Automl module

The Automl features allows you to train an automl model with the advantage of an added Sanatio report on the newly trained model.

The automl object is returned to the user with a validation report.

How to access the feature?

from sanatio.code_generation import generate_sanatio_pipeline

automl_object = generate_sanatio_pipeline(...parameters...)

Parameters required for initialization

Parameter Name	Data Type	Optional
X_train	Data Frame	The training data used for the model
y_train	Data Series	The truth value used to train the model
X_test	Data Frame	The testing feature data split
y_test	Data Series	The ground truth data for testing
classification	Boolean	True if a classification model
regression	Boolean	True if a regression model
time	Int	Amount of time in seconds that the automl model can take.

Example

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import pandas as pd

data = load_boston()
X,y = load_boston(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)

from sanatio.training.automl.pipeline_generation import generate_sanatio_pipeline

automl_object = generate_sanatio_pipeline(X_train=pd.DataFrame(X_train,columns=data.feature_names), 
                                          y_train=y_train, 
                                          X_test=pd.DataFrame(X_test,columns=data.feature_names), 
                                          y_test=y_test, 
                                          classification=False, regression=True, time=120)