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demo1.py
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demo1.py
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#coding:utf-8
import numpy as np
import pandas as pd
import matplotlib.pylab as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
import tensorflow as tf
import os
train_data = pd.read_csv('/Users/zhangguanqin/PycharmProjects/tatanic/tt/train.csv')
age = train_data[['Age','Survived','Fare','Parch','SibSp','Pclass']]
age_notnull = age.loc[(train_data.Age.notnull())]
age_isnull = age.loc[(train_data.Age.isnull())]
X = age_notnull.values[:,1:]
Y = age_notnull.values[:,0]
rfr = RandomForestRegressor(n_estimators=1000,n_jobs=-1)
rfr.fit(X,Y)
predictAges = rfr.predict(age_isnull.values[:,1:])
train_data.loc[(train_data.Age.isnull()),'Age'] = predictAges
train_data.loc[train_data['Sex']=='male','Sex'] = 0
train_data.loc[train_data['Sex']=='female','Sex'] = 1
train_data['Embarked'] = train_data['Embarked'].fillna('S')
train_data.loc[train_data['Embarked'] == 'S','Embarked'] = 0
train_data.loc[train_data['Embarked'] == 'C','Embarked'] = 1
train_data.loc[train_data['Embarked'] == 'Q','Embarked'] = 2
train_data.drop(['Cabin'],axis=1,inplace=True)
train_data['Deceased'] = train_data['Survived'].apply(lambda s: 1 - s)
#train_data.info()
dataset_X = train_data[['Sex','Age','Pclass','SibSp','Parch','Fare']]
dataset_Y = train_data[['Deceased','Survived']]
X_train,X_val,Y_train,Y_val = train_test_split(dataset_X.as_matrix(),
dataset_Y.as_matrix(),
test_size = 0.2,
random_state = 42)
x = tf.placeholder(tf.float32,shape = [None,6],name = 'input')
y = tf.placeholder(tf.float32,shape = [None,2],name = 'label')
weights1 = tf.Variable(tf.random_normal([6,6]),name = 'weights1')
bias1 = tf.Variable(tf.zeros([6]),name = 'bias1')
a = tf.nn.relu(tf.matmul(x,weights1) + bias1)
weights2 = tf.Variable(tf.random_normal([6,2]),name = 'weights2')
bias2 = tf.Variable(tf.zeros([2]),name = 'bias2')
z = tf.matmul(a,weights2) + bias2
y_pred = tf.nn.softmax(z)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=z))
correct_pred = tf.equal(tf.argmax(y,1),tf.argmax(y_pred,1))
acc_op = tf.reduce_mean(tf.cast(correct_pred,tf.float32))
train_op = tf.train.AdamOptimizer(0.001).minimize(cost)
saver = tf.train.Saver() # 在Saver声明之后定义的变量将不会被存储 # n
on_storable_variable = tf.Variable(777)
ckpt_dir = './ckpt_dir'
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
with tf.Session() as sess:
tf.global_variables_initializer().run()
ckpt = tf.train.latest_checkpoint(ckpt_dir)
if ckpt:
print('Restoring from checkpoint: %s' % ckpt)
saver.restore(sess, ckpt)
for epoch in range(30):
total_loss = 0.
for i in range(len(X_train)):
feed_dict = {x: [X_train[i]],y:[Y_train[i]]}
_,loss = sess.run([train_op,cost],feed_dict=feed_dict)
total_loss +=loss
print('Epoch: %4d, total loss = %.12f' % (epoch,total_loss))
if epoch % 10 == 0:
accuracy = sess.run(acc_op,feed_dict={x:X_val,y:Y_val})
print("Accuracy on validation set: %.9f" % accuracy)
saver.save(sess, ckpt_dir + '/logistic.ckpt')
print('training complete!')
accuracy = sess.run(acc_op,feed_dict={x:X_val,y:Y_val})
print("Accuracy on validation set: %.9f" % accuracy)
pred = sess.run(y_pred,feed_dict={x:X_val})
correct = np.equal(np.argmax(pred,1),np.argmax(Y_val,1))
numpy_accuracy = np.mean(correct.astype(np.float32))
print("Accuracy on validation set (numpy): %.9f" % numpy_accuracy)
saver.save(sess, ckpt_dir + '/logistic.ckpt')
saver = tf.train.Saver() # 在Saver声明之后定义的变量将不会被存储 # non_storable_variable = tf.Variable(777) ckpt_dir = './ckpt_dir' if not os.path.exists(ckpt_dir): os.makedirs(ckpt_dir) with tf.Session() as sess: tf.global_variables_initializer().run() ckpt = tf.train.latest_checkpoint(ckpt_dir) if ckpt: print('Restoring from checkpoint: %s' % ckpt) saver.restore(sess, ckpt) for epoch in range(30): total_loss = 0. for i in range(len(X_train)): feed_dict = {x: [X_train[i]],y:[Y_train[i]]} _,loss = sess.run([train_op,cost],feed_dict=feed_dict) total_loss +=loss print('Epoch: %4d, total loss = %.12f' % (epoch,total_loss)) if epoch % 10 == 0: accuracy = sess.run(acc_op,feed_dict={x:X_val,y:Y_val}) print("Accuracy on validation set: %.9f" % accuracy) saver.save(sess, ckpt_dir + '/logistic.ckpt') print('training complete!') accuracy = sess.run(acc_op,feed_dict={x:X_val,y:Y_val}) print("Accuracy on validation set: %.9f" % accuracy) pred = sess.run(y_pred,feed_dict={x:X_val}) correct = np.equal(np.argmax(pred,1),np.argmax(Y_val,1)) numpy_accuracy = np.mean(correct.astype(np.float32)) print("Accuracy on validation set (numpy): %.9f" % numpy_accuracy) saver.save(sess, ckpt_dir + '/logistic.ckpt') '''
#测试数据的清洗和训练数据一样，两者可以共同完成
test_data = pd.read_csv('/Users/zhangguanqin/PycharmProjects/tatanic/tt/test.csv') #数据清洗, 数据预处理
test_data.loc[test_data['Sex']=='male','Sex'] = 0
test_data.loc[test_data['Sex']=='female','Sex'] = 1
age = test_data[['Age','Sex','Parch','SibSp','Pclass']]
age_notnull = age.loc[(test_data.Age.notnull())]
age_isnull = age.loc[(test_data.Age.isnull())]
X = age_notnull.values[:,1:]
Y = age_notnull.values[:,0]
rfr = RandomForestRegressor(n_estimators=1000,n_jobs=-1)
rfr.fit(X,Y)
predictAges = rfr.predict(age_isnull.values[:,1:])
test_data.loc[(test_data.Age.isnull()),'Age'] = predictAges
test_data['Embarked'] = test_data['Embarked'].fillna('S')
test_data.loc[test_data['Embarked'] == 'S','Embarked'] = 0
test_data.loc[test_data['Embarked'] == 'C','Embarked'] = 1
test_data.loc[test_data['Embarked'] == 'Q','Embarked'] = 2
test_data.drop(['Cabin'],axis=1,inplace=True) #特征选择
X_test = test_data[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']] #评估模型
predictions = np.argmax(sess.run(y_pred, feed_dict={x: X_test}), 1) #保存结果
submission = pd.DataFrame({ "PassengerId": test_data["PassengerId"], "Survived": predictions })
submission.to_csv("titanic-submission.csv", index=False)