Welcome to pandas-ml’s documentation!¶

Contents:

What’s new¶

v0.6.1¶

Enhancement¶

Support pandas v0.24.0.

v0.6.0¶

Enhancement¶

Support pandas v0.22.0 and scikit-learn 0.20.0.

API Change¶

ModelFrame.model_selection.describe now returns ModelFrame compat with GridSearchCV.cv_results_

Deprecation¶

Drop support of pandas v0.18.x or earlier
Drop support of scikit-learn v0.18.x or earlier.

v0.5.0¶

Enhancement¶

Support pandas v0.21.0.

v0.4.0¶

Enhancement¶

Support scikit-learn v0.17.x and v0.18.0.
Support imbalanced-learn via .imbalance accessor. See Handling imbalanced data.
Added pandas_ml.ConfusionMatrix class for easier classification results evaluation. See Confusion matrix.

Bug Fix¶

ModelFrame.columns may not be preserved via .transform using FunctionTransformer, KernelCenterer, MaxAbsScaler and RobustScaler.

v0.3.1¶

Enhancement¶

inverse_transform now reverts original ModelFrame.columns information.

Bug Fix¶

Assigning Series to ModelFrame.data property raises TypeError

v0.3.0¶

Enhancement¶

Support xgboost via ModelFrame.xgboost accessor.

v0.2.0¶

Enhancement¶

ModelFrame.transform can preserve column names for some sklearn.preprocessing transformation.
Added ModelSeries.fit, transform, fit_transform and inverse_transform for preprocessing purpose.
ModelFrame can be initialized from statsmodels datasets.
ModelFrame.cross_validation.iterate and ModelFrame.cross_validation.train_test_split now keep index of original dataset, and added reset_index keyword to control this behaviour.

Bug Fix¶

target kw may be ignored when initializing ModelFrame with np.ndarray and columns kwds.
linear_model.enet_path doesn’t accept additional keywords.
Initializing ModelFrame with named Series may have duplicated target columns.
ModelFrame.target_name may not be preserved when sliced.

v0.1.1¶

Enhancement¶

Added sklearn.learning_curve, neural_network, random_projection

v0.1.0¶

Initial Release

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 pandas_ml as pdml

>>> df = pdml.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 'pandas_ml.core.frame.ModelFrame'>

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

>>> 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 = pdml.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 = pdml.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 = pdml.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 maniluplate ModelFrame like pandas.DataFrame. Because ModelFrame inherits pandas.DataFrame, all the pandas methods / functions can be applied to ModelFrame.

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 'pandas_ml.core.series.ModelSeries'>

>>> subset = df[['A', 'B']]
>>> subset
   A  B
a  1  2
b  2  3
c  3  4

>>> type(subset)
<class 'pandas_ml.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. Also, columns / value assignment are supported as the same as pandas.DataFrame.

>>> 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.

>>> df2.target_name
'A'

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

If the specified column doesn’t exist in ModelFrame, it should reset target to None. Current target will be regarded as data.

>>> 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 pandas-ml.

Basics¶

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

>>> import pandas_ml as pdml
>>> import sklearn.datasets as datasets

>>> df = pdml.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. Some accessors has its abbreviated versions.

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 preserves the most recently used estimator in estimator atribute, and predicted results in predicted attibute.

>>> df.estimator
KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=3, n_init=10,
    n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,
    verbose=0)

>>> df.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. The last results are saved as corresponding ModelFrame properties.

`ModelFrame` method	`ModelFrame` property
`ModelFrame.fit`	(None)
`ModelFrame.transform`	(None)
`ModelFrame.fit_transform`	(None)
`ModelFrame.inverse_transform`	(None)
`ModelFrame.predict`	`ModelFrame.predicted`
`ModelFrame.fit_predict`	`ModelFrame.predicted`
`ModelFrame.score`	(None)
`ModelFrame.predict_proba`	`ModelFrame.proba`
`ModelFrame.predict_log_proba`	`ModelFrame.log_proba`
`ModelFrame.decision_function`	`ModelFrame.decision`

Note

If you access to a property before calling ModelFrame methods, ModelFrame automatically calls corresponding method of the latest estimator and return the result.

