Methods

Summary

method	class parent	truncated documentation
__eq__	SkBase	Compares two objects, more precisely, compares the parameters which define the object.
__init__	BaseEstimatorDebugInformation
__init__	MLCache
__init__	PipelineCache
__init__	ApproximateNMFPredictor	kwargs should contains parameters for sklearn.decomposition.NMF. The parameter force_positive …
__init__	CategoriesToIntegers
__init__	ClassifierAfterKMeans
__init__	DecisionTreeLogisticRegression	constructor
__init__	_DecisionTreeLogisticRegressionNode	constructor
__init__	ExtendedFeatures
__init__	IntervalRegressor
__init__	ConstraintKMeans
__init__	KMeansL1L2
__init__	PiecewiseClassifier
__init__	PiecewiseEstimator
__init__	PiecewiseRegressor
__init__	PiecewiseTreeRegressor
__init__	PredictableTSNE
__init__	CustomizedMultilayerPerceptron
__init__	QuantileMLPRegressor	See sklearn.neural_networks.MLPRegressor
__init__	QuantileLinearRegression
__init__	BaseReciprocalTransformer
__init__	FunctionReciprocalTransformer
__init__	PermutationReciprocalTransformer
__init__	TransformedTargetClassifier2
__init__	TransformedTargetRegressor2
__init__	TransferTransformer
__init__	SearchEnginePredictions
__init__	SearchEngineVectors
__init__	SkBase	Stores the parameters, see SkLearnParameters, it keeps a copy of the parameters to easily implements …
__init__	SkBaseClassifier	constructor
__init__	SkBaseLearner	constructor
__init__	SkBaseRegressor	constructor
__init__	SkBaseTransform	Stores the parameters.
__init__	SkBaseTransformLearner
__init__	SkBaseTransformStacking
__init__	SkLearnParameters	Stores parameters as members of the class itself.
__init__	ARTimeSeriesRegressor
__init__	BaseReciprocalTimeSeriesTransformer
__init__	BaseTimeSeries
__init__	DummyTimeSeriesRegressor
__init__	TimeSeriesDifference
__init__	TimeSeriesDifferenceInv
__len__	MLCache	Returns the number of cached items.
__repr__	BaseEstimatorDebugInformation	usual
__repr__	ClassifierAfterKMeans	Overloads repr as scikit-learn now relies on the constructor signature.
__repr__	SearchEnginePredictions	usual
__repr__	SearchEngineVectors	usual
__repr__	SkBase	usual
__repr__	SkBaseTransformLearner	usual
__repr__	SkBaseTransformStacking	usual
__repr__	SkLearnParameters	usual
__str__	CategoriesToIntegers	usual
_applies_preprocessing	BaseTimeSeries	Applies the preprocessing to the series.
_applies_preprocessing_inv	BaseTimeSeries	Applies the preprocessing to the series.
_apply	TransformedTargetClassifier2	Calls predict, predict_proba or decision_function using the base classifier, applying inverse.
_apply_predict_method	PiecewiseClassifier	Generic predict method, works for predict_proba and decision_function as well.
_apply_predict_method	PiecewiseEstimator	Generic predict method, works for predict_proba and decision_function as well.
_apply_predict_method	PiecewiseRegressor	Generic predict method, works for predict_proba and decision_function as well.
_backprop	CustomizedMultilayerPerceptron	Computes the MLP loss function and its corresponding derivatives with respect to each parameter: weights and bias …
_backprop	QuantileMLPRegressor	Computes the MLP loss function and its corresponding derivatives with respect to each parameter: weights and bias …
_base_fit_predict	BaseTimeSeries	Trains the preprocessing and returns the modified X, y, sample_weight.
_build_schema	CategoriesToIntegers	Concatenates all the categories given the information stored in _categories.
_check_is_fitted	PermutationReciprocalTransformer
_check_is_fitted	TransformedTargetClassifier2
_find_closest	PermutationReciprocalTransformer
_first_pass	SearchEngineVectors	Finds the closest n_neighbors.
_fit	PipelineCache
_fit_knn	SearchEngineVectors	Fits the nearest neighbors.
_fit_l1	KMeansL1L2	Computes k-means clustering with norm ‘L1’.
_fit_parallel	DecisionTreeLogisticRegression	Implements the parallel strategy.
