# Path: gbdt/_forest.py
# class Forest:
#     def __init__(self, forest):
#         if isinstance(forest, six.text_type):
#             self._forest = libgbdt.Forest(forest)
#         elif isinstance(forest, libgbdt.Forest):
#             self._forest = forest
#         else:
#             raise TypeError('Unsupported forest type: {0}'.format(type(forest)))
# 
#     def predict(self, data_store):
#         """Computes prediction scores for data_store."""
#         return self._forest.predict(data_store._data_store)
# 
#     def predict_at_checkpoints(self, data_store, checkpoints):
#         """Computes prediction scores for data_store at different checkpoints. At each checkpoint n,
#            We compute prediction scores for the sub forest from the first tree to nth tree.
#         """
#         import tempfile
# 
#         output_dir = tempfile.gettempdir()
# 
#         self._forest.predict_and_output(data_store._data_store, checkpoints, output_dir)
#         for p in checkpoints:
#             yield p, [float(line) for line in open(output_dir + '/forest.{}.score'.format(p))
#                       if line.strip()]
# 
#     def feature_importance(self):
#         """Outputs list of feature importances in descending order."""
#         return self._forest.feature_importance()
# 
#     def features(self):
#         """Outputs list of features."""
#         return [f for (f, imp) in self._forest.feature_importance()]
# 
#     def feature_importance_bar_chart(self, color=None):
#         try:
#             from matplotlib import pyplot as plt
#             import numpy
#         except ImportError:
#             raise ImportError('Please install matplotlib and numpy.')
# 
#         fimps = self.feature_importance()
#         importances = [v for _, v in fimps]
#         features = [f for f,_ in fimps]
#         ind = -numpy.arange(len(fimps))
# 
#         _, ax = plt.subplots()
#         plt.barh(ind, importances, align='center', color=color)
#         ax.set_yticks(ind)
#         ax.set_yticklabels(features)
#         ax.set_xlabel('Feature importance')
# 
#     def __str__(self):
#         return self._forest.as_json()
# 
# Path: gbdt/_libgbdt.py



from ._forest import Forest
from ._libgbdt import libgbdt 
        import json

def train(data_store,
          config,
          y=[],

# Path: gbdt/_forest.py
# class Forest:
#     def __init__(self, forest):
#         if isinstance(forest, six.text_type):
#             self._forest = libgbdt.Forest(forest)
#         elif isinstance(forest, libgbdt.Forest):
#             self._forest = forest
#         else:
#             raise TypeError('Unsupported forest type: {0}'.format(type(forest)))
# 
#     def predict(self, data_store):
#         """Computes prediction scores for data_store."""
#         return self._forest.predict(data_store._data_store)
# 
#     def predict_at_checkpoints(self, data_store, checkpoints):
#         """Computes prediction scores for data_store at different checkpoints. At each checkpoint n,
#            We compute prediction scores for the sub forest from the first tree to nth tree.
#         """
#         import tempfile
# 
#         output_dir = tempfile.gettempdir()
# 
#         self._forest.predict_and_output(data_store._data_store, checkpoints, output_dir)
#         for p in checkpoints:
#             yield p, [float(line) for line in open(output_dir + '/forest.{}.score'.format(p))
#                       if line.strip()]
# 
#     def feature_importance(self):
#         """Outputs list of feature importances in descending order."""
#         return self._forest.feature_importance()
# 
#     def features(self):
#         """Outputs list of features."""
#         return [f for (f, imp) in self._forest.feature_importance()]
# 
#     def feature_importance_bar_chart(self, color=None):
#         try:
#             from matplotlib import pyplot as plt
#             import numpy
#         except ImportError:
#             raise ImportError('Please install matplotlib and numpy.')
# 
#         fimps = self.feature_importance()
#         importances = [v for _, v in fimps]
#         features = [f for f,_ in fimps]
#         ind = -numpy.arange(len(fimps))
# 
#         _, ax = plt.subplots()
#         plt.barh(ind, importances, align='center', color=color)
#         ax.set_yticks(ind)
#         ax.set_yticklabels(features)
#         ax.set_xlabel('Feature importance')
# 
#     def __str__(self):
#         return self._forest.as_json()
# 
# Path: gbdt/_libgbdt.py



# Path: gbdt/_gbdt.py
from ._forest import Forest
from ._libgbdt import libgbdt 
        import json


def train(data_store,
          config,
          y=[],

          features=[],