-
Notifications
You must be signed in to change notification settings - Fork 0
/
逻辑回归.py
680 lines (591 loc) · 22.8 KB
/
逻辑回归.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
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
#coding=utf-8
"""
This tutorial introduces logistic regression using Theano and stochastic
gradient descent.
Logistic regression is a probabilistic, linear classifier. It is parametrized
by a weight matrix :math:`W` and a bias vector :math:`b`. Classification is
done by projecting data points onto a set of hyperplanes, the distance to
which is used to determine a class membership probability.
Mathematically, this can be written as:
.. math::
P(Y=i|x, W,b) &= softmax_i(W x + b) \\
&= \frac {e^{W_i x + b_i}} {\sum_j e^{W_j x + b_j}}
The output of the model or prediction is then done by taking the argmax of
the vector whose i'th element is P(Y=i|x).
.. math::
y_{pred} = argmax_i P(Y=i|x,W,b)
This tutorial presents a stochastic gradient descent optimization method
suitable for large datasets.
References:
- textbooks: "Pattern Recognition and Machine Learning" -
Christopher M. Bishop, section 4.3.2
"""
__docformat__ = 'restructedtext en'
import cPickle
import os
import sys
import time
import MySQLdb
import numpy
import theano
import theano.tensor as T
class LogisticRegression(object):
"""Multi-class Logistic Regression Class
The logistic regression is fully described by a weight matrix :math:`W`
and bias vector :math:`b`. Classification is done by projecting data
points onto a set of hyperplanes, the distance to which is used to
determine a class membership probability.
"""
def __init__(self, input, n_in, n_out):
""" Initialize the parameters of the logistic regression
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
# start-snippet-1
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
self.W = theano.shared(
value=numpy.zeros(
(n_in, n_out),
dtype=theano.config.floatX
),
name='W',
borrow=True
)
# initialize the baises b as a vector of n_out 0s
self.b = theano.shared(
value=numpy.zeros(
(n_out,),
dtype=theano.config.floatX
),
name='b',
borrow=True
)
# symbolic expression for computing the matrix of class-membership
# probabilities
# Where:
# W is a matrix where column-k represent the separation hyper plain for
# class-k
# x is a matrix where row-j represents input training sample-j
# b is a vector where element-k represent the free parameter of hyper
# plain-k
self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)
# symbolic description of how to compute prediction as class whose
# probability is maximal
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
# end-snippet-1
# parameters of the model
self.params = [self.W, self.b]
def pred(self):
return self.y_pred
def negative_log_likelihood(self, y):
"""Return the mean of the negative log-likelihood of the prediction
of this model under a given target distribution.
.. math::
\frac{1}{|\mathcal{D}|} \mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
\frac{1}{|\mathcal{D}|} \sum_{i=0}^{|\mathcal{D}|}
\log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
\ell (\theta=\{W,b\}, \mathcal{D})
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
Note: we use the mean instead of the sum so that
the learning rate is less dependent on the batch size
"""
# start-snippet-2
# y.shape[0] is (symbolically) the number of rows in y, i.e.,
# number of examples (call it n) in the minibatch
# T.arange(y.shape[0]) is a symbolic vector which will contain
# [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of
# Log-Probabilities (call it LP) with one row per example and
# one column per class LP[T.arange(y.shape[0]),y] is a vector
# v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,
# LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is
# the mean (across minibatch examples) of the elements in v,
# i.e., the mean log-likelihood across the minibatch.
return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
# end-snippet-2
def errors(self, y):
"""Return a float representing the number of errors in the minibatch
over the total number of examples of the minibatch ; zero one
loss over the size of the minibatch
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
"""
# check if y has same dimension of y_pred
if y.ndim != self.y_pred.ndim:
raise TypeError(
'y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', self.y_pred.type)
)
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
return T.mean(T.neq(self.y_pred, y))
else:
raise NotImplementedError()
def load_data():
''' Loads the dataset
:type dataset: string
:param dataset: the path to the dataset (here MNIST)
'''
#############
# LOAD DATA #
#############
# Download the MNIST dataset if it is not present
conn= MySQLdb.connect(
host='localhost',
port = 3306,
user='root',
passwd='scut2428',
db ='data',
)
cur=conn.cursor()
cur.execute("select * from stockList")
stock=cur.fetchall()
print len(stock)
#b[]表示所有股票的所有数据b[0]代表第一个股票的数据
b=[]
for stockname in stock:
#print type(stockname)
stockname=''.join(stockname).split('.')
