expandas.skaccessors package¶

Subpackages¶

expandas.skaccessors.test package
- Submodules
- Module contents

Submodules¶

class expandas.skaccessors.cluster.ClusterMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.cluster.

Attributes

bicluster Property to access sklearn.cluster.bicluster

Methods

`affinity_propagation`(args, *kwargs)	Call `sklearn.cluster.affinity_propagation` using automatic mapping.
`dbscan`(args, *kwargs)	Call `sklearn.cluster.dbscan` using automatic mapping.
`k_means`(n_clusters, args, *kwargs)	Call `sklearn.cluster.k_means` using automatic mapping.
`mean_shift`(args, *kwargs)	Call `sklearn.cluster.mean_shift` using automatic mapping.
`spectral_clustering`(args, *kwargs)	Call `sklearn.cluster.spectral_clustering` using automatic mapping.

affinity_propagation(*args, **kwargs)¶

Call sklearn.cluster.affinity_propagation using automatic mapping.

S: ModelFrame.data

bicluster¶: Property to access sklearn.cluster.bicluster

dbscan(*args, **kwargs)¶

Call sklearn.cluster.dbscan using automatic mapping.

X: ModelFrame.data

k_means(n_clusters, *args, **kwargs)¶

Call sklearn.cluster.k_means using automatic mapping.

X: ModelFrame.data

mean_shift(*args, **kwargs)¶

Call sklearn.cluster.mean_shift using automatic mapping.

X: ModelFrame.data

spectral_clustering(*args, **kwargs)¶

Call sklearn.cluster.spectral_clustering using automatic mapping.

affinity: ModelFrame.data

class expandas.skaccessors.covariance.CovarianceMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.covariance.

Methods

`empirical_covariance`(args, *kwargs)	Call `sklearn.covariance.empirical_covariance` using automatic mapping.
`ledoit_wolf`(args, *kwargs)	Call `sklearn.covariance.ledoit_wolf` using automatic mapping.
`oas`(args, *kwargs)	Call `sklearn.covariance.oas` using automatic mapping.

empirical_covariance(*args, **kwargs)¶

Call sklearn.covariance.empirical_covariance using automatic mapping.

X: ModelFrame.data

ledoit_wolf(*args, **kwargs)¶

Call sklearn.covariance.ledoit_wolf using automatic mapping.

X: ModelFrame.data

oas(*args, **kwargs)¶

Call sklearn.covariance.oas using automatic mapping.

X: ModelFrame.data

class expandas.skaccessors.cross_validation.CrossValidationMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.cross_validation.

Methods

`StratifiedShuffleSplit`(args, *kwargs)	Instanciate `sklearn.cross_validation.StratifiedShuffleSplit` using automatic mapping.
`check_cv`(cv, args, *kwargs)	Call `sklearn.cross_validation.check_cv` using automatic mapping.
`cross_val_score`(estimator, args, *kwargs)	Call `sklearn.cross_validation.cross_val_score` using automatic mapping.
`iterate`(cv)	Generate `ModelFrame` using iterators for cross validation
`permutation_test_score`(estimator, *args, ...)	Call `sklearn.cross_validation.permutation_test_score` using automatic mapping.
`train_test_split`(args, *kwargs)	Call `sklearn.cross_validation.train_test_split` using automatic mapping.

StratifiedShuffleSplit(*args, **kwargs)¶

Instanciate sklearn.cross_validation.StratifiedShuffleSplit using automatic mapping.

y: ModelFrame.target

check_cv(cv, *args, **kwargs)¶

Call sklearn.cross_validation.check_cv using automatic mapping.

X: ModelFrame.data
y: ModelFrame.target

cross_val_score(estimator, *args, **kwargs)¶

Call sklearn.cross_validation.cross_val_score using automatic mapping.

X: ModelFrame.data
y: ModelFrame.target

iterate(cv)¶

Generate ModelFrame using iterators for cross validation

Parameters:	cv : cross validation iterator
Returns:	generated : generator of `ModelFrame`

permutation_test_score(estimator, *args, **kwargs)¶

Call sklearn.cross_validation.permutation_test_score using automatic mapping.

X: ModelFrame.data
y: ModelFrame.target

train_test_split(*args, **kwargs)¶: Call sklearn.cross_validation.train_test_split using automatic mapping.

class expandas.skaccessors.decomposition.DecompositionMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.decomposition.

Methods

`dict_learning`(n_components, alpha, *args, ...)	Call `sklearn.decomposition.dict_learning` using automatic mapping.
`dict_learning_online`(args, *kwargs)	Call `sklearn.decomposition.dict_learning_online` using automatic mapping.
`fastica`(args, *kwargs)	Call `sklearn.decomposition.fastica` using automatic mapping.
`sparse_encode`(dictionary, args, *kwargs)	Call `sklearn.decomposition.sparce_encode` using automatic mapping.

dict_learning(n_components, alpha, *args, **kwargs)¶

Call sklearn.decomposition.dict_learning using automatic mapping.

