Welcome to expandas’s documentation!¶

Contents:

Data Handling¶

Data Preparation¶

This section describes how to prepare basic data format named ModelFrame. ModelFrame defines a metadata to specify target (response variable) and data (explanatory variable / features). Using these metadata, ModelFrame can call other statistics/ML functions in more simple way.

You can create ModelFrame as the same manner as pandas.DataFrame. The below example shows how to create basic ModelFrame, which DOESN’T have target values.

>>> import expandas as expd

>>> df = expd.ModelFrame({'A': [1, 2, 3], 'B': [2, 3, 4],
...                       'C': [3, 4, 5]}, index=['A', 'B', 'C'])
>>> df
   A  B  C
A  1  2  3
B  2  3  4
C  3  4  5

>>> type(df)
<class 'expandas.core.frame.ModelFrame'>

You can check whether the created ModelFrame has target values using ModelFrame.has_target() function.

>>> df.has_target()
False

Target values can be specifyied via target keyword. You can simply pass a column name to be handled as target. Target column name can be confirmed via target_name property.

>>> df2 = expd.ModelFrame({'A': [1, 2, 3], 'B': [2, 3, 4],
...                        'C': [3, 4, 5]}, target='A')
>>> df2
   A  B  C
0  1  2  3
1  2  3  4
2  3  4  5

>>> df2.has_target()
True

>>> df2.target_name
'A'

Also, you can pass any list-likes to be handled as a target. In this case, target column will be named as ”.target”.

>>> df3 = expd.ModelFrame({'A': [1, 2, 3], 'B': [2, 3, 4],
...                        'C': [3, 4, 5]}, target=[4, 5, 6])
>>> df3
   .target  A  B  C
0        4  1  2  3
1        5  2  3  4
2        6  3  4  5

>>> df3.has_target()
True

>>> df3.target_name
'.target'

Also, you can pass pandas.DataFrame and pandas.Series as data and target.

>>> import pandas as pd
df4 = expd.ModelFrame({'A': [1, 2, 3], 'B': [2, 3, 4],
...                    'C': [3, 4, 5]}, target=pd.Series([4, 5, 6]))
>>> df4
   .target  A  B  C
0        4  1  2  3
1        5  2  3  4
2        6  3  4  5

>>> df4.has_target()
True

>>> df4.target_name
'.target'

Note

Target values are mandatory to perform operations which require response variable, such as regression and supervised learning.

Data Manipulation¶

You can access to each property as the same as pandas.DataFrame. Sliced results will be ModelSeries (simple wrapper for pandas.Series to support some data manipulation) or ModelFrame

>>> df
   A  B  C
A  1  2  3
B  2  3  4
C  3  4  5

>>> sliced = df['A']
>>> sliced
A    1
B    2
C    3
Name: A, dtype: int64

>>> type(sliced)
<class 'expandas.core.series.ModelSeries'>

>>> subset = df[['A', 'B']]
>>> subset
   A  B
A  1  2
B  2  3
C  3  4

>>> type(subset)
<class 'expandas.core.frame.ModelFrame'>

ModelFrame has a special properties data to access data (features) and target to access target.

>>> df2
   A  B  C
0  1  2  3
1  2  3  4
2  3  4  5

>>> df2.target_name
'A'

>>> df2.data
   B  C
0  2  3
1  3  4
2  4  5

>>> df2.target
0    1
1    2
2    3
Name: A, dtype: int64

You can update data and target via properties, in addition to standard pandas.DataFrame ways.

>>> df2.target = [9, 9, 9]
>>> df2
   A  B  C
0  9  2  3
1  9  3  4
2  9  4  5

>>> df2.data = pd.DataFrame({'X': [1, 2, 3], 'Y': [4, 5, 6]})
>>> df2
   A  X  Y
0  9  1  4
1  9  2  5
2  9  3  6

>>> df2['X'] = [0, 0, 0]
>>> df2
   A  X  Y
0  9  0  4
1  9  0  5
2  9  0  6

You can change target column specifying target_name property. Specifying a column which doesn’t exist in ModelFrame results in target column to be data column.

