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_mcnn.py
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_mcnn.py
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# todo keras/tesnorflow memory problem when search over network parameters
# currently just deleting EVERY model and retraining the best parameters
# at the end, see **1
__author__ = "Aaron Bostrom, James Large"
import gc
import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow import keras
from sktime_dl.classification._classifier import BaseDeepClassifier
from sktime_dl.utils import check_and_clean_data
from sktime_dl.utils import check_is_fitted
from sklearn.utils import check_random_state
class MCNNClassifier(BaseDeepClassifier):
"""Multi-scale Convolutional Neural Network (MCNN).
Adapted from the implementation from Fawaz et. al
https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mcnn.py
Network originally defined in:
@article{cui2016multi,
title={Multi-scale convolutional neural networks for time series
classification},
author={Cui, Zhicheng and Chen, Wenlin and Chen, Yixin},
journal={arXiv preprint arXiv:1603.06995},
year={2016}
}
"""
def __init__(
self,
pool_factors=[2, 3, 5],
filter_sizes=[0.05, 0.1, 0.2],
window_size=0.2,
nb_train_batch=10,
nb_epochs=200,
max_train_batch_size=256,
slice_ratio=0.9,
random_state=0,
verbose=False,
model_name="mcnn",
model_save_directory=None,
):
"""
:param pool_factors: array of shape
:param filter_sizes: array of shape
:param window_size: int,
:param nb_train_batch: int,
:param nb_epochs: int, the number of epochs to train the model
:param max_train_batch_size: int,
:param slice_ratio: int,
:param random_state: int, seed to any needed random actions
:param verbose: boolean, whether to output extra information
:param model_name: string, the name of this model for printing and
file writing purposes
:param model_save_directory: string, if not None; location to save
the trained keras model in hdf5 format
"""
super(MCNNClassifier, self).__init__(
model_save_directory=model_save_directory,
model_name=model_name
)
self.random_state = random_state
self.verbose = verbose
self.pool_factors = (
pool_factors # used for hyperparameters grid search
)
self.filter_sizes = (
filter_sizes # used for hyperparameters grid search
)
self.window_size = window_size
self.nb_train_batch = nb_train_batch
self.nb_epochs = nb_epochs
self.max_train_batch_size = max_train_batch_size
self.slice_ratio = slice_ratio
self._is_fitted = False
def set_hyperparameters(self):
# *******set up the ma and ds********#
self.ma_base = 5
self.ma_step = 6
self.ma_num = 1
self.ds_base = 2
self.ds_step = 1
self.ds_num = 4
def slice_data(self, data_x, data_y, slice_ratio):
n = data_x.shape[0]
length = data_x.shape[1]
n_dim = data_x.shape[2] # for MTS
length_sliced = int(length * slice_ratio)
increase_num = (
length - length_sliced + 1
) # if increase_num =5, it means one ori becomes 5 new instances.
n_sliced = n * increase_num
new_x = np.zeros((n_sliced, length_sliced, n_dim))
new_y = None
if data_y is not None:
nb_classes = data_y.shape[1]
new_y = np.zeros((n_sliced, nb_classes))
for i in range(n):
for j in range(increase_num):
new_x[i * increase_num + j, :, :] = data_x[
i, j: j + length_sliced, :
]
if data_y is not None:
new_y[i * increase_num + j] = np.int_(
data_y[i].astype(np.float32)
)
return new_x, new_y
def _downsample(self, data_x, sample_rate, offset=0):
num = data_x.shape[0]
length_x = data_x.shape[1]
num_dim = data_x.shape[2] # for MTS
last_one = 0
if length_x % sample_rate > offset:
last_one = 1
new_length = int(np.floor(length_x / sample_rate)) + last_one
output = np.zeros((num, new_length, num_dim))
for i in range(new_length):
output[:, i] = np.array(data_x[:, offset + sample_rate * i])
return output
def _movingavrg(self, data_x, window_size):
num = data_x.shape[0]
length_x = data_x.shape[1]
num_dim = data_x.shape[2] # for MTS
output_len = length_x - window_size + 1
output = np.zeros((num, output_len, num_dim))
for i in range(output_len):
output[:, i] = np.mean(data_x[:, i: i + window_size], axis=1)
return output
def movingavrg(self, data_x, window_base, step_size, num):
if num == 0:
return (None, [])
out = self._movingavrg(data_x, window_base)
data_lengths = [out.shape[1]]
for i in range(1, num):
window_size = window_base + step_size * i
if window_size > data_x.shape[1]:
continue
new_series = self._movingavrg(data_x, window_size)
data_lengths.append(new_series.shape[1])
