Metadata-Version: 2.1
Name: metats
Version: 0.2.1
Summary: Meta-Learning for Time Series Forecasting
Author-email: Sasan Barak <s.barak@soton.ac.uk>, Amirabbas Asadi <amir137825@gmail.com>, Mohammad Joshaghani <mjoshaghani10@gmail.com>
License: MIT License
        
        Copyright (c) 2021 Amirabbas Asadi
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://drsasanbarak.github.io/metats/
Keywords: timeseries,metalearning,forecasting,unsupervised learning,deeplearning,machine learning
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE

# MetaTS | Meta-Learning for Global Time Series Forecasting
![example workflow](https://github.com/amirabbasasadi/metats/actions/workflows/main.yml/badge.svg)
[![PyPI version fury.io](https://badge.fury.io/py/metats.svg)](https://pypi.python.org/pypi/metats/)
[![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg)](https://www.python.org/)
[![GitHub license](https://img.shields.io/github/license/amirabbasasadi/metats.svg)](https://github.com/amirabbasasadi/metats/blob/master/LICENSE)
![image](https://user-images.githubusercontent.com/8543469/176514410-bf8efea2-fb54-4903-a0ee-169c9595958a.png)

## Features:
- Generating meta features
    - Statistical features : TsFresh, User defined features
    - Automated feature extraction using Deep Unsupervised Learning : Deep AutoEncoder (MLP, LSTM, GRU, ot custom model)
- Supporting sktime and darts libraries for base-forecasters
- Providing a Meta-Learning pipeline

## Quick Start

### Installing the package
```
pip install metats
```

### Generating a toy dataset by sampling from two different processes
```python
from metats.datasets import ETSDataset

ets_generator = ETSDataset({'A,N,N': 512,
                            'M,M,M': 512}, length=30, freq=4)

data, labels = ets_generator.load(return_family=True)
colors = list(map(lambda x: (x=='A,N,N')*1, labels))
```

### Normalizing the time series
```python
from sklearn.preprocessing import StandardScaler

scaled_data = StandardScaler().fit_transform(data.T)
data = scaled_data.T[:, :, None]
```
### Checking How data looks like
```python
import matplotlib.pyplot as plt
_ = plt.plot(data[10, :, 0])
```
![image](https://user-images.githubusercontent.com/8543469/176520933-64be6613-c64b-4a6c-baa7-d1c0ca13a7b2.png)

### Generating the meta-features
#### Statistical features using TsFresh
```python
from metats.features.statistical import TsFresh

stat_features = TsFresh().transform(data)
```
#### Deep Unsupervised Features
##### Training an AutoEncoder
```python
from metats.features.unsupervised import DeepAutoEncoder
from metats.features.deep import AutoEncoder, MLPEncoder, MLPDecoder

enc = MLPEncoder(input_size=1, input_length=30, latent_size=8, hidden_layers=(16,))
dec = MLPDecoder(input_size=1, input_length=30, latent_size=8, hidden_layers=(16,))

ae = AutoEncoder(encoder=enc, decoder=dec)
ae_feature = DeepAutoEncoder(auto_encoder=ae, epochs=150, verbose=True)

ae_feature.fit(data)
```
##### Generating features using the auto-encoder
```python
deep_features = ae_feature.transform(data)
```

#### Visualizing both statistical and deep meta-features
Dimensionality reduction using UMAP for visualization
```python
from umap import UMAP
deep_reduced = UMAP().fit_transform(deep_features)
stat_reduced = UMAP().fit_transform(stat_features)
```
Visualizing the statistical features:
```python
plt.scatter(stat_reduced[:512, 0], stat_reduced[:512, 1], c='#e74c3c', label='ANN')
plt.scatter(stat_reduced[512:, 0], stat_reduced[512:, 1], c='#9b59b6', label='MMM')
plt.legend()
plt.title('TsFresh Meta-Features')
_ = plt.show()
```
And similarly the auto encoder's features
```python
plt.scatter(deep_reduced[:512, 0], deep_reduced[:512, 1], c='#e74c3c', label='ANN')
plt.scatter(deep_reduced[512:, 0], deep_reduced[512:, 1], c='#9b59b6', label='MMM')
plt.legend()
plt.title('Deep Unsupervised Meta-Features')
_ = plt.show()
```
![image](https://user-images.githubusercontent.com/8543469/176526565-e26cbd0c-2b20-4848-995e-e12632bde8e3.png)
![image](https://user-images.githubusercontent.com/8543469/176526711-989e1ac3-2af8-4d27-a90d-ea6007594f36.png)



## Meta-Learning Pipeline
Creating a meta-learning pipeline with selection strategy:
```python
from metats.pipeline import MetaLearning

pipeline = MetaLearning(method='selection', loss='mse')
```
Adding AutoEncoder features:
```python
from metats.features.unsupervised import DeepAutoEncoder
from metats.features.deep import AutoEncoder, MLPEncoder, MLPDecoder

enc = MLPEncoder(input_size=1, input_length=23, latent_size=8, hidden_layers=(16,))
dec = MLPDecoder(input_size=1, input_length=23, latent_size=8, hidden_layers=(16,))

ae = AutoEncoder(encoder=enc, decoder=dec)
ae_features = DeepAutoEncoder(auto_encoder=ae, epochs=200, verbose=True)

pipeline.add_feature(ae_features)
```
You can add as many features as you like:
```python
from metats.features.statistical import TsFresh

stat_features = TsFresh()
pipeline.add_feature(stat_features)
```
Adding two sktime forecaster as base-forecasters
```python
from sktime.forecasting.naive import NaiveForecaster
from sktime.forecasting.compose import make_reduction
from sklearn.neighbors import KNeighborsRegressor

regressor = KNeighborsRegressor(n_neighbors=1)
forecaster1 = make_reduction(regressor, window_length=15, strategy="recursive")

forecaster2 = NaiveForecaster() 

pipeline.add_forecaster(forecaster1)
pipeline.add_forecaster(forecaster2)
```
Specify some meta-learner
```python
from sklearn.ensemble import RandomForestClassifier

pipeline.add_metalearner(RandomForestClassifier())
```

Training the pipeline
```python
pipeline.fit(data, fh=7)
```
Prediction for another set of data
```python
pipeline.predict(data, fh=7)
```

## About the package
### Contributors
- Sasan Barak
- Amirabbas Asadi


We wish to see your name in the list of contributors, So we are waiting for pull requests!
