/
_fcn.py
326 lines (285 loc) · 11.4 KB
/
_fcn.py
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
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
"""Fully Convolutional Network (FCN) for regression."""
__maintainer__ = []
__all__ = ["FCNRegressor"]
import gc
import os
import time
from copy import deepcopy
from sklearn.utils import check_random_state
from aeon.networks import FCNNetwork
from aeon.regression.deep_learning.base import BaseDeepRegressor
class FCNRegressor(BaseDeepRegressor):
"""Fully Convolutional Network (FCN).
Adapted from the implementation used in [1]_.
Parameters
----------
n_layers : int, default = 3
number of convolution layers
n_filters : int or list of int, default = [128,256,128]
number of filters used in convolution layers
kernel_size : int or list of int, default = [8,5,3]
size of convolution kernel
dilation_rate : int or list of int, default = 1
the dilation rate for convolution
strides : int or list of int, default = 1
the strides of the convolution filter
padding : str or list of str, default = "same"
the type of padding used for convolution
activation : str or list of str, default = "relu"
activation used after the convolution
use_bias : bool or list of bool, default = True
whether or not ot use bias in convolution
n_epochs : int, default = 2000
the number of epochs to train the model
batch_size : int, default = 16
the number of samples per gradient update.
use_mini_batch_size : bool, default = False,
whether or not to use the mini batch size formula
random_state : int, RandomState instance or None, default=None
If `int`, random_state is the seed used by the random number generator;
If `RandomState` instance, random_state is the random number generator;
If `None`, the random number generator is the `RandomState` instance used
by `np.random`.
Seeded random number generation can only be guaranteed on CPU processing,
GPU processing will be non-deterministic.
verbose : boolean, default = False
whether to output extra information
output_activation : str, default = "linear",
the output activation of the regressor
loss : string, default="mean_squared_error"
fit parameter for the keras model
metrics : list of strings, default="mean_squared_error",
The evaluation metrics to use during training. If
a single string metric is provided, it will be
used as the only metric. If a list of metrics are
provided, all will be used for evaluation.
optimizer : keras.optimizers object, default = Adam(lr=0.01)
specify the optimizer and the learning rate to be used.
file_path : str, default = "./"
file path to save best model
save_best_model : bool, default = False
Whether or not to save the best model, if the
modelcheckpoint callback is used by default,
this condition, if True, will prevent the
automatic deletion of the best saved model from
file and the user can choose the file name
save_last_model : bool, default = False
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
best_file_name : str, default = "best_model"
The name of the file of the best model, if
save_best_model is set to False, this parameter
is discarded
last_file_name : str, default = "last_model"
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
callbacks : keras.callbacks, default = None
Notes
-----
Adapted from the implementation from Fawaz et. al
https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/fcn.py
References
----------
.. [1] Zhao et. al, Convolutional neural networks for time series classification,
Journal of Systems Engineering and Electronics, 28(1):2017.
Examples
--------
>>> from aeon.regression.deep_learning import FCNRegressor
>>> from aeon.testing.utils.data_gen import make_example_3d_numpy
>>> X, y = make_example_3d_numpy(n_cases=10, n_channels=1, n_timepoints=12,
... return_y=True, regression_target=True,
... random_state=0)
>>> rgs = FCNRegressor(n_epochs=20,batch_size=4) # doctest: +SKIP
>>> rgs.fit(X, y) # doctest: +SKIP
FCNRegressor(...)
"""
def __init__(
self,
n_layers=3,
n_filters=None,
kernel_size=None,
dilation_rate=1,
strides=1,
padding="same",
activation="relu",
file_path="./",
save_best_model=False,
save_last_model=False,
best_file_name="best_model",
last_file_name="last_model",
n_epochs=2000,
batch_size=16,
use_mini_batch_size=False,
callbacks=None,
verbose=False,
output_activation="linear",
loss="mse",
metrics="mean_squared_error",
random_state=None,
use_bias=True,
optimizer=None,
):
self.n_layers = n_layers
self.kernel_size = kernel_size
self.n_filters = n_filters
self.strides = strides
self.activation = activation
self.dilation_rate = dilation_rate
self.padding = padding
self.use_bias = use_bias
self.output_activation = output_activation
self.callbacks = callbacks
self.n_epochs = n_epochs
self.use_mini_batch_size = use_mini_batch_size
self.verbose = verbose
self.loss = loss
self.metrics = metrics
self.random_state = random_state
self.optimizer = optimizer
self.file_path = file_path
self.save_best_model = save_best_model
self.save_last_model = save_last_model
self.best_file_name = best_file_name
self.history = None
super().__init__(batch_size=batch_size, last_file_name=last_file_name)
self._network = FCNNetwork(
n_layers=self.n_layers,
kernel_size=self.kernel_size,
n_filters=self.n_filters,
strides=self.strides,
padding=self.padding,
dilation_rate=self.dilation_rate,
activation=self.activation,
use_bias=self.use_bias,
)
def build_model(self, input_shape, **kwargs):
"""Construct a compiled, un-trained, keras model that is ready for training.
