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August 9, 2018 16:25
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Grid search for H2O penalized GLM
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| def glm_grid(X, y, train, valid, family): | |
| """ Wrapper function for penalized GLM with alpha and lambda search. | |
| :param X: List of inputs. | |
| :param y: Name of target variable. | |
| :param train: Name of training H2OFrame. | |
| :param valid: Name of validation H2OFrame. | |
| :param: family: 'gaussian' for linear regression; 'binomial' for logistic. | |
| :return: Best H2Omodel from H2OGeneralizedLinearEstimator | |
| """ | |
| import h2o | |
| from h2o.estimators.glm import H2OGeneralizedLinearEstimator | |
| from h2o.grid.grid_search import H2OGridSearch | |
| if family == 'binomial': | |
| train[y] = train[y].asfactor() | |
| valid[y] = valid[y].asfactor() | |
| h2o.init() | |
| alpha_opts = [0.01, 0.25, 0.5, 0.99] # always keep some L2 | |
| hyper_parameters = {'alpha': alpha_opts} | |
| # initialize grid search | |
| grid = H2OGridSearch( | |
| H2OGeneralizedLinearEstimator( | |
| family=family, | |
| lambda_search=True, | |
| seed=12345), | |
| hyper_params=hyper_parameters) | |
| # train grid | |
| grid.train(y=y, | |
| x=X, | |
| training_frame=train, | |
| validation_frame=valid) | |
| # show grid search results | |
| print(grid.show()) | |
| best = grid.get_grid()[0] | |
| print(best) | |
| # plot top frame values | |
| yhat_frame = valid.cbind(best.predict(valid)) | |
| print(yhat_frame[0:10, [y, 'predict']]) | |
| # plot sorted predictions | |
| if family == 'gaussian' | |
| yhat_frame_df = yhat_frame[[y, 'predict']].as_data_frame() | |
| yhat_frame_df.sort_values(by='predict', inplace=True) | |
| yhat_frame_df.reset_index(inplace=True, drop=True) | |
| _ = yhat_frame_df.plot(title='Ranked Predictions Plot') | |
| # select best model | |
| return best |
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