def Polynomial_Model(data):
"""Model for Polynomial Regression Model"""
X = data[['AXp-0d', 'AXp-1d', 'AXp-2d', 'ETA_eta_L',
'ETA_epsilon_3']].values
Y = data[['e_gap_alpha']].values
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
test_size=.25,
random_state=1234)
# Model
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train) # transfer to normal distribution
X_test = sc_X.transform(X_test)
L_R = LinearRegression()
Poly_Reg = PolynomialFeatures(degree=2)
X_poly = Poly_Reg.fit_transform(X_train)
Poly_Reg.fit(X_poly, Y_train)
L_R.fit(X_poly, Y_train)
Ytrain_Pred = L_R.predict(Poly_Reg.fit_transform(X_train))
Ytest_Pred = L_R.predict(Poly_Reg.fit_transform(X_test))
PLMscore = r2_score(Y_test, Ytest_Pred)
# Figure
figure = plt.figure()
plt.scatter(Y_train, Ytrain_Pred, color='blue')
plt.scatter(Y_test, Ytest_Pred, color='red')
plt.plot([0, 4], [0, 4], lw=4, color='black')
plt.title('$Polynomial \ Regression$')
plt.xlabel('$<Eg> \ Actual \ [eV]$')
plt.ylabel('$<Eg> \ Predict \ [eV]$')
print('The score of this regression in this case is: ', PLMscore)
return PLMscore
def Random_Forest_Reg(data):
# import data
X = data[['AXp-0d', 'AXp-1d', 'AXp-2d', 'ETA_eta_L',
'ETA_epsilon_3']].values
y = data[['e_gap_alpha']].values
# split test and train dataset
X_train, X_test, y_train, y_test = train_test_split(X,
y.ravel(),
test_size=.25,
random_state=1234)
regressor = RandomForestRegressor(n_estimators=300,
random_state=123,
min_samples_split=15)
regressor.fit(X_train, y_train.ravel())
# Predicting a new result with the Random Forest Regression
Ytrain_Pred = regressor.predict(X_train)
Ytest_Pred = regressor.predict(X_test)
RFR_score = r2_score(y_test, Ytest_Pred)
# Visualising the Random Forest Regression results
# in higher resolution and smoother curve
plt.scatter(y_train, Ytrain_Pred, color='blue')
plt.scatter(y_test, Ytest_Pred, color='red')
plt.plot([0, 4], [0, 4], lw=4, color='black')
plt.title('$Random \ Forest \ Regression$')
plt.xlabel('$<Eg> \ Actual \ [eV]$')
plt.ylabel('$<Eg> \ Predict \ [eV]$')
return RFR_score
def Random_Forest_Model(data): # Pending
"""Model for Random Forest Model"""
# Choose the Predictors
X = data[['AXp-0d', 'AXp-1d', 'AXp-2d',
'ETA_eta_L', 'ETA_epsilon_3']].values
Y = data[['e_gap_alpha']].values
X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=.25, random_state=1234)
# Model
regressor = RandomForestRegressor(n_estimators=300, random_state=0,
min_samples_split=15)
regressor.fit(X_train, Y_train)
Ytrain_Pred = regressor.predict(X_train)
Ytest_Pred = regressor.predict(X_test)
RFMscore = r2_score(Y_test, Ytest_Pred)
# Figure
figure = plt.figure()
plt.scatter(Y_train, Ytrain_Pred, color='blue')
plt.scatter(Y_test, Ytest_Pred, color='red')
plt.plot([0, 4], [0, 4], lw=4, color='black')
plt.title('$Random \ Forest \ Regression$')
plt.xlabel('$<Eg> \ Actual \ [eV]$')
plt.ylabel('$<Eg> \ Predict \ [eV]$')
# Score of the Model
print('The score of this regression in this case is: ',
RFMscore)
return RFMscore
def Multiple_Linear_Model(data):
"""Model for Multiple_Linear_Regression, Return a Figure"""
# Choose the Predictors
X = data[['AXp-0d', 'AXp-1d', 'AXp-2d',
'ETA_eta_L', 'ETA_epsilon_3']].values
Y = data[['e_gap_alpha']].values
X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=.25, random_state=1234)
# Model
MLR = linear_model.LinearRegression()
MLR.fit(X_train, Y_train)
Test_Predictions = MLR.predict(X_test)
Train_Predictions = MLR.predict(X_train)
MLRscore = r2_score(Y_test, Test_Predictions)
# Plot the figure
figure = plt.figure()
plt.scatter(Y_train, Train_Predictions, color='blue')
plt.scatter(Y_test, Test_Predictions, color='r')
plt.plot([0, 4], [0, 4], lw=4, color='black')
plt.title('$Multiple \ Linear \ Regression$')
plt.xlabel('$<Eg> \ Actual \ [eV]$')
plt.ylabel('$<Eg> \ Predict \ [eV]$')
plt.show()
print('The score of this regression in this case is: ', MLRscore)
return MLRscore
def neural_network_model(data):
"""Neural net work model"""
X = data[['AXp-0d', 'AXp-1d', 'AXp-2d', 'ETA_eta_L',
'ETA_epsilon_3']].values
Y = data[['e_gap_alpha']].values
X_train_pn, X_test_pn, y_train, y_test = train_test_split(X, Y,
test_size=0.25,
random_state=1234
)
# create the scaler from the training data only and keep it for later use
X_train_scaler = StandardScaler().fit(X_train_pn)
# apply the scaler transform to the training data
X_train = X_train_scaler.transform(X_train_pn)
X_test = X_train_scaler.transform(X_test_pn)
def neural_model():
# assemble the structure
model = Sequential()
model.add(Dense(5, input_dim=5, kernel_initializer='normal',
activation='relu',
kernel_regularizer=regularizers.l2(0.01)))
model.add(Dense(8, kernel_initializer='normal', activation='relu',
kernel_regularizer=regularizers.l2(0.01)))
model.add(Dense(20, kernel_initializer='normal', activation='relu',
kernel_regularizer=regularizers.l2(0.01)))
# model.add(Dense(4, kernel_initializer='normal',activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
# compile the model
model.compile(loss='mean_squared_error', optimizer='adam')
return model
# initialize the andom seed as this is used to generate
# the starting weights
np.random.seed(1234)
# create the NN framework
estimator = KerasRegressor(build_fn=neural_model,
epochs=1200, batch_size=25000, verbose=0)
history = estimator.fit(X_train, y_train, validation_split=0.33,
epochs=1200, batch_size=10000, verbose=0)
print("final MSE for train is %.2f and for validation is %.2f" %
(history.history['loss'][-1], history.history['val_loss'][-1]))
plt.figure(figsize=(5, 5))
prediction = estimator.predict(X_test)
plt.scatter(y_train, estimator.predict(X_train), color='blue')
plt.scatter(y_test, prediction, color='red')
plt.plot([0, 4], [0, 4], lw=4, color='black')
plt.title('$Neural \ Network \ Model$')
plt.xlabel('$<Eg> \ Actual \ [eV]$')
plt.ylabel('$<Eg> \ Predict \ [eV]$')
return r2_score(y_test, prediction)
def test_r2_score():
d = evaluation.r2_score(df1,df2)
assert d == 0.625, "Error: The function of calculating R2 score is wrong!"
return