import tensorflow as tf
import json
import pandas as pd
import time
import sys
import os
from datetime import date, timedelta


sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
import utils.configUtils as configUtils

ITEM_NUMBER_COLUMNS = ["item_"+c for c in ["smart_rank2"]]
embedding_columns = ["itemid","userid"] + ["item_"+c for c in ["doctor_id","hospital_id","merchant_id"]]
multi_columns = ["tags_v3","first_demands","second_demands","first_solutions","second_solutions","first_positions","second_positions"]
one_hot_columns = ["user_os"] + ["item_"+c for c in ["service_type","doctor_type","doctor_famous","hospital_city_tag_id","hospital_type","hospital_is_high_quality"]]
# history_columns = ["userRatedHistory"]

# 数据加载
# data_path_train = "/Users/zhigangzheng/Desktop/work/guoyu/service_sort/train/part-00000-a61205d1-ad4e-4fa7-895d-ad8db41189e6-c000.csv"
# data_path_test = "/Users/zhigangzheng/Desktop/work/guoyu/service_sort/train/part-00000-a61205d1-ad4e-4fa7-895d-ad8db41189e6-c000.csv"

VERSION = configUtils.SERVICE_VERSION
trainDay = time.strftime("%Y%m%d%H", time.localtime())
data_path_train = "/data/files/service_feature_{}_train.csv".format(VERSION)
data_path_test = "/data/files/service_feature_{}_test.csv".format(VERSION)
model_file = configUtils.SERVICE_MODEL_PATH + "/" + trainDay

def is_float(s):
    try:
        float(s)
        return True
    except ValueError:
        return False

# 数据类型转换
def csvTypeConvert(columns,df,data_vocab):
    df["label"] = df["label"].astype("int")
    for k in columns:
        # 离散na值填充
        if data_vocab.get(k):
            df[k] = df[k].fillna("-1")
            df[k] = df[k].astype("string")
        elif k != "label":
            # df[k] = df[k].map(lambda x:x if is_float(x) else 0)
            df[k] = df[k].fillna(0)
            df[k] = df[k].astype("float")
    # print(df.dtypes)
    return df

def loadData(data_path):
    print("读取数据...")
    timestmp1 = int(round(time.time() * 1000))
    df = pd.read_csv(data_path, sep="|")
    timestmp2 = int(round(time.time() * 1000))
    print("读取数据耗时ms:{}".format(timestmp2 - timestmp1))
    return df

def getWeight(x):
    res = 1
    try:
        p = int(x)
        if p > 0 and p <= 5:
            res = 2
        elif p > 5 and p <= 10:
            res = 3
        elif p > 10:
            res = 4

    except Exception as e:
        print(e)

    return res


def getDataSet(df,shuffleSize = 10000,batchSize=128):
    # print(df.dtypes)
    labels = df.pop('label')
    # df["rating"] = df["rating"].map(getWeight)
    # weights = df.pop('rating')
    dataSet = tf.data.Dataset.from_tensor_slices((dict(df), labels)).shuffle(shuffleSize).batch(batchSize)
    return dataSet

def getTrainColumns(train_columns,data_vocab):
    emb_columns = []
    number_columns = []
    oneHot_columns = []
    dataColumns = []
    inputs = {}
    # 离散特征
    for feature in train_columns:
        if data_vocab.get(feature):
            if feature.count("__")>0:
                cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature,vocabulary_list=data_vocab[feature])
                col = tf.feature_column.embedding_column(cat_col, 5)
                emb_columns.append(col)
                dataColumns.append(feature)
                inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
            elif feature in one_hot_columns or feature.count("Bucket") > 0:
                cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature, vocabulary_list=data_vocab[feature])
#                 col = tf.feature_column.indicator_column(cat_col)
                col = tf.feature_column.embedding_column(cat_col, 3)
                oneHot_columns.append(col)
                dataColumns.append(feature)
                inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
            else:
                cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature,vocabulary_list=data_vocab[feature])
                col = tf.feature_column.embedding_column(cat_col, 10)
                emb_columns.append(col)
                dataColumns.append(feature)
                inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')

        elif feature in ITEM_NUMBER_COLUMNS:
            col = tf.feature_column.numeric_column(feature)
            number_columns.append(col)
            dataColumns.append(feature)
            inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='float32')

    return emb_columns,number_columns,oneHot_columns,dataColumns,inputs


def train(emb_columns, number_columns, oneHot_columns, inputs, train_dataset):
    wide = tf.keras.layers.DenseFeatures(emb_columns + number_columns + oneHot_columns)(inputs)
    deep = tf.keras.layers.Dense(64, activation='relu')(wide)
    deep = tf.keras.layers.Dropout(0.2)(deep)
    concat_layer = tf.keras.layers.concatenate([wide, deep], axis=1)

