Sklearn-pandas
This module provides a bridge between Scikit-Learn's machine learning methods and pandas-style Data Frames.
In particular, it provides:
- A way to map
DataFrame
columns to transformations, which are later recombined into features. - A compatibility shim for old
scikit-learn
versions to cross-validate a pipeline that takes a pandasDataFrame
as input. This is only needed forscikit-learn<0.16.0
(see #11 for details). It is deprecated and will likely be dropped inskearn-pandas==2.0
. - A
CategoricalImputer
that replaces null-like values with the mode and works with string columns.
Installation
You can install sklearn-pandas
with pip
:
# pip install sklearn-pandas
Tests
The examples in this file double as basic sanity tests. To run them, use doctest
, which is included with python:
# python -m doctest README.rst
Usage
Import
Import what you need from the sklearn_pandas
package. The choices are:
DataFrameMapper
, a class for mapping pandas data frame columns to different sklearn transformationscross_val_score
, similar tosklearn.cross_validation.cross_val_score
but working on pandas DataFrames
For this demonstration, we will import both:
>>> from sklearn_pandas import DataFrameMapper, cross_val_score
For these examples, we'll also use pandas, numpy, and sklearn:
>>> import pandas as pd >>> import numpy as np >>> import sklearn.preprocessing, sklearn.decomposition, \ ... sklearn.linear_model, sklearn.pipeline, sklearn.metrics >>> from sklearn.feature_extraction.text import CountVectorizer
Load some Data
Normally you'll read the data from a file, but for demonstration purposes we'll create a data frame from a Python dict:
>>> data = pd.DataFrame({'pet': ['cat', 'dog', 'dog', 'fish', 'cat', 'dog', 'cat', 'fish'], ... 'children': [4., 6, 3, 3, 2, 3, 5, 4], ... 'salary': [90, 24, 44, 27, 32, 59, 36, 27]})
Transformation Mapping
Map the Columns to Transformations
The mapper takes a list of tuples. The first is a column name from the pandas DataFrame, or a list containing one or multiple columns (we will see an example with multiple columns later). The second is an object which will perform the transformation which will be applied to that column. The third is optional and is a dictionary containing the transformation options, if applicable (see "custom column names for transformed features" below).
Let's see an example:
>>> mapper = DataFrameMapper([ ... ('pet', sklearn.preprocessing.LabelBinarizer()), ... (['children'], sklearn.preprocessing.StandardScaler()) ... ])
The difference between specifying the column selector as 'column'
(as a simple string) and ['column']
(as a list with one element) is the shape of the array that is passed to the transformer. In the first case, a one dimensional array will be passed, while in the second case it will be a 2-dimensional array with one column, i.e. a column vector.
This behaviour mimics the same pattern as pandas' dataframes __getitem__
indexing:
>>> data['children'].shape (8,) >>> data[['children']].shape (8, 1)
Be aware that some transformers expect a 1-dimensional input (the label-oriented ones) while some others, like OneHotEncoder
or Imputer
, expect 2-dimensional input, with the shape [n_samples, n_features]
.
Test the Transformation
We can use the fit_transform
shortcut to both fit the model and see what transformed data looks like. In this and the other examples, output is rounded to two digits with np.round
to account for rounding errors on different hardware:
>>> np.round(mapper.fit_transform(data.copy()), 2) array([[ 1. , 0. , 0. , 0.21], [ 0. , 1. , 0. , 1.88], [ 0. , 1. , 0. , -0.63], [ 0. , 0. , 1. , -0.63], [ 1. , 0. , 0. , -1.46], [ 0. , 1. , 0. , -0.63], [ 1. , 0. , 0. , 1.04], [ 0. , 0. , 1. , 0.21]])
Note that the first three columns are the output of the LabelBinarizer
(corresponding to _cat_, _dog_, and _fish_ respectively) and the fourth column is the standardized value for the number of children. In general, the columns are ordered according to the order given when the DataFrameMapper
is constructed.
Now that the transformation is trained, we confirm that it works on new data:
>>> sample = pd.DataFrame({'pet': ['cat'], 'children': [5.]}) >>> np.round(mapper.transform(sample), 2) array([[ 1. , 0. , 0. , 1.04]])
Output features names
In certain cases, like when studying the feature importances for some model, we want to be able to associate the original features to the ones generated by the dataframe mapper. We can do so by inspecting the automatically generated
transformed_names_
attribute of the mapper after transformation:>>> mapper.transformed_names_ ['pet_cat', 'pet_dog', 'pet_fish', 'children']
Custom column names for transformed features
We can provide a custom name for the transformed features, to be used instead of the automatically generated one, by specifying it as the third argument of the feature definition:
>>> mapper_alias = DataFrameMapper([ ... (['children'], sklearn.preprocessing.StandardScaler(), ... {'alias': 'children_scaled'}) ... ]) >>> _ = mapper_alias.fit_transform(data.copy()) >>> mapper_alias.transformed_names_ ['children_scaled']
Passing Series/DataFrames to the transformers
By default the transformers are passed a numpy array of the selected columns
as input. This is because sklearn
transformers are historically designed to
work with numpy arrays, not with pandas dataframes, even though their basic
indexing interfaces are similar.
