Skip to content

Instantly share code, notes, and snippets.

@noklam
Created November 18, 2020 06:42
Show Gist options
  • Save noklam/0a1d261318621227c3bac30a50a0301b to your computer and use it in GitHub Desktop.
Save noklam/0a1d261318621227c3bac30a50a0301b to your computer and use it in GitHub Desktop.
lightgbm_bug_trains.py
# TRAINS - Example of LightGBM integration
#
import lightgbm as lgb
import pandas as pd
from sklearn.metrics import mean_squared_error
from trains import Task
task = Task.init(project_name="examples", task_name="LIGHTgbm")
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': 31,
'learning_rate': 0.05,
'feature_fraction': 0.9,
'bagging_fraction': 0.8,
'bagging_freq': 5,
'verbose': 0
}
print('Starting training...')
# Train
gbm = lgb.train(
params=params,
lgb_train=lgb_train,
num_boost_round=20,
valid_sets=lgb_eval,
early_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)
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment