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timeseries_fastai

This repository aims to implement TimeSeries classification/regression algorithms. It makes extensive use of fastai V2!

I recommend to use Ignacio's tsai for a more complete and robust timeseries fastai based library. It is well documented and implemetns way more models that me here.

Installation

You will need to install fastai V2 from here and then you can do from within the environment where you installed fastai V2:

pip install timeseries_fastai

and you are good to go.

TL;DR

git clone https://github.com/fastai/fastai
cd fastai
conda env create -f environment.yml
source activate fastai
pip install fastai timeseries_fastai

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

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

InceptionTime: Finding AlexNet for Time SeriesClassification

The original paper repo is here

Results

You can run the benchmark using:

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

Default Values:

results_inception = pd.read_csv(Path.cwd().parent/'inception.csv', index_col=0)
display_df(results_inception)
<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>acc</th> <th>acc_max</th> <th>train_loss</th> <th>val_loss</th> </tr> <tr> <th>task</th> <th></th> <th></th> <th></th> <th></th> </tr> </thead> <tbody> <tr> <th>ACSF1</th> <td>0.82</td> <td>0.85</td> <td>0.77</td> <td>0.62</td> </tr> <tr> <th>Adiac</th> <td>0.77</td> <td>0.77</td> <td>0.81</td> <td>0.89</td> </tr> <tr> <th>ArrowHead</th> <td>0.70</td> <td>0.76</td> <td>0.28</td> <td>1.21</td> </tr> <tr> <th>BME</th> <td>0.85</td> <td>0.88</td> <td>0.21</td> <td>0.79</td> </tr> <tr> <th>Beef</th> <td>0.77</td> <td>0.83</td> <td>0.50</td> <td>0.53</td> </tr> <tr> <th>BeetleFly</th> <td>0.70</td> <td>0.85</td> <td>0.14</td> <td>0.79</td> </tr> <tr> <th>BirdChicken</th> <td>0.95</td> <td>0.95</td> <td>0.14</td> <td>0.20</td> </tr> <tr> <th>CBF</th> <td>0.95</td> <td>0.97</td> <td>0.22</td> <td>0.24</td> </tr> <tr> <th>Car</th> <td>0.60</td> <td>0.68</td> <td>0.33</td> <td>1.23</td> </tr> <tr> <th>Chinatown</th> <td>0.95</td> <td>0.96</td> <td>0.05</td> <td>0.27</td> </tr> <tr> <th>ChlorineConcentration</th> <td>0.82</td> <td>0.82</td> <td>0.28</td> <td>0.48</td> </tr> <tr> <th>CinCECGTorso</th> <td>0.58</td> <td>0.60</td> <td>0.42</td> <td>1.60</td> </tr> <tr> <th>Coffee</th> <td>0.71</td> <td>0.82</td> <td>0.16</td> <td>0.71</td> </tr> <tr> <th>Computers</th> <td>0.66</td> <td>0.72</td> <td>0.24</td> <td>0.72</td> </tr> <tr> <th>CricketX</th> <td>0.72</td> <td>0.73</td> <td>0.49</td> <td>0.88</td> </tr> <tr> <th>CricketY</th> <td>0.71</td> <td>0.72</td> <td>0.53</td> <td>0.84</td> </tr> <tr> <th>CricketZ</th> <td>0.77</td> <td>0.78</td> <td>0.52</td> <td>0.79</td> </tr> <tr> <th>Crop</th> <td>0.78</td> <td>0.78</td> <td>0.56</td> <td>0.76</td> </tr> <tr> <th>DiatomSizeReduction</th> <td>0.93</td> <td>0.96</td> <td>0.22</td> <td>0.22</td> </tr> <tr> <th>DistalPhalanxOutlineAgeGroup</th> <td>0.71</td> <td>0.75</td> <td>0.18</td> <td>0.80