However we can pass a dataframe/series to the transformers to handle custom cases initializing the dataframe mapper with ``input_df=True`:
>>> from sklearn.base import TransformerMixin >>> class DateEncoder(TransformerMixin): ... def fit(self, X, y=None): ... return self ... ... def transform(self, X): ... dt = X.dt ... return pd.concat([dt.year, dt.month, dt.day], axis=1) >>> dates_df = pd.DataFrame( ... {'dates': pd.date_range('2015-10-30', '2015-11-02')}) >>> mapper_dates = DataFrameMapper([ ... ('dates', DateEncoder()) ... ], input_df=True) >>> mapper_dates.fit_transform(dates_df) array([[2015, 10, 30], [2015, 10, 31], [2015, 11, 1], [2015, 11, 2]])
Outputting a dataframe
By default the output of the dataframe mapper is a numpy array. This is so because most sklearn estimators expect a numpy array as input. If however we want the output of the mapper to be a dataframe, we can do so using the parameter df_out
when creating the mapper:
>>> mapper_df = DataFrameMapper([ ... ('pet', sklearn.preprocessing.LabelBinarizer()), ... (['children'], sklearn.preprocessing.StandardScaler()) ... ], df_out=True) >>> np.round(mapper_df.fit_transform(data.copy()), 2) pet_cat pet_dog pet_fish children 0 1.0 0.0 0.0 0.21 1 0.0 1.0 0.0 1.88 2 0.0 1.0 0.0 -0.63 3 0.0 0.0 1.0 -0.63 4 1.0 0.0 0.0 -1.46 5 0.0 1.0 0.0 -0.63 6 1.0 0.0 0.0 1.04 7 0.0 0.0 1.0 0.21
The names for the columns are the same ones present in the transformed_names_
attribute.
Note this does not work together with the default=True
or sparse=True
arguments to the mapper.
Transform Multiple Columns
Transformations may require multiple input columns. In these cases, the column names can be specified in a list:
>>> mapper2 = DataFrameMapper([ ... (['children', 'salary'], sklearn.decomposition.PCA(1)) ... ])
Now running fit_transform
will run PCA on the children
and salary
columns and return the first principal component:
>>> np.round(mapper2.fit_transform(data.copy()), 1) array([[ 47.6], [-18.4], [ 1.6], [-15.4], [-10.4], [ 16.6], [ -6.4], [-15.4]])
Multiple transformers for the same column
Multiple transformers can be applied to the same column specifying them in a list:
>>> mapper3 = DataFrameMapper([ ... (['age'], [sklearn.preprocessing.Imputer(), ... sklearn.preprocessing.StandardScaler()])]) >>> data_3 = pd.DataFrame({'age': [1, np.nan, 3]}) >>> mapper3.fit_transform(data_3) array([[-1.22474487], [ 0. ], [ 1.22474487]])
Columns that don't need any transformation
Only columns that are listed in the DataFrameMapper are kept. To keep a column but don't apply any transformation to it, use None as transformer:
>>> mapper3 = DataFrameMapper([ ... ('pet', sklearn.preprocessing.LabelBinarizer()), ... ('children', None) ... ]) >>> np.round(mapper3.fit_transform(data.copy())) array([[ 1., 0., 0., 4.], [ 0., 1., 0., 6.], [ 0., 1., 0., 3.], [ 0., 0., 1., 3.], [ 1., 0., 0., 2.], [ 0., 1., 0., 3.], [ 1., 0., 0., 5.], [ 0., 0., 1., 4.]])
Applying a default transformer
A default transformer can be applied to columns not explicitly selected
passing it as the default
argument to the mapper:
>>> mapper4 = DataFrameMapper([ ... ('pet', sklearn.preprocessing.LabelBinarizer()), ... ('children', None) ... ], default=sklearn.preprocessing.StandardScaler()) >>> np.round(mapper4.fit_transform(data.copy()), 1) array([[ 1. , 0. , 0. , 4. , 2.3], [ 0. , 1. , 0. , 6. , -0.9], [ 0. , 1. , 0. , 3. , 0.1], [ 0. , 0. , 1. , 3. , -0.7], [ 1. , 0. , 0. , 2. , -0.5], [ 0. , 1. , 0. , 3. , 0.8], [ 1. , 0. , 0. , 5. , -0.3], [ 0. , 0. , 1. , 4. , -0.7]])
Using default=False
(the default) drops unselected columns. Using
default=None
pass the unselected columns unchanged.
