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stringlengths 2
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| docstring
stringlengths 0
281k
⌀ | code
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sequencelengths 768
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16,419 | presidio_analyzer.recognizer_result | __hash__ |
Hash the result data by using all class fields.
:return: int
| def __hash__(self):
"""
Hash the result data by using all class fields.
:return: int
"""
return hash(
f"{str(self.start)} {str(self.end)} {str(self.score)} {self.entity_type}"
)
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16,420 | presidio_analyzer.recognizer_result | __init__ | null | def __init__(
self,
entity_type: str,
start: int,
end: int,
score: float,
analysis_explanation: AnalysisExplanation = None,
recognition_metadata: Dict = None,
):
self.entity_type = entity_type
self.start = start
self.end = end
self.score = score
self.analysis_explanation = analysis_explanation
if not recognition_metadata:
self.logger.debug(
"recognition_metadata should be passed, "
"containing a recognizer_name value"
)
self.recognition_metadata = recognition_metadata
| (self, entity_type: str, start: int, end: int, score: float, analysis_explanation: Optional[presidio_analyzer.analysis_explanation.AnalysisExplanation] = None, recognition_metadata: Optional[Dict] = None) | [
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16,421 | presidio_analyzer.recognizer_result | __repr__ | Return a string representation of the instance. | def __repr__(self) -> str:
"""Return a string representation of the instance."""
return self.__str__()
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16,422 | presidio_analyzer.recognizer_result | __str__ | Return a string representation of the instance. | def __str__(self) -> str:
"""Return a string representation of the instance."""
return (
f"type: {self.entity_type}, "
f"start: {self.start}, "
f"end: {self.end}, "
f"score: {self.score}"
)
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16,423 | presidio_analyzer.recognizer_result | append_analysis_explanation_text | Add text to the analysis explanation. | def append_analysis_explanation_text(self, text: str) -> None:
"""Add text to the analysis explanation."""
if self.analysis_explanation:
self.analysis_explanation.append_textual_explanation_line(text)
| (self, text: str) -> NoneType | [
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16,424 | presidio_analyzer.recognizer_result | contained_in |
Check if self is contained in a different RecognizerResult.
:return: true if contained
| def contained_in(self, other: "RecognizerResult") -> bool:
"""
Check if self is contained in a different RecognizerResult.
:return: true if contained
"""
return self.start >= other.start and self.end <= other.end
| (self, other: presidio_analyzer.recognizer_result.RecognizerResult) -> bool | [
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16,425 | presidio_analyzer.recognizer_result | contains |
Check if one result is contained or equal to another result.
:param other: another RecognizerResult
:return: bool
| def contains(self, other: "RecognizerResult") -> bool:
"""
Check if one result is contained or equal to another result.
:param other: another RecognizerResult
:return: bool
"""
return self.start <= other.start and self.end >= other.end
| (self, other: presidio_analyzer.recognizer_result.RecognizerResult) -> bool | [
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16,426 | presidio_analyzer.recognizer_result | equal_indices |
Check if the indices are equal between two results.
:param other: another RecognizerResult
:return:
| def equal_indices(self, other: "RecognizerResult") -> bool:
"""
Check if the indices are equal between two results.
:param other: another RecognizerResult
:return:
"""
return self.start == other.start and self.end == other.end
| (self, other: presidio_analyzer.recognizer_result.RecognizerResult) -> bool | [
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] |
16,427 | presidio_analyzer.recognizer_result | has_conflict |
Check if two recognizer results are conflicted or not.
I have a conflict if:
1. My indices are the same as the other and my score is lower.
2. If my indices are contained in another.
:param other: RecognizerResult
:return:
| def has_conflict(self, other: "RecognizerResult") -> bool:
"""
Check if two recognizer results are conflicted or not.
I have a conflict if:
1. My indices are the same as the other and my score is lower.
2. If my indices are contained in another.
:param other: RecognizerResult
:return:
"""
if self.equal_indices(other):
return self.score <= other.score
return other.contains(self)
| (self, other: presidio_analyzer.recognizer_result.RecognizerResult) -> bool | [
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] |
16,428 | presidio_analyzer.recognizer_result | intersects |
Check if self intersects with a different RecognizerResult.
:return: If intersecting, returns the number of
intersecting characters.
If not, returns 0
| def intersects(self, other: "RecognizerResult") -> int:
"""
Check if self intersects with a different RecognizerResult.
:return: If intersecting, returns the number of
intersecting characters.
If not, returns 0
"""
# if they do not overlap the intersection is 0
if self.end < other.start or other.end < self.start:
return 0
# otherwise the intersection is min(end) - max(start)
return min(self.end, other.end) - max(self.start, other.start)
| (self, other: presidio_analyzer.recognizer_result.RecognizerResult) -> int | [
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16,430 | presidio_analyzer.remote_recognizer | RemoteRecognizer |
A configuration for a recognizer that runs on a different process / remote machine.
:param supported_entities: A list of entities this recognizer can identify
:param name: name of recognizer
:param supported_language: The language this recognizer can detect entities in
:param version: Version of this recognizer
| class RemoteRecognizer(ABC, EntityRecognizer):
"""
A configuration for a recognizer that runs on a different process / remote machine.
:param supported_entities: A list of entities this recognizer can identify
:param name: name of recognizer
:param supported_language: The language this recognizer can detect entities in
:param version: Version of this recognizer
"""
def __init__(
self,
supported_entities: List[str],
name: Optional[str],
supported_language: str,
version: str,
context: Optional[List[str]] = None,
):
super().__init__(
supported_entities=supported_entities,
name=name,
supported_language=supported_language,
version=version,
context=context,
)
def load(self): # noqa D102
pass
@abstractmethod
def analyze(
self, text: str, entities: List[str], nlp_artifacts: NlpArtifacts
): # noqa ANN201
"""
Call an external service for PII detection.
:param text: text to be analyzed
:param entities: Entities that should be looked for
:param nlp_artifacts: Additional metadata from the NLP engine
:return: List of identified PII entities
"""
# 1. Call the external service.
# 2. Translate results into List[RecognizerResult]
pass
@abstractmethod
def get_supported_entities(self) -> List[str]: # noqa D102
pass
| (supported_entities: List[str], name: Optional[str], supported_language: str, version: str, context: Optional[List[str]] = None) | [
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16,431 | presidio_analyzer.remote_recognizer | __init__ | null | def __init__(
self,
supported_entities: List[str],
name: Optional[str],
supported_language: str,
version: str,
context: Optional[List[str]] = None,
):
super().__init__(
supported_entities=supported_entities,
name=name,
supported_language=supported_language,
version=version,
context=context,
)
| (self, supported_entities: List[str], name: Optional[str], supported_language: str, version: str, context: Optional[List[str]] = None) | [
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16,432 | presidio_analyzer.remote_recognizer | analyze |
Call an external service for PII detection.
:param text: text to be analyzed
:param entities: Entities that should be looked for
:param nlp_artifacts: Additional metadata from the NLP engine
:return: List of identified PII entities
| @abstractmethod
def analyze(
self, text: str, entities: List[str], nlp_artifacts: NlpArtifacts
): # noqa ANN201
"""
Call an external service for PII detection.
:param text: text to be analyzed
:param entities: Entities that should be looked for
:param nlp_artifacts: Additional metadata from the NLP engine
:return: List of identified PII entities
"""
# 1. Call the external service.
# 2. Translate results into List[RecognizerResult]
pass
| (self, text: str, entities: List[str], nlp_artifacts: presidio_analyzer.nlp_engine.nlp_artifacts.NlpArtifacts) | [
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] |
16,434 | presidio_analyzer.remote_recognizer | get_supported_entities | null | @abstractmethod
def get_supported_entities(self) -> List[str]: # noqa D102
pass
| (self) -> List[str] | [
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16,458 | sklearn.ensemble._weight_boosting | AdaBoostClassifier | An AdaBoost classifier.
An AdaBoost [1]_ classifier is a meta-estimator that begins by fitting a
classifier on the original dataset and then fits additional copies of the
classifier on the same dataset but where the weights of incorrectly
classified instances are adjusted such that subsequent classifiers focus
more on difficult cases.
This class implements the algorithm based on [2]_.
Read more in the :ref:`User Guide <adaboost>`.
.. versionadded:: 0.14
Parameters
----------
estimator : object, default=None
The base estimator from which the boosted ensemble is built.
Support for sample weighting is required, as well as proper
``classes_`` and ``n_classes_`` attributes. If ``None``, then
the base estimator is :class:`~sklearn.tree.DecisionTreeClassifier`
initialized with `max_depth=1`.
.. versionadded:: 1.2
`base_estimator` was renamed to `estimator`.
n_estimators : int, default=50
The maximum number of estimators at which boosting is terminated.
In case of perfect fit, the learning procedure is stopped early.
Values must be in the range `[1, inf)`.
learning_rate : float, default=1.0
Weight applied to each classifier at each boosting iteration. A higher
learning rate increases the contribution of each classifier. There is
a trade-off between the `learning_rate` and `n_estimators` parameters.
Values must be in the range `(0.0, inf)`.
algorithm : {'SAMME', 'SAMME.R'}, default='SAMME.R'
If 'SAMME.R' then use the SAMME.R real boosting algorithm.
``estimator`` must support calculation of class probabilities.
If 'SAMME' then use the SAMME discrete boosting algorithm.
The SAMME.R algorithm typically converges faster than SAMME,
achieving a lower test error with fewer boosting iterations.
.. deprecated:: 1.4
`"SAMME.R"` is deprecated and will be removed in version 1.6.
'"SAMME"' will become the default.
random_state : int, RandomState instance or None, default=None
Controls the random seed given at each `estimator` at each
boosting iteration.
Thus, it is only used when `estimator` exposes a `random_state`.
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
Attributes
----------
estimator_ : estimator
The base estimator from which the ensemble is grown.
.. versionadded:: 1.2
`base_estimator_` was renamed to `estimator_`.
estimators_ : list of classifiers
The collection of fitted sub-estimators.
classes_ : ndarray of shape (n_classes,)
The classes labels.
n_classes_ : int
The number of classes.
estimator_weights_ : ndarray of floats
Weights for each estimator in the boosted ensemble.
estimator_errors_ : ndarray of floats
Classification error for each estimator in the boosted
ensemble.
feature_importances_ : ndarray of shape (n_features,)
The impurity-based feature importances if supported by the
``estimator`` (when based on decision trees).
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
See Also
--------
AdaBoostRegressor : An AdaBoost regressor that begins by fitting a
regressor on the original dataset and then fits additional copies of
the regressor on the same dataset but where the weights of instances
are adjusted according to the error of the current prediction.
GradientBoostingClassifier : GB builds an additive model in a forward
stage-wise fashion. Regression trees are fit on the negative gradient
of the binomial or multinomial deviance loss function. Binary
classification is a special case where only a single regression tree is
induced.
sklearn.tree.DecisionTreeClassifier : A non-parametric supervised learning
method used for classification.
Creates a model that predicts the value of a target variable by
learning simple decision rules inferred from the data features.
References
----------
.. [1] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of
on-Line Learning and an Application to Boosting", 1995.
.. [2] :doi:`J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class adaboost."
Statistics and its Interface 2.3 (2009): 349-360.
<10.4310/SII.2009.v2.n3.a8>`
Examples
--------
>>> from sklearn.ensemble import AdaBoostClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=1000, n_features=4,
... n_informative=2, n_redundant=0,
... random_state=0, shuffle=False)
>>> clf = AdaBoostClassifier(n_estimators=100, algorithm="SAMME", random_state=0)
>>> clf.fit(X, y)
AdaBoostClassifier(algorithm='SAMME', n_estimators=100, random_state=0)
>>> clf.predict([[0, 0, 0, 0]])
array([1])
>>> clf.score(X, y)
0.96...
| class AdaBoostClassifier(
_RoutingNotSupportedMixin, ClassifierMixin, BaseWeightBoosting
):
"""An AdaBoost classifier.
An AdaBoost [1]_ classifier is a meta-estimator that begins by fitting a
classifier on the original dataset and then fits additional copies of the
classifier on the same dataset but where the weights of incorrectly
classified instances are adjusted such that subsequent classifiers focus
more on difficult cases.
This class implements the algorithm based on [2]_.
Read more in the :ref:`User Guide <adaboost>`.
.. versionadded:: 0.14
Parameters
----------
estimator : object, default=None
The base estimator from which the boosted ensemble is built.
Support for sample weighting is required, as well as proper
``classes_`` and ``n_classes_`` attributes. If ``None``, then
the base estimator is :class:`~sklearn.tree.DecisionTreeClassifier`
initialized with `max_depth=1`.
.. versionadded:: 1.2
`base_estimator` was renamed to `estimator`.
n_estimators : int, default=50
The maximum number of estimators at which boosting is terminated.
In case of perfect fit, the learning procedure is stopped early.
Values must be in the range `[1, inf)`.
learning_rate : float, default=1.0
Weight applied to each classifier at each boosting iteration. A higher
learning rate increases the contribution of each classifier. There is
a trade-off between the `learning_rate` and `n_estimators` parameters.
Values must be in the range `(0.0, inf)`.
algorithm : {'SAMME', 'SAMME.R'}, default='SAMME.R'
If 'SAMME.R' then use the SAMME.R real boosting algorithm.
``estimator`` must support calculation of class probabilities.
If 'SAMME' then use the SAMME discrete boosting algorithm.
The SAMME.R algorithm typically converges faster than SAMME,
achieving a lower test error with fewer boosting iterations.
.. deprecated:: 1.4
`"SAMME.R"` is deprecated and will be removed in version 1.6.
