diff --git a/aeon/classification/deep_learning/__init__.py b/aeon/classification/deep_learning/__init__.py
index 26ff063855..ba36690cfe 100644
--- a/aeon/classification/deep_learning/__init__.py
+++ b/aeon/classification/deep_learning/__init__.py
@@ -3,6 +3,7 @@
__all__ = [
"BaseDeepClassifier",
"CNNClassifier",
+ "TimeCNNClassifier",
"EncoderClassifier",
"FCNClassifier",
"InceptionTimeClassifier",
@@ -13,7 +14,7 @@
"LITETimeClassifier",
"IndividualLITEClassifier",
]
-from aeon.classification.deep_learning._cnn import CNNClassifier
+from aeon.classification.deep_learning._cnn import CNNClassifier, TimeCNNClassifier
from aeon.classification.deep_learning._encoder import EncoderClassifier
from aeon.classification.deep_learning._fcn import FCNClassifier
from aeon.classification.deep_learning._inception_time import (
diff --git a/aeon/classification/deep_learning/_cnn.py b/aeon/classification/deep_learning/_cnn.py
index 15fa496f3a..6f8a13a5ae 100644
--- a/aeon/classification/deep_learning/_cnn.py
+++ b/aeon/classification/deep_learning/_cnn.py
@@ -1,19 +1,27 @@
-"""Time Convolutional Neural Network (CNN) for classification."""
+"""Time Convolutional Neural Network (CNN) classifier."""
-__maintainer__ = []
-__all__ = ["CNNClassifier"]
+__maintainer__ = ["hadifawaz1999"]
+__all__ = ["CNNClassifier", "TimeCNNClassifier"]
import gc
import os
import time
from copy import deepcopy
+from deprecated.sphinx import deprecated
from sklearn.utils import check_random_state
from aeon.classification.deep_learning.base import BaseDeepClassifier
-from aeon.networks import CNNNetwork
+from aeon.networks import CNNNetwork, TimeCNNNetwork
+# TODO: remove v0.12.0
+@deprecated(
+ version="0.10.0",
+ reason="CNNClassifier has been renamed to TimeCNNClassifier"
+ "and will be removed in 0.12.0.",
+ category=FutureWarning,
+)
class CNNClassifier(BaseDeepClassifier):
"""
Time Convolutional Neural Network (CNN).
@@ -76,12 +84,17 @@ class CNNClassifier(BaseDeepClassifier):
save_last_model : bool, default = False
Whether to save the last model, last epoch trained, using the base class method
save_last_model_to_file.
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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.
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
Notes
-----
@@ -120,8 +133,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
verbose=False,
loss="mean_squared_error",
metrics=None,
@@ -144,7 +159,9 @@ def __init__(
self.file_path = file_path
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.verbose = verbose
self.loss = loss
self.metrics = metrics
@@ -243,6 +260,329 @@ def _fit(self, X, y):
self.input_shape = X.shape[1:]
self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
+ if self.verbose:
+ self.training_model_.summary()
+
+ self.file_name_ = (
+ self.best_file_name if self.save_best_model else str(time.time_ns())
+ )
+
+ if self.callbacks is None:
+ self.callbacks_ = [
+ tf.keras.callbacks.ModelCheckpoint(
+ filepath=self.file_path + self.file_name_ + ".keras",
+ monitor="loss",
+ save_best_only=True,
+ ),
+ ]
+ else:
+ self.callbacks_ = self._get_model_checkpoint_callback(
+ callbacks=self.callbacks,
+ file_path=self.file_path,
+ file_name=self.file_name_,
+ )
+
+ self.history = self.training_model_.fit(
+ X,
+ y_onehot,
+ batch_size=self.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 classifiers, 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`.
+ """
+ param1 = {
+ "n_epochs": 10,
+ "batch_size": 4,
+ "avg_pool_size": 4,
+ }
+
+ test_params = [param1]
+
+ return test_params
+
+
+class TimeCNNClassifier(BaseDeepClassifier):
+ """
+ Time Convolutional Neural Network (CNN).
+
+ Adapted from the implementation used in [1]_.
+
+ Parameters
+ ----------
+ n_layers : int, default = 2
+ The number of convolution layers in the network.
+ kernel_size : int or list of int, default = 7
+ Kernel size of convolution layers, if not a list, the same kernel size
+ is used for all layer, len(list) should be n_layers.
+ n_filters : int or list of int, default = [6, 12]
+ Number of filters for each convolution layer, if not a list, the same n_filters
+ is used in all layers.
+ avg_pool_size : int or list of int, default = 3
+ The size of the average pooling layer, if not a list, the same
+ max pooling size is used for all convolution layer.
+ activation : str or list of str, default = "sigmoid"
+ Keras activation function used in the model for each layer, if not a list,
+ the same activation is used for all layers.
+ padding : str or list of str, default = 'valid'
+ The method of padding in convolution layers, if not a list, the same padding
+ used for all convolution layers.
+ strides : int or list of int, default = 1
+ The strides of kernels in the convolution and max pooling layers, if not a
+ list, the same strides are used for all layers.
+ dilation_rate : int or list of int, default = 1
+ The dilation rate of the convolution layers, if not a list, the same dilation
+ rate is used all over the network.
+ use_bias : bool or list of bool, default = True
+ Condition on whether to use bias values for convolution layers,
+ if not a list, the same condition is used for all layers.
+ 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.
+ 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.
+ verbose : boolean, default = False
+ Whether to output extra information.
+ loss : string, default = "mean_squared_error"
+ Fit parameter for the keras model.
+ optimizer : keras.optimizer, default = keras.optimizers.Adam()
+ metrics : list of strings, default = ["accuracy"]
+ callbacks : keras.callbacks, default = model_checkpoint
+ To save best model on training loss.
+ file_path : file_path for the best model
+ Only used if checkpoint is used as callback.
+ save_best_model : bool, default = False
+ Whether 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 to save the last model, last epoch trained, using the base class method
+ save_last_model_to_file.
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
+ 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.
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
+
+ Notes
+ -----
+ Adapted from the implementation from Fawaz et. al
+ https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/cnn.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.classification.deep_learning import TimeCNNClassifier
+ >>> from aeon.datasets import load_unit_test
+ >>> X_train, y_train = load_unit_test(split="train")
+ >>> X_test, y_test = load_unit_test(split="test")
+ >>> cnn = TimeCNNClassifier(n_epochs=20, batch_size=4) # doctest: +SKIP
+ >>> cnn.fit(X_train, y_train) # doctest: +SKIP
+ TimeCNNClassifier(...)
+ """
+
+ def __init__(
+ self,
+ n_layers=2,
+ kernel_size=7,
+ n_filters=None,
+ avg_pool_size=3,
+ activation="sigmoid",
+ padding="valid",
+ strides=1,
+ dilation_rate=1,
+ n_epochs=2000,
+ batch_size=16,
+ callbacks=None,
+ file_path="./",
+ save_best_model=False,
+ save_last_model=False,
+ save_init_model=False,
+ best_file_name="best_model",
+ last_file_name="last_model",
+ init_file_name="init_model",
+ verbose=False,
+ loss="mean_squared_error",
+ metrics=None,
+ random_state=None,
+ use_bias=True,
+ optimizer=None,
+ ):
+ self.n_layers = n_layers
+ self.kernel_size = kernel_size
+ self.n_filters = n_filters
+ self.padding = padding
+ self.strides = strides
+ self.dilation_rate = dilation_rate
+ self.avg_pool_size = avg_pool_size
+ self.activation = activation
+ self.use_bias = use_bias
+
+ self.n_epochs = n_epochs
+ self.callbacks = callbacks
+ self.file_path = file_path
+ self.save_best_model = save_best_model
+ self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
+ self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
+ self.verbose = verbose
+ self.loss = loss
+ self.metrics = metrics
+ self.optimizer = optimizer
+
+ self.history = None
+
+ super().__init__(
+ batch_size=batch_size,
+ random_state=random_state,
+ last_file_name=last_file_name,
+ )
+
+ self._network = TimeCNNNetwork(
+ n_layers=self.n_layers,
+ kernel_size=self.kernel_size,
+ n_filters=self.n_filters,
+ avg_pool_size=self.avg_pool_size,
+ activation=self.activation,
+ padding=self.padding,
+ strides=self.strides,
+ dilation_rate=self.dilation_rate,
+ use_bias=self.use_bias,
+ )
+
+ def build_model(self, input_shape, n_classes, **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)
+ n_classes : int
+ The number of classes, which becomes the size of the output layer
+
+ Returns
+ -------
+ output : a compiled Keras Model
+ """
+ import numpy as np
+ import tensorflow as tf
+
+ if self.metrics is None:
+ metrics = ["accuracy"]
+ else:
+ metrics = self.metrics
+
+ 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=n_classes, activation=self.activation, use_bias=self.use_bias
+ )(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=metrics,
+ )
+
+ return model
+
+ def _fit(self, X, y):
+ """Fit the classifier 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 class labels of shape (n_cases,).
+
+
+ Returns
+ -------
+ self : object
+ """
+ import tensorflow as tf
+
+ y_onehot = self.convert_y_to_keras(y)
+ # Transpose to conform to Keras input style.
+ X = X.transpose(0, 2, 1)
+
+ self.input_shape = X.shape[1:]
+ self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
+
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/classification/deep_learning/_encoder.py b/aeon/classification/deep_learning/_encoder.py
index 7f839456f2..2765c4cbbe 100644
--- a/aeon/classification/deep_learning/_encoder.py
+++ b/aeon/classification/deep_learning/_encoder.py
@@ -52,6 +52,8 @@ class EncoderClassifier(BaseDeepClassifier):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file.
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -60,6 +62,9 @@ class EncoderClassifier(BaseDeepClassifier):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded.
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
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;
@@ -102,8 +107,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
verbose=False,
loss="categorical_crossentropy",
metrics=None,
@@ -124,7 +131,9 @@ def __init__(
self.file_path = file_path
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.n_epochs = n_epochs
self.verbose = verbose
self.loss = loss
@@ -225,6 +234,9 @@ def _fit(self, X, y):
self.input_shape = X.shape[1:]
self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/classification/deep_learning/_fcn.py b/aeon/classification/deep_learning/_fcn.py
index e77457646b..ea14036b87 100644
--- a/aeon/classification/deep_learning/_fcn.py
+++ b/aeon/classification/deep_learning/_fcn.py
@@ -1,6 +1,6 @@
-"""Fully Convolutional Network (FCN) for classification."""
+"""Fully Convolutional Network (FCN) classifier."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = ["FCNClassifier"]
import gc
@@ -69,6 +69,8 @@ class FCNClassifier(BaseDeepClassifier):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file.
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -77,6 +79,10 @@ class FCNClassifier(BaseDeepClassifier):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded.
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if
+ save_init_model is set to False,
+ this parameter is discarded.
callbacks : keras.callbacks, default = None
Notes
@@ -112,8 +118,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
n_epochs=2000,
batch_size=16,
use_mini_batch_size=False,
@@ -145,7 +153,9 @@ def __init__(
self.file_path = file_path
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.history = None
@@ -238,6 +248,9 @@ def _fit(self, X, y):
self.input_shape = X.shape[1:]
self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/classification/deep_learning/_inception_time.py b/aeon/classification/deep_learning/_inception_time.py
index d5cc3cec56..6b377d565a 100644
--- a/aeon/classification/deep_learning/_inception_time.py
+++ b/aeon/classification/deep_learning/_inception_time.py
@@ -1,6 +1,6 @@
-"""InceptionTime classifier."""
+"""InceptionTime and Inception classifiers."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = ["InceptionTimeClassifier"]
import gc
@@ -103,6 +103,8 @@ class InceptionTimeClassifier(BaseClassifier):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -111,6 +113,9 @@ class InceptionTimeClassifier(BaseClassifier):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
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;
@@ -181,8 +186,10 @@ def __init__(
file_path="./",
save_last_model=False,
save_best_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
batch_size=64,
use_mini_batch_size=False,
n_epochs=1500,
@@ -218,8 +225,10 @@ def __init__(
self.save_last_model = save_last_model
self.save_best_model = save_best_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
self.last_file_name = last_file_name
+ self.init_file_name = init_file_name
self.callbacks = callbacks
self.random_state = random_state
@@ -229,7 +238,7 @@ def __init__(
self.metrics = metrics
self.optimizer = optimizer
- self.classifers_ = []
+ self.classifiers_ = []
super().__init__()
@@ -247,7 +256,7 @@ def _fit(self, X, y):
-------
self : object
"""
- self.classifers_ = []
+ self.classifiers_ = []
rng = check_random_state(self.random_state)
for n in range(0, self.n_classifiers):
@@ -269,8 +278,10 @@ def _fit(self, X, y):
file_path=self.file_path,
save_best_model=self.save_best_model,
save_last_model=self.save_last_model,
+ save_init_model=self.save_init_model,
best_file_name=self.best_file_name + str(n),
last_file_name=self.last_file_name + str(n),
+ init_file_name=self.init_file_name + str(n),
batch_size=self.batch_size,
use_mini_batch_size=self.use_mini_batch_size,
n_epochs=self.n_epochs,
@@ -282,7 +293,7 @@ def _fit(self, X, y):
verbose=self.verbose,
)
cls.fit(X, y)
- self.classifers_.append(cls)
+ self.classifiers_.append(cls)
gc.collect()
return self
@@ -323,7 +334,7 @@ def _predict_proba(self, X) -> np.ndarray:
"""
probs = np.zeros((X.shape[0], self.n_classes_))
- for cls in self.classifers_:
+ for cls in self.classifiers_:
probs += cls._predict_proba(X)
probs = probs / self.n_classifiers
@@ -437,14 +448,19 @@ class IndividualInceptionClassifier(BaseDeepClassifier):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
+ 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
+ is discarded.
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
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;
@@ -504,8 +520,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
batch_size=64,
use_mini_batch_size=False,
n_epochs=1500,
@@ -538,7 +556,9 @@ def __init__(
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.callbacks = callbacks
self.verbose = verbose
@@ -652,6 +672,9 @@ def _fit(self, X, y):
mini_batch_size = self.batch_size
self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/classification/deep_learning/_lite_time.py b/aeon/classification/deep_learning/_lite_time.py
index ef2479a949..f16e136d71 100644
--- a/aeon/classification/deep_learning/_lite_time.py
+++ b/aeon/classification/deep_learning/_lite_time.py
@@ -1,6 +1,6 @@
-"""LITETime classifier."""
+"""LITETime and LITE classifiers."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = ["LITETimeClassifier"]
import gc
@@ -60,6 +60,8 @@ class LITETimeClassifier(BaseClassifier):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -68,6 +70,9 @@ class LITETimeClassifier(BaseClassifier):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
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;
@@ -120,8 +125,10 @@ def __init__(
file_path="./",
save_last_model=False,
save_best_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
batch_size=64,
use_mini_batch_size=False,
n_epochs=1500,
@@ -146,8 +153,10 @@ def __init__(
self.save_last_model = save_last_model
self.save_best_model = save_best_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
self.last_file_name = last_file_name
+ self.init_file_name = init_file_name
self.callbacks = callbacks
self.random_state = random_state
@@ -157,7 +166,7 @@ def __init__(
self.metrics = metrics
self.optimizer = optimizer
- self.classifers_ = []
+ self.classifiers_ = []
super().__init__()
@@ -175,7 +184,7 @@ def _fit(self, X, y):
-------
self : object
"""
- self.classifers_ = []
+ self.classifiers_ = []
rng = check_random_state(self.random_state)
for n in range(0, self.n_classifiers):
@@ -185,8 +194,10 @@ def _fit(self, X, y):
file_path=self.file_path,
save_best_model=self.save_best_model,
save_last_model=self.save_last_model,
+ save_init_model=self.save_init_model,
best_file_name=self.best_file_name + str(n),
last_file_name=self.last_file_name + str(n),
+ init_file_name=self.init_file_name + str(n),
batch_size=self.batch_size,
use_mini_batch_size=self.use_mini_batch_size,
n_epochs=self.n_epochs,
@@ -198,7 +209,7 @@ def _fit(self, X, y):
verbose=self.verbose,
)
cls.fit(X, y)
- self.classifers_.append(cls)
+ self.classifiers_.append(cls)
gc.collect()
return self
@@ -239,7 +250,7 @@ def _predict_proba(self, X) -> np.ndarray:
"""
probs = np.zeros((X.shape[0], self.n_classes_))
- for cls in self.classifers_:
+ for cls in self.classifiers_:
probs += cls._predict_proba(X)
probs = probs / self.n_classifiers
@@ -318,6 +329,8 @@ class IndividualLITEClassifier(BaseDeepClassifier):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -326,6 +339,9 @@ class IndividualLITEClassifier(BaseDeepClassifier):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
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;
@@ -369,8 +385,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
batch_size=64,
use_mini_batch_size=False,
n_epochs=1500,
@@ -393,7 +411,9 @@ def __init__(
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.callbacks = callbacks
self.verbose = verbose
@@ -495,6 +515,9 @@ def _fit(self, X, y):
mini_batch_size = self.batch_size
self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/classification/deep_learning/_mlp.py b/aeon/classification/deep_learning/_mlp.py
index 1624aa9904..48eb8f711e 100644
--- a/aeon/classification/deep_learning/_mlp.py
+++ b/aeon/classification/deep_learning/_mlp.py
@@ -1,6 +1,6 @@
-"""Multi Layer Perceptron Network (MLP) for classification."""