</td> </tr> <tr> <th>DistalPhalanxOutlineCorrect</th> <td>0.74</td> <td>0.78</td> <td>0.16</td> <td>0.57</td> </tr> <tr> <th>DistalPhalanxTW</th> <td>0.62</td> <td>0.68</td> <td>0.27</td> <td>1.22</td> </tr> <tr> <th>ECG200</th> <td>0.87</td> <td>0.91</td> <td>0.15</td> <td>0.30</td> </tr> <tr> <th>ECG5000</th> <td>0.94</td> <td>0.94</td> <td>0.17</td> <td>0.27</td> </tr> <tr> <th>ECGFiveDays</th> <td>0.92</td> <td>0.94</td> <td>0.14</td> <td>0.21</td> </tr> <tr> <th>EOGHorizontalSignal</th> <td>0.36</td> <td>0.40</td> <td>0.63</td> <td>2.05</td> </tr> <tr> <th>EOGVerticalSignal</th> <td>0.37</td> <td>0.39</td> <td>0.79</td> <td>2.00</td> </tr> <tr> <th>Earthquakes</th> <td>0.75</td> <td>0.75</td> <td>0.12</td> <td>0.89</td> </tr> <tr> <th>ElectricDevices</th> <td>0.71</td> <td>0.72</td> <td>0.36</td> <td>1.20</td> </tr> <tr> <th>EthanolLevel</th> <td>0.32</td> <td>0.36</td> <td>0.61</td> <td>1.81</td> </tr> <tr> <th>FaceAll</th> <td>0.77</td> <td>0.78</td> <td>0.46</td> <td>0.84</td> </tr> <tr> <th>FaceFour</th> <td>0.83</td> <td>0.89</td> <td>0.29</td> <td>0.57</td> </tr> <tr> <th>FacesUCR</th> <td>0.83</td> <td>0.83</td> <td>0.51</td> <td>0.73</td> </tr> <tr> <th>FiftyWords</th> <td>0.67</td> <td>0.69</td> <td>0.70</td> <td>1.27</td> </tr> <tr> <th>Fish</th> <td>0.83</td> <td>0.83</td> <td>0.45</td> <td>1.69</td> </tr> <tr> <th>FordA</th> <td>0.95</td> <td>0.95</td> <td>0.18</td> <td>0.13</td> </tr> <tr> <th>FordB</th> <td>0.83</td> <td>0.85</td> <td>0.16</td> <td>0.38</td> </tr> <tr> <th>FreezerRegularTrain</th> <td>0.98</td> <td>0.99</td> <td>0.20</td> <td>0.10</td> </tr> <tr> <th>FreezerSmallTrain</th> <td>0.71</td> <td>0.81</td> <td>0.21</td> <td>1.54</td> </tr> <tr> <th>Fungi</th> <td>0.77</td> <td>0.85</td> <td>0.31</td> <td>0.68</td> </tr> <tr> <th>GunPoint</th> <td>0.95</td> <td>0.97</td> <td>0.17</td> <td>0.14</td> </tr> <tr> <th>GunPointAgeSpan</th> <td>0.97</td> <td>0.98</td> <td>0.25</td> <td>0.08</td> </tr> <tr> <th>GunPointMaleVersusFemale</th> <td>1.00</td> <td>1.00</td> <td>0.17</td> <td>0.02</td> </tr> <tr> <th>GunPointOldVersusYoung</th> <td>1.00</td> <td>1.00</td> <td>0.13</td> <td>0.01</td> </tr> <tr> <th>Ham</th> <td>0.55</td> <td>0.66</td> <td>0.21</td> <td>1.12</td> </tr> <tr> <th>HandOutlines</th> <td>0.89</td> <td>0.91</td> <td>0.25</td> <td>0.29</td> </tr> <tr> <th>Haptics</th> <td>0.38</td> <td>0.43</td> <td>0.44</td> <td>1.94</td> </tr> <tr> <th>Herring</th> <td>0.61</td> <td>0.70</td> <td>0.19</td> <td>0.82</td> </tr> <tr> <th>HouseTwenty</th> <td>0.85</td> <td>0.88</td> <td>0.18</td> <td>0.39</td> </tr> <tr> <th>InlineSkate</th> <td>0.30</td> <td>0.31</td> <td>0.95</td> <td>2.05</td> </tr> <tr> <th>InsectEPGRegularTrain</th> <td>1.00</td> <td>1.00</td> <td>0.28</td> <td>0.08</td> </tr> <tr> <th>InsectEPGSmallTrain</th> <td>0.80</td> <td>1.00</td> <td>0.49</td> <td>0.48</td> </tr> <tr> <th>InsectWingbeatSound</th> <td>0.55</td> <td>0.56</td> <td>0.65</td> <td>1.27</td> </tr> <tr> <th>ItalyPowerDemand</th> <td>0.96</td> <td>0.96</td> <td>0.14</td> <td>0.16</td> </tr> <tr> <th