df2['open2'] = df['open2']
print(df2.tail())
df3 = df2.tail(200)
df3.plot(rot=15)
#4
print('\n#4,计算预测结果')
df5 = pd.DataFrame()
df5['y_test'] = df2['xopen']
df5['y_pred'] = df2['open2']
a1, df5x = ztq.ai_acc_xed2x(df5['y_test'], df5['y_pred'], ky0=5)
print(df5x.tail())
print('\na1,', a1)
a1, df5x, a20 = ztq.ai_acc_xed2ext(df5['y_test'], df5['y_pred'], ky0=5)
print('\na20,', a20)
'''
n_df9,3671,n_dfk,3655
acc-kok: 99.56%, MAE:42.65, MSE:2463.18, RMSE:49.63
y_test y_pred ysub ysub2 y_test_div ysubk
date
2017-04-25 3132.918 3185.180 -52.262 52.262 3132.918 1.668
2017-04-26 3131.350 3191.560 -60.210 60.210 3131.350 1.923
2017-04-27 3144.022 3203.079 -59.057 59.057 3144.022 1.878
2017-04-28 3147.228 3205.589 -58.361 58.361 3147.228 1.854
2017-05-02 3138.307 3194.470 -56.163 56.163 3138.307 1.790
a1, 99.564
n_df9,3671,n_dfk,3659
mx.summary()
plot_model(mx, to_file='tmp/lstm010mx.png')
#4 模型训练
print('\n#4 模型训练 fit')
tbCallBack = keras.callbacks.TensorBoard(log_dir=rlog,
write_graph=True,
write_images=True)
tn0 = arrow.now()
mx.fit(x_train,
y_train,
epochs=2000,
batch_size=512 * 24,
callbacks=[tbCallBack])
tn = zt.timNSec('', tn0, True)
mx.save('tmp/lstm010mx.dat')
#5 利用模型进行预测 predict
print('\n#5 模型预测 predict')
tn0 = arrow.now()
y_pred = mx.predict(x_test)
tn = zt.timNSec('', tn0, True)
df_test['y_pred'] = zdat.ds4x(y_pred, df_test.index, True)
df_test.to_csv('tmp/df_lstm010mx.csv', index=False)
#6
print('\n#6 acc准确度分析')
print('\nky0=10')
df = df_test
dacc, dfx, a10 = ztq.ai_acc_xed2ext(df.y, df.y_pred, ky0=10, fgDebug=True)
print('\n#6 模型预测 predict')
y_pred = model.predict(x_test)
print(y_pred)
print('type(y_pred):', type(y_pred))
#7 整理预测数据
print('\n#7 整理预测数据 ')
print('\n acc.xed')
y2x = y_pred.flatten()[:]
print('\n y2x')
print(y2x)
print('type(y2x):', type(y2x))
ds2y = zdat.ds4x(y2x, df_test.index)
df_test['y_pred'] = ds2y
print('\ndf_test')
print(df_test.tail(9))
print('\n acc.xed')
dacc, df2, a10 = ztq.ai_acc_xed2ext(df_test.y,
df_test.y_pred,
ky0=5,
fgDebug=True)
#
#
#8 draw
print('\n#8 绘制图形')
v1 = [df_test.x, df_test.y, 300, 'blue', 0.2]
v2 = [df_test.x, df_test.y_pred, 50, 'red', 0.6]
zdr.dr_mul_scatter(vlst=[v1, v2])