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305 lines
13 KiB
305 lines
13 KiB
# -*- coding: utf-8 -*-
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"""
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@Time : 2019/9/12 15:30
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@Author : 杰森·家乐森
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@File : ANN_train.py
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@Software: PyCharm
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"""
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import datetime
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import json
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import os
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import time
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import config
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import requests
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import tensorflow as tf
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from sklearn.preprocessing import MinMaxScaler
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from tensorflow.keras import backend
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from tensorflow.keras import layers
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from tensorflow.keras.models import load_model
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from tensorflow.keras.models import model_from_json
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os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
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def get_history_value(points, time, interval, typedata):
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# url="http://192.168.1.201:8080/openPlant/getMultiplePointHistorys"
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url = f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat"
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headers = {"Content-Type": "application/json;charset=utf-8"} # ,"token":get_token()
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point_array = points.split(",")
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time_span = time.split(";")
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value_array = []
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for item in point_array:
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value_group = []
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for time_piece in time_span:
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st = time_piece.split(",")[0]
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et = time_piece.split(",")[1]
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para = {"ItemName": item, "StartingTime": st, "TerminalTime": et, "SamplingPeriod": interval}
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response = requests.get(url, headers=headers, params=para)
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value = eval(str(response.text).replace("\"", "").replace("null", str(np.nan)))
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for row in value:
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value_group.append(row[1])
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value_array.append(value_group)
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valuetrs = np.array(value_array)
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typeArr = list(enumerate(typedata.split(",")))
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data_x = [(valuetrs.T)[:, item[0]].tolist() for item in typeArr if item[1] == "0"]
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data_y = [(valuetrs.T)[:, item[0]].tolist() for item in typeArr if item[1] == "1"]
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x_data = np.array(data_x).T
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y_data = np.array(data_y).T
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return x_data, y_data, valuetrs
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def rmse(y_true, y_pred):
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return backend.sqrt(backend.mean(tf.keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
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def TrainOffline(x_data, y_data, hidden_layers, epochsdata):
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# 计算原来每一列数据的最大值
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valuetrs = np.hstack((x_data, y_data))
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# valuetrs = valuetrs
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train_max, train_min, train_ave = [], [], []
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for row in valuetrs.T:
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train_max.append(np.max(row))
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train_min.append(np.min(row))
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train_ave.append(np.mean(row))
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mms1 = MinMaxScaler()
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mms2 = MinMaxScaler()
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mms3 = MinMaxScaler()
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x_normal = mms1.fit_transform(x_data)
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y_normal = mms2.fit_transform(y_data)
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x_train = x_normal
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y_train = y_normal
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# 构建网络结构
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model = tf.keras.Sequential()
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model.add(layers.Dense(units=hidden_layers[0], input_dim=x_data.shape[1], activation="sigmoid"))
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for i in range(len(hidden_layers) - 1):
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model.add(layers.Dense(units=hidden_layers[i + 1], activation="sigmoid"))
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model.add(layers.Dense(units=y_data.shape[1]))
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# 选择损失函数，优化方法
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model.compile(loss="mse", optimizer="adam", metrics=["accuracy"])
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# 训练模型
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history = model.fit(x_train, y_train, epochs=epochsdata, batch_size=80)
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path = os.getcwd() + "\\ModelOline";
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if not os.path.exists(path):
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os.makedirs(path)
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now = datetime.datetime.now()
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# filepath=path + "\\M"+"_"+str(now).replace(" ","-").replace(".","-").replace(":","-")+".h5"
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# model.save(filepath)
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filepath = model.to_json()
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model_weight = model.get_weights()
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test_data = model.predict(x_normal, batch_size=400)
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# 反归一化
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predict_data = mms2.inverse_transform(test_data)
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with tf.compat.v1.Session():
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spe = rmse(test_data, y_normal).eval()
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limit = 3 * spe
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mimusdata = predict_data - y_data
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mimusdatares = mimusdata.T
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pre_min, pre_max, pre_ave, pre_s = [], [], [], []
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for row in mimusdatares:
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pre_max.append(np.max(row))
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pre_min.append(np.min(row))
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pre_ave.append(np.mean(row))
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pre_s.append(np.std(row) * 3)
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result, mms1new, mms2new = {}, {}, {}
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mms1new["data_max_"] = mms1.data_max_.tolist()
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mms1new["data_min_"] = mms1.data_min_.tolist()
