Methods

Summary

method class parent truncated documentation
__eq__ SkBase Compares two objects, more precisely, compares the parameters which define the object.
__init__ CategoriesToIntegers  
__init__ ClassifierAfterKMeans  
__init__ ExtendedFeatures  
__init__ PiecewiseClassifier  
__init__ PiecewiseEstimator  
__init__ PiecewiseRegression  
__init__ DecisionTreeLinearRegressor  
__init__ PredictableTSNE  
__init__ CustomizedMultilayerPerceptron  
__init__ QuantileMLPRegressor Parameters ———- loss: loss function, by default 'absolute_loss' kwargs: see sklearn.neural_networks.MLPRegressor
__init__ QuantileLinearRegression Parameters ———- fit_intercept: boolean, optional, default True whether to calculate the …
__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.
__repr__ SearchEnginePredictions usual
__repr__ SearchEngineVectors usual
__repr__ SkBase usual
__repr__ SkBaseTransformLearner usual
__repr__ SkBaseTransformStacking usual
__repr__ SkLearnParameters usual
__str__ CategoriesToIntegers usual
_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 PiecewiseRegression 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 …
_build_schema CategoriesToIntegers Concatenates all the categories given the information stored in _categories.
_first_pass SearchEngineVectors Finds the closest n_neighbors.
_fit_knn SearchEngineVectors Fits the nearest neighbors.
_fit_poly ExtendedFeatures Fitting method for the polynomial features.
_get_feature_names_poly ExtendedFeatures Returns feature names for output features for the polynomial features.
_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 PiecewiseRegression  
_modify_loss_derivatives CustomizedMultilayerPerceptron Modifies the loss derivatives.
_modify_loss_derivatives QuantileMLPRegressor Modifies the loss derivatives.
_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_poly ExtendedFeatures Transforms data to polynomial features.
_transform_poly_slow ExtendedFeatures Transforms data to polynomial features.
_validate_input QuantileMLPRegressor  
decision_function ClassifierAfterKMeans Calls decision_function.
decision_function PiecewiseClassifier Computes the predictions probabilities. Parameters ———- X: features, X is converted into …
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. …
fit CategoriesToIntegers Makes the list of all categories in input X. X must be a dataframe. Parameters ———- …
fit ClassifierAfterKMeans Runs a k-means on each class then trains a classifier on the extended set of features. Parameters …
fit ExtendedFeatures Compute number of output features. Parameters ———- X : array-like, shape (n_samples, n_features) …
fit PiecewiseClassifier Trains the binner and an estimator on every bucket. Parameters ———- X: features, …
fit PiecewiseEstimator Trains the binner and an estimator on every bucket. Parameters ———- X: features, …
fit PiecewiseRegression Trains the binner and an estimator on every bucket. Parameters ———- X: features, …
fit DecisionTreeLinearRegressor Réinterprète le paramètre criterion.
fit PredictableTSNE Trains a TSNE then trains an estimator to approximate its outputs. Parameters ———- …
fit QuantileLinearRegression Fits a linear model with L1 norm which is equivalent to a quantile regression. Parameters …
fit TransferTransformer The function does nothing. Parameters ———- X: unused y: unused sample_weight: …
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_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_feature_names ExtendedFeatures Returns feature names for output features. Parameters ———- input_features : list of string, …
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.
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 ClassifierAfterKMeans Runs the predictions.
predict PiecewiseClassifier Computes the predictions. Parameters ———- X: features, X is converted into an array if …
predict PiecewiseRegression Computes the predictions. Parameters ———- X: features, X is converted into an array if …
predict QuantileMLPRegressor Predicts using the multi-layer perceptron model. Parameters ———- X : {array-like, sparse …
predict SkBaseLearner Predicts.
predict_proba ClassifierAfterKMeans Converts predictions into probabilities.
predict_proba PiecewiseClassifier Computes the predictions probabilities. Parameters ———- X: features, X is converted into …
predict_proba SkBaseClassifier Returns probability estimates for the test data X.
score QuantileMLPRegressor Returns mean absolute error regression loss. Parameters ———- X : array-like, shape = (n_samples, …
score QuantileLinearRegression Returns Mean absolute error regression loss. Parameters ———- X : array-like, shape = (n_samples, …
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.
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_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. Parameters ———- X : array-like, shape [n_samples, n_features] …
transform PredictableTSNE Runs the predictions. Parameters ———- X : numpy array or sparse matrix of shape [n_samples,n_features] …
transform TransferTransformer Runs the predictions. Parameters ———- X : numpy array or sparse matrix of shape [n_samples,n_features] …
transform SkBaseTransform Transforms the data.
transform SkBaseTransformLearner Predictions, output of the embedded learner.
transform SkBaseTransformStacking Calls the learners predictions to convert the features.
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 PiecewiseRegression 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.