out = np.concatenate([out, new_series], axis=1)
return (out, data_lengths)
def downsample(self, data_x, base, step_size, num):
# the case for dataset JapaneseVowels MTS
if data_x.shape[1] == 26:
return (None, []) # too short to apply downsampling
if num == 0:
return (None, [])
out = self._downsample(data_x, base, 0)
data_lengths = [out.shape[1]]
# for offset in range(1,base): #for the base case
# new_series = _downsample(data_x, base, offset)
# data_lengths.append( new_series.shape[1] )
# out = np.concatenate( [out, new_series], axis = 1)
for i in range(1, num):
sample_rate = base + step_size * i
if sample_rate > data_x.shape[1]:
continue
for offset in range(0, 1): # sample_rate):
new_series = self._downsample(data_x, sample_rate, offset)
data_lengths.append(new_series.shape[1])
out = np.concatenate([out, new_series], axis=1)
return (out, data_lengths)
def train(self, x_train, y_train, pool_factor, filter_size):
# split train into validation set with validation_size = 0.2 train_size
x_train, x_val, y_train, y_val = train_test_split(
x_train, y_train, test_size=0.2
)
ori_len = x_train.shape[1] # original_length of time series
kernel_size = int(ori_len * filter_size)
# restrict slice ratio when data lenght is too large
current_slice_ratio = self.slice_ratio
if ori_len > 500:
current_slice_ratio = (
self.slice_ratio if self.slice_ratio > 0.98 else 0.98
)
increase_num = (
ori_len - int(ori_len * current_slice_ratio) + 1
) # this can be used as the bath size
# print(increase_num)
train_batch_size = int(
x_train.shape[0] * increase_num / self.nb_train_batch
)
current_n_train_batch = self.nb_train_batch
if train_batch_size > self.max_train_batch_size:
# limit the train_batch_size
current_n_train_batch = int(x_train.shape[0] * increase_num /
self.max_train_batch_size)
# data augmentation by slicing the length of the series
x_train, y_train = self.slice_data(
x_train, y_train, current_slice_ratio
)
x_val, y_val = self.slice_data(x_val, y_val, current_slice_ratio)
train_set_x, train_set_y = x_train, y_train
valid_set_x, valid_set_y = x_val, y_val
valid_num = valid_set_x.shape[0]
# print("increase factor is ", increase_num, ', ori len', ori_len)
valid_num_batch = int(valid_num / increase_num)
length_train = train_set_x.shape[1] # length after slicing.
current_window_size = (
int(length_train * self.window_size)
if self.window_size < 1
else int(self.window_size)
)
ds_num_max = length_train / (pool_factor * current_window_size)
current_ds_num = int(min(self.ds_num, ds_num_max))
ma_train, ma_lengths = self.movingavrg(
train_set_x, self.ma_base, self.ma_step, self.ma_num
)
ma_valid, ma_lengths = self.movingavrg(
valid_set_x, self.ma_base, self.ma_step, self.ma_num
)
ds_train, ds_lengths = self.downsample(
train_set_x, self.ds_base, self.ds_step, current_ds_num
)
ds_valid, ds_lengths = self.downsample(
valid_set_x, self.ds_base, self.ds_step, current_ds_num
)
# concatenate directly
data_lengths = [length_train]
# downsample part:
if ds_lengths != []:
data_lengths += ds_lengths
train_set_x = np.concatenate([train_set_x, ds_train], axis=1)
valid_set_x = np.concatenate([valid_set_x, ds_valid], axis=1)
# moving average part
if ma_lengths != []:
data_lengths += ma_lengths
train_set_x = np.concatenate([train_set_x, ma_train], axis=1)
valid_set_x = np.concatenate([valid_set_x, ma_valid], axis=1)
# print("Data length:", data_lengths)
n_train_size = train_set_x.shape[0]
# n_valid_size = valid_set_x.shape[0]
batch_size = int(n_train_size / current_n_train_batch)
n_train_batches = int(n_train_size / batch_size)
# data_dim = train_set_x.shape[1]
num_dim = train_set_x.shape[2] # For MTS
nb_classes = train_set_y.shape[1]
self.input_shapes, max_length = self.get_list_of_input_shapes(
data_lengths, num_dim
)
model = self.build_sub_model(
self.input_shapes, nb_classes, pool_factor, kernel_size
)
# print('submodel built', model)
if self.verbose:
model.summary()
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
max_before_stopping = 500
best_validation_loss = np.inf
# best_iter = 0
valid_loss = 0.0
epoch = 0
done_looping = False
num_no_update_epoch = 0
epoch_avg_cost = float("inf")
# epoch_avg_err = float("inf")
while (epoch < self.nb_epochs) and (not done_looping):
epoch = epoch + 1
epoch_train_err = 0.0
epoch_cost = 0.0
num_no_update_epoch += 1
if num_no_update_epoch == max_before_stopping:
break
for minibatch_index in range(n_train_batches):
iteration = (epoch - 1) * n_train_batches + minibatch_index
