class Predictor(BaseEstimator):
'''Predictor: modify this class to create a predictor of
your choice. This could be your own algorithm, of one for the scikit-learn
models, for which you choose the hyper-parameters.'''
def __init__(self):
'''This method initializes the predictor.'''
self.mod = BaggingRegressor(base_estimator=RandomForestRegressor(
n_estimators=50))
print("PREDICTOR=" + self.mod.__str__())
def fit(self, X, y):
''' This is the training method: parameters are adjusted with training data.'''
self.mod = self.mod.fit(X, y)
return self
def predict(self, X):
''' This is called to make predictions on test data. Predicted classes are output.'''
return self.mod.predict(X)
def save(self, path="./"):
pickle.dump(self, open(path + '_model.pickle', "w"))
def load(self, path="./"):
self = pickle.load(open(path + '_model.pickle'))
return self
class Predictor(BaseEstimator):
def __init__(self):
'''This method initializes the predictor.'''
self.mod = BaggingRegressor(base_estimator=RandomForestRegressor(
n_estimators=50, max_depth=None, n_jobs=-1))
print("PREDICTOR=" + self.mod.__str__())
def fit(self, X, y):
''' This is the training method: parameters are adjusted with training data.'''
self.mod = self.mod.fit(X, y)
return self
def predict(self, X):
''' This is called to make predictions on test data. Predicted classes are output.'''
return self.mod.predict(X)
def save(self, path="./"):
pickle.dump(self, open(path + '_model.pickle', "w"))
def load(self, path="./"):
self = pickle.load(open(path + '_model.pickle'))
return self