class r07546035_DummyRegression(regression):
def trainAlgo(self):
self.model = DummyRegressor(strategy=self.param['strategy'],
quantile=self.param['quantile'])
self.model.fit(self.inputData['X'],
self.outputData['y'],
sample_weight=None)
def predictAlgo(self):
self.result['y'] = self.model.predict(self.inputData['X'])
def get_paramsAlgo(self):
self.model.get_params(self)
def train_dummy_regressors(features, target):
for strat in ['mean', 'median']:
dr = DummyRegressor(strategy=strat)
dr.fit(features, y=target.flatten())
dummy_score = (100 * dr.score(features, target))
print('{:.1f} % score for a dummy regressor using the {} stragety'.format(
dummy_score,
dr.get_params()['strategy']))
class PerfectClassifierMeanRegressor():
def fit(self,X: pd.DataFrame, y: pd.Series):
self.X = X
self.y = y
self.regressor = DummyRegressor(strategy='mean')
def cross_val(self,scoring,k=10):
self.scores = {}
for name, scorer in scoring.items():
for split in ['train','test']:
self.scores[split+'_'+name] = []
splitter = KFold(n_splits=k,shuffle=True,random_state=7)
for train_index, test_index in splitter.split(self.X,self.y):
X_train = self.X.values[train_index]
y_train = self.y.values[train_index]
X_test = self.X.values[test_index]
y_test = self.y.values[test_index]
# get test y class labels for perfect classification
y_test_binary = (y_test != 0)
y_train_binary = (y_train != 0)
self.regressor.fit(X_train,y_train.reshape(-1,1))
reg_pred_test = self.regressor.predict(X_test).flatten()
reg_pred_train = self.regressor.predict(X_train).flatten()
y_pred_test = np.multiply(y_test_binary,reg_pred_test)
y_pred_train = np.multiply(y_train_binary,reg_pred_train)
for name, scorer in scoring.items():
self.scores['test_'+name].append(scorer(y_test,y_pred_test))
self.scores['train_'+name].append(scorer(y_train,y_pred_train))
return self.scores
def get_params(self):
return(self.regressor.get_params())
