# -*- coding:utf-8 -*-
import tensorflow as tf
import tensorflow.keras.backend as K
from hyperts.utils import consts
from hyperts.framework.dl import layers
from hyperts.framework.dl import BaseDeepEstimator
from hypernets.utils import logging
logger = logging.get_logger(__name__)
[docs]def InceptionTimeModel(task, window, continuous_columns, categorical_columns, blocks=3,
cnn_filters=32, bottleneck_size=32, kernel_size_list=(1, 3, 5, 8, 12),
shortcut=True, short_filters=64, nb_outputs=1, **kwargs):
"""Inception Time Model (InceptionTime).
Parameters
----------
task : Str - Only 'classification' is supported.
window : Positive Int - Length of the time series sequences for a sample.
continuous_columns: CategoricalColumn class.
Contains some information(name, column_names, input_dim, dtype,
input_name) about continuous variables.
categorical_columns: CategoricalColumn class.
Contains some information(name, vocabulary_size, embedding_dim,
dtype, input_name) about categorical variables.
blocks : Int - The depth of the net architecture.
cnn_filters: Int - The number of cnn filters.
bottleneck_size: Int - The number of bottleneck (a cnn layer).
kernel_size_list: Tuple - The kernel size of cnn for a inceptionblock.
shortcut : Bool - Whether to use shortcut opration.
short_filters: Int - The number of filters of shortcut conv1d layer.
nb_outputs : Int - The number of classes, default 1.
"""
if task not in consts.TASK_LIST_CLASSIFICATION + consts.TASK_LIST_REGRESSION:
raise ValueError(f'Unsupported task type {task}.')
kernel_size_list = list(filter(lambda x: x < window, kernel_size_list))
K.clear_session()
continuous_inputs, categorical_inputs = layers.build_input_head(window, continuous_columns, categorical_columns)
denses = layers.build_denses(continuous_columns, continuous_inputs)
embeddings = layers.build_embeddings(categorical_columns, categorical_inputs)
if embeddings is not None:
denses = layers.LayerNormalization(name='layer_norm')(denses)
x = layers.Concatenate(axis=-1, name='concat_embeddings_dense_inputs')([denses, embeddings])
else:
x = denses
s = x
for i in range(blocks):
x = layers.InceptionBlock(filters=cnn_filters,
bottleneck_size=bottleneck_size,
kernel_size_list=kernel_size_list,
name=f'inceptionblock_{i}')(x)
if shortcut and i % 3 == 2:
x = layers.Conv1D(short_filters, 1, padding='same', use_bias=False, name=f'shortconv_{i}')(x)
s = layers.Shortcut(filters=short_filters, name=f'shortcut_{i}')(s)
x = layers.Add(name=f'add_x_s_{i}')([x, s])
x = layers.Activation('relu', name=f'relu_x_s_{i}')(x)
s = x
x = layers.GlobalAveragePooling1D(name='globeal_avg_pool')(x)
outputs = layers.build_output_tail(x, task, nb_outputs)
all_inputs = list(continuous_inputs.values()) + list(categorical_inputs.values())
model = tf.keras.models.Model(inputs=all_inputs, outputs=[outputs], name=f'InceptionTime')
return model
[docs]class InceptionTime(BaseDeepEstimator):
"""InceptionTime Estimator.
Parameters
----------
task : Str - Support forecast, classification, and regression.
See hyperts.utils.consts for details.
blocks : Int - The depth of the net architecture.
default = 3.
cnn_filters: Int - The number of cnn filters.
default = 32.
bottleneck_size: Int - The number of bottleneck (a cnn layer).
default = 32.
kernel_size_list: Tuple - The kernel size of cnn for a inceptionblock.
default = (1, 3, 5, 8, 12).
shortcut : Bool - Whether to use shortcut opration.
default = True.
short_filters: Int - The number of filters of shortcut conv1d layer.
default = 64.
timestamp : Str or None - Timestamp name, the forecast task must be given,
default None.
window : Positive Int - Length of the time series sequences for a sample,
default = 3.
