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RandomForestRegressor.py
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RandomForestRegressor.py
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import numpy as np
from sklearn import datasets,ensemble,tree
from sklearn.metrics import mean_squared_error
class RandomForestRegressor:
def __init__(self,base_estimator,n_estimators=10,min_samples_leaf=5,min_samples_split=15):
self.base_estimator=base_estimator
self.n_estimators=n_estimators
self.min_samples_split=min_samples_split
self.min_samples_leaf=min_samples_leaf
self.estimators_=[]
def fit(self,X,y):
for t in range(self.n_estimators):
estimator_t=self.base_estimator(random_state=True,min_samples_split=self.min_samples_split,min_samples_leaf=self.min_samples_leaf)
estimator_t.fit(X,y)
self.estimators_.append(estimator_t)
def predict(self,X):
preds=[]
for t in range(self.n_estimators):
preds.append(self.estimators_[t].predict(X))
return np.mean(np.array(preds),axis=0)
if __name__=='__main__':
breast_data = datasets.load_boston()
X, y = breast_data.data, breast_data.target
X_train, y_train = X[:400], y[:400]
X_test, y_test = X[400:], y[400:]
tinyml_decisiontree_reg=tree.DecisionTreeRegressor(min_samples_split=20, min_samples_leaf=5,random_state=True)
tinyml_decisiontree_reg.fit(X_train, y_train)
decisiontree_pred=tinyml_decisiontree_reg.predict(X_test)
print('base estimator:',mean_squared_error(y_test,decisiontree_pred))
tinyml_rf_reg=RandomForestRegressor(n_estimators=100, base_estimator=tree.DecisionTreeRegressor)
tinyml_rf_reg.fit(X_train,y_train)
y_pred=tinyml_rf_reg.predict(X_test)
print('tinyml rf mse:',mean_squared_error(y_test,y_pred))
sklearn_rf_reg=ensemble.RandomForestRegressor(n_estimators=100, min_samples_leaf=5, min_samples_split=20, random_state=False)
sklearn_rf_reg.fit(X_train, y_train)
sklearn_pred=sklearn_rf_reg.predict(X_test)
print('sklearn mse:',mean_squared_error(y_test,sklearn_pred))