Following example shows to perform PCA, then revert principal components back to original space. inverse_transform should revert the original columns.

>>> 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 'pandas_ml.core.frame.ModelFrame'>

>>> transformed.inverse_transform(estimator)
     .target  sepal length  sepal width  petal length  petal width
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]

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

Use Module Level Functions¶

Some scikit-learn modules define functions which handle data without instanciating estimators. You can call these functions from accessor methods directly, and ModelFrame will pass corresponding data on background. Following example shows to use sklearn.cluster.k_means function to perform K-means.

Important

When you use module level function, ModelFrame.predicted WILL NOT be updated. Thus, using estimator is recommended.

# no need to pass data explicitly
# sklearn.cluster.kmeans returns centroids, cluster labels and inertia
>>> c, l, i = df.cluster.k_means(n_clusters=3)
>>> l
0     1
1     1
2     1
...
147    2
148    2
149    0
Length: 150, dtype: int32

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. model_selection.train_test_split splits data to training and test set. You can access to the function via model_selection accessor.

>>> train_df, test_df = df.model_selection.train_test_split()
>>> train_df
     .target  sepal length  sepal width  petal length  petal width
124        2           6.7          3.3           5.7          2.1
117        2           7.7          3.8           6.7          2.2
123        2           6.3          2.7           4.9          1.8
65         1           6.7          3.1           4.4          1.4
133        2           6.3          2.8           5.1          1.5
..       ...           ...          ...           ...          ...
93         1           5.0          2.3           3.3          1.0
46         0           5.1          3.8           1.6          0.2
121        2           5.6          2.8           4.9          2.0
91         1           6.1          3.0           4.6          1.4
147        2           6.5          3.0           5.2          2.0

[112 rows x 5 columns]


>>> test_df
     .target  sepal length  sepal width  petal length  petal width
146        2           6.3          2.5           5.0          1.9
75         1           6.6          3.0           4.4          1.4
138        2           6.0          3.0           4.8          1.8
77         1           6.7          3.0           5.0          1.7
36         0           5.5          3.5           1.3          0.2
..       ...           ...          ...           ...          ...
14         0           5.8          4.0           1.2          0.2
141        2           6.9          3.1           5.1          2.3
100        2           6.3          3.3           6.0          2.5
83         1           6.0          2.7           5.1          1.6
114        2           5.8          2.8           5.1          2.4

[38 rows x 5 columns]

You can iterate over Splitter classes via ModelFrame.model_selection.split which returns ModelFrame corresponding to training and test data.

>>> kf = df.model_selection.KFold(n_splits=3)
>>> for train_df, test_df in df.model_selection.iterate(kf):
...    print('training set shape: ', train_df.shape,
...          'test set shape: ', test_df.shape)
training set shape:  (112, 5) test set shape:  (38, 5)
training set shape:  (112, 5) test set shape:  (38, 5)
training set shape:  (112, 5) test set shape:  (38, 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 = pdml.ModelFrame(datasets.load_digits())
>>> cv = df.model_selection.GridSearchCV(df.svm.SVC(C=1), tuned_parameters,
...                                      cv=5)

>>> 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.model_selection has a describe function to organize each grid search result as ModelFrame accepting estimator.

>>> df.model_selection.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

Handling imbalanced data¶

This section describes how to use imbalanced-learn functionalities via pandas-ml to handle imbalanced data.

Sampling¶

Assuming we have ModelFrame which has imbalanced target values. The ModelFrame has data with 80 observations labeld with 0 and 20 observations labeled with 1.

