Quick Start

import xarray as xr

List available datasets

To view available datasets, you can use the list_datasets function.

from pyrregular import list_datasets

print(list_datasets())

['Abf.h5', 'Ais.h5', 'AllGestureWiimoteX.h5', 'AllGestureWiimoteY.h5', 'AllGestureWiimoteZ.h5', 'Animals.h5', 'AsphaltObstaclesCoordinates.h5', 'AsphaltPavementTypeCoordinates.h5', 'AsphaltRegularityCoordinates.h5', 'CharacterTrajectories.h5', 'CombinedTrajectories.h5', 'DodgerLoopDay.h5', 'DodgerLoopGame.h5', 'DodgerLoopWeekend.h5', 'Garment.h5', 'Geolife.h5', 'GeolifeSupervised.h5', 'GestureMidAirD1.h5', 'GestureMidAirD2.h5', 'GestureMidAirD3.h5', 'GesturePebbleZ1.h5', 'GesturePebbleZ2.h5', 'JapaneseVowels.h5', 'Ldfpa.h5', 'MelbournePedestrian.h5', 'Mimic3.h5', 'PLAID.h5', 'Pamap2.h5', 'Physionet2012.h5', 'Physionet2019.h5', 'PickupGestureWiimoteZ.h5', 'Seabirds.h5', 'ShakeGestureWiimoteZ.h5', 'SpokenArabicDigits.h5', 'TDrive.h5', 'Taxi.h5', 'Vehicles.h5']

Loading the dataset from the online repository

Loading a dataset is as from the online repo (https://huggingface.co/datasets/splandi/pyrregular) is as simple as calling the load_dataset function with the dataset name.

from pyrregular import load_dataset

ds = load_dataset("Garment.h5")

The dataset is loaded as an xarray dataset. The dataset is saved in the default os cache directory, which can be found with:

import pooch
print(pooch.os_cache("pyrregular"))

You can also use xarray to directly load a local file. In this case, you have to specify our backend as pyrregular in the engine argument.

import xarray as xr
ds = xr.load_dataset("path/to/file.h5", engine="pyrregular")

You can view the underlying DataArray by calling the data variable.

da = ds.data

da

<xarray.DataArray 'data' (ts_id: 24, signal_id: 9, time_id: 59)> Size: 329kB
<COO: shape=(24, 9, 59), dtype=float64, nnz=10267, fill_value=nan>
Coordinates:
    day                     (time_id) <U9 2kB 'Thursday' ... 'Wednesday'
    department              (ts_id) <U9 864B 'finishing' ... 'sweing'
    productivity_binary     (ts_id) int32 96B 1 0 1 1 1 1 1 1 ... 1 1 0 0 0 0 1
    productivity_class      (ts_id) <U4 384B 'high' 'low' ... 'low' 'high'
    productivity_numerical  (ts_id) float32 96B 0.8126 0.6283 ... 0.7005 0.7503
    quarter                 (time_id) <U8 2kB 'Quarter1' ... 'Quarter2'
  * signal_id               (signal_id) <U21 756B 'idle_men' ... 'wip'
    split                   (ts_id) <U5 480B 'train' 'train' ... 'train' 'train'
    team                    (ts_id) int32 96B 1 10 11 12 2 3 4 ... 3 4 5 6 7 8 9
  * time_id                 (time_id) datetime64[ns] 472B 2015-01-01T01:00:00...
  * ts_id                   (ts_id) <U12 1kB 'finishing_1' ... 'sweing_9'
Attributes:
    _fixed_at:  2024-12-04T21:50:44.408790-12:00
    _is_fixed:  True
    author:     ['NA']
    configs:    {'default': {'task': 'classification', 'split': 'split', 'tar...
    license:    CC BY 4.0
    source:     https://archive.ics.uci.edu/dataset/597/productivity+predicti...
    title:      Productivity Prediction of Garment Employees

xarray.DataArray

'data'

• ts_id: 24
• signal_id: 9
• time_id: 59

• <COO: nnz=10267, fill_value=nan>

  Format	coo
  Data Type	float64
  Shape	(24, 9, 59)
  nnz	10267
  Density	0.8056340238543629
  Read-only	True
  Size	320.8K
  Storage ratio	3.22

• Coordinates: (11)
  • day
    (time_id)
    <U9
    'Thursday' ... 'Wednesday'

    array(['Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday',
           'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday',
           'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday'],
          dtype='<U9')

