feature_engineering.py 10.8 KB
Newer Older
张彦钊's avatar
张彦钊 committed
1 2
import pandas as pd
import pymysql
张彦钊's avatar
张彦钊 committed
3
import datetime
张彦钊's avatar
张彦钊 committed
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
import tensorflow as tf

def con_sql(db,sql):
    cursor = db.cursor()
    try:
        cursor.execute(sql)
        result = cursor.fetchall()
        df = pd.DataFrame(list(result))
    except Exception:
        print("发生异常", Exception)
        df = pd.DataFrame()
    finally:
        db.close()
    return df


def get_data():
    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select max(stat_date) from esmm_train_data"
    validate_date = con_sql(db, sql)[0].values.tolist()[0]
    print("validate_date:" + validate_date)
    temp = datetime.datetime.strptime(validate_date, "%Y-%m-%d")
    start = (temp - datetime.timedelta(days=30)).strftime("%Y-%m-%d")
    print(start)
    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    sql = "select e.y,e.z,e.stat_date,e.ucity_id,e.clevel1_id,e.ccity_name," \
          "u.device_type,u.manufacturer,u.channel,c.top,cid_time.time " \
          "from esmm_train_data e left join user_feature u on e.device_id = u.device_id " \
          "left join cid_type_top c on e.device_id = c.device_id left join cid_time on e.cid_id = cid_time.cid_id " \
          "where e.stat_date >= '{}'".format(start)
    df = con_sql(db, sql)
    print(df.shape)
    df = df.rename(columns={0: "y", 1: "z", 2: "stat_date", 3: "ucity_id", 4: "clevel1_id", 5: "ccity_name",
                            6: "device_type", 7: "manufacturer", 8: "channel", 9: "top", 10: "time"})
    print("esmm data ok")
    print(df.head(2))
    df = df.fillna("na")
    print(df.count())
    ucity_id = {v:i for i,v in df["ucity_id"].unique()}
    clevel1_id = {v:i for i,v in df["clevel1_id"].unique()}
    ccity_name = {v:i for i,v in df["ccity_name"].unique()}
    device_type = {v:i for i,v in df["device_type"].unique()}
    manufacturer = {v:i for i,v in df["manufacturer"].unique()}
    channel = {v:i for i,v in df["channel"].unique()}
    top = {v:i for i,v in df["top"].unique()}
    time = {v:i for i,v in df["time"].unique()}

    df["ucity_id"] = df["ucity_id"].map(ucity_id)
    df["clevel1_id"] = df["clevel1_id"].map(clevel1_id)
    df["ccity_name"] = df["ccity_name"].map(ccity_name)
    df["device_type"] = df["device_type"].map(device_type)
    df["manufacturer"] = df["manufacturer"].map(manufacturer)
    df["channel"] = df["channel"].map(channel)
    df["top"] = df["top"].map(top)
    df["time"] = df["time"].map(time)

    train = df.loc[df["stat_date"] == validate_date]
    test = df.loc[df["stat_date"] != validate_date]

    features = ["ucity_id","clevel1_id","ccity_name","device_type","manufacturer","channel","top","time"]

    train_values = train[features].values
    train_labels = train[["y","z"]].values
    test_values = test[features].values
    test_labels = test[["y","z"]].values

    ucity_id_max = len(ucity_id)
    clevel1_id_max = len(clevel1_id)
    ccity_name_max = len(ccity_name)
    device_type_max = len(device_type)
    manufacturer_max = len(manufacturer)
    channel_max = len(channel)
    top_max = len(top)
    time_max = len(time)

    return train_values,train_labels,test_values,test_labels,ucity_id_max,clevel1_id_max,ccity_name_max,\
           device_type_max,manufacturer_max,channel_max,top_max,time_max


def get_inputs():
    ucity_id = tf.placeholder(tf.int32, [None, 1], name="ucity_id")
    clevel1_id = tf.placeholder(tf.int32, [None, 1], name="clevel1_id")
    ccity_name = tf.placeholder(tf.int32, [None, 1], name="ccity_name")
    device_type = tf.placeholder(tf.int32, [None, 1], name="device_type")
    manufacturer = tf.placeholder(tf.int32, [None, 1], name="manufacturer")
    channel = tf.placeholder(tf.int32, [None, 1], name="channel")
    top = tf.placeholder(tf.int32, [None, 1], name="top")
    time = tf.placeholder(tf.int32, [None, 1], name="time")

