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1. 列表内容

  Test模型开发

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom sklearn.ensemble import RandomForestRegressorfrom sklearn.model_selection import train_test_splitfrom sklearn.multioutput import MultiOutputRegressorplt.ion()#prepare DatainputLabel=["WindSpeed","GenPower","GenSpeed","OutDoorTemp","NacelleTemp"]outputLabel=["GenBearDETemp","GenBearNDETemp","GenStatorUTemp","GenStatorVTemp", "GenStatorWTemp"]dataPath="E:/DevRawModelProject/RawModelData/test/goodt20014001f.csv"data_testPath="E:/DevRawModelProject/RawModelData/test/t20014001_test.csv"df=pd.read_csv(dataPath)df_test=pd.read_csv(data_testPath)X_train, y_train=(df[inputLabel].values,df[outputLabel].values)X_test, y_test=(df_test[inputLabel].values,df_test[outputLabel].values)

#cal mainmax_depth =8max_depth=max_depth#random_state=2regr_rf = RandomForestRegressor(max_depth=max_depth)regr_rf.fit(X_train, y_train)

RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,           max_features='auto', max_leaf_nodes=None,           min_impurity_split=1e-07, min_samples_leaf=1,           min_samples_split=2, min_weight_fraction_leaf=0.0,           n_estimators=10, n_jobs=1, oob_score=False, random_state=None,           verbose=0, warm_start=False)

#test modely_rf = regr_rf.predict(X_train)y_test_rf = regr_rf.predict(X_test)varR=np.var(y_train[:,2]-y_rf[:,2])varT=np.var(y_train[:,2])r2=1-varR/varTprint(r2)

0.820108692169

plt.plot(y_test[:,0])plt.plot(y_test_rf[:,0],'r')

[<matplotlib.lines.Line2D at 0x1018f9b0>]

print(y_rf.shape)#plot data#plt.scatter(X_train,y_train,c="red", marker="+",alpha=0.5,label="Data")plt.scatter(y_train[:,0],y_rf[:,0],c="cornflowerblue", alpha=0.5,label="predict")plt.scatter(y_train[:,1],y_rf[:,1],marker="+",c="red", alpha=0.3,label="predict")#plt.show()

(27627, 5)<matplotlib.collections.PathCollection at 0xeffacf8>

from mpl_toolkits.mplot3d import Axes3D  fig = plt.figure()  ax = fig.add_subplot(111, projection='3d')  X = X_train[:,0]Y = X_train[:,1]Z = y_train[:,2]ax.scatter(X, Y, Z) #ax = fig.add_subplot(111, projection='3d')  X = X_train[:,0]Y = X_train[:,1]Z =y_rf[:,2]ax.scatter(X, Y, Z,c="red", marker="+",alpha=0.5,label="Data")plt.show() 

plt.scatter(y_train[:,0],y_train[:,1],c="cornflowerblue",marker="+",alpha=0.5,label="predict")plt.scatter(y_rf[:,0],y_rf[:,1],marker="+",c="red", alpha=0.3,label="predict")

<matplotlib.collections.PathCollection at 0xd7a1a58>

df.columns

Index(['Unnamed: 0', 'TablePart', 'TurbineID', 'real_time', 'WindSpeed',       'GenPower', 'GenSpeed', 'OutDoorTemp', 'NacelleTemp', 'GenBearDETemp',       'GenBearNDETemp', 'GenStatorUTemp', 'GenStatorVTemp', 'GenStatorWTemp',       'con1'],      dtype='object')

plt.plot(y_train[:,0])plt.plot(y_rf[:,0])

[<matplotlib.lines.Line2D at 0xb97ee80>]

plt.plot(y_train[:,1])plt.plot(y_rf[:,1])

[<matplotlib.lines.Line2D at 0xf07def0>]

plt.plot(y_train[:,1]-y_rf[:,1])

[<matplotlib.lines.Line2D at 0xf128dd8>]

plt.plot(y_train[:,0]-y_rf[:,0])

[<matplotlib.lines.Line2D at 0xf18dd30>]

plt.plot(y_train[:,2]-y_rf[:,2])

[<matplotlib.lines.Line2D at 0xf1db080>]

plt.plot(X_train[:,2],y_train[:,2]-y_rf[:,2],'*')#plt.title("T")plt.show()

import numpy as npvarR=np.var(y_train[:,2]-y_rf[:,2])varT=np.var(y_train[:,2])r2=1-varR/varTprint(r2)

0.962437941174

y_test_rf = regr_rf.predict(X_test)

plt.plot(X_test[:,2],y_test[:,2]-y_test_rf[:,2],'*')#plt.title("T")plt.show()

plt.plot(y_test[:,0],y_test_rf[:,0],'*')#plt.title("T")plt.show()

import numpy as npvarR=np.var(y_test[:,2]-y_test_rf[:,2])varT=np.var(y_test[:,2])r2=1-varR/varTprint(r2)

0.703730968599

df.WindSpeed.min()

1.02

plt.plot(df['WindSpeed'],df['GenPower'],"+")plt.plot(df_test['WindSpeed'],df_test['GenPower'],"o")

[<matplotlib.lines.Line2D at 0xf4e4470>]

plt.plot(y_test[:,0]-y_test_rf[:,0])plt.plot(y_test[:,1]-y_test_rf[:,1])

[<matplotlib.lines.Line2D at 0xfd5e208>]

plt.plot(y_test[:,0])plt.plot(y_test_rf[:,0],'r')

[<matplotlib.lines.Line2D at 0xfd2a6d8>]

plt.plot(y_train[:,2]-y_rf[:,2],y_train[:,3]-y_rf[:,3],'*')#plt.title("T")plt.show()