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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}" )
(self)
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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__()
(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}" )
(self) -> str
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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
(self, iboost, X, y, sample_weight, random_state)
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0.019552139565348625, -0.00020062158000655472, 0.045183755457401276 ]
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)
(decision, n_classes)
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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
(self, append=True, random_state=None)
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16,478
sklearn.base
_more_tags
null
def _more_tags(self): return {"requires_y": True}
(self)
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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." )
(self)
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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
(self, X)
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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)
(self, X)
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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)
(self, X)
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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
(self, X)
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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)
(self, X)
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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
(self, estimator=None, *, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None)
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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_))
(self, X)
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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
(self, X, y, cv=5)
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16,552
imodels.util.automl
predict
null
def predict(self, X): return self.est_.predict(X)
(self, 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)
(self, 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
(self, X, chain_number)
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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
(self, X, y, chain_number)
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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
(self, X, chain_number)
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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()]
(self, X: numpy.ndarray, y: numpy.ndarray)
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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
(self, X, chain_number)
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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
(self, i)
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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
(self, feature_labels, verbose=False)
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16,626
imodels.discretization.mdlp
_apply_cutpoints
null
def _apply_cutpoints(self): return self.discretizer._apply_cutpoints()
(self)
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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
(self, X)
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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()
(self, X, y)
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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
(self, X, y, undiscretized_features=[])
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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
(self, discretized_X)
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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)
(self, X, return_onehot=True)
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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
(n_bins=2, dcols=[], encode='onehot', strategy='quantile', onehot_drop='if_binary')
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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)
(self, 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
(self, discretized_df, X)
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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))
(self)
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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
(self)
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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
(self, X, y=None)
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0.014120681211352348, 0.030711067840456963, 0.09901444613933563, 0.01724080555140972 ]
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. """
(self, *, transform=None)
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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
(self, X)
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