X: ModelFrame.data

dict_learning_online(*args, **kwargs)¶

Call sklearn.decomposition.dict_learning_online using automatic mapping.

X: ModelFrame.data

fastica(*args, **kwargs)¶

Call sklearn.decomposition.fastica using automatic mapping.

X: ModelFrame.data

sparse_encode(dictionary, *args, **kwargs)¶

Call sklearn.decomposition.sparce_encode using automatic mapping.

X: ModelFrame.data

class expandas.skaccessors.ensemble.EnsembleMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.ensemble.

Attributes

partial_dependence Property to access sklearn.ensemble.partial_dependence

partial_dependence¶: Property to access sklearn.ensemble.partial_dependence

class expandas.skaccessors.ensemble.PartialDependenceMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Methods

`partial_dependence`(gbrt, target_variables, ...)	Call `sklearn.ensemble.partial_dependence` using automatic mapping.
`plot_partial_dependence`(gbrt, features, **kwargs)	Call `sklearn.ensemble.plot_partial_dependence` using automatic mapping.

partial_dependence(gbrt, target_variables, **kwargs)¶

Call sklearn.ensemble.partial_dependence using automatic mapping.

X: ModelFrame.data

plot_partial_dependence(gbrt, features, **kwargs)¶

Call sklearn.ensemble.plot_partial_dependence using automatic mapping.

X: ModelFrame.data

class expandas.skaccessors.feature_extraction.FeatureExtractionMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.feature_extraction.

Attributes

`image`	Property to access `sklearn.feature_extraction.image`
`text`	Property to access `sklearn.feature_extraction.text`

image¶: Property to access sklearn.feature_extraction.image

text¶: Property to access sklearn.feature_extraction.text

class expandas.skaccessors.feature_selection.FeatureSelectionMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.feature_selection.

class expandas.skaccessors.gaussian_process.GaussianProcessMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.gaussian_process.

Attributes

`correlation_models`	Property to access `sklearn.gaussian_process.correlation_models`
`regression_models`	Property to access `sklearn.gaussian_process.regression_models`

correlation_models¶: Property to access sklearn.gaussian_process.correlation_models

regression_models¶: Property to access sklearn.gaussian_process.regression_models

class expandas.skaccessors.gaussian_process.RegressionModelsMethods(df, module_name=None, attrs=None)¶: Bases: expandas.core.accessor.AccessorMethods

class expandas.skaccessors.grid_search.GridSearchMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.grid_search.

Methods

describe(estimator) Describe grid search results

describe(estimator)¶

Describe grid search results

Parameters:	estimator : fitted grid search estimator
Returns:	described : `ModelFrame`

class expandas.skaccessors.isotonic.IsotonicMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.isotonic.

Attributes

IsotonicRegression sklearn.isotonic.IsotonicRegression

Methods

`check_increasing`(args, *kwargs)	Call `sklearn.isotonic.check_increasing` using automatic mapping.
`isotonic_regression`(args, *kwargs)	Call `sklearn.isotonic.isotonic_regression` using automatic mapping.

IsotonicRegression¶: sklearn.isotonic.IsotonicRegression

check_increasing(*args, **kwargs)¶

Call sklearn.isotonic.check_increasing using automatic mapping.

x: ModelFrame.index
y: ModelFrame.target

isotonic_regression(*args, **kwargs)¶

Call sklearn.isotonic.isotonic_regression using automatic mapping.

y: ModelFrame.target

class expandas.skaccessors.learning_curve.LearningCurveMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.learning_curve.

Methods

`learning_curve`(estimator, args, *kwargs)	Call `sklearn.lerning_curve.learning_curve` using automatic mapping.
`validation_curve`(estimator, param_name, ...)	Call `sklearn.learning_curve.validation_curve` using automatic mapping.

learning_curve(estimator, *args, **kwargs)¶

Call sklearn.lerning_curve.learning_curve using automatic mapping.

X: ModelFrame.data
y: ModelFrame.target

validation_curve(estimator, param_name, param_range, *args, **kwargs)¶

Call sklearn.learning_curve.validation_curve using automatic mapping.

X: ModelFrame.data
y: ModelFrame.target

class expandas.skaccessors.linear_model.LinearModelMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.linear_model.

