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ffm-baseline
Commit 5471b149 authored by 张彦钊's avatar 张彦钊
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pandas 映射

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tensnsorflow/feature_engineering.py
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...@@ -27,204 +27,50 @@ def get_data(): ...@@ -27,204 +27,50 @@ def get_data():
print(start) print(start)
db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test') 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," \ 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 " \ "u.device_type,u.manufacturer,u.channel,c.top,df.level2_ids,e.device_id,cut.time " \
"from esmm_train_data e left join user_feature u on e.device_id = u.device_id " \ "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 " \ "left join cid_type_top c on e.device_id = c.device_id " \
"left join diary_feat df on e.cid_id = df.diary_id " \
"left join cid_time_cut cut on e.cid_id = cut.cid " \
"where e.stat_date >= '{}'".format(start) "where e.stat_date >= '{}'".format(start)
df = con_sql(db, sql) df = con_sql(db, sql)
print(df.shape) # print(df.shape)
df = df.rename(columns={0: "y", 1: "z", 2: "stat_date", 3: "ucity_id", 4: "clevel1_id", 5: "ccity_name", 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"}) 6: "device_type", 7: "manufacturer", 8: "channel", 9: "top", 10: "level2_ids",
11: "device_id", 12: "time"})
print("esmm data ok") print("esmm data ok")
print(df.head(2)) # print(df.head(2)
df = df.fillna("na") print("before")
print(df.count()) print(df.shape)
ucity_id = {v:i for i,v in df["ucity_id"].unique()} print("after")
clevel1_id = {v:i for i,v in df["clevel1_id"].unique()} df = df.drop_duplicates()
ccity_name = {v:i for i,v in df["ccity_name"].unique()} features = ["ucity_id", "clevel1_id", "ccity_name", "device_type", "manufacturer",
device_type = {v:i for i,v in df["device_type"].unique()} "channel", "top", "level2_ids", "time", "stat_date"]
manufacturer = {v:i for i,v in df["manufacturer"].unique()} df = df.drop_duplicates(features)
channel = {v:i for i,v in df["channel"].unique()} print(df.shape)
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): unique_values = []
cvr_layer_1 = tf.layers.dense(esmm_embedding_layer, 200, activation=tf.nn.relu) for i in features:
cvr_layer_2 = tf.layers.dense(cvr_layer_1, 80, activation=tf.nn.relu) df[i] = df[i].astype("str")
cvr_layer_3 = tf.layers.dense(cvr_layer_2, 2) # [nonbuy, buy] df[i] = df[i].fillna("lost")
cvr_prob = tf.nn.softmax(cvr_layer_3) + 0.00000001 df[i] = df[i] + i
return cvr_prob unique_values.extend(list(df[i].unique()))
print(df.head(2))
def define_ctr_cvr_layer(esmm_embedding_layer): value_map = {v: k for k, v in enumerate(unique_values)}
layer_1 = tf.layers.dense(esmm_embedding_layer, 128 , activation=tf.nn.relu) for i in features:
layer_2 = tf.layers.dense(layer_1, 16, activation=tf.nn.relu) df[i] = df[i].map(value_map)
layer_3 = tf.layers.dense(layer_2, 2) print("类别总数")
ctr_prob = tf.nn.softmax(layer_3) + 0.00000001 print(len(value_map))
cvr_prob = tf.nn.softmax(layer_3) + 0.00000001 print(df.head(2))
return ctr_prob, cvr_prob
if __name__ == '__main__': if __name__ == '__main__':
embed_dim = 6 get_data()
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)
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