forked from scikit-learn/scikit-learn
-
Notifications
You must be signed in to change notification settings - Fork 6
/
multiclass.py
406 lines (311 loc) · 12.7 KB
/
multiclass.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
"""
Multiclass algorithms
======================
This module implements multiclass learning algorithms:
- one-vs-the-rest / one-vs-all
- one-vs-one
- error correcting output codes
The estimators provided in this module are meta-estimators: they require a base
estimator to be provided in their constructor. For example, it is possible to
use these estimators to turn a binary classifier or a regressor into a
multiclass classifier. It is also possible to use these estimators with
multiclass estimators in the hope that their accuracy or runtime performance
improves.
"""
# Author: Mathieu Blondel <[email protected]>
#
# License: BSD Style.
import numpy as np
from .base import BaseEstimator, ClassifierMixin, clone
from .preprocessing import LabelBinarizer
from .metrics.pairwise import euclidean_distances
from .utils import check_random_state
def fit_binary(estimator, X, y):
"""Fit a single binary estimator."""
estimator = clone(estimator)
estimator.fit(X, y)
return estimator
def predict_binary(estimator, X):
"""Make predictions using a single binary estimator."""
if hasattr(estimator, "decision_function"):
return np.ravel(estimator.decision_function(X))
else:
# probabilities of the positive class
return estimator.predict_proba(X)[:, 1]
def check_estimator(estimator):
"""Make sure that an estimator implements the necessary methods."""
if not hasattr(estimator, "decision_function") and \
not hasattr(estimator, "predict_proba"):
raise ValueError("The base estimator should implement "
"decision_function or predict_proba!")
def fit_ovr(estimator, X, y):
"""Fit a one-vs-the-rest strategy."""
check_estimator(estimator)
lb = LabelBinarizer()
Y = lb.fit_transform(y)
estimators = [fit_binary(estimator, X, Y[:, i]) for i in range(Y.shape[1])]
return estimators, lb
def predict_ovr(estimators, label_binarizer, X):
"""Make predictions using the one-vs-the-rest strategy."""
Y = np.array([predict_binary(e, X) for e in estimators]).T
return label_binarizer.inverse_transform(Y)
class OneVsRestClassifier(BaseEstimator, ClassifierMixin):
"""One-vs-the-rest multiclass strategy
Also known as one-vs-all, this strategy consists in fitting one classifier
per class. For each classifier, the class is fitted against all the other
classes. In addition to its computational efficiency (only `n_classes`
classifiers are needed), one advantage of this approach is its
interpretability. Since each class is represented by one and one classifier
only, it is possible to gain knowledge about the class by inspecting its
corresponding classifier. This is the most commonly used strategy and is a
fair default choice.
Parameters
----------
estimator : estimator object
An estimator object implementing `fit` and one of `decision_function`
or `predict_proba`.
Attributes
----------
estimators_ : list of `n_classes` estimators
Estimators used for predictions.
label_binarizer_ : LabelBinarizer object
Object used to transform multiclass labels to binary labels and
vice-versa.
"""
def __init__(self, estimator):
self.estimator = estimator
def fit(self, X, y):
"""Fit underlying estimators.
Parameters
----------
X: {array-like, sparse matrix}, shape = [n_samples, n_features]
Data.
y : array-like, shape = [n_samples]
Multi-class targets.
Returns
-------
self
"""
self.estimators_, self.label_binarizer_ = fit_ovr(self.estimator, X, y)
return self
def predict(self, X):
"""Predict multi-class targets using underlying estimators.
Parameters
----------
X: {array-like, sparse matrix}, shape = [n_samples, n_features]
Data.
Returns
-------
y : array-like, shape = [n_samples]
Predicted multi-class targets.
"""
if not hasattr(self, "estimators_"):
raise ValueError("The object hasn't been fitted yet!")
return predict_ovr(self.estimators_, self.label_binarizer_, X)
def fit_ovo_binary(estimator, X, y, i, j):
"""Fit a single binary estimator (one-vs-one)."""
cond = np.logical_or(y == i, y == j)
y = y[cond]
y[y == i] = 0
y[y == j] = 1
ind = np.arange(X.shape[0])
return fit_binary(estimator, X[ind[cond]], y)
def fit_ovo(estimator, X, y):
"""Fit a one-vs-one strategy."""
classes = np.unique(y)
n_classes = classes.shape[0]
estimators = [fit_ovo_binary(estimator, X, y, classes[i], classes[j])
for i in range(n_classes) for j in range(i + 1, n_classes)]
return estimators, classes
def predict_ovo(estimators, classes, X):
"""Make predictions using the one-vs-one strategy."""
n_samples = X.shape[0]
n_classes = classes.shape[0]
votes = np.zeros((n_samples, n_classes))
k = 0
for i in range(n_classes):
for j in range(i + 1, n_classes):
pred = estimators[k].predict(X)
votes[pred == 0, i] += 1
votes[pred == 1, j] += 1
k += 1
return classes[votes.argmax(axis=1)]
class OneVsOneClassifier(BaseEstimator, ClassifierMixin):
"""One-vs-one multiclass strategy
This strategy consists in fitting one classifier per class pair.
At prediction time, the class which received the most votes is selected.
Since it requires to fit `n_classes * (n_classes - 1) / 2` classifiers,
this method is usually slower than one-vs-the-rest, due to its
O(n_classes^2) complexity. However, this method may be advantageous for
algorithms such as kernel algorithms which don't scale well with
`n_samples`. This is because each individual learning problem only involves
a small subset of the data whereas, with one-vs-the-rest, the complete
dataset is used `n_classes` times.
