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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.
# ==============================================================================
"""Tests for LogisticRegressor."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib import layers
from tensorflow.contrib.framework.python.ops import variables
from tensorflow.contrib.layers.python.layers import optimizers
from tensorflow.contrib.learn.python.learn.datasets import base
from tensorflow.contrib.learn.python.learn.estimators import logistic_regressor
from tensorflow.contrib.learn.python.learn.estimators import metric_key
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.losses import losses
from tensorflow.python.platform import test
def _iris_data_input_fn():
# Converts iris data to a logistic regression problem.
iris = base.load_iris()
ids = np.where((iris.target == 0) | (iris.target == 1))
features = constant_op.constant(iris.data[ids], dtype=dtypes.float32)
labels = constant_op.constant(iris.target[ids], dtype=dtypes.float32)
labels = array_ops.reshape(labels, labels.get_shape().concatenate(1))
return features, labels
def _logistic_regression_model_fn(features, labels, mode):
_ = mode
logits = layers.linear(
features,
1,
weights_initializer=init_ops.zeros_initializer(),
# Intentionally uses really awful initial values so that
# AUC/precision/recall/etc will change meaningfully even on a toy dataset.
biases_initializer=init_ops.constant_initializer(-10.0))
predictions = math_ops.sigmoid(logits)
loss = losses.sigmoid_cross_entropy(labels, logits)
train_op = optimizers.optimize_loss(
loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1)
return predictions, loss, train_op
class LogisticRegressorTest(test.TestCase):
def test_fit_and_evaluate_metrics(self):
"""Tests basic fit and evaluate, and checks the evaluation metrics."""
regressor = logistic_regressor.LogisticRegressor(
model_fn=_logistic_regression_model_fn)
# Get some (intentionally horrible) baseline metrics.
regressor.fit(input_fn=_iris_data_input_fn, steps=1)
eval_metrics = regressor.evaluate(input_fn=_iris_data_input_fn, steps=1)
self.assertNear(
0.0, eval_metrics[metric_key.MetricKey.PREDICTION_MEAN], err=1e-3)
self.assertNear(
0.5, eval_metrics[metric_key.MetricKey.LABEL_MEAN], err=1e-6)
self.assertNear(
0.5, eval_metrics[metric_key.MetricKey.ACCURACY_BASELINE], err=1e-6)
self.assertNear(0.5, eval_metrics[metric_key.MetricKey.AUC], err=1e-6)
self.assertNear(
0.5, eval_metrics[metric_key.MetricKey.ACCURACY_MEAN % 0.5], err=1e-6)
self.assertNear(
0.0, eval_metrics[metric_key.MetricKey.PRECISION_MEAN % 0.5], err=1e-6)
self.assertNear(
0.0, eval_metrics[metric_key.MetricKey.RECALL_MEAN % 0.5], err=1e-6)
# Train for more steps and check the metrics again.
regressor.fit(input_fn=_iris_data_input_fn, steps=100)
eval_metrics = regressor.evaluate(input_fn=_iris_data_input_fn, steps=1)
# Mean prediction moves from ~0.0 to ~0.5 as we stop predicting all 0's.
self.assertNear(
0.5, eval_metrics[metric_key.MetricKey.PREDICTION_MEAN], err=1e-2)
# Label mean and baseline both remain the same at 0.5.
self.assertNear(
0.5, eval_metrics[metric_key.MetricKey.LABEL_MEAN], err=1e-6)
self.assertNear(
0.5, eval_metrics[metric_key.MetricKey.ACCURACY_BASELINE], err=1e-6)
# AUC improves from 0.5 to 1.0.
self.assertNear(1.0, eval_metrics[metric_key.MetricKey.AUC], err=1e-6)
# Accuracy improves from 0.5 to >0.9.
self.assertTrue(
eval_metrics[metric_key.MetricKey.ACCURACY_MEAN % 0.5] > 0.9)
# Precision improves from 0.0 to 1.0.
self.assertNear(
1.0, eval_metrics[metric_key.MetricKey.PRECISION_MEAN % 0.5], err=1e-6)
# Recall improves from 0.0 to >0.9.
self.assertTrue(eval_metrics[metric_key.MetricKey.RECALL_MEAN % 0.5] > 0.9)
if __name__ == '__main__':
test.main()
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