Methods

`lars_path`(args, *kwargs)	Call `sklearn.linear_model.lars_path` using automatic mapping.
`lasso_path`(args, *kwargs)	Call `sklearn.linear_model.lasso_path` using automatic mapping.
`lasso_stability_path`(args, *kwargs)	Call `sklearn.linear_model.lasso_stability_path` using automatic mapping.
`orthogonal_mp_gram`(args, *kwargs)	Call `sklearn.linear_model.orthogonal_mp_gram` using automatic mapping.

lars_path(*args, **kwargs)¶

Call sklearn.linear_model.lars_path using automatic mapping.

X: ModelFrame.data
y: ModelFrame.target

lasso_path(*args, **kwargs)¶

Call sklearn.linear_model.lasso_path using automatic mapping.

X: ModelFrame.data
y: ModelFrame.target

lasso_stability_path(*args, **kwargs)¶

Call sklearn.linear_model.lasso_stability_path using automatic mapping.

X: ModelFrame.data
y: ModelFrame.target

orthogonal_mp_gram(*args, **kwargs)¶

Call sklearn.linear_model.orthogonal_mp_gram using automatic mapping.

Gram: ModelFrame.data.T.dot(ModelFrame.data)
Xy: ModelFrame.data.T.dot(ModelFrame.target)

class expandas.skaccessors.manifold.ManifoldMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.manifold.

Methods

`locally_linear_embedding`(n_neighbors, ...)	Call `sklearn.manifold.locally_linear_embedding` using automatic mapping.
`spectral_embedding`(args, *kwargs)	Call `sklearn.manifold.spectral_embedding` using automatic mapping.

locally_linear_embedding(n_neighbors, n_components, *args, **kwargs)¶

Call sklearn.manifold.locally_linear_embedding using automatic mapping.

X: ModelFrame.data

spectral_embedding(*args, **kwargs)¶

Call sklearn.manifold.spectral_embedding using automatic mapping.

adjacency: ModelFrame.data

class expandas.skaccessors.metrics.MetricsMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.metrics.

Attributes

pairwise Not implemented

Methods

`auc`([kind, reorder])	Calcurate AUC of ROC curve or precision recall curve
`average_precision_score`(args, *kwargs)	Call `sklearn.metrics.average_precision_score` using automatic mapping.
`confusion_matrix`(args, *kwargs)	Call `sklearn.metrics.confusion_matrix` using automatic mapping.
`consensus_score`(args, *kwargs)	Not implemented
`f1_score`(args, *kwargs)	Call `sklearn.metrics.f1_score` using automatic mapping.
`fbeta_score`(beta, args, *kwargs)	Call `sklearn.metrics.fbeta_score` using automatic mapping.
`hinge_loss`(args, *kwargs)	Call `sklearn.metrics.hinge_loss` using automatic mapping.
`log_loss`(args, *kwargs)	Call `sklearn.metrics.log_loss` using automatic mapping.
`precision_recall_curve`(args, *kwargs)	Call `sklearn.metrics.precision_recall_curve` using automatic mapping.
`precision_recall_fscore_support`(args, *kwargs)	Call `sklearn.metrics.precision_recall_fscore_support` using automatic mapping.
`precision_score`(args, *kwargs)	Call `sklearn.metrics.precision_score` using automatic mapping.
`recall_score`(args, *kwargs)	Call `sklearn.metrics.recall_score` using automatic mapping.
`roc_auc_score`(args, *kwargs)	Call `sklearn.metrics.roc_auc_score` using automatic mapping.
`roc_curve`(args, *kwargs)	Call `sklearn.metrics.roc_curve` using automatic mapping.
`silhouette_samples`(args, *kwargs)	Call `sklearn.metrics.silhouette_samples` using automatic mapping.
`silhouette_score`(args, *kwargs)	Call `sklearn.metrics.silhouette_score` using automatic mapping.

auc(kind='roc', reorder=False, **kwargs)¶

Calcurate AUC of ROC curve or precision recall curve

Parameters:	kind : {‘roc’, ‘precision_recall_curve’}
Returns:	float : AUC

average_precision_score(*args, **kwargs)¶

Call sklearn.metrics.average_precision_score using automatic mapping.

y_true: ModelFrame.target
y_score: ModelFrame.decision

confusion_matrix(*args, **kwargs)¶

Call sklearn.metrics.confusion_matrix using automatic mapping.

y_true: ModelFrame.target
y_pred: ModelFrame.predicted

consensus_score(*args, **kwargs)¶: Not implemented

f1_score(*args, **kwargs)¶

Call sklearn.metrics.f1_score using automatic mapping.

y_true: ModelFrame.target
y_pred: ModelFrame.predicted

fbeta_score(beta, *args, **kwargs)¶

Call sklearn.metrics.fbeta_score using automatic mapping.