+"""Multi Layer Perceptron Network (MLP) classifier."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = ["MLPClassifier"]
import gc
@@ -51,6 +51,8 @@ class MLPClassifier(BaseDeepClassifier):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -59,6 +61,9 @@ class MLPClassifier(BaseDeepClassifier):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
optimizer : keras.optimizer, default=keras.optimizers.Adadelta(),
metrics : list of strings, default=["accuracy"],
activation : string or a tf callable, default="sigmoid"
@@ -101,8 +106,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
random_state=None,
activation="sigmoid",
use_bias=True,
@@ -119,7 +126,9 @@ def __init__(
self.file_path = file_path
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.optimizer = optimizer
self.history = None
@@ -204,6 +213,9 @@ def _fit(self, X, y):
self.input_shape = X.shape[1:]
self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/classification/deep_learning/_resnet.py b/aeon/classification/deep_learning/_resnet.py
index ccd6d11f0d..963faec26b 100644
--- a/aeon/classification/deep_learning/_resnet.py
+++ b/aeon/classification/deep_learning/_resnet.py
@@ -1,6 +1,6 @@
-"""Residual Network (ResNet) for classification."""
+"""Residual Network (ResNet) classifier."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = ["ResNetClassifier"]
import gc
@@ -75,12 +75,17 @@ class ResNetClassifier(BaseDeepClassifier):
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.
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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.
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
verbose : boolean, default = False
whether to output extra information
loss : string, default = "mean_squared_error"
@@ -131,8 +136,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
optimizer=None,
):
self.n_residual_blocks = n_residual_blocks
@@ -153,7 +160,9 @@ def __init__(
self.file_path = file_path
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.optimizer = optimizer
self.history = None
@@ -250,6 +259,9 @@ def _fit(self, X, y):
self.input_shape = X.shape[1:]
self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/classification/deep_learning/_tapnet.py b/aeon/classification/deep_learning/_tapnet.py
index 4bfe51e4c2..2ad048827b 100644
--- a/aeon/classification/deep_learning/_tapnet.py
+++ b/aeon/classification/deep_learning/_tapnet.py
@@ -1,6 +1,6 @@
-"""Time Convolutional Neural Network (CNN) for classification."""
+"""Time series Attentional Prototype Network (TapNet) Classifier."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = [
"TapNetClassifier",
]
diff --git a/aeon/classification/deep_learning/base.py b/aeon/classification/deep_learning/base.py
index 45974298a0..837945eead 100644
--- a/aeon/classification/deep_learning/base.py
+++ b/aeon/classification/deep_learning/base.py
@@ -5,7 +5,7 @@
because we can generalise tags, _predict and _predict_proba
"""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = ["BaseDeepClassifier"]
from abc import ABC, abstractmethod
diff --git a/aeon/classification/deep_learning/tests/test_deep_classifier_base.py b/aeon/classification/deep_learning/tests/test_deep_classifier_base.py
index 203c256eef..e0c4157c7a 100644
--- a/aeon/classification/deep_learning/tests/test_deep_classifier_base.py
+++ b/aeon/classification/deep_learning/tests/test_deep_classifier_base.py
@@ -10,7 +10,7 @@
from aeon.testing.data_generation import make_example_2d_numpy_collection
from aeon.utils.validation._dependencies import _check_soft_dependencies
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
class _DummyDeepClassifier(BaseDeepClassifier):
diff --git a/aeon/classification/deep_learning/tests/test_random_state_deep_learning.py b/aeon/classification/deep_learning/tests/test_random_state_deep_learning.py
index 4647de8b00..b426169cce 100644
--- a/aeon/classification/deep_learning/tests/test_random_state_deep_learning.py
+++ b/aeon/classification/deep_learning/tests/test_random_state_deep_learning.py
@@ -12,35 +12,37 @@
__maintainer__ = ["hadifawaz1999"]
+_deep_cls_classes = [
+ member[1] for member in inspect.getmembers(deep_learning, inspect.isclass)
+]
+
+
@pytest.mark.skipif(
not _check_soft_dependencies(["tensorflow"], severity="none"),
reason="skip test if required soft dependency not available",
)
-def test_random_state_deep_learning_cls():
+@pytest.mark.parametrize("deep_cls", _deep_cls_classes)
+def test_random_state_deep_learning_cls(deep_cls):
"""Test Deep Classifier seeding."""
- random_state = 42
-
- X, y = make_example_3d_numpy(random_state=random_state)
-
- deep_cls_classes = [
- member[1] for member in inspect.getmembers(deep_learning, inspect.isclass)
- ]
-
- for i in range(len(deep_cls_classes)):
- if (
- "BaseDeepClassifier" in str(deep_cls_classes[i])
- or "InceptionTimeClassifier" in str(deep_cls_classes[i])
- or "LITETimeClassifier" in str(deep_cls_classes[i])
- or "TapNetClassifier" in str(deep_cls_classes[i])
- ):
- continue
-
- deep_cls1 = deep_cls_classes[i](random_state=random_state, n_epochs=4)
+ if not (
+ deep_cls.__name__
+ in [
+ "BaseDeepClassifier",
+ "InceptionTimeClassifier",
+ "LITETimeClassifier",
+ "TapNetClassifier",
+ ]
+ ):
+ random_state = 42
+
+ X, y = make_example_3d_numpy(random_state=random_state)
+
+ deep_cls1 = deep_cls(random_state=random_state, n_epochs=4)
deep_cls1.fit(X, y)
layers1 = deep_cls1.training_model_.layers[1:]
- deep_cls2 = deep_cls_classes[i](random_state=random_state, n_epochs=4)
+ deep_cls2 = deep_cls(random_state=random_state, n_epochs=4)
deep_cls2.fit(X, y)
layers2 = deep_cls2.training_model_.layers[1:]
diff --git a/aeon/classification/deep_learning/tests/test_saving_loading_deep_learning_cls.py b/aeon/classification/deep_learning/tests/test_saving_loading_deep_learning_cls.py
new file mode 100644
index 0000000000..d90393a369
--- /dev/null
+++ b/aeon/classification/deep_learning/tests/test_saving_loading_deep_learning_cls.py
@@ -0,0 +1,83 @@
+"""Unit tests for classifiers deep learners save/load functionalities."""
+
+import inspect
+import os
+import tempfile
+import time
+
+import numpy as np
+import pytest
+
+from aeon.classification import deep_learning
+from aeon.testing.data_generation import make_example_3d_numpy
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+__maintainer__ = ["hadifawaz1999"]
+
+
+_deep_cls_classes = [
+ member[1] for member in inspect.getmembers(deep_learning, inspect.isclass)
+]
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="skip test if required soft dependency not available",
+)
+@pytest.mark.parametrize("deep_cls", _deep_cls_classes)
+def test_saving_loading_deep_learning_cls(deep_cls):
+ """Test Deep Classifier saving."""
+ with tempfile.TemporaryDirectory() as tmp:
+ if not (
+ deep_cls.__name__
+ in [
+ "BaseDeepClassifier",
+ "InceptionTimeClassifier",
+ "LITETimeClassifier",
+ "TapNetClassifier",
+ ]
+ ):
+ if tmp[-1] != "/":
+ tmp = tmp + "/"
+ curr_time = str(time.time_ns())
+ last_file_name = curr_time + "last"
+ best_file_name = curr_time + "best"
+ init_file_name = curr_time + "init"
+
+ X, y = make_example_3d_numpy()
+
+ deep_cls_train = deep_cls(
+ n_epochs=2,
+ save_best_model=True,
+ save_last_model=True,
+ save_init_model=True,
+ best_file_name=best_file_name,
+ last_file_name=last_file_name,
+ init_file_name=init_file_name,
+ file_path=tmp,
+ )
+ deep_cls_train.fit(X, y)
+
+ deep_cls_best = deep_cls()
+ deep_cls_best.load_model(
+ model_path=os.path.join(tmp, best_file_name + ".keras"),
+ classes=np.unique(y),
+ )
+ ypred_best = deep_cls_best.predict(X)
+ assert len(ypred_best) == len(y)
+
+ deep_cls_last = deep_cls()
+ deep_cls_last.load_model(
+ model_path=os.path.join(tmp, last_file_name + ".keras"),
+ classes=np.unique(y),
+ )
+ ypred_last = deep_cls_last.predict(X)
+ assert len(ypred_last) == len(y)
+
+ deep_cls_init = deep_cls()
+ deep_cls_init.load_model(
+ model_path=os.path.join(tmp, init_file_name + ".keras"),
+ classes=np.unique(y),
+ )
+ ypred_init = deep_cls_init.predict(X)
+ assert len(ypred_init) == len(y)
diff --git a/aeon/classification/dictionary_based/__init__.py b/aeon/classification/dictionary_based/__init__.py
index 832eae10a5..29f5970edc 100644
--- a/aeon/classification/dictionary_based/__init__.py
+++ b/aeon/classification/dictionary_based/__init__.py
@@ -10,10 +10,12 @@
"WEASEL_V2",
"MUSE",
"REDCOMETS",
+ "MrSQMClassifier",
]
from aeon.classification.dictionary_based._boss import BOSSEnsemble, IndividualBOSS
from aeon.classification.dictionary_based._cboss import ContractableBOSS
+from aeon.classification.dictionary_based._mrsqm import MrSQMClassifier
from aeon.classification.dictionary_based._muse import MUSE
from aeon.classification.dictionary_based._redcomets import REDCOMETS
from aeon.classification.dictionary_based._tde import (
diff --git a/aeon/classification/shapelet_based/_mrsqm.py b/aeon/classification/dictionary_based/_mrsqm.py
similarity index 92%
rename from aeon/classification/shapelet_based/_mrsqm.py
rename to aeon/classification/dictionary_based/_mrsqm.py
index a3b1d4cc57..6c9304a144 100644
--- a/aeon/classification/shapelet_based/_mrsqm.py
+++ b/aeon/classification/dictionary_based/_mrsqm.py
@@ -1,13 +1,12 @@
"""Multiple Representations Sequence Miner (MrSQM) Classifier."""
-__maintainer__ = []
+__maintainer__ = ["TonyBagnall"]
__all__ = ["MrSQMClassifier"]
from typing import List, Union
import numpy as np
import pandas as pd
-from deprecated.sphinx import deprecated
from aeon.classification import BaseClassifier
@@ -31,13 +30,6 @@ def _from_numpy3d_to_nested_dataframe(X):
return df
-# TODO: Move in v0.11.0
-@deprecated(
- version="0.10.0",
- reason="MrSQMClassifier will be moved to the dictionary_based package in version "
- "0.11.0 at the request of the author.",
- category=FutureWarning,
-)
class MrSQMClassifier(BaseClassifier):
"""
Multiple Representations Sequence Miner (MrSQM) classifier.
@@ -46,7 +38,7 @@ class MrSQMClassifier(BaseClassifier):
MrSQM is not included in all extras as it requires gcc and fftw
(http://www.fftw.org/index.html) to be installed for Windows and some Linux OS.
- Overview: MrSQM is an efficient time series classifier utilising symbolic
+ Overview: MrSQM is a time series classifier utilising symbolic
representations of time series. MrSQM implements four different feature selection
strategies (R,S,RS,SR) that can quickly select subsequences from multiple symbolic
representations of time series data.
@@ -92,7 +84,7 @@ class MrSQMClassifier(BaseClassifier):
Examples
--------
- >>> from aeon.classification.shapelet_based import MrSQMClassifier
+ >>> from aeon.classification.dictionary_based import MrSQMClassifier
>>> from aeon.testing.data_generation import make_example_3d_numpy
>>> X, y = make_example_3d_numpy(random_state=0)
>>> clf = MrSQMClassifier(random_state=0) # doctest: +SKIP
@@ -102,7 +94,7 @@ class MrSQMClassifier(BaseClassifier):
"""
_tags = {
- "X_inner_type": "numpy3D", # we don't like this, but it's the only input!
+ "X_inner_type": "numpy3D",
"algorithm_type": "shapelet",
"cant-pickle": True,
"python_dependencies": "mrsqm",
diff --git a/aeon/classification/shapelet_based/__init__.py b/aeon/classification/shapelet_based/__init__.py
index 28702fe52f..3b76ddddec 100644
--- a/aeon/classification/shapelet_based/__init__.py
+++ b/aeon/classification/shapelet_based/__init__.py
@@ -1,7 +1,6 @@
"""Shapelet based time series classifiers."""
__all__ = [
- "MrSQMClassifier",
"ShapeletTransformClassifier",
"RDSTClassifier",
"SASTClassifier",
@@ -10,7 +9,6 @@
]
from aeon.classification.shapelet_based._ls import LearningShapeletClassifier
-from aeon.classification.shapelet_based._mrsqm import MrSQMClassifier
from aeon.classification.shapelet_based._rdst import RDSTClassifier
from aeon.classification.shapelet_based._rsast import RSASTClassifier
from aeon.classification.shapelet_based._sast import SASTClassifier
diff --git a/aeon/clustering/_k_means.py b/aeon/clustering/_k_means.py
index c7fadd2f8e..8c46a5df9c 100644
--- a/aeon/clustering/_k_means.py
+++ b/aeon/clustering/_k_means.py
@@ -326,6 +326,9 @@ def _check_params(self, X: np.ndarray) -> None:
# Invalid distance passed for ba so default to dba
self._average_params["distance"] = "dtw"
+ if "random_state" not in self._average_params:
+ self._average_params["random_state"] = self._random_state
+
self._averaging_method = _resolve_average_callable(self.averaging_method)
if self.n_clusters > X.shape[0]:
diff --git a/aeon/clustering/deep_learning/_ae_fcn.py b/aeon/clustering/deep_learning/_ae_fcn.py
index 322a614a01..a9f33751ce 100644
--- a/aeon/clustering/deep_learning/_ae_fcn.py
+++ b/aeon/clustering/deep_learning/_ae_fcn.py
@@ -1,6 +1,6 @@
"""Deep Learning Auto-Encoder using FCN Network."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = ["AEFCNClusterer"]
import gc
diff --git a/aeon/clustering/deep_learning/_ae_resnet.py b/aeon/clustering/deep_learning/_ae_resnet.py
index 19ab56549c..2d1ccf13e0 100644
--- a/aeon/clustering/deep_learning/_ae_resnet.py
+++ b/aeon/clustering/deep_learning/_ae_resnet.py
@@ -1,6 +1,6 @@
"""Residual Network (ResNet) for clustering."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = ["AEResNetClusterer"]
import gc
diff --git a/aeon/clustering/deep_learning/tests/test_deep_clusterer_base.py b/aeon/clustering/deep_learning/tests/test_deep_clusterer_base.py
index cc8e952e85..421aa69a85 100644
--- a/aeon/clustering/deep_learning/tests/test_deep_clusterer_base.py
+++ b/aeon/clustering/deep_learning/tests/test_deep_clusterer_base.py
@@ -9,7 +9,7 @@
from aeon.testing.mock_estimators import MockDeepClusterer
from aeon.utils.validation._dependencies import _check_soft_dependencies
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
@pytest.mark.skipif(
diff --git a/aeon/datasets/_data_loaders.py b/aeon/datasets/_data_loaders.py
index 4f16e02eea..19f83ff52b 100644
--- a/aeon/datasets/_data_loaders.py
+++ b/aeon/datasets/_data_loaders.py
@@ -962,7 +962,7 @@ def load_forecasting(name, extract_path=None, return_metadata=False):
extract_path : optional (default = None)
Path of the location for the data file. If none, data is written to
os.path.dirname(__file__)/data/
- return_metadata : boolean, default = True
+ return_metadata : boolean, default = False
If True, returns a tuple (data, metadata)
Returns
diff --git a/aeon/forecasting/compose/_reduce.py b/aeon/forecasting/compose/_reduce.py
index 05335e5217..8a6a1551cc 100644
--- a/aeon/forecasting/compose/_reduce.py
+++ b/aeon/forecasting/compose/_reduce.py
@@ -27,7 +27,7 @@
from aeon.forecasting.base._fh import _index_range
from aeon.regression.base import BaseRegressor
from aeon.transformations._legacy.compose import FeatureUnion
-from aeon.transformations.summarize import WindowSummarizer
+from aeon.transformations._legacy.summarize import WindowSummarizer
from aeon.utils.datetime import _shift
from aeon.utils.index_functions import get_time_index
from aeon.utils.sklearn import is_sklearn_regressor
@@ -1839,7 +1839,7 @@ def _predict(self, X=None, fh=None):
def _fit_shifted(self, y, X=None, fh=None):
"""Fit to training data."""
- from aeon.transformations.lag import Lag, ReducerTransform
+ from aeon.transformations._legacy.lag import Lag, ReducerTransform
impute_method = self.impute_method
lags = self._lags
@@ -1918,7 +1918,7 @@ def _predict_shifted(self, fh=None, X=None):
def _fit_concurrent(self, y, X=None, fh=None):
"""Fit to training data."""
- from aeon.transformations.lag import Lag, ReducerTransform
+ from aeon.transformations._legacy.lag import Lag, ReducerTransform
impute_method = self.impute_method
@@ -1983,7 +1983,7 @@ def _fit_concurrent(self, y, X=None, fh=None):
def _predict_concurrent(self, X=None, fh=None):
"""Fit to training data."""
