pandas_ml.skaccessors package¶
Submodules¶
-
class
pandas_ml.skaccessors.cluster.
ClusterMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.cluster
.-
affinity_propagation
(*args, **kwargs)¶ Call
sklearn.cluster.affinity_propagation
using automatic mapping.S
:ModelFrame.data
-
bicluster
¶ Property to access
sklearn.cluster.bicluster
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dbscan
(*args, **kwargs)¶ Call
sklearn.cluster.dbscan
using automatic mapping.X
:ModelFrame.data
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k_means
(n_clusters, *args, **kwargs)¶ Call
sklearn.cluster.k_means
using automatic mapping.X
:ModelFrame.data
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mean_shift
(*args, **kwargs)¶ Call
sklearn.cluster.mean_shift
using automatic mapping.X
:ModelFrame.data
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spectral_clustering
(*args, **kwargs)¶ Call
sklearn.cluster.spectral_clustering
using automatic mapping.affinity
:ModelFrame.data
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-
class
pandas_ml.skaccessors.covariance.
CovarianceMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.covariance
.-
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
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oas
(*args, **kwargs)¶ Call
sklearn.covariance.oas
using automatic mapping.X
:ModelFrame.data
-
-
class
pandas_ml.skaccessors.cross_decomposition.
CrossDecompositionMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.cross_decomposition
.
-
class
pandas_ml.skaccessors.decomposition.
DecompositionMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.decomposition
.-
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
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sparse_encode
(dictionary, *args, **kwargs)¶ Call
sklearn.decomposition.sparce_encode
using automatic mapping.X
:ModelFrame.data
-
-
class
pandas_ml.skaccessors.ensemble.
EnsembleMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.ensemble
.-
partial_dependence
¶ Property to access
sklearn.ensemble.partial_dependence
-
-
class
pandas_ml.skaccessors.ensemble.
PartialDependenceMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
-
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
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-
class
pandas_ml.skaccessors.feature_extraction.
FeatureExtractionMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.feature_extraction
.-
image
¶ Property to access
sklearn.feature_extraction.image
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text
¶ Property to access
sklearn.feature_extraction.text
-
-
class
pandas_ml.skaccessors.feature_selection.
FeatureSelectionMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.feature_selection
.
-
class
pandas_ml.skaccessors.gaussian_process.
GaussianProcessMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.gaussian_process
.-
correlation_models
¶ Property to access
sklearn.gaussian_process.correlation_models
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regression_models
¶ Property to access
sklearn.gaussian_process.regression_models
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-
class
pandas_ml.skaccessors.gaussian_process.
RegressionModelsMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
-
class
pandas_ml.skaccessors.isotonic.
IsotonicMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.isotonic
.-
IsotonicRegression
¶ sklearn.isotonic.IsotonicRegression
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check_increasing
(*args, **kwargs)¶ Call
sklearn.isotonic.check_increasing
using automatic mapping.x
:ModelFrame.index
y
:ModelFrame.target
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isotonic_regression
(*args, **kwargs)¶ Call
sklearn.isotonic.isotonic_regression
using automatic mapping.y
:ModelFrame.target
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-
class
pandas_ml.skaccessors.linear_model.
LinearModelMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.linear_model
.-
enet_path
(*args, **kwargs)¶ Call
sklearn.linear_model.enet_path
using automatic mapping.X
:ModelFrame.data
y
:ModelFrame.target
-
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
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lasso_stability_path
(*args, **kwargs)¶ Call
sklearn.linear_model.lasso_stability_path
using automatic mapping.X
:ModelFrame.data
y
:ModelFrame.target
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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)
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-
class
pandas_ml.skaccessors.manifold.
ManifoldMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.manifold
.-
locally_linear_embedding
(n_neighbors, n_components, *args, **kwargs)¶ Call
sklearn.manifold.locally_linear_embedding
using automatic mapping.X
:ModelFrame.data
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spectral_embedding
(*args, **kwargs)¶ Call
sklearn.manifold.spectral_embedding
using automatic mapping.adjacency
:ModelFrame.data
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class
pandas_ml.skaccessors.metrics.
MetricsMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.metrics
.-
auc
(kind='roc', reorder=False, **kwargs)¶ Calcurate AUC of ROC curve or precision recall curve
Parameters: - kind : {‘roc’, ‘precision_recall_curve’}
Returns: - float : AUC
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average_precision_score
(*args, **kwargs)¶ Call
sklearn.metrics.average_precision_score
using automatic mapping.y_true
:ModelFrame.target
y_score
:ModelFrame.decision
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confusion_matrix
(*args, **kwargs)¶ Call
sklearn.metrics.confusion_matrix
using automatic mapping.y_true
:ModelFrame.target
y_pred
:ModelFrame.predicted
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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
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fbeta_score
(beta, *args, **kwargs)¶ Call
sklearn.metrics.fbeta_score
using automatic mapping.y_true
:ModelFrame.target
y_pred
:ModelFrame.predicted
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hinge_loss
(*args, **kwargs)¶ Call
sklearn.metrics.hinge_loss
using automatic mapping.y_true
:ModelFrame.target
y_pred_decision
:ModelFrame.decision
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log_loss
(*args, **kwargs)¶ Call
sklearn.metrics.log_loss
using automatic mapping.y_true
:ModelFrame.target
y_pred
:ModelFrame.proba
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pairwise
¶ Not implemented
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precision_recall_curve
(*args, **kwargs)¶ Call
sklearn.metrics.precision_recall_curve
using automatic mapping.y_true
:ModelFrame.target
y_probas_pred
:ModelFrame.decision
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precision_recall_fscore_support
(*args, **kwargs)¶ Call
sklearn.metrics.precision_recall_fscore_support
using automatic mapping.y_true
:ModelFrame.target
y_pred
:ModelFrame.predicted
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precision_score
(*args, **kwargs)¶ Call
sklearn.metrics.precision_score
using automatic mapping.y_true
:ModelFrame.target
y_pred
:ModelFrame.predicted
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recall_score
(*args, **kwargs)¶ Call
sklearn.metrics.recall_score
using automatic mapping.y_true
:ModelFrame.target
y_true
:ModelFrame.predicted
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roc_auc_score
(*args, **kwargs)¶ Call
sklearn.metrics.roc_auc_score
using automatic mapping.y_true
:ModelFrame.target
y_score
:ModelFrame.decision
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roc_curve
(*args, **kwargs)¶ Call
sklearn.metrics.roc_curve
using automatic mapping.y_true
:ModelFrame.target
y_score
:ModelFrame.decision
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silhouette_samples
(*args, **kwargs)¶ Call
sklearn.metrics.silhouette_samples
using automatic mapping.X
:ModelFrame.data
labels
:ModelFrame.predicted
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silhouette_score
(*args, **kwargs)¶ Call
sklearn.metrics.silhouette_score
using automatic mapping.X
:ModelFrame.data
labels
:ModelFrame.predicted
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class
pandas_ml.skaccessors.model_selection.
ModelSelectionMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.model_selection
.-
StratifiedShuffleSplit
(*args, **kwargs)¶ Instanciate
sklearn.cross_validation.StratifiedShuffleSplit
using automatic mapping.y
:ModelFrame.target
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check_cv
(cv, *args, **kwargs)¶ Call
sklearn.cross_validation.check_cv
using automatic mapping.X
:ModelFrame.data
y
:ModelFrame.target
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cross_val_score
(estimator, *args, **kwargs)¶ Call
sklearn.cross_validation.cross_val_score
using automatic mapping.X
:ModelFrame.data
y
:ModelFrame.target
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describe
(estimator)¶ Describe grid search results
Parameters: - estimator : fitted grid search estimator
Returns: - described :
ModelFrame
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iterate
(cv, reset_index=False)¶ deprecated. Use .split
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learning_curve
(estimator, *args, **kwargs)¶ Call
sklearn.lerning_curve.learning_curve
using automatic mapping.X
:ModelFrame.data
y
:ModelFrame.target
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permutation_test_score
(estimator, *args, **kwargs)¶ Call
sklearn.cross_validation.permutation_test_score
using automatic mapping.X
:ModelFrame.data
y
:ModelFrame.target
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split
(cv, reset_index=False)¶ Generate
ModelFrame
using iterators for cross validationParameters: - cv : cross validation iterator
- reset_index : bool
logical value whether to reset index, default False
Returns: - generated : generator of
ModelFrame
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train_test_split
(reset_index=False, *args, **kwargs)¶ Call
sklearn.cross_validation.train_test_split
using automatic mapping.Parameters: - reset_index : bool
logical value whether to reset index, default False
- kwargs : keywords passed to
cross_validation.train_test_split
Returns: - train, test : tuple of
ModelFrame
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validation_curve
(estimator, param_name, param_range, *args, **kwargs)¶ Call
sklearn.learning_curve.validation_curve
using automatic mapping.X
:ModelFrame.data
y
:ModelFrame.target
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class
pandas_ml.skaccessors.neighbors.
NeighborsMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.neighbors
.
-
class
pandas_ml.skaccessors.pipeline.
PipelineMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.pipeline
.-
make_pipeline
¶ sklearn.pipeline.make_pipeline
-
make_union
¶ sklearn.pipeline.make_union
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-
class
pandas_ml.skaccessors.preprocessing.
PreprocessingMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.preprocessing
.-
add_dummy_feature
(value=1.0)¶ Call
sklearn.preprocessing.add_dummy_feature
using automatic mapping.X
:ModelFrame.data
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class
pandas_ml.skaccessors.svm.
SVMMethods
(df, module_name=None, attrs=None)¶ Bases:
pandas_ml.core.accessor._AccessorMethods
Accessor to
sklearn.svm
.-
l1_min_c
(*args, **kwargs)¶ Call
sklearn.svm.l1_min_c
using automatic mapping.X
:ModelFrame.data
y
:ModelFrame.target
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liblinear
¶ Not implemented
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libsvm
¶ Not implemented
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libsvm_sparse
¶ Not implemented
-