-
Notifications
You must be signed in to change notification settings - Fork 526
/
_split.py
482 lines (403 loc) · 18.4 KB
/
_split.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
# Copyright (c) 2019-2021, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import cudf
import cupy as cp
import cupyx
import numpy as np
from cuml.common.memory_utils import _strides_to_order
from numba import cuda
from typing import Union
def _stratify_split(X, stratify, labels, n_train, n_test, x_numba, y_numba,
random_state):
"""
Function to perform a stratified split based on stratify column.
Based on scikit-learn stratified split implementation.
Parameters
----------
X, y: Shuffled input data and labels
stratify: column to be stratified on.
n_train: Number of samples in train set
n_test: number of samples in test set
x_numba: Determines whether the data should be converted to numba
y_numba: Determines whether the labales should be converted to numba
Returns
-------
X_train, X_test: Data X divided into train and test sets
y_train, y_test: Labels divided into train and test sets
"""
x_cudf = False
labels_cudf = False
if isinstance(X, cudf.DataFrame):
x_cudf = True
elif hasattr(X, "__cuda_array_interface__"):
X = cp.asarray(X)
x_order = _strides_to_order(X.__cuda_array_interface__['strides'],
cp.dtype(X.dtype))
# labels and stratify will be only cp arrays
if isinstance(labels, cudf.Series):
labels_cudf = True
labels = labels.values
elif hasattr(labels, "__cuda_array_interface__"):
labels = cp.asarray(labels)
elif isinstance(stratify, cudf.DataFrame):
# ensuring it has just one column
if labels.shape[1] != 1:
raise ValueError('Expected one column for labels, but found df'
'with shape = %d' % (labels.shape))
labels_cudf = True
labels = labels[0].values
labels_order = _strides_to_order(
labels.__cuda_array_interface__['strides'],
cp.dtype(labels.dtype))
# Converting to cupy array removes the need to add an if-else block
# for startify column
if isinstance(stratify, cudf.Series):
stratify = stratify.values
elif hasattr(stratify, "__cuda_array_interface__"):
stratify = cp.asarray(stratify)
elif isinstance(stratify, cudf.DataFrame):
# ensuring it has just one column
if stratify.shape[1] != 1:
raise ValueError('Expected one column, but found column'
'with shape = %d' % (stratify.shape))
stratify = stratify[0].values
classes, stratify_indices = cp.unique(stratify, return_inverse=True)
n_classes = classes.shape[0]
class_counts = cp.bincount(stratify_indices)
if cp.min(class_counts) < 2:
raise ValueError("The least populated class in y has only 1"
" member, which is too few. The minimum"
" number of groups for any class cannot"
" be less than 2.")
if n_train < n_classes:
raise ValueError('The train_size = %d should be greater or '
'equal to the number of classes = %d' % (n_train,
n_classes))
class_indices = cp.split(cp.argsort(stratify_indices),
cp.cumsum(class_counts)[:-1].tolist())
X_train = None
# random_state won't be None or int, that's handled earlier
if isinstance(random_state, np.random.RandomState):
random_state = cp.random.RandomState(seed=random_state.get_state()[1])
# Break ties
n_i = _approximate_mode(class_counts, n_train, random_state)
class_counts_remaining = class_counts - n_i
t_i = _approximate_mode(class_counts_remaining, n_test, random_state)
for i in range(n_classes):
permutation = random_state.permutation(class_counts[i].item())
perm_indices_class_i = class_indices[i].take(permutation)
y_train_i = cp.array(labels[perm_indices_class_i[:n_i[i]]],
order=labels_order)
y_test_i = cp.array(labels[perm_indices_class_i[n_i[i]:n_i[i] +
t_i[i]]],
order=labels_order)
if hasattr(X, "__cuda_array_interface__") or \
isinstance(X, cupyx.scipy.sparse.csr_matrix):
X_train_i = cp.array(X[perm_indices_class_i[:n_i[i]]],
order=x_order)
X_test_i = cp.array(X[perm_indices_class_i[n_i[i]:n_i[i] +
t_i[i]]],
order=x_order)
if X_train is None:
X_train = cp.array(X_train_i, order=x_order)
y_train = cp.array(y_train_i, order=labels_order)
X_test = cp.array(X_test_i, order=x_order)
y_test = cp.array(y_test_i, order=labels_order)
else:
X_train = cp.concatenate([X_train, X_train_i], axis=0)
X_test = cp.concatenate([X_test, X_test_i], axis=0)
y_train = cp.concatenate([y_train, y_train_i], axis=0)
y_test = cp.concatenate([y_test, y_test_i], axis=0)
elif x_cudf:
X_train_i = X.iloc[perm_indices_class_i[:n_i[i]]]
X_test_i = X.iloc[perm_indices_class_i[n_i[i]:n_i[i] + t_i[i]]]
if X_train is None:
X_train = X_train_i
y_train = y_train_i
X_test = X_test_i
y_test = y_test_i
else:
X_train = cudf.concat([X_train, X_train_i], ignore_index=False)
X_test = cudf.concat([X_test, X_test_i], ignore_index=False)
y_train = cp.concatenate([y_train, y_train_i], axis=0)
y_test = cp.concatenate([y_test, y_test_i], axis=0)
if x_numba:
X_train = cuda.as_cuda_array(X_train)
X_test = cuda.as_cuda_array(X_test)
elif x_cudf:
X_train = cudf.DataFrame(X_train)
X_test = cudf.DataFrame(X_test)
if y_numba:
y_train = cuda.as_cuda_array(y_train)
y_test = cuda.as_cuda_array(y_test)
elif labels_cudf:
y_train = cudf.Series(y_train)
y_test = cudf.Series(y_test)
return X_train, X_test, y_train, y_test
def _approximate_mode(class_counts, n_draws, rng):
"""
CuPy implementataiton based on scikit-learn approximate_mode method.
