built-in estimators, we should still rely on
`self.__sklearn_tags__()`. `get_tags(est... | get_tags | python | scikit-learn/scikit-learn | sklearn/utils/_tags.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_tags.py | BSD-3-Clause |
def ignore_warnings(obj=None, category=Warning):
"""Context manager and decorator to ignore warnings.
Note: Using this (in both variants) will clear all warnings
from all python modules loaded. In case you need to test
cross-module-warning-logging, this is not your tool of choice.
Parameters
-... | Context manager and decorator to ignore warnings.
Note: Using this (in both variants) will clear all warnings
from all python modules loaded. In case you need to test
cross-module-warning-logging, this is not your tool of choice.
Parameters
----------
obj : callable, default=None
calla... | ignore_warnings | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def __call__(self, fn):
"""Decorator to catch and hide warnings without visual nesting."""
@wraps(fn)
def wrapper(*args, **kwargs):
with warnings.catch_warnings():
warnings.simplefilter("ignore", self.category)
return fn(*args, **kwargs)
retu... | Decorator to catch and hide warnings without visual nesting. | __call__ | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def assert_allclose(
actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True
):
"""dtype-aware variant of numpy.testing.assert_allclose
This variant introspects the least precise floating point dtype
in the input argument and automatically sets the relative tolerance
paramete... | dtype-aware variant of numpy.testing.assert_allclose
This variant introspects the least precise floating point dtype
in the input argument and automatically sets the relative tolerance
parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64
in scikit-learn).
`atol` is always left to 0.... | assert_allclose | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def assert_allclose_dense_sparse(x, y, rtol=1e-07, atol=1e-9, err_msg=""):
"""Assert allclose for sparse and dense data.
Both x and y need to be either sparse or dense, they
can't be mixed.
Parameters
----------
x : {array-like, sparse matrix}
First array to compare.
y : {array-li... | Assert allclose for sparse and dense data.
Both x and y need to be either sparse or dense, they
can't be mixed.
Parameters
----------
x : {array-like, sparse matrix}
First array to compare.
y : {array-like, sparse matrix}
Second array to compare.
rtol : float, default=1e-... | assert_allclose_dense_sparse | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def _delete_folder(folder_path, warn=False):
"""Utility function to cleanup a temporary folder if still existing.
Copy from joblib.pool (for independence).
"""
try:
if os.path.exists(folder_path):
# This can fail under windows,
# but will succeed when called by atexit
... | Utility function to cleanup a temporary folder if still existing.
Copy from joblib.pool (for independence).
| _delete_folder | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def create_memmap_backed_data(data, mmap_mode="r", return_folder=False):
"""
Parameters
----------
data
mmap_mode : str, default='r'
return_folder : bool, default=False
"""
temp_folder = tempfile.mkdtemp(prefix="sklearn_testing_")
atexit.register(functools.partial(_delete_folder, te... |
Parameters
----------
data
mmap_mode : str, default='r'
return_folder : bool, default=False
| create_memmap_backed_data | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def _get_func_name(func):
"""Get function full name.
Parameters
----------
func : callable
The function object.
Returns
-------
name : str
The function name.
"""
parts = []
module = inspect.getmodule(func)
if module:
parts.append(module.__name__)
... | Get function full name.
Parameters
----------
func : callable
The function object.
Returns
-------
name : str
The function name.
| _get_func_name | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def check_docstring_parameters(func, doc=None, ignore=None):
"""Helper to check docstring.
Parameters
----------
func : callable
The function object to test.
doc : str, default=None
Docstring if it is passed manually to the test.
ignore : list, default=None
Parameters to... | Helper to check docstring.
Parameters
----------
func : callable
The function object to test.
doc : str, default=None
Docstring if it is passed manually to the test.
ignore : list, default=None
Parameters to ignore.
Returns
-------
incorrect : list
A lis... | check_docstring_parameters | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def _check_item_included(item_name, args):
"""Helper to check if item should be included in checking."""
if args.include is not True and item_name not in args.include:
return False
if args.exclude is not None and item_name in args.exclude:
return False
return True | Helper to check if item should be included in checking. | _check_item_included | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def _get_diff_msg(docstrings_grouped):
"""Get message showing the difference between type/desc docstrings of all objects.
