Pandas integration with sklearn
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.. highlight:: python
This module provides a bridge between Scikit-Learn <http://scikit-learn.org/stable>
's machine learning methods and pandas <https://pandas.pydata.org>
-style Data Frames.
In particular, it provides a way to map DataFrame
columns to transformations, which are later recombined into features.
You can install sklearn-pandas
with pip
::
# pip install sklearn-pandas
or conda-forge::
# conda install -c conda-forge sklearn-pandas
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
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 transformationsFor this demonstration, we will import both::
>>> from sklearn_pandas import DataFrameMapper
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, \
... sklearn.compose
>>> 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]})
Map the Columns to Transformations
The mapper takes a list of tuples. Each tuple has three elements:
make_column_selector <https://scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_selector.html>
__.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']
Alternatively, you can also specify prefix and/or suffix to add to the column name. For example::
mapper_alias = DataFrameMapper([ ... (['children'], sklearn.preprocessing.StandardScaler(), {'prefix': 'standard_scaled_'}), ... (['children'], sklearn.preprocessing.StandardScaler(), {'suffix': 'raw'}) ... ]) _ = mapper_alias.fit_transform(data.copy()) mapper_alias.transformed_names ['standard_scaled_children', 'children_raw']
Dynamic Columns
In some situations the columns are not known before hand and we would like to dynamically select them during the fit operation. As shown below, in such situations you can provide either a custom callable or use make_column_selector <https://scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_selector.html>
__.
::
>>> class GetColumnsStartingWith:
... def __init__(self, start_str):
... self.pattern = start_str
...
... def __call__(self, X:pd.DataFrame=None):
... return [c for c in X.columns if c.startswith(self.pattern)]
...
>>> df = pd.DataFrame({
... 'sepal length (cm)': [1.0, 2.0, 3.0],
... 'sepal width (cm)': [1.0, 2.0, 3.0],
... 'petal length (cm)': [1.0, 2.0, 3.0],
... 'petal width (cm)': [1.0, 2.0, 3.0]
... })
>>> t = DataFrameMapper([
... (
... sklearn.compose.make_column_selector(dtype_include=float),
... sklearn.preprocessing.StandardScaler(),
... {'alias': 'x'}
... ),
... (
... GetColumnsStartingWith('petal'),
... None,
... {'alias': 'petal'}
... )], df_out=True, default=False)
>>> t.fit(df).transform(df).shape
(3, 6)
>>> t.transformed_names_
['x_0', 'x_1', 'x_2', 'x_3', 'petal_0', 'petal_1']
Above we use make_column_selector
to select all columns that are of type float and also use a custom callable function to select columns that start with the word 'petal'.
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]])
We can also specify this option per group of columns instead of for the whole mapper::
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 0.21
1 0 1 0 1.88
2 0 1 0 -0.63
3 0 0 1 -0.63
4 1 0 0 -1.46
5 0 1 0 -0.63
6 1 0 0 1.04
7 0 0 1 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.
Dropping columns explictly
Sometimes it is required to drop a specific column/ list of columns.
For this purpose, drop_cols
argument for DataFrameMapper
can be used.
Default value is None
::
>>> mapper_df = DataFrameMapper([
... ('pet', sklearn.preprocessing.LabelBinarizer()),
... (['children'], sklearn.preprocessing.StandardScaler())
... ], drop_cols=['salary'])
Now running fit_transform
will run transformations on 'pet' and 'children' and drop 'salary' column::
np.round(mapper_df.fit_transform(data.copy()), 1) array([[ 1. , 0. , 0. , 0.2], [ 0. , 1. , 0. , 1.9], [ 0. , 1. , 0. , -0.6], [ 0. , 0. , 1. , -0.6], [ 1. , 0. , 0. , -1.5], [ 0. , 1. , 0. , -0.6], [ 1. , 0. , 0. , 1. ], [ 0. , 0. , 1. , 0.2]])
Transformations may require multiple input columns. In these
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::
>>> from sklearn.impute import SimpleImputer
>>> mapper3 = DataFrameMapper([
... (['age'], [SimpleImputer(),
... 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.
Same transformer for the multiple columns
Sometimes it is required to apply the same transformation to several dataframe columns.
To simplify this process, the package provides gen_features
function which accepts a list
of columns and feature transformer class (or list of classes), and generates a feature definition,
acceptable by DataFrameMapper
.
