timeseries-fastai 0.0.1

A timeseries lib on top of fastai2

timeseries_fastai

This repository aims to implement TimeSeries classification/regression algorithms. It makes extensive use of fastai training methods.

Installation

In short, if you have anaconda, execute:

$ pip install fastai_timeseries

Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

The original paper repo is here is implemented in Keras/Tf.

• Notebook 01: This is a basic notebook that implements the Deep Learning models proposed in Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline.

InceptionTime: Finding AlexNet for Time SeriesClassification

The original paper repo is here

• Notebook 02: Added InceptionTime architecture from InceptionTime: Finding AlexNet for Time SeriesClassification.

Results

Results for InceptionTime

You can run the benchmark using:

$python ucr.py --arch='inception' --tasks='all' --filename='inception.csv' --mixup=0.2

Default Values:

• lr = 1e-3
• opt = 'ranger'
• epochs = 40
• fp16 = True

import pandas as pd
from pathlib import Path

results_inception = pd.read_csv(Path.cwd().parent/'inception.csv', index_col=0)
results_inception

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	acc	acc_max	train_loss	val_loss
task
Adiac	0.063939	0.063939	3.254499	4.328088

Getting Started

from timeseries_fastai.imports import *
from timeseries_fastai.core import *
from timeseries_fastai.data import *
from timeseries_fastai.models import *

ucr_path = untar_data(URLs.UCR)

df_train, df_test = load_df_ucr(ucr_path, 'StarLightCurves')

Loading files from: /home/tc256760/.fastai/data/Univariate2018_arff/StarLightCurves

df = stack_train_valid(df_train, df_test)

x_cols = df.columns[0:-2].to_list()

df.head()

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	att1	att2	att3	att4	att5	att6	att7	att8	att9	att10	...	att1017	att1018	att1019	att1020	att1021	att1022	att1023	att1024	target	valid_col
0	0.537303	0.531103	0.528503	0.529403	0.533603	0.540903	0.551103	0.564003	0.579603	0.597603	...	0.545903	0.543903	0.541003	0.537203	0.532303	0.526403	0.519503	0.511403	b'3'	False
1	0.588398	0.593898	0.599098	0.604098	0.608798	0.613397	0.617797	0.622097	0.626097	0.630097	...	0.246499	0.256199	0.266499	0.277399	0.288799	0.300899	0.313599	0.326899	b'3'	False
2	-0.049900	-0.041500	-0.033400	-0.025600	-0.018100	-0.010800	-0.003800	0.003000	0.009600	0.015900	...	-0.161601	-0.149201	-0.136401	-0.123201	-0.109701	-0.095901	-0.081701	-0.067100	b'1'	False
3	1.337005	1.319805	1.302905	1.286305	1.270005	1.254005	1.238304	1.223005	1.208104	1.193504	...	1.298505	1.307705	1.316505	1.324905	1.332805	1.340205	1.347005	1.353205	b'3'	False
4	0.769801	0.775301	0.780401	0.785101	0.789401	0.793301	0.796801	0.799901	0.802601	0.805101	...	0.744501	0.747301	0.750701	0.754801	0.759501	0.765001	0.771301	0.778401	b'3'	False

5 rows × 1026 columns

dls = TSDataLoaders.from_df(df, x_cols=x_cols, label_col='target', valid_col='valid_col', bs=16)

dls.vocab

(#3) [b'1',b'2',b'3']

inception = create_inception(1, len(dls.vocab))

learn = Learner(dls, inception, metrics=[accuracy])

learn.fit_one_cycle(5, 1e-3)

epoch	train_loss	valid_loss	accuracy	time
0	0.346574	0.216900	0.926906	00:15
1	0.262913	0.274731	0.888417	00:15
2	0.194458	0.131948	0.968917	00:15
3	0.154791	0.126375	0.967581	00:15
4	0.109763	0.101232	0.973774	00:15

interp = ClassificationInterpretation.from_learner(learn)
interp.plot_confusion_matrix()