#print stockname
stockname='stock'+stockname[0]+'_'+stockname[-1]
#set[]:表示的是每个股票各个属性的所有数据 set[0]代表某个股票的open数据
set=[]
for n in ['open','high','low','close','volume']:
cur.execute("select "+n+" from %s"%stockname)
a=cur.fetchall()
#a=str(a)
#print type(a)
set.append(a)
#print a
#print set[0][1]
b.append(set)
print len(b[230][1])
print len(b[0][0])
train_set_x_x=[]
#data_set 表示的是300只股票所有数据 data_set[0]表示300只股票的open数据
data_set=[]
for j in range(len(b[0])):
data=[]
#print type(data)
for index in range(len(b)):
data.append(b[index][j])
#data[index]=list(data[index])
#print type(data[index])
#data=numpy.matrix(data)
#print type(data)
#print data.shape
data_set.append(data)
#data_set[0][0]=numpy.array(data_set[0][0])
#data_set[0]=numpy.asarray(list(data_set[0]))
#print data_set[0].shape[1]
#print len(data_set[0])
#print type(data_set[0])
#print list(data_set[0]).shape()
#print type(data_set[0][0])
#print type(data_set[0][0][0])
#print len(data_set[0])
all_data_set=numpy.array(data_set)
#print all_data_set.shape
print len(all_data_set)
print all_data_set[0][0][0]
#print (data_set[4][1][1])
# Load the dataset
#10天的数据作为训练,递推1天
train_set_x=[]
test_set_x=[]
#valid_set_x=[]
for i in range(64):
if (i<62):
for j in range(len(all_data_set)):
train_set_x.append(all_data_set[j][0:,i*242:(i+1)*242])
if (i>=10):
for k in range(len(all_data_set)):
test_set_x.append(all_data_set[k][0:,i*242:(i+1)*242])
'''
for i in range(len(all_data_set)):
k=0
while k<41:
train_set_x.append(all_data_set[i][0:,k*242:(k+1)*242])
k=k+1
while k<48:
test_set_x.append(all_data_set[i][0:,k*242:k*242+10*242])
k=k+1
while k<55:
valid_set_x.append(all_data_set[i][0:,k*242:k*242+10*242])
k=k+1
'''
print train_set_x[0][0]
print test_set_x[-1][0]
temp_train_set=numpy.array(train_set_x)
temp_test_set=numpy.array(test_set_x)
#temp_valid_set=numpy.array(valid_set_x)
train_set_x=[]
test_set_x=[]
#valid_set_x=[]
print len(temp_train_set)
print len(temp_test_set)
#将4维的训练集变为2维
for i in temp_train_set:
g=[]
for j in i:
for k in j:
g.extend(k)
train_set_x.append(g)
for i in temp_test_set:
g=[]
for j in i:
for k in j:
g.extend(k)
test_set_x.append(g)
'''
for i in temp_valid_set:
g=[]
for j in i:
for k in j:
g.extend(k)
valid_set_x.append(g)
'''
#对训练集进行矩阵化
train_set_x=numpy.array(train_set_x)
test_set_x=numpy.array(test_set_x)
#valid_set_x=numpy.array(valid_set_x)
#train_set_x=numpy.array(train_set_x)
#print train_set_x.shape
#print len(test_set_x[0])
#print len(valid_set_x)
print train_set_x[0][1]
print len(train_set_x[0])
print test_set_x[0][1]
print len(test_set_x[0])
#取target
cur.execute("select * from IF_CFE")
data_set_y=cur.fetchall()
all_data_set_y=[]
num=0
print len(data_set_y)
while num+543<len(data_set_y):
if(data_set_y[num+272][0]>data_set_y[num+543][3]):
target=0
else:
target=1
all_data_set_y.append(target)
num=num+272
print len(all_data_set_y)
print all_data_set_y[0]
'''
temp=[]
temp.extend(all_data_set_y)
#同一时间段的不同训练数据有着同一个target
for l in range(4):
all_data_set_y.extend(temp)
print len(all_data_set_y)
print all_data_set_y
'''
train_set_y=[]
test_set_y=[]
n=0
for i in range(len(train_set_x)):
if ((i+1)%5==0):
train_set_y.append(all_data_set_y[n])
n=n+1
else:
v=all_data_set_y[n]
train_set_y.append(v)
k=10
for j in range(len(test_set_x)):
if ((j+1)%5==0):
test_set_y.append(all_data_set_y[k])
k=k+1
else:
test_set_y.append(all_data_set_y[k])
#valid_set_y=[]
print len(test_set_y)
print len(train_set_y)
print test_set_y
'''
for l in range(5):
train_set_y.extend(all_data_set_y[l*55:41+l*55])
