y,
random_state=10,
test_size=0.3)
w = range(1, len(y_test) + 1)
reg = RandomForestRegressor(n_estimators=30,
criterion='mae',
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features='auto',
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
bootstrap=True,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False)
reg.fit(x_train, y_train)
y_predict = reg.predict(x_test)
fnc.errors(y_test, y_predict)
# plt.plot(w,y_predict,color='red')
# plt.plot(w,y_test,color='blue')
# plt.show()
data.to_csv("withoutOutliers.csv")
fnc.plot_act_pred(y_test, y_predict)
precompute=False,
copy_X=True,
max_iter=1000,
tol=0.0001,
warm_start=False,
positive=False,
random_state=None,
selection='cyclic')
reg.fit(x_train, y_train)
y_predict = reg.predict(x_test)
err.append(mean_squared_error(y_test, y_predict))
err1.append(mean_absolute_error(y_test, y_predict))
err2.append(r2_score(y_test, y_predict))
err3.append(median_absolute_error(y_test, y_predict))
err4.append(explained_variance_score(y_test, y_predict))
errors(y_test, y_predict)
print("*******************************************************")
def normalization(data):
# data = np.array(data)
# data = ((data - np.mean(data)) /
# np.std(data))
# data = pd.DataFrame(data)
return data
err = normalization(err)
err1 = normalization(err1)
err2 = normalization(err2)
err3 = normalization(err3)
model.add(
Dense(12, input_dim=12, kernel_initializer='normal',
activation='relu'))
model.add(
Dense(12, input_dim=12, kernel_initializer='normal',
activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
# compile model
model.compile(loss='mean_squared_error', optimizer='adam')
return model
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
x_train, x_test, y_train, y_test = train_test_split(X, Y)
# evaluate model with standardized dataset
estimator = KerasRegressor(build_fn=baseline_model,
nb_epoch=1,
batch_size=5,
verbose=0)
kfold = KFold(n_splits=30, random_state=seed)
results = cross_val_score(estimator, X, Y, cv=kfold)
print("Results: %.2f (%.2f) MSE" % (results.mean(), results.std()))
estimator.fit(x_train, y_train)
y_pred = estimator.predict(x_test)
# print(y_pred.shape())
fnc.errors(y_test, y_pred)
