8000 Return TensorForest feature importances in inference dict instead of … · jbenjos/tensorflow@5c21d55 · GitHub
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Return TensorForest feature importances in inference dict instead of using tf.Print.
Change: 150659388
1 parent 4593809 commit 5c21d55

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‎tensorflow/contrib/tensor_forest/client/eval_metrics.py

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@@ -30,6 +30,8 @@
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INFERENCE_PROB_NAME = prediction_key.PredictionKey.CLASSES
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INFERENCE_PRED_NAME = prediction_key.PredictionKey.PROBABILITIES
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FEATURE_IMPORTANCE_NAME = 'global_feature_importance'
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def _top_k_generator(k):
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def _top_k(probabilities, targets):

‎tensorflow/contrib/tensor_forest/client/random_forest.py

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@@ -28,7 +28,6 @@
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import logging_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import state_ops
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from tensorflow.python.platform import tf_logging as logging
@@ -130,6 +129,10 @@ def _model_fn(features, labels, mode):
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inference[eval_metrics.INFERENCE_PRED_NAME] = math_ops.argmax(
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inference[eval_metrics.INFERENCE_PROB_NAME], 1)
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if report_feature_importances:
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inference[eval_metrics.FEATURE_IMPORTANCE_NAME] = (
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graph_builder.feature_importances())
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# labels might be None if we're doing prediction (which brings up the
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# question of why we force everything to adhere to a single model_fn).
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loss_deps = []
@@ -149,10 +152,7 @@ def _model_fn(features, labels, mode):
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with ops.control_dependencies(loss_deps):
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training_loss = graph_builder.training_loss(
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features, labels, name=LOSS_NAME)
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if report_feature_importances and mode == model_fn_lib.ModeKeys.EVAL:
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training_loss = logging_ops.Print(training_loss,
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[graph_builder.feature_importances()],
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summarize=1000)
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# Put weights back in
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if weights is not None:
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features[weights_name] = weights

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