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/__init__.py#L984
It is the mostly likely outcome of drawing n_draws many
samples from the population given by class_counts.
Parameters
----------
class_counts : ndarray of int
Population per class.
n_draws : int
Number of draws (samples to draw) from the overall population.
rng : random state
Used to break ties.
Returns
-------
sampled_classes : cupy array of int
Number of samples drawn from each class.
np.sum(sampled_classes) == n_draws
"""
# this computes a bad approximation to the mode of the
# multivariate hypergeometric given by class_counts and n_draws
continuous = n_draws * class_counts / class_counts.sum()
# floored means we don't overshoot n_samples, but probably undershoot
floored = cp.floor(continuous)
# we add samples according to how much "left over" probability
# they had, until we arrive at n_samples
need_to_add = int(n_draws - floored.sum())
if need_to_add > 0:
remainder = continuous - floored
values = cp.sort(cp.unique(remainder))[::-1]
# add according to remainder, but break ties
# randomly to avoid biases
for value in values:
inds, = cp.where(remainder == value)
# if we need_to_add less than what's in inds
# we draw randomly from them.
# if we need to add more, we add them all and
# go to the next value
add_now = min(len(inds), need_to_add)
inds = rng.choice(inds, size=add_now, replace=False)
floored[inds] += 1
need_to_add -= add_now
if need_to_add == 0:
break
return floored.astype(int)
def train_test_split(X,
y=None,
test_size: Union[float,
int] = None,
train_size: Union[float,
int] = None,
shuffle: bool = True,
random_state: Union[int,
cp.random.RandomState,
np.random.RandomState] = None,
stratify=None):
"""
Partitions device data into four collated objects, mimicking
Scikit-learn's `train_test_split
<https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html>`_.
Parameters
----------
X : cudf.DataFrame or cuda_array_interface compliant device array
Data to split, has shape (n_samples, n_features)
y : str, cudf.Series or cuda_array_interface compliant device array
Set of labels for the data, either a series of shape (n_samples) or
the string label of a column in X (if it is a cuDF DataFrame)
containing the labels
train_size : float or int, optional
If float, represents the proportion [0, 1] of the data
to be assigned to the training set. If an int, represents the number
of instances to be assigned to the training set. Defaults to 0.8
shuffle : bool, optional
Whether or not to shuffle inputs before splitting
random_state : int, CuPy RandomState or NumPy RandomState optional
If shuffle is true, seeds the generator. Unseeded by default
stratify: cudf.Series or cuda_array_interface compliant device array,
optional parameter. When passed, the input is split using this
as column to startify on. Default=None
Examples
--------
.. code-block:: python
import cudf
from cuml.model_selection import train_test_split
# Generate some sample data
df = cudf.DataFrame({'x': range(10),
'y': [0, 1] * 5})
print(f'Original data: {df.shape[0]} elements')
# Suppose we want an 80/20 split
X_train, X_test, y_train, y_test = train_test_split(df, 'y',
train_size=0.8)
print(f'X_train: {X_train.shape[0]} elements')
print(f'X_test: {X_test.shape[0]} elements')
print(f'y_train: {y_train.shape[0]} elements')
print(f'y_test: {y_test.shape[0]} elements')
# Alternatively, if our labels are stored separately
labels = df['y']
df = df.drop(['y'], axis=1)
# we can also do
X_train, X_test, y_train, y_test = train_test_split(df, labels,
train_size=0.8)
Output:
.. code-block:: python
Original data: 10 elements
X_train: 8 elements
X_test: 2 elements
y_train: 8 elements
y_test: 2 elements
Returns
-------
X_train, X_test, y_train, y_test : cudf.DataFrame or array-like objects
Partitioned dataframes if X and y were cuDF objects. If `y` was
provided as a column name, the column was dropped from `X`.
Partitioned numba device arrays if X and y were Numba device arrays.
Partitioned CuPy arrays for any other input.