- from aeon.transformations.lag import Lag
+ from aeon.transformations._legacy.lag import Lag
if X is not None and self._X is not None:
X_pool = X.combine_first(self._X)
@@ -2178,8 +2178,8 @@ def _fit(self, y, X=None, fh=None):
-------
self : reference to self
"""
- from aeon.transformations._legacy.impute import Imputer
- from aeon.transformations.lag import Lag
+ from aeon.transformations._legacy.lag import Lag
+ from aeon.transformations.impute import Imputer
impute_method = self.impute_method
@@ -2265,8 +2265,8 @@ def _predict(self, X=None, fh=None):
def _predict_out_of_sample(self, X_pool, fh):
"""Recursive reducer: predict out of sample (ahead of cutoff)."""
# very similar to _predict_concurrent of DirectReductionForecaster - refactor?
- from aeon.transformations._legacy.impute import Imputer
- from aeon.transformations.lag import Lag
+ from aeon.transformations._legacy.lag import Lag
+ from aeon.transformations.impute import Imputer
fh_idx = self._get_expected_pred_idx(fh=fh)
y_cols = self._y.columns
@@ -2332,8 +2332,8 @@ def _predict_out_of_sample(self, X_pool, fh):
def _predict_in_sample(self, X_pool, fh):
"""Recursive reducer: predict out of sample (in past of of cutoff)."""
- from aeon.transformations._legacy.impute import Imputer
- from aeon.transformations.lag import Lag
+ from aeon.transformations._legacy.lag import Lag
+ from aeon.transformations.impute import Imputer
fh_idx = self._get_expected_pred_idx(fh=fh)
y_cols = self._y.columns
diff --git a/aeon/forecasting/compose/tests/test_pipeline.py b/aeon/forecasting/compose/tests/test_pipeline.py
index 82e75b36ce..d0bc059a4c 100644
--- a/aeon/forecasting/compose/tests/test_pipeline.py
+++ b/aeon/forecasting/compose/tests/test_pipeline.py
@@ -19,10 +19,10 @@
from aeon.testing.mock_estimators import MockForecaster, MockTransformer
from aeon.testing.utils.estimator_checks import _assert_array_almost_equal
from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
-from aeon.transformations._legacy.impute import Imputer
+from aeon.transformations._legacy.outlier_detection import HampelFilter
from aeon.transformations.detrend import Detrender
from aeon.transformations.hierarchical.aggregate import Aggregator
-from aeon.transformations.outlier_detection import HampelFilter
+from aeon.transformations.impute import Imputer
from aeon.utils.index_functions import get_window
from aeon.utils.validation._dependencies import _check_soft_dependencies
diff --git a/aeon/forecasting/compose/tests/test_reduce_global.py b/aeon/forecasting/compose/tests/test_reduce_global.py
deleted file mode 100644
index 042f6a9cfb..0000000000
--- a/aeon/forecasting/compose/tests/test_reduce_global.py
+++ /dev/null
@@ -1,306 +0,0 @@
-"""Test extraction of features across (shifted) windows."""
-
-__maintainer__ = []
-
-from aeon.utils.validation._dependencies import _check_soft_dependencies
-
-# HistGradientBoostingRegressor requires experimental flag in old sklearn versions
-if _check_soft_dependencies("sklearn<1.0", severity="none"):
- from sklearn.experimental import enable_hist_gradient_boosting # noqa
-
-import numpy as np
-import pandas as pd
-import pytest
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.linear_model import LinearRegression
-from sklearn.pipeline import make_pipeline
-
-from aeon.datasets import load_airline
-from aeon.forecasting.base import ForecastingHorizon
-from aeon.forecasting.compose import make_reduction
-from aeon.forecasting.model_selection import temporal_train_test_split
-from aeon.performance_metrics.forecasting import mean_absolute_percentage_error
-from aeon.testing.data_generation import _make_hierarchical
-from aeon.testing.data_generation._legacy import get_examples
-from aeon.transformations.summarize import WindowSummarizer
-
-# Load data that will be the basis of tests
-y = load_airline()
-y_multi = get_examples("pd-multiindex")[0]
-
-# y Train will be univariate data set
-y_train, y_test = temporal_train_test_split(y)
-
-# y_int = y.copy()
-# y_int.index = [i for i in range(len(y_int))]
-
-# y_train_int, y_test_int = temporal_train_test_split(y_int)
-
-# Create traina nd test panel sample data
-mi = pd.MultiIndex.from_product([[0], y_train.index], names=["instances", "timepoints"])
-y_group1 = pd.DataFrame(y_train.values, index=mi, columns=["y"])
-
-mi = pd.MultiIndex.from_product([[1], y_train.index], names=["instances", "timepoints"])
-y_group2 = pd.DataFrame(y_train.values, index=mi, columns=["y"])
-
-y_train_grp = pd.concat([y_group1, y_group2])
-
-mi = pd.MultiIndex.from_product([[0], y_test.index], names=["instances", "timepoints"])
-y_group1 = pd.DataFrame(y_test.values, index=mi, columns=["y"])
-
-mi = pd.MultiIndex.from_product([[1], y_test.index], names=["instances", "timepoints"])
-y_group2 = pd.DataFrame(y_test.values, index=mi, columns=["y"])
-
-y_test_grp = pd.concat([y_group1, y_group2])
-
-# Get hierachical data
-y_train_hier = get_examples("pd_multiindex_hier")[0]
-
-# Create unbalanced hierachical data
-X = y_train_hier.reset_index().copy()
-X = X[~((X["bar"] == 2) & (X["foo"] == "b"))]
-X = X[["foo", "bar"]].drop_duplicates()
-
-time_names = y_train.index.names[-1]
-timeframe = y_train.index.to_frame()
-
-X2 = X.merge(timeframe, how="cross")
-
-freq_inferred = y_train.index.freq
-
-x_names = X.columns
-if not isinstance(x_names, list):
- x_names = x_names.to_list()
-
-y_train_reset = y_train.reset_index()
-
-X3 = X2.merge(y_train_reset, on="Period")
-
-freq_inferred = y_train.index.freq
-
-y_train_hier_unequal = X3.groupby(x_names, as_index=True).apply(
- lambda df: df.drop(x_names, axis=1).set_index(time_names).asfreq(freq_inferred)
-)
-
-# Create integer index data
-y_numeric = y_train.copy()
-y_numeric.index = pd.to_numeric(y_numeric.index)
-
-
-# Get different WindowSummarizer functions
-kwargs = WindowSummarizer.get_test_params()[0]
-kwargs_alternames = WindowSummarizer.get_test_params()[1]
-kwargs_variant = WindowSummarizer.get_test_params()[2]
-
-
-def check_eval(test_input, expected):
- """Test which columns are returned for different arguments.
-
- For a detailed description what these arguments do,
- and how theyinteract see docstring of DateTimeFeatures.
- """
- if test_input is not None:
- assert len(test_input) == len(expected)
- assert all([a == b for a, b in zip(test_input, expected)])
- else:
- assert expected is None
-
-
-@pytest.mark.parametrize(
- "y, index_names",
- [
- (
- y_train_grp,
- ["instances", "timepoints"],
- ),
- (
- y_train,
- [None],
- ),
- (
- y_numeric,
- [None],
- ),
- (
- y_train_hier_unequal,
- ["foo", "bar", "Period"],
- ),
- ],
-)
-def test_recursive_reduction(y, index_names):
- """Test index columns match input."""
- regressor = make_pipeline(
- RandomForestRegressor(random_state=1),
- )
-
- forecaster2 = make_reduction(
- regressor,
- scitype="tabular-regressor",
- transformers=[WindowSummarizer(**kwargs, n_jobs=1)],
- window_length=None,
- strategy="recursive",
- pooling="global",
- )
-
- forecaster2.fit(y, fh=[1, 2])
- y_pred = forecaster2.predict(fh=[1, 2, 12])
- check_eval(y_pred.index.names, index_names)
-
-
-@pytest.mark.parametrize(
- "y, index_names",
- [
- (
- y_train_grp,
- ["instances", "timepoints"],
- ),
- (
- y_train,
- [None],
- ),
- (
- y_numeric,
- [None],
- ),
- (
- y_train_hier_unequal,
- ["foo", "bar", "Period"],
- ),
- ],
-)
-def test_direct_reduction(y, index_names):
- """Test index columns match input."""
- regressor = make_pipeline(
- RandomForestRegressor(random_state=1),
- )
-
- forecaster2 = make_reduction(
- regressor,
- transformers=[WindowSummarizer(**kwargs, n_jobs=1)],
- window_length=None,
- strategy="recursive",
- pooling="global",
- )
-
- forecaster2.fit(y, fh=[1, 2])
- y_pred = forecaster2.predict(fh=[1, 2, 12])
- check_eval(y_pred.index.names, index_names)
-
-
-@pytest.mark.parametrize(
- "y, index_names",
- [
- (
- y_train_grp,
- ["instances", "timepoints"],
- ),
- (
- y_train,
- [None],
- ),
- (
- y_numeric,
- [None],
- ),
- (
- y_train_hier_unequal,
- ["foo", "bar", "Period"],
- ),
- ],
-)
-def test_list_reduction(y, index_names):
- """Test index columns match input."""
- regressor = make_pipeline(
- RandomForestRegressor(random_state=1),
- )
-
- forecaster2 = make_reduction(
- regressor,
- transformers=[WindowSummarizer(**kwargs), WindowSummarizer(**kwargs_variant)],
- window_length=None,
- strategy="recursive",
- pooling="global",
- )
-
- forecaster2.fit(y, fh=[1, 2, 12])
- y_pred = forecaster2.predict(fh=[1, 2, 12])
- check_eval(y_pred.index.names, index_names)
-
-
-def test_equality_transfo_nontranso():
- """Test that recursive reducers return same results for global / local forecasts."""
- y = load_airline()[:36]
- y_train, y_test = temporal_train_test_split(y, test_size=12)
- fh = ForecastingHorizon(y_test.index, is_relative=False)
-
- lag_vec = list(range(12, 0, -1))
- kwargs = {
- "lag_feature": {
- "lag": lag_vec,
- }
- }
-
- regressor = RandomForestRegressor(random_state=42)
-
- forecaster = make_reduction(regressor, window_length=12, strategy="recursive")
- forecaster.fit(y_train)
- y_pred = forecaster.predict(fh)
- recursive_without = mean_absolute_percentage_error(y_test, y_pred, symmetric=False)
- forecaster = make_reduction(
- regressor,
- window_length=None,
- strategy="recursive",
- transformers=[WindowSummarizer(**kwargs, n_jobs=1)],
- pooling="global",
- )
-
- forecaster.fit(y_train)
- y_pred = forecaster.predict(fh)
- recursive_global = mean_absolute_percentage_error(y_test, y_pred, symmetric=False)
- np.testing.assert_almost_equal(recursive_without, recursive_global)
-
-
-def test_nofreq_pass():
- """Test that recursive reducers return same results with / without freq given."""
- regressor = make_pipeline(
- LinearRegression(),
- )
-
- kwargs = {
- "lag_feature": {
- "lag": [1],
- }
- }
-
- forecaster_global = make_reduction(
- regressor,
- scitype="tabular-regressor",
- transformers=[WindowSummarizer(**kwargs, n_jobs=1, truncate="bfill")],
- window_length=None,
- strategy="recursive",
- pooling="global",
- )
-
- forecaster_global_freq = make_reduction(
- regressor,
- scitype="tabular-regressor",
- transformers=[WindowSummarizer(**kwargs, n_jobs=1, truncate="bfill")],
- window_length=None,
- strategy="recursive",
- pooling="global",
- )
-
- y = _make_hierarchical(
- hierarchy_levels=(100,), min_timepoints=1000, max_timepoints=1000
- )
-
- y_no_freq = y.reset_index().set_index(["h0", "time"])
-
- forecaster_global.fit(y)
- forecaster_global_freq.fit(y_no_freq)
-
- y_pred_global = forecaster_global.predict(fh=[1, 2])
- y_pred_nofreq = forecaster_global_freq.predict(fh=[1, 2])
- np.testing.assert_almost_equal(
- y_pred_global["c0"].values, y_pred_nofreq["c0"].values
- )
diff --git a/aeon/networks/__init__.py b/aeon/networks/__init__.py
index b7ec3ca75a..df56152dea 100644
--- a/aeon/networks/__init__.py
+++ b/aeon/networks/__init__.py
@@ -4,6 +4,7 @@
"BaseDeepNetwork",
"BaseDeepLearningNetwork",
"CNNNetwork",
+ "TimeCNNNetwork",
"EncoderNetwork",
"FCNNetwork",
"InceptionNetwork",
@@ -18,7 +19,7 @@
from aeon.networks._ae_bgru import AEBiGRUNetwork
from aeon.networks._ae_fcn import AEFCNNetwork
from aeon.networks._ae_resnet import AEResNetNetwork
-from aeon.networks._cnn import CNNNetwork
+from aeon.networks._cnn import CNNNetwork, TimeCNNNetwork
from aeon.networks._encoder import EncoderNetwork
from aeon.networks._fcn import FCNNetwork
from aeon.networks._inception import InceptionNetwork
diff --git a/aeon/networks/_ae_bgru.py b/aeon/networks/_ae_bgru.py
index 7fc2616f5f..5e2e78a71e 100644
--- a/aeon/networks/_ae_bgru.py
+++ b/aeon/networks/_ae_bgru.py
@@ -1,4 +1,6 @@
-"""Implement Auto-Encoder based on Bidirectional GRUs."""
+"""Auto-Encoder using Bidirectional GRU Network (AEBiGRUNetwork)."""
+
+__maintainer__ = ["aadya940", "hadifawaz1999"]
from aeon.networks.base import BaseDeepLearningNetwork
diff --git a/aeon/networks/_ae_fcn.py b/aeon/networks/_ae_fcn.py
index 37bbdf1aa5..1cef7d5c31 100644
--- a/aeon/networks/_ae_fcn.py
+++ b/aeon/networks/_ae_fcn.py
@@ -1,6 +1,6 @@
"""Auto-Encoder using Fully Convolutional Network (FCN)."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
import numpy as np
diff --git a/aeon/networks/_ae_resnet.py b/aeon/networks/_ae_resnet.py
index 3b817efe8e..4578dd8092 100644
--- a/aeon/networks/_ae_resnet.py
+++ b/aeon/networks/_ae_resnet.py
@@ -1,4 +1,7 @@
-"""Residual Network (ResNet) (minus the final output layer)."""
+"""Auto-Encoder using Residual Network (AEResNetNetwork)."""
+
+__maintainer__ = ["hadifawaz1999"]
+
import numpy as np
diff --git a/aeon/networks/_cnn.py b/aeon/networks/_cnn.py
index cb40b8f373..aafb9875c4 100644
--- a/aeon/networks/_cnn.py
+++ b/aeon/networks/_cnn.py
@@ -1,10 +1,19 @@
-"""Time Convolutional Neural Network (CNN) (minus the final output layer)."""
+"""Time Convolutional Neural Network (TimeCNNNetwork)."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
+
+from deprecated.sphinx import deprecated
from aeon.networks.base import BaseDeepLearningNetwork
+# TODO: remove v0.12.0
+@deprecated(
+ version="0.10.0",
+ reason="CNNNetwork has been renamed to TimeCNNNetwork"
+ "and will be removed in 0.12.0.",
+ category=FutureWarning,
+)
class CNNNetwork(BaseDeepLearningNetwork):
"""Establish the network structure for a CNN.
@@ -167,3 +176,167 @@ def build_network(self, input_shape, **kwargs):
flatten_layer = tf.keras.layers.Flatten()(conv)
return input_layer, flatten_layer
+
+
+class TimeCNNNetwork(BaseDeepLearningNetwork):
+ """Establish the network structure for a CNN.
+
+ Adapted from the implementation used in [1]_.
+
+ Parameters
+ ----------
+ n_layers : int, default = 2
+ The number of convolution layers in the network.
+ kernel_size : int or list of int, default = 7
+ Kernel size of convolution layers, if not a list, the same kernel size is
+ used for all layer, len(list) should be n_layers.
+ n_filters : int or list of int, default = [6, 12]
+ Number of filters for each convolution layer, if not a list, the same
+ `n_filters` is used in all layers.
+ avg_pool_size : int or list of int, default = 3
+ The size of the average pooling layer, if not a list, the same max pooling
+ size is used for all convolution layer.
+ activation : str or list of str, default = "sigmoid"
+ Keras activation function used in the model for each layer, if not a list,
+ the same activation is used for all layers.
+ padding : str or list of str, default = "valid"
+ The method of padding in convolution layers, if not a list, the same padding
+ used for all convolution layers.
+ strides : int or list of int, default = 1
+ The strides of kernels in the convolution and max pooling layers, if not a list,
+ the same strides are used for all layers.
+ dilation_rate : int or list of int, default = 1
+ The dilation rate of the convolution layers, if not a list, the same dilation
+ rate is used all over the network.
+ use_bias : bool or list of bool, default = True
+ Condition on whether or not to use bias values for convolution layers, if not
+ a list, the same condition is used for all layers.
+
+ Notes
+ -----
+ Adapted from source code
+ https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/cnn.py
+
+ References
+ ----------
+ .. [1] Zhao et al. Convolutional neural networks for time series classification,
+ Journal of Systems Engineering and Electronics 28(1), 162--169, 2017
+ """
+
+ def __init__(
+ self,
+ n_layers=2,
+ kernel_size=7,
+ n_filters=None,
+ avg_pool_size=3,
+ activation="sigmoid",
+ padding="valid",
+ strides=1,
+ dilation_rate=1,
+ use_bias=True,
+ ):
+ self.n_layers = n_layers
+ self.n_filters = n_filters
+ self.kernel_size = kernel_size
+ self.avg_pool_size = avg_pool_size
+ self.activation = activation
+ self.padding = padding
+ self.strides = strides
+ self.dilation_rate = dilation_rate
+ self.use_bias = use_bias
+
+ super().__init__()
+
+ def build_network(self, input_shape, **kwargs):
+ """
+ Construct a network and return its input and output layers.