Feature selection and other supervised transformations
DataFrameMapper
supports transformers that require both X and y arguments. An example of this is feature selection. Treating the 'pet' column as the target, we will select the column that best predicts it.
>>> from sklearn.feature_selection import SelectKBest, chi2 >>> mapper_fs = DataFrameMapper([(['children','salary'], SelectKBest(chi2, k=1))]) >>> mapper_fs.fit_transform(data[['children','salary']], data['pet']) array([[ 90.], [ 24.], [ 44.], [ 27.], [ 32.], [ 59.], [ 36.], [ 27.]])
Working with sparse features
DataFrameMapper``s will return a dense feature array by default. Setting ``sparse=True
in the mapper will return a sparse array whenever any of the extracted features is sparse. Example:
>>> mapper5 = DataFrameMapper([ ... ('pet', CountVectorizer()), ... ], sparse=True) >>> type(mapper5.fit_transform(data)) <class 'scipy.sparse.csr.csr_matrix'>
The stacking of the sparse features is done without ever densifying them.
Cross-Validation
Now that we can combine features from pandas DataFrames, we may want to use cross-validation to see whether our model works. scikit-learn<0.16.0
provided features for cross-validation, but they expect numpy data structures and won't work with DataFrameMapper
.
To get around this, sklearn-pandas provides a wrapper on sklearn's cross_val_score
function which passes a pandas DataFrame to the estimator rather than a numpy array:
>>> pipe = sklearn.pipeline.Pipeline([ ... ('featurize', mapper), ... ('lm', sklearn.linear_model.LinearRegression())]) >>> np.round(cross_val_score(pipe, X=data.copy(), y=data.salary, scoring='r2'), 2) array([ -1.09, -5.3 , -15.38])
Sklearn-pandas' cross_val_score
function provides exactly the same interface as sklearn's function of the same name.
CategoricalImputer
Since the scikit-learn
Imputer
transformer currently only works with
numbers, sklearn-pandas
provides an equivalent helper transformer that do
work with strings, substituting null values with the most frequent value in
that column.
Example:
>>> from sklearn_pandas import CategoricalImputer >>> data = np.array(['a', 'b', 'b', np.nan], dtype=object) >>> imputer = CategoricalImputer() >>> imputer.fit_transform(data) array(['a', 'b', 'b', 'b'], dtype=object)
Changelog
1.4.0 (2017-05-13)
- Allow specifying a custom name (alias) for transformed columns (#83).
- Capture output columns generated names in
transformed_names_
attribute (#78). - Add
CategoricalImputer
that replaces null-like values with the mode for string-like columns. - Add
input_df
init argument to allow inputting a dataframe/series to the transformers instead of a numpy array (#60).
1.3.0 (2017-01-21)
- Make the mapper return dataframes when
df_out=True
(#70, #74). - Update imports to avoid deprecation warnings in sklearn 0.18 (#68).
1.2.0 (2016-10-02)
- Deprecate custom cross-validation shim classes.
- Require
scikit-learn>=0.15.0
. Resolves #49. - Allow applying a default transformer to columns not selected explicitly in the mapper. Resolves #55.
- Allow specifying an optional
y
argument during transform for supervised transformations. Resolves #58.
1.1.0 (2015-12-06)
- Delete obsolete
PassThroughTransformer
. If no transformation is desired for a given column, useNone
as transformer. - Factor out code in several modules, to avoid having everything in
__init__.py
. - Use custom
TransformerPipeline
class to allow transformation steps accepting only a X argument. Fixes #46. - Add compatibility shim for unpickling mappers with list of transformers created before 1.0.0. Fixes #45.
1.0.0 (2015-11-28)
- Change version numbering scheme to SemVer.
- Use
sklearn.pipeline.Pipeline
instead of copying its code. Resolves #43. - Raise
KeyError
when selecting unexistent columns in the dataframe. Fixes #30. - Return sparse feature array if any of the features is sparse and
sparse
argument isTrue
. Defaults toFalse
to avoid potential breaking of existing code. Resolves #34. - Return model and prediction in custom CV classes. Fixes #27.
0.0.12 (2015-11-07)
- Allow specifying a list of transformers to use sequentially on the same column.
Credits
The code for DataFrameMapper
is based on code originally written by Ben Hamner.
Other contributors:
- Arnau Gil Amat
- Cal Paterson
- Gustavo Sena Mafra
- Israel Saeta Pérez
- Jeremy Howard
- Olivier Grisel
- Paul Butler
- Ritesh Agrawal
- Vitaley Zaretskey
- Zac Stewart