File size: 7,404 Bytes
97b6013 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 |
# Copyright 2017 Google, Inc. 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.
# ==============================================================================
"""Functions to generate or load datasets for supervised learning."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import namedtuple
import numpy as np
from sklearn.datasets import make_classification
MAX_SEED = 4294967295
class Dataset(namedtuple("Dataset", "data labels")):
"""Helper class for managing a supervised learning dataset.
Args:
data: an array of type float32 with N samples, each of which is the set
of features for that sample. (Shape (N, D_i), where N is the number of
samples and D_i is the number of features for that sample.)
labels: an array of type int32 or int64 with N elements, indicating the
class label for the corresponding set of features in data.
"""
# Since this is an immutable object, we don't need to reserve slots.
__slots__ = ()
@property
def size(self):
"""Dataset size (number of samples)."""
return len(self.data)
def batch_indices(self, num_batches, batch_size):
"""Creates indices of shuffled minibatches.
Args:
num_batches: the number of batches to generate
batch_size: the size of each batch
Returns:
batch_indices: a list of minibatch indices, arranged so that the dataset
is randomly shuffled.
Raises:
ValueError: if the data and labels have different lengths
"""
if len(self.data) != len(self.labels):
raise ValueError("Labels and data must have the same number of samples.")
batch_indices = []
# Follows logic in mnist.py to ensure we cover the entire dataset.
index_in_epoch = 0
dataset_size = len(self.data)
dataset_indices = np.arange(dataset_size)
np.random.shuffle(dataset_indices)
for _ in range(num_batches):
start = index_in_epoch
index_in_epoch += batch_size
if index_in_epoch > dataset_size:
# Finished epoch, reshuffle.
np.random.shuffle(dataset_indices)
# Start next epoch.
start = 0
index_in_epoch = batch_size
end = index_in_epoch
batch_indices.append(dataset_indices[start:end].tolist())
return batch_indices
def noisy_parity_class(n_samples,
n_classes=2,
n_context_ids=5,
noise_prob=0.25,
random_seed=None):
"""Returns a randomly generated sparse-to-sparse dataset.
The label is a parity class of a set of context classes.
Args:
n_samples: number of samples (data points)
n_classes: number of class labels (default: 2)
n_context_ids: how many classes to take the parity of (default: 5).
noise_prob: how often to corrupt the label (default: 0.25)
random_seed: seed used for drawing the random data (default: None)
Returns:
dataset: A Dataset namedtuple containing the generated data and labels
"""
np.random.seed(random_seed)
x = np.random.randint(0, n_classes, [n_samples, n_context_ids])
noise = np.random.binomial(1, noise_prob, [n_samples])
y = (np.sum(x, 1) + noise) % n_classes
return Dataset(x.astype("float32"), y.astype("int32"))
def random(n_features, n_samples, n_classes=2, sep=1.0, random_seed=None):
"""Returns a randomly generated classification dataset.
Args:
n_features: number of features (dependent variables)
n_samples: number of samples (data points)
n_classes: number of class labels (default: 2)
sep: separation of the two classes, a higher value corresponds to
an easier classification problem (default: 1.0)
random_seed: seed used for drawing the random data (default: None)
Returns:
dataset: A Dataset namedtuple containing the generated data and labels
"""
# Generate the problem data.
x, y = make_classification(n_samples=n_samples,
n_features=n_features,
n_informative=n_features,
n_redundant=0,
n_classes=n_classes,
class_sep=sep,
random_state=random_seed)
return Dataset(x.astype("float32"), y.astype("int32"))
def random_binary(n_features, n_samples, random_seed=None):
"""Returns a randomly generated dataset of binary values.
Args:
n_features: number of features (dependent variables)
n_samples: number of samples (data points)
random_seed: seed used for drawing the random data (default: None)
Returns:
dataset: A Dataset namedtuple containing the generated data and labels
"""
random_seed = (np.random.randint(MAX_SEED) if random_seed is None
else random_seed)
np.random.seed(random_seed)
x = np.random.randint(2, size=(n_samples, n_features))
y = np.zeros((n_samples, 1))
return Dataset(x.astype("float32"), y.astype("int32"))
def random_symmetric(n_features, n_samples, random_seed=None):
"""Returns a randomly generated dataset of values and their negatives.
Args:
n_features: number of features (dependent variables)
n_samples: number of samples (data points)
random_seed: seed used for drawing the random data (default: None)
Returns:
dataset: A Dataset namedtuple containing the generated data and labels
"""
random_seed = (np.random.randint(MAX_SEED) if random_seed is None
else random_seed)
np.random.seed(random_seed)
x1 = np.random.normal(size=(int(n_samples/2), n_features))
x = np.concatenate((x1, -x1), axis=0)
y = np.zeros((n_samples, 1))
return Dataset(x.astype("float32"), y.astype("int32"))
def random_mlp(n_features, n_samples, random_seed=None, n_layers=6, width=20):
"""Returns a generated output of an MLP with random weights.
Args:
n_features: number of features (dependent variables)
n_samples: number of samples (data points)
random_seed: seed used for drawing the random data (default: None)
n_layers: number of layers in random MLP
width: width of the layers in random MLP
Returns:
dataset: A Dataset namedtuple containing the generated data and labels
"""
random_seed = (np.random.randint(MAX_SEED) if random_seed is None
else random_seed)
np.random.seed(random_seed)
x = np.random.normal(size=(n_samples, n_features))
y = x
n_in = n_features
scale_factor = np.sqrt(2.) / np.sqrt(n_features)
for _ in range(n_layers):
weights = np.random.normal(size=(n_in, width)) * scale_factor
y = np.dot(y, weights).clip(min=0)
n_in = width
y = y[:, 0]
y[y > 0] = 1
return Dataset(x.astype("float32"), y.astype("int32"))
EMPTY_DATASET = Dataset(np.array([], dtype="float32"),
np.array([], dtype="int32"))
|