+
+ Parameters
+ ----------
+ input_shape : tuple
+ The shape of the data fed into the input layer.
+
+ Returns
+ -------
+ input_layer : a keras layer
+ output_layer : a keras layer
+ """
+ import tensorflow as tf
+
+ self._n_filters_ = [6, 12] if self.n_filters is None else self.n_filters
+
+ if isinstance(self.kernel_size, list):
+ assert len(self.kernel_size) == self.n_layers
+ self._kernel_size = self.kernel_size
+ else:
+ self._kernel_size = [self.kernel_size] * self.n_layers
+
+ if isinstance(self._n_filters_, list):
+ assert len(self._n_filters_) == self.n_layers
+ self._n_filters = self._n_filters_
+ else:
+ self._n_filters = [self._n_filters_] * self.n_layers
+
+ if isinstance(self.avg_pool_size, list):
+ assert len(self.avg_pool_size) == self.n_layers
+ self._avg_pool_size = self.avg_pool_size
+ else:
+ self._avg_pool_size = [self.avg_pool_size] * self.n_layers
+
+ if isinstance(self.activation, list):
+ assert len(self.activation) == self.n_layers
+ self._activation = self.activation
+ else:
+ self._activation = [self.activation] * self.n_layers
+
+ if isinstance(self.padding, list):
+ assert len(self.padding) == self.n_layers
+ self._padding = self.padding
+ else:
+ self._padding = [self.padding] * self.n_layers
+
+ if isinstance(self.strides, list):
+ assert len(self.strides) == self.n_layers
+ self._strides = self.strides
+ else:
+ self._strides = [self.strides] * self.n_layers
+
+ if isinstance(self.dilation_rate, list):
+ assert len(self.dilation_rate) == self.n_layers
+ self._dilation_rate = self.dilation_rate
+ else:
+ self._dilation_rate = [self.dilation_rate] * self.n_layers
+
+ if isinstance(self.use_bias, list):
+ assert len(self.use_bias) == self.n_layers
+ self._use_bias = self.use_bias
+ else:
+ self._use_bias = [self.use_bias] * self.n_layers
+
+ input_layer = tf.keras.layers.Input(input_shape)
+
+ if input_shape[0] < 60:
+ self._padding = ["same"] * self.n_layers
+
+ x = input_layer
+
+ for i in range(self.n_layers):
+ conv = tf.keras.layers.Conv1D(
+ filters=self._n_filters[i],
+ kernel_size=self._kernel_size[i],
+ strides=self._strides[i],
+ padding=self._padding[i],
+ dilation_rate=self._dilation_rate[i],
+ activation=self._activation[i],
+ use_bias=self._use_bias[i],
+ )(x)
+
+ conv = tf.keras.layers.AveragePooling1D(pool_size=self._avg_pool_size[i])(
+ conv
+ )
+
+ x = conv
+
+ flatten_layer = tf.keras.layers.Flatten()(conv)
+
+ return input_layer, flatten_layer
diff --git a/aeon/networks/_encoder.py b/aeon/networks/_encoder.py
index cc600aa932..e2423fc03c 100644
--- a/aeon/networks/_encoder.py
+++ b/aeon/networks/_encoder.py
@@ -1,4 +1,4 @@
-"""Encoder Classifier."""
+"""Encoder Network (EncoderNetwork)."""
__maintainer__ = ["hadifawaz1999"]
@@ -46,7 +46,7 @@ class EncoderNetwork(BaseDeepLearningNetwork):
"""
_config = {
- "python_dependencies": ["tensorflow", "tensorflow-addons"],
+ "python_dependencies": ["tensorflow"],
"python_version": "<3.12",
"structure": "encoder",
}
diff --git a/aeon/networks/_fcn.py b/aeon/networks/_fcn.py
index 9b6c10de96..fa4e3f763a 100644
--- a/aeon/networks/_fcn.py
+++ b/aeon/networks/_fcn.py
@@ -1,6 +1,7 @@
-"""Fully Convolutional Network (FCN) (minus the final output layer)."""
+"""Fully Convolutional Network (FCNNetwork)."""
+
+__maintainer__ = ["hadifawaz1999"]
-__maintainer__ = []
from aeon.networks.base import BaseDeepLearningNetwork
diff --git a/aeon/networks/_inception.py b/aeon/networks/_inception.py
index 1b209dd8dc..4c8abaa449 100644
--- a/aeon/networks/_inception.py
+++ b/aeon/networks/_inception.py
@@ -1,6 +1,6 @@
-"""Inception Network."""
+"""Inception Network (InceptionNetwork)."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
from aeon.networks.base import BaseDeepLearningNetwork
diff --git a/aeon/networks/_lite.py b/aeon/networks/_lite.py
index 64a5961ab4..df19fba0d0 100644
--- a/aeon/networks/_lite.py
+++ b/aeon/networks/_lite.py
@@ -1,6 +1,7 @@
-"""LITE Network."""
+"""LITE Network (LITENetwork)."""
+
+__maintainer__ = ["hadifawaz1999"]
-__maintainer__ = []
from aeon.networks.base import BaseDeepLearningNetwork
diff --git a/aeon/networks/_mlp.py b/aeon/networks/_mlp.py
index 537fadce0b..84b1570ff7 100644
--- a/aeon/networks/_mlp.py
+++ b/aeon/networks/_mlp.py
@@ -1,6 +1,7 @@
-"""Multi Layer Perceptron (MLP) (minus the final output layer)."""
+"""Multi Layer Perceptron Network (MLPNetwork)."""
+
+__maintainer__ = ["hadifawaz1999"]
-__maintainer__ = []
from aeon.networks.base import BaseDeepLearningNetwork
diff --git a/aeon/networks/_resnet.py b/aeon/networks/_resnet.py
index 91aaf5b154..d1ab38883a 100644
--- a/aeon/networks/_resnet.py
+++ b/aeon/networks/_resnet.py
@@ -1,6 +1,7 @@
-"""Residual Network (ResNet) (minus the final output layer)."""
+"""Residual Network (ResNetNetwork)."""
+
+__maintainer__ = ["hadifawaz1999"]
-__maintainer__ = []
from aeon.networks.base import BaseDeepLearningNetwork
diff --git a/aeon/networks/_tapnet.py b/aeon/networks/_tapnet.py
index 4e06d942c3..720925f2d2 100644
--- a/aeon/networks/_tapnet.py
+++ b/aeon/networks/_tapnet.py
@@ -1,6 +1,6 @@
-"""Time Convolutional Neural Network (CNN) (minus the final output layer)."""
+"""Time series Attentional Prototype Network (TapNetNetwork)."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
import math
diff --git a/aeon/networks/tests/test_all_networks.py b/aeon/networks/tests/test_all_networks.py
index 37ce19bf07..106a5b8b4f 100644
--- a/aeon/networks/tests/test_all_networks.py
+++ b/aeon/networks/tests/test_all_networks.py
@@ -35,7 +35,7 @@ def test_all_networks_functionality(network):
"""Test the functionality of all networks."""
input_shape = (100, 2)
- if not (network.__name__ in ["BaseDeepLearningNetwork", "EncoderNetwork"]):
+ if not (network.__name__ in ["BaseDeepLearningNetwork"]):
if _check_soft_dependencies(
network._config["python_dependencies"], severity="none"
) and _check_python_version(network._config["python_version"], severity="none"):
@@ -62,3 +62,75 @@ def test_all_networks_functionality(network):
)
else:
pytest.skip(f"{network.__name__} not to be tested since its a base class.")
+
+
+@pytest.mark.parametrize("network", _networks)
+def test_all_networks_params(network):
+ """Test the functionality of all networks."""
+ input_shape = (100, 2)
+
+ if network.__name__ in ["BaseDeepLearningNetwork", "EncoderNetwork"]:
+ pytest.skip(f"{network.__name__} not to be tested since its a base class.")
+
+ if network._config["structure"] == "auto-encoder":
+ pytest.skip(
+ f"{network.__name__} not to be tested (AE networks have their own tests)."
+ )
+
+ if not (
+ _check_soft_dependencies(
+ network._config["python_dependencies"], severity="none"
+ )
+ and _check_python_version(network._config["python_version"], severity="none")
+ ):
+ pytest.skip(
+ f"{network.__name__} dependencies not satisfied or invalid \
+ Python version."
+ )
+
+ # check with default parameters
+ my_network = network()
+ my_network.build_network(input_shape=input_shape)
+
+ # check with list parameters
+ params = dict()
+ for attrname in [
+ "kernel_size",
+ "n_filters",
+ "avg_pool_size",
+ "activation",
+ "padding",
+ "strides",
+ "dilation_rate",
+ "use_bias",
+ ]:
+
+ # Exceptions to fix
+ if (
+ attrname in ["kernel_size", "padding"]
+ and network.__name__ == "TapNetNetwork"
+ ):
+ continue
+ # LITENetwork does not seem to work with list args
+ if network.__name__ == "LITENetwork":
+ continue
+
+ # Here we use 'None' string as default to differentiate with None values
+ attr = getattr(my_network, attrname, "None")
+ if attr != "None":
+ if attr is None:
+ attr = 3
+ elif isinstance(attr, list):
+ attr = attr[0]
+ else:
+ if network.__name__ in ["ResNetNetwork"]:
+ attr = [attr] * my_network.n_conv_per_residual_block
+ elif network.__name__ in ["InceptionNetwork"]:
+ attr = [attr] * my_network.depth
+ else:
+ attr = [attr] * my_network.n_layers
+ params[attrname] = attr
+
+ if params:
+ my_network = network(**params)
+ my_network.build_network(input_shape=input_shape)
diff --git a/aeon/networks/tests/test_cnn.py b/aeon/networks/tests/test_cnn.py
new file mode 100644
index 0000000000..c859397b34
--- /dev/null
+++ b/aeon/networks/tests/test_cnn.py
@@ -0,0 +1,22 @@
+"""Tests for the CNN Model."""
+
+import pytest
+
+from aeon.networks import TimeCNNNetwork
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+__maintainer__ = []
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="Tensorflow soft dependency unavailable.",
+)
+def test_cnn_input_shape_padding():
+ """Test of CNN network with input_shape < 60."""
+ input_shape = (40, 2)
+ network = TimeCNNNetwork()
+ input_layer, output_layer = network.build_network(input_shape=input_shape)
+
+ assert input_layer is not None
+ assert output_layer is not None
diff --git a/aeon/networks/tests/test_inception.py b/aeon/networks/tests/test_inception.py
new file mode 100644
index 0000000000..afd046883f
--- /dev/null
+++ b/aeon/networks/tests/test_inception.py
@@ -0,0 +1,74 @@
+"""Tests for the Inception Model."""
+
+import pytest
+
+from aeon.networks import InceptionNetwork
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+__maintainer__ = []
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="Tensorflow soft dependency unavailable.",
+)
+def test_inceptionnetwork_bottleneck():
+ """Test of Inception network without bottleneck."""
+ input_shape = (100, 2)
+ inception = InceptionNetwork(use_bottleneck=False)
+ input_layer, output_layer = inception.build_network(input_shape=input_shape)
+
+ assert input_layer is not None
+ assert output_layer is not None
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="Tensorflow soft dependency unavailable.",
+)
+def test_inceptionnetwork_max_pooling():
+ """Test of Inception network without max pooling."""
+ input_shape = (100, 2)
+ inception = InceptionNetwork(use_max_pooling=False)
+ input_layer, output_layer = inception.build_network(input_shape=input_shape)
+
+ assert input_layer is not None
+ assert output_layer is not None
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="Tensorflow soft dependency unavailable.",
+)
+def test_inceptionnetwork_custom_filters():
+ """Test of Inception network with custom filters."""
+ input_shape = (100, 2)
+ inception = InceptionNetwork(use_custom_filters=True)
+ input_layer, output_layer = inception.build_network(input_shape=input_shape)
+
+ assert input_layer is not None
+ assert output_layer is not None
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="Tensorflow soft dependency unavailable.",
+)
+def test_inceptionnetwork_list_parameters():
+ """Test of Inception network with list parameters not in test_all_networks."""
+ input_shape = (100, 2)
+ depth = 6
+ n_conv_per_layer = [3] * depth
+ use_max_pooling = [True] * depth
+ max_pool_size = [3] * depth
+
+ inception = InceptionNetwork(
+ depth=depth,
+ n_conv_per_layer=n_conv_per_layer,
+ use_max_pooling=use_max_pooling,
+ max_pool_size=max_pool_size,
+ )
+ input_layer, output_layer = inception.build_network(input_shape=input_shape)
+
+ assert input_layer is not None
+ assert output_layer is not None
diff --git a/aeon/regression/deep_learning/__init__.py b/aeon/regression/deep_learning/__init__.py
index 13077e96e8..eb69f1e0f0 100644
--- a/aeon/regression/deep_learning/__init__.py
+++ b/aeon/regression/deep_learning/__init__.py
@@ -1,7 +1,9 @@
"""Deep learning based regressors."""
__all__ = [
+ "BaseDeepRegressor",
"CNNRegressor",
+ "TimeCNNRegressor",
"FCNRegressor",
"InceptionTimeRegressor",
"IndividualInceptionRegressor",
@@ -13,7 +15,7 @@
"MLPRegressor",
]
-from aeon.regression.deep_learning._cnn import CNNRegressor
+from aeon.regression.deep_learning._cnn import CNNRegressor, TimeCNNRegressor
from aeon.regression.deep_learning._encoder import EncoderRegressor
from aeon.regression.deep_learning._fcn import FCNRegressor
from aeon.regression.deep_learning._inception_time import (
@@ -27,3 +29,4 @@
from aeon.regression.deep_learning._mlp import MLPRegressor
from aeon.regression.deep_learning._resnet import ResNetRegressor
from aeon.regression.deep_learning._tapnet import TapNetRegressor
+from aeon.regression.deep_learning.base import BaseDeepRegressor
diff --git a/aeon/regression/deep_learning/_cnn.py b/aeon/regression/deep_learning/_cnn.py
index c5854b2438..988e823c63 100644
--- a/aeon/regression/deep_learning/_cnn.py
+++ b/aeon/regression/deep_learning/_cnn.py
@@ -1,19 +1,27 @@
-"""Time Convolutional Neural Network (CNN) for regression."""
+"""Time Convolutional Neural Network (TimeCNN) regressor."""
-__maintainer__ = []
-__all__ = ["CNNRegressor"]
+__maintainer__ = ["hadifawaz1999"]
+__all__ = ["CNNRegressor", "TimeCNNRegressor"]
import gc
import os
import time
from copy import deepcopy
+from deprecated.sphinx import deprecated
from sklearn.utils import check_random_state
-from aeon.networks import CNNNetwork
+from aeon.networks import CNNNetwork, TimeCNNNetwork
from aeon.regression.deep_learning.base import BaseDeepRegressor
+# TODO: remove v0.12.0
+@deprecated(
+ version="0.10.0",
+ reason="CNNRegressor has been renamed to TimeCNNRegressor"
+ "and will be removed in 0.12.0.",
+ category=FutureWarning,
+)
class CNNRegressor(BaseDeepRegressor):
"""Time Series Convolutional Neural Network (CNN).
@@ -86,6 +94,8 @@ class CNNRegressor(BaseDeepRegressor):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -94,6 +104,9 @@ class CNNRegressor(BaseDeepRegressor):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
Notes
-----
@@ -133,8 +146,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
verbose=False,
loss="mse",
output_activation="linear",
@@ -151,7 +166,9 @@ def __init__(
self.file_path = file_path
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.strides = strides
self.dilation_rate = dilation_rate
self.callbacks = callbacks
@@ -253,6 +270,337 @@ def _fit(self, X, y):
self.input_shape = X.shape[1:]
self.training_model_ = self.build_model(self.input_shape)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
+ if self.verbose:
+ self.training_model_.summary()
+
+ self.file_name_ = (
+ self.best_file_name if self.save_best_model else str(time.time_ns())
+ )
+
+ if self.callbacks is None:
+ self.callbacks_ = [
+ tf.keras.callbacks.ModelCheckpoint(
+ filepath=self.file_path + self.file_name_ + ".keras",
+ monitor="loss",
+ save_best_only=True,
+ ),
+ ]
+ else:
+ self.callbacks_ = self._get_model_checkpoint_callback(
+ callbacks=self.callbacks,
+ file_path=self.file_path,
+ file_name=self.file_name_,
+ )
+
+ self.history = self.training_model_.fit(
+ X,
+ y,
+ batch_size=self.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,
+ "avg_pool_size": 4,
+ }
+
+ return [param]
+
+
+class TimeCNNRegressor(BaseDeepRegressor):
+ """Time Series Convolutional Neural Network (CNN).
+
+ Adapted from the implementation used in [1]_.
+
+ Parameters
+ ----------
+ n_layers : int, default = 2,
+ the number of convolution layers in the network
+ kernel_size : int or list of int, default = 7,
+ kernel size of convolution layers, if not a list, the same kernel size
+ is used for all layer, len(list) should be n_layers
+ n_filters : int or list of int, default = [6, 12],
+ number of filters for each convolution layer, if not a list, the same n_filters
+ is used in all layers.