>LargeKitchenAppliances</th> <td>0.85</td> <td>0.86</td> <td>0.28</td> <td>0.69</td> </tr> <tr> <th>Lightning2</th> <td>0.70</td> <td>0.77</td> <td>0.18</td> <td>0.73</td> </tr> <tr> <th>Lightning7</th> <td>0.71</td> <td>0.73</td> <td>0.46</td> <td>1.10</td> </tr> <tr> <th>Mallat</th> <td>0.65</td> <td>0.66</td> <td>0.43</td> <td>1.37</td> </tr> <tr> <th>Meat</th> <td>0.93</td> <td>0.95</td> <td>0.25</td> <td>0.26</td> </tr> <tr> <th>MedicalImages</th> <td>0.72</td> <td>0.75</td> <td>0.40</td> <td>0.85</td> </tr> <tr> <th>MelbournePedestrian</th> <td>0.10</td> <td>0.10</td> <td>nan</td> <td>nan</td> </tr> <tr> <th>MiddlePhalanxOutlineAgeGroup</th> <td>0.53</td> <td>0.60</td> <td>0.20</td> <td>1.28</td> </tr> <tr> <th>MiddlePhalanxOutlineCorrect</th> <td>0.77</td> <td>0.81</td> <td>0.17</td> <td>0.46</td> </tr> <tr> <th>MiddlePhalanxTW</th> <td>0.49</td> <td>0.59</td> <td>0.34</td> <td>1.37</td> </tr> <tr> <th>MixedShapesRegularTrain</th> <td>0.93</td> <td>0.93</td> <td>0.35</td> <td>0.25</td> </tr> <tr> <th>MixedShapesSmallTrain</th> <td>0.80</td> <td>0.81</td> <td>0.42</td> <td>0.64</td> </tr> <tr> <th>MoteStrain</th> <td>0.75</td> <td>0.76</td> <td>0.09</td> <td>0.52</td> </tr> <tr> <th>NonInvasiveFetalECGThorax1</th> <td>0.92</td> <td>0.93</td> <td>0.66</td> <td>0.32</td> </tr> <tr> <th>NonInvasiveFetalECGThorax2</th> <td>0.93</td> <td>0.93</td> <td>0.59</td> <td>0.27</td> </tr> <tr> <th>OSULeaf</th> <td>0.82</td> <td>0.84</td> <td>0.43</td> <td>0.58</td> </tr> <tr> <th>OliveOil</th> <td>0.77</td> <td>0.80</td> <td>0.27</td> <td>0.74</td> </tr> <tr> <th>PhalangesOutlinesCorrect</th> <td>0.81</td> <td>0.83</td> <td>0.17</td> <td>0.46</td> </tr> <tr> <th>Phoneme</th> <td>0.22</td> <td>0.22</td> <td>0.79</td> <td>3.25</td> </tr> <tr> <th>PigAirwayPressure</th> <td>0.12</td> <td>0.14</td> <td>2.33</td> <td>4.06</td> </tr> <tr> <th>PigArtPressure</th> <td>0.47</td> <td>0.47</td> <td>1.25</td> <td>2.25</td> </tr> <tr> <th>PigCVP</th> <td>0.30</td> <td>0.33</td> <td>1.69</td> <td>2.97</td> </tr> <tr> <th>Plane</th> <td>1.00</td> <td>1.00</td> <td>0.35</td> <td>0.07</td> </tr> <tr> <th>PowerCons</th> <td>0.98</td> <td>0.98</td> <td>0.17</td> <td>0.10</td> </tr> <tr> <th>ProximalPhalanxOutlineAgeGroup</th> <td>0.83</td> <td>0.87</td> <td>0.22</td> <td>0.53</td> </tr> <tr> <th>ProximalPhalanxOutlineCorrect</th> <td>0.88</td> <td>0.89</td> <td>0.17</td> <td>0.34</td> </tr> <tr> <th>ProximalPhalanxTW</th> <td>0.78</td> <td>0.80</td> <td>0.28</td> <td>0.78</td> </tr> <tr> <th>RefrigerationDevices</th> <td>0.50</td> <td>0.56</td> <td>0.27</td> <td>1.35</td> </tr> <tr> <th>Rock</th> <td>0.58</td> <td>0.78</td> <td>0.29</td> <td>1.43</td> </tr> <tr> <th>ScreenType</th> <td>0.42</td> <td>0.43</td> <td>0.33</td> <td>1.41</td> </tr> <tr> <th>SemgHandGenderCh2</th> <td>0.73</td> <td>0.79</td> <td>0.21</td> <td>0.52</td> </tr> <tr> <th>SemgHandMovementCh2</th> <td>0.35</td> <td>0.40</td> <td>0.43</td> <td>1.56</td> </tr> <tr> <th>SemgHandSubjectCh2</th> <td>0.52</td> <td>0.52</td> <td>0.39</td> <td>1.13</td> </tr> <tr> <th