test_set_y.extend(all_data_set_y[41+l*55:48+l*55])
valid_set_y.extend(all_data_set_y[48+l*55:55+l*55])
#print train_set_y
#print len(train_set_y)
#print len(test_set_y)
#print len(valid_set_y)
#print valid_set_y
#train_set_x=numpy.array(train_set_x)
#test_set_x=numpy.array(test_set_x)
#valid_set_x=numpy.array(valid_set_x)
#train_set_y=numpy.array(train_set_y)
#test_set_y=numpy.array(test_set_y)
#valid_set_y=numpy.array(valid_set_y)
print type(train_set_x),'a'
print type(train_set_y),'b'
'''
train_set=[train_set_x,train_set_y]
test_set=[test_set_x,test_set_y]
#valid_set=[valid_set_x,valid_set_y]
#train_set=numpy.array(train_set)
#test_set=numpy.array(test_set)
#valid_set=numpy.array(valid_set)
print type(train_set_x)
print type(test_set)
print test_set[1],'ad'
'''
all_data_set_x=[]
cur.execute("select * from stock000001_SZ")
set_x=cur.fetchall()
print set_x
all_data_set_x.extend(set_x)
all_data_set_y=[]
n=0
while n<len(all_data_set_x):
day=n/242
if(all_data_set_x[n][3]<all_data_set_x[(day+1)*242-1][3]):
target=[0]
all_data_set_y.extend(target)
else:
target=[1]
all_data_set_y.extend(target)
n=n+1
train_set_x=all_data_set_x[epoch*5*242:15*242+epoch*5*242]
test_set_x=all_data_set_x[15*242+epoch*5*242:18*242+epoch*5*242]
valid_set_x=all_data_set_x[18*242+epoch*5*242:20*242+epoch*5*242]
train_set_y=all_data_set_y[epoch*20*242:46*242+epoch*20*242]
test_set_y=all_data_set_y[46*242+epoch*20*242:56*242+epoch*20*242]
valid_set_y=all_data_set_y[56*242+epoch*20*242:66*242+epoch*20*242]
train_set=[train_set_x,train_set_y]
test_set=[test_set_x,test_set_y]
valid_set=[valid_set_x,valid_set_y]
'''
#train_set, valid_set, test_set format: tuple(input, target)
#input is an numpy.ndarray of 2 dimensions (a matrix)
#witch row's correspond to an example. target is a
#numpy.ndarray of 1 dimensions (vector)) that have the same length as
#the number of rows in the input. It should give the target
#target to the example with the same index in the input.
def shared_dataset(data_xy, borrow=True):
""" Function that loads the dataset into shared variables
The reason we store our dataset in shared variables is to allow
Theano to copy it into the GPU memory (when code is run on GPU).
Since copying data into the GPU is slow, copying a minibatch everytime
is needed (the default behaviour if the data is not in a shared
variable) would lead to a large decrease in performance.
"""
data_x, data_y = data_xy
shared_x = theano.shared(numpy.asarray(data_x,
dtype=theano.config.floatX),
borrow=borrow)
shared_y = theano.shared(numpy.asarray(data_y,
dtype=theano.config.floatX),
borrow=borrow)
# When storing data on the GPU it has to be stored as floats
# therefore we will store the labels as ``floatX`` as well
# (``shared_y`` does exactly that). But during our computations
# we need them as ints (we use labels as index, and if they are
# floats it doesn't make sense) therefore instead of returning
# ``shared_y`` we will have to cast it to int. This little hack
# lets ous get around this issue
return shared_x, T.cast(shared_y, 'int32')
test_set_x, test_set_y = shared_dataset(test_set)
#valid_set_x, valid_set_y = shared_dataset(valid_set)
train_set_x, train_set_y = shared_dataset(train_set)
print train_set_x,type(train_set_y)
rval = [(train_set_x, train_set_y),(test_set_x, test_set_y)]
return rval
def sgd_optimization_mnist(learning_rate=0.13, n_epochs=20,
batch_size=10):
"""
Demonstrate stochastic gradient descent optimization of a log-linear
model
This is demonstrated on MNIST.