y_true: ModelFrame.target
y_pred: ModelFrame.predicted

hinge_loss(*args, **kwargs)¶

Call sklearn.metrics.hinge_loss using automatic mapping.

y_true: ModelFrame.target
y_pred_decision: ModelFrame.decision

log_loss(*args, **kwargs)¶

Call sklearn.metrics.log_loss using automatic mapping.

y_true: ModelFrame.target
y_pred: ModelFrame.proba

pairwise¶: Not implemented

precision_recall_curve(*args, **kwargs)¶

Call sklearn.metrics.precision_recall_curve using automatic mapping.

y_true: ModelFrame.target
y_probas_pred: ModelFrame.decision

precision_recall_fscore_support(*args, **kwargs)¶

Call sklearn.metrics.precision_recall_fscore_support using automatic mapping.

y_true: ModelFrame.target
y_pred: ``ModelFrame.predicted`

precision_score(*args, **kwargs)¶

Call sklearn.metrics.precision_score using automatic mapping.

y_true: ModelFrame.target
y_pred: ModelFrame.predicted

recall_score(*args, **kwargs)¶

Call sklearn.metrics.recall_score using automatic mapping.

y_true: ModelFrame.target
y_true: ModelFrame.predicted

roc_auc_score(*args, **kwargs)¶

Call sklearn.metrics.roc_auc_score using automatic mapping.

y_true: ModelFrame.target
y_score: ModelFrame.decision

roc_curve(*args, **kwargs)¶

Call sklearn.metrics.roc_curve using automatic mapping.

y_true: ModelFrame.target
y_score: ModelFrame.decision

silhouette_samples(*args, **kwargs)¶

Call sklearn.metrics.silhouette_samples using automatic mapping.

X: ModelFrame.data
labels: ModelFrame.predicted

silhouette_score(*args, **kwargs)¶

Call sklearn.metrics.silhouette_score using automatic mapping.

X: ModelFrame.data
labels: ModelFrame.predicted

class expandas.skaccessors.multiclass.MultiClassMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.multiclass.

Attributes

`OneVsOneClassifier`	`sklearn.multiclass.OneVsOneClassifier`
`OneVsRestClassifier`	`sklearn.multiclass.OneVsRestClassifier`
`OutputCodeClassifier`	`sklearn.multiclass.OutputCodeClassifier`

Methods

`fit_ecoc`(args, *kwargs)	Deprecated
`fit_ovo`(args, *kwargs)	Deprecated
`fit_ovr`(args, *kwargs)	Deprecated
`predict_ecoc`(args, *kwargs)	Deprecated
`predict_ovo`(args, *kwargs)	Deprecated
`predict_ovr`(args, *kwargs)	Deprecated

OneVsOneClassifier¶: sklearn.multiclass.OneVsOneClassifier

OneVsRestClassifier¶: sklearn.multiclass.OneVsRestClassifier

OutputCodeClassifier¶: sklearn.multiclass.OutputCodeClassifier

fit_ecoc(*args, **kwargs)¶: Deprecated

fit_ovo(*args, **kwargs)¶: Deprecated

fit_ovr(*args, **kwargs)¶: Deprecated

predict_ecoc(*args, **kwargs)¶: Deprecated

predict_ovo(*args, **kwargs)¶: Deprecated

predict_ovr(*args, **kwargs)¶: Deprecated

class expandas.skaccessors.neighbors.NeighborsMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.neighbors.

class expandas.skaccessors.pipeline.PipelineMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.pipeline.

Attributes

`make_pipeline`	`sklearn.pipeline.make_pipeline`
`make_union`	`sklearn.pipeline.make_union`

make_pipeline¶: sklearn.pipeline.make_pipeline

make_union¶: sklearn.pipeline.make_union

class expandas.skaccessors.preprocessing.PreprocessingMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.preprocessing.

Methods

add_dummy_feature([value]) Call sklearn.preprocessing.add_dummy_feature using automatic mapping.

add_dummy_feature(value=1.0)¶

Call sklearn.preprocessing.add_dummy_feature using automatic mapping.

X: ModelFrame.data

class expandas.skaccessors.svm.SVMMethods(df, module_name=None, attrs=None)¶

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.svm.

Attributes

`liblinear`	Not implemented
`libsvm`	Not implemented
`libsvm_sparse`	Not implemented

Methods

l1_min_c(*args, **kwargs) Call sklearn.svm.l1_min_c using automatic mapping.

l1_min_c(*args, **kwargs)¶

Call sklearn.svm.l1_min_c using automatic mapping.

X: ModelFrame.data
y: ModelFrame.target

liblinear¶: Not implemented

libsvm¶: Not implemented

libsvm_sparse¶: Not implemented

expandas.skaccessors package¶

Subpackages¶

Submodules¶

Module contents¶