`docstrings_grouped` keys should be the type/desc docstrings and values are a list
of objects with that docstring. Objects with the same type/desc docstring are
thus grouped togethe... | Get message showing the difference between type/desc docstrings of all objects.
`docstrings_grouped` keys should be the type/desc docstrings and values are a list
of objects with that docstring. Objects with the same type/desc docstring are
thus grouped together.
| _get_diff_msg | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def _check_consistency_items(
items_docs,
type_or_desc,
section,
n_objects,
descr_regex_pattern="",
ignore_types=tuple(),
):
"""Helper to check docstring consistency of all `items_docs`.
If item is not present in all objects, checking is skipped and warning raised.
If `regex` provid... | Helper to check docstring consistency of all `items_docs`.
If item is not present in all objects, checking is skipped and warning raised.
If `regex` provided, match descriptions to all descriptions.
Parameters
----------
items_doc : dict of dict of str
Dictionary where the key is the strin... | _check_consistency_items | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def assert_docstring_consistency(
objects,
include_params=False,
exclude_params=None,
include_attrs=False,
exclude_attrs=None,
include_returns=False,
exclude_returns=None,
descr_regex_pattern=None,
ignore_types=tuple(),
):
r"""Check consistency between docstring parameters/attrib... | Check consistency between docstring parameters/attributes/returns of objects.
Checks if parameters/attributes/returns have the same type specification and
description (ignoring whitespace) across `objects`. Intended to be used for
related classes/functions/data descriptors.
Entries that do not appear ... | assert_docstring_consistency | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def assert_run_python_script_without_output(source_code, pattern=".+", timeout=60):
"""Utility to check assertions in an independent Python subprocess.
The script provided in the source code should return 0 and the stdtout +
stderr should not match the pattern `pattern`.
This is a port from cloudpickl... | Utility to check assertions in an independent Python subprocess.
The script provided in the source code should return 0 and the stdtout +
stderr should not match the pattern `pattern`.
This is a port from cloudpickle https://github.com/cloudpipe/cloudpickle
Parameters
----------
source_code :... | assert_run_python_script_without_output | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def _convert_container(
container,
constructor_name,
columns_name=None,
dtype=None,
minversion=None,
categorical_feature_names=None,
):
"""Convert a given container to a specific array-like with a dtype.
Parameters
----------
container : array-like
The container to conve... | Convert a given container to a specific array-like with a dtype.
Parameters
----------
container : array-like
The container to convert.
constructor_name : {"list", "tuple", "array", "sparse", "dataframe", "series", "index", "slice", "sparse_csr", "sparse_csc", "sparse_cs... | _convert_container | python | scikit-learn/scikit-learn | sklearn/utils/_testing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py | BSD-3-Clause |
def _attach_unique(y):
"""Attach unique values of y to y and return the result.
The result is a view of y, and the metadata (unique) is not attached to y.
"""
if not isinstance(y, np.ndarray):
return y
try:
# avoid recalculating unique in nested calls.
if "unique" in y.dtype... | Attach unique values of y to y and return the result.
The result is a view of y, and the metadata (unique) is not attached to y.
| _attach_unique | python | scikit-learn/scikit-learn | sklearn/utils/_unique.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_unique.py | BSD-3-Clause |
def attach_unique(*ys, return_tuple=False):
"""Attach unique values of ys to ys and return the results.
The result is a view of y, and the metadata (unique) is not attached to y.
IMPORTANT: The output of this function should NEVER be returned in functions.
This is to avoid this pattern:
.. code::... | Attach unique values of ys to ys and return the results.
The result is a view of y, and the metadata (unique) is not attached to y.
IMPORTANT: The output of this function should NEVER be returned in functions.
This is to avoid this pattern:
.. code:: python
y = np.array([1, 2, 3])
y ... | attach_unique | python | scikit-learn/scikit-learn | sklearn/utils/_unique.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_unique.py | BSD-3-Clause |
def _cached_unique(y, xp=None):
"""Return the unique values of y.
Use the cached values from dtype.metadata if present.
