Adjust LightGBM example

examples/frameworks/lightgbm/lightgbm_example.py

@@ -1,75 +1,104 @@
 # ClearML - Example of LightGBM integration
 #
 import lightgbm as lgb
+import matplotlib.pyplot as plt
 import pandas as pd
 from sklearn.metrics import mean_squared_error
 
 from clearml import Task
 
-# Connecting ClearML with the current process,
-# from here on everything is logged automatically
-task = Task.init(project_name="examples", task_name="LightGBM")
 
-print('Loading data...')
+def main():
+    # Connecting ClearML with the current process,
+    # from here on everything is logged automatically
+    task = Task.init(project_name="examples", task_name="LightGBM")
 
-# Load or create your dataset
+    print('Loading data...')
+
+    # Load or create your dataset
+
+    df_train = pd.read_csv(
+        'https://raw.githubusercontent.com/microsoft/LightGBM/master/examples/regression/regression.train',
+        header=None, sep='\t'
+    )
+    df_test = pd.read_csv(
+        'https://raw.githubusercontent.com/microsoft/LightGBM/master/examples/regression/regression.test',
+        header=None, sep='\t'
+    )
+
+    y_train = df_train[0]
+    y_test = df_test[0]
+    X_train = df_train.drop(0, axis=1)
+    X_test = df_test.drop(0, axis=1)
+
+    # Create dataset for lightgbm
+    lgb_train = lgb.Dataset(X_train, y_train)
+    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
+
+    # Specify your configurations as a dict
+    params = {
+        'boosting_type': 'gbdt',
+        'objective': 'regression',
+        'metric': {'l2', 'l1'},
+        'num_leaves': 200,
+        'max_depth': 0,
+        'learning_rate': 0.05,
+        'feature_fraction': 0.9,
+        'bagging_fraction': 0.8,
+        'bagging_freq': 5,
+        'verbose': 0,
+        'force_col_wise': True,
+        'deterministic': True,
+    }
+
+    evals_result = {}  # to record eval results for plotting
+
+    print('Starting training...')
+
+    # Train
+    gbm = lgb.train(
+        params,
+        lgb_train,
+        num_boost_round=500,
+        valid_sets=[lgb_train, lgb_eval],
+        feature_name=[f'f{i + 1}' for i in range(X_train.shape[-1])],
+        categorical_feature=[21],
+        callbacks=[
+            lgb.record_evaluation(evals_result),
+        ],
+    )
+
+    print('Saving model...')
+
+    # Save model to file
+    gbm.save_model('model.txt')
+
+    print('Plotting metrics recorded during training...')
+
+    ax = lgb.plot_metric(evals_result, metric='l1')
+    plt.show()
+
+    print('Plotting feature importances...')
+
+    ax = lgb.plot_importance(gbm, max_num_features=10)
+    plt.show()
+
+    print('Plotting split value histogram...')
+
+    ax = lgb.plot_split_value_histogram(gbm, feature='f26', bins='auto')
+    plt.show()
+
+    print('Loading model to predict...')
+
+    # Load model to predict
+    bst = lgb.Booster(model_file='model.txt')
+
+    # Can only predict with the best iteration (or the saving iteration)
+    y_pred = bst.predict(X_test)
+
+    # Eval with loaded model
+    print("The rmse of loaded model's prediction is:", mean_squared_error(y_test, y_pred) ** 0.5)
 
 
-df_train = pd.read_csv(
-    'https://raw.githubusercontent.com/microsoft/LightGBM/master/examples/regression/regression.train',
-    header=None, sep='\t'
-)
-df_test = pd.read_csv(
-    'https://raw.githubusercontent.com/microsoft/LightGBM/master/examples/regression/regression.test',
-    header=None, sep='\t'
-)
-
-y_train = df_train[0]
-y_test = df_test[0]
-X_train = df_train.drop(0, axis=1)
-X_test = df_test.drop(0, axis=1)
-
-# Create dataset for lightgbm
-lgb_train = lgb.Dataset(X_train, y_train)
-lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
-
-# Specify your configurations as a dict
-params = {
-    'boosting_type': 'gbdt',
-    'objective': 'regression',
-    'metric': {'l2', 'l1'},
-    'num_leaves': 31,
-    'learning_rate': 0.05,
-    'feature_fraction': 0.9,
-    'bagging_fraction': 0.8,
-    'bagging_freq': 5,
-    'verbose': 0,
-    'force_col_wise': True,
-}
-
-print('Starting training...')
-
-# Train
-gbm = lgb.train(
-    params,
-    lgb_train,
-    num_boost_round=20,
-    valid_sets=[lgb_eval],
-    callbacks=[lgb.early_stopping(stopping_rounds=5)],
-)
-
-print('Saving model...')
-
-# Save model to file
-gbm.save_model('model.txt')
-
-print('Loading model to predict...')
-
-# Load model to predict
-bst = lgb.Booster(model_file='model.txt')
-
-# Can only predict with the best iteration (or the saving iteration)
-y_pred = bst.predict(X_test)
-
-# Eval with loaded model
-print("The rmse of loaded model's prediction is:", mean_squared_error(y_test, y_pred) ** 0.5)
+if __name__ == '__main__':
+    main()

examples/frameworks/lightgbm/requirements.txt

@@ -1,4 +1,5 @@
 lightgbm
 scikit-learn
 pandas
+matplotlib
 clearml