def evaluate_list(model):
evaluate_list = []
#4. 평가, 예측
y_predict = model.predict(x_pred)
print(y_predict.shape)
print(y_pred.shape)
# r2_list
r2 = r2_score(y_pred, y_predict)
evaluate_list.append(r2)
print('r2 : ', r2)
# rmse_list
rmse = mse_(y_pred, y_predict, squared=False)
evaluate_list.append(rmse)
print('rmse : ', rmse)
# mae_list
mae = mae_(y_pred, y_predict)
evaluate_list.append(mae)
print('mae : ', mae)
# mse_list
mse = mse_(y_pred, y_predict, squared=True)
evaluate_list.append(mse)
print('mse : ', mse)
return evaluate_list
callbacks=[reduce_lr, cp])
# from tensorflow.keras.models import load_model
# model = load_model('./data/hdf5/1_0del.hdf5', custom_objects={'leaky_relu': tf.nn.leaky_relu})
# 4. 평가, 예측
loss, mae = model.evaluate(x_pred, y_pred, batch_size=1024)
y_predict = model.predict(x_pred)
from sklearn.metrics import mean_squared_error as mse_
from sklearn.metrics import mean_absolute_error as mae_
# r2_list
r2 = r2_score(y_pred, y_predict)
print('r2 : ', r2)
# rmse_list
rmse = mse_(y_pred, y_predict, squared=False)
print('rmse : ', rmse)
# mae_list
mae = mae_(y_pred, y_predict)
print('mae : ', mae)
# mse_list
mse = mse_(y_pred, y_predict, squared=True)
print('mse : ', mse)
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 4))
chart = fig.add_subplot(1, 1, 1)
chart.plot(y_pred, marker='o', color='blue', label='실제값')
chart.plot(y_predict, marker='^', color='red', label='예측값')
plt.legend(loc='best')
#coding:utf-8 import numpy as np import os import pandas as pd from sklearn.metrics import mean_squared_error as mse_ #path source file1 = "E:\\houseprice\\33803.csv" file2 = "E:\\houseprice\\19502.csv" save = "E:\\houseprice\\result.csv" #read data data1 = pd.read_csv(file1, header=None) data2 = pd.read_csv(file2, header=None) #distance dist1 = 33803 dist2 = 19501 dist = np.sqrt(mse_(data1, data2)) #余弦定理 cos1 = (dist1 * dist1 + dist * dist - dist2 * dist2) / (2 * dist1 * dist) cos2 = (dist2 * dist2 + dist * dist - dist1 * dist1) / (2 * dist2 * dist) w1 = dist1 * cos1 / dist w2 = dist2 * cos2 / dist result = np.array(data1) * w2 + np.array(data2) * w1 result = pd.DataFrame(result) result.to_csv(save, index=None, header=None)
def RMSE(y_test, y_predict):
return np.sqrt(mse_(y_test, y_predict))
#coding:utf-8
import numpy as np
import pandas as pd
from scipy import interpolate as inp
from sklearn.metrics import mean_absolute_error as mae_
from sklearn.metrics import mean_squared_error as mse_
#read data
file = "rst\\rst_cnn+mlp_300.csv"
data = pd.read_csv(file, header=None)
data = np.array(data)
data1 = data
for i in range(6):
d = data[i * 96 * 21:(i + 1) * 96 * 21, :]
label = d[:, 0]
pred = d[:, 1]
x = range(96 * 21)
m = inp.UnivariateSpline(x, pred, s=len(pred) / 4)
y_spline = m(x)
data1[i * 96 * 21:(i + 1) * 96 * 21, 1] = y_spline
print(i + 1)
print(mse_(label, pred))
print(mse_(label, y_spline))
print(mae_(data1[:, 0], data1[:, 1]))
data1 = pd.DataFrame(data1)
data1.to_csv("rst\\rst_cnn+mlp_adjustedhist_spline.csv",
header=None,
index=None)