'"SAMME"' will become the default.
random_state : int, RandomState instance or None, default=None
Controls the random seed given at each `estimator` at each
boosting iteration.
Thus, it is only used when `estimator` exposes a `random_state`.
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
Attributes
----------
estimator_ : estimator
The base estimator from which the ensemble is grown.
.. versionadded:: 1.2
`base_estimator_` was renamed to `estimator_`.
estimators_ : list of classifiers
The collection of fitted sub-estimators.
classes_ : ndarray of shape (n_classes,)
The classes labels.
n_classes_ : int
The number of classes.
estimator_weights_ : ndarray of floats
Weights for each estimator in the boosted ensemble.
estimator_errors_ : ndarray of floats
Classification error for each estimator in the boosted
ensemble.
feature_importances_ : ndarray of shape (n_features,)
The impurity-based feature importances if supported by the
``estimator`` (when based on decision trees).
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
See Also
--------
AdaBoostRegressor : An AdaBoost regressor that begins by fitting a
regressor on the original dataset and then fits additional copies of
the regressor on the same dataset but where the weights of instances
are adjusted according to the error of the current prediction.
GradientBoostingClassifier : GB builds an additive model in a forward
stage-wise fashion. Regression trees are fit on the negative gradient
of the binomial or multinomial deviance loss function. Binary
classification is a special case where only a single regression tree is
induced.
sklearn.tree.DecisionTreeClassifier : A non-parametric supervised learning
method used for classification.
Creates a model that predicts the value of a target variable by
learning simple decision rules inferred from the data features.
References
----------
.. [1] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of
on-Line Learning and an Application to Boosting", 1995.
.. [2] :doi:`J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class adaboost."
Statistics and its Interface 2.3 (2009): 349-360.
<10.4310/SII.2009.v2.n3.a8>`
Examples
--------
>>> from sklearn.ensemble import AdaBoostClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=1000, n_features=4,
... n_informative=2, n_redundant=0,
... random_state=0, shuffle=False)
>>> clf = AdaBoostClassifier(n_estimators=100, algorithm="SAMME", random_state=0)
>>> clf.fit(X, y)
AdaBoostClassifier(algorithm='SAMME', n_estimators=100, random_state=0)
>>> clf.predict([[0, 0, 0, 0]])
array([1])
>>> clf.score(X, y)
0.96...
"""
# TODO(1.6): Modify _parameter_constraints for "algorithm" to only check
# for "SAMME"
_parameter_constraints: dict = {
**BaseWeightBoosting._parameter_constraints,
"algorithm": [
StrOptions({"SAMME", "SAMME.R"}),
],
}
# TODO(1.6): Change default "algorithm" value to "SAMME"
def __init__(
self,
estimator=None,
*,
n_estimators=50,
learning_rate=1.0,
algorithm="SAMME.R",
random_state=None,
):
super().__init__(
estimator=estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state,
)
self.algorithm = algorithm
def _validate_estimator(self):
"""Check the estimator and set the estimator_ attribute."""
super()._validate_estimator(default=DecisionTreeClassifier(max_depth=1))
# TODO(1.6): Remove, as "SAMME.R" value for "algorithm" param will be
# removed in 1.6
# SAMME-R requires predict_proba-enabled base estimators
if self.algorithm != "SAMME":
warnings.warn(
(
"The SAMME.R algorithm (the default) is deprecated and will be"
" removed in 1.6. Use the SAMME algorithm to circumvent this"
" warning."
),
FutureWarning,
)
if not hasattr(self.estimator_, "predict_proba"):
raise TypeError(
"AdaBoostClassifier with algorithm='SAMME.R' requires "
"that the weak learner supports the calculation of class "
"probabilities with a predict_proba method.\n"
"Please change the base estimator or set "
"algorithm='SAMME' instead."
)
if not has_fit_parameter(self.estimator_, "sample_weight"):
raise ValueError(
f"{self.estimator.__class__.__name__} doesn't support sample_weight."
)
# TODO(1.6): Redefine the scope of the `_boost` and `_boost_discrete`
# functions to be the same since SAMME will be the default value for the
# "algorithm" parameter in version 1.6. Thus, a distinguishing function is
# no longer needed. (Or adjust code here, if another algorithm, shall be
# used inst | (estimator=None, *, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None) | [
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16,459 | sklearn.ensemble._base | __getitem__ | Return the index'th estimator in the ensemble. | def __getitem__(self, index):
"""Return the index'th estimator in the ensemble."""
return self.estimators_[index]
| (self, index) | [
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16,461 | sklearn.ensemble._weight_boosting | __init__ | null | def __init__(
self,
estimator=None,
*,
n_estimators=50,
learning_rate=1.0,
algorithm="SAMME.R",
random_state=None,
):
super().__init__(
estimator=estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state,
)
self.algorithm = algorithm
| (self, estimator=None, *, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None) | [
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16,462 | sklearn.ensemble._base | __iter__ | Return iterator over estimators in the ensemble. | def __iter__(self):
"""Return iterator over estimators in the ensemble."""
return iter(self.estimators_)
| (self) | [
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16,463 | sklearn.ensemble._base | __len__ | Return the number of estimators in the ensemble. | def __len__(self):
"""Return the number of estimators in the ensemble."""
return len(self.estimators_)
| (self) | [
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] |
16,467 | sklearn.ensemble._weight_boosting | _boost | Implement a single boost.
Perform a single boost according to the real multi-class SAMME.R
algorithm or to the discrete SAMME algorithm and return the updated
sample weights.
Parameters
----------
iboost : int
The index of the current boost iteration.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples.
y : array-like of shape (n_samples,)
The target values (class labels).
sample_weight : array-like of shape (n_samples,)
The current sample weights.
random_state : RandomState instance
The RandomState instance used if the base estimator accepts a
`random_state` attribute.
Returns
-------
sample_weight : array-like of shape (n_samples,) or None
The reweighted sample weights.
If None then boosting has terminated early.
estimator_weight : float
The weight for the current boost.
If None then boosting has terminated early.
estimator_error : float
The classification error for the current boost.
If None then boosting has terminated early.
| def _boost(self, iboost, X, y, sample_weight, random_state):
"""Implement a single boost.
Perform a single boost according to the real multi-class SAMME.R
algorithm or to the discrete SAMME algorithm and return the updated
sample weights.
Parameters
----------
iboost : int
The index of the current boost iteration.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples.
y : array-like of shape (n_samples,)
The target values (class labels).
sample_weight : array-like of shape (n_samples,)
The current sample weights.
random_state : RandomState instance
The RandomState instance used if the base estimator accepts a
`random_state` attribute.
Returns
-------
sample_weight : array-like of shape (n_samples,) or None
The reweighted sample weights.
If None then boosting has terminated early.
estimator_weight : float
The weight for the current boost.
If None then boosting has terminated early.
estimator_error : float
The classification error for the current boost.
If None then boosting has terminated early.
"""
if self.algorithm == "SAMME.R":
return self._boost_real(iboost, X, y, sample_weight, random_state)
else: # elif self.algorithm == "SAMME":
return self._boost_discrete(iboost, X, y, sample_weight, random_state)
| (self, iboost, X, y, sample_weight, random_state) | [
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16,468 | sklearn.ensemble._weight_boosting | _boost_discrete | Implement a single boost using the SAMME discrete algorithm. | def _boost_discrete(self, iboost, X, y, sample_weight, random_state):
"""Implement a single boost using the SAMME discrete algorithm."""
estimator = self._make_estimator(random_state=random_state)
estimator.fit(X, y, sample_weight=sample_weight)
y_predict = estimator.predict(X)
if iboost == 0:
self.classes_ = getattr(estimator, "classes_", None)
self.n_classes_ = len(self.classes_)
# Instances incorrectly classified
incorrect = y_predict != y
# Error fraction
estimator_error = np.mean(np.average(incorrect, weights=sample_weight, axis=0))
# Stop if classification is perfect
if estimator_error <= 0:
return sample_weight, 1.0, 0.0
n_classes = self.n_classes_
# Stop if the error is at least as bad as random guessing
if estimator_error >= 1.0 - (1.0 / n_classes):
self.estimators_.pop(-1)
if len(self.estimators_) == 0:
raise ValueError(
"BaseClassifier in AdaBoostClassifier "
"ensemble is worse than random, ensemble "
"can not be fit."
)
return None, None, None
# Boost weight using multi-class AdaBoost SAMME alg
estimator_weight = self.learning_rate * (
np.log((1.0 - estimator_error) / estimator_error) + np.log(n_classes - 1.0)
)
# Only boost the weights if it will fit again
if not iboost == self.n_estimators - 1:
# Only boost positive weights
sample_weight = np.exp(
np.log(sample_weight)
+ estimator_weight * incorrect * (sample_weight > 0)
)
return sample_weight, estimator_weight, estimator_error
| (self, iboost, X, y, sample_weight, random_state) | [
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16,469 | sklearn.ensemble._weight_boosting | _boost_real | Implement a single boost using the SAMME.R real algorithm. | def _boost_real(self, iboost, X, y, sample_weight, random_state):
"""Implement a single boost using the SAMME.R real algorithm."""
estimator = self._make_estimator(random_state=random_state)
estimator.fit(X, y, sample_weight=sample_weight)
y_predict_proba = estimator.predict_proba(X)
if iboost == 0:
self.classes_ = getattr(estimator, "classes_", None)
self.n_classes_ = len(self.classes_)
y_predict = self.classes_.take(np.argmax(y_predict_proba, axis=1), axis=0)
# Instances incorrectly classified
incorrect = y_predict != y
# Error fraction
estimator_error = np.mean(np.average(incorrect, weights=sample_weight, axis=0))
# Stop if classification is perfect
if estimator_error <= 0:
return sample_weight, 1.0, 0.0
# Construct y coding as described in Zhu et al [2]:
#
# y_k = 1 if c == k else -1 / (K - 1)
#
# where K == n_classes_ and c, k in [0, K) are indices along the second
# axis of the y coding with c being the index corresponding to the true
# class label.
n_classes = self.n_classes_
classes = self.classes_
y_codes = np.array([-1.0 / (n_classes - 1), 1.0])
y_coding = y_codes.take(classes == y[:, np.newaxis])
# Displace zero probabilities so the log is defined.
# Also fix negative elements which may occur with
# negative sample weights.
proba = y_predict_proba # alias for readability
np.clip(proba, np.finfo(proba.dtype).eps, None, out=proba)
# Boost weight using multi-class AdaBoost SAMME.R alg
estimator_weight = (
-1.0
* self.learning_rate
* ((n_classes - 1.0) / n_classes)
* xlogy(y_coding, y_predict_proba).sum(axis=1)
)
# Only boost the weights if it will fit again
if not iboost == self.n_estimators - 1:
# Only boost positive weights
sample_weight *= np.exp(
estimator_weight * ((sample_weight > 0) | (estimator_weight < 0))
)
return sample_weight, 1.0, estimator_error
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16,470 | sklearn.ensemble._weight_boosting | _check_X | null | def _check_X(self, X):
# Only called to validate X in non-fit methods, therefore reset=False
return self._validate_data(
X,
accept_sparse=["csr", "csc"],
ensure_2d=True,
allow_nd=True,
dtype=None,
reset=False,
)
| (self, X) | [
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16,473 | sklearn.ensemble._weight_boosting | _compute_proba_from_decision | Compute probabilities from the decision function.
This is based eq. (15) of [1] where:
p(y=c|X) = exp((1 / K-1) f_c(X)) / sum_k(exp((1 / K-1) f_k(X)))
= softmax((1 / K-1) * f(X))
References
----------
.. [1] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost",
2009.
| @staticmethod
def _compute_proba_from_decision(decision, n_classes):
"""Compute probabilities from the decision function.
This is based eq. (15) of [1] where:
p(y=c|X) = exp((1 / K-1) f_c(X)) / sum_k(exp((1 / K-1) f_k(X)))
= softmax((1 / K-1) * f(X))
References
----------
.. [1] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost",
2009.
"""
if n_classes == 2:
decision = np.vstack([-decision, decision]).T / 2
else:
decision /= n_classes - 1
return softmax(decision, copy=False)
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16,477 | sklearn.ensemble._base | _make_estimator | Make and configure a copy of the `estimator_` attribute.
Warning: This method should be used to properly instantiate new
sub-estimators.
| def _make_estimator(self, append=True, random_state=None):
"""Make and configure a copy of the `estimator_` attribute.
Warning: This method should be used to properly instantiate new
sub-estimators.
"""
estimator = clone(self.estimator_)
estimator.set_params(**{p: getattr(self, p) for p in self.estimator_params})
if random_state is not None:
_set_random_states(estimator, random_state)
if append:
self.estimators_.append(estimator)
return estimator
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16,478 | sklearn.base | _more_tags | null | def _more_tags(self):
return {"requires_y": True}
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16,482 | sklearn.ensemble._weight_boosting | _validate_estimator | Check the estimator and set the estimator_ attribute. | def _validate_estimator(self):
"""Check the estimator and set the estimator_ attribute."""
super()._validate_estimator(default=DecisionTreeClassifier(max_depth=1))
# TODO(1.6): Remove, as "SAMME.R" value for "algorithm" param will be
# removed in 1.6
# SAMME-R requires predict_proba-enabled base estimators
if self.algorithm != "SAMME":
warnings.warn(
(
"The SAMME.R algorithm (the default) is deprecated and will be"
" removed in 1.6. Use the SAMME algorithm to circumvent this"
" warning."