This function does NOT cache the values in y, i.e. it doesn't change y.
Call `attach_unique` to attach the unique values to y.
"""
try:
if y.dtype.metadata is not No... | Return the unique values of y.
Use the cached values from dtype.metadata if present.
This function does NOT cache the values in y, i.e. it doesn't change y.
Call `attach_unique` to attach the unique values to y.
| _cached_unique | python | scikit-learn/scikit-learn | sklearn/utils/_unique.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_unique.py | BSD-3-Clause |
def cached_unique(*ys, xp=None):
"""Return the unique values of ys.
Use the cached values from dtype.metadata if present.
This function does NOT cache the values in y, i.e. it doesn't change y.
Call `attach_unique` to attach the unique values to y.
Parameters
----------
*ys : sequence of... | Return the unique values of ys.
Use the cached values from dtype.metadata if present.
This function does NOT cache the values in y, i.e. it doesn't change y.
Call `attach_unique` to attach the unique values to y.
Parameters
----------
*ys : sequence of array-like
Input data arrays.
... | cached_unique | python | scikit-learn/scikit-learn | sklearn/utils/_unique.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_unique.py | BSD-3-Clause |
def _message_with_time(source, message, time):
"""Create one line message for logging purposes.
Parameters
----------
source : str
String indicating the source or the reference of the message.
message : str
Short message.
time : int
Time in seconds.
"""
start_m... | Create one line message for logging purposes.
Parameters
----------
source : str
String indicating the source or the reference of the message.
message : str
Short message.
time : int
Time in seconds.
| _message_with_time | python | scikit-learn/scikit-learn | sklearn/utils/_user_interface.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_user_interface.py | BSD-3-Clause |
def _print_elapsed_time(source, message=None):
"""Log elapsed time to stdout when the context is exited.
Parameters
----------
source : str
String indicating the source or the reference of the message.
message : str, default=None
Short message. If None, nothing will be printed.
... | Log elapsed time to stdout when the context is exited.
Parameters
----------
source : str
String indicating the source or the reference of the message.
message : str, default=None
Short message. If None, nothing will be printed.
Returns
-------
context_manager
Prin... | _print_elapsed_time | python | scikit-learn/scikit-learn | sklearn/utils/_user_interface.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_user_interface.py | BSD-3-Clause |
def test_get_namespace_ndarray_creation_device():
"""Check expected behavior with device and creation functions."""
X = numpy.asarray([1, 2, 3])
xp_out, _ = get_namespace(X)
full_array = xp_out.full(10, fill_value=2.0, device="cpu")
assert_allclose(full_array, [2.0] * 10)
with pytest.raises(Va... | Check expected behavior with device and creation functions. | test_get_namespace_ndarray_creation_device | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_array_api.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_array_api.py | BSD-3-Clause |
def test_asarray_with_order(array_api):
"""Test _asarray_with_order passes along order for NumPy arrays."""
xp = pytest.importorskip(array_api)
X = xp.asarray([1.2, 3.4, 5.1])
X_new = _asarray_with_order(X, order="F", xp=xp)
X_new_np = numpy.asarray(X_new)
assert X_new_np.flags["F_CONTIGUOUS"] | Test _asarray_with_order passes along order for NumPy arrays. | test_asarray_with_order | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_array_api.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_array_api.py | BSD-3-Clause |
def test_convert_estimator_to_array_api():
"""Convert estimator attributes to ArrayAPI arrays."""
xp = pytest.importorskip("array_api_strict")
X_np = numpy.asarray([[1.3, 4.5]])
est = SimpleEstimator().fit(X_np)
new_est = _estimator_with_converted_arrays(est, lambda array: xp.asarray(array))
a... | Convert estimator attributes to ArrayAPI arrays. | test_convert_estimator_to_array_api | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_array_api.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_array_api.py | BSD-3-Clause |
def test_bunch_attribute_deprecation():
"""Check that bunch raises deprecation message with `__getattr__`."""
bunch = Bunch()
values = np.asarray([1, 2, 3])
msg = (
"Key: 'values', is deprecated in 1.3 and will be "
"removed in 1.5. Please use 'grid_values' instead"
)
bunch._set_... | Check that bunch raises deprecation message with `__getattr__`. | test_bunch_attribute_deprecation | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_bunch.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_bunch.py | BSD-3-Clause |
def test_get_chunk_n_rows_warns():
"""Check that warning is raised when working_memory is too low."""
row_bytes = 1024 * 1024 + 1
max_n_rows = None
working_memory = 1
expected = 1
warn_msg = (
"Could not adhere to working_memory config. Currently 1MiB, 2MiB required."
