Automated g4 rollback of changelist 162273792

PiperOrigin-RevId: 162296574

1 files changed, 0 insertions, 527 deletions

diff --git a/tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py b/tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py
deleted file mode 100644
index 296ac7220d..0000000000
--- a/tensorflow/contrib/tensor_forest/python/kernel_tests/model_ops_kernel_test.py
+++ /dev/null
@@ -1,527 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for tf.contrib.tensor_forest.ops.tree_predictions_op."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from google.protobuf import descriptor_pool
-from google.protobuf import text_format
-
-from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2
-from tensorflow.contrib.tensor_forest.python import tensor_forest
-from tensorflow.contrib.tensor_forest.python import tensor_forest_v4
-from tensorflow.contrib.tensor_forest.python.ops import data_ops
-from tensorflow.contrib.tensor_forest.python.ops import model_ops
-
-from tensorflow.python.framework import test_util
-from tensorflow.python.ops import resources
-from tensorflow.python.platform import googletest
-
-
-TREE_PROTO_DENSE = """
-decision_tree {
-  nodes {
-    binary_node {
-      left_child_id {
-        value: 1
-      }
-      right_child_id {
-        value: 2
-      }
-      inequality_left_child_test {
-        feature_id {
-          id {
-            value: "0"
-          }
-        }
-        threshold {
-          float_value: 0
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 1
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 10
-        }
-        value {
-          float_value: 10
-        }
-        value {
-          float_value: 80
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 2
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 50
-        }
-        value {
-          float_value: 25
-        }
-        value {
-          float_value: 25
-        }
-      }
-    }
-  }
-}
-"""
-
-# TODO(gilberth): This currently has to load inequality_left_child_test
-# in the tree because of a proto parsing error (MatchingValuesTest not
-# found in the descriptor pool).
-TREE_PROTO_SPARSE = """
-decision_tree {
-  nodes {
-    binary_node {
-      left_child_id {
-        value: 1
-      }
-      right_child_id {
-        value: 2
-      }
-      inequality_left_child_test {
-        feature_id {
-          id {
-            value: "1"
-          }
-        }
-        threshold {
-          float_value: 10
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 1
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 10
-        }
-        value {
-          float_value: 10
-        }
-        value {
-          float_value: 80
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 2
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 50
-        }
-        value {
-          float_value: 25
-        }
-        value {
-          float_value: 25
-        }
-      }
-    }
-  }
-}
-"""
-
-FEATURE_COUNT_TREE = """
-decision_tree {
-  nodes {
-    binary_node {
-      left_child_id {
-        value: 1
-      }
-      right_child_id {
-        value: 2
-      }
-      inequality_left_child_test {
-        feature_id {
-          id {
-            value: "0"
-          }
-        }
-        threshold {
-          float_value: 0
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 1
-    }
-    binary_node {
-      left_child_id {
-        value: 3
-      }
-      right_child_id {
-        value: 4
-      }
-      inequality_left_child_test {
-        feature_id {
-          id {
-            value: "1"
-          }
-        }
-        threshold {
-          float_value: 0
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 2
-    }
-    binary_node {
-      left_child_id {
-        value: 5
-      }
-      right_child_id {
-        value: 6
-      }
-      inequality_left_child_test {
-        feature_id {
-          id {
-            value: "0"
-          }
-        }
-        threshold {
-          float_value: 10
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 3
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 10
-        }
-        value {
-          float_value: 10
-        }
-        value {
-          float_value: 80
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 4
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 50
-        }
-        value {
-          float_value: 25
-        }
-        value {
-          float_value: 25
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 5
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 50
-        }
-        value {
-          float_value: 25
-        }
-        value {
-          float_value: 25
-        }
-      }
-    }
-  }
-  nodes {
-    node_id {
-      value: 6
-    }
-    leaf {
-      vector {
-        value {
-          float_value: 50
-        }
-        value {
-          float_value: 25
-        }
-        value {
-          float_value: 25
-        }
-      }
-    }
-  }
-}
-"""
-
-
-def get_v4_params(num_classes, num_features, regression):
-  params = tensor_forest.ForestHParams()
-  params.num_classes = num_classes
-  params.num_features = num_features
-  params.regression = regression
-  v4_params = tensor_forest_v4.V4ForestHParams(params)
-  v4_params.params_proto = tensor_forest_v4.build_params_proto(v4_params)
-  v4_params.serialized_params_proto = (
-      v4_params.params_proto.SerializeToString())
-  return v4_params
-
-
-def get_dense_data_spec():
-  spec_proto = data_ops.TensorForestDataSpec()
-  f1 = spec_proto.dense.add()
-  f1.name = 'f1'
-  f1.original_type = data_ops.DATA_FLOAT
-  f1.size = 1
-
-  f2 = spec_proto.dense.add()
-  f2.name = 'f2'
-  f2.original_type = data_ops.DATA_FLOAT
-  f2.size = 1
-  spec_proto.dense_features_size = 2
-  return spec_proto.SerializeToString()
-
-
-def get_sparse_data_spec():
-  spec_proto = data_ops.TensorForestDataSpec()
-  f1 = spec_proto.dense.add()
-  f1.name = 'f1'
-  f1.original_type = data_ops.DATA_CATEGORICAL