),
FutureWarning,
)
if not hasattr(self.estimator_, "predict_proba"):
raise TypeError(
"AdaBoostClassifier with algorithm='SAMME.R' requires "
"that the weak learner supports the calculation of class "
"probabilities with a predict_proba method.\n"
"Please change the base estimator or set "
"algorithm='SAMME' instead."
)
if not has_fit_parameter(self.estimator_, "sample_weight"):
raise ValueError(
f"{self.estimator.__class__.__name__} doesn't support sample_weight."
)
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16,484 | sklearn.ensemble._weight_boosting | decision_function | Compute the decision function of ``X``.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns
-------
score : ndarray of shape of (n_samples, k)
The decision function of the input samples. The order of
outputs is the same as that of the :term:`classes_` attribute.
Binary classification is a special cases with ``k == 1``,
otherwise ``k==n_classes``. For binary classification,
values closer to -1 or 1 mean more like the first or second
class in ``classes_``, respectively.
| def decision_function(self, X):
"""Compute the decision function of ``X``.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns
-------
score : ndarray of shape of (n_samples, k)
The decision function of the input samples. The order of
outputs is the same as that of the :term:`classes_` attribute.
Binary classification is a special cases with ``k == 1``,
otherwise ``k==n_classes``. For binary classification,
values closer to -1 or 1 mean more like the first or second
class in ``classes_``, respectively.
"""
check_is_fitted(self)
X = self._check_X(X)
n_classes = self.n_classes_
classes = self.classes_[:, np.newaxis]
# TODO(1.6): Remove, because "algorithm" param will be deprecated in 1.6
if self.algorithm == "SAMME.R":
# The weights are all 1. for SAMME.R
pred = sum(
_samme_proba(estimator, n_classes, X) for estimator in self.estimators_
)
else: # self.algorithm == "SAMME"
pred = sum(
np.where(
(estimator.predict(X) == classes).T,
w,
-1 / (n_classes - 1) * w,
)
for estimator, w in zip(self.estimators_, self.estimator_weights_)
)
pred /= self.estimator_weights_.sum()
if n_classes == 2:
pred[:, 0] *= -1
return pred.sum(axis=1)
return pred
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16,486 | sklearn.utils._metadata_requests | get_metadata_routing | Raise `NotImplementedError`.
This estimator does not support metadata routing yet. | def get_metadata_routing(self):
"""Raise `NotImplementedError`.
This estimator does not support metadata routing yet."""
raise NotImplementedError(
f"{self.__class__.__name__} has not implemented metadata routing yet."
)
| (self) | [
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16,488 | sklearn.ensemble._weight_boosting | predict | Predict classes for X.
The predicted class of an input sample is computed as the weighted mean
prediction of the classifiers in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns
-------
y : ndarray of shape (n_samples,)
The predicted classes.
| def predict(self, X):
"""Predict classes for X.
The predicted class of an input sample is computed as the weighted mean
prediction of the classifiers in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns
-------
y : ndarray of shape (n_samples,)
The predicted classes.
"""
pred = self.decision_function(X)
if self.n_classes_ == 2:
return self.classes_.take(pred > 0, axis=0)
return self.classes_.take(np.argmax(pred, axis=1), axis=0)
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16,489 | sklearn.ensemble._weight_boosting | predict_log_proba | Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the weighted mean predicted class log-probabilities of the classifiers
in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns
-------
p : ndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of
outputs is the same of that of the :term:`classes_` attribute.
| def predict_log_proba(self, X):
"""Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the weighted mean predicted class log-probabilities of the classifiers
in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns
-------
p : ndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of
outputs is the same of that of the :term:`classes_` attribute.
"""
return np.log(self.predict_proba(X))
| (self, X) | [
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16,490 | sklearn.ensemble._weight_boosting | predict_proba | Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the weighted mean predicted class probabilities of the classifiers
in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns
-------
p : ndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of
outputs is the same of that of the :term:`classes_` attribute.
| def predict_proba(self, X):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the weighted mean predicted class probabilities of the classifiers
in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns
-------
p : ndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of
outputs is the same of that of the :term:`classes_` attribute.
"""
check_is_fitted(self)
n_classes = self.n_classes_
if n_classes == 1:
return np.ones((_num_samples(X), 1))
decision = self.decision_function(X)
return self._compute_proba_from_decision(decision, n_classes)
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16,491 | sklearn.base | score |
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 : array-like of shape (n_samples, n_features)
Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for `X`.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
score : float
Mean accuracy of ``self.predict(X)`` w.r.t. `y`.
| 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
----------
X : array-like of shape (n_samples, n_features)
Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for `X`.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
score : float
Mean accuracy of ``self.predict(X)`` w.r.t. `y`.
"""
from .metrics import accuracy_score
return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
| (self, X, y, sample_weight=None) | [
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16,492 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``sample_weight`` parameter in ``fit``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**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 available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: sklearn.ensemble._weight_boosting.AdaBoostClassifier, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._weight_boosting.AdaBoostClassifier | [
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16,494 | sklearn.utils._metadata_requests | set_score_request | Request metadata passed to the ``score`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``score`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``score``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``sample_weight`` parameter in ``score``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**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 available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: sklearn.ensemble._weight_boosting.AdaBoostClassifier, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._weight_boosting.AdaBoostClassifier | [
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16,495 | sklearn.ensemble._weight_boosting | staged_decision_function | Compute decision function of ``X`` for each boosting iteration.
This method allows monitoring (i.e. determine error on testing set)
after each boosting iteration.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Yields
------
score : generator of ndarray of shape (n_samples, k)
The decision function of the input samples. The order of
outputs is the same of that of the :term:`classes_` attribute.
Binary classification is a special cases with ``k == 1``,
otherwise ``k==n_classes``. For binary classification,
values closer to -1 or 1 mean more like the first or second
class in ``classes_``, respectively.
| def staged_decision_function(self, X):
"""Compute decision function of ``X`` for each boosting iteration.
This method allows monitoring (i.e. determine error on testing set)
after each boosting iteration.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Yields
------
score : generator of ndarray of shape (n_samples, k)
The decision function of the input samples. The order of
outputs is the same of that of the :term:`classes_` attribute.
Binary classification is a special cases with ``k == 1``,
otherwise ``k==n_classes``. For binary classification,
values closer to -1 or 1 mean more like the first or second
class in ``classes_``, respectively.
"""
check_is_fitted(self)
X = self._check_X(X)
n_classes = self.n_classes_
classes = self.classes_[:, np.newaxis]
pred = None
norm = 0.0
for weight, estimator in zip(self.estimator_weights_, self.estimators_):
norm += weight
# TODO(1.6): Remove, because "algorithm" param will be deprecated in
# 1.6
if self.algorithm == "SAMME.R":
# The weights are all 1. for SAMME.R
current_pred = _samme_proba(estimator, n_classes, X)
else: # elif self.algorithm == "SAMME":
current_pred = np.where(
(estimator.predict(X) == classes).T,
weight,
-1 / (n_classes - 1) * weight,
)
if pred is None:
pred = current_pred
else:
pred += current_pred
if n_classes == 2:
tmp_pred = np.copy(pred)
tmp_pred[:, 0] *= -1
yield (tmp_pred / norm).sum(axis=1)
else:
yield pred / norm
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16,496 | sklearn.ensemble._weight_boosting | staged_predict | Return staged predictions for X.
The predicted class of an input sample is computed as the weighted mean
prediction of the classifiers in the ensemble.
This generator method yields the ensemble prediction after each
iteration of boosting and therefore allows monitoring, such as to
determine the prediction on a test set after each boost.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Yields
------
y : generator of ndarray of shape (n_samples,)
The predicted classes.
| def staged_predict(self, X):
"""Return staged predictions for X.
The predicted class of an input sample is computed as the weighted mean
prediction of the classifiers in the ensemble.
This generator method yields the ensemble prediction after each
iteration of boosting and therefore allows monitoring, such as to
determine the prediction on a test set after each boost.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Yields
------
y : generator of ndarray of shape (n_samples,)
The predicted classes.
"""
X = self._check_X(X)
n_classes = self.n_classes_
classes = self.classes_
if n_classes == 2:
for pred in self.staged_decision_function(X):
yield np.array(classes.take(pred > 0, axis=0))
else:
for pred in self.staged_decision_function(X):
yield np.array(classes.take(np.argmax(pred, axis=1), axis=0))
| (self, X) | [
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] |
16,497 | sklearn.ensemble._weight_boosting | staged_predict_proba | Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the weighted mean predicted class probabilities of the classifiers
in the ensemble.
This generator method yields the ensemble predicted class probabilities
after each iteration of boosting and therefore allows monitoring, such
as to determine the predicted class probabilities on a test set after
each boost.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Yields
------
p : generator of ndarray of shape (n_samples,)
The class probabilities of the input samples. The order of
outputs is the same of that of the :term:`classes_` attribute.
| def staged_predict_proba(self, X):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the weighted mean predicted class probabilities of the classifiers
in the ensemble.
This generator method yields the ensemble predicted class probabilities
after each iteration of boosting and therefore allows monitoring, such
as to determine the predicted class probabilities on a test set after
each boost.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Yields
------
p : generator of ndarray of shape (n_samples,)
The class probabilities of the input samples. The order of
outputs is the same of that of the :term:`classes_` attribute.
"""
n_classes = self.n_classes_
for decision in self.staged_decision_function(X):
yield self._compute_proba_from_decision(decision, n_classes)
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16,498 | sklearn.ensemble._weight_boosting | staged_score | Return staged scores for X, y.
This generator method yields the ensemble score after each iteration of
boosting and therefore allows monitoring, such as to determine the
score on a test set after each boost.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
y : array-like of shape (n_samples,)
Labels for X.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Yields
------
z : float
| def staged_score(self, X, y, sample_weight=None):
"""Return staged scores for X, y.
This generator method yields the ensemble score after each iteration of
boosting and therefore allows monitoring, such as to determine the
score on a test set after each boost.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
y : array-like of shape (n_samples,)
Labels for X.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Yields
------
z : float
"""
X = self._check_X(X)
for y_pred in self.staged_predict(X):
if is_classifier(self):
yield accuracy_score(y, y_pred, sample_weight=sample_weight)
else:
yield r2_score(y, y_pred, sample_weight=sample_weight)
| (self, X, y, sample_weight=None) | [
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16,499 | sklearn.ensemble._weight_boosting | AdaBoostRegressor | An AdaBoost regressor.
An AdaBoost [1] regressor is a meta-estimator that begins by fitting a
regressor on the original dataset and then fits additional copies of the
regressor on the same dataset but where the weights of instances are
adjusted according to the error of the current prediction. As such,
subsequent regressors focus more on difficult cases.
This class implements the algorithm known as AdaBoost.R2 [2].
Read more in the :ref:`User Guide <adaboost>`.
.. versionadded:: 0.14
Parameters
----------
estimator : object, default=None
The base estimator from which the boosted ensemble is built.
If ``None``, then the base estimator is
:class:`~sklearn.tree.DecisionTreeRegressor` initialized with
`max_depth=3`.
.. versionadded:: 1.2
`base_estimator` was renamed to `estimator`.
n_estimators : int, default=50
The maximum number of estimators at which boosting is terminated.
In case of perfect fit, the learning procedure is stopped early.
Values must be in the range `[1, inf)`.
learning_rate : float, default=1.0
Weight applied to each regressor at each boosting iteration. A higher
learning rate increases the contribution of each regressor. There is
a trade-off between the `learning_rate` and `n_estimators` parameters.
Values must be in the range `(0.0, inf)`.
loss : {'linear', 'square', 'exponential'}, default='linear'
The loss function to use when updating the weights after each
boosting iteration.
random_state : int, RandomState instance or None, default=None
Controls the random seed given at each `estimator` at each
boosting iteration.
Thus, it is only used when `estimator` exposes a `random_state`.
In addition, it controls the bootstrap of the weights used to train the
`estimator` at each boosting iteration.
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
Attributes
----------
estimator_ : estimator
The base estimator from which the ensemble is grown.
.. versionadded:: 1.2
`base_estimator_` was renamed to `estimator_`.
estimators_ : list of regressors
The collection of fitted sub-estimators.
estimator_weights_ : ndarray of floats
Weights for each estimator in the boosted ensemble.
estimator_errors_ : ndarray of floats
Regression error for each estimator in the boosted ensemble.
feature_importances_ : ndarray of shape (n_features,)
The impurity-based feature importances if supported by the
``estimator`` (when based on decision trees).
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
See Also
--------
AdaBoostClassifier : An AdaBoost classifier.
GradientBoostingRegressor : Gradient Boosting Classification Tree.
sklearn.tree.DecisionTreeRegressor : A decision tree regressor.
References
----------
.. [1] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of
on-Line Learning and an Application to Boosting", 1995.
.. [2] H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
Examples
--------
>>> from sklearn.ensemble import AdaBoostRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=4, n_informative=2,
... random_state=0, shuffle=False)
>>> regr = AdaBoostRegressor(random_state=0, n_estimators=100)
>>> regr.fit(X, y)
AdaBoostRegressor(n_estimators=100, random_state=0)
>>> regr.predict([[0, 0, 0, 0]])
array([4.7972...])
>>> regr.score(X, y)
0.9771...
| class AdaBoostRegressor(_RoutingNotSupportedMixin, RegressorMixin, BaseWeightBoosting):
"""An AdaBoost regressor.
An AdaBoost [1] regressor is a meta-estimator that begins by fitting a
regressor on the original dataset and then fits additional copies of the
regressor on the same dataset but where the weights of instances are
adjusted according to the error of the current prediction. As such,
subsequent regressors focus more on difficult cases.
This class implements the algorithm known as AdaBoost.R2 [2].