For example, consider a dataset with three categorical columns, 'col1', 'col2', and 'col3',
To binarize each of them, one could pass column names and LabelBinarizer
transformer class
into generator, and then use returned definition as features
argument for DataFrameMapper
::
>>> from sklearn_pandas import gen_features
>>> feature_def = gen_features(
... columns=['col1', 'col2', 'col3'],
... classes=[sklearn.preprocessing.LabelEncoder]
... )
>>> feature_def
[('col1', [LabelEncoder()], {}), ('col2', [LabelEncoder()], {}), ('col3', [LabelEncoder()], {})]
>>> mapper5 = DataFrameMapper(feature_def)
>>> data5 = pd.DataFrame({
... 'col1': ['yes', 'no', 'yes'],
... 'col2': [True, False, False],
... 'col3': ['one', 'two', 'three']
... })
>>> mapper5.fit_transform(data5)
array([[1, 1, 0],
[0, 0, 2],
[1, 0, 1]])
If it is required to override some of transformer parameters, then a dict with 'class' key and transformer parameters should be provided. For example, consider a dataset with missing values. Then the following code could be used to override default imputing strategy::
>>> from sklearn.impute import SimpleImputer
>>> import numpy as np
>>> feature_def = gen_features(
... columns=[['col1'], ['col2'], ['col3']],
... classes=[{'class': SimpleImputer, 'strategy':'most_frequent'}]
... )
>>> mapper6 = DataFrameMapper(feature_def)
>>> data6 = pd.DataFrame({
... 'col1': [np.nan, 1, 1, 2, 3],
... 'col2': [True, False, np.nan, np.nan, True],
... 'col3': [0, 0, 0, np.nan, np.nan]
... })
>>> mapper6.fit_transform(data6)
array([[1.0, True, 0.0],
[1.0, False, 0.0],
[1.0, True, 0.0],
[2.0, True, 0.0],
[3.0, True, 0.0]], dtype=object)
You can also specify global prefix or suffix for the generated transformed column names using the prefix and suffix parameters::
>>> feature_def = gen_features(
... columns=['col1', 'col2', 'col3'],
... classes=[sklearn.preprocessing.LabelEncoder],
... prefix="lblencoder_"
... )
>>> mapper5 = DataFrameMapper(feature_def)
>>> data5 = pd.DataFrame({
... 'col1': ['yes', 'no', 'yes'],
... 'col2': [True, False, False],
... 'col3': ['one', 'two', 'three']
... })
>>> _ = mapper5.fit_transform(data5)
>>> mapper5.transformed_names_
['lblencoder_col1', 'lblencoder_col2', 'lblencoder_col3']
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
A DataFrameMapper
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.
Using NumericalTransformer
While you can use FunctionTransformation
to generate arbitrary transformers, it can present serialization issues
when pickling. Use NumericalTransformer
instead, which takes the function name as a string parameter and hence
can be easily serialized.
::
>>> from sklearn_pandas import NumericalTransformer
>>> mapper5 = DataFrameMapper([
... ('children', NumericalTransformer('log')),
... ])
>>> mapper5.fit_transform(data)
array([[1.38629436],
[1.79175947],
[1.09861229],
[1.09861229],
[0.69314718],
[1.09861229],
[1.60943791],
[1.38629436]])
Changing Logging level
You can change log level to info to print time take to fit/transform features. Setting it to higher level will stop printing elapsed time. Below example shows how to change logging level.
::
>>> import logging
>>> logging.getLogger('sklearn_pandas').setLevel(logging.INFO)
2.2.0 (2021-05-07)
2.1.0 (2021-02-26)
2.0.4 (2020-11-06)
2.0.3 (2020-11-06)
2.0.2 (2020-10-01)
DataFrameMapper
drop_cols attribute naming consistency with scikit-learn and initialization.2.0.1 (2020-09-07)
2.0.0 (2020-08-01)
NumericalTransformer
for common numerical transformations. Currently it implements log and log1pdrop_cols
argument to DataframeMapper. This can be used to explicitly drop columns1.8.0 (2018-12-01)
FunctionTransformer
class (#117).1.7.0 (2018-08-15)
get_names
(#160).numpy==1.14
and python==3.6
(#154).strategy
and fill_value
parameters to CategoricalImputer
to allow imputing1.6.0 (2017-10-28)
gen_feature
helper function to help generating the same transformation for multiple columns (#126).1.5.0 (2017-06-24)
estimator.get_feature_names()
if present.__init__
to be compatible withsklearn>=0.20
(#76).1.4.0 (2017-05-13)
transformed_names_
attribute (#78).CategoricalImputer
that replaces null-like values with the modeinput_df
init argument to allow inputting a dataframe/series to the1.3.0 (2017-01-21)
df_out=True
(#70, #74).1.2.0 (2016-10-02)
scikit-learn>=0.15.0
. Resolves #49.y
argument during transform for1.1.0 (2015-12-06)
PassThroughTransformer
. If no transformation is desired for a given column, use None
as transformer.__init__.py
.TransformerPipeline
class to allow transformation steps accepting only a X argument. Fixes #46.1.0.0 (2015-11-28)
sklearn.pipeline.Pipeline
instead of copying its code. Resolves #43.KeyError
when selecting unexistent columns in the dataframe. Fixes #30.sparse
argument is True
. Defaults to False
to avoid potential breaking of existing code. Resolves #34.0.0.12 (2015-11-07)
The code for DataFrameMapper
is based on code originally written by Ben Hamner <https://github.com/benhamner>
__.
Other contributors: