我們開發了一個人工神經網路 (ANN)，我們將資料拆分為訓練資料和測驗資料train_test_split。由于我們想要對我們的性能分數進行更好和更普遍的估計，因此我們希望用 k 折來拆分資料。
現在，我們將資料分成 70% 的訓練資料和 30% 的測驗資料 train_test_split
def splitData(dataframe):
X = dataframe[Predictors].values
y = dataframe[TargetVariable].values
### Sandardization of data ###
PredictorScaler = StandardScaler()
TargetVarScaler = StandardScaler()
# Storing the fit object for later reference
PredictorScalerFit = PredictorScaler.fit(X)
TargetVarScalerFit = TargetVarScaler.fit(y)
# Generating the standardized values of X and y
X = PredictorScalerFit.transform(X)
y = TargetVarScalerFit.transform(y)
# Split the data into training and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
return X_train, X_test, y_train, y_test, PredictorScalerFit, TargetVarScalerFit
我們如何用 k-fold 拆分我們的資料？
并且，我們就在我們的假設是，性能得分將導致更好，更廣義的估計，如果用K-fold交叉驗證，而不是train_test_split？
我們的主要
if __name__ == '__main__':
print('--------------')
# ...
dataframe = pd.read_csv("/.../file.csv")
# Splitting data into training and tesing data
X_train, X_test, y_train, y_test, PredictorScalerFit, TargetVarScalerFit = splitData(dataframe=dataframe)
# Making the Regression Artificial Neural Network (ANN)
ann = ANN(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test, PredictorScalerFit=PredictorScalerFit, TargetVarScalerFit=TargetVarScalerFit)
# Evaluation of the performance of the Aritifical Neural Network (ANN)
performanceEvaluation(y_test_orig=ann['temp'], y_test_pred=ann['Predicted_temp'])
我們的 ANN 預測功能
def ANN(X_train, y_train, X_test, y_test, TargetVarScalerFit, PredictorScalerFit):
# Making the regression Artificial Neural Network (ANN) Model
model = make_regression_ann()
# Fitting the ANN to the Training set
model.fit(X_train, y_train, batch_size=5, epochs=50, verbose=1)
# Generating Predictions on testing data
Predictions = model.predict(X_test)
# Scaling the predicted temp data back to original price scale
Predictions = TargetVarScalerFit.inverse_transform(Predictions)
# Scaling the y_test temp data back to original temp scale
y_test_orig = TargetVarScalerFit.inverse_transform(y_test)
# Scaling the test data back to original scale
Test_Data = PredictorScalerFit.inverse_transform(X_test)
TestingData = pd.DataFrame(data=Test_Data, columns=Predictors)
TestingData['temp'] = y_test_orig
TestingData['Predicted_temp'] = Predictions
TestingData.head()
TestingData.to_csv("TestingData.csv")
return TestingData
制作回歸模型
def make_regression_ann(initializer='normal', activation='relu', optimizer='adam', loss='mse'):
# create ANN model
model = Sequential()