  • department
    (ts_id)
    <U9
    'finishing' ... 'sweing'

    array(['finishing', 'finishing', 'finishing', 'finishing', 'finishing',
           'finishing', 'finishing', 'finishing', 'finishing', 'finishing',
           'finishing', 'finishing', 'sweing', 'sweing', 'sweing', 'sweing',
           'sweing', 'sweing', 'sweing', 'sweing', 'sweing', 'sweing',
           'sweing', 'sweing'], dtype='<U9')

  • productivity_binary
    (ts_id)
    int32
    1 0 1 1 1 1 1 1 ... 1 1 1 0 0 0 0 1

    array([1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
           0, 1], dtype=int32)

  • productivity_class
    (ts_id)
    <U4
    'high' 'low' ... 'low' 'high'

    array(['high', 'low', 'high', 'high', 'high', 'high', 'high', 'high',
           'low', 'low', 'low', 'low', 'high', 'high', 'high', 'high', 'high',
           'high', 'high', 'low', 'low', 'low', 'low', 'high'], dtype='<U4')

  • productivity_numerical
    (ts_id)
    float32
    0.8126 0.6283 ... 0.7005 0.7503

    array([0.812625  , 0.62833333, 0.8740278 , 0.9228395 , 0.81927085,
           0.78663194, 0.813309  , 0.75885   , 0.39472222, 0.625625  ,
           0.625625  , 0.5058889 , 0.75005084, 0.750608  , 0.8000345 ,
           0.800402  , 0.7501407 , 0.80007184, 0.75005084, 0.7005569 ,
           0.7002465 , 0.6505965 , 0.70050526, 0.7503473 ], dtype=float32)

  • quarter
    (time_id)
    <U8
    'Quarter1' ... 'Quarter2'

    array(['Quarter1', 'Quarter1', 'Quarter1', 'Quarter1', 'Quarter1',
           'Quarter1', 'Quarter2', 'Quarter2', 'Quarter2', 'Quarter2',
           'Quarter2', 'Quarter2', 'Quarter3', 'Quarter3', 'Quarter3',
           'Quarter3', 'Quarter3', 'Quarter3', 'Quarter4', 'Quarter4',
           'Quarter4', 'Quarter4', 'Quarter4', 'Quarter4', 'Quarter5',
           'Quarter5', 'Quarter1', 'Quarter1', 'Quarter1', 'Quarter1',
           'Quarter1', 'Quarter1', 'Quarter2', 'Quarter2', 'Quarter2',
           'Quarter2', 'Quarter2', 'Quarter2', 'Quarter3', 'Quarter3',
           'Quarter3', 'Quarter3', 'Quarter3', 'Quarter4', 'Quarter4',
           'Quarter4', 'Quarter4', 'Quarter4', 'Quarter4', 'Quarter1',
           'Quarter1', 'Quarter1', 'Quarter1', 'Quarter1', 'Quarter1',
           'Quarter2', 'Quarter2', 'Quarter2', 'Quarter2'], dtype='<U8')

  • signal_id
    (signal_id)
    <U21
    'idle_men' 'idle_time' ... 'wip'

    array(['idle_men', 'idle_time', 'incentive', 'no_of_style_change',
           'no_of_workers', 'over_time', 'smv', 'targeted_productivity', 'wip'],
          dtype='<U21')

  • split
    (ts_id)
    <U5
    'train' 'train' ... 'train' 'train'

    array(['train', 'train', 'test', 'train', 'train', 'test', 'train',
           'train', 'train', 'test', 'train', 'train', 'test', 'train',
           'train', 'test', 'train', 'train', 'train', 'train', 'test',
           'train', 'train', 'train'], dtype='<U5')

  • team
    (ts_id)
    int32
    1 10 11 12 2 3 4 ... 3 4 5 6 7 8 9

    array([ 1, 10, 11, 12,  2,  3,  4,  5,  6,  7,  8,  9,  1, 10, 11, 12,  2,
            3,  4,  5,  6,  7,  8,  9], dtype=int32)

  • time_id
    (time_id)
    datetime64[ns]
    2015-01-01T01:00:00 ... 2015-03-...

