def _larscv(*,
train,
test,
x_predict=None,
metrics,
fit_intercept=True,
verbose=False,
max_iter=500,
normalize=True,
precompute='auto',
cv=None,
max_n_alphas=1000,
n_jobs=None,
eps=2.220446049250313e-16,
copy_X=True):
"""For more info visit :
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LarsCV.html#sklearn.linear_model.LarsCV
"""
model = LarsCV(fit_intercept=fit_intercept,
verbose=verbose,
max_iter=max_iter,
normalize=normalize,
precompute=precompute,
cv=cv,
max_n_alphas=max_n_alphas,
n_jobs=n_jobs,
eps=eps,
copy_X=copy_X)
model.fit(train[0], train[1])
model_name = 'LarsCV'
y_hat = model.predict(test[0])
if metrics == 'mse':
accuracy = _mse(test[1], y_hat)
if metrics == 'rmse':
accuracy = _rmse(test[1], y_hat)
if metrics == 'mae':
accuracy = _mae(test[1], y_hat)
if x_predict is None:
return (model_name, accuracy, None)
y_predict = model.predict(x_predict)
return (model_name, accuracy, y_predict)
def Lars_regression(self, X_train, y_train, X_test, y_test):
my_cv = RepeatedKFold(n_splits=10, n_repeats=10, random_state=42)
best_model = LarsCV(cv=my_cv, n_jobs=-1)
best_model.fit(X_train, y_train)
y_pred = best_model.predict(X_test)
mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
return best_model, mse, mae, r2
class _LarsCVImpl:
def __init__(self, **hyperparams):
self._hyperparams = hyperparams
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
if y is not None:
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
class LarsCvClass:
"""
Name : LarsCV
Attribute : None
Method : predict, predict_by_cv, save_model
"""
def __init__(self):
# 알고리즘 이름
self._name = 'larscv'
# 기본 경로
self._f_path = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir))
# 경고 메시지 삭제
warnings.filterwarnings('ignore')
# 원본 데이터 로드
data = pd.read_csv(self._f_path + "/regression/resource/regression_sample.csv", sep=",", encoding="utf-8")
# 학습 및 테스트 데이터 분리
self._x = (data["year"] <= 2017)
self._y = (data["year"] >= 2018)
# 학습 데이터 분리
self._x_train, self._y_train = self.preprocessing(data[self._x])
# 테스트 데이터 분리
self._x_test, self._y_test = self.preprocessing(data[self._y])
# 모델 선언
self._model = LarsCV(normalize=False)
# 모델 학습
self._model.fit(self._x_train, self._y_train)
# 데이터 전처리
def preprocessing(self, data):
# 학습
x = []
# 레이블
y = []
# 기준점(7일)
base_interval = 7
# 기온
temps = list(data["temperature"])
for i in range(len(temps)):
if i < base_interval:
continue
y.append(temps[i])
xa = []
for p in range(base_interval):
d = i + p - base_interval
xa.append(temps[d])
x.append(xa)
return x, y
# 일반 예측
def predict(self, save_img=False, show_chart=False):
# 예측
y_pred = self._model.predict(self._x_test)
# 스코어 정보
score = r2_score(self._y_test, y_pred)
# 리포트 확인
if hasattr(self._model, 'coef_') and hasattr(self._model, 'intercept_'):
print(f'Coef = {self._model.coef_}')
print(f'intercept = {self._model.intercept_}')
print(f'Score = {score}')
# 이미지 저장 여부
if save_img:
self.save_chart_image(y_pred, show_chart)
# 예측 값 & 스코어
return [list(y_pred), score]
# CV 예측(Cross Validation)
def predict_by_cv(self):
# Regression 알고리즘은 실 프로젝트 상황에 맞게 Cross Validation 구현
return False
# GridSearchCV 예측
def predict_by_gs(self):
pass
# 모델 저장 및 갱신
def save_model(self, renew=False):
# 모델 저장
if not renew:
# 처음 저장
joblib.dump(self._model, self._f_path + f'/model/{self._name}_rg.pkl')
else:
# 기존 모델 대체
if os.path.isfile(self._f_path + f'/model/{self._name}_rg.pkl'):
os.rename(self._f_path + f'/model/{self._name}_rg.pkl',
self._f_path + f'/model/{str(self._name) + str(time.time())}_rg.pkl')
joblib.dump(self._model, self._f_path + f'/model/{self._name}_rg.pkl')
# 회귀 차트 저장
def save_chart_image(self, data, show_chart):
# 사이즈
plt.figure(figsize=(15, 10), dpi=100)
# 레이블
plt.plot(self._y_test, c='r')
# 예측 값
plt.plot(data, c='b')
# 이미지로 저장
plt.savefig('./chart_images/tenki-kion-lr.png')
# 차트 확인(Optional)
if show_chart:
plt.show()
def __del__(self):
del self._x_train, self._x_test, self._y_train, self._y_test, self._x, self._y, self._model
# (High Dimensional) Linear Regression #
#####################################################################
#####################################################################
## Scikit Learn ##
#####################################################################
lasso_model = LassoCV()
lasso_model.fit(x_train_values, y_train_values)
lasso_model_predictions = lasso_model.predict(x_test_values)
generate_submission_file(lasso_model_predictions, test_data["Id"],
"../results/" + user + "_LassoCV.csv")
lars_model = LarsCV()
lars_model.fit(x_train_values, y_train_values)
lars_model_predictions = lars_model.predict(x_test_values)
generate_submission_file(lars_model_predictions, test_data["Id"],
"../results/" + user + "_LarsCV.csv")
lassolars_model = LassoLarsCV()
lassolars_model.fit(x_train_values, y_train_values)
lassolars_model_predictions = lassolars_model.predict(x_test_values)
generate_submission_file(lassolars_model_predictions, test_data["Id"],
"../results/" + user + "_LassoLarsCV.csv")
en_model = ElasticNetCV()
en_model.fit(x_train_values, y_train_values)
en_model_predictions = en_model.predict(x_test_values)
generate_submission_file(en_model_predictions, test_data["Id"],
"../results/" + user + "_ElasticNetCV.csv")