horizon : Positive Int - Length of the prediction horizon,
default = 1.
forecast_length : Positive Int - Step of the forecast outputs,
default = 1.
metrics : Str - List of metrics to be evaluated by the model during training and testing,
default = 'auto'.
monitor_metric : Str - Quality indicators monitored during neural network training.
default = 'val_loss'.
optimizer : Str or keras Instance - for example, 'adam', 'sgd', and so on.
default = 'auto'.
learning_rate : Positive Float - The optimizer's learning rate,
default = 0.001.
loss : Str - Loss function, for forecsting or regression, optional {'auto', 'mae', 'mse', 'huber_loss',
'mape'}, for classification, optional {'auto', 'categorical_crossentropy', 'binary_crossentropy},
default = 'auto'.
reducelr_patience : Positive Int - The number of epochs with no improvement after which learning rate
will be reduced, default = 5.
earlystop_patience : Positive Int - The number of epochs with no improvement after which training
will be stopped, default = 5.
summary : Bool - Whether to output network structure information,
default = True.
continuous_columns: CategoricalColumn class.
Contains some information(name, column_names, input_dim, dtype,
input_name) about continuous variables.
categorical_columns: CategoricalColumn class.
Contains some information(name, vocabulary_size, embedding_dim,
dtype, input_name) about categorical variables.
"""
def __init__(self,
task,
blocks=3,
cnn_filters=32,
bottleneck_size=32,
kernel_size_list=(1, 3, 5, 8, 12),
shortcut=True,
short_filters=64,
timestamp=None,
window=3,
horizon=1,
forecast_length=1,
metrics='auto',
monitor_metric='val_loss',
optimizer='auto',
learning_rate=0.001,
loss='auto',
reducelr_patience=5,
earlystop_patience=10,
summary=True,
continuous_columns=None,
categorical_columns=None,
**kwargs):
self.blocks = blocks
self.cnn_filters = cnn_filters
self.bottleneck_size = bottleneck_size
self.kernel_size_list = kernel_size_list
self.shortcut = shortcut
self.short_filters = short_filters
self.metrics = metrics
self.optimizer = optimizer
self.learning_rate = learning_rate
self.loss = loss
self.summary = summary
self.model_kwargs = kwargs.copy()
super(InceptionTime, self).__init__(task=task,
timestamp=timestamp,
window=window,
horizon=horizon,
forecast_length=forecast_length,
monitor_metric=monitor_metric,
reducelr_patience=reducelr_patience,
earlystop_patience=earlystop_patience,
continuous_columns=continuous_columns,
categorical_columns=categorical_columns)
def _build_estimator(self, **kwargs):
model_params = {
'task': self.task,
'window': self.window,
'blocks': self.blocks,
'continuous_columns': self.continuous_columns,
'categorical_columns': self.categorical_columns,
'cnn_filters': self.cnn_filters,
'bottleneck_size': self.bottleneck_size,
'kernel_size_list': self.kernel_size_list,
'nb_outputs': self.meta.classes_,
'shortcut': self.shortcut,
'short_filters': self.short_filters,
}
model_params = {**model_params, **self.model_kwargs, **kwargs}
return InceptionTimeModel(**model_params)
def _fit(self, train_X, train_y, valid_X, valid_y, **kwargs):
train_ds = self._from_tensor_slices(X=train_X, y=train_y,
batch_size=kwargs['batch_size'],
epochs=kwargs['epochs'],
shuffle=True)
valid_ds = self._from_tensor_slices(X=valid_X, y=valid_y,
batch_size=kwargs.pop('batch_size'),
epochs=kwargs['epochs'],
shuffle=False)
model = self._build_estimator()
if self.summary and kwargs['verbose'] != 0:
model.summary()
else:
logger.info(f'Number of current InceptionTime params: {model.count_params()}')
model = self._compile_model(model, self.optimizer, self.learning_rate)
history = model.fit(train_ds, validation_data=valid_ds, **kwargs)
return model, history
@tf.function(experimental_relax_shapes=True)
def _predict(self, X):
return self.model(X, training=False)