    #     deep = tf.keras.layers.Dense(64, activation='relu')(deep)
    #     deep = tf.keras.layers.Dropout(0.5)(deep)
    output_layer = tf.keras.layers.Dense(1, activation='sigmoid')(concat_layer)
    #     output_layer = FM(1)(deep)

    model = tf.keras.Model(inputs, output_layer)

    # compile the model, set loss function, optimizer and evaluation metrics
    model.compile(
        loss='binary_crossentropy',
        optimizer='adam',
        metrics=['accuracy', tf.keras.metrics.AUC(curve='ROC'), tf.keras.metrics.AUC(curve='PR')])

    # train the model
    print("train start...")
    model.fit(train_dataset, epochs=5)
    print("train end...")
    print("train save...")

    model.save(model_file, include_optimizer=False, save_format='tf')
    return model


def evaluate(model,test_dataset):
    if not model:
        print("加载模型中")
        model = tf.keras.models.load_model(model_file)
    # evaluate the model
    timestmp1 = int(round(time.time()))
    print("evaluate:")
    test_loss, test_accuracy, test_roc_auc, test_pr_auc = model.evaluate(test_dataset)
    print('\n\nTest Loss {}, Test Accuracy {}, Test ROC AUC {}, Test PR AUC {}'.format(test_loss, test_accuracy,
                                                                                       test_roc_auc, test_pr_auc))
    print("验证耗时s:", int(round(time.time())) - timestmp1)


def predict(model_path,df):
    print("加载模型中...")
    model_new = tf.keras.models.load_model(model_path)
    # model_new.summary()
    print("模型加载完成...")
    # model = tf.keras.models.model_from_json(model.to_json)
    n = 1000
    dd = dict(df.sample(n=n))
    for i in range(10):
        timestmp1 = int(round(time.time() * 1000))
        model_new.predict(dd, batch_size=10000)
        print("测试样本数：{},测试耗时ms:{}".format(n, int(round(time.time() * 1000)) - timestmp1))

def addDays(n, format="%Y%m%d"):
    return (date.today() + timedelta(days=n)).strftime(format)

if __name__ == '__main__':

    curTime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
    print("train_service执行时间:{}".format(curTime))
    splitTimestamp = int(time.mktime(time.strptime(addDays(-1), "%Y%m%d")))

    # redis中加载数据字典
    print("加载模型字典...")
    data_vocab = json.load(open(configUtils.VOCAB_PATH,mode='r',encoding='utf-8'))
    print("字典keys:",str(data_vocab.keys()))
    # data_vocab = getDataVocabFromRedis(VERSION)
    assert data_vocab

    timestmp1 = int(round(time.time()))
    df_train = loadData(data_path_train)
    print(df_train.dtypes)

    df_test = df_train.loc[df_train['timestamp']>=splitTimestamp]
    df_train = df_train.loc[df_train['timestamp'] < splitTimestamp]
    # df_test = loadData(data_path_test)
    timestmp2 = int(round(time.time()))
    print("读取数据耗时s:{}".format(timestmp2 - timestmp1))

    # 获取训练列
    columns = df_train.columns.tolist()
    print("原始数据列：")
    print(columns)
    emb_columns,number_columns,oneHot_columns, datasColumns,inputs = getTrainColumns(columns, data_vocab)
    print("训练列：")
    print(datasColumns)

    df_train = df_train[datasColumns + ["label"]]
    df_test = df_test[datasColumns + ["label"]]

    trainSize = df_train["label"].count()
    print("trainSize:{}".format(trainSize))
    testSize = df_test["label"].count()
    print("trainSize:{},testSize{}".format(trainSize,testSize))

    # 数据类型转换
    df_train = csvTypeConvert(datasColumns,df_train,data_vocab)
    df_test = csvTypeConvert(datasColumns,df_test,data_vocab)

    # 获取训练数据
    train_data = getDataSet(df_train,shuffleSize=trainSize,)


    print("train start...")
    timestmp3 = int(round(time.time()))
    model = train(emb_columns,number_columns,oneHot_columns,inputs,train_data)
    timestmp4 = int(round(time.time()))
    print("train end...耗时h:{}".format((timestmp4 - timestmp3)/60/60))

    if(testSize > 0):
        test_data = getDataSet(df_test,shuffleSize=testSize)
        evaluate(model,test_data)


    # predict(model_file,test_data)

    pass