Preprocessing

Preprocessing¶

dask_ml.preprocessing contains some scikit-learn style transformers that can be used in Pipelines to perform various data transformations as part of the model fitting process. These transformers will work well on dask collections (dask.array, dask.dataframe), NumPy arrays, or pandas dataframes. They’ll fit and transform in parallel.

Scikit-Learn Clones¶

Some of the transformers are (mostly) drop-in replacements for their scikit-learn counterparts.

`MinMaxScaler`([feature_range, copy, clip])	Transform features by scaling each feature to a given range.
`QuantileTransformer`(*[, n_quantiles, ...])	Transforms features using quantile information.
`RobustScaler`(*[, with_centering, ...])	Scale features using statistics that are robust to outliers.
`StandardScaler`(*[, copy, with_mean, with_std])	Standardize features by removing the mean and scaling to unit variance.
`LabelEncoder`([use_categorical])	Encode labels with value between 0 and n_classes-1.
`OneHotEncoder`(n_values, ...)	Encode categorical integer features as a one-hot numeric array.
`PolynomialFeatures`([degree, ...])	Generate polynomial and interaction features.

These can be used just like the scikit-learn versions, except that:

They operate on dask collections in parallel
.transform will return a dask.array or dask.dataframe when the input is a dask collection

See sklearn.preprocessing for more information about any particular transformer. Scikit-learn does have some transforms that are alternatives to the large-memory tasks that Dask serves. These include FeatureHasher (a good alternative to DictVectorizer and CountVectorizer) and HashingVectorizer (best suited for use in text over CountVectorizer). They are not stateful, which allows easy use with Dask with map_partitions:

In [1]: import dask.bag as db

In [2]: from sklearn.feature_extraction import FeatureHasher

In [3]: D = [{'dog': 1, 'cat':2, 'elephant':4}, {'dog': 2, 'run': 5}]

In [4]: b = db.from_sequence(D)

In [5]: h = FeatureHasher()

In [6]: b.map_partitions(h.transform).compute()
Out[6]: 
[<Compressed Sparse Row sparse matrix of dtype 'float64'
 	with 3 stored elements and shape (1, 1048576)>,
 <Compressed Sparse Row sparse matrix of dtype 'float64'
 	with 2 stored elements and shape (1, 1048576)>]

Note

dask_ml.preprocessing.LabelEncoder and dask_ml.preprocessing.OneHotEncoder will use the categorical dtype information for a dask or pandas Series with a pandas.api.types.CategoricalDtype. This improves performance, but may lead to different encodings depending on the categories. See the class docstrings for more.

Encoding Categorical Features¶

dask_ml.preprocessing.OneHotEncoder can be useful for “one-hot” (or “dummy”) encoding features.

See the scikit-learn documentation for a full discussion. This section focuses only on the differences from scikit-learn.

Dask-ML Supports pandas’ Categorical dtype¶

Dask-ML supports and uses the type information from pandas Categorical dtype. See https://pandas.pydata.org/pandas-docs/stable/categorical.html for an introduction. For large datasets, using categorical dtypes is crucial for achieving performance.

This will have a couple effects on the learned attributes and transformed values.

The learned categories_ may differ. Scikit-Learn requires the categories to be sorted. With a CategoricalDtype the categories do not need to be sorted.
The output of OneHotEncoder.transform() will be the same type as the input. Passing a pandas DataFrame returns a pandas Dataframe, instead of a NumPy array. Likewise, a Dask DataFrame returns a Dask DataFrame.

Dask-ML’s Sparse Support¶

The default behavior of OneHotEncoder is to return a sparse array. Scikit-Learn returns a SciPy sparse matrix for ndarrays passed to transform.