+ avg_pool_size : int or list of int, default = 3,
+ the size of the average pooling layer, if not a list, the same
+ max pooling size is used
+ for all convolution layer
+ output_activation : str, default = "linear",
+ the output activation for the regressor
+ activation : str or list of str, default = "sigmoid",
+ keras activation function used in the model for each layer,
+ if not a list, the same
+ activation is used for all layers
+ padding : str or list of str, default = 'valid',
+ the method of padding in convolution layers, if not a list,
+ the same padding used
+ for all convolution layers
+ strides : int or list of int, default = 1,
+ the strides of kernels in the convolution and max pooling layers,
+ if not a list, the same strides are used for all layers
+ dilation_rate : int or list of int, default = 1,
+ the dilation rate of the convolution layers, if not a list,
+ the same dilation rate is used all over the network
+ use_bias : bool or list of bool, default = True,
+ condition on whether or not to use bias values for convolution layers,
+ if not a list, the same condition is used for all layers
+ 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.
+ 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.
+ verbose : boolean, default = False
+ whether to output extra information
+ loss : string, default="mean_squared_error"
+ fit parameter for the keras model
+ optimizer : keras.optimizer, default=keras.optimizers.Adam(),
+ metrics : str or list of str, 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.
+ callbacks : keras.callbacks, default=model_checkpoint to save best
+ model on training loss
+ file_path : file_path for the best model (if checkpoint is used as callback)
+ 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
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
+ 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
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
+
+ Notes
+ -----
+ Adapted from the implementation from Fawaz et. al
+ https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/cnn.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 TimeCNNRegressor
+ >>> from aeon.testing.data_generation 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 = TimeCNNRegressor(n_epochs=20, bacth_size=4) # doctest: +SKIP
+ >>> rgs.fit(X, y) # doctest: +SKIP
+ TimeCNNRegressor(...)
+ """
+
+ def __init__(
+ self,
+ n_layers=2,
+ kernel_size=7,
+ n_filters=None,
+ avg_pool_size=3,
+ activation="sigmoid",
+ padding="valid",
+ strides=1,
+ dilation_rate=1,
+ n_epochs=2000,
+ batch_size=16,
+ callbacks=None,
+ file_path="./",
+ save_best_model=False,
+ save_last_model=False,
+ save_init_model=False,
+ best_file_name="best_model",
+ last_file_name="last_model",
+ init_file_name="init_model",
+ verbose=False,
+ loss="mse",
+ output_activation="linear",
+ metrics="mean_squared_error",
+ random_state=None,
+ use_bias=True,
+ optimizer=None,
+ ):
+ self.n_layers = n_layers
+ self.avg_pool_size = avg_pool_size
+ self.padding = padding
+ self.n_filters = n_filters
+ self.kernel_size = kernel_size
+ self.file_path = file_path
+ self.save_best_model = save_best_model
+ self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
+ self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
+ self.strides = strides
+ self.dilation_rate = dilation_rate
+ self.callbacks = callbacks
+ self.n_epochs = n_epochs
+ self.verbose = verbose
+ self.loss = loss
+ self.output_activation = output_activation
+ self.metrics = metrics
+ self.random_state = random_state
+ self.activation = activation
+ self.use_bias = use_bias
+ self.optimizer = optimizer
+
+ self.history = None
+
+ super().__init__(
+ batch_size=batch_size,
+ last_file_name=last_file_name,
+ )
+
+ self._network = TimeCNNNetwork(
+ n_layers=self.n_layers,
+ kernel_size=self.kernel_size,
+ n_filters=self.n_filters,
+ avg_pool_size=self.avg_pool_size,
+ activation=self.activation,
+ padding=self.padding,
+ strides=self.strides,
+ dilation_rate=self.dilation_rate,
+ 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
+ from tensorflow import keras
+
+ 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 = keras.layers.Dense(units=1, activation=self.output_activation)(
+ output_layer
+ )
+
+ self.optimizer_ = (
+ keras.optimizers.Adam() if self.optimizer is None else self.optimizer
+ )
+
+ model = 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.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/regression/deep_learning/_encoder.py b/aeon/regression/deep_learning/_encoder.py
index be65733d0f..4b73047c05 100644
--- a/aeon/regression/deep_learning/_encoder.py
+++ b/aeon/regression/deep_learning/_encoder.py
@@ -1,6 +1,6 @@
"""Encoder Regressor."""
-__author__ = ["AnonymousCodes911"]
+__author__ = ["AnonymousCodes911", "hadifawaz1999"]
__all__ = ["EncoderRegressor"]
import gc
@@ -54,6 +54,8 @@ class EncoderRegressor(BaseDeepRegressor):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file.
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -62,6 +64,9 @@ class EncoderRegressor(BaseDeepRegressor):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded.
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
n_epochs:
The number of times the entire training dataset
will be passed forward and backward
@@ -121,8 +126,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
verbose=False,
loss="mean_squared_error",
metrics="mean_squared_error",
@@ -144,7 +151,9 @@ def __init__(
self.file_path = file_path
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.n_epochs = n_epochs
self.verbose = verbose
self.loss = loss
@@ -239,6 +248,9 @@ def _fit(self, X, y):
self.input_shape = X.shape[1:]
self.training_model_ = self.build_model(self.input_shape)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/regression/deep_learning/_fcn.py b/aeon/regression/deep_learning/_fcn.py
index 7c136cae34..374feb5b93 100644
--- a/aeon/regression/deep_learning/_fcn.py
+++ b/aeon/regression/deep_learning/_fcn.py
@@ -1,6 +1,6 @@
-"""Fully Convolutional Network (FCN) for regression."""
+"""Fully Convolutional Network (FCN) regressor."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = ["FCNRegressor"]
import gc
@@ -75,6 +75,8 @@ class FCNRegressor(BaseDeepRegressor):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -83,6 +85,9 @@ class FCNRegressor(BaseDeepRegressor):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
callbacks : keras.callbacks, default = None
Notes
@@ -119,8 +124,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
n_epochs=2000,
batch_size=16,
use_mini_batch_size=False,
@@ -153,7 +160,9 @@ def __init__(
self.file_path = file_path
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.history = None
@@ -239,6 +248,9 @@ def _fit(self, X, y):
self.input_shape = X.shape[1:]
self.training_model_ = self.build_model(self.input_shape)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/regression/deep_learning/_inception_time.py b/aeon/regression/deep_learning/_inception_time.py
index dc14749d01..a5bd459545 100644
--- a/aeon/regression/deep_learning/_inception_time.py
+++ b/aeon/regression/deep_learning/_inception_time.py
@@ -1,6 +1,6 @@
-"""InceptionTime regressor."""
+"""InceptionTime and Inception regressors."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = ["InceptionTimeRegressor"]
import gc
@@ -107,6 +107,8 @@ class InceptionTimeRegressor(BaseRegressor):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -115,6 +117,9 @@ class InceptionTimeRegressor(BaseRegressor):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
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;
@@ -187,8 +192,10 @@ def __init__(
file_path="./",
save_last_model=False,
save_best_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
batch_size=64,
use_mini_batch_size=False,
n_epochs=1500,
@@ -223,8 +230,10 @@ def __init__(
self.save_last_model = save_last_model
self.save_best_model = save_best_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
self.last_file_name = last_file_name
+ self.init_file_name = init_file_name
self.callbacks = callbacks
self.random_state = random_state
@@ -275,8 +284,10 @@ def _fit(self, X, y):
file_path=self.file_path,
save_best_model=self.save_best_model,
save_last_model=self.save_last_model,
+ save_init_model=self.save_init_model,
best_file_name=self.best_file_name + str(n),
last_file_name=self.last_file_name + str(n),
+ init_file_name=self.init_file_name + str(n),
batch_size=self.batch_size,
use_mini_batch_size=self.use_mini_batch_size,
n_epochs=self.n_epochs,
@@ -424,6 +435,8 @@ class IndividualInceptionRegressor(BaseDeepRegressor):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -432,6 +445,9 @@ class IndividualInceptionRegressor(BaseDeepRegressor):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
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;
@@ -492,8 +508,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
batch_size=64,
use_mini_batch_size=False,
n_epochs=1500,
@@ -526,7 +544,9 @@ def __init__(
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.callbacks = callbacks
self.random_state = random_state
@@ -626,6 +646,9 @@ def _fit(self, X, y):
mini_batch_size = self.batch_size
self.training_model_ = self.build_model(self.input_shape_)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/regression/deep_learning/_lite_time.py b/aeon/regression/deep_learning/_lite_time.py
index 8b2e32f200..5a2079df94 100644
--- a/aeon/regression/deep_learning/_lite_time.py
+++ b/aeon/regression/deep_learning/_lite_time.py
@@ -1,6 +1,6 @@
-"""LITETime Regressor."""
+"""LITETime and LITE regressors."""
-__author__ = ["aadya940"]
+__author__ = ["aadya940", "hadifawaz1999"]
__all__ = ["IndividualLITERegressor", "LITETimeRegressor"]
import gc
@@ -61,6 +61,8 @@ class LITETimeRegressor(BaseRegressor):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -69,6 +71,9 @@ class LITETimeRegressor(BaseRegressor):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
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;
@@ -122,8 +127,10 @@ def __init__(
file_path="./",
save_last_model=False,
save_best_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
batch_size=64,
use_mini_batch_size=False,
n_epochs=1500,
@@ -149,8 +156,10 @@ def __init__(
self.save_last_model = save_last_model
self.save_best_model = save_best_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
self.last_file_name = last_file_name
+ self.init_file_name = init_file_name
self.callbacks = callbacks
self.random_state = random_state
@@ -188,8 +197,10 @@ def _fit(self, X, y):
file_path=self.file_path,
save_best_model=self.save_best_model,
save_last_model=self.save_last_model,
+ save_init_model=self.save_init_model,
best_file_name=self.best_file_name + str(n),
last_file_name=self.last_file_name + str(n),
+ init_file_name=self.init_file_name + str(n),
batch_size=self.batch_size,
use_mini_batch_size=self.use_mini_batch_size,
n_epochs=self.n_epochs,
@@ -302,6 +313,8 @@ class IndividualLITERegressor(BaseDeepRegressor):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -310,6 +323,9 @@ class IndividualLITERegressor(BaseDeepRegressor):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
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;
@@ -354,8 +370,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
batch_size=64,
use_mini_batch_size=False,
n_epochs=1500,
@@ -379,7 +397,9 @@ def __init__(
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.callbacks = callbacks
self.random_state = random_state
@@ -476,6 +496,9 @@ def _fit(self, X, y):
mini_batch_size = self.batch_size
self.training_model_ = self.build_model(self.input_shape)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/regression/deep_learning/_mlp.py b/aeon/regression/deep_learning/_mlp.py
index a8883f3f12..cb9907fe7c 100644
--- a/aeon/regression/deep_learning/_mlp.py
+++ b/aeon/regression/deep_learning/_mlp.py
@@ -1,6 +1,6 @@
-"""Multi Layer Perceptron Network (MLP) for Regression."""
+"""Multi Layer Perceptron Network (MLP) regressor."""
-__author__ = ["Aadya-Chinubhai"]
+__author__ = ["Aadya-Chinubhai", "hadifawaz1999"]
__all__ = ["MLPRegressor"]
import gc
@@ -47,6 +47,8 @@ class MLPRegressor(BaseDeepRegressor):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -55,6 +57,9 @@ class MLPRegressor(BaseDeepRegressor):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
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;
@@ -100,8 +105,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
random_state=None,
activation="relu",
output_activation="linear",
@@ -118,7 +125,9 @@ def __init__(
self.file_path = file_path
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.optimizer = optimizer
self.random_state = random_state
self.output_activation = output_activation
@@ -202,6 +211,9 @@ def _fit(self, X, y):
self.training_model_ = self.build_model(self.input_shape)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/regression/deep_learning/_resnet.py b/aeon/regression/deep_learning/_resnet.py
index b9a411891c..48f2d3c5f8 100644
--- a/aeon/regression/deep_learning/_resnet.py
+++ b/aeon/regression/deep_learning/_resnet.py
@@ -1,6 +1,6 @@
-"""Residual Network (ResNet) for regression."""
+"""Residual Network (ResNet) regressor."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = ["ResNetRegressor"]
import gc
@@ -82,6 +82,8 @@ class ResNetRegressor(BaseDeepRegressor):
Whether or not to save the last model, last
epoch trained, using the base class method
save_last_model_to_file
+ save_init_model : bool, default = False
+ Whether to save the initialization of the model.
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
@@ -90,6 +92,9 @@ class ResNetRegressor(BaseDeepRegressor):
The name of the file of the last model, if
save_last_model is set to False, this parameter
is discarded
+ init_file_name : str, default = "init_model"
+ The name of the file of the init model, if save_init_model is set to False,
+ this parameter is discarded.
verbose : boolean, default = False
whether to output extra information
loss : string, default="mean_squared_error"
@@ -147,8 +152,10 @@ def __init__(
file_path="./",
save_best_model=False,
save_last_model=False,
+ save_init_model=False,
best_file_name="best_model",
last_file_name="last_model",
+ init_file_name="init_model",
optimizer=None,
):
self.n_residual_blocks = n_residual_blocks
@@ -171,7 +178,9 @@ def __init__(
self.file_path = file_path
self.save_best_model = save_best_model
self.save_last_model = save_last_model
+ self.save_init_model = save_init_model
self.best_file_name = best_file_name
+ self.init_file_name = init_file_name
self.optimizer = optimizer
self.history = None
@@ -261,6 +270,9 @@ def _fit(self, X, y):
self.input_shape = X.shape[1:]
self.training_model_ = self.build_model(self.input_shape)
+ if self.save_init_model:
+ self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
if self.verbose:
self.training_model_.summary()
diff --git a/aeon/regression/deep_learning/_tapnet.py b/aeon/regression/deep_learning/_tapnet.py
index 380d2ee533..4a03f02c66 100644
--- a/aeon/regression/deep_learning/_tapnet.py
+++ b/aeon/regression/deep_learning/_tapnet.py
@@ -1,6 +1,6 @@
-"""Time Convolutional Neural Network (CNN) for classification."""
+"""Time series Attentional Prototype Network (TapNet) regressor."""
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
__all__ = [
"TapNetRegressor",
]
diff --git a/aeon/regression/deep_learning/tests/test_deep_regressor_base.py b/aeon/regression/deep_learning/tests/test_deep_regressor_base.py
index 000b39173d..9dad88f0b2 100644
--- a/aeon/regression/deep_learning/tests/test_deep_regressor_base.py
+++ b/aeon/regression/deep_learning/tests/test_deep_regressor_base.py
@@ -10,7 +10,7 @@
from aeon.testing.data_generation import make_example_2d_numpy_collection
from aeon.utils.validation._dependencies import _check_soft_dependencies
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
class _DummyDeepRegressor(BaseDeepRegressor):
diff --git a/aeon/regression/deep_learning/tests/test_saving_loading_deep_learning_cls.py b/aeon/regression/deep_learning/tests/test_saving_loading_deep_learning_cls.py
new file mode 100644
index 0000000000..736d99baf3
--- /dev/null
+++ b/aeon/regression/deep_learning/tests/test_saving_loading_deep_learning_cls.py
@@ -0,0 +1,79 @@
+"""Unit tests for regressors deep learners save/load functionalities."""
+
+import inspect
+import os
+import tempfile
+import time
+
+import pytest
+
+from aeon.regression import deep_learning
+from aeon.testing.data_generation import make_example_3d_numpy
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+__maintainer__ = ["hadifawaz1999"]
+
+
+_deep_rgs_classes = [
+ member[1] for member in inspect.getmembers(deep_learning, inspect.isclass)
+]
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="skip test if required soft dependency not available",
+)
+@pytest.mark.parametrize("deep_rgs", _deep_rgs_classes)
+def test_saving_loading_deep_learning_rgs(deep_rgs):
+ """Test Deep Regressor saving."""