Parameters
----------
estimator : estimator object
An estimator object implementing `fit` and `predict`.
Attributes
----------
estimators_ : list of `n_classes * (n_classes - 1) / 2` estimators
Estimators used for predictions.
classes_ : numpy array of shape [n_classes]
Array containing labels.
"""
def __init__(self, estimator):
self.estimator = estimator
def fit(self, X, y):
"""Fit underlying estimators.
Parameters
----------
X: {array-like, sparse matrix}, shape = [n_samples, n_features]
Data.
y : numpy array of shape [n_samples]
Multi-class targets.
Returns
-------
self
"""
self.estimators_, self.classes_ = fit_ovo(self.estimator, X, y)
return self
def predict(self, X):
"""Predict multi-class targets using underlying estimators.
Parameters
----------
X: {array-like, sparse matrix}, shape = [n_samples, n_features]
Data.
Returns
-------
y : numpy array of shape [n_samples]
Predicted multi-class targets.
"""
if not hasattr(self, "estimators_"):
raise ValueError("The object hasn't been fitted yet!")
return predict_ovo(self.estimators_, self.classes_, X)
def fit_ecoc(estimator, X, y, code_size=1.5, random_state=None):
"""
Fit an error-correcting output-code strategy.
Parameters
----------
estimator : estimator object
An estimator object implementing `fit` and one of `decision_function`
or `predict_proba`.
code_size: float, optional
Percentage of the number of classes to be used to create the code book.
random_state: numpy.RandomState, optional
The generator used to initialize the codebook. Defaults to
numpy.random.
Returns
--------
estimators : list of `int(n_classes * code_size)` estimators
Estimators used for predictions.
classes : numpy array of shape [n_classes]
Array containing labels.
code_book_: numpy array of shape [n_classes, code_size]
Binary array containing the code of each class.
"""
check_estimator(estimator)
random_state = check_random_state(random_state)
classes = np.unique(y)
n_classes = classes.shape[0]
code_size = int(n_classes * code_size)
# FIXME: there are more elaborate methods than generating the codebook
# randomly.
code_book = random_state.random_sample((n_classes, code_size))
code_book[code_book > 0.5] = 1
if hasattr(estimator, "decision_function"):
code_book[code_book != 1] = -1
else:
code_book[code_book != 1] = 0
cls_idx = dict((c, i) for i, c in enumerate(classes))
Y = np.array([code_book[cls_idx[y[i]]] for i in xrange(X.shape[0])])
estimators = [fit_binary(estimator, X, Y[:, i])
for i in range(Y.shape[1])]
return estimators, classes, code_book
def predict_ecoc(estimators, classes, code_book, X):
"""Make predictions using the error-correcting output-code strategy."""
Y = np.array([predict_binary(e, X) for e in estimators]).T
pred = euclidean_distances(Y, code_book).argmin(axis=1)
return classes[pred]
class OutputCodeClassifier(BaseEstimator, ClassifierMixin):
"""(Error-Correcting) Output-Code multiclass strategy
Output-code based strategies consist in representing each class with a
binary code (an array of 0s and 1s). At fitting time, one binary
classifier per bit in the code book is fitted. At prediction time, the
classifiers are used to project new points in the class space and the class
closest to the points is chosen. The main advantage of these strategies is
that the number of classifiers used can be controlled by the user, either
for compressing the model (0 < code_size < 1) or for making the model more
robust to errors (code_size > 1). See the documentation for more details.
Parameters
----------
estimator : estimator object
An estimator object implementing `fit` and one of `decision_function`
or `predict_proba`.
code_size: float
Percentage of the number of classes to be used to create the code book.
A number between 0 and 1 will require fewer classifiers than
one-vs-the-rest. A number greater than 1 will require more classifiers
than one-vs-the-rest.
random_state: numpy.RandomState, optional
The generator used to initialize the codebook. Defaults to
numpy.random.
Attributes
----------
estimators_ : list of `int(n_classes * code_size)` estimators
Estimators used for predictions.
classes_ : numpy array of shape [n_classes]
Array containing labels.
code_book_: numpy array of shape [n_classes, code_size]
Binary array containing the code of each class.
References
----------
* [1] "Solving multiclass learning problems via error-correcting ouput
codes",
Dietterich T., Bakiri G.,
Journal of Artificial Intelligence Research 2,
1995.
* [2] "The error coding method and PICTs",
James G., Hastie T.,
Journal of Computational and Graphical statistics 7,
1998.
* [3] "The Elements of Statistical Learning",
Hastie T., Tibshirani R., Friedman J., page 606 (second-edition)
2008.
"""
def __init__(self, estimator, code_size=1.5, random_state=None):
if (code_size <= 0):
raise ValueError("code_size should be greater than 0!")
self.estimator = estimator
self.code_size = code_size
self.random_state = random_state
def fit(self, X, y):
"""Fit underlying estimators.
Parameters
----------
X: {array-like, sparse matrix}, shape = [n_samples, n_features]
Data.
y : numpy array of shape [n_samples]
Multi-class targets.
Returns
-------
self
"""
self.estimators_, self.classes_, self.code_book_ = \
fit_ecoc(self.estimator, X, y, self.code_size, self.random_state)
return self
def predict(self, X):
"""Predict multi-class targets using underlying estimators.
Parameters
----------
X: {array-like, sparse matrix}, shape = [n_samples, n_features]
Data.
Returns
-------
y : numpy array of shape [n_samples]
Predicted multi-class targets.
"""
if not hasattr(self, "estimators_"):
raise ValueError("The object hasn't been fitted yet!")
return predict_ecoc(self.estimators_, self.classes_,
self.code_book_, X)