#!/usr/bin/env python
# coding: utf-8

# # Deep learning based time series classification in aeon
# 
# There are a range of deep learning based classification algorithms in the toolkit.
# The networks that are common to classification, regression and clustering are in the
# `networks` module. Our deep learning classifiers are based those used in deep
# learning bake off [1] and recent experimentation [2]. [3] provides an extensive recent
# review of related deep learning work.

# 
# A list of all deep learning classifiers

# In[3]:


import warnings

from aeon.registry import all_estimators

warnings.filterwarnings("ignore")
all_estimators("classifier", filter_tags={"algorithm_type": "deeplearning"})


# he use case for deep learning classifiers is identical to that of all classifiers.
# However, you need to have tensorflow and tensorflow-probability installed in your
# environment. If you have a GPU correctly installed the classifiers should use them,
# although it is worth checking the output.
# 

# 

# In[2]:


from sklearn.metrics import accuracy_score

from aeon.classification.deep_learning import CNNClassifier
from aeon.datasets import load_basic_motions  # multivariate dataset
from aeon.datasets import load_italy_power_demand  # univariate dataset

italy, italy_labels = load_italy_power_demand(split="train")
italy_test, italy_test_labels = load_italy_power_demand(split="test")
motions, motions_labels = load_basic_motions(split="train")
motions_test, motions_test_labels = load_basic_motions(split="train")
cnn = CNNClassifier()
cnn.fit(italy, italy_labels)
y_pred = cnn.predict(italy_test)
accuracy_score(italy_test_labels, y_pred)


# 

# ### Classifier Details
# 
# The deep learning bake off [1] found that the Residual Network (ResNet) was the best
# performing architecture for TSC. ResNet has the following network structure.
# 
# 
# <img src="./img/resnet.png" width="600" alt="ROCKET.">
# 
# The InceptionTime deep learning algorithm Subsequent to [1],
# InceptionTime is an ensemble of five SingleInceptionTime deep learning
# classifiers. Each base classifier shares the same architecture based on
# Inception modules. Diversity is achieved through randomly intialising weights.
# A SingleInceptionTimeClassifier has the following structure.
# 
# <img src="./img/inception_module.png" width="600" alt="ROCKET.">
# 
# A SingleInceptionTimeClassifier is structured as follows.
# 
# <img src="./img/inception.png" width="600" alt="ROCKET.">

# ## References
# 
# [1] Fawaz et al. (2019) "Deep learning for time series classification: a review" Data
#  Mining and Knowledge Discovery. 33(4): 917-963
# 
# [2] Fawaz et al. (2020) "InceptionTime: finding AlexNet for time series classification.
# Data Mining and Knowledge Discovery. 34(6): 1936-1962
# 
# [3] Foumani et al. (2023) "Deep Learning for Time Series Classification and Extrinsic
# Regression: A Current Survey" ArXiv https://arxiv.org/pdf/2302.02515.pdf
# 
# [4]  https://github.com/MSD-IRIMAS/CF-4-TSC

#