1# Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================== 15"""Test data utilities (deprecated). 16 17This module and all its submodules are deprecated. See 18[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) 19for migration instructions. 20""" 21 22from __future__ import absolute_import 23from __future__ import division 24from __future__ import print_function 25 26import numpy as np 27from tensorflow.contrib.learn.python.learn.datasets import base 28from tensorflow.python.framework import constant_op 29from tensorflow.python.framework import dtypes 30 31 32def get_quantile_based_buckets(feature_values, num_buckets): 33 quantiles = np.percentile( 34 np.array(feature_values), 35 ([100 * (i + 1.) / (num_buckets + 1.) for i in range(num_buckets)])) 36 return list(quantiles) 37 38 39def prepare_iris_data_for_logistic_regression(): 40 # Converts iris data to a logistic regression problem. 41 iris = base.load_iris() 42 ids = np.where((iris.target == 0) | (iris.target == 1)) 43 return base.Dataset(data=iris.data[ids], target=iris.target[ids]) 44 45 46def iris_input_multiclass_fn(): 47 iris = base.load_iris() 48 return { 49 'feature': constant_op.constant( 50 iris.data, dtype=dtypes.float32) 51 }, constant_op.constant( 52 iris.target, shape=(150, 1), dtype=dtypes.int32) 53 54 55def iris_input_logistic_fn(): 56 iris = prepare_iris_data_for_logistic_regression() 57 return { 58 'feature': constant_op.constant( 59 iris.data, dtype=dtypes.float32) 60 }, constant_op.constant( 61 iris.target, shape=(100, 1), dtype=dtypes.int32) 62