100、TensorFlow实现FFM Field-awared FM模型

'''
Created on 2017年11月15日

@author: weizhen
'''
import tensorflow as tf
import pandas as pd
import numpy as np
import os
input_x_size = 80;
field_size = 8;
vector_dimension = 3;
total_plan_train_steps = 1000;
MODEL_SAVE_PATH = "TFModel"
MODEL_NAME = "FFM"
BATCH_SIZE = 1;

def createTwoDimensionWeight(input_x_size,
field_size,
vector_dimension):
weights = tf.truncated_normal([int(input_x_size * (input_x_size + 1) / 2),
field_size,
vector_dimension
])
tf_weights = tf.Variable(weights);
return tf_weights;

def createOneDimensionWeight(input_x_size):
weights = tf.truncated_normal([input_x_size])
tf_weights = tf.Variable(weights)
return tf_weights;

def createZeroDimensionWeight():
weights = tf.truncated_normal([1])
tf_weights = tf.Variable(weights)
return tf_weights;

def inference(input_x, input_x_field):
"""计算回归模型输出的值"""
zeroWeights = createZeroDimensionWeight();  # 随机初始化常数项的权重
oneDimWeights = createOneDimensionWeight(input_x_size);  # 随机初始化一次项的权重

secondValue = tf.reduce_sum(tf.multiply(oneDimWeights, input_x, name="secondVale"));  # 计算一次项的权重和x的点积，和点积后的和
firstTwoValue = tf.add(zeroWeights, secondValue, name="firstTwoValue");  # 常数项和一次项的值

thirdWeight = createTwoDimensionWeight(input_x_size,  # 创建二次项的权重变量
field_size,
vector_dimension);

thirdValue = tf.Variable(0.0, dtype=tf.float32);  # 初始化二次项的和为0
input_shape = input_x_size;  # 得到输入训练数据的大小
for i in range(input_shape):
featureIndex1 = i;  # 第一个特征的索引编号
fieldIndex1 = int(input_x_field[i]);  # 第一个特征所在域的索引编号
for j in range(i + 1, input_shape):
featureIndex2 = j;  # 第二个特征的索引编号
fieldIndex2 = int(input_x_field[j]);  # 第二个特征的所在域索引编号
vectorLeft = tf.convert_to_tensor([[featureIndex1, fieldIndex2, 0], [featureIndex1, fieldIndex2, 1], [featureIndex1, fieldIndex2, 2]])
weightLeft = tf.gather_nd(thirdWeight, vectorLeft)
weightLeftAfterCut = tf.squeeze(weightLeft)

vectorRight = tf.convert_to_tensor([[featureIndex2, fieldIndex1, 0], [featureIndex2, fieldIndex1, 1], [featureIndex2, fieldIndex1, 2]])
weightRight = tf.gather_nd(thirdWeight, vectorRight)
weightRightAfterCut = tf.squeeze(weightRight)
tempValue = tf.reduce_sum(tf.multiply(weightLeftAfterCut, weightRightAfterCut))

indices2 = [i]
indices3 = [j]

xi = tf.squeeze(tf.gather_nd(input_x, indices2));
xj = tf.squeeze(tf.gather_nd(input_x, indices3));

product = tf.reduce_sum(tf.multiply(xi, xj));

secondItemVal = tf.multiply(tempValue, product)

tf.assign(thirdValue, tf.add(thirdValue, secondItemVal))

fowardY = tf.add(firstTwoValue, thirdValue)

return fowardY;