Read more in the :ref:`User Guide <adaboost>`.
.. versionadded:: 0.14
Parameters
----------
estimator : object, default=None
The base estimator from which the boosted ensemble is built.
If ``None``, then the base estimator is
:class:`~sklearn.tree.DecisionTreeRegressor` initialized with
`max_depth=3`.
.. versionadded:: 1.2
`base_estimator` was renamed to `estimator`.
n_estimators : int, default=50
The maximum number of estimators at which boosting is terminated.
In case of perfect fit, the learning procedure is stopped early.
Values must be in the range `[1, inf)`.
learning_rate : float, default=1.0
Weight applied to each regressor at each boosting iteration. A higher
learning rate increases the contribution of each regressor. There is
a trade-off between the `learning_rate` and `n_estimators` parameters.
Values must be in the range `(0.0, inf)`.
loss : {'linear', 'square', 'exponential'}, default='linear'
The loss function to use when updating the weights after each
boosting iteration.
random_state : int, RandomState instance or None, default=None
Controls the random seed given at each `estimator` at each
boosting iteration.
Thus, it is only used when `estimator` exposes a `random_state`.
In addition, it controls the bootstrap of the weights used to train the
`estimator` at each boosting iteration.
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
Attributes
----------
estimator_ : estimator
The base estimator from which the ensemble is grown.
.. versionadded:: 1.2
`base_estimator_` was renamed to `estimator_`.
estimators_ : list of regressors
The collection of fitted sub-estimators.
estimator_weights_ : ndarray of floats
Weights for each estimator in the boosted ensemble.
estimator_errors_ : ndarray of floats
Regression error for each estimator in the boosted ensemble.
feature_importances_ : ndarray of shape (n_features,)
The impurity-based feature importances if supported by the
``estimator`` (when based on decision trees).
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
See Also
--------
AdaBoostClassifier : An AdaBoost classifier.
GradientBoostingRegressor : Gradient Boosting Classification Tree.
sklearn.tree.DecisionTreeRegressor : A decision tree regressor.
References
----------
.. [1] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of
on-Line Learning and an Application to Boosting", 1995.
.. [2] H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
Examples
--------
>>> from sklearn.ensemble import AdaBoostRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=4, n_informative=2,
... random_state=0, shuffle=False)
>>> regr = AdaBoostRegressor(random_state=0, n_estimators=100)
>>> regr.fit(X, y)
AdaBoostRegressor(n_estimators=100, random_state=0)
>>> regr.predict([[0, 0, 0, 0]])
array([4.7972...])
>>> regr.score(X, y)
0.9771...
"""
_parameter_constraints: dict = {
**BaseWeightBoosting._parameter_constraints,
"loss": [StrOptions({"linear", "square", "exponential"})],
}
def __init__(
self,
estimator=None,
*,
n_estimators=50,
learning_rate=1.0,
loss="linear",
random_state=None,
):
super().__init__(
estimator=estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state,
)
self.loss = loss
self.random_state = random_state
def _validate_estimator(self):
"""Check the estimator and set the estimator_ attribute."""
super()._validate_estimator(default=DecisionTreeRegressor(max_depth=3))
def _boost(self, iboost, X, y, sample_weight, random_state):
"""Implement a single boost for regression
Perform a single boost according to the AdaBoost.R2 algorithm and
return the updated sample weights.
Parameters
----------
iboost : int
The index of the current boost iteration.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples.
y : array-like of shape (n_samples,)
The target values (class labels in classification, real numbers in
regression).
sample_weight : array-like of shape (n_samples,)
The current sample weights.
random_state : RandomState
The RandomState instance used if the base estimator accepts a
`random_state` attribute.
Controls also the bootstrap of the weights used to train the weak
learner.
replacement.
Returns
-------
sample_weight : array-like of shape (n_samples,) or None
The reweighted sample weights.
If None then boosting has terminated early.
estimator_weight : float
The weight for the current boost.
If None then boosting has terminated early.
estimator_error : float
The regression error for the current boost.
If None then boosting has terminated early.
"""
estimator = self._make_estimator(random_state=random_state)
# Weighted sampling of the training set with replacement
bootstrap_idx = random_state.choice(
np.arange(_num_samples(X)),
size=_num_samples(X),
replace=True,
p=sample_weight,
)
# Fit on the bootstrapped sample and obtain a prediction
# for all samples in the training set
X_ = _safe_indexing(X, bootstrap_idx)
y_ = _safe_indexing(y, bootstrap_idx)
estimator.fit(X_, y_)
y_predict = estimator.predict(X)
error_vect = np.abs(y_predict - y)
sample_mask = sample_weight > 0
masked_sample_weight = sample_weight[sample_mask]
masked_error_vector = error_vect[sample_mask]
error_max = masked_error_vector.max()
if error_max != 0:
masked_error_vector /= error_max
if self.loss == "square":
masked_error_vector **= 2
elif self.loss == "exponential":
masked_error_vector = 1.0 - np.exp(-masked_error_vector)
# Calculate the average loss
estimator_error = (masked_sample_weight * masked_error_vector).sum()
if estimator_error <= 0:
# Stop if fit is perfect
return sample_weight, 1.0, 0.0
elif estimator_error >= 0.5:
# Discard current estimator only if it isn't the only one
if len(self.estimators_) > 1:
self.estimators_.pop(-1)
return None, None, N | (estimator=None, *, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None) | [
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16,502 | sklearn.ensemble._weight_boosting | __init__ | null | def __init__(
self,
estimator=None,
*,
n_estimators=50,
learning_rate=1.0,
loss="linear",
random_state=None,
):
super().__init__(
estimator=estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state,
)
self.loss = loss
self.random_state = random_state
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] |
16,508 | sklearn.ensemble._weight_boosting | _boost | Implement a single boost for regression
Perform a single boost according to the AdaBoost.R2 algorithm and
return the updated sample weights.
Parameters
----------
iboost : int
The index of the current boost iteration.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples.
y : array-like of shape (n_samples,)
The target values (class labels in classification, real numbers in
regression).
sample_weight : array-like of shape (n_samples,)
The current sample weights.
random_state : RandomState
The RandomState instance used if the base estimator accepts a
`random_state` attribute.
Controls also the bootstrap of the weights used to train the weak
learner.
replacement.
Returns
-------
sample_weight : array-like of shape (n_samples,) or None
The reweighted sample weights.
If None then boosting has terminated early.
estimator_weight : float
The weight for the current boost.
If None then boosting has terminated early.
estimator_error : float
The regression error for the current boost.
If None then boosting has terminated early.
| def _boost(self, iboost, X, y, sample_weight, random_state):
"""Implement a single boost for regression
Perform a single boost according to the AdaBoost.R2 algorithm and
return the updated sample weights.
Parameters
----------
iboost : int
The index of the current boost iteration.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples.
y : array-like of shape (n_samples,)
The target values (class labels in classification, real numbers in
regression).
sample_weight : array-like of shape (n_samples,)
The current sample weights.
random_state : RandomState
The RandomState instance used if the base estimator accepts a
`random_state` attribute.
Controls also the bootstrap of the weights used to train the weak
learner.
replacement.
Returns
-------
sample_weight : array-like of shape (n_samples,) or None
The reweighted sample weights.
If None then boosting has terminated early.
estimator_weight : float
The weight for the current boost.
If None then boosting has terminated early.
estimator_error : float
The regression error for the current boost.
If None then boosting has terminated early.
"""
estimator = self._make_estimator(random_state=random_state)
# Weighted sampling of the training set with replacement
bootstrap_idx = random_state.choice(
np.arange(_num_samples(X)),
size=_num_samples(X),
replace=True,
p=sample_weight,
)
# Fit on the bootstrapped sample and obtain a prediction
# for all samples in the training set
X_ = _safe_indexing(X, bootstrap_idx)
y_ = _safe_indexing(y, bootstrap_idx)
estimator.fit(X_, y_)
y_predict = estimator.predict(X)
error_vect = np.abs(y_predict - y)
sample_mask = sample_weight > 0
masked_sample_weight = sample_weight[sample_mask]
masked_error_vector = error_vect[sample_mask]
error_max = masked_error_vector.max()
if error_max != 0:
masked_error_vector /= error_max
if self.loss == "square":
masked_error_vector **= 2
elif self.loss == "exponential":
masked_error_vector = 1.0 - np.exp(-masked_error_vector)
# Calculate the average loss
estimator_error = (masked_sample_weight * masked_error_vector).sum()
if estimator_error <= 0:
# Stop if fit is perfect
return sample_weight, 1.0, 0.0
elif estimator_error >= 0.5:
# Discard current estimator only if it isn't the only one
if len(self.estimators_) > 1:
self.estimators_.pop(-1)
return None, None, None
beta = estimator_error / (1.0 - estimator_error)
# Boost weight using AdaBoost.R2 alg
estimator_weight = self.learning_rate * np.log(1.0 / beta)
if not iboost == self.n_estimators - 1:
sample_weight[sample_mask] *= np.power(
beta, (1.0 - masked_error_vector) * self.learning_rate
)
return sample_weight, estimator_weight, estimator_error
| (self, iboost, X, y, sample_weight, random_state) | [
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16,513 | sklearn.ensemble._weight_boosting | _get_median_predict | null | def _get_median_predict(self, X, limit):
# Evaluate predictions of all estimators
predictions = np.array([est.predict(X) for est in self.estimators_[:limit]]).T
# Sort the predictions
sorted_idx = np.argsort(predictions, axis=1)
# Find index of median prediction for each sample
weight_cdf = stable_cumsum(self.estimator_weights_[sorted_idx], axis=1)
median_or_above = weight_cdf >= 0.5 * weight_cdf[:, -1][:, np.newaxis]
median_idx = median_or_above.argmax(axis=1)
median_estimators = sorted_idx[np.arange(_num_samples(X)), median_idx]
# Return median predictions
return predictions[np.arange(_num_samples(X)), median_estimators]
| (self, X, limit) | [
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16,521 | sklearn.ensemble._weight_boosting | _validate_estimator | Check the estimator and set the estimator_ attribute. | def _validate_estimator(self):
"""Check the estimator and set the estimator_ attribute."""
super()._validate_estimator(default=DecisionTreeRegressor(max_depth=3))
| (self) | [
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16,526 | sklearn.ensemble._weight_boosting | predict | Predict regression value for X.
The predicted regression value of an input sample is computed
as the weighted median prediction of the regressors in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns
-------
y : ndarray of shape (n_samples,)
The predicted regression values.
| def predict(self, X):
"""Predict regression value for X.
The predicted regression value of an input sample is computed
as the weighted median prediction of the regressors in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns
-------
y : ndarray of shape (n_samples,)
The predicted regression values.
"""
check_is_fitted(self)
X = self._check_X(X)
return self._get_median_predict(X, len(self.estimators_))
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] |
16,527 | sklearn.base | score | Return the coefficient of determination of the prediction.
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_true - y_true.mean()) ** 2).sum()``.
The best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always predicts
the expected value of `y`, disregarding the input features, would get
a :math:`R^2` score of 0.0.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed
kernel matrix or a list of generic objects instead with shape
``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``
is the number of samples used in the fitting for the estimator.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
True values for `X`.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
score : float
:math:`R^2` of ``self.predict(X)`` w.r.t. `y`.
Notes
-----
The :math:`R^2` score used when calling ``score`` on a regressor uses
``multioutput='uniform_average'`` from version 0.23 to keep consistent
with default value of :func:`~sklearn.metrics.r2_score`.
This influences the ``score`` method of all the multioutput
regressors (except for
:class:`~sklearn.multioutput.MultiOutputRegressor`).
| def score(self, X, y, sample_weight=None):
"""Return the coefficient of determination of the prediction.
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_true - y_true.mean()) ** 2).sum()``.
The best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always predicts
the expected value of `y`, disregarding the input features, would get
a :math:`R^2` score of 0.0.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed
kernel matrix or a list of generic objects instead with shape
``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``
is the number of samples used in the fitting for the estimator.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
True values for `X`.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
score : float
:math:`R^2` of ``self.predict(X)`` w.r.t. `y`.
Notes
-----
The :math:`R^2` score used when calling ``score`` on a regressor uses
``multioutput='uniform_average'`` from version 0.23 to keep consistent
with default value of :func:`~sklearn.metrics.r2_score`.
This influences the ``score`` method of all the multioutput
regressors (except for
:class:`~sklearn.multioutput.MultiOutputRegressor`).
"""
from .metrics import r2_score
y_pred = self.predict(X)
return r2_score(y, y_pred, sample_weight=sample_weight)
| (self, X, y, sample_weight=None) | [
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] |
16,531 | sklearn.ensemble._weight_boosting | staged_predict | Return staged predictions for X.
The predicted regression value of an input sample is computed
as the weighted median prediction of the regressors in the ensemble.
This generator method yields the ensemble prediction after each
iteration of boosting and therefore allows monitoring, such as to
determine the prediction on a test set after each boost.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples.
Yields
------
y : generator of ndarray of shape (n_samples,)
The predicted regression values.
| def staged_predict(self, X):
"""Return staged predictions for X.
The predicted regression value of an input sample is computed
as the weighted median prediction of the regressors in the ensemble.
This generator method yields the ensemble prediction after each
iteration of boosting and therefore allows monitoring, such as to
determine the prediction on a test set after each boost.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples.
Yields
------
y : generator of ndarray of shape (n_samples,)
The predicted regression values.