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: the path of the MNIST dataset file from
http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz
"""
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
# generate symbolic variables for input (x and y represent a
# minibatch)
x = T.matrix('x') # data, presented as rasterized images
y = T.ivector('y') # labels, presented as 1D vector of [int] labels
# construct the logistic regression class
# Each MNIST image has size 28*28
classifier = LogisticRegression(input=x, n_in=300*242, n_out=2)
# the cost we minimize during training is the negative log likelihood of
# the model in symbolic format
cost = classifier.negative_log_likelihood(y)
datasets = load_data()
train_set_x, train_set_y = datasets[0]
print train_set_x.get_value().shape
#print train_set_x.get_value()
#valid_set_x, valid_set_y = datasets[2]
test_set_x, test_set_y = datasets[1]
#print test_set_y
# compute number of minibatches for training, validation and testing
n_train_batches = (train_set_x.get_value(borrow=True).shape[0]-50) / batch_size+1
print n_train_batches
#n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = (test_set_x.get_value(borrow=True).shape[0]) / batch_size
# compiling a Theano function that computes the mistakes that are made by
# the model on a minibatch
test_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]
}
)
'''
validate_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]
}
)
'''
pred_model=theano.function(
inputs=[index],
outputs=classifier.pred(),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
#y: valid_set_y[index * batch_size: (index + 1) * batch_size]
}
)
# compute the gradient of cost with respect to theta = (W,b)
g_W = T.grad(cost=cost, wrt=classifier.W)
g_b = T.grad(cost=cost, wrt=classifier.b)
#g_W,g_b = T.grad(cost=cost,wrt=[classifier.W,classifier.b])
# start-snippet-3
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs.
updates = [(classifier.W, classifier.W - learning_rate * g_W),
(classifier.b, classifier.b - learning_rate * g_b)]
# compiling a Theano function `train_model` that returns the cost, but in
# the same time updates the parameter of the model based on the rules
# defined in `updates`
train_model = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: index * batch_size+50],
y: train_set_y[index * batch_size: index * batch_size+50]
}
)
#classify = theano.function(inputs=[x], outputs=classifier.y_pred)
# end-snippet-3
###############
# TRAIN MODEL #
###############
print '... training the model'
# early-stopping parameters
patience = 5000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = numpy.inf
test_score = 0.
start_time = time.clock()
done_looping = False
epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_model(minibatch_index)
#print minibatch_avg_cost
predition=[pred_model(i) for i in xrange(n_test_batches)]
print predition
print classifier.W.get_value()
# iteration number
iter = (epoch - 1) * n_train_batches + minibatch_index
#print minibatch_index + 1
#print validation_frequency
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [validate_model(i)
for i in xrange(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
predition=[pred_model(i)
for i in xrange(n_valid_batches)]
print predition
#print minibatch_index + 1
print(
'epoch %i, minibatch %i/%i, validation error %f %%' %
(
epoch,
minibatch_index + 1,
n_train_batches,
this_validation_loss * 100.
)
)
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
# test it on the test set
test_losses = [test_model(i)
for i in xrange(n_test_batches)]
test_score = numpy.mean(test_losses)
print(
(
' epoch %i, minibatch %i/%i, test error of'
' best model %f %%'
) %
(
epoch,
minibatch_index + 1,
n_train_batches,
test_score * 100.
)
)
#print classifier.y_pred
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print(
(
'Optimization complete with best validation score of %f %%,'
'with test performance %f %%'
)
% (best_validation_loss * 100., test_score * 100.)
)
print 'The code run for %d epochs, with %f epochs/sec' % (
epoch, 1. * epoch / (end_time - start_time))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.1fs' % ((end_time - start_time)))
if __name__ == '__main__':
sgd_optimization_mnist()