)
with pyt... | Check that warning is raised when working_memory is too low. | test_get_chunk_n_rows_warns | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_chunking.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_chunking.py | BSD-3-Clause |
def test_class_weight_does_not_contains_more_classes():
"""Check that class_weight can contain more labels than in y.
Non-regression test for #22413
"""
tree = DecisionTreeClassifier(class_weight={0: 1, 1: 10, 2: 20})
# Does not raise
tree.fit([[0, 0, 1], [1, 0, 1], [1, 2, 0]], [0, 0, 1]) | Check that class_weight can contain more labels than in y.
Non-regression test for #22413
| test_class_weight_does_not_contains_more_classes | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_class_weight.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_class_weight.py | BSD-3-Clause |
def test_compute_sample_weight_sparse(csc_container):
"""Check that we can compute weight for sparse `y`."""
y = csc_container(np.asarray([[0], [1], [1]]))
sample_weight = compute_sample_weight("balanced", y)
assert_allclose(sample_weight, [1.5, 0.75, 0.75]) | Check that we can compute weight for sparse `y`. | test_compute_sample_weight_sparse | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_class_weight.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_class_weight.py | BSD-3-Clause |
def test_check_estimator_with_class_removed():
"""Test that passing a class instead of an instance fails."""
msg = "Passing a class was deprecated"
with raises(TypeError, match=msg):
check_estimator(LogisticRegression) | Test that passing a class instead of an instance fails. | test_check_estimator_with_class_removed | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_mutable_default_params():
"""Test that constructor cannot have mutable default parameters."""
msg = (
"Parameter 'p' of estimator 'HasMutableParameters' is of type "
"object which is not allowed"
)
# check that the "default_constructible" test checks for mutable parameters
c... | Test that constructor cannot have mutable default parameters. | test_mutable_default_params | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_check_set_params():
"""Check set_params doesn't fail and sets the right values."""
# check that values returned by get_params match set_params
msg = "get_params result does not match what was passed to set_params"
with raises(AssertionError, match=msg):
check_set_params("test", Modifies... | Check set_params doesn't fail and sets the right values. | test_check_set_params | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_check_estimator_not_fail_fast():
"""Check the contents of the results returned with on_fail!="raise".
This results should contain details about the observed failures, expected
or not.
"""
check_results = check_estimator(BaseEstimator(), on_fail=None)
assert isinstance(check_results, li... | Check the contents of the results returned with on_fail!="raise".
This results should contain details about the observed failures, expected
or not.
| test_check_estimator_not_fail_fast | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_check_estimator_sparse_tag():
"""Test that check_estimator_sparse_tag raises error when sparse tag is
misaligned."""
class EstimatorWithSparseConfig(BaseEstimator):
def __init__(self, tag_sparse, accept_sparse, fit_error=None):
self.tag_sparse = tag_sparse
self.acce... | Test that check_estimator_sparse_tag raises error when sparse tag is
misaligned. | test_check_estimator_sparse_tag | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def run_tests_without_pytest():
"""Runs the tests in this file without using pytest."""
main_module = sys.modules["__main__"]
test_functions = [
getattr(main_module, name)
for name in dir(main_module)
if name.startswith("test_")
]
test_cases = [unittest.FunctionTestCase(fn) f... | Runs the tests in this file without using pytest. | run_tests_without_pytest | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_xfail_count_with_no_fast_fail():
"""Test that the right number of xfail warnings are raised when on_fail is "warn".
It also checks the number of raised EstimatorCheckFailedWarning, and checks the
output of check_estimator.
"""
est = NuSVC()
expected_failed_checks = _get_expected_failed... | Test that the right number of xfail warnings are raised when on_fail is "warn".