"""
check_is_fitted(self)
X = self._check_X(X)
for i, _ in enumerate(self.estimators_, 1):
yield self._get_median_predict(X, limit=i)
| (self, X) | [
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] |
16,533 | imodels.util.automl | AutoInterpretableClassifier | null | class AutoInterpretableClassifier(AutoInterpretableModel, ClassifierMixin):
...
| (param_grid=None, refit=True) | [
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16,535 | imodels.util.automl | __init__ | null | def __init__(self, param_grid=None, refit=True):
if param_grid is None:
if isinstance(self, ClassifierMixin):
self.param_grid = self.PARAM_GRID_DEFAULT_CLASSIFICATION
elif isinstance(self, RegressorMixin):
self.param_grid = self.PARAM_GRID_DEFAULT_REGRESSION
else:
self.param_grid = param_grid
self.refit = refit
| (self, param_grid=None, refit=True) | [
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16,549 | imodels.util.automl | fit | null | def fit(self, X, y, cv=5):
self.pipe_ = Pipeline([("est", BaseEstimator())]
) # Placeholder Estimator
if isinstance(self, ClassifierMixin):
scoring = "roc_auc"
elif isinstance(self, RegressorMixin):
scoring = "r2"
self.est_ = GridSearchCV(
self.pipe_, self.param_grid, scoring=scoring, cv=cv, refit=self.refit)
self.est_.fit(X, y)
return self
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16,552 | imodels.util.automl | predict | null | def predict(self, X):
return self.est_.predict(X)
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16,553 | imodels.util.automl | predict_proba | null | def predict_proba(self, X):
return self.est_.predict_proba(X)
| (self, X) | [
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16,554 | imodels.util.automl | score | null | def score(self, X, y):
return self.est_.score(X, y)
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16,555 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
cv : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``cv`` parameter in ``fit``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**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 available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: imodels.util.automl.AutoInterpretableClassifier, *, cv: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.util.automl.AutoInterpretableClassifier | [
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16,557 | imodels.util.automl | AutoInterpretableRegressor | null | class AutoInterpretableRegressor(AutoInterpretableModel, RegressorMixin):
...
| (param_grid=None, refit=True) | [
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16,581 | imodels.experimental.bartpy.sklearnmodel | BART | null | class BART(SklearnModel):
@staticmethod
def _get_n_nodes(trees):
nodes = 0
for tree in trees:
nodes += len(tree.decision_nodes)
return nodes
@property
def sample_complexity(self):
# samples = self._model_samples
# trees = [s.trees for s in samples]
complexities = [self._get_n_nodes(t) for t in self.trees]
return np.sum(complexities)
@staticmethod
def sub_forest(trees, n_nodes):
nodes = 0
for i, tree in enumerate(trees):
nodes += len(tree.decision_nodes)
if nodes >= n_nodes:
return trees[0:i + 1]
@property
def trees(self):
trs = [s.trees for s in self._model_samples]
return trs
def update_complexity(self, i):
samples_complexity = [self._get_n_nodes(t) for t in self.trees]
# complexity_sum = 0
arg_sort_complexity = np.argsort(samples_complexity)
self._model_samples = self._model_samples[arg_sort_complexity[:i + 1]]
return self
| (n_trees: int = 200, n_chains: int = 4, sigma_a: float = 0.001, sigma_b: float = 0.001, n_samples: int = 200, n_burn: int = 200, thin: float = 0.1, alpha: float = 0.95, beta: float = 2.0, store_in_sample_predictions: bool = False, store_acceptance_trace: bool = False, tree_sampler: imodels.experimental.bartpy.samplers.treemutation.TreeMutationSampler = <imodels.experimental.bartpy.samplers.unconstrainedtree.treemutation.UnconstrainedTreeMutationSampler object at 0x7f16638312a0>, initializer: Optional[imodels.experimental.bartpy.initializers.initializer.Initializer] = None, n_jobs=-1, classification: bool = False, max_rules=None) | [
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16,583 | imodels.experimental.bartpy.sklearnmodel | __init__ | null | def __init__(self,
n_trees: int = 200,
n_chains: int = 4,
sigma_a: float = 0.001,
sigma_b: float = 0.001,
n_samples: int = 200,
n_burn: int = 200,
thin: float = 0.1,
alpha: float = 0.95,
beta: float = 2.,
store_in_sample_predictions: bool = False,
store_acceptance_trace: bool = False,
tree_sampler: TreeMutationSampler = get_tree_sampler(0.5, 0.5),
initializer: Optional[Initializer] = None,
n_jobs=-1,
classification: bool = False,
max_rules=None):
self.n_trees = n_trees
self.n_chains = n_chains
self.sigma_a = sigma_a
self.sigma_b = sigma_b
self.n_burn = n_burn
self.n_samples = n_samples
self.p_grow = 0.5
self.p_prune = 0.5
self.alpha = alpha
self.beta = beta
self.thin = thin
self.n_jobs = n_jobs
self.store_in_sample_predictions = store_in_sample_predictions
self.store_acceptance_trace = store_acceptance_trace
self.columns = None
self.tree_sampler = tree_sampler
self.initializer = initializer
self.schedule = SampleSchedule(self.tree_sampler, LeafNodeSampler(), SigmaSampler())
self.sampler = ModelSampler(self.schedule)
self.classification = classification
self.max_rules = max_rules
self.sigma, self.data, self.model, self._prediction_samples, self._model_samples, self.extract = [None] * 6
| (self, n_trees: int = 200, n_chains: int = 4, sigma_a: float = 0.001, sigma_b: float = 0.001, n_samples: int = 200, n_burn: int = 200, thin: float = 0.1, alpha: float = 0.95, beta: float = 2.0, store_in_sample_predictions: bool = False, store_acceptance_trace: bool = False, tree_sampler: imodels.experimental.bartpy.samplers.treemutation.TreeMutationSampler = <imodels.experimental.bartpy.samplers.unconstrainedtree.treemutation.UnconstrainedTreeMutationSampler object at 0x7f16638312a0>, initializer: Optional[imodels.experimental.bartpy.initializers.initializer.Initializer] = None, n_jobs=-1, classification: bool = False, max_rules=None) | [
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16,587 | imodels.experimental.bartpy.sklearnmodel | _chain_pred_arr | null | def _chain_pred_arr(self, X, chain_number):
chain_len = int(self.n_samples)
samples_chain = self._model_samples[chain_number * chain_len: (chain_number + 1) * chain_len]
predictions_transformed = [x.predict(X) for x in samples_chain]
return predictions_transformed
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16,590 | imodels.experimental.bartpy.sklearnmodel | _combine_chains | null | @staticmethod
def _combine_chains(extract: List[Chain]) -> Chain:
keys = list(extract[0].keys())
combined = {}
for key in keys:
combined[key] = np.concatenate([chain[key] for chain in extract], axis=0)
return combined
| (extract: List[Mapping[str, Union[List[Any], numpy.ndarray]]]) -> Mapping[str, Union[List[Any], numpy.ndarray]] | [
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16,591 | imodels.experimental.bartpy.sklearnmodel | _construct_model | null | def _construct_model(self, X: np.ndarray, y: np.ndarray) -> Model:
if len(X) == 0 or X.shape[1] == 0:
raise ValueError("Empty covariate matrix passed")
self.data = self._convert_covariates_to_data(X, y)
self.sigma = Sigma(self.sigma_a, self.sigma_b, self.data.y.normalizing_scale, self.classification)
self.model = Model(self.data,
self.sigma,
n_trees=self.n_trees,
alpha=self.alpha,
beta=self.beta,
initializer=self.initializer,
classification=self.classification)
n_trees = self.n_trees if self.initializer is None else self.initializer.n_trees
self.n_trees = n_trees
return self.model
| (self, X: numpy.ndarray, y: numpy.ndarray) -> imodels.experimental.bartpy.model.Model | [
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16,592 | imodels.experimental.bartpy.sklearnmodel | _convert_covariates_to_data | null | @staticmethod
def _convert_covariates_to_data(X: np.ndarray, y: np.ndarray) -> Data:
from copy import deepcopy
if type(X) == pd.DataFrame:
X: pd.DataFrame = X
X = X.values
return Data(deepcopy(X), deepcopy(y), normalize=True)
| (X: numpy.ndarray, y: numpy.ndarray) -> imodels.experimental.bartpy.data.Data | [
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16,595 | imodels.experimental.bartpy.sklearnmodel | _get_n_nodes | null | @staticmethod
def _get_n_nodes(trees):
nodes = 0
for tree in trees:
nodes += len(tree.decision_nodes)
return nodes
| (trees) | [
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16,598 | imodels.experimental.bartpy.sklearnmodel | _out_of_sample_predict | null | def _out_of_sample_predict(self, X):
samples = self._model_samples
predictions_transformed = [x.predict(X) for x in samples]
predictions = self.data.y.unnormalize_y(np.mean(predictions_transformed, axis=0))
if self.classification:
predictions = scipy.stats.norm.cdf(predictions)
return predictions
| (self, X) | [
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16,603 | imodels.experimental.bartpy.sklearnmodel | between_chains_var | null | def between_chains_var(self, X):
all_predictions = np.stack([self.data.y.unnormalize_y(x.predict(X)) for x in self._model_samples], axis=1)
def _get_var(preds_arr):
mean_pred = preds_arr.mean(axis=1)
var = np.mean((preds_arr - np.expand_dims(mean_pred, 1)) ** 2)
return var
total_var = _get_var(all_predictions)
within_chain_var = 0
for c in range(self.n_chains):
chain_preds = self._chain_pred_arr(X, c)
within_chain_var += _get_var(np.stack(chain_preds, axis=1))
return total_var - within_chain_var
| (self, X) | [
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16,604 | imodels.experimental.bartpy.sklearnmodel | chain_mse_std | null | def chain_mse_std(self, X, y, chain_number):
predictions_transformed = self._chain_pred_arr(X, chain_number)
predictions_std = np.std(
[mean_squared_error(self.data.y.unnormalize_y(preds), y) for preds in predictions_transformed])
return predictions_std
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16,605 | imodels.experimental.bartpy.sklearnmodel | chain_predictions | null | def chain_predictions(self, X, chain_number):
predictions_transformed = self._chain_pred_arr(X, chain_number)
preds_arr = [self.data.y.unnormalize_y(preds) for preds in predictions_transformed]
return preds_arr
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16,606 | imodels.experimental.bartpy.sklearnmodel | f_chains |
List of methods to run MCMC chains
Useful for running multiple models in parallel
Returns
-------
List[Callable[[], Extract]]
List of method to run individual chains
Length of n_chains
| def f_chains(self) -> List[Callable[[], Chain]]:
"""
List of methods to run MCMC chains
Useful for running multiple models in parallel
Returns
-------
List[Callable[[], Extract]]
List of method to run individual chains
Length of n_chains
"""
return [delayed_run_chain() for _ in range(self.n_chains)]
| (self) -> List[Callable[[], Mapping[str, Union[List[Any], numpy.ndarray]]]] | [
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16,607 | imodels.experimental.bartpy.sklearnmodel | f_delayed_chains |
Access point for getting access to delayed methods for running chains
Useful for when you want to run multiple instances of the model in parallel
e.g. when calculating a null distribution for feature importance
Parameters
----------
X: np.ndarray
Covariate matrix
y: np.ndarray
Target array
Returns
-------
List[Callable[[], ChainExtract]]
| def f_delayed_chains(self, X: np.ndarray, y: np.ndarray):
"""
Access point for getting access to delayed methods for running chains
Useful for when you want to run multiple instances of the model in parallel
e.g. when calculating a null distribution for feature importance
Parameters
----------
X: np.ndarray
Covariate matrix
y: np.ndarray
Target array
Returns
-------
List[Callable[[], ChainExtract]]
"""
return [delayed(x)(self, X, y) for x in self.f_chains()]
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16,608 | imodels.experimental.bartpy.sklearnmodel | fit |
Learn the model based on training data
Parameters
----------
X: pd.DataFrame
training covariates
y: np.ndarray
training targets
Returns
-------
SklearnModel
self with trained parameter values
| def fit(self, X: Union[np.ndarray, pd.DataFrame], y: np.ndarray) -> 'SklearnModel':
"""
Learn the model based on training data
Parameters
----------
X: pd.DataFrame
training covariates
y: np.ndarray
training targets
Returns
-------
SklearnModel
self with trained parameter values
"""
self.model = self._construct_model(X, y)
self.extract = Parallel(n_jobs=self.n_jobs)(self.f_delayed_chains(X, y))
self.combined_chains = self._combine_chains(self.extract)
self._model_samples, self._prediction_samples = self.combined_chains["model"], self.combined_chains[
"in_sample_predictions"]
self._acceptance_trace = self.combined_chains["acceptance"]
self._likelihood = self.combined_chains["likelihood"]
self._probs = self.combined_chains["probs"]
self.fitted_ = True
return self
| (self, X: Union[numpy.ndarray, pandas.core.frame.DataFrame], y: numpy.ndarray) -> imodels.experimental.bartpy.sklearnmodel.SklearnModel | [
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16,609 | imodels.experimental.bartpy.sklearnmodel | fit_predict | null | def fit_predict(self, X, y):
self.fit(X, y)
if self.store_in_sample_predictions:
return self.predict()
else:
return self.predict(X)
| (self, X, y) | [
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16,610 | imodels.experimental.bartpy.sklearnmodel | from_extract |
Create a copy of the model using an extract
Useful for doing operations on extracts created in external processes like feature selection
Parameters
----------
extract: Extract
samples produced by delayed chain methods
X: np.ndarray
Covariate matrix
y: np.ndarray
Target variable
Returns
-------
SklearnModel
Copy of the current model with samples
| def from_extract(self, extract: List[Chain], X: np.ndarray, y: np.ndarray) -> 'SklearnModel':
"""
Create a copy of the model using an extract