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mms1new["data_range_"] = mms1.data_range_.tolist()
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mms1new["min_"] = mms1.min_.tolist()
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mms1new["scale_"] = mms1.scale_.tolist()
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mms2new["data_max_"] = mms2.data_max_.tolist()
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mms2new["data_min_"] = mms2.data_min_.tolist()
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mms2new["data_range_"] = mms2.data_range_.tolist()
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mms2new["min_"] = mms2.min_.tolist()
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mms2new["scale_"] = mms2.scale_.tolist()
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result["filename"] = filepath
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result["mms1"] = mms1new
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result["mms2"] = mms2new
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result["train_max"] = np.array(train_max).tolist()
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result["train_min"] = np.array(train_min).tolist()
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result["train_ave"] = np.array(train_ave).tolist()
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result["pre_max"] = np.array(pre_max).tolist()
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result["pre_min"] = np.array(pre_min).tolist()
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result["pre_ave"] = np.array(pre_ave).tolist()
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result["pre_s"] = np.array(pre_s).tolist()
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result["pre_s_ave"] = np.mean(np.array(pre_s)).tolist()
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result["limit"] = limit
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result["weight"] = [model_weight[index].tolist() for index in range(len(model_weight))]
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return json.dumps(result)
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def Train(x_data, y_data, hidden_layers, valuetrs, epochsdata):
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# 计算原来每一列数据的最大值
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# x_data = np.array(x_data)
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# y_data = np.array(y_data)
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valuetrs = valuetrs
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train_max, train_min, train_ave = [], [], []
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for row in valuetrs.T:
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train_max.append(np.max(row))
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train_min.append(np.min(row))
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train_ave.append(np.mean(row))
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mms1 = MinMaxScaler()
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mms2 = MinMaxScaler()
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mms3 = MinMaxScaler()
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x_normal = mms1.fit_transform(x_data)
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y_normal = mms2.fit_transform(y_data)
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x_train = x_normal
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y_train = y_normal
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# 构建网络结构
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model = tf.keras.Sequential()
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model.add(layers.Dense(units=hidden_layers[0], input_dim=x_data.shape[1], activation="sigmoid"))
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for i in range(len(hidden_layers) - 1):
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model.add(layers.Dense(units=hidden_layers[i + 1], activation="sigmoid"))
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model.add(layers.Dense(units=y_data.shape[1]))
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model.summary()
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# 选择损失函数，优化方法
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model.compile(loss="mse", optimizer="adam", metrics=["accuracy"])
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# 训练模型
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history = model.fit(x_train, y_train, epochs=epochsdata, batch_size=80)
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path = os.getcwd() + "\\ModelOline";
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if not os.path.exists(path):
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os.makedirs(path)
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now = datetime.datetime.now()
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# filepath=path + "\\M"+"_"+str(now).replace(" ","-").replace(".","-").replace(":","-")+".h5"
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# model.save(filepath)
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filepath = model.to_json()
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model_weight = model.get_weights()
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test_data = model.predict(x_normal, batch_size=400)
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# 反归一化
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predict_data = mms2.inverse_transform(test_data)
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with tf.compat.v1.Session():
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spe = rmse(y_normal, test_data).eval()
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limit = 3 * spe
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mimusdata = predict_data - y_data
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mimusdatares = mimusdata.T
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pre_min, pre_max, pre_ave, pre_s = [], [], [], []
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for row in mimusdatares:
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pre_max.append(np.max(row))
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pre_min.append(np.min(row))
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pre_ave.append(np.mean(row))
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pre_s.append(np.std(row) * 3)
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result, mms1new, mms2new = {}, {}, {}
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mms1new["data_max_"] = mms1.data_max_.tolist()
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mms1new["data_min_"] = mms1.data_min_.tolist()
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mms1new["data_range_"] = mms1.data_range_.tolist()
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mms1new["min_"] = mms1.min_.tolist()
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mms1new["scale_"] = mms1.scale_.tolist()
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mms2new["data_max_"] = mms2.data_max_.tolist()
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mms2new["data_min_"] = mms2.data_min_.tolist()
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mms2new["data_range_"] = mms2.data_range_.tolist()
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mms2new["min_"] = mms2.min_.tolist()
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mms2new["scale_"] = mms2.scale_.tolist()
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result["filename"] = filepath
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result["mms1"] = mms1new
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result["mms2"] = mms2new
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result["train_max"] = np.array(train_max).tolist()
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result["train_min"] = np.array(train_min).tolist()
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result["train_ave"] = np.array(train_ave).tolist()
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result["pre_max"] = np.array(pre_max).tolist()
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result["pre_min"] = np.array(pre_min).tolist()
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result["pre_ave"] = np.array(pre_ave).tolist()
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result["pre_s"] = np.array(pre_s).tolist()
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result["pre_s_ave"] = np.mean(np.array(pre_s)).tolist()
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result["limit"] = limit
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result["weight"] = [model_weight[index].tolist() for index in range(len(model_weight))]