In aeon, time series are stored in numpy arrays of shape (d,m), where d
is the number of dimensions, m is the series length. Keras/tensorflow assume
data is in shape (m,d). This method also assumes (m,d). Transpose should
happen in fit.
Parameters
----------
input_shape : tuple
The shape of the data fed into the input layer, should be (m,d)
Returns
-------
output : a compiled Keras Model
"""
import numpy as np
import tensorflow as tf
rng = check_random_state(self.random_state)
self.random_state_ = rng.randint(0, np.iinfo(np.int32).max)
tf.keras.utils.set_random_seed(self.random_state_)
input_layer, output_layer = self._network.build_network(input_shape, **kwargs)
output_layer = tf.keras.layers.Dense(
units=1,
activation=self.output_activation,
)(output_layer)
self.optimizer_ = (
tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
)
model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
model.compile(
loss=self.loss,
optimizer=self.optimizer_,
metrics=self._metrics,
)
return model
def _fit(self, X, y):
"""Fit the regressor on the training set (X, y).
Parameters
----------
X : np.ndarray
The training input samples of shape (n_cases, n_channels, n_timepoints).
y : np.ndarray
The training data target values of shape (n_cases,).
Returns
-------
self : object
"""
import tensorflow as tf
# Transpose to conform to Keras input style.
X = X.transpose(0, 2, 1)
if isinstance(self.metrics, str):
self._metrics = [self.metrics]
else:
self._metrics = self.metrics
self.input_shape = X.shape[1:]
self.training_model_ = self.build_model(self.input_shape)
if self.verbose:
self.training_model_.summary()
if self.use_mini_batch_size:
mini_batch_size = min(self.batch_size, X.shape[0] // 10)
else:
mini_batch_size = self.batch_size
self.file_name_ = (
self.best_file_name if self.save_best_model else str(time.time_ns())
)
self.callbacks_ = (
[
tf.keras.callbacks.ReduceLROnPlateau(
monitor="loss", factor=0.5, patience=50, min_lr=0.0001
),
tf.keras.callbacks.ModelCheckpoint(
filepath=self.file_path + self.file_name_ + ".keras",
monitor="loss",
save_best_only=True,
),
]
if self.callbacks is None
else self.callbacks
)
self.history = self.training_model_.fit(
X,
y,
batch_size=mini_batch_size,
epochs=self.n_epochs,
verbose=self.verbose,
callbacks=self.callbacks_,
)
try:
self.model_ = tf.keras.models.load_model(
self.file_path + self.file_name_ + ".keras", compile=False
)
if not self.save_best_model:
os.remove(self.file_path + self.file_name_ + ".keras")
except FileNotFoundError:
self.model_ = deepcopy(self.training_model_)
if self.save_last_model:
self.save_last_model_to_file(file_path=self.file_path)
gc.collect()
return self
@classmethod
def get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
----------
parameter_set : str, default="default"
Name of the set of test parameters to return, for use in tests. If no
special parameters are defined for a value, will return `"default"` set.
For Regressors, a "default" set of parameters should be provided for
general testing, and a "results_comparison" set for comparing against
previously recorded results if the general set does not produce suitable
probabilities to compare against.
Returns
-------
params : dict or list of dict, default={}
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
`create_test_instance` uses the first (or only) dictionary in `params`.
"""
param = {
"n_epochs": 10,
"batch_size": 4,
"use_bias": False,
"n_layers": 1,
"n_filters": 5,
"kernel_size": 3,
"padding": "valid",
"strides": 2,
}
return [param]