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# Copyright 2016 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.
# ==============================================================================
"""A tf.learn implementation of tensor_forest (extremely random forests)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import numpy as np
import six
from tensorflow.contrib import framework as contrib_framework
from tensorflow.contrib.learn.python.learn import monitors as mon
from tensorflow.contrib.learn.python.learn.estimators import estimator
from tensorflow.contrib.learn.python.learn.estimators import run_config
from tensorflow.contrib.tensor_forest.client import eval_metrics
from tensorflow.contrib.tensor_forest.data import data_ops
from tensorflow.contrib.tensor_forest.python import tensor_forest
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
class LossMonitor(mon.EveryN):
"""Terminates training when training loss stops decreasing."""
def __init__(self,
early_stopping_rounds,
every_n_steps):
super(LossMonitor, self).__init__(every_n_steps=every_n_steps)
self.early_stopping_rounds = early_stopping_rounds
self.min_loss = None
self.min_loss_step = 0
def set_estimator(self, est):
"""This function gets called in the same graph as _get_train_ops."""
super(LossMonitor, self).set_estimator(est)
self._loss_op_name = est.training_loss.name
def every_n_step_end(self, step, outputs):
super(LossMonitor, self).every_n_step_end(step, outputs)
current_loss = outputs[self._loss_op_name]
if self.min_loss is None or current_loss < self.min_loss:
self.min_loss = current_loss
self.min_loss_step = step
return step - self.min_loss_step >= self.early_stopping_rounds
class TensorForestEstimator(estimator.BaseEstimator):
"""An estimator that can train and evaluate a random forest."""
def __init__(self, params, device_assigner=None, model_dir=None,
graph_builder_class=tensor_forest.RandomForestGraphs,
master='', accuracy_metric=None,
tf_random_seed=None, config=None):
self.params = params.fill()
self.accuracy_metric = (accuracy_metric or
('r2' if self.params.regression else 'accuracy'))
self.data_feeder = None
self.device_assigner = (
device_assigner or tensor_forest.RandomForestDeviceAssigner())
self.graph_builder_class = graph_builder_class
self.training_args = {}
self.construction_args = {}
super(TensorForestEstimator, self).__init__(model_dir=model_dir,
config=config)
def predict_proba(
self, x=None, input_fn=None, batch_size=None, as_iterable=False):
"""Returns prediction probabilities for given features (classification).
Args:
x: features.
input_fn: Input function. If set, x and y must be None.
batch_size: Override default batch size.
as_iterable: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
Returns:
Numpy array of predicted probabilities (or an iterable of predicted
probabilities if as_iterable is True).
Raises:
ValueError: If both or neither of x and input_fn were given.
"""
return super(TensorForestEstimator, self).predict(
x=x, input_fn=input_fn, batch_size=batch_size, as_iterable=as_iterable)
def predict(
self, x=None, input_fn=None, axis=None, batch_size=None,
as_iterable=False):
"""Returns predictions for given features.
Args:
x: features.
input_fn: Input function. If set, x must be None.
axis: Axis on which to argmax (for classification).
Last axis is used by default.
batch_size: Override default batch size.
as_iterable: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
Returns:
Numpy array of predicted classes or regression values (or an iterable of
predictions if as_iterable is True).
"""
probabilities = self.predict_proba(
x=x, input_fn=input_fn, batch_size=batch_size, as_iterable=as_iterable)
if self.params.regression:
return probabilities
else:
if as_iterable:
return (np.argmax(p, axis=0) for p in probabilities)
else:
return np.argmax(probabilities, axis=1)
def _get_train_ops(self, features, targets):
"""Method that builds model graph and returns trainer ops.
Args:
features: `Tensor` or `dict` of `Tensor` objects.
targets: `Tensor` or `dict` of `Tensor` objects.
Returns:
Tuple of train `Operation` and loss `Tensor`.
"""
features, spec = data_ops.ParseDataTensorOrDict(features)
labels = data_ops.ParseLabelTensorOrDict(targets)
graph_builder = self.graph_builder_class(
self.params, device_assigner=self.device_assigner,
**self.construction_args)
epoch = None
if self.data_feeder:
epoch = self.data_feeder.make_epoch_variable()
train = control_flow_ops.group(
graph_builder.training_graph(
features, labels, data_spec=spec, epoch=epoch,
**self.training_args),
state_ops.assign_add(contrib_framework.get_global_step(), 1))
self.training_loss = graph_builder.training_loss(features, targets)
return train, self.training_loss
def _get_predict_ops(self, features):
graph_builder = self.graph_builder_class(
self.params, device_assigner=self.device_assigner, training=False,
**self.construction_args)
features, spec = data_ops.ParseDataTensorOrDict(features)
return graph_builder.inference_graph(features, data_spec=spec)
def _get_eval_ops(self, features, targets, metrics):
features, spec = data_ops.ParseDataTensorOrDict(features)
labels = data_ops.ParseLabelTensorOrDict(targets)
graph_builder = self.graph_builder_class(
self.params, device_assigner=self.device_assigner, training=False,
**self.construction_args)
probabilities = graph_builder.inference_graph(features, data_spec=spec)
# One-hot the labels.
if not self.params.regression:
labels = math_ops.to_int64(array_ops.one_hot(math_ops.to_int64(
array_ops.squeeze(labels)), self.params.num_classes, 1, 0))
if metrics is None:
metrics = {self.accuracy_metric:
eval_metrics.get_metric(self.accuracy_metric)}
result = {}
for name, metric in six.iteritems(metrics):
result[name] = metric(probabilities, labels)
return result
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