>>> df2.target_name
'A'

>>> df2.target_name = 'X'
>>> df2.target_name
'X'

>>> df2.target_name = 'XXXX'
>>> df2.has_target()
False

>>> df2.data
   A  X  Y
0  9  0  4
1  9  0  5
2  9  0  6

Use scikit-learn¶

This section describes how to use scikit-learn functionalities via expandas.

Basics¶

You can create ModelFrame instance from scikit-learn datasets directly.

>>> import expandas as expd
>>> import sklearn.datasets as datasets

>>> df = expd.ModelFrame(datasets.load_iris())
>>> df.head()
   .target  sepal length (cm)  sepal width (cm)  petal length (cm)  \
0        0                5.1               3.5                1.4
1        0                4.9               3.0                1.4
2        0                4.7               3.2                1.3
3        0                4.6               3.1                1.5
4        0                5.0               3.6                1.4

   petal width (cm)
0               0.2
1               0.2
2               0.2
3               0.2
4               0.2

# make columns be readable
>>> df.columns = ['.target', 'sepal length', 'sepal width', 'petal length', 'petal width']

ModelFrame has accessor methods which makes easier access to scikit-learn namespace.

>>> df.cluster.KMeans
<class 'sklearn.cluster.k_means_.KMeans'>

Following table shows scikit-learn module and corresponding ModelFrame module.

`scikit-learn`	`ModelFrame` accessor
`sklearn.cluster`	`ModelFrame.cluster`
`sklearn.covariance`	`ModelFrame.covariance`
`sklearn.cross_validation`	`ModelFrame.cross_validation`
`sklearn.decomposition`	`ModelFrame.decomposition`
`sklearn.datasets`	(not accesible from accessor)
`sklearn.dummy`	`ModelFrame.dummy`
`sklearn.ensemble`	`ModelFrame.ensemble`
`sklearn.feature_extraction`	`ModelFrame.feature_extraction`
`sklearn.gaussian_process`	`ModelFrame.gaussian_process` (WIP)
`sklearn.grid_search`	`ModelFrame.grid_search`
`sklearn.isotonic`	`ModelFrame.isotonic` (WIP)
`sklearn.kernel_approximation`	`ModelFrame.kernel_approximation`
`sklearn.lda`	`ModelFrame.lda`
`sklearn.linear_model`	`ModelFrame.linear_model`
`sklearn.manifold`	`ModelFrame.manifold`
`sklearn.metrics`	`ModelFrame.metrics`
`sklearn.mixture`	`ModelFrame.mixture`
`sklearn.multiclass`	`ModelFrame.multiclass`
`sklearn.naive_bayes`	`ModelFrame.naive_bayes`
`sklearn.neighbors`	`ModelFrame.neighbors`
`sklearn.cross_decomposition`	`ModelFrame.cross_decomposition` (WIP)
`sklearn.pipeline`	`ModelFrame.pipeline`
`sklearn.preprocessing`	`ModelFrame.preprocessing`
`sklearn.qda`	`ModelFrame.qda`
`sklearn.semi_supervised`	`ModelFrame.semi_supervised`
`sklearn.svm`	`ModelFrame.svm`
`sklearn.tree`	`ModelFrame.tree`
`sklearn.utils`	(not accesible from accessor)

Thus, you can instanciate each estimator via ModelFrame accessors. Once create an estimator, you can pass it to ModelFrame.fit then predict. ModelFrame automatically uses its data and target properties for each operations.

>>> estimator = df.cluster.KMeans(n_clusters=3)
>>> df.fit(estimator)

>>> predicted = df.predict(estimator)
>>> predicted
0    1
1    1
2    1
...
147    2
148    2
149    0
Length: 150, dtype: int32

ModelFrame has following methods corresponding to various scikit-learn estimators.

