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

# # The ROCKET transform
# 
# ## Overview
# 
# ROCKET [1] transforms time series using random convolutional kernels (random length, weights, bias, dilation, and padding). ROCKET computes two features from the resulting feature maps: the max, and the proportion of positive values (or ppv). The transformed features are used to train a linear classifier.
# 
# [1] Dempster A, Petitjean F, Webb GI (2019) ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. [arXiv:1910.13051](https://arxiv.org/abs/1910.13051)
# 
# ***
# 
# ## Contents
# 
# 1. Imports
# 2. Univariate Time Series
# 3. Multivariate Time Series
# 4. Pipeline Example
# 
# ***
# 
# ## 1 Imports
# 
# Import example data, ROCKET, and a classifier (`RidgeClassifierCV` from scikit-learn), as well as NumPy and `make_pipeline` from scikit-learn.
# 
# **Note**: ROCKET compiles (via Numba) on import, which may take a few seconds.

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# !pip install --upgrade numba


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import numpy as np
from sklearn.linear_model import RidgeClassifierCV
from sklearn.pipeline import make_pipeline

from aeon.datasets import load_basic_motions  # multivariate dataset
from aeon.datasets import load_gunpoint  # univariate dataset
from aeon.transformations.collection.convolution_based import Rocket


# ## 2 Univariate Time Series
# 
# We can transform the data using ROCKET and separately fit a classifier, or we can use ROCKET together with a classifier in a pipeline (section 4, below).
# 
# ### 2.1 Load the Training Data
# For more details on the data set, see the [univariate time series classification
# notebook](https://github.com/aeon-toolkit/aeon/tree/main/examples/classification/classification.ipynb).

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X_train, y_train = load_gunpoint(split="train")
X_train = X_train[:5, :, :]
y_train = y_train[:5]
print(X_train.shape)


# ### 2.2 Initialise ROCKET and Transform the Training Data

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rocket = Rocket(n_kernels=100)  # by default, ROCKET uses 10,000 kernels
rocket.fit(X_train)
X_train_transform = rocket.transform(X_train)
print(X_train_transform.shape)


# ### 2.3 Fit a Classifier

# We recommend using `RidgeClassifierCV` from scikit-learn for smaller datasets (fewer than approx. 20K training examples), and using logistic regression trained using stochastic gradient descent for larger datasets.

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classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))
classifier.fit(X_train_transform, y_train)


# ### 2.4 Load and Transform the Test Data

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X_test, y_test = load_gunpoint(split="test")
X_test_transform = rocket.transform(X_test)


# ### 2.5 Classify the Test Data

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classifier.score(X_test_transform, y_test)


# ***
# 
# ## 3 Multivariate Time Series
# 
# We can use ROCKET in exactly the same way for multivariate time series.
# 
# ### 3.1 Load the Training Data

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X_train, y_train = load_basic_motions(split="train")


# ### 3.2 Initialise ROCKET and Transform the Training Data

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rocket = Rocket(n_kernels=100)  # by default, ROCKET uses 10,000 kernels
rocket.fit(X_train)
X_train_transform = rocket.transform(X_train)
X_train_transform.shape


# ### 3.3 Fit a Classifier

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classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))
classifier.fit(X_train_transform, y_train)


# ### 3.4 Load and Transform the Test Data

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X_test, y_test = load_basic_motions(split="test")
X_test_transform = rocket.transform(X_test)


# ### 3.5 Classify the Test Data

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classifier.score(X_test_transform, y_test)


# ***
# 
# ## 4 Pipeline Example
# 
# We can use ROCKET together with `RidgeClassifierCV` (or another classifier) in a pipeline.  We can then use the pipeline like a self-contained classifier, with a single call to `fit`, and without having to separately transform the data, etc.
# 
# ### 4.1 Initialise the Pipeline

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rocket_pipeline = make_pipeline(
    Rocket(), RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))
)


# ### 4.2 Load and Fit the Training Data

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# it is necessary to pass y_train to the pipeline
# y_train is not used for the transform, but it is used by the classifier
rocket_pipeline.fit(X_train, y_train)


# ### 4.3 Load and Classify the Test Data

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rocket_pipeline.score(X_test, y_test)


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