add lightgbm regressor.

Author: is

sklearn_model_selection_lightgbm.py

@@ -0,0 +1,60 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import lightgbm as lgb
+
+from sklearn import model_selection
+
+from sklearn import ensemble
+from sklearn import datasets
+
+from sklearn.utils import shuffle
+
+X = list('aabcccdddd')
+print(X)
+k_fold = model_selection.KFold(n_splits=10)
+for train_indices, test_indices in k_fold.split(X):
+  print('Train: %s | Test: %s' % (
+    train_indices, test_indices))
+
+boston = datasets.load_boston()
+X, y = shuffle(boston.data, boston.target, random_state=13)
+X = X.astype(np.float32)
+y = y.astype(np.float32)
+
+params = {
+  'n_estimators': 200,
+  'max_depth':4,
+  'min_samples_split': 2, 
+  'learning_rate':0.01,
+  'loss': 'ls'
+}
+
+models = []
+for i in [100, 200, 300, 400, 500, 600, 700]:
+  params.update(n_estimators=i)
+  models.append([str(i), ensemble.GradientBoostingRegressor(**params)])
+for i in [100, 200, 300, 400, 500, 600, 700]:
+  models.append(['g' + str(i), lgb.LGBMRegressor(
+    objective='regression', num_leaves=31,learning_rate=0.05,n_estimators=i)])
+
+seed = 13
+scoring = 'neg_mean_squared_log_error'
+results = []
+names = []
+for name, model in models:
+  kfold = model_selection.KFold(n_splits=10, random_state=seed)
+  cv_results = model_selection.cross_val_score(
+    model, X, y, cv=kfold, scoring=scoring)
+  results.append(cv_results)
+  names.append(name)
+  msg = "%s: %f (%f), %f %f" % (name,
+    cv_results.mean(), cv_results.std(),
+    cv_results.min(), cv_results.max())
+  print(msg)
+  
+fig = plt.figure()
+fig.suptitle('Algorithm Comparison')
+ax = fig.add_subplot(111)
+plt.boxplot(results)
+ax.set_xticklabels(names)
+plt.show()