def read_csv():
f = open('train_sales_data.csv')
df = pd.read_csv(f)
y = np.array(df['UNIT_SALES'])
x1 = np.array(df['ITEM_NBR'])
x2 = np.array(df['STORE_NBR'])
x3 = np.array(df['CITY_GUAYAQUIL'])
x4 = np.array(df['CITY_BABAHOY'])
x5 = np.array(df['CITY_PLAYAS'])
x6 = np.array(df['CITY_LOJA'])
x7 = np.array(df['CITY_EL_CARMEN'])
x8 = np.array(df['CITY_LATACUNGA'])
x9 = np.array(df['CITY_GUARAN'])
x10 = np.array(df['CITY_CUENC'])
x11 = np.array(df['CITY_ESMERALDAS'])
x12 = np.array(df['CITY_QUITO'])
x13 = np.array(df['CITY_CAYAMBE'])
x14 = np.array(df['CITY_SALINAS'])
x15 = np.array(df['CITY_RIOBAMBA'])
x16 = np.array(df['CITY_SANTO_DOMINGO'])
x17 = np.array(df['CITY_DAULE'])
x18 = np.array(df['CITY_MACHALA'])
x19 = np.array(df['CITY_MACHALA_1'])
x20 = np.array(df['CITY_QUEVEDO'])
x21 = np.array(df['STATE_AZUAY'])
x22 = np.array(df['STATE_BOLIVAR'])
x23 = np.array(df['STATE_CHIMBORAZO'])
x24 = np.array(df['STATE_COTOPAXI'])
x25 = np.array(df['STATE_EL_ORO'])
x26 = np.array(df['STATE_ESMERALDAS'])
x27 = np.array(df['STATE_GUAYAS'])
x28 = np.array(df['STATE_IMBABURA'])
x29 = np.array(df['STATE_LOJA'])
x30 = np.array(df['STATE_LOS_RIOS'])
x31 = np.array(df['STATE_MANABI'])
x32 = np.array(df['STATE_PICHINCHA'])
x33 = np.array(df['STATE_SANTA_ELENA'])
x34 = np.array(df['STATE_SANTO_DOMINGO_DE_LOS'])
x35 = np.array(df['STATE_TUNGURAHUA'])
x36 = np.array(df['N_CLUSTER_1'])
x37 = np.array(df['N_CLUSTER_2'])
x38 = np.array(df['N_CLUSTER_3'])
x39 = np.array(df['N_CLUSTER_4'])
x40 = np.array(df['N_CLUSTER_5'])
x41 = np.array(df['N_CLUSTER_6'])
x42 = np.array(df['N_CLUSTER_7'])
x43 = np.array(df['N_CLUSTER_8'])
x44 = np.array(df['N_CLUSTER_9'])
x45 = np.array(df['N_CLUSTER_10'])
x46 = np.array(df['N_CLUSTER_11'])
x47 = np.array(df['N_CLUSTER_12'])
x48 = np.array(df['N_CLUSTER_13'])
x49 = np.array(df['N_CLUSTER_14'])
x50 = np.array(df['N_CLUSTER_15'])
x51 = np.array(df['N_CLUSTER_16'])
x52 = np.array(df['N_CLUSTER_17'])
x53 = np.array(df['FAMILY_CLEANING'])
x54 = np.array(df['FAMILY_BREAD_BAKERY'])
x55 = np.array(df['FAMILY_LIQUOR_WINE_BEER'])
x56 = np.array(df['FAMILY_PREPARED_FOODS'])
x57 = np.array(df['FAMILY_MEATS'])
x58 = np.array(df['FAMILY_BEAUTY'])
x59 = np.array(df['FAMILY_HARDWARE'])
x60 = np.array(df['FAMILY_BEVERAGES'])
x61 = np.array(df['FAMILY_DAIRY'])
x62 = np.array(df['FAMILY_GROCERY_II'])
x63 = np.array(df['FAMILY_POULTRY'])
x64 = np.array(df['FAMILY_SEAFOOD'])
x65 = np.array(df['FAMILY_LAWN_AND_GARDEN'])
x66 = np.array(df['FAMILY_EGGS'])
x67 = np.array(df['FAMILY_DELI'])
x68 = np.array(df['FAMILY_LINGERIE'])
x69 = np.array(df['FAMILY_FROZEN_FOODS'])
x70 = np.array(df['FAMILY_AUTOMOTIVE'])
x71 = np.array(df['FAMILY_GROCERY_I'])
x72 = np.array(df['FAMILY_PERSONAL_CARE'])
x73 = np.array(df['PERISHABLE_TRUE'])
x74 = np.array(df['TYPE_HOLIDAY'])
x75 = np.array(df['TYPE_WORK_DAY'])
x76 = np.array(df['LOCALE_NATIONAL'])
x77 = np.array(df['LOCALE_NAME_ECUADOR'])
x78 = np.array(df['LOCALE_PRIMER_DIA_DEL_ANO'])
x79 = np.array(df['LOCALE_RECUPERO_PUENTE_NAVIDAD'])
x80 = np.array(df['LOCALE_RECUPERO_PUENTE'])
x81 = np.array(df["FIELD_CATEGORY"])

train_x, train_y, train_x_field = [], [], []
for j in range(80):
train_x_field.append(x81[j])
print(x81[j])

for i in range(y.shape[0]):
train_x_temp = []
train_y_temp = []

train_x_temp.append(x1[i])
train_x_temp.append(x2[i])
train_x_temp.append(x3[i])
train_x_temp.append(x4[i])