>>> import numpy as np
>>> import pandas_ml as pdml
>>> df = pdml.ModelFrame(np.random.randn(100, 5),
...                      target=np.array([0, 1]).repeat([80, 20]),
...                      columns=list('ABCDE'))
>>> df
    .target         A         B         C         D         E
0         0  1.467859  1.637449  0.175770  0.189108  0.775139
1         0 -1.706293 -0.598930 -0.343427  0.355235 -1.348378
2         0  0.030542  0.393779 -1.891991  0.041062  0.055530
3         0  0.320321 -1.062963 -0.416418 -0.629776  1.126027
..      ...       ...       ...       ...       ...       ...
96        1 -1.199039  0.055702  0.675555 -0.416601 -1.676259
97        1 -1.264182 -0.167390 -0.939794 -0.638733 -0.806794
98        1 -0.616754  1.667483 -1.858449 -0.259630  1.236777
99        1 -1.374068 -0.400435 -1.825555  0.824052 -0.335694

[100 rows x 6 columns]

>>> df.target.value_counts()
0    80
1    20
Name: .target, dtype: int64

You can access imbalanced-learn namespace via .imbalance accessor. Passing instanciated under-sampling class to ModelFrame.fit_sample returns under sampled ModelFrame (Note that .index is reset).

>>> sampler = df.imbalance.under_sampling.ClusterCentroids()
>>> sampler
ClusterCentroids(n_jobs=-1, random_state=None, ratio='auto')

>>> sampled = df.fit_sample(sampler)
>>> sampled
    .target         A         B         C         D         E
0         1  0.232841 -1.364282  1.436854  0.563796 -0.372866
1         1 -0.159551  0.473617 -2.024209  0.760444 -0.820403
2         1  1.495356 -2.144495  0.076485  1.219948  0.382995
3         1 -0.736887  1.399623  0.557098  0.621909 -0.507285
..      ...       ...       ...       ...       ...       ...
36        0  0.429978 -1.421307  0.771368  1.704277  0.645590
37        0  1.408448  0.132760 -1.082301 -1.195149  0.155057
38        0  0.362793 -0.682171  1.026482  0.663343 -2.371229
39        0 -0.796293 -0.196428 -0.747574  2.228031 -0.468669

[40 rows x 6 columns]

>>> sampled.target.value_counts()
1    20
0    20
Name: .target, dtype: int64

As the same manner, you can perform over-sampling.

>>> sampler = df.imbalance.over_sampling.SMOTE()
>>> sampler
SMOTE(k=5, kind='regular', m=10, n_jobs=-1, out_step=0.5, random_state=None,
ratio='auto')

>>> sampled = df.fit_sample(sampler)
>>> sampled
     .target         A         B         C         D         E
0          0  1.467859  1.637449  0.175770  0.189108  0.775139
1          0 -1.706293 -0.598930 -0.343427  0.355235 -1.348378
2          0  0.030542  0.393779 -1.891991  0.041062  0.055530
3          0  0.320321 -1.062963 -0.416418 -0.629776  1.126027
..       ...       ...       ...       ...       ...       ...
156        1 -1.279399  0.218171 -0.487836 -0.573564  0.582580
157        1 -0.736964  0.239095 -0.422025 -0.841780  0.221591
158        1 -0.273911 -0.305608 -0.886088  0.062414 -0.001241
159        1  0.073145 -0.167884 -0.781611 -0.016734 -0.045330

[160 rows x 6 columns]'

>>> sampled.target.value_counts()
1    80
0    80
Name: .target, dtype: int64

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

`imbalanced-learn`	`ModelFrame` accessor
`imblearn.under_sampling`	`ModelFrame.imbalance.under_sampling`
`imblearn.over_sampling`	`ModelFrame.imbalance.over_sampling`
`imblearn.combine`	`ModelFrame.imbalance.combine`
`imblearn.ensemble`	`ModelFrame.imbalance.ensemble`

Use XGBoost¶

This section describes how to use XGBoost functionalities via pandas-ml.