"""
if isinstance(y, str):
# Use the column with name `str` as y
if isinstance(X, cudf.DataFrame):
name = y
y = X[name]
X = X.drop(name, axis=1)
else:
raise TypeError("X needs to be a cuDF Dataframe when y is a \
string")
# todo: this check will be replaced with upcoming improvements
# to input_utils
#
if y is not None:
if not hasattr(X, "__cuda_array_interface__") and not \
isinstance(X, cudf.DataFrame):
raise TypeError("X needs to be either a cuDF DataFrame, Series or \
a cuda_array_interface compliant array.")
if not hasattr(y, "__cuda_array_interface__") and not \
isinstance(y, cudf.DataFrame):
raise TypeError("y needs to be either a cuDF DataFrame, Series or \
a cuda_array_interface compliant array.")
if X.shape[0] != y.shape[0]:
raise ValueError("X and y must have the same first dimension"
"(found {} and {})".format(
X.shape[0],
y.shape[0]))
else:
if not hasattr(X, "__cuda_array_interface__") and not \
isinstance(X, cudf.DataFrame):
raise TypeError("X needs to be either a cuDF DataFrame, Series or \
a cuda_array_interface compliant object.")
if isinstance(train_size, float):
if not 0 <= train_size <= 1:
raise ValueError("proportion train_size should be between"
"0 and 1 (found {})".format(train_size))
if isinstance(train_size, int):
if not 0 <= train_size <= X.shape[0]:
raise ValueError(
"Number of instances train_size should be between 0 and the"
"first dimension of X (found {})".format(train_size))
if isinstance(test_size, float):
if not 0 <= test_size <= 1:
raise ValueError("proportion test_size should be between"
"0 and 1 (found {})".format(train_size))
if isinstance(test_size, int):
if not 0 <= test_size <= X.shape[0]:
raise ValueError(
"Number of instances test_size should be between 0 and the"
"first dimension of X (found {})".format(test_size))
x_numba = cuda.devicearray.is_cuda_ndarray(X)
y_numba = cuda.devicearray.is_cuda_ndarray(y)
# Determining sizes of splits
if isinstance(train_size, float):
train_size = int(X.shape[0] * train_size)
if test_size is None:
if train_size is None:
train_size = int(X.shape[0] * 0.75)
test_size = X.shape[0] - train_size
if isinstance(test_size, float):
test_size = int(X.shape[0] * test_size)
if train_size is None:
train_size = X.shape[0] - test_size
elif isinstance(test_size, int):
if train_size is None:
train_size = X.shape[0] - test_size
if shuffle:
# Shuffle the data
if random_state is None or isinstance(random_state, int):
idxs = cp.arange(X.shape[0])
random_state = cp.random.RandomState(seed=random_state)
elif isinstance(random_state, cp.random.RandomState):
idxs = cp.arange(X.shape[0])
elif isinstance(random_state, np.random.RandomState):
idxs = np.arange(X.shape[0])
else:
raise TypeError("`random_state` must be an int, NumPy RandomState \
or CuPy RandomState.")
random_state.shuffle(idxs)
if isinstance(X, cudf.DataFrame) or isinstance(X, cudf.Series):
X = X.iloc[idxs]
elif hasattr(X, "__cuda_array_interface__"):
# numba (and therefore rmm device_array) does not support
# fancy indexing
X = cp.asarray(X)[idxs]
if isinstance(y, cudf.DataFrame) or isinstance(y, cudf.Series):
y = y.iloc[idxs]
elif hasattr(y, "__cuda_array_interface__"):
y = cp.asarray(y)[idxs]
if stratify is not None:
if isinstance(stratify, cudf.DataFrame) or \
isinstance(stratify, cudf.Series):
stratify = stratify.iloc[idxs]
elif hasattr(stratify, "__cuda_array_interface__"):
stratify = cp.asarray(stratify)[idxs]
split_return = _stratify_split(X,
stratify,
y,
train_size,
test_size,
x_numba,
y_numba,
random_state)
return split_return
# If not stratified, perform train_test_split splicing
if hasattr(X, "__cuda_array_interface__"):
x_order = _strides_to_order(X.__cuda_array_interface__['strides'],
cp.dtype(X.dtype))
if hasattr(y, "__cuda_array_interface__"):
y_order = _strides_to_order(y.__cuda_array_interface__['strides'],
cp.dtype(y.dtype))
if hasattr(X, "__cuda_array_interface__") or \
isinstance(X, cupyx.scipy.sparse.csr_matrix):
X_train = cp.array(X[0:train_size], order=x_order)
X_test = cp.array(X[-1 * test_size:], order=x_order)
if y is not None:
y_train = cp.array(y[0:train_size], order=y_order)
y_test = cp.array(y[-1 * test_size:], order=y_order)
elif isinstance(X, cudf.DataFrame):
X_train = X.iloc[0:train_size]
X_test = X.iloc[-1 * test_size:]
if y is not None:
if isinstance(y, cudf.Series):
y_train = y.iloc[0:train_size]
y_test = y.iloc[-1 * test_size:]
elif hasattr(y, "__cuda_array_interface__") or \
isinstance(y, cupyx.scipy.sparse.csr_matrix):
y_train = cp.array(y[0:train_size], order=y_order)
y_test = cp.array(y[-1 * test_size:], order=y_order)
if x_numba:
X_train = cuda.as_cuda_array(X_train)
X_test = cuda.as_cuda_array(X_test)
if y_numba:
y_train = cuda.as_cuda_array(y_train)
y_test = cuda.as_cuda_array(y_test)
if y is not None:
return X_train, X_test, y_train, y_test
else:
return X_train, X_test