train_x_temp.append(x5[i])
train_x_temp.append(x6[i])
train_x_temp.append(x7[i])
train_x_temp.append(x8[i])

train_x_temp.append(x9[i])
train_x_temp.append(x10[i])
train_x_temp.append(x11[i])
train_x_temp.append(x12[i])

train_x_temp.append(x13[i])
train_x_temp.append(x14[i])
train_x_temp.append(x15[i])
train_x_temp.append(x16[i])

train_x_temp.append(x17[i])
train_x_temp.append(x18[i])
train_x_temp.append(x19[i])
train_x_temp.append(x20[i])

train_x_temp.append(x21[i])
train_x_temp.append(x22[i])
train_x_temp.append(x23[i])
train_x_temp.append(x24[i])

train_x_temp.append(x25[i])
train_x_temp.append(x26[i])
train_x_temp.append(x27[i])
train_x_temp.append(x28[i])

train_x_temp.append(x29[i])
train_x_temp.append(x30[i])
train_x_temp.append(x31[i])
train_x_temp.append(x32[i])

train_x_temp.append(x33[i])
train_x_temp.append(x34[i])
train_x_temp.append(x35[i])
train_x_temp.append(x36[i])

train_x_temp.append(x37[i])
train_x_temp.append(x38[i])
train_x_temp.append(x39[i])
train_x_temp.append(x40[i])

train_x_temp.append(x41[i])
train_x_temp.append(x42[i])
train_x_temp.append(x43[i])
train_x_temp.append(x44[i])

train_x_temp.append(x45[i])
train_x_temp.append(x46[i])
train_x_temp.append(x47[i])
train_x_temp.append(x48[i])

train_x_temp.append(x49[i])
train_x_temp.append(x50[i])
train_x_temp.append(x51[i])
train_x_temp.append(x52[i])

train_x_temp.append(x53[i])
train_x_temp.append(x54[i])
train_x_temp.append(x55[i])
train_x_temp.append(x56[i])

train_x_temp.append(x57[i])
train_x_temp.append(x58[i])
train_x_temp.append(x59[i])
train_x_temp.append(x60[i])

train_x_temp.append(x61[i])
train_x_temp.append(x62[i])
train_x_temp.append(x63[i])
train_x_temp.append(x64[i])

train_x_temp.append(x65[i])
train_x_temp.append(x66[i])
train_x_temp.append(x67[i])
train_x_temp.append(x68[i])

train_x_temp.append(x69[i])
train_x_temp.append(x70[i])
train_x_temp.append(x71[i])
train_x_temp.append(x72[i])

train_x_temp.append(x73[i])
train_x_temp.append(x74[i])
train_x_temp.append(x75[i])
train_x_temp.append(x76[i])

train_x_temp.append(x77[i])
train_x_temp.append(x78[i])
train_x_temp.append(x79[i])
train_x_temp.append(x80[i])

train_y_temp.append(y[i])

train_x.append(train_x_temp);
train_y.append(train_y_temp);
f.close();
return (train_x, train_y, train_x_field)

1 if __name__ == "__main__":
2     global_step = tf.Variable(0, trainable=False)
3     (train_x, train_y, train_x_field) = read_csv();
4     input_x = tf.placeholder(tf.float32, [None, 80])
5     input_y = tf.placeholder(tf.float32, [None, 1])
6     y_ = inference(input_x, train_x_field)
7     cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=y_, labels=input_y);
8     train_step = tf.train.GradientDescentOptimizer(0.001, name="GradientDescentOptimizer").minimize(cross_entropy, global_step=global_step);
9
10     saver = tf.train.Saver();
11     with tf.Session() as sess:
12         tf.global_variables_initializer().run()
13         for i in range(total_plan_train_steps):
14             input_x_batch = train_x[int(i * BATCH_SIZE):int((i + 1) * BATCH_SIZE)]
15             input_y_batch = train_y[int(i * BATCH_SIZE):int((i + 1) * BATCH_SIZE)]
16
17             predict_loss , steps = sess.run([train_step, global_step], feed_dict={input_x:input_x_batch, input_y:input_y_batch})
18             if (i + 1) % 2 == 0:
19                 print("After  {step} training   step(s)   ,   loss    on    training    batch   is  {predict_loss} "
20                        .format(step=steps, predict_loss=predict_loss))
21
22                 saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=steps)
23                 writer = tf.summary.FileWriter(os.path.join(MODEL_SAVE_PATH, MODEL_NAME), tf.get_default_graph())
24                 writer.close()