Use scikit-learn digits dataset as sample data.

>>> import pandas_ml as pdml
>>> import sklearn.datasets as datasets

>>> df = pdml.ModelFrame(datasets.load_digits())
>>> df.head()
   .target  0  1  2 ...  60  61  62  63
0        0  0  0  5 ...  10   0   0   0
1        1  0  0  0 ...  16  10   0   0
2        2  0  0  0 ...  11  16   9   0
3        3  0  0  7 ...  13   9   0   0
4        4  0  0  0 ...  16   4   0   0

[5 rows x 65 columns]

As an estimator, XGBClassifier and XGBRegressor are available via xgboost accessor. See XGBoost Scikit-learn API for details.

>>> df.xgboost.XGBClassifier
<class 'xgboost.sklearn.XGBClassifier'>

>>> df.xgboost.XGBRegressor
<class 'xgboost.sklearn.XGBRegressor'>

You can use these estimators like scikit-learn estimators.

>>> train_df, test_df = df.model_selection.train_test_split()

>>> estimator = df.xgboost.XGBClassifier()

>>> train_df.fit(estimator)
XGBClassifier(base_score=0.5, colsample_bytree=1, gamma=0, learning_rate=0.1,
       max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,
       n_estimators=100, nthread=-1, objective='multi:softprob', seed=0,
       silent=True, subsample=1)

>>> predicted = test_df.predict(estimator)

>>> predicted
1371    2
1090    3
1299    2
...
1286    8
1632    3
538     2
dtype: int64

>>> test_df.metrics.confusion_matrix()
Predicted   0   1   2   3 ...   6   7   8   9
Target                    ...
0          53   0   0   0 ...   0   0   1   0
1           0  46   0   0 ...   0   0   0   0
2           0   0  51   1 ...   0   0   1   0
3           0   0   0  33 ...   0   0   1   0
4           0   0   0   0 ...   0   0   0   1
5           0   0   0   0 ...   1   0   0   1
6           0   0   0   0 ...  39   0   1   0
7           0   0   0   0 ...   0  40   0   1
8           1   0   0   0 ...   1   0  32   2
9           0   1   0   0 ...   0   1   1  51

[10 rows x 10 columns]

Also, plotting functions are available via xgboost accessor.

>>> train_df.xgboost.plot_importance()
# importance plot will be displayed

XGBoost estimators can be passed to other scikit-learn APIs. Following example shows to perform a grid search.

>>> tuned_parameters = [{'max_depth': [3, 4]}]
>>> cv = df.model_selection.GridSearchCV(df.xgb.XGBClassifier(), tuned_parameters, cv=5)

>>> df.fit(cv)
>>> df.model_selection.describe(cv)
       mean       std  max_depth
0  0.917641  0.032600          3
1  0.919310  0.026644          4

Use patsy¶

This section describes data transformation using patsy. ModelFrame.transform can accept patsy style formula.

>>> import pandas_ml as pdml

# create modelframe which doesn't have target
>>> df = pdml.ModelFrame({'X': [1, 2, 3], 'Y': [2, 3, 4],
...                       'Z': [3, 4, 5]}, index=['a', 'b', 'c'])

>>> df
   X  Y  Z
a  1  2  3
b  2  3  4
c  3  4  5

# transform with patsy formula
>>> transformed = df.transform('Z ~ Y + X')
>>> transformed
   Z  Intercept  Y  X
a  3          1  2  1
b  4          1  3  2
c  5          1  4  3

# transformed data should have target specified by formula
>>> transformed.target
a    3
b    4
c    5
Name: Z, dtype: float64

>>> transformed.data
   Intercept  Y  X
a          1  2  1
b          1  3  2
c          1  4  3

If you do not want intercept, specify with 0.

>>> df.transform('Z ~ Y + 0')
   Z  Y
a  3  2
b  4  3
c  5  4

Also, you can use formula which doesn’t have left side.