>ShapeletSim</th> <td>0.99</td> <td>1.00</td> <td>0.14</td> <td>0.12</td> </tr> <tr> <th>ShapesAll</th> <td>0.80</td> <td>0.80</td> <td>0.89</td> <td>0.83</td> </tr> <tr> <th>SmallKitchenAppliances</th> <td>0.65</td> <td>0.66</td> <td>0.43</td> <td>1.60</td> </tr> <tr> <th>SmoothSubspace</th> <td>0.96</td> <td>0.97</td> <td>0.23</td> <td>0.15</td> </tr> <tr> <th>SonyAIBORobotSurface1</th> <td>0.87</td> <td>0.90</td> <td>0.08</td> <td>0.29</td> </tr> <tr> <th>SonyAIBORobotSurface2</th> <td>0.75</td> <td>0.79</td> <td>0.15</td> <td>0.54</td> </tr> <tr> <th>StarLightCurves</th> <td>0.98</td> <td>0.98</td> <td>0.22</td> <td>0.09</td> </tr> <tr> <th>Strawberry</th> <td>0.97</td> <td>0.98</td> <td>0.15</td> <td>0.09</td> </tr> <tr> <th>SwedishLeaf</th> <td>0.94</td> <td>0.94</td> <td>0.52</td> <td>0.27</td> </tr> <tr> <th>Symbols</th> <td>0.83</td> <td>0.87</td> <td>0.39</td> <td>0.61</td> </tr> <tr> <th>SyntheticControl</th> <td>1.00</td> <td>1.00</td> <td>0.31</td> <td>0.04</td> </tr> <tr> <th>ToeSegmentation1</th> <td>0.93</td> <td>0.97</td> <td>0.16</td> <td>0.17</td> </tr> <tr> <th>ToeSegmentation2</th> <td>0.88</td> <td>0.91</td> <td>0.15</td> <td>0.27</td> </tr> <tr> <th>Trace</th> <td>1.00</td> <td>1.00</td> <td>0.29</td> <td>0.02</td> </tr> <tr> <th>TwoLeadECG</th> <td>0.91</td> <td>0.92</td> <td>0.10</td> <td>0.26</td> </tr> <tr> <th>TwoPatterns</th> <td>1.00</td> <td>1.00</td> <td>0.25</td> <td>0.01</td> </tr> <tr> <th>UMD</th> <td>0.92</td> <td>0.94</td> <td>0.25</td> <td>0.26</td> </tr> <tr> <th>UWaveGestureLibraryAll</th> <td>0.91</td> <td>0.91</td> <td>0.41</td> <td>0.31</td> </tr> <tr> <th>UWaveGestureLibraryX</th> <td>0.82</td> <td>0.82</td> <td>0.46</td> <td>0.56</td> </tr> <tr> <th>UWaveGestureLibraryY</th> <td>0.73</td> <td>0.73</td> <td>0.50</td> <td>0.78</td> </tr> <tr> <th>UWaveGestureLibraryZ</th> <td>0.74</td> <td>0.74</td> <td>0.48</td> <td>0.72</td> </tr> <tr> <th>Wafer</th> <td>1.00</td> <td>1.00</td> <td>0.05</td> <td>0.01</td> </tr> <tr> <th>Wine</th> <td>0.48</td> <td>0.63</td> <td>0.19</td> <td>1.07</td> </tr> <tr> <th>WordSynonyms</th> <td>0.62</td> <td>0.62</td> <td>0.61</td> <td>1.60</td> </tr> <tr> <th>Worms</th> <td>0.77</td> <td>0.78</td> <td>0.41</td> <td>0.70</td> </tr> <tr> <th>WormsTwoClass</th> <td>0.73</td> <td>0.81</td> <td>0.22</td> <td>0.56</td> </tr> <tr> <th>Yoga</th> <td>0.86</td> <td>0.86</td> <td>0.24</td> <td>0.33</td> </tr> </tbody> </table>

Getting Started

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

PATH = Path.cwd().parent

df_train, df_test = load_df_ucr(PATH, 'Adiac')

Loading files from: /home/tcapelle/SteadySun/timeseries_fastai/Adiac

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

dls = TSDataLoaders.from_dfs(df_train, df_test, x_cols=x_cols, label_col='target', bs=16)

dls.show_batch()

png

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

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

learn.fit_one_cycle(1, 1e-3)

epoch     train_loss  valid_loss  accuracy  time    
0         3.934007    3.640701    0.043478  00:03