dalian-university/丁浩测试/JiaYueTest.py

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
from metrics import RMSE
import lightgbm as lgb

# root_path = './dataset/data/'
root_path = './dataset/shandong/Dataset_training/'
# 设置图大小
width = 16
height = 9
plt.figure(figsize=(width, height))
# 设置全局字体样式
plt.rcParams['font.family'] = 'Microsoft YaHei'
# max_power = 22
# 定义sigmoid函数
def sigmoid(x, k, x0,max_power,min_power):
    y = max_power / (1 + np.exp(-k * (x - x0)))+min_power
    return y


def contact_all():
    df = None
    csvpath = (r"./dataset/shandong/Dataset_training/concat_all.csv")
    for id in range(6):
        df_NWP = pd.read_csv(r"./dataset/shandong/Dataset_training/NWP/NWP_{}.csv".format(id))
        df_PowerRE = pd.read_csv(r"./dataset/shandong/Dataset_training/power/power_{}.csv".format(id))
        df_raw = pd.merge(df_NWP, df_PowerRE, on="C_TIME")
        all_features = ['C_TIME', 'C_WS90', 'C_WS100',  'C_REAL_VALUE']
        df_raw = df_raw[all_features]
        print(len(df_raw))
        if df is None:
            df = df_raw
        else:
            df = pd.concat([df, df_raw], axis=0)
    print(len(df))
    df.to_csv(csvpath, index=False)


def show_cz_plot():
    df = pd.read_csv(root_path + 'concat102.csv')
    # 创建一些样本数据
    x1 = df['C_WS100'].values
    dif = df['C_WS'].values - x1
    x1 = np.arange(1, len(df)+1)
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(64, 14))
    # 绘制散点图
    ax1.plot(x1, dif, color='red', label='turbine-102')
    # 添加标题和标签
    ax1.set_title('折线图')
    ax1.set_xlabel('时间点')
    ax1.set_ylabel('NWP与机头风速差值')
    ax1.legend()

    # plt.scatter(dif, y1, color='blue', label='数据集2')
    # plt.scatter(x3, y3, color='green', label='数据集3')
    # 绘制散点图

    df = pd.read_csv(root_path + 'concat103.csv')
    # 创建一些样本数据
    x1 = df['C_WS100'].values
    y1 = df['C_ACTIVE_POWER'].values
    dif = df['C_WS'].values - x1
    x1 = np.arange(1, len(df) + 1)
    ax2.plot(x1, dif, color='blue', label='turbine-103')
    # 添加标题和标签
    ax2.set_title('折线图')
    ax2.set_xlabel('时间点')
    ax2.set_ylabel('NWP与机头风速差值')
    ax2.legend()
    plt.show()


def show_cz_scatter():
    df = pd.read_csv(root_path + 'concat102.csv')
    # 创建一些样本数据
    x1 = df['C_WS100'].values
    dif = df['C_WS'].values - x1
    x1 = np.arange(1, len(df)+1)
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(64, 14))
    # 绘制散点图
    ax1.scatter(x1, dif, color='red', label='turbine-102')
    # 添加标题和标签
    ax1.set_title('散点图')
    ax1.set_xlabel('时间点')
    ax1.set_ylabel('NWP与机头风速差值')
    ax1.legend()

    # plt.scatter(dif, y1, color='blue', label='数据集2')
    # plt.scatter(x3, y3, color='green', label='数据集3')
    # 绘制散点图

    df = pd.read_csv(root_path + 'concat103.csv')
    # 创建一些样本数据
    x1 = df['C_WS100'].values
    y1 = df['C_ACTIVE_POWER'].values
    dif = df['C_WS'].values - x1
    x1 = np.arange(1, len(df) + 1)
    ax2.scatter(x1, dif, color='blue', label='turbine-103')
    # 添加标题和标签
    ax2.set_title('散点图')
    ax2.set_xlabel('时间点')
    ax2.set_ylabel('NWP与机头风速差值')
    ax2.legend()
    plt.show()



def show_WSandPower_nihe():
    df = pd.read_csv(root_path + 'concat_all.csv')
    # 读样本数据
    x1 = df['C_WS100'].values
    y1 = df['C_REAL_VALUE'].values

