teradataml.opensource.td_lightgbm = <teradataml.opensource._class.Lightgbm object> | ||
DESCRIPTION: Interface object to access exposed classes and functions of lightgbm opensource package. All the classes and functions can be run and attributes can be accessed using the object created by "td_lightgbm" interface object. Refer Teradata Python Package User Guide for more information about OpenML and exposed interface objects. PARAMETERS: None RETURNS: None EXAMPLES: # Load example data. >>> load_example_data("openml", ["test_classification"]) >>> df = DataFrame("test_classification") >>> df.head(3) col2 col3 col4 label col1 -2.560430 0.402232 -1.100742 -2.959588 0 -3.587546 0.291819 -1.850169 -4.331055 0 -3.697436 1.576888 -0.461220 -3.598652 0 # Get the feature and label data. >>> df_x = df.select(df.columns[:-1]) >>> df_y = df.select(df.columns[-1]) >>> from teradataml import td_lightgbm # Example 1: Train the model using train() function. # Create lightgbm Dataset object. >>> lgbm_data = td_lightgbm.Dataset(data=df_x, label=df_y, free_raw_data=False) >>> lgbm_data <lightgbm.basic.Dataset object at ...> # Train the model. >>> model = td_lightgbm.train(params={}, train_set=lgbm_data, num_boost_round=30, valid_sets=[lgbm_data]) [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000043 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 532 [LightGBM] [Info] Number of data points in the train set: 400, number of used features: 4 [1] valid_0's l2: 0.215811 [2] valid_0's l2: 0.188138 [3] valid_0's l2: 0.166146 ... ... [29] valid_0's l2: 0.042255 [30] valid_0's l2: 0.0416953 >>> model <lightgbm.basic.Booster object at ...> # Example 2: Train the model using LGBMClassifier sklearn object. # Create lightgbm sklearn object. >>> lgbm_cl = td_lightgbm.LGBMClassifier() >>> lgbm_cl LGBMClassifier() # Fit/train the model using fit() function. >>> lgbm_cl.fit(df_x, df_y) LGBMClassifier() # Perform prediction. >>> lgbm_cl.predict(df_x).head(3) col1 col2 col3 col4 lgbmclassifier_predict_1 0 1.105026 -1.949894 -1.537164 0.073171 1 1 1.878349 0.577289 1.795746 2.762539 1 2 -1.130582 -0.020296 -0.710234 -1.440991 0 # Access attributes. >>> lgbm_cl.feature_importances_ array([ 0, 20, 10, 10]) |