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__

ExtendedFeatures

__init__

IntervalRegressor

__init__

PiecewiseClassifier

__init__

PiecewiseEstimator

__init__

PiecewiseRegressor

__init__

PiecewiseTreeRegressor

__init__

PredictableTSNE

__init__

CustomizedMultilayerPerceptron

__init__

QuantileMLPRegressor

Parameters ———- 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.

__len__

MLCache

Returns the number of cached items.

__repr__

BaseEstimatorDebugInformation

usual

__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

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 …

_build_schema

CategoriesToIntegers

Concatenates all the categories given the information stored in _categories.

_first_pass

SearchEngineVectors

Finds the closest n_neighbors.

_fit

PipelineCache

_fit_knn

SearchEngineVectors

Fits the nearest neighbors.

_fit_poly

ExtendedFeatures

Fitting method for the polynomial features.

_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.

_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_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.

count

MLCache

Retrieves the number of times an elements was retrieved from the cache.

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. …

display

BaseEstimatorDebugInformation

Displays the first

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. 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

IntervalRegressor

Trains the binner and an estimator on every bucket. Parameters ———- X: 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

PiecewiseRegressor

Trains the binner and an estimator on every bucket. Parameters ———- X: features, …

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. 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

MLCache

Retrieves an element from the cache.

get_feature_names

ExtendedFeatures

Returns feature names for output features. Parameters ———- input_features : list of string, …

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.

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

IntervalRegressor

Computes the average predictions. Parameters ———- X: features, X is converted into an array …

predict

PiecewiseClassifier

Computes the predictions. Parameters ———- X: features, X is converted into an array if …

predict

PiecewiseRegressor

Computes the predictions. Parameters ———- X: features, X is converted into an array if …

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. Parameters ———- X : {array-like, sparse …

predict

SkBaseLearner

Predicts.

predict_all

IntervalRegressor

Computes the predictions for all estimators. Parameters ———- X: features, X is converted …

predict_leaves

PiecewiseTreeRegressor

Returns the leave index for each observation of X.

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.

predict_sorted

IntervalRegressor

Computes the predictions for all estimators. Sorts them for all observations. Parameters ———- …

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_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. 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

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.