101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
|
# Sebastian Raschka 2014-2020
|
||
|
|
# mlxtend Machine Learning Library Extensions
|
||
|
|
# Author: Sebastian Raschka <sebastianraschka.com>
|
||
|
|
#
|
||
|
|
# License: BSD 3 clause
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from mlxtend.utils import check_Xy
|
||
|
|
|
||
|
|
|
||
|
|
def shuffle_arrays_unison(arrays, random_seed=None):
|
||
|
|
"""Shuffle NumPy arrays in unison.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
arrays : array-like, shape = [n_arrays]
|
||
|
|
A list of NumPy arrays.
|
||
|
|
random_seed : int (default: None)
|
||
|
|
Sets the random state.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
----------
|
||
|
|
shuffled_arrays : A list of NumPy arrays after shuffling.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
|
||
|
|
>>> import numpy as np
|
||
|
|
>>> from mlxtend.preprocessing import shuffle_arrays_unison
|
||
|
|
>>> X1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
|
||
|
|
>>> y1 = np.array([1, 2, 3])
|
||
|
|
>>> X2, y2 = shuffle_arrays_unison(arrays=[X1, y1], random_seed=3)
|
||
|
|
>>> assert(X2.all() == np.array([[4, 5, 6], [1, 2, 3], [7, 8, 9]]).all())
|
||
|
|
>>> assert(y2.all() == np.array([2, 1, 3]).all())
|
||
|
|
>>>
|
||
|
|
|
||
|
|
For more usage examples, please see
|
||
|
|
http://rasbt.github.io/mlxtend/user_guide/preprocessing/shuffle_arrays_unison/
|
||
|
|
"""
|
||
|
|
if random_seed:
|
||
|
|
np.random.seed(random_seed)
|
||
|
|
n = len(arrays[0])
|
||
|
|
for a in arrays:
|
||
|
|
assert(len(a) == n)
|
||
|
|
idx = np.random.permutation(n)
|
||
|
|
return [a[idx] for a in arrays]
|
||
|
|
|
||
|
|
|
||
|
|
def shuffled_split(X, y, shuffle=True, train_size=0.75, random_seed=None):
|
||
|
|
"""Splits feature and target arrays into training and test subsets.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like, shape = [n_samples, n_features]
|
||
|
|
Initial dataset, where n_samples is the number of samples and
|
||
|
|
n_features is the number of features.
|
||
|
|
y : array-like, shape = [n_samples]
|
||
|
|
Target values.
|
||
|
|
shuffle : bool (default: True)
|
||
|
|
Doesn't shuffle the arrays if False
|
||
|
|
train_size : float (default: 0.75)
|
||
|
|
Proportion of data in the training arrays. For example, 0.75 will
|
||
|
|
put 75% of the data into the training array, and 25% of the data
|
||
|
|
into the test array.
|
||
|
|
random_seed : int (default: None)
|
||
|
|
Sets the random state.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
----------
|
||
|
|
X_train : array-like, shape = [n_samples * train_size, n_features]
|
||
|
|
Training dataset, where n_samples is the number of samples and
|
||
|
|
n_features is the number of features.
|
||
|
|
y_train : array-like, shape = [n_samples * train_size]
|
||
|
|
Training target values.
|
||
|
|
X_test : array-like, shape = [n_samples * (1-train_size), n_features]
|
||
|
|
Dataset for testing, where n_samples is the number of samples and
|
||
|
|
n_features is the number of features.
|
||
|
|
y_test : array-like, shape = [n_samples * (1-train_size)]
|
||
|
|
Target values for testing.
|
||
|
|
|
||
|
|
For usage examples, please see
|
||
|
|
http://rasbt.github.io/mlxtend/user_guide/preprocessing/shuffled_split/
|
||
|
|
|
||
|
|
"""
|
||
|
|
check_Xy(X, y, y_int=False)
|
||
|
|
|
||
|
|
if train_size <= 0.0 or train_size >= 1.0:
|
||
|
|
raise ValueError('train_size must be a float in the range (0.0, 1.0)')
|
||
|
|
|
||
|
|
if shuffle:
|
||
|
|
X_ary, y_ary = shuffle_arrays_unison(arrays=[X.copy(), y.copy()],
|
||
|
|
random_seed=random_seed)
|
||
|
|
else:
|
||
|
|
X_ary, y_ary = X.copy(), y.copy()
|
||
|
|
|
||
|
|
train_absize = round(train_size * y.shape[0])
|
||
|
|
|
||
|
|
X_train, y_train = X_ary[:train_absize], y_ary[:train_absize]
|
||
|
|
X_test, y_test = X_ary[train_absize:], y_ary[train_absize:]
|
||
|
|
return X_train, y_train, X_test, y_test
|