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binary_classification.py
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binary_classification.py
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#!/usr/bin/env python
#coding=gbk
# ==============================================================================
# \file binary_classification.py
# \author chenghuige
# \date 2015-11-30 16:06:52.693026
# \Description
# ==============================================================================
import sys
import tensorflow as tf
import numpy as np
from sklearn.metrics import roc_auc_score
import melt
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')
flags.DEFINE_integer('num_epochs', 120, 'Number of epochs to run trainer.')
flags.DEFINE_integer('batch_size', 500, 'Batch size. Must divide evenly into the dataset sizes.')
flags.DEFINE_string('train', './corpus/feature.normed.rand.12000.0_2.txt', 'train file')
flags.DEFINE_string('test', './corpus/feature.normed.rand.12000.1_2.txt', 'test file')
flags.DEFINE_string('method', 'logistic', 'currently support logistic/mlp')
#----for mlp
flags.DEFINE_integer('hidden_size', 20, 'Hidden unit size')
trainset_file = FLAGS.train
testset_file = FLAGS.test
learning_rate = FLAGS.learning_rate
num_epochs = FLAGS.num_epochs
batch_size = FLAGS.batch_size
method = FLAGS.method
trainset = melt.load_dataset(trainset_file)
print "finish loading train set ",trainset_file
testset = melt.load_dataset(testset_file)
print "finish loading test set ", testset_file
assert(trainset.num_features == testset.num_features)
num_features = trainset.num_features
print 'num_features: ', num_features
print 'trainSet size: ', trainset.num_instances()
print 'testSet size: ', testset.num_instances()
print 'batch_size:', batch_size, ' learning_rate:', learning_rate, ' num_epochs:', num_epochs
trainer = melt.gen_binary_classification_trainer(trainset)
class LogisticRegresssion:
def model(self, X, w):
return melt.matmul(X,w)
def forward(self, trainer):
w = melt.init_weights([trainer.num_features, 1])
py_x = self.model(trainer.X, w)
return py_x
class Mlp:
def model(self, X, w_h, w_o):
h = tf.nn.sigmoid(melt.matmul(X, w_h)) # this is a basic mlp, think 2 stacked logistic regressions
return tf.matmul(h, w_o) # note that we dont take the softmax at the end because our cost fn does that for us
def forward(self, trainer):
w_h = melt.init_weights([trainer.num_features, FLAGS.hidden_size]) # create symbolic variables
w_o = melt.init_weights([FLAGS.hidden_size, 1])
py_x = self.model(trainer.X, w_h, w_o)
return py_x
def gen_algo(method):
if method == 'logistic':
return LogisticRegresssion()
elif method == 'mlp':
return Mlp()
else:
print method, ' is not supported right now'
exit(-1)
algo = gen_algo(method)
py_x = algo.forward(trainer)
Y = trainer.Y
cost = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(py_x, Y))
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # construct optimizer
predict_op = tf.nn.sigmoid(py_x)
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
teX, teY = testset.full_batch()
num_train_instances = trainset.num_instances()
for i in range(num_epochs):
predicts, cost_ = sess.run([predict_op, cost], feed_dict = trainer.gen_feed_dict(teX, teY))
print i, 'auc:', roc_auc_score(teY, predicts), 'cost:', cost_ / len(teY)
for start, end in zip(range(0, num_train_instances, batch_size), range(batch_size, num_train_instances, batch_size)):
trX, trY = trainset.mini_batch(start, end)
sess.run(train_op, feed_dict = trainer.gen_feed_dict(trX, trY))
predicts, cost_ = sess.run([predict_op, cost], feed_dict = trainer.gen_feed_dict(teX, teY))
print 'final ', 'auc:', roc_auc_score(teY, predicts),'cost:', cost_ / len(teY)