# create modelframe which has target
>>> df2 = pdml.ModelFrame({'X': [1, 2, 3], 'Y': [2, 3, 4],'Z': [3, 4, 5]},
...                       target =[7, 8, 9], index=['a', 'b', 'c'])

>>> df2
   .target  X  Y  Z
a        7  1  2  3
b        8  2  3  4
c        9  3  4  5

# overwrite data with transformed data
>>> df2.data = df2.transform('Y + Z')
>>> df2
   .target  Intercept  Y  Z
a        7          1  2  3
b        8          1  3  4
c        9          1  4  5

# data has been updated based on formula
>>> df2.data
   Intercept  Y  Z
a          1  2  3
b          1  3  4
c          1  4  5

# target is not changed
>>> df2.target
a    7
b    8
c    9
Name: .target, dtype: int64

Below example is performing deviation coding via patsy formula.

>>> df3 = pdml.ModelFrame({'X': [1, 2, 3, 4, 5], 'Y': [1, 3, 2, 2, 1],
...                        'Z': [1, 1, 1, 2, 2]}, target='Z',
...                        index=['a', 'b', 'c', 'd', 'e'])

>>> df3.transform('C(X, Sum)')
   Intercept  C(X, Sum)[S.1]  C(X, Sum)[S.2]  C(X, Sum)[S.3]  C(X, Sum)[S.4]
a          1               1               0               0               0
b          1               0               1               0               0
c          1               0               0               1               0
d          1               0               0               0               1
e          1              -1              -1              -1              -1

>>> df3.transform('C(Y, Sum)')
   Intercept  C(Y, Sum)[S.1]  C(Y, Sum)[S.2]
a          1               1               0
b          1              -1              -1
c          1               0               1
d          1               0               1
e          1               1               0

Confusion matrix¶

Import ConfusionMatrix

from pandas_ml import ConfusionMatrix

Define actual values (y_true) and predicted values (y_pred)

y_true = ['rabbit', 'cat', 'rabbit', 'rabbit', 'cat', 'dog', 'dog', 'rabbit', 'rabbit', 'cat', 'dog', 'rabbit']
y_pred = ['cat', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'cat', 'rabbit', 'rabbit']

Let’s define a (non binary) confusion matrix

confusion_matrix = ConfusionMatrix(y_true, y_pred)
print("Confusion matrix:\n%s" % confusion_matrix)

You can see it

Predicted  cat  dog  rabbit  __all__
Actual
cat          3    0       0        3
dog          0    1       2        3
rabbit       2    1       3        6
__all__      5    2       5       12

Matplotlib plot of a confusion matrix¶

Inside a IPython notebook add this line as first cell

%matplotlib inline

You can plot confusion matrix using:

import matplotlib.pyplot as plt

confusion_matrix.plot()

If you are not using inline mode, you need to use to show confusion matrix plot.

plt.show()

$confusion\_matrix$

confusion_matrix

Matplotlib plot of a normalized confusion matrix¶

confusion_matrix.plot(normalized=True)
plt.show()

$confusion\_matrix\_norm$

confusion_matrix_norm

Binary confusion matrix¶

If actual values (y_true) and predicted values (y_pred) are bool, ConfusionMatrix outputs binary confusion matrix.

y_true = [ True,  True, False, False, False,  True, False,  True,  True,
           False,  True, False, False, False, False, False,  True, False,
            True,  True,  True,  True, False, False, False,  True, False,
            True, False, False, False, False,  True,  True, False, False,
           False,  True,  True,  True,  True, False, False, False, False,
            True, False, False, False, False, False, False, False, False,
           False,  True,  True, False,  True, False,  True,  True,  True,
           False, False,  True, False,  True, False, False,  True, False,
           False, False, False, False, False, False, False,  True, False,
            True,  True,  True,  True, False, False,  True, False,  True,
            True, False,  True, False,  True, False, False,  True,  True,
           False, False,  True,  True, False, False, False, False, False,
           False,  True,  True, False]

