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

# `KDD2024 Tutorial / A Hands-On Introduction to Time Series Classification and Regression`

# # Part9: Hybrid Estimators for Time Series Machine Learning in `aeon`
# 
# `aeon` contains two hard coded hybrid classifiers, HIVE-COTEV1 (HC1) and HIVE-COTEV2
# (HC2). We will just use HC2 in this example.
# 

# In[ ]:


get_ipython().system('pip install aeon==0.11.0')
get_ipython().system('mkdir -p data')
get_ipython().system('wget -nc https://raw.githubusercontent.com/aeon-tutorials/KDD-2024/main/Notebooks/data/KDD_MTSC_TRAIN.ts -P data/')
get_ipython().system('wget -nc https://raw.githubusercontent.com/aeon-tutorials/KDD-2024/main/Notebooks/data/KDD_MTSC_TEST.ts -P data/')
get_ipython().system('wget -nc https://raw.githubusercontent.com/aeon-tutorials/KDD-2024/main/Notebooks/data/KDD_UTSC_TRAIN.ts -P data/')
get_ipython().system('wget -nc https://raw.githubusercontent.com/aeon-tutorials/KDD-2024/main/Notebooks/data/KDD_UTSC_TEST.ts -P data/')
get_ipython().system('wget -nc https://raw.githubusercontent.com/aeon-tutorials/KDD-2024/main/Notebooks/data/KDD_MTSER_TRAIN.ts -P data/')
get_ipython().system('wget -nc https://raw.githubusercontent.com/aeon-tutorials/KDD-2024/main/Notebooks/data/KDD_MTSER_TEST.ts -P data/')
get_ipython().system('wget -nc https://raw.githubusercontent.com/aeon-tutorials/KDD-2024/main/Notebooks/data/KDD_UTSER_TRAIN.ts -P data/')
get_ipython().system('wget -nc https://raw.githubusercontent.com/aeon-tutorials/KDD-2024/main/Notebooks/data/KDD_UTSER_TEST.ts -P data/')


# In[1]:


# There are some deprecation warnings present in the notebook, we will ignore them.
# Remove this cell if you are interested in finding out what is changing soon, for
# aeon there will be big changes in out v1.0.0 release!
import warnings

warnings.filterwarnings("ignore")


# In[2]:


# load EEG example here
from aeon.datasets import load_from_tsfile

# Load the univariate EEG TSC dataset as a 3D numpy array
X_train_utsc, y_train_utsc = load_from_tsfile("./data/KDD_UTSC_TRAIN.ts")
X_test_utsc, y_test_utsc = load_from_tsfile("./data/KDD_UTSC_TEST.ts")

print("Single channel EEG TSC problem train shape:", X_train_utsc.shape)
print("Single channel EEG TSC problem test shape:", X_test_utsc.shape)

# Load the univariate EEG TSC dataset as a 3D numpy array
X_train_mtsc, y_train_mtsc = load_from_tsfile("./data/KDD_MTSC_TRAIN.ts")
X_test_mtsc, y_test_mtsc = load_from_tsfile("./data/KDD_MTSC_TEST.ts")

print("Four channel EEG TSC problem train shape:", X_train_mtsc.shape)
print("Single channel EEG TSC problem test shape:", X_test_mtsc.shape)

# Load the univariate EEG TSER dataset as a 3D numpy array
X_train_utser, y_train_utser = load_from_tsfile("./data/KDD_UTSER_TRAIN.ts")
X_test_utser, y_test_utser = load_from_tsfile("./data/KDD_UTSER_TEST.ts")

print("Single channel EEG TSER problem train shape:", X_train_utsc.shape)
print("Single channel EEG TSER problem test shape:", X_test_utsc.shape)

# Load the univariate EEG TSER dataset as a 3D numpy array
X_train_mtser, y_train_mtser = load_from_tsfile("./data/KDD_MTSER_TRAIN.ts")
X_test_mtser, y_test_mtser = load_from_tsfile("./data/KDD_MTSER_TEST.ts")

print("Four channel EEG TSER problem train shape:", X_train_mtser.shape)
print("Single channel EEG TSER problem test shape:", X_test_mtser.shape)


# HC2 is not designed to be fast on small examples. For ease of example we can set the contract
# to reduce the run time and set number of jobs and can be recovered like this.

# In[3]:


from aeon.classification.hybrid import HIVECOTEV2
hc2 = HIVECOTEV2(n_jobs =-1, time_limit_in_minutes=2)
hc2.fit(X_train_utsc, y_train_utsc)
print("Single channel EEG TSC problem accuracy:", hc2.score(X_test_utsc, y_test_utsc))
print("STC weight = " + str(hc2.stc_weight_))
print("DrCIF weight = " + str(hc2.drcif_weight_))
print("Arsenal weight = " + str(hc2.arsenal_weight_))
print("TDE weight = " + str(hc2.tde_weight_))


# ## Make your own hybrid estimator
# 
# HC2 is hard coded as an estimator so we can recreate results and use it as the
# authors proposed it. However, you can make your own weighted ensemble.

# In[4]:


from aeon.classification import DummyClassifier
from aeon.classification.convolution_based import RocketClassifier
from aeon.classification.feature_based import FreshPRINCEClassifier
from aeon.classification.compose._ensemble import ClassifierEnsemble

clf = [DummyClassifier(), RocketClassifier(), FreshPRINCEClassifier()]
ens = ClassifierEnsemble(clf, weights=[0.1, 0.3, 0.6])
ens.fit(X_train_utsc, y_train_utsc)
print("Some ensemble accuracy:", ens.score(X_test_utsc, y_test_utsc))


#