Add LightGBM support

examples/frameworks/lightgbm/requirements.txt

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+lightgbm
+scikit-learn
+pandas

examples/frameworks/lightgbm/train_with_lightbgm.py

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+# 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,
+    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)