y_pred = [False, False, False, False, False,  True, False, False,  True,
       False,  True, False, False, False, False, False, False, False,
        True,  True,  True,  True, False, False, False, False, False,
       False, False, False, False, False,  True, False, False, False,
       False,  True, False, False, False, False, False, False, False,
        True, False, False, False, False, False, False, False, False,
       False,  True, False, False, False, False, False, False, False,
       False, False,  True, False, False, False, False,  True, False,
       False, False, False, False, False, False, False,  True, False,
       False,  True, False, False, False, False,  True, False,  True,
        True, False, False, False,  True, False, False,  True,  True,
       False, False,  True,  True, False, False, False, False, False,
       False,  True, False, False]

binary_confusion_matrix = ConfusionMatrix(y_true, y_pred)
print("Binary confusion matrix:\n%s" % binary_confusion_matrix)

It display as a nicely labeled Pandas DataFrame

Binary confusion matrix:
Predicted  False  True  __all__
Actual
False         67     0       67
True          21    24       45
__all__       88    24      112

You can get useful attributes such as True Positive (TP), True Negative (TN) …

print(binary_confusion_matrix.TP)

Matplotlib plot of a binary confusion matrix¶

binary_confusion_matrix.plot()
plt.show()

$binary\_confusion\_matrix$

binary_confusion_matrix

Matplotlib plot of a normalized binary confusion matrix¶

binary_confusion_matrix.plot(normalized=True)
plt.show()

$binary\_confusion\_matrix\_norm$

binary_confusion_matrix_norm

Seaborn plot of a binary confusion matrix (ToDo)¶

binary_confusion_matrix.plot(backend='seaborn')

Confusion matrix and class statistics¶

Overall statistics and class statistics of confusion matrix can be easily displayed.

y_true = [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200]
y_pred = [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200]
cm = ConfusionMatrix(y_true, y_pred)
cm.print_stats()

You should get:

Confusion Matrix:

Classes  100  200  500  600  __all__
Actual
100        0    0    0    0        0
200        9    6    1    0       16
500        1    1    1    0        3
600        1    0    0    0        1
__all__   11    7    2    0       20


Overall Statistics:

Accuracy: 0.35
95% CI: (0.1539092047845412, 0.59218853453282805)
No Information Rate: ToDo
P-Value [Acc > NIR]: 0.978585644357
Kappa: 0.0780141843972
Mcnemar's Test P-Value: ToDo


Class Statistics:

Classes                                 100         200         500   600
Population                               20          20          20    20
Condition positive                        0          16           3     1
Condition negative                       20           4          17    19
Test outcome positive                    11           7           2     0
Test outcome negative                     9          13          18    20
TP: True Positive                         0           6           1     0
TN: True Negative                         9           3          16    19
FP: False Positive                       11           1           1     0
FN: False Negative                        0          10           2     1
TPR: Sensivity                          NaN       0.375   0.3333333     0
TNR=SPC: Specificity                   0.45        0.75   0.9411765     1
PPV: Pos Pred Value = Precision           0   0.8571429         0.5   NaN
NPV: Neg Pred Value                       1   0.2307692   0.8888889  0.95
FPR: False-out                         0.55        0.25  0.05882353     0
FDR: False Discovery Rate                 1   0.1428571         0.5   NaN
FNR: Miss Rate                          NaN       0.625   0.6666667     1
ACC: Accuracy                          0.45        0.45        0.85  0.95
F1 score                                  0   0.5217391         0.4     0
MCC: Matthews correlation coefficient   NaN   0.1048285    0.326732   NaN
Informedness                            NaN       0.125   0.2745098     0
Markedness                                0  0.08791209   0.3888889   NaN
Prevalence                                0         0.8        0.15  0.05
LR+: Positive likelihood ratio          NaN         1.5    5.666667   NaN
LR-: Negative likelihood ratio          NaN   0.8333333   0.7083333     1
DOR: Diagnostic odds ratio              NaN         1.8           8   NaN
FOR: False omission rate                  0   0.7692308   0.1111111  0.05