x = train_set_x[
minibatch_index
* batch_size: (minibatch_index + 1) * batch_size]
y = train_set_y[
minibatch_index
* batch_size: (minibatch_index + 1) * batch_size]
x = self.split_input_for_model(x, self.input_shapes)
# print('\t pre train batch')
cost_ij, accuracy = model.train_on_batch(x, y)
# print('\t post train batch')
train_err = 1 - accuracy
epoch_train_err = epoch_train_err + train_err
epoch_cost = epoch_cost + cost_ij
if (iteration + 1) % validation_frequency == 0:
valid_losses = []
for i in range(valid_num_batch):
x = valid_set_x[
i * (increase_num): (i + 1) * (increase_num)
]
y_pred = model.predict_on_batch(
self.split_input_for_model(x, self.input_shapes)
)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
label = np.argmax(valid_set_y[i * increase_num])
(
unique_value,
sub_ind,
correspond_ind,
count,
) = np.unique(y_pred, True, True, True)
unique_value = unique_value.tolist()
curr_err = 1.0
if label in unique_value:
target_ind = unique_value.index(label)
count = count.tolist()
sorted_count = sorted(count)
if count[target_ind] == sorted_count[-1]:
if (len(sorted_count) > 1 and
sorted_count[-1] == sorted_count[-2]):
curr_err = 0.5 # tie
else:
curr_err = 0
valid_losses.append(curr_err)
valid_loss = sum(valid_losses) / float(len(valid_losses))
# print('...epoch%i,valid err: %.5f |' %
# (epoch, valid_loss))
# if we got the best validation score until now
if valid_loss <= best_validation_loss:
num_no_update_epoch = 0
# improve patience if loss improvement is good enough
if (
valid_loss
< best_validation_loss * improvement_threshold
):
patience = max(
patience, iteration * patience_increase
)
# save best validation score and iteration number
best_validation_loss = valid_loss
# best_iter = iteration
# save model in h5 format
# self.model.save(self.output_directory+'best_model.hdf5')
if patience <= iteration:
done_looping = True
break
epoch_avg_cost = epoch_cost / n_train_batches
# epoch_avg_err = epoch_train_err / n_train_batches
# print('train err %.5f, cost %.4f' % (epoch_avg_err,
# epoch_avg_cost))
if epoch_avg_cost == 0:
break
return best_validation_loss, model
def split_input_for_model(self, x, input_shapes):
res = []
indx = 0
for input_shape in input_shapes:
res.append(x[:, indx: indx + input_shape[0], :])
indx = indx + input_shape[0]
return res
def get_list_of_input_shapes(self, data_lengths, num_dim):
input_shapes = []
max_length = 0
for i in data_lengths:
input_shapes.append((i, num_dim))
max_length = max(max_length, i)
return input_shapes, max_length
def build_sub_model(
self, input_shapes, nb_classes, pool_factor, kernel_size
):
input_layers = []
stage_1_layers = []
for input_shape in input_shapes:
input_layer = keras.layers.Input(input_shape)
input_layers.append(input_layer)
conv_layer = keras.layers.Conv1D(
filters=256,
kernel_size=kernel_size,
padding="same",
activation="sigmoid",
kernel_initializer="glorot_uniform",
)(input_layer)
# should all concatenated have the same length
pool_size = int(int(conv_layer.shape[1]) / pool_factor)
max_layer = keras.layers.MaxPooling1D(pool_size=pool_size)(
conv_layer
)
# max_layer = keras.layers.GlobalMaxPooling1D()(conv_layer)
stage_1_layers.append(max_layer)
concat_layer = keras.layers.Concatenate(axis=-1)(stage_1_layers)
kernel_size = int(
min(kernel_size, int(concat_layer.shape[1]))
) # kernel shouldn't exceed the length
full_conv = keras.layers.Conv1D(
filters=256,
kernel_size=kernel_size,
padding="same",
activation="sigmoid",
kernel_initializer="glorot_uniform",
)(concat_layer)
pool_size = int(int(full_conv.shape[1]) / pool_factor)
full_max = keras.layers.MaxPooling1D(pool_size=pool_size)(full_conv)
full_max = keras.layers.Flatten()(full_max)
fully_connected = keras.layers.Dense(
units=256,
activation="sigmoid",
kernel_initializer="glorot_uniform",
)(full_max)
output_layer = keras.layers.Dense(
units=nb_classes,
activation="softmax",
kernel_initializer="glorot_uniform",
)(fully_connected)
model = keras.models.Model(inputs=input_layers, outputs=output_layer)
model.compile(
loss="categorical_crossentropy",
optimizer=keras.optimizers.Adam(lr=0.1),
metrics=["accuracy"],
)
return model
def fit(self, X, y, input_checks=True, **kwargs):
"""
Fit the classifier on the training set (X, y)
----------
X : a nested pd.Dataframe, or (if input_checks=False) array-like of
shape = (n_instances, series_length, n_dimensions)
The training input samples. If a 2D array-like is passed,
n_dimensions is assumed to be 1.