ModelFrame.fit
ModelFrame.transform
ModelFrame.fit_transform
ModelFrame.inverse_transform
ModelFrame.predict
ModelFrame.fit_predict
ModelFrame.score
ModelFrame.predict_proba
ModelFrame.predict_log_proba
ModelFrame.decision_function

Following example shows to perform PCA, then revert principal components back to original space.

>>> estimator = df.decomposition.PCA()
>>> df.fit(estimator)

>>> transformed = df.transform(estimator)
>>> transformed.head()
   .target         0         1         2         3
0        0 -2.684207 -0.326607  0.021512  0.001006
1        0 -2.715391  0.169557  0.203521  0.099602
2        0 -2.889820  0.137346 -0.024709  0.019305
3        0 -2.746437  0.311124 -0.037672 -0.075955
4        0 -2.728593 -0.333925 -0.096230 -0.063129

>>> type(transformed)
<class 'expandas.core.frame.ModelFrame'>

>>> transformed.inverse_transform(estimator)
     .target    0    1    2    3
0          0  5.1  3.5  1.4  0.2
1          0  4.9  3.0  1.4  0.2
2          0  4.7  3.2  1.3  0.2
3          0  4.6  3.1  1.5  0.2
4          0  5.0  3.6  1.4  0.2
..       ...  ...  ...  ...  ...
145        2  6.7  3.0  5.2  2.3
146        2  6.3  2.5  5.0  1.9
147        2  6.5  3.0  5.2  2.0
148        2  6.2  3.4  5.4  2.3
149        2  5.9  3.0  5.1  1.8

[150 rows x 5 columns]

Note

columns information will be lost once transformed to principal components.

ModelFrame preserves last predicted result in predicted attibute. If ModelFrame both has target and predicted values, the model evaluation can be performed using functions available in ModelFrame.metrics.

>>> estimator = df.svm.SVC()
>>> df.fit(estimator)

>>> df.predict(estimator)
0    0
1    0
2    0
...
147    2
148    2
149    2
Length: 150, dtype: int64

>>> df.predicted
0    0
1    0
2    0
...
147    2
148    2
149    2
Length: 150, dtype: int64

>>> df.metrics.confusion_matrix()
Predicted   0   1   2
Target
0          50   0   0
1           0  48   2
2           0   0  50

Pipeline¶

ModelFrame can handle pipeline as the same as normal estimators.

>>> estimators = [('reduce_dim', df.decomposition.PCA()),
...               ('svm', df.svm.SVC())]
>>> pipe = df.pipeline.Pipeline(estimators)
>>> df.fit(pipe)

>>> df.predict(pipe)
0    0
1    0
2    0
...
147    2
148    2
149    2
Length: 150, dtype: int64

Above expression is the same as below:

>>> df2 = df.copy()
>>> df2 = df2.fit_transform(df2.decomposition.PCA())
>>> svm = df2.svm.SVC()
>>> df2.fit(svm)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)
>>> df2.predict(svm)
0     0
1     0
2     0
...
147    2
148    2
149    2
Length: 150, dtype: int64

Cross Validation¶

scikit-learn has some classes for cross validation. cross_validation.train_test_split splits data to training and test set. You can access to the function via cross_validation accessor.

>>> train_df, test_df = df.cross_validation.train_test_split()
>>> train_df
     .target  sepal length  sepal width  petal length  petal width
0          0           4.8          3.4           1.9          0.2
1          1           6.3          3.3           4.7          1.6
2          0           4.8          3.4           1.6          0.2
3          2           7.7          2.6           6.9          2.3
4          0           5.4          3.4           1.7          0.2
..       ...           ...          ...           ...          ...
107        0           5.1          3.7           1.5          0.4
108        1           6.7          3.1           4.7          1.5
109        0           4.7          3.2           1.3          0.2
110        0           5.8          4.0           1.2          0.2
111        0           5.1          3.5           1.4          0.2

[112 rows x 5 columns]

>>> test_df
    .target  sepal length  sepal width  petal length  petal width
0         2           6.3          2.7           4.9          1.8
1         0           4.5          2.3           1.3          0.3
2         2           5.8          2.8           5.1          2.4
3         0           4.3          3.0           1.1          0.1
4         0           5.0          3.0           1.6          0.2
..      ...           ...          ...           ...          ...
33        1           6.7          3.1           4.4          1.4
34        0           4.6          3.6           1.0          0.2
35        1           5.7          3.0           4.2          1.2
36        1           5.9          3.0           4.2          1.5
37        2           6.4          2.8           5.6          2.1

[38 rows x 5 columns]

Also, there are some iterative classes which returns indexes for training sets and test sets. You can slice ModelFrame using these indexes.