-  f1.size = 10
-  spec_proto.dense_features_size = 0
-
-  return spec_proto.SerializeToString()
-
-
-class ModelOpsPredictionsTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.nothing = []
-
-  def testDense(self):
-    input_data = [[-1., 0.], [-1., 2.],  # node 1
-                  [1., 0.], [1., -2.]]  # node 2
-
-    v4_params = get_v4_params(3, 2, False)
-
-    tree_proto = generic_tree_model_pb2.Model()
-    text_format.Merge(TREE_PROTO_DENSE, tree_proto,
-                      descriptor_pool=descriptor_pool.Default())
-
-    with self.test_session():
-      tree = model_ops.tree_variable(
-          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
-      resources.initialize_resources(resources.shared_resources()).run()
-
-      predictions = model_ops.tree_predictions_v4(
-          tree,
-          input_data,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          input_spec=get_dense_data_spec(),
-          params=v4_params.serialized_params_proto)
-
-      leaves = model_ops.traverse_tree_v4(
-          tree,
-          input_data,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          input_spec=get_dense_data_spec(),
-          params=v4_params.serialized_params_proto)
-
-      self.assertAllClose([[0.1, 0.1, 0.8], [0.1, 0.1, 0.8],
-                           [0.5, 0.25, 0.25], [0.5, 0.25, 0.25]],
-                          predictions.eval())
-
-      self.assertAllEqual([1, 1, 2, 2], leaves.eval())
-
-  def testSparseInput(self):
-    sparse_shape = [4, 10]
-    sparse_indices = [[0, 0], [0, 1], [0, 9],
-                      [1, 0], [1, 1],
-                      [2, 1],
-                      [3, 6]]
-    sparse_values = [3.0, 5.0, 0.5,
-                     15.0, 10.0,
-                     20.0,
-                     20.0]
-
-    v4_params = get_v4_params(3, 10, False)
-
-    tree_proto = generic_tree_model_pb2.Model()
-    text_format.Merge(TREE_PROTO_SPARSE, tree_proto,
-                      descriptor_pool=descriptor_pool.Default())
-
-    with self.test_session():
-      tree = model_ops.tree_variable(
-          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
-      resources.initialize_resources(resources.shared_resources()).run()
-
-      predictions = model_ops.tree_predictions_v4(
-          tree,
-          self.nothing,
-          sparse_indices,
-          sparse_values,
-          sparse_shape,
-          input_spec=get_sparse_data_spec(),
-          params=v4_params.serialized_params_proto)
-
-      leaves = model_ops.traverse_tree_v4(
-          tree,
-          self.nothing,
-          sparse_indices,
-          sparse_values,
-          sparse_shape,
-          input_spec=get_sparse_data_spec(),
-          params=v4_params.serialized_params_proto)
-
-      self.assertAllClose([[0.1, 0.1, 0.8], [0.1, 0.1, 0.8],
-                           [0.5, 0.25, 0.25], [0.1, 0.1, 0.8]],
-                          predictions.eval())
-
-      self.assertAllEqual([1, 1, 2, 1], leaves.eval())
-
-  def testNoInput(self):
-    v4_params = get_v4_params(3, 4, False)
-
-    tree_proto = generic_tree_model_pb2.Model()
-    text_format.Merge(TREE_PROTO_DENSE, tree_proto,
-                      descriptor_pool=descriptor_pool.Default())
-
-    with self.test_session():
-      tree = model_ops.tree_variable(
-          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
-      resources.initialize_resources(resources.shared_resources()).run()
-
-      predictions = model_ops.tree_predictions_v4(
-          tree,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          input_spec=get_dense_data_spec(),
-          params=v4_params.serialized_params_proto)
-
-      self.assertEqual((0, 3), predictions.eval().shape)
-
-
-class ModelOpsUpdateTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.nothing = []
-
-  def testBasic(self):
-    input_labels = [0, 1, 2, 1]
-    leaves = [1, 2, 1, 2]
-    weights = [10, 10, 10, 10]
-
-    # For checking the resulting leaves after update.
-    input_data = [[-1., 0.],  # node 1
-                  [1., 0.]]   # node 2
-
-    v4_params = get_v4_params(3, 2, False)
-
-    tree_proto = generic_tree_model_pb2.Model()
-    text_format.Merge(TREE_PROTO_DENSE, tree_proto,
-                      descriptor_pool=descriptor_pool.Default())
-
-    with self.test_session():
-      tree = model_ops.tree_variable(
-          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
-      resources.initialize_resources(resources.shared_resources()).run()
-
-      model_ops.update_model_v4(
-          tree,
-          leaves,
-          input_labels,
-          weights,
-          params=v4_params.serialized_params_proto).run()
-
-      predictions = model_ops.tree_predictions_v4(
-          tree,
-          input_data,
-          self.nothing,
-          self.nothing,
-          self.nothing,
-          input_spec=get_dense_data_spec(),
-          params=v4_params.serialized_params_proto)
-
-      self.assertAllClose([[0.16666, 0.08333, 0.75],
-                           [0.416666, 0.375, 0.208333]],
-                          predictions.eval(),
-                          rtol=0.0001, atol=0.0001)
-
-
-class ModelOpsFeatureCountsTest(test_util.TensorFlowTestCase):
-
-  def setUp(self):
-    self.nothing = []
-
-  def testBasic(self):
-    v4_params = get_v4_params(3, 4, False)
-
-    tree_proto = generic_tree_model_pb2.Model()
-    text_format.Merge(FEATURE_COUNT_TREE, tree_proto,
-                      descriptor_pool=descriptor_pool.Default())
-
-    with self.test_session():
-      tree = model_ops.tree_variable(
-          v4_params, tree_proto.SerializeToString(), None, 'tree-0')
-      resources.initialize_resources(resources.shared_resources()).run()
-
-      feature_counts = model_ops.feature_usage_counts(
-          tree,
-          params=v4_params.serialized_params_proto)
-
-      self.assertAllEqual([2, 1, 0, 0], feature_counts.eval())
-
-
-if __name__ == '__main__':
-  googletest.main()