def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
y_pred = self.run().predict(X_test)
rmse = compute_rmse(y_pred, y_test)
# self.mlflow_log_param('model', "LinearRegression")
# self.mlflow_log_param('rmse', rmse)
return rmse
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
y_pred = self.pipeline.predict(X_test)
rmse = compute_rmse(y_pred, y_test)
self.mlflow_log_metric('rmse', rmse)
self.mlflow_log_param("model", 'linear')
return round(rmse, 2)
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
y_pred = self.pipeline.predict(X_test)
rmse = compute_rmse(y_pred, y_test)
if self.mlflow:
self.mlflow_log_metric('rmse', rmse)
return rmse
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
y_pred = self.pipeline.predict(X_test)
rmse = compute_rmse(y_pred, y_test)
print(rmse)
self.mlflow_log_metric("rmse", rmse)
self.mlflow_log_param("model", self.pipeline.get_params()['linear_model'])
return rmse
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
y_pred = self.pipeline.predict(X_test)
rmse = compute_rmse(y_pred, y_test)
print(f'ID:{trainer.mlflow_experiment_id}')
self.mlflow_log_param('model', str(self.pipeline.get_params()['model'])
.strip('()'))
self.mlflow_log_metric('rmse', rmse)
return rmse
def compute_rmse(self, X_test, y_test, show=False):
if self.pipeline is None:
raise ("Cannot evaluate an empty pipeline")
y_pred = self.pipeline.predict(X_test)
if show:
res = pd.DataFrame(y_test)
res["pred"] = y_pred
print(colored(res.sample(5), "blue"))
rmse = compute_rmse(y_pred, y_test)
return round(rmse, 3)
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
y_pred = self.pipeline.predict(X_test)
rmse_ = compute_rmse(y_pred, y_test)
print(f"RMSE = {rmse_}")
self.experiment_name = EXPERIMENT_NAME
self.mlflow_log_metric("rmse", rmse_)
self.mlflow_log_param("model", self.estimator)
self.mlflow_log_param("student_name", myname)
return rmse_
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
y_pred = self.best_model.predict(X_test)
#y_pred = self.pipeline.predict(X_test)
rmse = compute_rmse(y_pred, y_test)
# for model in ["a", "b"]: on peut itérer sur des modèles avec des runs différents en bouclant
# self.mlflow_run()
# self.mlflow_log_metric("rmse",rmse)
# self.mlflow_log_param("model",self.pipeline.get_params()['linear_model'])
# self.mlflow_log_param("truc",model)
# for key,value in self.best_params.items():
# self.mlflow_log_param(key,value)
self.mlflow_run()
self.mlflow_log_metric("rmse", rmse)
self.mlflow_log_param(
"model",
str(self.best_model.get_params()['linear_model'])[:20])
for key, value in self.best_params.items():
self.mlflow_log_param(key, value)
return rmse
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
a=self.pipeline.predict(X_test)
self.mlflow_log_param('model', 'KnnRegressor')
self.mlflow_log_metric('rmse', compute_rmse(a,y_test))
return compute_rmse(a,y_test)
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
'''returns the value of the RMSE'''
y_pred = self.pipeline.predict(X_test)
rmse = compute_rmse(y_pred, y_test)
return rmse
def evaluate(self, X_test, y_test):
'''returns the value of the RMSE'''
y_pred = self.pipeline.predict(X_test)
rmse = compute_rmse(y_pred, y_test)
return rmse
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
y_pred = self.pipeline.predict(X_test)
score = compute_rmse(y_pred, y_test)
self.mlflow_log_metric("test rmse", score)
return score
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
y_pred = self.pipeline.predict(X_test)
self.rmse = compute_rmse(y_pred, y_test)
self.mlflow_log_param('model', self.model)
self.mlflow_log_metric('rmse', self.rmse)
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
model = self.run()
y_pred = model.predict(X_test)
return compute_rmse(y_pred, y_test)
def evaluate(self):
"""evaluates the pipeline on df_test and return the RMSE"""
print(compute_rmse(self.pipeline.predict(self.X_test), self.y_test))
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
pipe_trained = self.run()
y_pred = pipe_trained.predict(X_test)
rmse = compute_rmse(y_pred, y_test)
return rmse
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
y_pred = self.pipeline.predict(X_test)
rmse = compute_rmse(y_pred, y_test)
print(f"rmse = {rmse}")
def compute_rmse(self, X, y):
y_pred = self.pipeline.predict(X)
rmse = compute_rmse(y_pred, y)
return round(rmse, 3)
def test_rmse():
y_true = np.array((34, 37, 44, 47, 48, 48, 46, 43, 32, 27, 26, 24))
y_pred = np.array((37, 40, 46, 44, 46, 50, 45, 44, 34, 30, 22, 23))
assert round(compute_rmse(y_pred, y_true),
2) == 2.43, "RMSE calculation is not right"
def evaluate(self, X_test, y_test):
if self.pipeline is None:
raise ("Cannot evaluate an empty pipeline")
y_pred = self.pipeline.predict(X_test)
rmse = compute_rmse(y_pred, y_test)
return round(rmse, 3)
def evaluate(self, X_test, y_test):
"""evaluates the pipeline on df_test and return the RMSE"""
y_pred = self.pipeline.predict(X_test)
rmse = round(compute_rmse(y_pred, y_test), 2)
self.mlflow_log_metric("rmse", rmse)
return rmse
def evaluate(self, X_test, y_test, estimator):
"""evaluates the pipeline on df_test and return the RMSE"""
y_pred = self.pipeline.predict(X_test)
rmse = compute_rmse(y_pred, y_test)
def evaluate(self):
return compute_rmse(self.pipeline.predict(self.X_test), self.y_test)