From a8746de9ebc266f86ccec201b04e0dfae1251d29 Mon Sep 17 00:00:00 2001
From: Alex Burlacu <alex.burlacu@clear.ml>
Date: Thu, 25 May 2023 17:43:13 +0300
Subject: [PATCH] Adjust LightGBM example
---
.../frameworks/lightgbm/lightgbm_example.py | 155 +++++++++++-------
examples/frameworks/lightgbm/requirements.txt | 1 +
2 files changed, 93 insertions(+), 63 deletions(-)
diff --git a/examples/frameworks/lightgbm/lightgbm_example.py b/examples/frameworks/lightgbm/lightgbm_example.py
index 16034374..8e1614cb 100644
--- a/examples/frameworks/lightgbm/lightgbm_example.py
+++ b/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()
diff --git a/examples/frameworks/lightgbm/requirements.txt b/examples/frameworks/lightgbm/requirements.txt
index ac7ee02e..8508c428 100644
--- a/examples/frameworks/lightgbm/requirements.txt
+++ b/examples/frameworks/lightgbm/requirements.txt
@@ -1,4 +1,5 @@
lightgbm
scikit-learn
pandas
+matplotlib
clearml
\ No newline at end of file