Statistics are also available as an OrderedDict using:

cm.stats()

API:

pandas_ml.core package¶

Submodules¶

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

Bases: pandas_ml.core.generic.ModelPredictor, pandas.core.frame.DataFrame

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

Parameters:	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`

calibration¶: Property to access sklearn.calibration

cls¶: alias of pandas_ml.skaccessors.gaussian_process.GaussianProcessMethods

cluster¶: Property to access sklearn.cluster. See pandas_ml.skaccessors.cluster

covariance¶: Property to access sklearn.covariance. See pandas_ml.skaccessors.covariance

cross_decomposition¶: Property to access sklearn.cross_decomposition

da¶: Property to access sklearn.discriminant_analysis

data¶

Return data (explanatory variable / features)

Returns:	data : `ModelFrame`

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

Call estimator’s decision_function method.

Parameters:	args : arguments passed to decision_function method kwargs : keyword arguments passed to decision_function method
Returns:	returned : decisions

decomposition¶: Property to access sklearn.decomposition

discriminant_analysis¶: Property to access sklearn.discriminant_analysis

dummy¶: Property to access sklearn.dummy

ensemble¶: Property to access sklearn.ensemble. See pandas_ml.skaccessors.ensemble

feature_extraction¶: Property to access sklearn.feature_extraction. See pandas_ml.skaccessors.feature_extraction

feature_selection¶: Property to access sklearn.feature_selection. See pandas_ml.skaccessors.feature_selection

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

Call estimator’s fit_predict method.

Parameters:	args : arguments passed to fit_predict method kwargs : keyword arguments passed to fit_predict method
Returns:	returned : predicted result

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

Call estimator’s fit_resample method.

Parameters:	args : arguments passed to fit_resample method kwargs : keyword arguments passed to fit_resample method
Returns:	returned : resampling result

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

Call estimator’s fit_sample method.

Parameters:	args : arguments passed to fit_sample method kwargs : keyword arguments passed to fit_sample method
Returns:	returned : sampling result

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

Call estimator’s fit_transform method.

Parameters:	args : arguments passed to fit_transform method kwargs : keyword arguments passed to fit_transform method
Returns:	returned : transformed result

gaussian_process¶: Property to access sklearn.gaussian_process. See pandas_ml.skaccessors.gaussian_process

gp¶: Property to access sklearn.gaussian_process. See pandas_ml.skaccessors.gaussian_process

groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False)¶

Group DataFrame or Series using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Parameters:

by : mapping, function, label, or list of labels: Used to determine the groups for the groupby. If by is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see .align() method). If an ndarray is passed, the values are used as-is determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted a (single) key.
axis : {0 or ‘index’, 1 or ‘columns’}, default 0: Split along rows (0) or columns (1).
level : int, level name, or sequence of such, default None: If the axis is a MultiIndex (hierarchical), group by a particular level or levels.
as_index : bool, default True: For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output.
sort : bool, default True: Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group.
group_keys : bool, default True: When calling apply, add group keys to index to identify pieces.
squeeze : bool, default False: Reduce the dimensionality of the return type if possible, otherwise return a consistent type.
observed : bool, default False: This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

New in version 0.23.0.
**kwargs: Optional, only accepts keyword argument ‘mutated’ and is passed to groupby.

Returns:

DataFrameGroupBy or SeriesGroupBy: Depends on the calling object and returns groupby object that contains information about the groups.