Useful for doing operations on extracts created in external processes like feature selection
Parameters
----------
extract: Extract
samples produced by delayed chain methods
X: np.ndarray
Covariate matrix
y: np.ndarray
Target variable
Returns
-------
SklearnModel
Copy of the current model with samples
"""
new_model = deepcopy(self)
combined_chain = self._combine_chains(extract)
self._model_samples, self._prediction_samples = combined_chain["model"], combined_chain["in_sample_predictions"]
self._acceptance_trace = combined_chain["acceptance"]
new_model.data = self._convert_covariates_to_data(X, y)
return new_model
| (self, extract: List[Mapping[str, Union[List[Any], numpy.ndarray]]], X: numpy.ndarray, y: numpy.ndarray) -> imodels.experimental.bartpy.sklearnmodel.SklearnModel | [
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16,613 | imodels.experimental.bartpy.sklearnmodel | l2_error |
Calculate the squared errors for each row in the covariate matrix
Parameters
----------
X: np.ndarray
Covariate matrix
y: np.ndarray
Target array
Returns
-------
np.ndarray
Squared error for each observation
| def l2_error(self, X=None, y=None) -> np.ndarray:
"""
Calculate the squared errors for each row in the covariate matrix
Parameters
----------
X: np.ndarray
Covariate matrix
y: np.ndarray
Target array
Returns
-------
np.ndarray
Squared error for each observation
"""
return np.square(self.residuals(X, y))
| (self, X=None, y=None) -> numpy.ndarray | [
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16,614 | imodels.experimental.bartpy.sklearnmodel | predict |
Predict the target corresponding to the provided covariate matrix
If X is None, will predict based on training covariates
Prediction is based on the mean of all samples
Parameters
----------
X: pd.DataFrame
covariates to predict from
Returns
-------
np.ndarray
predictions for the X covariates
| def predict(self, X: np.ndarray = None) -> np.ndarray:
"""
Predict the target corresponding to the provided covariate matrix
If X is None, will predict based on training covariates
Prediction is based on the mean of all samples
Parameters
----------
X: pd.DataFrame
covariates to predict from
Returns
-------
np.ndarray
predictions for the X covariates
"""
if X is None and self.store_in_sample_predictions:
return self.data.y.unnormalize_y(np.mean(self._prediction_samples, axis=0))
elif X is None and not self.store_in_sample_predictions:
raise ValueError(
"In sample predictions only possible if model.store_in_sample_predictions is `True`. Either set the parameter to True or pass a non-None X parameter")
else:
predictions = self._out_of_sample_predict(X)
if self.classification:
return np.round(predictions, 0)
return predictions
| (self, X: Optional[numpy.ndarray] = None) -> numpy.ndarray | [
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16,615 | imodels.experimental.bartpy.sklearnmodel | predict_chain | null | def predict_chain(self, X, chain_number):
predictions_transformed = self._chain_pred_arr(X, chain_number)
predictions = self.data.y.unnormalize_y(np.mean(predictions_transformed, axis=0))
if self.classification:
predictions = scipy.stats.norm.cdf(predictions)
return predictions
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16,616 | imodels.experimental.bartpy.sklearnmodel | predict_proba | null | def predict_proba(self, X: np.ndarray = None) -> np.ndarray:
preds = self._out_of_sample_predict(X)
return np.stack([preds, 1 - preds], axis=1)
| (self, X: Optional[numpy.ndarray] = None) -> numpy.ndarray | [
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16,617 | imodels.experimental.bartpy.sklearnmodel | residuals |
Array of error for each observation
Parameters
----------
X: np.ndarray
Covariate matrix
y: np.ndarray
Target array
Returns
-------
np.ndarray
Error for each observation
| def residuals(self, X=None, y=None) -> np.ndarray:
"""
Array of error for each observation
Parameters
----------
X: np.ndarray
Covariate matrix
y: np.ndarray
Target array
Returns
-------
np.ndarray
Error for each observation
"""
if y is None:
return self.model.data.y.unnormalized_y - self.predict(X)
else:
return y - self.predict(X)
| (self, X=None, y=None) -> numpy.ndarray | [
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16,618 | imodels.experimental.bartpy.sklearnmodel | rmse |
The total RMSE error of the model
The sum of squared errors over all observations
Parameters
----------
X: np.ndarray
Covariate matrix
y: np.ndarray
Target array
Returns
-------
float
The total summed L2 error for the model
| def rmse(self, X, y) -> float:
"""
The total RMSE error of the model
The sum of squared errors over all observations
Parameters
----------
X: np.ndarray
Covariate matrix
y: np.ndarray
Target array
Returns
-------
float
The total summed L2 error for the model
"""
return np.sqrt(np.sum(self.l2_error(X, y)))
| (self, X, y) -> float | [
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16,622 | imodels.experimental.bartpy.sklearnmodel | sub_forest | null | @staticmethod
def sub_forest(trees, n_nodes):
nodes = 0
for i, tree in enumerate(trees):
nodes += len(tree.decision_nodes)
if nodes >= n_nodes:
return trees[0:i + 1]
| (trees, n_nodes) | [
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16,623 | imodels.experimental.bartpy.sklearnmodel | update_complexity | null | def update_complexity(self, i):
samples_complexity = [self._get_n_nodes(t) for t in self.trees]
# complexity_sum = 0
arg_sort_complexity = np.argsort(samples_complexity)
self._model_samples = self._model_samples[arg_sort_complexity[:i + 1]]
return self
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16,624 | imodels.discretization.mdlp | BRLDiscretizer | null | class BRLDiscretizer:
def __init__(self, feature_labels, verbose=False):
self.feature_labels_original = feature_labels
self.verbose = verbose
def fit(self, X, y, undiscretized_features=[]):
# check which features are numeric (to be discretized)
self.discretized_features = []
X_str_disc = self._encode_strings(X)
for fi in range(X_str_disc.shape[1]):
# if not string, has values other than 0 and 1, and not specified as undiscretized
if (
isinstance(X_str_disc[0][fi], numbers.Number)
and (not set(np.unique(X_str_disc[:, fi])).issubset({0, 1}))
and (len(self.feature_labels) == 0 or
len(undiscretized_features) == 0 or
self.feature_labels[fi] not in undiscretized_features
)
):
self.discretized_features.append(self.feature_labels[fi])
if len(self.discretized_features) > 0:
if self.verbose:
print(
"Warning: non-categorical data found. Trying to discretize. (Please convert categorical values to "
"strings, and/or specify the argument 'undiscretized_features', to avoid this.)")
X_str_and_num_disc = self.discretize(X_str_disc, y)
self.discretized_X = X_str_and_num_disc
else:
self.discretizer = None
return
def discretize(self, X, y):
'''Discretize the features specified in self.discretized_features
'''
if self.verbose:
print("Discretizing ", self.discretized_features, "...")
D = pd.DataFrame(np.hstack((X, np.expand_dims(y, axis=1))), columns=list(self.feature_labels) + ["y"])
self.discretizer = MDLPDiscretizer(dataset=D, class_label="y", features=self.discretized_features)
cat_data = pd.DataFrame(np.zeros_like(X))
for i in range(len(self.feature_labels)):
label = self.feature_labels[i]
if label in self.discretized_features:
new_column = label + " : " + self.discretizer._data[label].astype(str)
cat_data.iloc[:, i] = new_column
else:
cat_data.iloc[:, i] = D[label]
return np.array(cat_data).tolist()
def _encode_strings(self, X):
# handle string data
X_str_disc = pd.DataFrame([])
for fi in range(X.shape[1]):
if issubclass(type(X[0][fi]), str):
new_columns = pd.get_dummies(X[:, fi])
new_columns.columns = [self.feature_labels_original[fi] + '_' + value for value in new_columns.columns]
new_columns_colon_format = new_columns.apply(lambda s: s.name + ' : ' + s.astype(str))
X_str_disc = pd.concat([X_str_disc, new_columns_colon_format], axis=1)
else:
X_str_disc = pd.concat([X_str_disc, pd.Series(X[:, fi], name=self.feature_labels_original[fi])], axis=1)
self.feature_labels = list(X_str_disc.columns)
return X_str_disc.values
def transform(self, X, return_onehot=True):
if type(X) in [pd.DataFrame, pd.Series]:
X = X.values
if self.discretizer is None:
return pd.DataFrame(X, columns=self.feature_labels_original)
self.data = pd.DataFrame(self._encode_strings(X), columns=self.feature_labels)
self._apply_cutpoints()
D = np.array(self.data)
# prepend feature labels
Dl = np.copy(D).astype(str).tolist()
for i in range(len(Dl)):
for j in range(len(Dl[0])):
Dl[i][j] = self.feature_labels[j] + " : " + Dl[i][j]
if not return_onehot:
return Dl
else:
return self.get_onehot_df(Dl)
@property
def onehot_df(self):
return self.get_onehot_df(self.discretized_X)
def get_onehot_df(self, discretized_X):
'''Create readable one-hot encoded DataFrame from discretized features
'''
data = list(discretized_X[:])
X_colname_removed = data.copy()
replace_str_entries_func = lambda s: s.split(' : ')[1] if type(s) is str else s
for i in range(len(data)):
X_colname_removed[i] = list(map(replace_str_entries_func, X_colname_removed[i]))
X_df_categorical = pd.DataFrame(X_colname_removed, columns=self.feature_labels)
X_df_onehot = pd.get_dummies(X_df_categorical)
return X_df_onehot
@property
def data(self):
return self.discretizer._data
@data.setter
def data(self, value):
self.discretizer._data = value
def _apply_cutpoints(self):
return self.discretizer._apply_cutpoints()
| (feature_labels, verbose=False) | [
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16,625 | imodels.discretization.mdlp | __init__ | null | def __init__(self, feature_labels, verbose=False):
self.feature_labels_original = feature_labels
self.verbose = verbose
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16,626 | imodels.discretization.mdlp | _apply_cutpoints | null | def _apply_cutpoints(self):
return self.discretizer._apply_cutpoints()
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16,627 | imodels.discretization.mdlp | _encode_strings | null | def _encode_strings(self, X):
# handle string data
X_str_disc = pd.DataFrame([])
for fi in range(X.shape[1]):
if issubclass(type(X[0][fi]), str):
new_columns = pd.get_dummies(X[:, fi])
new_columns.columns = [self.feature_labels_original[fi] + '_' + value for value in new_columns.columns]
new_columns_colon_format = new_columns.apply(lambda s: s.name + ' : ' + s.astype(str))
X_str_disc = pd.concat([X_str_disc, new_columns_colon_format], axis=1)
else:
X_str_disc = pd.concat([X_str_disc, pd.Series(X[:, fi], name=self.feature_labels_original[fi])], axis=1)
self.feature_labels = list(X_str_disc.columns)
return X_str_disc.values
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16,628 | imodels.discretization.mdlp | discretize | Discretize the features specified in self.discretized_features
| def discretize(self, X, y):
'''Discretize the features specified in self.discretized_features
'''
if self.verbose:
print("Discretizing ", self.discretized_features, "...")
D = pd.DataFrame(np.hstack((X, np.expand_dims(y, axis=1))), columns=list(self.feature_labels) + ["y"])
self.discretizer = MDLPDiscretizer(dataset=D, class_label="y", features=self.discretized_features)
cat_data = pd.DataFrame(np.zeros_like(X))
for i in range(len(self.feature_labels)):
label = self.feature_labels[i]
if label in self.discretized_features:
new_column = label + " : " + self.discretizer._data[label].astype(str)
cat_data.iloc[:, i] = new_column
else:
cat_data.iloc[:, i] = D[label]
return np.array(cat_data).tolist()
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16,629 | imodels.discretization.mdlp | fit | null | def fit(self, X, y, undiscretized_features=[]):
# check which features are numeric (to be discretized)
self.discretized_features = []
X_str_disc = self._encode_strings(X)
for fi in range(X_str_disc.shape[1]):
# if not string, has values other than 0 and 1, and not specified as undiscretized
if (
isinstance(X_str_disc[0][fi], numbers.Number)
and (not set(np.unique(X_str_disc[:, fi])).issubset({0, 1}))
and (len(self.feature_labels) == 0 or
len(undiscretized_features) == 0 or
self.feature_labels[fi] not in undiscretized_features
)
):
self.discretized_features.append(self.feature_labels[fi])
if len(self.discretized_features) > 0:
if self.verbose:
print(
"Warning: non-categorical data found. Trying to discretize. (Please convert categorical values to "
"strings, and/or specify the argument 'undiscretized_features', to avoid this.)")
X_str_and_num_disc = self.discretize(X_str_disc, y)
self.discretized_X = X_str_and_num_disc
else:
self.discretizer = None
return
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16,630 | imodels.discretization.mdlp | get_onehot_df | Create readable one-hot encoded DataFrame from discretized features
| def get_onehot_df(self, discretized_X):
'''Create readable one-hot encoded DataFrame from discretized features
'''
data = list(discretized_X[:])
X_colname_removed = data.copy()
replace_str_entries_func = lambda s: s.split(' : ')[1] if type(s) is str else s
for i in range(len(data)):
X_colname_removed[i] = list(map(replace_str_entries_func, X_colname_removed[i]))
X_df_categorical = pd.DataFrame(X_colname_removed, columns=self.feature_labels)
X_df_onehot = pd.get_dummies(X_df_categorical)
return X_df_onehot
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16,631 | imodels.discretization.mdlp | transform | null | def transform(self, X, return_onehot=True):
if type(X) in [pd.DataFrame, pd.Series]:
X = X.values
if self.discretizer is None:
return pd.DataFrame(X, columns=self.feature_labels_original)
self.data = pd.DataFrame(self._encode_strings(X), columns=self.feature_labels)
self._apply_cutpoints()
D = np.array(self.data)
# prepend feature labels
Dl = np.copy(D).astype(str).tolist()
for i in range(len(Dl)):
for j in range(len(Dl[0])):
Dl[i][j] = self.feature_labels[j] + " : " + Dl[i][j]
if not return_onehot:
return Dl
else:
return self.get_onehot_df(Dl)
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16,632 | sklearn.base | BaseEstimator | Base class for all estimators in scikit-learn.