    targets = tf.placeholder(tf.float32, [None, 2], name="targets")
    LearningRate = tf.placeholder(tf.float32, name="LearningRate")
    return ucity_id,clevel1_id,ccity_name,device_type,manufacturer,channel,top,time,targets,LearningRate


def define_embedding_layers(combiner,embed_dim,ucity_id, ucity_id_max, clevel1_id_max,clevel1_id,
                            ccity_name_max,ccity_name,device_type_max,device_type,manufacturer_max,
                            manufacturer,channel,channel_max,top,top_max,time,time_max):
    ucity_id_embed_matrix = tf.Variable(tf.random_normal([ucity_id_max, embed_dim], 0, 0.001))
    ucity_id_embed_layer = tf.nn.embedding_lookup(ucity_id_embed_matrix, ucity_id)
    if combiner == "sum":
        ucity_id_embed_layer = tf.reduce_sum(ucity_id_embed_layer, axis=1, keep_dims=True)

    clevel1_id_embed_matrix = tf.Variable(tf.random_uniform([clevel1_id_max, embed_dim], 0, 0.001))
    clevel1_id_embed_layer = tf.nn.embedding_lookup(clevel1_id_embed_matrix, clevel1_id)
    if combiner == "sum":
        clevel1_id_embed_layer = tf.reduce_sum(clevel1_id_embed_layer, axis=1, keep_dims=True)

    ccity_name_embed_matrix = tf.Variable(tf.random_uniform([ccity_name_max, embed_dim], 0, 0.001))
    ccity_name_embed_layer = tf.nn.embedding_lookup(ccity_name_embed_matrix,ccity_name)
    if combiner == "sum":
        ccity_name_embed_layer = tf.reduce_sum(ccity_name_embed_layer, axis=1, keep_dims=True)

    device_type_embed_matrix = tf.Variable(tf.random_uniform([device_type_max, embed_dim], 0, 0.001))
    device_type_embed_layer = tf.nn.embedding_lookup(device_type_embed_matrix, device_type)
    if combiner == "sum":
        device_type_embed_layer = tf.reduce_sum(device_type_embed_layer, axis=1, keep_dims=True)

    manufacturer_embed_matrix = tf.Variable(tf.random_uniform([manufacturer_max, embed_dim], 0, 0.001))
    manufacturer_embed_layer = tf.nn.embedding_lookup(manufacturer_embed_matrix, manufacturer)
    if combiner == "sum":
        manufacturer_embed_layer = tf.reduce_sum(manufacturer_embed_layer, axis=1, keep_dims=True)

    channel_embed_matrix = tf.Variable(tf.random_uniform([channel_max, embed_dim], 0, 0.001))
    channel_embed_layer = tf.nn.embedding_lookup(channel_embed_matrix, channel)
    if combiner == "sum":
        channel_embed_layer = tf.reduce_sum(channel_embed_layer, axis=1, keep_dims=True)

    top_embed_matrix = tf.Variable(tf.random_uniform([top_max, embed_dim], 0, 0.001))
    top_embed_layer = tf.nn.embedding_lookup(top_embed_matrix, top)
    if combiner == "sum":
        top_embed_layer = tf.reduce_sum(top_embed_layer, axis=1, keep_dims=True)

    time_embed_matrix = tf.Variable(tf.random_uniform([time_max, embed_dim], 0, 0.001))
    time_embed_layer = tf.nn.embedding_lookup(time_embed_matrix, time)
    if combiner == "sum":
        time_embed_layer = tf.reduce_sum(time_embed_layer, axis=1, keep_dims=True)

    esmm_embedding_layer = tf.concat([ucity_id_embed_layer, clevel1_id_embed_layer,ccity_name_embed_layer,
                                      device_type_embed_layer,manufacturer_embed_layer,channel_embed_layer,
                                      top_embed_layer,time_embed_layer], axis=1)
    esmm_embedding_layer = tf.reshape(esmm_embedding_layer, [-1, embed_dim * 8])
    return esmm_embedding_layer

def define_ctr_layer(esmm_embedding_layer):
    ctr_layer_1 = tf.layers.dense(esmm_embedding_layer, 200, activation=tf.nn.relu)
    ctr_layer_2 = tf.layers.dense(ctr_layer_1, 80, activation=tf.nn.relu)
    ctr_layer_3 = tf.layers.dense(ctr_layer_2, 2) # [nonclick, click]
    ctr_prob = tf.nn.softmax(ctr_layer_3) + 0.00000001
    return ctr_prob