Module contents¶

pandas_ml.skaccessors package¶

Subpackages¶

pandas_ml.skaccessors.test package¶

Submodules¶

class pandas_ml.skaccessors.test.test_multioutput.TestMultiOutput¶

Bases: pandas_ml.util.testing.TestCase

test_multioutput()¶

test_objectmapper()¶

Module contents¶

Submodules¶

class pandas_ml.skaccessors.base.Bunch¶: Bases: object

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

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 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

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

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

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

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

regression_models¶: Property to access sklearn.gaussian_process.regression_models

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

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 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

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 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

spectral_embedding(*args, **kwargs)¶

Call sklearn.manifold.spectral_embedding using automatic mapping.

adjacency: ModelFrame.data

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

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 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

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

describe(estimator)¶

Describe grid search results

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

iterate(cv, reset_index=False)¶: deprecated. Use .split

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

Call sklearn.lerning_curve.learning_curve using automatic mapping.

X: ModelFrame.data
y: ModelFrame.target

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

Call sklearn.cross_validation.permutation_test_score using automatic mapping.

X: ModelFrame.data
y: ModelFrame.target

split(cv, reset_index=False)¶

Generate ModelFrame using iterators for cross validation

Parameters:	cv : cross validation iterator reset_index : bool logical value whether to reset index, default False
Returns:	generated : generator of `ModelFrame`

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`

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 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

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

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

liblinear¶: Not implemented

libsvm¶: Not implemented

libsvm_sparse¶: Not implemented

Module contents¶

pandas_ml.xgboost package¶

Subpackages¶

pandas_ml.xgboost.test package¶

Submodules¶

Module contents¶

Submodules¶

class pandas_ml.xgboost.base.XGBoostMethods(df, module_name=None, attrs=None)¶

Bases: pandas_ml.core.accessor._AccessorMethods

Accessor to xgboost.

XGBClassifier¶

XGBRegressor¶

plot_importance(ax=None, height=0.2, xlim=None, title='Feature importance', xlabel='F score', ylabel='Features', grid=True, **kwargs)¶

Plot importance based on fitted trees.

Parameters:

ax : matplotlib Axes, default None: Target axes instance. If None, new figure and axes will be created.
height : float, default 0.2: Bar height, passed to ax.barh()
xlim : tuple, default None: Tuple passed to axes.xlim()
title : str, default “Feature importance”: Axes title. To disable, pass None.
xlabel : str, default “F score”: X axis title label. To disable, pass None.
ylabel : str, default “Features”: Y axis title label. To disable, pass None.
kwargs :: Other keywords passed to ax.barh()

Returns:

ax : matplotlib Axes

plot_tree(num_trees=0, rankdir='UT', ax=None, **kwargs)¶

Plot specified tree.

Parameters:	booster : Booster, XGBModel Booster or XGBModel instance num_trees : int, default 0 Specify the ordinal number of target tree rankdir : str, default “UT” Passed to graphiz via graph_attr ax : matplotlib Axes, default None Target axes instance. If None, new figure and axes will be created. kwargs : Other keywords passed to to_graphviz
Returns:	ax : matplotlib Axes

to_graphviz(num_trees=0, rankdir='UT', yes_color='#0000FF', no_color='#FF0000', **kwargs)¶

Convert specified tree to graphviz instance. IPython can automatically plot the returned graphiz instance. Otherwise, you shoud call .render() method of the returned graphiz instance.

Parameters:	num_trees : int, default 0 Specify the ordinal number of target tree rankdir : str, default “UT” Passed to graphiz via graph_attr yes_color : str, default ‘#0000FF’ Edge color when meets the node condigion. no_color : str, default ‘#FF0000’ Edge color when doesn’t meet the node condigion. kwargs : Other keywords passed to graphviz graph_attr
Returns:	ax : matplotlib Axes

Parameters:	obj : pandas object axis : int, default 0 level : int, default None Level of MultiIndex groupings : list of Grouping objects Most users should ignore this exclusions : array-like, optional List of columns to exclude name : string Most users should ignore this
Returns:	Attributes groups : dict {group name -> group labels} len(grouped) : int Number of groups