+ with tempfile.TemporaryDirectory() as tmp:
+ if not (
+ deep_rgs.__name__
+ in [
+ "BaseDeepRegressor",
+ "InceptionTimeRegressor",
+ "LITETimeRegressor",
+ "TapNetRegressor",
+ ]
+ ):
+ if tmp[-1] != "/":
+ tmp = tmp + "/"
+ curr_time = str(time.time_ns())
+ last_file_name = curr_time + "last"
+ best_file_name = curr_time + "best"
+ init_file_name = curr_time + "init"
+
+ X, y = make_example_3d_numpy()
+
+ deep_rgs_train = deep_rgs(
+ n_epochs=2,
+ save_best_model=True,
+ save_last_model=True,
+ save_init_model=True,
+ best_file_name=best_file_name,
+ last_file_name=last_file_name,
+ init_file_name=init_file_name,
+ file_path=tmp,
+ )
+ deep_rgs_train.fit(X, y)
+
+ deep_rgs_best = deep_rgs()
+ deep_rgs_best.load_model(
+ model_path=os.path.join(tmp, best_file_name + ".keras"),
+ )
+ ypred_best = deep_rgs_best.predict(X)
+ assert len(ypred_best) == len(y)
+
+ deep_rgs_last = deep_rgs()
+ deep_rgs_last.load_model(
+ model_path=os.path.join(tmp, last_file_name + ".keras"),
+ )
+ ypred_last = deep_rgs_last.predict(X)
+ assert len(ypred_last) == len(y)
+
+ deep_rgs_init = deep_rgs()
+ deep_rgs_init.load_model(
+ model_path=os.path.join(tmp, init_file_name + ".keras"),
+ )
+ ypred_init = deep_rgs_init.predict(X)
+ assert len(ypred_init) == len(y)
diff --git a/aeon/testing/expected_results/classifier_results_reproduction.py b/aeon/testing/expected_results/classifier_results_reproduction.py
index 06c5231d2e..441a496442 100644
--- a/aeon/testing/expected_results/classifier_results_reproduction.py
+++ b/aeon/testing/expected_results/classifier_results_reproduction.py
@@ -23,6 +23,7 @@
WEASEL_V2,
BOSSEnsemble,
ContractableBOSS,
+ MrSQMClassifier,
TemporalDictionaryEnsemble,
)
from aeon.classification.distance_based import (
@@ -56,7 +57,6 @@
from aeon.classification.ordinal_classification import OrdinalTDE
from aeon.classification.shapelet_based import (
LearningShapeletClassifier,
- MrSQMClassifier,
SASTClassifier,
ShapeletTransformClassifier,
)
diff --git a/aeon/testing/expected_results/regressor_results_reproduction.py b/aeon/testing/expected_results/regressor_results_reproduction.py
index d60feb2067..5c47340ef7 100644
--- a/aeon/testing/expected_results/regressor_results_reproduction.py
+++ b/aeon/testing/expected_results/regressor_results_reproduction.py
@@ -7,7 +7,7 @@
from aeon.regression.convolution_based import (
HydraRegressor,
MultiRocketHydraRegressor,
- RocketRegressor
+ RocketRegressor,
)
from aeon.regression.distance_based import KNeighborsTimeSeriesRegressor
from aeon.regression.feature_based import (
@@ -25,9 +25,8 @@
RandomIntervalSpectralEnsembleRegressor,
TimeSeriesForestRegressor,
)
-from aeon.regression.shapelet_based import (
- RDSTRegressor,
-)
+from aeon.regression.shapelet_based import RDSTRegressor
+
def _reproduce_regression_covid_3month(estimator):
X_train, y_train = load_covid_3month(split="train")
diff --git a/aeon/transformations/lag.py b/aeon/transformations/_legacy/lag.py
similarity index 98%
rename from aeon/transformations/lag.py
rename to aeon/transformations/_legacy/lag.py
index 93d14666fe..ff3ef76d0b 100644
--- a/aeon/transformations/lag.py
+++ b/aeon/transformations/_legacy/lag.py
@@ -6,7 +6,6 @@
import numpy as np
import pandas as pd
-from deprecated.sphinx import deprecated
from pandas.api.types import is_integer_dtype
from aeon.transformations.base import BaseTransformer
@@ -24,12 +23,6 @@ def _coerce_to_int(obj):
return obj
-# TODO: remove in v0.11.0
-@deprecated(
- version="0.10.0",
- reason="Lag will be removed in version 0.11.0.",
- category=FutureWarning,
-)
class Lag(BaseTransformer):
"""Lagging transformer. Lags time series by one or multiple lags.
@@ -298,12 +291,6 @@ def get_test_params(cls, parameter_set="default"):
return [params1, params2, params3]
-# TODO: remove in v0.11.0
-@deprecated(
- version="0.10.0",
- reason="ReducerTransform will be removed in version 0.11.0.",
- category=FutureWarning,
-)
class ReducerTransform(BaseTransformer):
"""Transformer for forecasting reduction. Prepares tabular X/y via lag and trafos.
diff --git a/aeon/transformations/outlier_detection.py b/aeon/transformations/_legacy/outlier_detection.py
similarity index 93%
rename from aeon/transformations/outlier_detection.py
rename to aeon/transformations/_legacy/outlier_detection.py
index 81c8e8b40a..26b9826aa0 100644
--- a/aeon/transformations/outlier_detection.py
+++ b/aeon/transformations/_legacy/outlier_detection.py
@@ -7,18 +7,11 @@
import numpy as np
import pandas as pd
-from deprecated.sphinx import deprecated
from aeon.forecasting.model_selection import SlidingWindowSplitter
from aeon.transformations.base import BaseTransformer
-# TODO: remove in v0.11.0
-@deprecated(
- version="0.10.0",
- reason="HampelFilter will be removed in version 0.11.0.",
- category=FutureWarning,
-)
class HampelFilter(BaseTransformer):
"""Use HampelFilter to detect outliers based on a sliding window.
@@ -46,14 +39,6 @@ class HampelFilter(BaseTransformer):
----------
.. [1] Hampel F. R., "The influence curve and its role in robust estimation",
Journal of the American Statistical Association, 69, 382β393, 1974
-
- Examples
- --------
- >>> from aeon.transformations.outlier_detection import HampelFilter
- >>> from aeon.datasets import load_airline
- >>> y = load_airline()
- >>> transformer = HampelFilter(window_length=10)
- >>> y_hat = transformer.fit_transform(y)
"""
_tags = {
diff --git a/aeon/transformations/_legacy/subset.py b/aeon/transformations/_legacy/subset.py
index 48facac3b5..e8dea367d3 100644
--- a/aeon/transformations/_legacy/subset.py
+++ b/aeon/transformations/_legacy/subset.py
@@ -8,106 +8,6 @@
from aeon.transformations.base import BaseTransformer
-class _IndexSubset(BaseTransformer):
- r"""Index subsetting transformer.
-
- In transform, subsets `X` to the indices in `y.index`.
- If `y` is None, returns `X` without subsetting.
- numpy-based `X` are interpreted as having a RangeIndex starting at n,
- where n is the number of numpy rows seen so far through `fit` and `update`.
- Non-pandas types are interpreted as having index as after conversion to the
- `"pd.DataFrame"` aeon type.
-
- Parameters
- ----------
- index_treatment : str, optional, one of "keep" (default) or "remove"
- determines which indices are kept in `Xt = transform(X, y)`
- "keep" = all indices in y also appear in Xt. If not present in X, NA is filled.
- "remove" = only indices that appear in both X and y are present in Xt.
- """
-
- _tags = {
- "input_data_type": "Series",
- # what is the abstract type of X: Series, or Panel
- "output_data_type": "Series",
- # what abstract type is returned: Primitives, Series, Panel
- "instancewise": True, # is this an instance-wise transform?
- "X_inner_type": ["pd.DataFrame", "pd.Series"],
- "y_inner_type": ["pd.DataFrame", "pd.Series"],
- "transform-returns-same-time-index": False,
- "fit_is_empty": False,
- "capability:multivariate": True,
- "capability:inverse_transform": False,
- "remember_data": True, # remember all data seen as _X
- }
-
- def __init__(self, index_treatment="keep"):
- self.index_treatment = index_treatment
- super().__init__()
-
- def _transform(self, X, y=None):
- """Transform X and return a transformed version.
-
- private _transform containing the core logic, called from transform
-
- Parameters
- ----------
- X : pd.DataFrame or pd.Series
- Data to be transformed
- y : pd.DataFrame or pd.Series
- Additional data, e.g., labels for transformation
-
- Returns
- -------
- Xt : pd.DataFrame or pd.Series, same type as X
- transformed version of X
- """
- if y is None:
- return X
-
- X = self._X
-
- index_treatment = self.index_treatment
- ind_X_and_y = X.index.intersection(y.index)
-
- if index_treatment == "remove":
- Xt = X.loc[ind_X_and_y]
- elif index_treatment == "keep":
- Xt = X.loc[ind_X_and_y]
- y_idx_frame = type(X)(index=y.index)
- Xt = Xt.combine_first(y_idx_frame)
- else:
- raise ValueError(
- f'index_treatment must be one of "remove", "keep", but found'
- f' "{index_treatment}"'
- )
- return Xt
-
- @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.
- There are currently no reserved values for transformers.
-
- 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`
- """
- params1 = {"index_treatment": "remove"}
- params2 = {"index_treatment": "keep"}
-
- return [params1, params2]
-
-
class _ColumnSelect(BaseTransformer):
r"""Column selection transformer.
diff --git a/aeon/transformations/summarize.py b/aeon/transformations/_legacy/summarize.py
similarity index 91%
rename from aeon/transformations/summarize.py
rename to aeon/transformations/_legacy/summarize.py
index edfbc28b37..3386cb9f68 100644
--- a/aeon/transformations/summarize.py
+++ b/aeon/transformations/_legacy/summarize.py
@@ -5,19 +5,12 @@
import numpy as np
import pandas as pd
-from deprecated.sphinx import deprecated
from joblib import Parallel, delayed
from aeon.transformations.base import BaseTransformer
from aeon.utils.multiindex import flatten_multiindex
-# TODO: remove in v0.11.0
-@deprecated(
- version="0.10.0",
- reason="WindowSummarizer will be removed in version 0.11.0.",
- category=FutureWarning,
-)
class WindowSummarizer(BaseTransformer):
"""
Transformer for extracting time series features.
@@ -132,55 +125,6 @@ class WindowSummarizer(BaseTransformer):
Contains all transformed columns as well as non-transformed columns.
The raw inputs to transformed columns will be dropped.
self: reference to self
-
- Examples
- --------
- >>> import pandas as pd
- >>> from aeon.transformations.summarize import WindowSummarizer
- >>> from aeon.datasets import load_airline, load_longley
- >>> from aeon.forecasting.naive import NaiveForecaster
- >>> from aeon.forecasting.base import ForecastingHorizon
- >>> from aeon.forecasting.compose import ForecastingPipeline
- >>> from aeon.forecasting.model_selection import temporal_train_test_split
- >>> y = load_airline()
- >>> kwargs = {
- ... "lag_feature": {
- ... "lag": [1],
- ... "mean": [[1, 3], [3, 6]],
- ... "std": [[1, 4]],
- ... }
- ... }
- >>> transformer = WindowSummarizer(**kwargs)
- >>> y_transformed = transformer.fit_transform(y)
-
- Example with transforming multiple columns of exogeneous features
- >>> y, X = load_longley()
- >>> y_train, y_test, X_train, X_test = temporal_train_test_split(y, X)
- >>> fh = ForecastingHorizon(X_test.index, is_relative=False)
- >>> # Example transforming only X
- >>> pipe = ForecastingPipeline(
- ... steps=[
- ... ("a", WindowSummarizer(n_jobs=1, target_cols=["POP", "GNPDEFL"])),
- ... ("b", WindowSummarizer(n_jobs=1, target_cols=["GNP"], **kwargs)),
- ... ("forecaster", NaiveForecaster(strategy="drift")),
- ... ]
- ... )
- >>> pipe_return = pipe.fit(y_train, X_train)
- >>> y_pred1 = pipe_return.predict(fh=fh, X=X_test)
-
- Example with transforming multiple columns of exogeneous features
- as well as the y column
- >>> Z_train = pd.concat([X_train, y_train], axis=1)
- >>> Z_test = pd.concat([X_test, y_test], axis=1)
- >>> pipe = ForecastingPipeline(
- ... steps=[
- ... ("a", WindowSummarizer(n_jobs=1, target_cols=["POP", "TOTEMP"])),
- ... ("b", WindowSummarizer(**kwargs, n_jobs=1, target_cols=["GNP"])),
- ... ("forecaster", NaiveForecaster(strategy="drift")),
- ... ]
- ... )
- >>> pipe_return = pipe.fit(y_train, Z_train)
- >>> y_pred2 = pipe_return.predict(fh=fh, X=Z_test)
"""
_tags = {
@@ -594,12 +538,6 @@ def _check_quantiles(quantiles):
return quantiles
-# TODO: remove in v0.11.0
-@deprecated(
- version="0.10.0",
- reason="SummaryTransformer will be removed in version 0.11.0.",
- category=FutureWarning,
-)
class SummaryTransformer(BaseTransformer):
"""Calculate summary value of a time series.
@@ -633,14 +571,6 @@ class SummaryTransformer(BaseTransformer):
-----
This provides a wrapper around pandas DataFrame and Series agg and
quantile methods.
-
- Examples
- --------
- >>> from aeon.transformations.summarize import SummaryTransformer
- >>> from aeon.datasets import load_airline
- >>> y = load_airline()
- >>> transformer = SummaryTransformer()
- >>> y_mean = transformer.fit_transform(y)
"""
_tags = {
@@ -742,12 +672,6 @@ def get_test_params(cls, parameter_set="default"):
return [params1, params2, params3]
-# TODO: remove in v0.11.0
-@deprecated(
- version="0.10.0",
- reason="PlateauFinder will be removed in version 0.11.0.",
- category=FutureWarning,
-)
class PlateauFinder(BaseTransformer):
"""
Plateau finder transformer.
@@ -833,12 +757,6 @@ def _transform(self, X, y=None):
return Xt
-# TODO: remove in v0.11.0
-@deprecated(
- version="0.10.0",
- reason="FittedParamExtractor will be removed in version 0.11.0.",
- category=FutureWarning,
-)
class FittedParamExtractor(BaseTransformer):
"""Fitted parameter extractor.
diff --git a/aeon/transformations/collection/convolution_based/tests/test_all_rockets.py b/aeon/transformations/collection/convolution_based/tests/test_all_rockets.py
index 30dceecdad..b9439a1eaa 100644
--- a/aeon/transformations/collection/convolution_based/tests/test_all_rockets.py
+++ b/aeon/transformations/collection/convolution_based/tests/test_all_rockets.py
@@ -9,6 +9,7 @@
MultiRocket,
Rocket,
)
+from aeon.transformations.collection.convolution_based._minirocket import _PPV
# Data used to test correctness of transform
uni_test_data = np.array(
@@ -213,3 +214,11 @@ def test_expected_basic_motions():
np.testing.assert_allclose(
X2[:5, :5], expected_basic_motions["MiniRocket"], rtol=1e-4
)
+
+
+def test_ppv():
+ """Test uncovered PPV function."""
+ a = np.float32(10.0)
+ b = np.float32(-5.0)
+ assert _PPV(a, b) == 1
+ assert _PPV(b, a) == 0
diff --git a/aeon/transformations/collection/dictionary_based/_sfa.py b/aeon/transformations/collection/dictionary_based/_sfa.py
index 9bf638e3d5..df3f51604a 100644
--- a/aeon/transformations/collection/dictionary_based/_sfa.py
+++ b/aeon/transformations/collection/dictionary_based/_sfa.py
@@ -46,7 +46,7 @@ class SFA(BaseCollectionTransformer):
Parameters
----------
word_length: int, default = 8
- length of word to shorten window to (using PAA)
+ length of word to shorten window to (using DFT)
alphabet_size: int, default = 4
number of values to discretise each value to
diff --git a/aeon/transformations/collection/dictionary_based/_sfa_fast.py b/aeon/transformations/collection/dictionary_based/_sfa_fast.py
index 91d0e326e9..044217158e 100644
--- a/aeon/transformations/collection/dictionary_based/_sfa_fast.py
+++ b/aeon/transformations/collection/dictionary_based/_sfa_fast.py
@@ -59,7 +59,7 @@ class SFAFast(BaseCollectionTransformer):
Parameters
----------
word_length : int, default = 8
- Length of word to shorten window to (using PAA).
+ Length of word to shorten window to (using DFT).
alphabet_size : int, default = 4
Number of values to discretise each value to.
window_size : int, default = 12
diff --git a/aeon/transformations/scaledlogit.py b/aeon/transformations/scaledlogit.py
deleted file mode 100644
index 3a1f7f1a7d..0000000000
--- a/aeon/transformations/scaledlogit.py
+++ /dev/null
@@ -1,214 +0,0 @@
-"""Implements the scaled logit transformation."""
-
-__maintainer__ = []
-__all__ = ["ScaledLogitTransformer"]
-
-from copy import deepcopy
-from warnings import warn
-
-import numpy as np
-from deprecated.sphinx import deprecated
-
-from aeon.transformations.base import BaseTransformer
-
-
-# TODO: remove in v0.11.0
-@deprecated(
- version="0.10.0",
- reason="ScaledLogitTransformer will be removed in version 0.11.0.",
- category=FutureWarning,
-)
-class ScaledLogitTransformer(BaseTransformer):
- r"""Scaled logit transform or Log transform.
-
- If both lower_bound and upper_bound are not None, a scaled logit transform is
- applied to the data. Otherwise, the transform applied is a log transform variation
- that ensures the resulting values from the inverse transform are bounded
- accordingly. The transform is applied to all scalar elements of the input array
- individually.
-
- Combined with an aeon.forecasting.compose.TransformedTargetForecaster, it ensures
- that the forecast stays between the specified bounds (lower_bound, upper_bound).
-
- Default is lower_bound = upper_bound = None, i.e., the identity transform.
-
- The logarithm transform is obtained for lower_bound = 0, upper_bound = None.
-
- Parameters
- ----------
- lower_bound : float, optional, default=None
- lower bound of inverse transform function
- upper_bound : float, optional, default=None
- upper bound of inverse transform function
-
- See Also
- --------
- aeon.transformations.boxcox._LogTransformer :
- Transformer input data using natural log. Can help normalize data and
- compress variance of the series.
- aeon.transformations.boxcox._BoxCoxTransformer :
- Applies Box-Cox power transformation. Can help normalize data and
- compress variance of the series.
- aeon.transformations.exponent.ExponentTransformer :
- Transform input data by raising it to an exponent. Can help compress
- variance of series if a fractional exponent is supplied.