>>> kf = df.cross_validation.KFold(n=150, n_folds=3)
>>> for train_index, test_index in kf:
...    print('training set shape: ', df.iloc[train_index, :].shape,
...          'test set shape: ', df.iloc[test_index, :].shape)
('training set shape: ', (100, 5), 'test set shape: ', (50, 5))
('training set shape: ', (100, 5), 'test set shape: ', (50, 5))
('training set shape: ', (100, 5), 'test set shape: ', (50, 5))

For further simplification, ModelFrame.cross_validation.iterate can accept such iterators and returns ModelFrame corresponding to training and test data.

>>> kf = df.cross_validation.KFold(n=150, n_folds=3)
>>> for train_df, test_df in df.cross_validation.iterate(kf):
...    print('training set shape: ', train_df.shape,
...          'test set shape: ', test_df.shape)
('training set shape: ', (100, 5), 'test set shape: ', (50, 5))
('training set shape: ', (100, 5), 'test set shape: ', (50, 5))
('training set shape: ', (100, 5), 'test set shape: ', (50, 5))

Grid Search¶

You can perform grid search using ModelFrame.fit.

>>> tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
...                     'C': [1, 10, 100]},
...                    {'kernel': ['linear'], 'C': [1, 10, 100]}]

>>> df = expd.ModelFrame(datasets.load_digits())
>>> cv = df.grid_search.GridSearchCV(df.svm.SVC(C=1), tuned_parameters,
...                                  cv=5, scoring='precision')

>>> df.fit(cv)

>>> cv.best_estimator_
SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)

In addition, ModelFrame.grid_search has a describe function to organize each grid search result as pd.DataFrame accepting estimator.

>>> df.grid_search.describe(cv)
       mean       std    C   gamma  kernel
0.974108  0.013139    1  0.0010     rbf
0.951416  0.020010    1  0.0001     rbf
0.975372  0.011280   10  0.0010     rbf
0.962534  0.020218   10  0.0001     rbf
0.975372  0.011280  100  0.0010     rbf
0.964695  0.016686  100  0.0001     rbf
0.951811  0.018410    1     NaN  linear
0.951811  0.018410   10     NaN  linear
0.951811  0.018410  100     NaN  linear

API:

expandas.core package¶

Submodules¶

expandas.core.accessor module¶

class expandas.core.accessor.AccessorMethods(df, module_name=None, attrs=None)¶

Bases: object

Accessor to related functionalities.

expandas.core.frame module¶

class expandas.core.frame.ModelFrame(data, target=None, *args, **kwargs)¶

Bases: pandas.core.frame.DataFrame

Data structure subclassing pandas.DataFrame to define a metadata to specify target (response variable) and data (explanatory variable / features).

data : same as pandas.DataFrame target : str or array-like

Column name or values to be used as target

args : arguments passed to pandas.DataFrame kwargs : keyword arguments passed to pandas.DataFrame

cluster = None¶

covariance = None¶

cross_decomposition = None¶

cross_validation = None¶

data¶

decision¶

Return current estimator’s decision function

probabilities : ModelFrame

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

Call estimator’s decision_function method.

args : arguments passed to decision_function method kwargs : keyword arguments passed to decision_function method

returned : decisions

decomposition = None¶

dummy = None¶

ensemble = None¶

estimator¶

Return most recently used estimator

estimator : estimator

feature_extraction = None¶

feature_selection = None¶

fit(estimator, *args, **kwargs)¶

Call estimator’s fit method.