_fit_perpendicular	DecisionTreeLogisticRegression	Implements the perpendicular strategy.
_fit_poly	ExtendedFeatures	Fitting method for the polynomial features.
_fit_preprocessing	BaseTimeSeries	Applies the preprocessing. X, y, sample_weight.
_fit_reglin	PiecewiseTreeRegressor	Fits linear regressions for all leaves. Sets attributes leaves_mapping_, betas_, leaves_index_. …
_get_feature_names_poly	ExtendedFeatures	Returns feature names for output features for the polynomial features.
_get_fit_params_steps	PipelineCache
_get_loss_function	CustomizedMultilayerPerceptron	Returns the loss functions.
_get_loss_function	QuantileMLPRegressor	Returns the loss functions.
_is_iterable	SearchEngineVectors	Tells if an objet is an iterator or not.
_mapping_train	PiecewiseClassifier
_mapping_train	PiecewiseEstimator
_mapping_train	PiecewiseRegressor
_mapping_train	PiecewiseTreeRegressor
_modify_loss_derivatives	CustomizedMultilayerPerceptron	Modifies the loss derivatives.
_modify_loss_derivatives	QuantileMLPRegressor	Modifies the loss derivatives.
_more_tags	TransformedTargetClassifier2
_more_tags	TransformedTargetRegressor2
_predict_l1	KMeansL1L2	Returns the distance of each point in X to every fit clusters.
_predict_reglin	PiecewiseTreeRegressor	Computes the predictions with a linear regression fitted with the observations mapped to each leave of the …
_prepare_fit	SearchEnginePredictionImages	Stores data in the class itself.
_prepare_fit	SearchEngineVectors	Stores data in the class itself.
_second_pass	SearchEngineVectors	Reorders the closest n_neighbors.
_set_method	SkBaseTransformLearner	Defines the method to use to convert the features into predictions.
_transform_l1	KMeansL1L2	Returns the distance of each point in X to every fit clusters.
_transform_poly	ExtendedFeatures	Transforms data to polynomial features.
_transform_poly_slow	ExtendedFeatures	Transforms data to polynomial features.
_validate_input	QuantileMLPRegressor
_word_ngrams	NGramsMixin	Turn tokens into a sequence of n-grams after stop words filtering
_word_ngrams	TraceableCountVectorizer
_word_ngrams	TraceableTfidfVectorizer
cache	MLCache	Caches one object.
cluster_edges	ConstraintKMeans	Computes edges between clusters based on a Delaunay …
constraint_kmeans	ConstraintKMeans	Completes the constraint k-means.
count	MLCache	Retrieves the number of times an elements was retrieved from the cache.
decision_function	ClassifierAfterKMeans	Calls decision_function.
decision_function	DecisionTreeLogisticRegression	Calls decision_function.
decision_function	PiecewiseClassifier	Computes the predictions probabilities.
decision_function	TransformedTargetClassifier2	Predicts using the base classifier, applying inverse.
decision_function	SkBaseLearner	Output of the model in case of a regressor, matrix with a score for each class and each sample for a classifier. …
decision_path	DecisionTreeLogisticRegression	Returns the decision path.
decision_path	_DecisionTreeLogisticRegressionNode	Returns the classification probabilities.
display	BaseEstimatorDebugInformation	Displays the first
enumerate_leaves_index	_DecisionTreeLogisticRegressionNode	Returns the leaves index.
fit	ApproximateNMFPredictor	Trains a sklearn.decomposition.NMF then a multi-output regressor.
fit	CategoriesToIntegers	Makes the list of all categories in input X. X must be a dataframe.
fit	ClassifierAfterKMeans	Runs a k-means on each class then trains a classifier on the extended set of features.
fit	DecisionTreeLogisticRegression	Builds the tree model.
fit	_DecisionTreeLogisticRegressionNode	Fits a logistic regression, then splits the sample into positive and negative examples, finally tries to fit …
fit	ExtendedFeatures	Compute number of output features.
fit	IntervalRegressor	Trains the binner and an estimator on every bucket.
fit	ConstraintKMeans	Compute k-means clustering.
fit	KMeansL1L2	Computes k-means clustering.
fit	PiecewiseClassifier	Trains the binner and an estimator on every bucket.
fit	PiecewiseEstimator	Trains the binner and an estimator on every bucket.
fit	PiecewiseRegressor	Trains the binner and an estimator on every bucket.
fit	PiecewiseTreeRegressor	Replaces the string stored in criterion by an instance of a class.
fit	PredictableTSNE	Trains a TSNE then trains an estimator to approximate its outputs.
fit	QuantileLinearRegression	Fits a linear model with L1 norm which is equivalent to a quantile regression. The implementation …
fit	FunctionReciprocalTransformer	Just defines fct and fct_inv.
fit	PermutationReciprocalTransformer	Defines a random permutation over the targets.
fit	TransformedTargetClassifier2	Fits the model according to the given training data.
fit	TransformedTargetRegressor2	Fits the model according to the given training data.
fit	TransferTransformer	The function does nothing.
fit	SearchEnginePredictions	Every vector comes with a list of metadata.
fit	SearchEnginePredictionImages	Processes images through the model and fits a k-nn.
fit	SearchEngineVectors	Every vector comes with a list of metadata.
fit	SkBase	Trains a model.
fit	SkBaseLearner	Trains a model.
fit	SkBaseTransform	Trains a model.
fit	SkBaseTransformLearner	Trains a model.
fit	SkBaseTransformStacking	Trains a model.
fit	ARTimeSeriesRegressor	Trains the model.
fit	BaseReciprocalTimeSeriesTransformer	Stores the first values.
fit	DummyTimeSeriesRegressor	Trains the model.
fit	TimeSeriesDifference	Stores the first values.
fit	TimeSeriesDifferenceInv	Checks that estimator is fitted.
fit_improve	_DecisionTreeLogisticRegressionNode	The method only works on a linear classifier, it changes the intercept in order to be within the constraints …
fit_transform	CategoriesToIntegers	Fits and transforms categories in numerical features based on the list of categories found by method fit. …
fit_transform	SkBaseTransform	Trains and transforms the data.
get	MLCache	Retrieves an element from the cache.
get_fct_inv	BaseReciprocalTransformer	Returns a trained transform which reverse the target after a predictor.
get_fct_inv	FunctionReciprocalTransformer	Returns a trained transform which reverse the target after a predictor.
get_fct_inv	PermutationReciprocalTransformer	Returns a trained transform which reverse the target after a predictor.
get_fct_inv	BaseReciprocalTimeSeriesTransformer	Returns the reverse tranform.
get_fct_inv	TimeSeriesDifference	Returns the reverse tranform.
get_feature_names	ExtendedFeatures	Returns feature names for output features.
get_leaves_index	DecisionTreeLogisticRegression	Returns the index of every leave.
get_params	ApproximateNMFPredictor	Returns the parameters of the estimator as a dictionary.
get_params	ClassifierAfterKMeans	Returns the parameters for both the clustering and the classifier.
get_params	SkBase	Returns the parameters which define the objet, all are needed to clone the object.
get_params	SkBaseTransformLearner	Returns the parameters mandatory to clone the class.
get_params	SkBaseTransformStacking	Returns the parameters which define the object. It follows scikit-learn API.
has_preprocessing	BaseTimeSeries	Tells if there is one preprocessing.
items	MLCache	Enumerates all cached items.
keys	MLCache	Enumerates all cached keys.
kneighbors	SearchEnginePredictions	Searches for neighbors close to X.
kneighbors	SearchEnginePredictionImages	Searches for neighbors close to the first image returned by iter_images. It returns the neighbors only …
kneighbors	SearchEngineVectors	Searches for neighbors close to X.
predict	ApproximateNMFPredictor	Predicts based on the multi-output regressor. The output has the same dimension as X.
predict	ClassifierAfterKMeans	Runs the predictions.
predict	DecisionTreeLogisticRegression	Runs the predictions.
predict	_DecisionTreeLogisticRegressionNode	Predicts
predict	IntervalRegressor	Computes the average predictions.
predict	ConstraintKMeans	Computes the predictions.
predict	KMeansL1L2	Predicts the closest cluster each sample in X belongs to. In the vector quantization literature, cluster_centers_ …
predict	PiecewiseClassifier	Computes the predictions.
predict	PiecewiseRegressor	Computes the predictions.
predict	PiecewiseTreeRegressor	Overloads method predict. Falls back into the predict from a decision tree is criterion is mse, mae, …
predict	QuantileMLPRegressor	Predicts using the multi-layer perceptron model.