Inheriting from this class provides default implementations of:
- setting and getting parameters used by `GridSearchCV` and friends;
- textual and HTML representation displayed in terminals and IDEs;
- estimator serialization;
- parameters validation;
- data validation;
- feature names validation.
Read more in the :ref:`User Guide <rolling_your_own_estimator>`.
Notes
-----
All estimators should specify all the parameters that can be set
at the class level in their ``__init__`` as explicit keyword
arguments (no ``*args`` or ``**kwargs``).
Examples
--------
>>> import numpy as np
>>> from sklearn.base import BaseEstimator
>>> class MyEstimator(BaseEstimator):
... def __init__(self, *, param=1):
... self.param = param
... def fit(self, X, y=None):
... self.is_fitted_ = True
... return self
... def predict(self, X):
... return np.full(shape=X.shape[0], fill_value=self.param)
>>> estimator = MyEstimator(param=2)
>>> estimator.get_params()
{'param': 2}
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
>>> y = np.array([1, 0, 1])
>>> estimator.fit(X, y).predict(X)
array([2, 2, 2])
>>> estimator.set_params(param=3).fit(X, y).predict(X)
array([3, 3, 3])
| class BaseEstimator(_HTMLDocumentationLinkMixin, _MetadataRequester):
"""Base class for all estimators in scikit-learn.
Inheriting from this class provides default implementations of:
- setting and getting parameters used by `GridSearchCV` and friends;
- textual and HTML representation displayed in terminals and IDEs;
- estimator serialization;
- parameters validation;
- data validation;
- feature names validation.
Read more in the :ref:`User Guide <rolling_your_own_estimator>`.
Notes
-----
All estimators should specify all the parameters that can be set
at the class level in their ``__init__`` as explicit keyword
arguments (no ``*args`` or ``**kwargs``).
Examples
--------
>>> import numpy as np
>>> from sklearn.base import BaseEstimator
>>> class MyEstimator(BaseEstimator):
... def __init__(self, *, param=1):
... self.param = param
... def fit(self, X, y=None):
... self.is_fitted_ = True
... return self
... def predict(self, X):
... return np.full(shape=X.shape[0], fill_value=self.param)
>>> estimator = MyEstimator(param=2)
>>> estimator.get_params()
{'param': 2}
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
>>> y = np.array([1, 0, 1])
>>> estimator.fit(X, y).predict(X)
array([2, 2, 2])
>>> estimator.set_params(param=3).fit(X, y).predict(X)
array([3, 3, 3])
"""
@classmethod
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 constructor to introspect
return []
# introspect the constructor arguments to find the model parameters
# to represent
init_signature = inspect.signature(init)
# Consider the constructor parameters excluding 'self'
parameters = [
p
for p in init_signature.parameters.values()
if p.name != "self" and p.kind != p.VAR_KEYWORD
]
for p in parameters:
if p.kind == p.VAR_POSITIONAL:
raise RuntimeError(
"scikit-learn estimators should always "
"specify their parameters in the signature"
" of their __init__ (no varargs)."
" %s with constructor %s doesn't "
" follow this convention." % (cls, init_signature)
)
# Extract and sort argument names excluding 'self'
return sorted([p.name for p in parameters])
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
-------
params : dict
Parameter names mapped to their values.
"""
out = dict()
for key in self._get_param_names():
value = getattr(self, key)
if deep and hasattr(value, "get_params") and not isinstance(value, type):
deep_items = value.get_params().items()
out.update((key + "__" + k, val) for k, val in deep_items)
out[key] = value
return out
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 update each component of a nested object.
Parameters
----------
**params : dict
Estimator parameters.
Returns
-------
self : estimator instance
Estimator instance.
"""
if not params:
# Simple optimization to gain speed (inspect is slow)
return self
valid_params = self.get_params(deep=True)
nested_params = defaultdict(dict) # grouped by prefix
for key, value in params.items():
key, delim, sub_key = key.partition("__")
if key not in valid_params:
local_valid_params = self._get_param_names()
raise ValueError(
f"Invalid parameter {key!r} for estimator {self}. "
f"Valid parameters are: {local_valid_params!r}."
)
if delim:
nested_params[key][sub_key] = value
else:
setattr(self, key, value)
valid_params[key] = value
for key, sub_params in nested_params.items():
valid_params[key].set_params(**sub_params)
return self
def __sklearn_clone__(self):
return _clone_parametrized(self)
def __repr__(self, N_CHAR_MAX=700):
# N_CHAR_MAX is the (approximate) maximum number of non-blank
# characters to render. We pass it as an optional parameter to ease
# the tests.
from .utils._pprint import _EstimatorPrettyPrinter
N_MAX_ELEMENTS_TO_SHOW = 30 # number of elements to show in sequences
# use ellipsis for sequences with a lot of elements
pp = _EstimatorPrettyPrinter(
compact=True,
indent=1,
indent_at_name=True,
n_max_elements_to_show=N_MAX_ELEMENTS_TO_SHOW,
)
repr_ = pp.pformat(self)
# Use bruteforce ellipsis when there are a lot of non-blank characters
n_nonblank = len("".join(repr_.split()))
if n_nonblank > N_CHAR_MAX:
lim = N_CHAR_MAX // 2 # apprx number of chars to keep on both ends
regex = r"^(\s*\S){%d}" % lim
# The regex '^(\s*\S){%d}' % n
# matches from the start of the string until the nth non-blank
# character:
# - ^ matches the start of string
# - (pattern){n} matches n repetitions of pattern
# - \s*\S matches a non-blank char following zero or more blanks
left_lim = re.match(regex, repr_).end()
right_lim = re.match(regex, repr_[::-1]).end()
if "\n" in repr_[left_lim:-right_lim]:
# The left side and right side aren't on the same line.
# To avoid weird cuts, e.g.:
# categoric...ore',
# we need to start the right side with an appropriate newline
# character so that it renders properly as:
# categoric...
# handle_unknown='ignore',
# so we add [^\n]*\n which matches until the next \n
regex += r"[^\n]*\n"
right_lim = re.match(regex, repr_[::-1]).end()
ellipsis = "..."
if left_lim + len(ellipsis) < len(repr_) - right_lim:
# Only add ellipsis if it results in a shorter repr
repr_ = repr_[:left_lim] + "..." + repr_[-right_lim:]
return repr_
def __getstate__(self):
if getattr(self, "__slots__", None):
raise TypeError(
"You cannot use `__slots__` in objects inheriting from "
"`sklearn.base.BaseEstimator`."
)
try:
state = super().__getstate__()
if state is None:
# For Python 3.11+, empty instance (no `__slots__`,
# and `__dict__`) will return a state equal to `None`.
state = self.__dict__.copy()
except AttributeError:
# Python < 3.11
state = self.__dict__.copy()
if type(self).__module__.startswith("sklearn."):
return dict(state.items(), _sklearn_version=__version__)
else:
return state
def __setstate__(self, state):
if type(self).__module__.startswith(" | () | [
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16,650 | imodels.discretization.discretizer | BasicDiscretizer |
Discretize numeric data into bins. Provides a wrapper around
KBinsDiscretizer from sklearn
Params
------
n_bins : int or array-like of shape (len(dcols),), default=2
Number of bins to discretize each feature into.
dcols : list of strings
The names of the columns to be discretized; by default,
discretize all float and int columns in X.
encode : {'onehot', 'ordinal'}, default='onehot'
Method used to encode the transformed result.
onehot
Encode the transformed result with one-hot encoding and
return a dense array.
ordinal
Return the bin identifier encoded as an integer value.
strategy : {'uniform', 'quantile', 'kmeans'}, default='quantile'
Strategy used to define the widths of the bins.
uniform
All bins in each feature have identical widths.
quantile
All bins in each feature have the same number of points.
kmeans
Values in each bin have the same nearest center of a 1D
k-means cluster.
onehot_drop : {‘first’, ‘if_binary’} or a array-like of shape (len(dcols),), default='if_binary'
Specifies a methodology to use to drop one of the categories
per feature when encode = "onehot".
None
Retain all features (the default).
‘first’
Drop the first y_str in each feature. If only one y_str
is present, the feature will be dropped entirely.
‘if_binary’
Drop the first y_str in each feature with two categories.
Features with 1 or more than 2 categories are left intact.
Attributes
----------
discretizer_ : object of class KBinsDiscretizer()
Primary discretization method used to bin numeric data
manual_discretizer_ : dictionary
Provides bin_edges to feed into _quantile_discretization()
and do quantile discretization manually for features where
KBinsDiscretizer() failed. Ignored if strategy != 'quantile'
or no errors in KBinsDiscretizer().
onehot_ : object of class OneHotEncoder()
One hot encoding fit. Ignored if encode != 'onehot'
Examples
--------
| class BasicDiscretizer(AbstractDiscretizer):
"""
Discretize numeric data into bins. Provides a wrapper around
KBinsDiscretizer from sklearn
Params
------
n_bins : int or array-like of shape (len(dcols),), default=2
Number of bins to discretize each feature into.
dcols : list of strings
The names of the columns to be discretized; by default,
discretize all float and int columns in X.
encode : {'onehot', 'ordinal'}, default='onehot'
Method used to encode the transformed result.
onehot
Encode the transformed result with one-hot encoding and
return a dense array.
ordinal
Return the bin identifier encoded as an integer value.
strategy : {'uniform', 'quantile', 'kmeans'}, default='quantile'
Strategy used to define the widths of the bins.
uniform
All bins in each feature have identical widths.
quantile
All bins in each feature have the same number of points.
kmeans
Values in each bin have the same nearest center of a 1D
k-means cluster.
onehot_drop : {‘first’, ‘if_binary’} or a array-like of shape (len(dcols),), default='if_binary'
Specifies a methodology to use to drop one of the categories
per feature when encode = "onehot".
None
Retain all features (the default).
‘first’
Drop the first y_str in each feature. If only one y_str
is present, the feature will be dropped entirely.
‘if_binary’
Drop the first y_str in each feature with two categories.
Features with 1 or more than 2 categories are left intact.
Attributes
----------
discretizer_ : object of class KBinsDiscretizer()
Primary discretization method used to bin numeric data
manual_discretizer_ : dictionary
Provides bin_edges to feed into _quantile_discretization()
and do quantile discretization manually for features where
KBinsDiscretizer() failed. Ignored if strategy != 'quantile'
or no errors in KBinsDiscretizer().
onehot_ : object of class OneHotEncoder()
One hot encoding fit. Ignored if encode != 'onehot'
Examples
--------
"""
def __init__(self, n_bins=2, dcols=[],
encode='onehot', strategy='quantile',
onehot_drop='if_binary'):
super().__init__(n_bins=n_bins, dcols=dcols,
encode=encode, strategy=strategy,
onehot_drop=onehot_drop)
def fit(self, X, y=None):
"""
Fit the estimator.
Parameters
----------
X : data frame of shape (n_samples, n_features)
(Training) data to be discretized.
y : Ignored. This parameter exists only for compatibility with
:class:`~sklearn.pipeline.Pipeline` and fit_transform method
Returns
-------
self
"""
# initialization and error checking
self._fit_preprocessing(X)
# apply KBinsDiscretizer to the selected columns
discretizer = KBinsDiscretizer(n_bins=self.n_bins,
encode='ordinal',
strategy=self.strategy)
discretizer.fit(X[self.dcols_])
self.discretizer_ = discretizer
if (self.encode == 'onehot') | (self.strategy == 'quantile'):
discretized_df = discretizer.transform(X[self.dcols_])
discretized_df = pd.DataFrame(discretized_df,
columns=self.dcols_,
index=X.index).astype(int)
# fix KBinsDiscretizer errors if any when strategy = "quantile"
if self.strategy == "quantile":
err_idx = np.where(discretized_df.nunique() != self.n_bins)[0]
self.manual_discretizer_ = dict()
for idx in err_idx:
col = self.dcols_[idx]
if X[col].nunique() > 1:
q_values = np.linspace(0, 1, self.n_bins[idx] + 1)
bin_edges = np.quantile(X[col], q_values)
discretized_df[col] = self._discretize_to_bins(X[col], bin_edges,
keep_pointwise_bins=True)
self.manual_discretizer_[col] = bin_edges
# fit onehot encoded X if specified
if self.encode == "onehot":
onehot = OneHotEncoder(drop=self.onehot_drop) # , sparse=False)
onehot.fit(discretized_df.astype(str))
self.onehot_ = onehot
return self
def transform(self, X):
"""
Discretize the data.
Parameters
----------
X : data frame of shape (n_samples, n_features)
Data to be discretized.
Returns
-------
X_discretized : data frame
Data with features in dcols transformed to the
binned space. All other features remain unchanged.