When passed a Dask Array, OneHotEncoder.transform() returns a Dask Array where each block is a scipy sparse matrix. SciPy sparse matrices don’t support the same API as the NumPy ndarray, so most methods won’t work on the result. Even basic things like compute will fail. To work around this, we currently recommend converting the sparse matrices to dense.

from dask_ml.preprocessing import OneHotEncoder
import dask.array as da
import numpy as np

enc = OneHotEncoder(sparse=True)
X = da.from_array(np.array([['A'], ['B'], ['A'], ['C']]), chunks=2)
enc = enc.fit(X)
result = enc.transform(X)
result

Each block of result is a scipy sparse matrix

result.blocks[0].compute()
# This would fail!
# result.compute()
# Convert to, say, pydata/sparse COO matrices instead
from sparse import COO

result.map_blocks(COO.from_scipy_sparse, dtype=result.dtype).compute()

Dask-ML’s sparse support for sparse data is currently in flux. Reach out if you have any issues.

Additional Tranformers¶

Other transformers are specific to dask-ml.

`Categorizer`([categories, columns])	Transform columns of a DataFrame to categorical dtype.
`DummyEncoder`([columns, drop_first])	Dummy (one-hot) encode categorical columns.
`OrdinalEncoder`([columns])	Ordinal (integer) encode categorical columns.

Both dask_ml.preprocessing.Categorizer and dask_ml.preprocessing.DummyEncoder deal with converting non-numeric data to numeric data. They are useful as a preprocessing step in a pipeline where you start with heterogenous data (a mix of numeric and non-numeric), but the estimator requires all numeric data.

In this toy example, we use a dataset with two columns. 'A' is numeric and 'B' contains text data. We make a small pipeline to

Categorize the text data
Dummy encode the categorical data
Fit a linear regression

In [7]: from dask_ml.preprocessing import Categorizer, DummyEncoder

In [8]: from sklearn.linear_model import LogisticRegression

In [9]: from sklearn.pipeline import make_pipeline

In [10]: import pandas as pd

In [11]: import dask.dataframe as dd

In [12]: df = pd.DataFrame({"A": [1, 2, 1, 2], "B": ["a", "b", "c", "c"]})

In [13]: X = dd.from_pandas(df, npartitions=2)

In [14]: y = dd.from_pandas(pd.Series([0, 1, 1, 0]), npartitions=2)

In [15]: pipe = make_pipeline(
   ....:    Categorizer(),
   ....:    DummyEncoder(),
   ....:    LogisticRegression(solver='lbfgs')
   ....: )
   ....: 

In [16]: pipe.fit(X, y)
Out[16]: 
Pipeline(steps=[('categorizer', Categorizer()),
                ('dummyencoder', DummyEncoder()),
                ('logisticregression', LogisticRegression())])

Categorizer will convert a subset of the columns in X to categorical dtype (see here for more about how pandas handles categorical data). By default, it converts all the object dtype columns.

DummyEncoder will dummy (or one-hot) encode the dataset. This replaces a categorical column with multiple columns, where the values are either 0 or 1, depending on whether the value in the original.

In [17]: df['B']
Out[17]: 
0    a
1    b
2    c
3    c
Name: B, dtype: object

In [18]: pd.get_dummies(df['B'])
Out[18]: 
       a      b      c
0   True  False  False
1  False   True  False
2  False  False   True
3  False  False   True

Wherever the original was 'a', the transformed now has a 1 in the a column and a 0 everywhere else.

Why was the Categorizer step necessary? Why couldn’t we operate directly on the object (string) dtype column? Doing this would be fragile, especially when using dask.dataframe, since the shape of the output would depend on the values present. For example, suppose that we just saw the first two rows in the training, and the last two rows in the tests datasets. Then, when training, our transformed columns would be:

In [19]: pd.get_dummies(df.loc[[0, 1], 'B'])
Out[19]: 
       a      b
0   True  False
1  False   True

while on the test dataset, they would be:

In [20]: pd.get_dummies(df.loc[[2, 3], 'B'])
Out[20]: 
      c
2  True
3  True

Which is incorrect! The columns don’t match.

When we categorize the data, we can be confident that all the possible values have been specified, so the output shape no longer depends on the values in the whatever subset of the data we currently see. Instead, it depends on the categories, which are identical in all the subsets.

Examples

Cross Validation