predict	TransformedTargetClassifier2	Predicts using the base classifier, applying inverse.
predict	TransformedTargetRegressor2	Predicts using the base regressor, applying inverse.
predict	SkBaseLearner	Predicts.
predict	ARTimeSeriesRegressor	Returns the prediction
predict	DummyTimeSeriesRegressor	Returns the prediction
predict_all	IntervalRegressor	Computes the predictions for all estimators.
predict_leaves	PiecewiseTreeRegressor	Returns the leave index for each observation of X.
predict_proba	ClassifierAfterKMeans	Converts predictions into probabilities.
predict_proba	DecisionTreeLogisticRegression	Converts predictions into probabilities.
predict_proba	_DecisionTreeLogisticRegressionNode	Returns the classification probabilities.
predict_proba	PiecewiseClassifier	Computes the predictions probabilities.
predict_proba	TransformedTargetClassifier2	Predicts using the base classifier, applying inverse.
predict_proba	SkBaseClassifier	Returns probability estimates for the test data X.
predict_sorted	IntervalRegressor	Computes the predictions for all estimators. Sorts them for all observations.
score	ConstraintKMeans	Returns the distances to all clusters.
score	QuantileMLPRegressor	Returns mean absolute error regression loss.
score	QuantileLinearRegression	Returns Mean absolute error regression loss.
score	TransformedTargetClassifier2	Scores the model with sklearn.metrics.accuracy_score.
score	TransformedTargetRegressor2	Scores the model with sklearn.metrics.r2_score.
score	SkBaseClassifier	Returns the mean accuracy on the given test data and labels.
score	SkBaseLearner	Returns the mean accuracy on the given test data and labels.
score	SkBaseRegressor	Returns the mean accuracy on the given test data and labels.
score	TimeSeriesRegressorMixin	Scores the prediction using ts_mape()
set_params	ClassifierAfterKMeans	Sets the parameters before training. Every parameter prefixed by 'e_' is an estimator parameter, every …
set_params	SkBase	Udpates parameters which define the object, all needed to clone the object.
set_params	SkBaseTransformLearner	Sets parameters.
set_params	SkBaseTransformStacking	Sets the parameters.
test_equality	SkBase	Compares two objects and checks parameters have the same values.
to_dict	SkLearnParameters	Returns parameters as a dictionary.
to_str	BaseEstimatorDebugInformation	Tries to produce a readable message.
to_zip	SearchEngineVectors	Saves the features and the metadata into a zipfile. The function does not save the k-nn.
transform	CategoriesToIntegers	Transforms categories in numerical features based on the list of categories found by method fit. X must …
transform	ExtendedFeatures	Transforms data to extended features.
transform	ConstraintKMeans	Computes the predictions.
transform	KMeansL1L2	Transforms X to a cluster-distance space. In the new space, each dimension is the distance to the cluster …
transform	PredictableTSNE	Runs the predictions.
transform	BaseReciprocalTransformer	Transforms X and y. Returns transformed X and y.
transform	FunctionReciprocalTransformer	Transforms X and y. Returns transformed X and y. If y is None, the returned value for y …
transform	PermutationReciprocalTransformer	Transforms X and y. Returns transformed X and y. If y is None, the returned value for y …
transform	TransferTransformer	Runs the predictions.
transform	SkBaseTransform	Transforms the data.
transform	SkBaseTransformLearner	Predictions, output of the embedded learner.
transform	SkBaseTransformStacking	Calls the learners predictions to convert the features.
transform	BaseReciprocalTimeSeriesTransformer	Transforms both X and y. Returns X and y, returns sample_weight as well if not None. The …
transform	TimeSeriesDifference	Transforms both X and y. Returns X and y, returns sample_weight as well if not None.
transform	TimeSeriesDifferenceInv	Transforms both X and y. Returns X and y, returns sample_weight as well if not None.
transform_bins	PiecewiseClassifier	Maps every row to a tree in self.estimators_.
transform_bins	PiecewiseEstimator	Maps every row to a tree in self.estimators_.
transform_bins	PiecewiseRegressor	Maps every row to a tree in self.estimators_.
transform_features	ClassifierAfterKMeans	Applies all the clustering objects on every observations and extends the list of features.
validate	SkLearnParameters	Verifies a parameter and its value.