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from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet, LogisticRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.svm import SVR
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
class RegressionModels:
def __init__(self):
self.data = None
self.X_train = None
self.X_test = None
self.y_train = None
self.y_test = None
self.models = {
'Linear Regression': LinearRegression(),
'Polynomial Regression': LinearRegression(),
'Ridge Regression': Ridge(),
'Lasso Regression': Lasso(),
'ElasticNet Regression': ElasticNet(),
'Logistic Regression': LogisticRegression(),
'Decision Tree Regression': DecisionTreeRegressor(),
'Random Forest Regression': RandomForestRegressor(),
'Gradient Boosting Regression': GradientBoostingRegressor(),
'Support Vector Regression (SVR)': SVR(),
'XGBoost': XGBRegressor(),
'LightGBM': LGBMRegressor()
}
def add_data(self, X, y):
self.data = (X, y)
def split_data(self, test_size=0.2, random_state=None):
if self.data is None:
raise ValueError("No data provided. Use add_data method to add data first.")
X, y = self.data
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
def fit(self, model_name):
if self.X_train is None or self.y_train is None:
raise ValueError("Data not split. Use split_data method to split data into training and testing sets.")
model = self.models[model_name]
model.fit(self.X_train, self.y_train)
def train(self, model_name):
if self.X_train is None or self.y_train is None or self.X_test is None:
raise ValueError("Data not split. Use split_data method to split data into training and testing sets.")
model = self.models[model_name]
model.fit(self.X_train, self.y_train)
y_pred = model.predict(self.X_test)
return y_pred
def evaluate(self, model_name):
if self.X_test is None or self.y_test is None:
raise ValueError("Data not split. Use split_data method to split data into training and testing sets.")
model = self.models[model_name]
y_pred = model.predict(self.X_test)
mse = mean_squared_error(self.y_test, y_pred)
r2 = r2_score(self.y_test, y_pred)
return mse, r2
def predict(self, model_name, X):
model = self.models[model_name]
return model.predict(X)
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