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return result
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def isnumber(limits):
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flag = True
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for item in limits:
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item = item.replace("-", "")
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if (item.isdigit() == False):
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flag = False
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break
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return flag
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def clearmain(info):
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try:
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points = info["point"].split(',')
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times = info["time"].split(';')
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epochs = info["iter"]
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layer = info["layer"]
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typedata = info["type"]
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condition = info["condition"].replace("=", "==").replace(">=", ">").replace("<=", "<")
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interval = 300000
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dead = info["dead"].split(',')
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limit = info["limit"].split(',')
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uplower = info["uplow"].split(';')
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count = 0
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ItemsInfo, SamplingTimePeriods = [], []
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Constraint = ""
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for i in range(len(points)):
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iteminfo = {}
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iteminfo["ItemName"] = points[i] # 加点
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if dead[i] == "1": # 判断是否参与死区清洗
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iteminfo["ClearDeadZone"] = "true"
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else:
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iteminfo["ClearDeadZone"] = "false"
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if limit[i] == "1": # 参与上下限清洗
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limits = uplower[i].split(',')
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if (isnumber(limits) == True): # 输入上下限正确
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count += 1
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Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[
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1] + " and "
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ItemsInfo.append(iteminfo)
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if count != 0:
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Constraint = Constraint[:len(Constraint) - 4:]
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else:
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Constraint = "1==1" # 没有上下限清洗
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Constraint += " and (" + condition + ")"
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Constraint = Constraint.replace("\n", " ")
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for i in range(len(times)):
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Eachsampletime = {}
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timess = times[i].split(',')
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Eachsampletime["StartingTime"] = timess[0]
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Eachsampletime["TerminalTime"] = timess[1]
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SamplingTimePeriods.append(Eachsampletime)
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url = f"http://{config._CLEAN_IP}/exawebapi/exatime/GetCleaningData?ItemsInfo=%s&SamplingTimePeriods=%s&Constraint=%s&SamplingPeriod=%s&DCount=6" % (
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ItemsInfo, SamplingTimePeriods, Constraint, interval)
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response = requests.get(url)
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content = json.loads(response.text)
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# data =np.array([item for item in content["ClearData"] if item ]).T
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valuetrs = np.array([item for item in content["ClearData"]]).T
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typeArr = list(enumerate(typedata.split(",")))
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data_x = [valuetrs[:, item[0]].tolist() for item in typeArr if item[1] == "0"]
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data_y = [valuetrs[:, item[0]].tolist() for item in typeArr if item[1] == "1"]
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data_x = np.array(data_x).T
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data_y = np.array(data_y).T
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result = Train(data_x, data_y, layer, valuetrs, eval(epochs))
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result["BeforeCleanSamNum"] = content["BeforeCleanSamNum"]
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result["AfterCleanSamNum"] = content["AfterCleanSamNum"]
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result["CleanOrNot"] = True
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return result
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except Exception as e:
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result = {"CleanOrNot": False}
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return result
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def main(info):
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points = info["point"]
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time1 = info["time"]
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interval = info["interval"]
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epochs = info["iter"]
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layer = info["layer"]
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datatype = info["type"]
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data_x, data_y, origndata = get_history_value(points, time1, interval, datatype)
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result = Train(data_x, data_y, layer, origndata, eval(epochs))
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return result
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if __name__ == "__main__":
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# info_str = '{"time":"2020-01-19 23:26:04,2020-01-25 20:42:16;2020-01-26 16:23:49,2020-02-03 11:36:42;2020-02-05 20:02:49,2020-02-06 05:51:40","condition":"1=1","interval":300000,"dead":"1,1,1","layer":["6"],"point":"JL_D1_10MILLA:SEP_TEMP.PNT,JL_D1_10FSSS20A:HFC10CT301.PNT,JL_D1_10FSSS20A:HFC10CT302.PNT","type":"1,0,0","limit":"0,0,0","uplow":",;,;,","iter":"100"}'
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# info_str = '{"iter":"800","dead":"1,1,1,1","point":"DH4_40HLF10CE101,DH4_40HLF10CT351,DH4_40HLF10CT352,DH4_40HLF10CY101","limit":"0,0,0,0","layer":["5"],"type":"0,0,0,1","time":"2020-10-06 19:17:46,2020-10-12 14:58:19","condition":"1=1","interval":300000,"uplow":"null,null;null,null;null,null;null,null"}'
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# info = json.loads(info_str)
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# result = clearmain(info)
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# print(result)
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info = {"iter":"800","Train_Data_X":[[7.0,7.0,7.0,7.0,7.0,7.0,7.0],[8.0,8.0,8.0,8.0,8.0,8.0,8.0],[1.0,1.0,1.0,1.0,1.0,1.0,1.0],[2.0,2.0,2.0,2.0,2.0,2.0,2.0],[3.0,3.0,3.0,3.0,3.0,3.0,3.0],[4.0,4.0,4.0,4.0,4.0,4.0,4.0],[5.0,5.0,5.0,5.0,5.0,5.0,5.0],[6.0,6.0,6.0,6.0,6.0,6.0,6.0],[7.0,7.0,7.0,7.0,7.0,7.0,7.0],[8.0,8.0,8.0,8.0,8.0,8.0,8.0],[1.0,1.0,1.0,1.0,1.0,1.0,1.0],[2.0,2.0,2.0,2.0,2.0,2.0,2.0]],"hide":["7","5","1"],"Train_Data_Y":[[7.0],[8.0],[1.0],[2.0],[3.0],[4.0],[5.0],[6.0],[7.0],[8.0],[1.0],[2.0]]}
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result = TrainOffline(np.array(info["Train_Data_X"]), np.array(info["Train_Data_Y"]),info["hide"], eval(info["iter"]))
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