args : arguments passed to fit method kwargs : keyword arguments passed to fit method

returned : None or fitted estimator

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

Call estimator’s fit_predict method.

args : arguments passed to fit_predict method kwargs : keyword arguments passed to fit_predict method

returned : predicted result

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

Call estimator’s fit_transform method.

args : arguments passed to fit_transform method kwargs : keyword arguments passed to fit_transform method

returned : transformed result

gaussian_process = None¶

grid_search = None¶

has_data()¶

Return whether ModelFrame has data

has_data : bool

has_target()¶

Return whether ModelFrame has target

has_target : bool

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

Call estimator’s inverse_transform method.

args : arguments passed to inverse_transform method kwargs : keyword arguments passed to inverse_transform method

returned : transformed result

isotonic = None¶

kernel_approximation = None¶

lda = None¶

linear_model = None¶

log_proba¶

Return current estimator’s log probabilities

probabilities : ModelFrame

manifold = None¶

metrics = None¶

mixture = None¶

multiclass = None¶

naive_bayes = None¶

neighbors = None¶

pipeline = None¶

predict(estimator, *args, **kwargs)¶

Call estimator’s predict method.

args : arguments passed to predict method kwargs : keyword arguments passed to predict method

returned : predicted result

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

Call estimator’s predict_log_proba method.

args : arguments passed to predict_log_proba method kwargs : keyword arguments passed to predict_log_proba method

returned : probabilities

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

Call estimator’s predict_proba method.

args : arguments passed to predict_proba method kwargs : keyword arguments passed to predict_proba method

returned : probabilities

predicted¶

Return current estimator’s predicted results

predicted : ModelSeries

preprocessing = None¶

proba¶

Return current estimator’s probabilities

probabilities : ModelFrame

qda = None¶

score(estimator, *args, **kwargs)¶

Call estimator’s score method.

args : arguments passed to score method kwargs : keyword arguments passed to score method

returned : score

semi_supervised = None¶

svm = None¶

target¶

target_name¶

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

Call estimator’s transform method.

args : arguments passed to transform method kwargs : keyword arguments passed to transform method

returned : transformed result

tree = None¶

expandas.core.series module¶

class expandas.core.series.ModelSeries(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)¶

Bases: pandas.core.series.Series

Wrapper for pandas.Series to support sklearn.preprocessing

preprocessing = None¶

to_frame(name=None)¶

Convert Series to DataFrame

name : object, default None: The passed name should substitute for the series name (if it has one).

data_frame : DataFrame

Module contents¶

expandas.skaccessors package¶

Subpackages¶

expandas.skaccessors.test package¶

Submodules¶

expandas.skaccessors.test.test_cluster module¶

expandas.skaccessors.test.test_covariance module¶

expandas.skaccessors.test.test_cross_decomposition module¶

expandas.skaccessors.test.test_cross_validation module¶

expandas.skaccessors.test.test_decomposition module¶

expandas.skaccessors.test.test_dummy module¶

expandas.skaccessors.test.test_ensemble module¶

expandas.skaccessors.test.test_feature_extraction module¶

expandas.skaccessors.test.test_feature_selection module¶

expandas.skaccessors.test.test_gaussian_process module¶

expandas.skaccessors.test.test_grid_search module¶

expandas.skaccessors.test.test_isotonic module¶

expandas.skaccessors.test.test_kernel_approximation module¶

expandas.skaccessors.test.test_lda module¶

expandas.skaccessors.test.test_linear_model module¶

expandas.skaccessors.test.test_manifold module¶

expandas.skaccessors.test.test_metrics module¶

expandas.skaccessors.test.test_mixture module¶

expandas.skaccessors.test.test_multiclass module¶

expandas.skaccessors.test.test_naive_bayes module¶

expandas.skaccessors.test.test_neighbors module¶

expandas.skaccessors.test.test_pipeline module¶

expandas.skaccessors.test.test_preprocessing module¶

expandas.skaccessors.test.test_qda module¶

expandas.skaccessors.test.test_semi_supervised module¶

expandas.skaccessors.test.test_svm module¶

expandas.skaccessors.test.test_tree module¶

Module contents¶

Submodules¶

expandas.skaccessors.cluster module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.cluster.