It also checks the number of raised EstimatorCheckFailedWarning, and checks the
output of check_estimator.
| test_xfail_count_with_no_fast_fail | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_check_estimator_callback():
"""Test that the callback is called with the right arguments."""
call_count = {"xfail": 0, "skipped": 0, "passed": 0, "failed": 0}
def callback(
*,
estimator,
check_name,
exception,
status,
expected_to_fail,
expect... | Test that the callback is called with the right arguments. | test_check_estimator_callback | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_check_outlier_contamination():
"""Check the test for the contamination parameter in the outlier detectors."""
# Without any parameter constraints, the estimator will early exit the test by
# returning None.
class OutlierDetectorWithoutConstraint(OutlierMixin, BaseEstimator):
"""Outlier... | Check the test for the contamination parameter in the outlier detectors. | test_check_outlier_contamination | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_check_estimator_cloneable_error():
"""Check that the right error is raised when the estimator is not cloneable."""
class NotCloneable(BaseEstimator):
def __sklearn_clone__(self):
raise NotImplementedError("This estimator is not cloneable.")
estimator = NotCloneable()
msg =... | Check that the right error is raised when the estimator is not cloneable. | test_check_estimator_cloneable_error | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_estimator_repr_error():
"""Check that the right error is raised when the estimator does not have a repr."""
class NotRepr(BaseEstimator):
def __repr__(self):
raise NotImplementedError("This estimator does not have a repr.")
estimator = NotRepr()
msg = "Repr of .* failed wi... | Check that the right error is raised when the estimator does not have a repr. | test_estimator_repr_error | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_check_classifier_not_supporting_multiclass():
"""Check that when the estimator has the wrong tags.classifier_tags.multi_class
set, the test fails."""
class BadEstimator(BaseEstimator):
# we don't actually need to define the tag here since we're running the test
# manually, and Base... | Check that when the estimator has the wrong tags.classifier_tags.multi_class
set, the test fails. | test_check_classifier_not_supporting_multiclass | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_check_estimator_callback_with_fast_fail_error():
"""Check that check_estimator fails correctly with on_fail='raise' and callback."""
with raises(
ValueError, match="callback cannot be provided together with on_fail='raise'"
):
check_estimator(LogisticRegression(), on_fail="raise", c... | Check that check_estimator fails correctly with on_fail='raise' and callback. | test_check_estimator_callback_with_fast_fail_error | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_check_mixin_order():
"""Test that the check raises an error when the mixin order is incorrect."""
class BadEstimator(BaseEstimator, TransformerMixin):
def fit(self, X, y=None):
return self
msg = "TransformerMixin comes before/left side of BaseEstimator"
with raises(Asserti... | Test that the check raises an error when the mixin order is incorrect. | test_check_mixin_order | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_estimator_checks.py | BSD-3-Clause |
def test_randomized_eigsh(dtype):
"""Test that `_randomized_eigsh` returns the appropriate components"""
rng = np.random.RandomState(42)
X = np.diag(np.array([1.0, -2.0, 0.0, 3.0], dtype=dtype))
# random rotation that preserves the eigenvalues of X
rand_rot = np.linalg.qr(rng.normal(size=X.shape))[... | Test that `_randomized_eigsh` returns the appropriate components | test_randomized_eigsh | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_extmath.py | BSD-3-Clause |
def test_randomized_eigsh_compared_to_others(k):
"""Check that `_randomized_eigsh` is similar to other `eigsh`
Tests that for a random PSD matrix, `_randomized_eigsh` provides results
comparable to LAPACK (scipy.linalg.eigh) and ARPACK
(scipy.sparse.linalg.eigsh).
Note: some versions of ARPACK do ... | Check that `_randomized_eigsh` is similar to other `eigsh`
Tests that for a random PSD matrix, `_randomized_eigsh` provides results
comparable to LAPACK (scipy.linalg.eigh) and ARPACK
(scipy.sparse.linalg.eigsh).
Note: some versions of ARPACK do not support k=n_features.
| test_randomized_eigsh_compared_to_others | python | scikit-learn/scikit-learn | sklearn/utils/tests/test_extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/tests/test_extmath.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.