import numpy as np

 import pandas as pd

 import tensorflow as tf

 from sklearn.model_selection import train_test_split

 ################################

 # Preparing Data

 ################################

 # read data from file

 data = pd.read_csv('data/train.csv')

 # fill nan values with 0

 data = data.fillna(0)

 # convert ['male', 'female'] values of Sex to [1, 0]

 data['Sex'] = data['Sex'].apply(lambda s: 1 if s == 'male' else 0)

 # 'Survived' is the label of one class,

 # add 'Deceased' as the other class

 data['Deceased'] = data['Survived'].apply(lambda s: 1 - s)

 # select features and labels for training

 dataset_X = data[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']].as_matrix()

 dataset_Y = data[['Deceased', 'Survived']].as_matrix()

 # split training data and validation set data

 X_train, X_val, y_train, y_val = train_test_split(dataset_X, dataset_Y,

                                                   test_size=0.2,

                                                   random_state=42)

 ################################

 # Constructing Dataflow Graph

 ################################

 # create symbolic variables

 X = tf.placeholder(tf.float32, shape=[None, 6])

 y = tf.placeholder(tf.float32, shape=[None, 2])

 # weights and bias are the variables to be trained

 weights = tf.Variable(tf.random_normal([6, 2]), name='weights')

 bias = tf.Variable(tf.zeros([2]), name='bias')

 y_pred = tf.nn.softmax(tf.matmul(X, weights) + bias)

 # Minimise cost using cross entropy

 # NOTE: add a epsilon(1e-10) when calculate log(y_pred),

 # otherwise the result will be -inf

 cross_entropy = - tf.reduce_sum(y * tf.log(y_pred + 1e-10),

                                 reduction_indices=1)

 cost = tf.reduce_mean(cross_entropy)

 # use gradient descent optimizer to minimize cost

 train_op = tf.train.GradientDescentOptimizer(0.001).minimize(cost)

 # calculate accuracy

 correct_pred = tf.equal(tf.argmax(y, 1), tf.argmax(y_pred, 1))

 acc_op = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

 ################################

 # Training and Evaluating the model

 ################################

 saver = tf.train.Saver()

 # use session to run the calculation

 with tf.Session() as sess:

     # variables have to be initialized at the first place

     tf.global_variables_initializer().run()

     # training loop

     for epoch in range(10):

         total_loss = 0.

         for i in range(len(X_train)):

             # prepare feed data and run

             feed_dict = {X: [X_train[i]], y: [y_train[i]]}

             _, loss = sess.run([train_op, cost], feed_dict=feed_dict)

             total_loss += loss

         # display loss per epoch

         print('Epoch: %04d, total loss=%.9f' % (epoch + 1, total_loss))

     saver_path = saver.save(sess,"wjy_data/model.ckpt")

     # Accuracy calculated by TensorFlow

     accuracy = sess.run(acc_op, feed_dict={X: X_val, y: y_val})

     print("Accuracy on validation set: %.9f" % accuracy)

     # Accuracy calculated by NumPy

     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)

     # predict on test data

     testdata = pd.read_csv('data/test.csv')

     testdata = testdata.fillna(0)

     # convert ['male', 'female'] values of Sex to [1, 0]

     testdata['Sex'] = testdata['Sex'].apply(lambda s: 1 if s == 'male' else 0)

     X_test = testdata[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']]

     predictions = np.argmax(sess.run(y_pred, feed_dict={X: X_test}), 1)

     submission = pd.DataFrame({

         "PassengerId": testdata["PassengerId"],

         "Survived": predictions

     })

     submission.to_csv("titanic-submission.csv", index=False)

 import numpy as np

 import pandas as pd

 import tensorflow as tf

 from sklearn.model_selection import train_test_split

 # create symbolic variables

 X = tf.placeholder(tf.float32, shape=[None, 6])

 y = tf.placeholder(tf.float32, shape=[None, 2])

 # weights and bias are the variables to be trained

 weights = tf.Variable(tf.random_normal([6, 2]), name='weights')

 bias = tf.Variable(tf.zeros([2]), name='bias')

 y_pred = tf.nn.softmax(tf.matmul(X, weights) + bias)

 # predict on test data

 testdata = pd.read_csv('data/test.csv')

 testdata = testdata.fillna(0)

 # convert ['male', 'female'] values of Sex to [1, 0]

 testdata['Sex'] = testdata['Sex'].apply(lambda s: 1 if s == 'male' else 0)

 X_test = testdata[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']]

 ################################

 # Training and Evaluating the model

 ################################

 saver = tf.train.Saver()

 # use session to run the calculation

 with tf.Session() as sess:

     # variables have to be initialized at the first place

     tf.global_variables_initializer().run()

     #save_path = saver.save(sess,"Saved_model/model.ckpt")

     saver.restore(sess,"wjy_data/model.ckpt")#加载模型

     predictions = np.argmax(sess.run(y_pred, feed_dict={X: X_test}), 1)

     submission = pd.DataFrame({

         "PassengerId": testdata["PassengerId"],

         "Survived": predictions

     })

     #saver = tf.train.Saver()

     submission.to_csv("titanic-submission.csv", index=False)