def define_cvr_layer(esmm_embedding_layer):
    cvr_layer_1 = tf.layers.dense(esmm_embedding_layer, 200, activation=tf.nn.relu)
    cvr_layer_2 = tf.layers.dense(cvr_layer_1, 80, activation=tf.nn.relu)
    cvr_layer_3 = tf.layers.dense(cvr_layer_2, 2) # [nonbuy, buy]
    cvr_prob = tf.nn.softmax(cvr_layer_3) + 0.00000001
    return cvr_prob

def define_ctr_cvr_layer(esmm_embedding_layer):
    layer_1 = tf.layers.dense(esmm_embedding_layer, 128 , activation=tf.nn.relu)
    layer_2 = tf.layers.dense(layer_1, 16, activation=tf.nn.relu)
    layer_3 = tf.layers.dense(layer_2, 2)
    ctr_prob = tf.nn.softmax(layer_3) + 0.00000001
    cvr_prob = tf.nn.softmax(layer_3) + 0.00000001
    return ctr_prob, cvr_prob
张彦钊's avatar
张彦钊 committed
168 169 170 171 172 173





if __name__ == '__main__':
张彦钊's avatar
张彦钊 committed
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
    embed_dim = 6
    combiner = "sum"
    train_values, train_labels, test_values, test_labels, ucity_id_max, clevel1_id_max, ccity_name_max, \
    device_type_max, manufacturer_max, channel_max, top_max, time_max = get_data()
    tf.reset_default_graph()
    train_graph = tf.Graph()
    with train_graph.as_default():
        ucity_id, clevel1_id, ccity_name, device_type, manufacturer, channel, top, \
        time, targets, LearningRate = get_inputs()

        esmm_embedding_layer = define_embedding_layers(combiner,embed_dim,ucity_id, ucity_id_max, clevel1_id_max,clevel1_id,
                            ccity_name_max,ccity_name,device_type_max,device_type,manufacturer_max,
                            manufacturer,channel,channel_max,top,top_max,time,time_max)

        ctr_prob, cvr_prob = define_ctr_cvr_layer(esmm_embedding_layer)

        with tf.name_scope("loss"):
            ctr_prob_one = tf.slice(ctr_prob, [0, 1], [-1, 1])  # [batch_size , 1]
            cvr_prob_one = tf.slice(cvr_prob, [0, 1], [-1, 1])  # [batchsize, 1 ]

            ctcvr_prob_one = ctr_prob_one * cvr_prob_one  # [ctr*cvr]
            ctcvr_prob = tf.concat([1 - ctcvr_prob_one, ctcvr_prob_one], axis=1)

            ctr_label = tf.slice(targets, [0, 0], [-1, 1])  # target: [click, buy]
            ctr_label = tf.concat([1 - ctr_label, ctr_label], axis=1)  # [1-click, click]

            cvr_label = tf.slice(targets, [0, 1], [-1, 1])
            ctcvr_label = tf.concat([1 - cvr_label, cvr_label], axis=1)

            # 单列,判断Click是否=1
            ctr_clk = tf.slice(targets, [0, 0], [-1, 1])
            ctr_clk_dup = tf.concat([ctr_clk, ctr_clk], axis=1)

            # clicked subset CVR loss
            cvr_loss = - tf.multiply(tf.log(cvr_prob) * ctcvr_label, ctr_clk_dup)
            # batch CTR loss
            ctr_loss = - tf.log(ctr_prob) * ctr_label  # -y*log(p)-(1-y)*log(1-p)
            # batch CTCVR loss
            ctcvr_loss = - tf.log(ctcvr_prob) * ctcvr_label

            # loss = tf.reduce_mean(ctr_loss + ctcvr_loss + cvr_loss)
            # loss = tf.reduce_mean(ctr_loss + ctcvr_loss)
            # loss = tf.reduce_mean(ctr_loss + cvr_loss)
            loss = tf.reduce_mean(cvr_loss)
            ctr_loss = tf.reduce_mean(ctr_loss)
            cvr_loss = tf.reduce_mean(cvr_loss)
            ctcvr_loss = tf.reduce_mean(ctcvr_loss)

        # 优化损失
        # train_op = tf.train.AdamOptimizer(lr).minimize(loss)  #cost
        global_step = tf.Variable(0, name="global_step", trainable=False)
        optimizer = tf.train.AdamOptimizer(lr)
        gradients = optimizer.compute_gradients(loss)  # cost
        train_op = optimizer.apply_gradients(gradients, global_step=global_step)

张彦钊's avatar
张彦钊 committed
229 230