    # 拟合数据
    # p0 = [1, np.median(x1),max(y1),min(y1)]
    p0 = [1, 7.3,max(y1),min(y1)]
    popt, pcov = curve_fit(sigmoid, x1, y1,p0=p0)
    y_fit = sigmoid(x1, *p0)
    trusts = []
    for i in range(len(y1)):
        if y1[i] > y_fit[i]-2 and y1[i]<y_fit[i]+2:
            trusts.append(i)
    # 绘制散点图
    plt.scatter(x1, y1, color='blue',  label='data')
    plt.scatter(x1, y_fit, color='red', label='fit')
    plt.scatter(x1[trusts],y1[trusts],color='green', label='trust')

    print("k = %f, x0 = %f" % (popt[0], popt[1]))
    plt.title('散点图')
    plt.xlabel('NWP100米风速值')
    plt.ylabel('功率值')
    # 添加图例
    plt.legend()
    # 显示图形
    plt.show()


def data_clean(df):
    # 读样本数据
    x1 = df['C_WS100'].values
    y1 = df['C_REAL_VALUE'].values
    # 拟合数据
    # p0 = [1, np.median(x1),max(y1),min(y1)]
    p0 = [1, 7.3,max(y1),min(y1)]
    # p0 = [1, (max(x1)+min(x1))/2, max(y1), min(y1)]
    popt, pcov = curve_fit(sigmoid, x1, y1,p0=p0)
    y_fit = sigmoid(x1, *p0)
    trusts = []
    for i in range(len(y1)):
        if y1[i] > y_fit[i]-2 and y1[i]<y_fit[i]+2:
            trusts.append(i)
    df = df.loc[trusts]
    df.reset_index(drop=True, inplace=True)
    return df


# 23/04/26 风电实验基准
def train_lgbmodel_feng(clean=False):
    print('------------generate features-----------------')
    index = 'shandong'
    df = pd.read_csv('./dataset/'+index+'/Dataset_training/concat_all.csv')
    all_features = ['C_WS100']
    border = int(len(df) * 0.8)
    valid_border = border+int(len(df) * 0.1)
    start =0 #int(len(df) * 0.5)
    df_train = df.loc[:border]
    df_valid = df.loc[border:valid_border]
    df_test = df.loc[valid_border:]
    if clean:
        df_test.reset_index(drop=True, inplace=True)
        df_train = data_clean(df_train)
    X_train = df_train[all_features]
    Y_train = df_train[['C_REAL_VALUE']]
    X_valid = df_valid[all_features]
    Y_valid = df_valid[['C_REAL_VALUE']]
    X_test = df_test[all_features]
    Y_test = df_test[['C_REAL_VALUE']]
    print("train shape{}{} test shape{}{}".format(X_train.shape, Y_train.shape, X_test.shape, Y_test.shape))
    print('------------training-----------------')
    model = lgb.LGBMRegressor(objective='regression', n_estimators=1000,
                              learning_rate=0.025, n_jobs=-1, random_state=630)
    # Train the model
    # model.fit(X_train, Y_train, eval_metric='rmse',
    #           eval_set=[(X_train, Y_train), (X_temp,Y_temp)],
    #           eval_names=['train', 'val'],
    #           early_stopping_rounds=20, verbose=0)
    model.fit(X_train, Y_train, eval_metric='rmse',
              eval_set=[(X_valid, Y_valid)],
              eval_names=['valid'],
              early_stopping_rounds=20, verbose=0)
    best_iteration = model.best_iteration_
    print(best_iteration)
    # --------------feature importance----------------
    '''
    feature_importance = pd.DataFrame()
    feature_importance['fea_name'] = all_features
    feature_importance['fea_imp'] = model.feature_importances_
    feature_importance = feature_importance.sort_values('fea_imp', ascending=False)
    print(feature_importance)
    '''
    # --------------feature importance----------------
    Y_pred = model.predict(X_test, num_iteration=best_iteration)
    Y_pred = np.maximum(Y_pred, 0)
    Y_pred = Y_pred.reshape([-1])
    Y_true = Y_test.values.reshape([-1])
    for li in range(len(Y_pred)):
        if Y_pred[li]<0.016:
            Y_pred[li] = 0
    print('------------testing-----------------')
    print("test shape{}{}".format( Y_pred.shape, Y_true.shape))
    mean = RMSE(Y_pred, Y_true)
    print("模型RMSE:{}".format(mean))
    print("模型准确率:{}%".format(100-mean/22.5*100))
    
if __name__=="__main__":
    print("main:")
    # contact_all()
    train_lgbmodel_feng(True)
    # show_WSandPower_nihe()