- aeon.transformations.exponent.SqrtTransformer :
- Transform input data by taking its square root. Can help compress
- variance of input series.
-
- Notes
- -----
- | The scaled logit transform is applied if both upper_bound and lower_bound are
- | not None:
- | :math:`log(\frac{x - a}{b - x})`, where a is the lower and b is the upper bound.
-
- | If upper_bound is None and lower_bound is not None the transform applied is
- | a log transform of the form:
- | :math:`log(x - a)`
-
- | If lower_bound is None and upper_bound is not None the transform applied is
- | a log transform of the form:
- | :math:`- log(b - x)`
-
- References
- ----------
- .. [1] Hyndsight - Forecasting within limits:
- https://robjhyndman.com/hyndsight/forecasting-within-limits/
- .. [2] Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and
- practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3.
- Accessed on January 24th 2022.
-
- Examples
- --------
- >>> import numpy as np
- >>> from aeon.datasets import load_airline
- >>> from aeon.transformations.scaledlogit import ScaledLogitTransformer
- >>> from aeon.forecasting.trend import PolynomialTrendForecaster
- >>> from aeon.forecasting.compose import TransformedTargetForecaster
- >>> y = load_airline()
- >>> fcaster = TransformedTargetForecaster([
- ... ("scaled_logit", ScaledLogitTransformer(0, 650)),
- ... ("poly", PolynomialTrendForecaster(degree=2))
- ... ])
- >>> fcaster.fit(y)
- TransformedTargetForecaster(...)
- >>> y_pred = fcaster.predict(fh = np.arange(32))
- """
-
- _tags = {
- "input_data_type": "Series",
- # what is the abstract type of X: Series, or Panel
- "output_data_type": "Series",
- # what abstract type is returned: Primitives, Series, Panel
- "instancewise": True, # is this an instance-wise transform?
- "X_inner_type": "np.ndarray",
- "y_inner_type": "None",
- "transform-returns-same-time-index": True,
- "fit_is_empty": True,
- "capability:multivariate": True,
- "capability:inverse_transform": True,
- "skip-inverse-transform": False,
- }
-
- def __init__(self, lower_bound=None, upper_bound=None):
- self.lower_bound = lower_bound
- self.upper_bound = upper_bound
-
- super().__init__()
-
- def _transform(self, X, y=None):
- """Transform X and return a transformed version.
-
- private _transform containing core logic, called from transform
-
- Parameters
- ----------
- X : 2D np.ndarray
- Data to be transformed
- y : data structure of type y_inner_type, default=None
- Ignored argument for interface compatibility
-
- Returns
- -------
- transformed version of X
- """
- if self.upper_bound is not None and np.any(X >= self.upper_bound):
- warn(
- "X in ScaledLogitTransformer should not have values "
- "greater than upper_bound",
- RuntimeWarning,
- )
-
- if self.lower_bound is not None and np.any(X <= self.lower_bound):
- warn(
- "X in ScaledLogitTransformer should not have values "
- "lower than lower_bound",
- RuntimeWarning,
- )
-
- if self.upper_bound and self.lower_bound:
- X_transformed = np.log((X - self.lower_bound) / (self.upper_bound - X))
- elif self.upper_bound is not None:
- X_transformed = -np.log(self.upper_bound - X)
- elif self.lower_bound is not None:
- X_transformed = np.log(X - self.lower_bound)
- else:
- X_transformed = deepcopy(X)
-
- return X_transformed
-
- def _inverse_transform(self, X, y=None):
- """Inverse transform, inverse operation to transform.
-
- private _inverse_transform containing core logic, called from inverse_transform
-
- Parameters
- ----------
- X : 2D np.ndarray
- Data to be inverse transformed
- y : data of y_inner_type, default=None
- Ignored argument for interface compatibility
-
- Returns
- -------
- inverse transformed version of X
- """
- if self.upper_bound and self.lower_bound:
- X_inv_transformed = (self.upper_bound * np.exp(X) + self.lower_bound) / (
- np.exp(X) + 1
- )
- elif self.upper_bound is not None:
- X_inv_transformed = self.upper_bound - np.exp(-X)
- elif self.lower_bound is not None:
- X_inv_transformed = np.exp(X) + self.lower_bound
- else:
- X_inv_transformed = deepcopy(X)
-
- return X_inv_transformed
-
- @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.
-
-
- 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`
- """
- test_params = [
- {"lower_bound": None, "upper_bound": None},
- {"lower_bound": -(10**6), "upper_bound": None},
- {"lower_bound": None, "upper_bound": 10**6},
- {"lower_bound": -(10**6), "upper_bound": 10**6},
- ]
- return test_params
diff --git a/aeon/transformations/tests/test_compose.py b/aeon/transformations/tests/test_compose.py
index f25ecba802..c49f12a57d 100644
--- a/aeon/transformations/tests/test_compose.py
+++ b/aeon/transformations/tests/test_compose.py
@@ -19,13 +19,13 @@
OptionalPassthrough,
TransformerPipeline,
)
-from aeon.transformations._legacy.impute import Imputer
from aeon.transformations._legacy.subset import _ColumnSelect
+from aeon.transformations._legacy.summarize import SummaryTransformer
from aeon.transformations._legacy.theta import (
_ThetaLinesTransformer as ThetaLinesTransformer,
)
from aeon.transformations.collection.pad import PaddingTransformer
-from aeon.transformations.summarize import SummaryTransformer
+from aeon.transformations.impute import Imputer
def test_dunder_mul():
diff --git a/aeon/transformations/tests/test_featureizer.py b/aeon/transformations/tests/test_featureizer.py
deleted file mode 100644
index f6b4b3e03c..0000000000
--- a/aeon/transformations/tests/test_featureizer.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""Test YtoX."""
-
-__maintainer__ = []
-
-from numpy.testing import assert_array_equal
-
-from aeon.datasets import load_longley
-from aeon.forecasting.model_selection import temporal_train_test_split
-from aeon.testing.mock_estimators import MockTransformer
-from aeon.transformations._legacy.compose import YtoX
-from aeon.transformations.lag import Lag
-
-y, X = load_longley()
-y_train, y_test, X_train, X_test = temporal_train_test_split(y, X)
-
-
-def test_featurized_values():
- """Test against plain transformation.
-
- Test to check that the featurized values are same as if transformation
- is done without YtoX.
- """
- lags = len(y_test)
- featurizer = YtoX() * MockTransformer() * Lag(lags)
- featurizer.fit(X_train, y_train)
- X_hat = featurizer.transform(X_test, y_test)
-
- exp_transformer = MockTransformer()
- expected_len = lags + len(y_test)
- y_hat = exp_transformer.fit_transform(y[-expected_len:])
- assert_array_equal(X_hat[f"lag_{lags}__TOTEMP"].values, y_hat.values)
diff --git a/aeon/transformations/tests/test_fittedparamextractor.py b/aeon/transformations/tests/test_fittedparamextractor.py
deleted file mode 100644
index 524f49255d..0000000000
--- a/aeon/transformations/tests/test_fittedparamextractor.py
+++ /dev/null
@@ -1,30 +0,0 @@
-"""Tests for FittedParamExtractor."""
-
-__maintainer__ = []
-__all__ = []
-
-import pytest
-
-from aeon.datasets import load_gunpoint
-from aeon.forecasting.exp_smoothing import ExponentialSmoothing
-from aeon.transformations.summarize import FittedParamExtractor
-from aeon.utils.validation._dependencies import _check_estimator_deps
-
-X_train, y_train = load_gunpoint("train", return_type="nested_univ")
-
-
-@pytest.mark.skipif(
- not _check_estimator_deps(ExponentialSmoothing, severity="none"),
- reason="skip test if required soft dependency for hmmlearn not available",
-)
-@pytest.mark.parametrize("param_names", ["initial_level"])
-def test_fitted_param_extractor(param_names):
- forecaster = ExponentialSmoothing()
- t = FittedParamExtractor(forecaster=forecaster, param_names=param_names)
- Xt = t.fit_transform(X_train)
- assert Xt.shape == (X_train.shape[0], len(t._check_param_names(param_names)))
-
- # check specific value
- forecaster.fit(X_train.iloc[47, 0])
- fitted_param = forecaster.get_fitted_params()[param_names]
- assert Xt.iloc[47, 0] == fitted_param
diff --git a/aeon/transformations/tests/test_lag.py b/aeon/transformations/tests/test_lag.py
deleted file mode 100644
index 92e1c8ef78..0000000000
--- a/aeon/transformations/tests/test_lag.py
+++ /dev/null
@@ -1,92 +0,0 @@
-"""Tests for Lag transformer."""
-
-__maintainer__ = []
-
-import itertools
-
-import pandas as pd
-import pytest
-
-from aeon.testing.data_generation._legacy import get_examples, make_series
-from aeon.transformations.lag import Lag
-
-# some examples with range vs time index, univariate vs multivariate (mv)
-X_range_idx = get_examples("pd.DataFrame")[0]
-X_range_idx_mv = get_examples("pd.DataFrame")[1]
-X_time_idx = make_series()
-X_time_idx_mv = make_series(n_columns=2)
-
-# all fixtures
-X_fixtures = [X_range_idx, X_range_idx_mv, X_time_idx, X_time_idx_mv]
-
-# fixtures with time index
-X_time_fixtures = [X_time_idx, X_time_idx_mv]
-
-index_outs = ["original", "extend", "shift"]
-
-
-@pytest.mark.parametrize("X", X_fixtures)
-@pytest.mark.parametrize("index_out", index_outs)
-def test_lag_fit_transform_out_index(X, index_out):
- """Test that index sets of fit_transform output behave as expected."""
- t = Lag(2, index_out=index_out)
- Xt = t.fit_transform(X)
-
- if index_out == "original":
- assert Xt.index.equals(X.index)
- elif index_out == "extend":
- assert X.index.isin(Xt.index).all()
- assert len(Xt) == len(X) + 2
- elif index_out == "shift":
- assert len(Xt) == len(X)
- assert X.index[2:].isin(Xt.index).all()
-
-
-@pytest.mark.parametrize("X", X_fixtures)
-@pytest.mark.parametrize("index_out", index_outs)
-@pytest.mark.parametrize("lag", [2, [2, 4], [-1, 0, 5]])
-def test_lag_fit_transform_columns(X, index_out, lag):
- """Test that columns of fit_transform output behave as expected."""
- t = Lag(lags=lag, index_out=index_out)
- Xt = t.fit_transform(X)
-
- if isinstance(lag, list):
- len_lag = len(lag)
- else:
- len_lag = 1
-
- def ncols(obj):
- if isinstance(obj, pd.DataFrame):
- return len(obj.columns)
- else:
- return 1
-
- assert ncols(Xt) == ncols(X) * len_lag
-
-
-@pytest.mark.parametrize("X", X_fixtures)
-@pytest.mark.parametrize("index_out", index_outs)
-@pytest.mark.parametrize("lags", [2, [2, 4]])
-def test_lag_fit_transform_column_names(X, index_out, lags):
- """Test expected column names."""
- t = Lag(lags=lags, index_out=index_out)
- Xt = t.fit_transform(X)
-
- if isinstance(Xt, pd.DataFrame):
- lag_col_names = set(Xt.columns)
-
- if isinstance(X, pd.DataFrame):
- col_names = X.columns
- elif isinstance(X, pd.Series):
- col_names = [X.name if X.name else 0]
- else:
- pass
-
- lags = [lags] if isinstance(lags, int) else lags
- expected = {
- f"lag_{lag}__{col}" for lag, col in itertools.product(lags, col_names)
- }
- assert lag_col_names == expected
-
- elif isinstance(Xt, pd.Series):
- assert Xt.name is None
diff --git a/aeon/transformations/tests/test_plateaufinder.py b/aeon/transformations/tests/test_plateaufinder.py
index d94dac896e..b5ebd53f2b 100644
--- a/aeon/transformations/tests/test_plateaufinder.py
+++ b/aeon/transformations/tests/test_plateaufinder.py
@@ -4,7 +4,7 @@
import pandas as pd
import pytest
-from aeon.transformations.summarize import PlateauFinder
+from aeon.transformations._legacy.summarize import PlateauFinder
@pytest.mark.parametrize("value", [np.nan, -10, 10, -0.5, 0.5])
diff --git a/aeon/transformations/tests/test_rockets.py b/aeon/transformations/tests/test_rockets.py
deleted file mode 100644
index cac8aeebb5..0000000000
--- a/aeon/transformations/tests/test_rockets.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""Tests for the rocket transformers."""
-
-import numpy as np
-
-from aeon.transformations.collection.convolution_based._minirocket import _PPV
-
-
-def test_ppv():
- """Test uncovered PPV function."""
- a = np.float32(10.0)
- b = np.float32(-5.0)
- assert _PPV(a, b) == 1
- assert _PPV(b, a) == 0
diff --git a/aeon/transformations/tests/test_summarize.py b/aeon/transformations/tests/test_summarize.py
deleted file mode 100644
index 6585b4f79b..0000000000
--- a/aeon/transformations/tests/test_summarize.py
+++ /dev/null
@@ -1,94 +0,0 @@
-"""Test functionality of summary transformer."""
-
-__maintainer__ = []
-import re
-
-import numpy as np
-import pandas as pd
-import pytest
-
-from aeon.testing.data_generation._legacy import make_series
-from aeon.transformations.summarize import ALLOWED_SUM_FUNCS, SummaryTransformer
-
-# Test individual summary functions + lists and tuples of all summary functions
-sum_funcs_to_test = [ALLOWED_SUM_FUNCS[0]] + [ALLOWED_SUM_FUNCS]
-sum_funcs_to_test.append(tuple(ALLOWED_SUM_FUNCS))
-quantiles_to_test = [0.7, [0.5, 0.25, 0.1], (0.3, 0.0002, 0.99), None]
-
-# Incorrect inputs to test to ensure they raise expected errors
-incorrect_sum_funcs_to_test = [
- "meen",
- "md",
- 0.25,
- ["mean", "medain"],
- np.array(["mean", "max"]),
-]
-incorrect_quantiles_to_test = [25, "0.25", "median", [0.25, 1.25], [0.25, "median"]]
-
-# Test functionality on pd.Series and pd.DataFrame (uni- and multi-variate) input
-y1 = make_series(n_timepoints=75)
-y2 = make_series(n_timepoints=75)
-y1.name, y2.name = "y1", "y2"
-y_df_uni = pd.DataFrame(y1)
-y_df_multi = pd.concat([y1, y2], axis=1)
-data_to_test = [y1, y_df_uni, y_df_multi]
-
-
-@pytest.mark.parametrize("y", data_to_test)
-@pytest.mark.parametrize("summary_arg", sum_funcs_to_test)
-@pytest.mark.parametrize("quantile_arg", quantiles_to_test)
-def test_summary_transformer_output_type(y, summary_arg, quantile_arg):
- """Test whether output is DataFrame of correct dimensions."""
- transformer = SummaryTransformer(
- summary_function=summary_arg, quantiles=quantile_arg
- )
- transformer.fit(y)
- yt = transformer.transform(y)
-
- output_is_dataframe = isinstance(yt, pd.DataFrame)
- assert output_is_dataframe
-
- # compute number of expected rows and columns
-
- # all test cases are single series, so single row
- expected_instances = 1
-
- # expected number of feature types = quantiles plus summaries
- expected_sum_features = 1 if isinstance(summary_arg, str) else len(summary_arg)
- if quantile_arg is None:
- expected_q_features = 0
- elif isinstance(quantile_arg, (int, float)):
- expected_q_features = 1
- else:
- expected_q_features = len(quantile_arg)
- expected_features = expected_sum_features + expected_q_features
-
- # for multivariate series, columns = no variables * no feature types
- if isinstance(y, pd.DataFrame):
- expected_features = len(y.columns) * expected_features
-
- assert yt.shape == (expected_instances, expected_features)
-
-
-@pytest.mark.parametrize("summary_arg", incorrect_sum_funcs_to_test)
-def test_summary_transformer_incorrect_summary_function_raises_error(summary_arg):
- """Test if correct errors are raised for invalid summary_function input."""
- msg = rf"""`summary_function` must be None, or str or a list or tuple made up of
- {ALLOWED_SUM_FUNCS}.
- """
- with pytest.raises(ValueError, match=re.escape(msg)):
- transformer = SummaryTransformer(summary_function=summary_arg, quantiles=None)
- transformer.fit_transform(data_to_test[0])
-
-
-@pytest.mark.parametrize("quantile_arg", incorrect_quantiles_to_test)
-def test_summary_transformer_incorrect_quantile_raises_error(quantile_arg):
- """Test if correct errors are raised for invalid quantiles input."""
- msg = """`quantiles` must be None, int, float or a list or tuple made up of
- int and float values that are between 0 and 1.
- """
- with pytest.raises(ValueError, match=msg):
- transformer = SummaryTransformer(
- summary_function="mean", quantiles=quantile_arg
- )
- transformer.fit_transform(data_to_test[0])
diff --git a/aeon/transformations/tests/test_window_summarizer.py b/aeon/transformations/tests/test_window_summarizer.py
deleted file mode 100644
index 636e3c78c6..0000000000
--- a/aeon/transformations/tests/test_window_summarizer.py
+++ /dev/null
@@ -1,171 +0,0 @@
-"""Test extraction of features across (shifted) windows."""
