Cross Validation
Cross Validation¶
See the scikit-learn cross validation documentation for a fuller discussion of cross validation. This document only describes the extensions made to support Dask arrays.
The simplest way to split one or more Dask arrays is with dask_ml.model_selection.train_test_split():
In [1]: import dask.array as da
In [2]: from dask_ml.datasets import make_regression
In [3]: from dask_ml.model_selection import train_test_split
In [4]: X, y = make_regression(n_samples=125, n_features=4, random_state=0, chunks=50)
In [5]: X
Out[5]: dask.array<normal, shape=(125, 4), dtype=float64, chunksize=(50, 4), chunktype=numpy.ndarray>
The interface for splitting Dask arrays is the same as scikit-learn’s version.
In [6]: X_train, X_test, y_train, y_test = train_test_split(X, y)
In [7]: X_train # A dask Array
Out[7]: dask.array<concatenate, shape=(112, 4), dtype=float64, chunksize=(45, 4), chunktype=numpy.ndarray>
In [8]: X_train.compute()[:3]
Out[8]:
array([[ 0.09281918, -0.44461239, 0.92946053, 0.42286638],
[ 0.12590048, -0.69540901, -1.18976616, -1.07897621],
[-0.66480354, -0.91242817, 0.97555562, -0.86313815]])
While it’s possible to pass dask arrays to sklearn.model_selection.train_test_split(), we recommend
using the Dask version for performance reasons: the Dask version is faster
for two reasons:
First, the Dask version shuffles blockwise. In a distributed setting, shuffling between blocks may require sending large amounts of data between machines, which can be slow. However, if there’s a strong pattern in your data, you’ll want to perform a full shuffle.
Second, the Dask version avoids allocating large intermediate NumPy arrays storing the indexes for slicing.
For very large datasets, creating and transmitting np.arange(n_samples) can be expensive.