y : array-like, shape = [n_instances]
The class labels.
input_checks: boolean
whether to check the X and y parameters
Returns
-------
self : object
"""
self.random_state = check_random_state(self.random_state)
self.set_hyperparameters()
X = check_and_clean_data(X, y, input_checks=input_checks)
y_onehot = self.convert_y(y)
# best_df_metrics = None
best_valid_loss = np.inf
# grid search
for pool_factor in self.pool_factors:
for filter_size in self.filter_sizes:
# print('pretrain')
valid_loss, model = self.train(
X, y_onehot, pool_factor, filter_size
)
# print('posttrain')
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
self.best_pool_factor = pool_factor
self.best_filter_size = filter_size
# self.model = model # see **1 below
# print('postbest', self.model)
# clear memory in all the ways... **1
del model
gc.collect()
keras.backend.clear_session()
# print('postclear',self.model)
_, self.model = self.train(X, y_onehot, pool_factor, filter_size)
self.save_trained_model()
self._is_fitted = True
return self
def predict_proba(self, X, input_checks=True, **kwargs):
"""
Find probability estimates for each class for all cases in X.
Parameters
----------
X : a nested pd.Dataframe, or (if input_checks=False) array-like of
shape = (n_instances, series_length, n_dimensions)
The training input samples. If a 2D array-like is passed,
n_dimensions is assumed to be 1.
input_checks: boolean
whether to check the X parameter
Returns
-------
output : array of shape = [n_instances, n_classes] of probabilities
"""
check_is_fitted(self)
X = check_and_clean_data(X, input_checks=input_checks)
ori_len = X.shape[1] # original_length of time series
# restrict slice ratio when data lenght is too large
current_slice_ratio = self.slice_ratio
if ori_len > 500:
current_slice_ratio = (
self.slice_ratio if self.slice_ratio > 0.98 else 0.98
)
increase_num = (
ori_len - int(ori_len * current_slice_ratio) + 1
) # this can be used as the bath size
# will need to slice at some poin
x_test, _ = self.slice_data(X, None, current_slice_ratio)
length_train = x_test.shape[1] # length after slicing.
current_window_size = (
int(length_train * self.window_size)
if self.window_size < 1
else int(self.window_size)
)
ds_num_max = length_train / (
self.best_pool_factor * current_window_size
)
current_ds_num = int(min(self.ds_num, ds_num_max))
# need to batch and downsample the test data.
ma_test, ma_lengths = self.movingavrg(
x_test, self.ma_base, self.ma_step, self.ma_num
)
ds_test, ds_lengths = self.downsample(
x_test, self.ds_base, self.ds_step, current_ds_num
)
test_set_x = x_test
# concatenate directly
data_lengths = [length_train]
# downsample part:
if ds_lengths != []:
data_lengths += ds_lengths
test_set_x = np.concatenate([test_set_x, ds_test], axis=1)
# moving average part
if ma_lengths != []:
data_lengths += ma_lengths
test_set_x = np.concatenate([test_set_x, ma_test], axis=1)
test_num = x_test.shape[0]
test_num_batch = int(test_num / increase_num)
# get the true predictions of the test set
y_predicted = []
for i in range(test_num_batch):
x = test_set_x[i * (increase_num): (i + 1) * (increase_num)]
preds = self.model.predict_on_batch(
self.split_input_for_model(x, self.input_shapes)
)
y_predicted.append(np.average(preds, axis=0))
y_pred = np.array(y_predicted)
return y_pred