"""
check_is_fitted(self)
# transform using KBinsDiscretizer
discretized_df = self.discretizer_.transform(
X[self.dcols_]).astype(int)
discretized_df = pd.DataFrame(discretized_df,
columns=self.dcols_,
index=X.index)
# fix KBinsDiscretizer errors (if any) when strategy = "quantile"
if self.strategy == "quantile":
for col in self.manual_discretizer_.keys():
bin_edges = self.manual_discretizer_[col]
discretized_df[col] = self._discretize_to_bins(X[col], bin_edges,
keep_pointwise_bins=True)
# return onehot encoded data if specified and
# join discretized columns with rest of X
X_discretized = self._transform_postprocessing(discretized_df, X)
return X_discretized
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16,652 | imodels.discretization.discretizer | __init__ | null | def __init__(self, n_bins=2, dcols=[],
encode='onehot', strategy='quantile',
onehot_drop='if_binary'):
super().__init__(n_bins=n_bins, dcols=dcols,
encode=encode, strategy=strategy,
onehot_drop=onehot_drop)
| (self, n_bins=2, dcols=[], encode='onehot', strategy='quantile', onehot_drop='if_binary') | [
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16,658 | imodels.discretization.discretizer | _discretize_to_bins |
Discretize data into bins of the form [a, b) given bin
edges/boundaries
Parameters
----------
x : array-like of shape (n_samples,)
Data vector to be discretized.
bin_edges : array-like
Values to serve as bin edges; should include min and
max values for the range of x
keep_pointwise_bins : boolean
If True, treat duplicate bin_edges as a pointwise bin,
i.e., [a, a]. If False, these bins are in effect ignored.
Returns
-------
xd: array of shape (n_samples,) where x has been
transformed to the binned space
| def _discretize_to_bins(self, x, bin_edges,
keep_pointwise_bins=False):
"""
Discretize data into bins of the form [a, b) given bin
edges/boundaries
Parameters
----------
x : array-like of shape (n_samples,)
Data vector to be discretized.
bin_edges : array-like
Values to serve as bin edges; should include min and
max values for the range of x
keep_pointwise_bins : boolean
If True, treat duplicate bin_edges as a pointwise bin,
i.e., [a, a]. If False, these bins are in effect ignored.
Returns
-------
xd: array of shape (n_samples,) where x has been
transformed to the binned space
"""
# ignore min and max values in bin generation
unique_edges = np.unique(bin_edges[1:-1])
if keep_pointwise_bins:
# note: min and max values are used to define pointwise bins
pointwise_bins = np.unique(
bin_edges[pd.Series(bin_edges).duplicated()])
else:
pointwise_bins = np.array([])
xd = np.zeros_like(x)
i = 1
for idx, split in enumerate(unique_edges):
if idx == (len(unique_edges) - 1): # uppermost bin
if (idx == 0) & (split in pointwise_bins):
# two bins total: (-inf, a], (a, inf)
indicator = x > split
else:
indicator = x >= split # uppermost bin: [a, inf)
else:
if split in pointwise_bins:
# create two bins: [a, a], (a, b)
indicator = (x > split) & (x < unique_edges[idx + 1]) #
if idx != 0:
xd[x == split] = i
i += 1
else:
# create bin: [a, b)
indicator = (x >= split) & (x < unique_edges[idx + 1])
xd[indicator] = i
i += 1
return xd.astype(int)
| (self, x, bin_edges, keep_pointwise_bins=False) | [
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16,659 | imodels.discretization.discretizer | _fit_preprocessing |
Initial checks before fitting the estimator.
Parameters
----------
X : data frame of shape (n_samples, n_features)
(Training) data to be discretized.
Returns
-------
self
| def _fit_preprocessing(self, X):
"""
Initial checks before fitting the estimator.
Parameters
----------
X : data frame of shape (n_samples, n_features)
(Training) data to be discretized.
Returns
-------
self
"""
# by default, discretize all numeric columns
if len(self.dcols) == 0:
numeric_cols = [
col for col in X.columns if is_numeric_dtype(X[col].dtype)]
self.dcols_ = numeric_cols
# error checking
self._validate_n_bins()
self._validate_args()
self._validate_dcols(X)
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16,666 | imodels.discretization.discretizer | _transform_postprocessing |
Final processing in transform method. Does one-hot encoding
(if specified) and joins discretized columns to the
un-transformed columns in X.
Parameters
----------
discretized_df : data frame of shape (n_sample, len(dcols))
Discretized data in the transformed bin space.
X : data frame of shape (n_samples, n_features)
Data to be discretized.
Returns
-------
X_discretized : data frame
Data with features in dcols transformed to the
binned space. All other features remain unchanged.
Encoded either as ordinal or one-hot.
| def _transform_postprocessing(self, discretized_df, X):
"""
Final processing in transform method. Does one-hot encoding
(if specified) and joins discretized columns to the
un-transformed columns in X.
Parameters
----------
discretized_df : data frame of shape (n_sample, len(dcols))
Discretized data in the transformed bin space.
X : data frame of shape (n_samples, n_features)
Data to be discretized.
Returns
-------
X_discretized : data frame
Data with features in dcols transformed to the
binned space. All other features remain unchanged.
Encoded either as ordinal or one-hot.
"""
discretized_df = discretized_df[self.dcols_]
# return onehot encoded X if specified
if self.encode == "onehot":
colnames = [str(col) for col in self.dcols_]
try:
onehot_col_names = self.onehot_.get_feature_names_out(colnames)
except:
onehot_col_names = self.onehot_.get_feature_names(
colnames) # older versions of sklearn
discretized_df = self.onehot_.transform(discretized_df.astype(str))
discretized_df = pd.DataFrame(discretized_df,
columns=onehot_col_names,
index=X.index).astype(int)
# join discretized columns with rest of X
cols = [col for col in X.columns if col not in self.dcols_]
X_discretized = pd.concat([discretized_df, X[cols]], axis=1)
return X_discretized
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16,667 | imodels.discretization.discretizer | _validate_args |
Check if encode, strategy arguments are valid.
| def _validate_args(self):
"""
Check if encode, strategy arguments are valid.
"""
valid_encode = ('onehot', 'ordinal')
if self.encode not in valid_encode:
raise ValueError("Valid options for 'encode' are {}. Got encode={!r} instead."
.format(valid_encode, self.encode))
valid_strategy = ('uniform', 'quantile', 'kmeans')
if (self.strategy not in valid_strategy):
raise ValueError("Valid options for 'strategy' are {}. Got strategy={!r} instead."
.format(valid_strategy, self.strategy))
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] |
16,669 | imodels.discretization.discretizer | _validate_dcols |
Check if dcols argument is valid.
| def _validate_dcols(self, X):
"""
Check if dcols argument is valid.
"""
for col in self.dcols_:
if col not in X.columns:
raise ValueError("{} is not a column in X.".format(col))
if not is_numeric_dtype(X[col].dtype):
raise ValueError("Cannot discretize non-numeric columns.")
| (self, X) | [
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16,670 | imodels.discretization.discretizer | _validate_n_bins |
Check if n_bins argument is valid.
| def _validate_n_bins(self):
"""
Check if n_bins argument is valid.
"""
orig_bins = self.n_bins
n_features = len(self.dcols_)
if isinstance(orig_bins, numbers.Number):
if not isinstance(orig_bins, numbers.Integral):
raise ValueError(
"{} received an invalid n_bins type. "
"Received {}, expected int.".format(
AbstractDiscretizer.__name__, type(orig_bins).__name__
)
)
if orig_bins < 2:
raise ValueError(
"{} received an invalid number "
"of bins. Received {}, expected at least 2.".format(
AbstractDiscretizer.__name__, orig_bins
)
)
self.n_bins = np.full(n_features, orig_bins, dtype=int)
else:
n_bins = check_array(orig_bins, dtype=int,
copy=True, ensure_2d=False)
if n_bins.ndim > 1 or n_bins.shape[0] != n_features:
raise ValueError(
"n_bins must be a scalar or array of shape (n_features,).")
bad_nbins_value = (n_bins < 2) | (n_bins != orig_bins)
violating_indices = np.where(bad_nbins_value)[0]
if violating_indices.shape[0] > 0:
indices = ", ".join(str(i) for i in violating_indices)
raise ValueError(
"{} received an invalid number "
"of bins at indices {}. Number of bins "
"must be at least 2, and must be an int.".format(
AbstractDiscretizer.__name__, indices
)
)
self.n_bins = n_bins
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16,672 | imodels.discretization.discretizer | fit |
Fit the estimator.
Parameters
----------
X : data frame of shape (n_samples, n_features)
(Training) data to be discretized.
y : Ignored. This parameter exists only for compatibility with
:class:`~sklearn.pipeline.Pipeline` and fit_transform method
Returns
-------
self
| def fit(self, X, y=None):
"""
Fit the estimator.
Parameters
----------
X : data frame of shape (n_samples, n_features)
(Training) data to be discretized.
y : Ignored. This parameter exists only for compatibility with
:class:`~sklearn.pipeline.Pipeline` and fit_transform method
Returns
-------
self
"""
# initialization and error checking
self._fit_preprocessing(X)
# apply KBinsDiscretizer to the selected columns
discretizer = KBinsDiscretizer(n_bins=self.n_bins,
encode='ordinal',
strategy=self.strategy)
discretizer.fit(X[self.dcols_])
self.discretizer_ = discretizer
if (self.encode == 'onehot') | (self.strategy == 'quantile'):
discretized_df = discretizer.transform(X[self.dcols_])
discretized_df = pd.DataFrame(discretized_df,
columns=self.dcols_,
index=X.index).astype(int)
# fix KBinsDiscretizer errors if any when strategy = "quantile"
if self.strategy == "quantile":
err_idx = np.where(discretized_df.nunique() != self.n_bins)[0]
self.manual_discretizer_ = dict()
for idx in err_idx:
col = self.dcols_[idx]
if X[col].nunique() > 1:
q_values = np.linspace(0, 1, self.n_bins[idx] + 1)
bin_edges = np.quantile(X[col], q_values)
discretized_df[col] = self._discretize_to_bins(X[col], bin_edges,
keep_pointwise_bins=True)
self.manual_discretizer_[col] = bin_edges
# fit onehot encoded X if specified
if self.encode == "onehot":
onehot = OneHotEncoder(drop=self.onehot_drop) # , sparse=False)
onehot.fit(discretized_df.astype(str))
self.onehot_ = onehot
return self
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16,673 | sklearn.base | fit_transform |
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 shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
**fit_params : dict
Additional fit parameters.
Returns
-------
X_new : ndarray array of shape (n_samples, n_features_new)
Transformed array.
| 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 update each component of a nested object.
Parameters
----------
**params : dict
Estimator parameters.
Returns
-------
self : estimator instance
Estimator instance.
"""
if not params:
# Simple optimization to gain speed (inspect is slow)
return self
valid_params = self.get_params(deep=True)
nested_params = defaultdict(dict) # grouped by prefix
for key, value in params.items():
key, delim, sub_key = key.partition("__")
if key not in valid_params:
local_valid_params = self._get_param_names()
raise ValueError(
f"Invalid parameter {key!r} for estimator {self}. "
f"Valid parameters are: {local_valid_params!r}."
)
if delim:
nested_params[key][sub_key] = value
else:
setattr(self, key, value)
valid_params[key] = value
for key, sub_params in nested_params.items():
valid_params[key].set_params(**sub_params)
return self
| (self, X, y=None, **fit_params) | [
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16,676 | sklearn.utils._set_output | set_output | 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"}, default=None
Configure output of `transform` and `fit_transform`.
- `"default"`: Default output format of a transformer
- `"pandas"`: DataFrame output
- `"polars"`: Polars output
- `None`: Transform configuration is unchanged
.. versionadded:: 1.4
`"polars"` option was added.
Returns
-------
self : estimator instance
Estimator instance.
| 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 dataframe. This is used to extract the metadata that should
be passed to `X_output`, e.g. pandas row index.
columns : callable, ndarray, or None
The column names or a callable that returns the column names. The
callable is useful if the column names require some computation. If `None`,
then no columns are passed to the container's constructor.
inplace : bool, default=False
Whether or not we intend to modify `X_output` in-place. However, it does
not guarantee that we return the same object if the in-place operation
is not possible.
Returns
-------
wrapped_output : container_type
`X_output` wrapped into the container type.
"""
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16,678 | imodels.discretization.discretizer | transform |
Discretize the data.
Parameters
----------
X : data frame of shape (n_samples, n_features)
Data to be discretized.
Returns
-------
X_discretized : data frame
Data with features in dcols transformed to the
binned space. All other features remain unchanged.
| def _transform_postprocessing(self, discretized_df, X):
"""
Final processing in transform method. Does one-hot encoding
(if specified) and joins discretized columns to the
un-transformed columns in X.
Parameters
----------
discretized_df : data frame of shape (n_sample, len(dcols))
Discretized data in the transformed bin space.
X : data frame of shape (n_samples, n_features)
Data to be discretized.
Returns
-------
X_discretized : data frame
Data with features in dcols transformed to the
binned space. All other features remain unchanged.
Encoded either as ordinal or one-hot.
"""
discretized_df = discretized_df[self.dcols_]
# return onehot encoded X if specified
if self.encode == "onehot":
colnames = [str(col) for col in self.dcols_]
try:
onehot_col_names = self.onehot_.get_feature_names_out(colnames)
except:
onehot_col_names = self.onehot_.get_feature_names(
colnames) # older versions of sklearn
discretized_df = self.onehot_.transform(discretized_df.astype(str))
discretized_df = pd.DataFrame(discretized_df,
columns=onehot_col_names,
index=X.index).astype(int)
# join discretized columns with rest of X
cols = [col for col in X.columns if col not in self.dcols_]
X_discretized = pd.concat([discretized_df, X[cols]], axis=1)
return X_discretized
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