# Defining the Input layer and FIRST hidden layer, both are same!
model.add(Dense(units=8, input_dim=7, kernel_initializer=initializer, activation=activation))
# Defining the Second layer of the model
# after the first layer we don't have to specify input_dim as keras configure it automatically
model.add(Dense(units=6, kernel_initializer=initializer, activation=activation))
# The output neuron is a single fully connected node
# Since we will be predicting a single number
model.add(Dense(1, kernel_initializer=initializer))
# Compiling the model
model.compile(loss=loss, optimizer=optimizer)
return model
我們生成性能分數的函式
def performanceEvaluation(y_test_orig, y_test_pred):
# Computing the Mean Absolute Percent Error
MAPE = mean_absolute_percentage_error(y_test_orig, y_test_pred)
# Computing R2 Score
r2 = r2_score(y_test_orig, y_test_pred)
# Computing Mean Square Error (MSE)
MSE = mean_squared_error(y_test_orig, y_test_pred)
# Computing Root Mean Square Error (RMSE)
RMSE = mean_squared_error(y_test_orig, y_test_pred, squared=False)
# Computing Mean Absolute Error (MAE)
MAE = mean_absolute_error(y_test_orig, y_test_pred)
# Computing Mean Bias Error (MBE)
MBE = np.mean(y_test_pred - y_test_orig) # here we calculate MBE
print('--------------')
print('The Coefficient of Determination (R2) of ANN model is:', r2)
print("The Root Mean Squared Error (RMSE) of ANN model is:", RMSE)
print("The Mean Squared Error (MSE) of ANN model is:", MSE)
print('The Mean Absolute Percent Error (MAPE) of ANN model is:', MAPE)
print("The Mean Absolute Error (MAE) of ANN model is:", MAE)
print("The Mean Bias Error (MBE) of ANN model is:", MBE)
print('--------------')
eval_list = [r2, RMSE, MSE, MAPE, MAE, MBE]
columns = ['Coefficient of Determination (R2)', 'Root Mean Square Error (RMSE)', 'Mean Squared Error (MSE)',
'Mean Absolute Percent Error (MAPE)', 'Mean Absolute Error (MAE)', 'Mean Bias Error (MBE)']
dataframe = pd.DataFrame([eval_list], columns=columns)
return dataframe
我們的成績
Coefficient of Determination (R2) Root Mean Square Error (RMSE) Mean Squared Error (MSE) Mean Absolute Percent Error (MAPE) Mean Absolute Error (MAE) Mean Bias Error (MBE)
0.982052940563799 0.7293977725380798 0.5320211105835124 0.0894734801108027 0.5711224962560332 0.049541171482965995
我們試圖達到的目標
將整個資料集拆分為 X (Predictors) , y (TargetVariable)
def splitData(dataframe):
X = dataframe[Predictors].values
y = dataframe[TargetVariable].values
### Sandardization of data ###
PredictorScaler = StandardScaler()
TargetVarScaler = StandardScaler()
# Storing the fit object for later reference
PredictorScalerFit = PredictorScaler.fit(X)
TargetVarScalerFit = TargetVarScaler.fit(y)
# Generating the standardized values of X and y
X = PredictorScalerFit.transform(X)
y = TargetVarScalerFit.transform(y)
return X, y
利用人工神經網路
def ANN_test(X,y):
# Fitting the ANN to the Training set
model = KerasRegressor(build_fn=make_regression_ann(), epochs=50, batch_size=5)
cv = KFold(n_splits=10, random_state=1, shuffle=True)
test = cross_val_score(model, X=X, y=y, cv=cv, scoring="neg_mean_squared_error", n_jobs=1)
print(test)
mean = test.mean()
print(mean)
使用此函式接收錯誤：
2021-12-24 16:16:47.909705: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
/Users/yusuf/PycharmProjects/MasterThesis-ArtificialNeuralNetwork/main.py:168: DeprecationWarning: KerasRegressor is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead.
model = KerasRegressor(build_fn=make_regression_ann(), epochs=50, batch_size=5)
2021-12-24 16:16:48.193312: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
[nan nan nan nan nan nan nan nan nan nan]
nan
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning:
10 fits failed out of a total of 10.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.
Below are more details about the failures:
--------------------------------------------------------------------------------
10 fits failed with the following error:
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/keras/wrappers/scikit_learn.py", line 152, in fit
self.model = self.build_fn(
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/keras/engine/base_layer.py", line 3076, in _split_out_first_arg
raise ValueError(
ValueError: The first argument to `Layer.call` must always be passed.
warnings.warn(some_fits_failed_message, FitFailedWarning)
We are operating on:
Mac Monterey, Version: 12.0.1
Tensorflow Version: 2.7.0
Keras Version: 2.7.0
Libraries
import os
import time
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pathlib import Path
from sklearn.metrics import accuracy_score, make_scorer, mean_absolute_percentage_error, mean_absolute_error, mean_squared_error, r2_score
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, GridSearchCV, KFold, cross_val_score
from keras.wrappers.scikit_learn import KerasRegressor
from keras.models import Sequential
from keras.layers import Dense
uj5u.com熱心網友回復：
您需要使用KerasRegressor將您的 keras 模型包裝為 scikit 學習模型。
在這里查看示例 1
from keras.wrappers.scikit_learn import KerasRegressor
model = KerasRegressor(build_fn=make_regression_ann, epochs=512, batch_size=3)
kfold = KFold(n_splits=10, random_state=seed)
scores = cross_val_score(model, x, y, cv=kfold)
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