-
-__maintainer__ = []
-
-import numpy as np
-import pandas as pd
-import pytest
-
-from aeon.datasets import load_airline, load_longley
-from aeon.forecasting.model_selection import temporal_train_test_split
-from aeon.testing.data_generation._legacy import get_examples
-from aeon.transformations.summarize import WindowSummarizer
-
-
-def check_eval(test_input, expected):
- """Test which columns are returned for different arguments.
-
- For a detailed description what these arguments do,
- and how theyinteract see docstring of DateTimeFeatures.
- """
- if test_input is not None:
- assert len(test_input) == len(expected)
- assert all([a == b for a, b in zip(test_input, expected)])
- else:
- assert expected is None
-
-
-# Load data that will be the basis of tests
-y = load_airline()
-y_pd = get_examples("pd.DataFrame")[0]
-y_series = get_examples("pd.Series")[0]
-y_multi = get_examples("pd-multiindex")[0]
-# y Train will be univariate data set
-y_train, y_test = temporal_train_test_split(y)
-
-# Create Panel sample data
-mi = pd.MultiIndex.from_product([[0], y.index], names=["instances", "timepoints"])
-y_group1 = pd.DataFrame(y.values, index=mi, columns=["y"])
-
-mi = pd.MultiIndex.from_product([[1], y.index], names=["instances", "timepoints"])
-y_group2 = pd.DataFrame(y.values, index=mi, columns=["y"])
-
-y_grouped = pd.concat([y_group1, y_group2])
-
-mi = pd.MultiIndex.from_product([[0], [0], y.index], names=["h1", "h2", "time"])
-y_hier1 = pd.DataFrame(y.values, index=mi, columns=["y"])
-
-mi = pd.MultiIndex.from_product([[0], [1], y.index], names=["h1", "h2", "time"])
-y_hier2 = pd.DataFrame(y.values, index=mi, columns=["y"])
-
-mi = pd.MultiIndex.from_product([[1], [0], y.index], names=["h1", "h2", "time"])
-y_hier3 = pd.DataFrame(y.values, index=mi, columns=["y"])
-
-mi = pd.MultiIndex.from_product([[1], [1], y.index], names=["h1", "h2", "time"])
-y_hier4 = pd.DataFrame(y.values, index=mi, columns=["y"])
-
-y_hierarchical = pd.concat([y_hier1, y_hier2, y_hier3, y_hier4])
-
-y_ll, X_ll = load_longley()
-y_ll_train, _, X_ll_train, X_ll_test = temporal_train_test_split(y_ll, X_ll)
-
-# Get different WindowSummarizer functions
-kwargs = WindowSummarizer.get_test_params()[0]
-kwargs_alternames = WindowSummarizer.get_test_params()[1]
-kwargs_variant = WindowSummarizer.get_test_params()[2]
-
-
-def count_gt100(x):
- """Count how many observations lie above threshold 100."""
- return np.sum((x > 100)[::-1])
-
-
-# Cannot be pickled in get_test_params, therefore here explicit
-kwargs_custom = {
- "lag_feature": {
- count_gt100: [[3, 2]],
- }
-}
-# Generate named and unnamed y
-y_train.name = None
-y_train_named = y_train.copy()
-y_train_named.name = "y"
-
-# Target for multivariate extraction
-Xtmvar = ["POP_lag_3", "POP_lag_6", "GNP_lag_3", "GNP_lag_6"]
-Xtmvar = Xtmvar + ["GNPDEFL", "UNEMP", "ARMED"]
-Xtmvar_none = ["GNPDEFL_lag_3", "GNPDEFL_lag_6", "GNP", "UNEMP", "ARMED", "POP"]
-
-
-@pytest.mark.parametrize(
- "kwargs, column_names, y, target_cols, truncate",
- [
- (
- kwargs,
- ["y_lag_1", "y_mean_1_3", "y_mean_1_12", "y_std_1_4"],
- y_train_named,
- None,
- None,
- ),
- (kwargs_alternames, Xtmvar, X_ll_train, ["POP", "GNP"], None),
- (kwargs_alternames, Xtmvar_none, X_ll_train, None, None),
- (
- kwargs,
- ["y_lag_1", "y_mean_1_3", "y_mean_1_12", "y_std_1_4"],
- y_group1,
- None,
- None,
- ),
- (
- kwargs,
- ["y_lag_1", "y_mean_1_3", "y_mean_1_12", "y_std_1_4"],
- y_grouped,
- None,
- None,
- ),
- (
- kwargs,
- ["y_lag_1", "y_mean_1_3", "y_mean_1_12", "y_std_1_4"],
- y_hierarchical,
- None,
- None,
- ),
- (
- None,
- ["var_0_lag_1", "var_1"],
- y_multi,
- None,
- None,
- ),
- (None, None, y_train, None, None),
- (None, ["a_lag_1"], y_pd, None, None),
- (kwargs_custom, ["a_count_gt100_3_2"], y_pd, None, None),
- (kwargs_alternames, ["0_lag_3", "0_lag_6"], y_train, None, "bfill"),
- (
- kwargs_variant,
- ["0_mean_1_7", "0_mean_8_7", "0_cov_1_28"],
- y_train,
- None,
- None,
- ),
- ],
-)
-def test_windowsummarizer(kwargs, column_names, y, target_cols, truncate):
- """Test columns match kwargs arguments."""
- if kwargs is not None:
- transformer = WindowSummarizer(
- **kwargs, target_cols=target_cols, truncate=truncate
- )
- else:
- transformer = WindowSummarizer(target_cols=target_cols, truncate=truncate)
- Xt = transformer.fit_transform(y)
- if Xt is not None:
- if isinstance(Xt, pd.DataFrame):
- Xt_columns = Xt.columns.to_list()
- else:
- Xt_columns = Xt.name
- else:
- Xt_columns = None
-
- # check that the index names are preserved
- assert y.index.names == Xt.index.names
-
- check_eval(Xt_columns, column_names)
-
-
-@pytest.mark.xfail(raises=ValueError)
-def test_wrong_column():
- """Test mismatch between X column names and target_cols."""
- transformer = WindowSummarizer(target_cols=["dummy"])
- Xt = transformer.fit_transform(X_ll_train)
- return Xt
diff --git a/docs/api_reference/classification.rst b/docs/api_reference/classification.rst
index ab36e9a19b..89a6261c3b 100644
--- a/docs/api_reference/classification.rst
+++ b/docs/api_reference/classification.rst
@@ -32,7 +32,9 @@ Deep learning
:toctree: auto_generated/
:template: class.rst
+ BaseDeepClassifier
CNNClassifier
+ TimeCNNClassifier
EncoderClassifier
FCNClassifier
InceptionTimeClassifier
@@ -56,6 +58,7 @@ Dictionary-based
ContractableBOSS
IndividualBOSS
IndividualTDE
+ MrSQMClassifier
MUSE
REDCOMETS
TemporalDictionaryEnsemble
@@ -133,7 +136,6 @@ Shapelet-based
:template: class.rst
LearningShapeletClassifier
- MrSQMClassifier
RDSTClassifier
SASTClassifier
ShapeletTransformClassifier
diff --git a/docs/api_reference/clustering.rst b/docs/api_reference/clustering.rst
index 1361f8b1a4..737a585631 100644
--- a/docs/api_reference/clustering.rst
+++ b/docs/api_reference/clustering.rst
@@ -20,6 +20,7 @@ Deep learning
BaseDeepClusterer
AEFCNClusterer
+ AEResNetClusterer
Clustering Algorithms
---------------------
diff --git a/docs/api_reference/distances.rst b/docs/api_reference/distances.rst
index ef79912320..c11ca70151 100644
--- a/docs/api_reference/distances.rst
+++ b/docs/api_reference/distances.rst
@@ -153,6 +153,9 @@ Shape Dynamic Time Warping (Shape DTW)
shape_dtw_pairwise_distance
shape_dtw_cost_matrix
shape_dtw_alignment_path
+ _pad_ts_collection_edges
+ _pad_ts_edges
+ _transform_subsequences
Squared
-------
diff --git a/docs/api_reference/networks.rst b/docs/api_reference/networks.rst
index 0a33613289..65b8aefa14 100644
--- a/docs/api_reference/networks.rst
+++ b/docs/api_reference/networks.rst
@@ -15,6 +15,7 @@ Deep learning networks
BaseDeepLearningNetwork
CNNNetwork
+ TimeCNNNetwork
EncoderNetwork
FCNNetwork
InceptionNetwork
@@ -22,3 +23,6 @@ Deep learning networks
ResNetNetwork
TapNetNetwork
AEFCNNetwork
+ AEResNetNetwork
+ LITENetwork
+ AEBiGRUNetwork
diff --git a/docs/api_reference/regression.rst b/docs/api_reference/regression.rst
index 2001e5e4b4..bb0ff961c3 100644
--- a/docs/api_reference/regression.rst
+++ b/docs/api_reference/regression.rst
@@ -51,16 +51,18 @@ Deep learning
:toctree: auto_generated/
:template: class.rst
+ BaseDeepRegressor
CNNRegressor
+ TimeCNNRegressor
EncoderRegressor
FCNRegressor
InceptionTimeRegressor
IndividualLITERegressor
IndividualInceptionRegressor
LITETimeRegressor
- LITETimeRegressor
ResNetRegressor
TapNetRegressor
+ MLPRegressor
Distance-based
--------------
diff --git a/docs/api_reference/transformations.rst b/docs/api_reference/transformations.rst
index 5f1ea860a4..9752d7c36a 100644
--- a/docs/api_reference/transformations.rst
+++ b/docs/api_reference/transformations.rst
@@ -74,21 +74,6 @@ features, usually a vector of floats, but can also be categorical.
When applied to collections or hierarchical data, the transformation result is a table
with as many rows as time series in the collection and a column for each feature.
-Summarization
-~~~~~~~~~~~~~
-
-These transformers extract simple summary features.
-
-.. currentmodule:: aeon.transformations.summarize
-
-.. autosummary::
- :toctree: auto_generated/
- :template: class.rst
-
- SummaryTransformer
- WindowSummarizer
- FittedParamExtractor
-
Shapelets, wavelets and convolution
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -172,23 +157,6 @@ These transformers extract larger collections of features.
TSFreshFeatureExtractor
Catch22
-Series-to-series transformers
------------------------------
-
-Series-to-series transformers transform individual time series into another time series.
-When applied to collections or hierarchical data, individual series are transformed
-through broadcasting.
-
-Lagging
-~~~~~~~
-
-.. currentmodule:: aeon.transformations.lag
-
-.. autosummary::
- :toctree: auto_generated/
- :template: class.rst
-
- Lag
Series transforms
~~~~~~~~~~~~~~~~~~~~~~~
@@ -206,14 +174,6 @@ Depending on the transformer, the transformation parameters can be fitted.
BoxCoxTransformer
LogTransformer
-.. currentmodule:: aeon.transformations.scaledlogit
-
-.. autosummary::
- :toctree: auto_generated/
- :template: class.rst
-
- ScaledLogitTransformer
-
.. currentmodule:: aeon.transformations.exponent
.. autosummary::
@@ -386,14 +346,6 @@ Bootstrap transformations
Outlier detection, changepoint detection
----------------------------------------
-.. currentmodule:: aeon.transformations.outlier_detection
-
-.. autosummary::
- :toctree: auto_generated/
- :template: class.rst
-
- HampelFilter
-
.. currentmodule:: aeon.transformations.series._clasp
.. autosummary::
diff --git a/docs/api_reference/visualisation.rst b/docs/api_reference/visualisation.rst
index 1555d6bbaf..4d7be0feb8 100644
--- a/docs/api_reference/visualisation.rst
+++ b/docs/api_reference/visualisation.rst
@@ -31,3 +31,4 @@ Visualisation
plot_time_series_with_change_points
plot_time_series_with_profiles
plot_cluster_algorithm
+ plot_network
diff --git a/docs/getting_started.md b/docs/getting_started.md
index 212c058dca..180d553a38 100644
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@@ -72,15 +72,14 @@ Quarter
1971 Q1 1.897371 1.987154 1.909734 3.657771 -0.1
```
-We commonly refer to the number of observations for a time series as `n_timepoints` or
-`n_timepoints`. If a series is multivariate, we refer to the dimensions as channels
+We commonly refer to the number of observations for a time series as `n_timepoints`. If a series is multivariate, we refer to the dimensions as channels
(to avoid confusion with the dimensions of array) and in code use `n_channels`.
Dimensions may also be referred to as variables.
Different parts of `aeon` work with single series or collections of series. The
`anomaly detection` and `segmentation` modules will commonly use single series input, while
`classification`, `regression` and `clustering` modules will use collections of time
-series. Collections of time series may also be referred to a Panels. Collections of
+series. Collections of time series may also be referred to as Panels. Collections of
time series will often be accompanied by an array of target variables.
```{code-block} python
diff --git a/examples/classification/deep_learning.ipynb b/examples/classification/deep_learning.ipynb
index e9fc4e2db9..1e9415c32f 100644
--- a/examples/classification/deep_learning.ipynb
+++ b/examples/classification/deep_learning.ipynb
@@ -201,6 +201,7 @@
"names.remove(\"IndividualInception\")\n",
"names.remove(\"IndividualLITE\")\n",
"names.remove(\"MLP\")\n",
+ "names.remove(\"TimeCNN\")\n",
"names.remove(\"TapNet\") # Multivariate only\n",
"\n",
"\n",
diff --git a/examples/transformations/transformations.ipynb b/examples/transformations/transformations.ipynb
deleted file mode 100644
index 53ff123f8d..0000000000
--- a/examples/transformations/transformations.ipynb
+++ /dev/null
@@ -1,890 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Transforming time series with aeon\n",
- "\n",
- "Transformers are objects that transform data from one representation to another. `aeon`\n",
- "contains time series specific transformers which can be used in\n",
- "pipelines in conjunction with other estimators.\n",
- "Note: the term \"transformer\" is used in deep learning to refer to specific neural\n",
- "network architectures. `aeon` transformers follow the `scikit-learn` design: they\n",
- "have `fit`, `transform` and `fit_transform` methods that combine the two functions.\n",
- "Some transformers also have `inverse_transform` that allows you to reverse the change.\n",
- "\n",
- "`aeon` distinguishes different types of transformer, depending on the input type accepted\n",
- "by the `fit` and `transform` methods. The main distinction is whether all series types\n",
- "(i.e. single time series, collections of time series, hierarchical time series) are accepted\n",
- "and implicitly converted, or whether only a singular input type (i.e. collections) is accepted."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Transformers\n",
- "\n",
- "General transformers (in the package `aeon/transformations`, `aeon/transformations`,\n",
- "`aeon/transformations/bootstrap` and `aeon/transformations/hierarchical`) aim to\n",
- "accept all input types, and will attempt to restructure the data or broadcast to multiple transformer\n",
- "objects if necessary to fit the input data to the data structure used by the transformer. For example,\n",
- "if the class excepts a singular series but is given a collection of series, a separate instance of the\n",
- "transformer is applied independently to each series. Transformers all extend the base class\n",
- "`BaseTransformer`. General transformations are mostly used for single series tasks such as forecasting\n",
- "and annotation, and are best used with `pd.Series` or `pd.DataFrame` input. Other valid data types will\n",
- "be accepted but are likely to be converted to another format internally, see the\n",
- "[data structures](../datasets/data_structures.ipynb) notebook for clarification of how\n",
- "best\n",
- "to store\n",
- "data with aeon.\n",
- "\n",
- "Transformers differ in terms of whether they convert time series into different time series\n",
- "(series-to-series transformation), or whether they convert series into feature vector(s)\n",
- "(series-to-vector transformation).\n",
- "\n",
- "To illustrate the difference, we compare two single-series transformers with different output:\n",
- "\n",
- "* the Box-Cox transformer `BoxCoxTransformer`, a series-to-series transformer using the\n",
- "[Box Cox power transform](https://en.wikipedia.org/wiki/Power_transform#Box%E2%80%93Cox_transformation).\n",
- "* the summary transformer `SummaryTransformer`, a series-to-vector transformer that\n",
- "finds summary statistics such as the mean an standard deviation of each series.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "outputs": [],
- "source": [
- "import warnings\n",
- "\n",
- "warnings.filterwarnings(\"ignore\")"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-03-01T16:16:25.993971Z",
- "start_time": "2024-03-01T16:16:25.989981Z"
- }
- }
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2024-03-01T16:16:26.098690Z",
- "start_time": "2024-03-01T16:16:26.088717Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": "pandas.core.series.Series"
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from aeon.datasets import load_airline\n",
- "from aeon.transformations._legacy._boxcox import _BoxCoxTransformer as BoxCoxTransformer\n",
- "from aeon.transformations.summarize import SummaryTransformer\n",
- "from aeon.visualisation import plot_series\n",
- "\n",
- "boxcox_trans = BoxCoxTransformer()\n",
- "summary_trans = SummaryTransformer()\n",
- "\n",
- "# airline is a single time series stored in a pd.Series\n",
- "airline = load_airline()\n",
- "\n",
- "type(airline)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "outputs": [
- {
- "data": {
- "text/plain": "Period\n1949-01 112.0\n1949-02 118.0\n1949-03 132.0\n1949-04 129.0\n1949-05 121.0\nFreq: M, Name: Number of airline passengers, dtype: float64"
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "airline[:5]"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-03-01T16:16:26.108664Z",
- "start_time": "2024-03-01T16:16:26.102680Z"
- }
- }
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "outputs": [
- {
- "data": {
- "text/plain": "(
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