affinity_propagation(*args, **kwargs)¶

bicluster = None¶

dbscan(*args, **kwargs)¶

k_means(n_clusters, *args, **kwargs)¶

mean_shift(*args, **kwargs)¶

spectral_clustering(*args, **kwargs)¶

expandas.skaccessors.covariance module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.covariance.

empirical_covariance(*args, **kwargs)¶

ledoit_wolf(*args, **kwargs)¶

oas(*args, **kwargs)¶

expandas.skaccessors.cross_validation module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.cross_validation.

StratifiedShuffleSplit(*args, **kwargs)¶

check_cv(cv, *args, **kwargs)¶

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

iterate(cv)¶

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

train_test_split(*args, **kwargs)¶

expandas.skaccessors.decomposition module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.decomposition.

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

dict_learning_online(*args, **kwargs)¶

fastica(*args, **kwargs)¶

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

expandas.skaccessors.ensemble module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.ensemble.

partial_dependence¶

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

Bases: expandas.core.accessor.AccessorMethods

partial_dependence(gbrt, target_variables, **kwargs)¶

plot_partial_dependence(gbrt, features, **kwargs)¶

expandas.skaccessors.feature_extraction module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.feature_extraction.

image = None¶

text = None¶

expandas.skaccessors.feature_selection module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.feature_selection.

expandas.skaccessors.gaussian_process module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.gaussian_process.

correlation_models¶

regression_models¶

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

expandas.skaccessors.grid_search module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.grid_search.

describe(estimator)¶: Return cross validation results as pd.DataFrame.

expandas.skaccessors.isotonic module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.isotonic.

IsotonicRegression¶

check_increasing(*args, **kwargs)¶

isotonic_regression(*args, **kwargs)¶

expandas.skaccessors.linear_model module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.linear_model.

lars_path(*args, **kwargs)¶

lasso_path(*args, **kwargs)¶

lasso_stability_path(*args, **kwargs)¶

orthogonal_mp_gram(*args, **kwargs)¶

expandas.skaccessors.manifold module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.manifold.

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

spectral_embedding(*args, **kwargs)¶

expandas.skaccessors.metrics module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.metrics.

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

average_precision_score(*args, **kwargs)¶

confusion_matrix(*args, **kwargs)¶

consensus_score(*args, **kwargs)¶

f1_score(*args, **kwargs)¶

fbeta_score(beta, *args, **kwargs)¶

hinge_loss(*args, **kwargs)¶

log_loss(*args, **kwargs)¶

pairwise¶

precision_recall_curve(*args, **kwargs)¶

precision_recall_fscore_support(*args, **kwargs)¶

precision_score(*args, **kwargs)¶

recall_score(*args, **kwargs)¶

roc_auc_score(*args, **kwargs)¶

roc_curve(*args, **kwargs)¶

silhouette_samples(*args, **kwargs)¶

silhouette_score(*args, **kwargs)¶

expandas.skaccessors.multiclass module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.multiclass.

OneVsOneClassifier¶

OneVsRestClassifier¶

OutputCodeClassifier¶

fit_ecoc(*args, **kwargs)¶

fit_ovo(*args, **kwargs)¶

fit_ovr(*args, **kwargs)¶

predict_ecoc(*args, **kwargs)¶

predict_ovo(*args, **kwargs)¶

predict_ovr(*args, **kwargs)¶

expandas.skaccessors.neighbors module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.neighbors.

expandas.skaccessors.pipeline module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.pipeline.

make_pipeline¶

make_union¶

expandas.skaccessors.preprocessing module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.preprocessing.

add_dummy_feature(value=1.0)¶

expandas.skaccessors.svm module¶

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

Bases: expandas.core.accessor.AccessorMethods

Accessor to sklearn.svm.

l1_min_c(*args, **kwargs)¶

liblinear¶

libsvm¶

libsvm_sparse¶