    array(['2015-01-01T01:00:00.000000000', '2015-01-03T01:00:00.000000000',
           '2015-01-04T01:00:00.000000000', '2015-01-05T01:00:00.000000000',
           '2015-01-06T01:00:00.000000000', '2015-01-07T01:00:00.000000000',
           '2015-01-08T01:00:00.000000000', '2015-01-10T01:00:00.000000000',
           '2015-01-11T01:00:00.000000000', '2015-01-12T01:00:00.000000000',
           '2015-01-13T01:00:00.000000000', '2015-01-14T01:00:00.000000000',
           '2015-01-15T01:00:00.000000000', '2015-01-17T01:00:00.000000000',
           '2015-01-18T01:00:00.000000000', '2015-01-19T01:00:00.000000000',
           '2015-01-20T01:00:00.000000000', '2015-01-21T01:00:00.000000000',
           '2015-01-22T01:00:00.000000000', '2015-01-24T01:00:00.000000000',
           '2015-01-25T01:00:00.000000000', '2015-01-26T01:00:00.000000000',
           '2015-01-27T01:00:00.000000000', '2015-01-28T01:00:00.000000000',
           '2015-01-29T01:00:00.000000000', '2015-01-31T01:00:00.000000000',
           '2015-02-01T01:00:00.000000000', '2015-02-02T01:00:00.000000000',
           '2015-02-03T01:00:00.000000000', '2015-02-04T01:00:00.000000000',
           '2015-02-05T01:00:00.000000000', '2015-02-07T01:00:00.000000000',
           '2015-02-08T01:00:00.000000000', '2015-02-09T01:00:00.000000000',
           '2015-02-10T01:00:00.000000000', '2015-02-11T01:00:00.000000000',
           '2015-02-12T01:00:00.000000000', '2015-02-14T01:00:00.000000000',
           '2015-02-15T01:00:00.000000000', '2015-02-16T01:00:00.000000000',
           '2015-02-17T01:00:00.000000000', '2015-02-18T01:00:00.000000000',
           '2015-02-19T01:00:00.000000000', '2015-02-22T01:00:00.000000000',
           '2015-02-23T01:00:00.000000000', '2015-02-24T01:00:00.000000000',
           '2015-02-25T01:00:00.000000000', '2015-02-26T01:00:00.000000000',
           '2015-02-28T01:00:00.000000000', '2015-03-01T01:00:00.000000000',
           '2015-03-02T01:00:00.000000000', '2015-03-03T01:00:00.000000000',
           '2015-03-04T01:00:00.000000000', '2015-03-05T01:00:00.000000000',
           '2015-03-07T01:00:00.000000000', '2015-03-08T01:00:00.000000000',
           '2015-03-09T01:00:00.000000000', '2015-03-10T01:00:00.000000000',
           '2015-03-11T01:00:00.000000000'], dtype='datetime64[ns]')

  • ts_id
    (ts_id)
    <U12
    'finishing_1' ... 'sweing_9'

    array(['finishing_1', 'finishing_10', 'finishing_11', 'finishing_12',
           'finishing_2', 'finishing_3', 'finishing_4', 'finishing_5',
           'finishing_6', 'finishing_7', 'finishing_8', 'finishing_9', 'sweing_1',
           'sweing_10', 'sweing_11', 'sweing_12', 'sweing_2', 'sweing_3',
           'sweing_4', 'sweing_5', 'sweing_6', 'sweing_7', 'sweing_8', 'sweing_9'],
          dtype='<U12')

• Indexes: (3)
  • signal_id
    PandasIndex

    PandasIndex(Index(['idle_men', 'idle_time', 'incentive', 'no_of_style_change',
           'no_of_workers', 'over_time', 'smv', 'targeted_productivity', 'wip'],
          dtype='object', name='signal_id'))

  • time_id
    PandasIndex

    PandasIndex(DatetimeIndex(['2015-01-01 01:00:00', '2015-01-03 01:00:00',
                   '2015-01-04 01:00:00', '2015-01-05 01:00:00',
                   '2015-01-06 01:00:00', '2015-01-07 01:00:00',
                   '2015-01-08 01:00:00', '2015-01-10 01:00:00',
                   '2015-01-11 01:00:00', '2015-01-12 01:00:00',
                   '2015-01-13 01:00:00', '2015-01-14 01:00:00',
                   '2015-01-15 01:00:00', '2015-01-17 01:00:00',
                   '2015-01-18 01:00:00', '2015-01-19 01:00:00',
                   '2015-01-20 01:00:00', '2015-01-21 01:00:00',
                   '2015-01-22 01:00:00', '2015-01-24 01:00:00',
                   '2015-01-25 01:00:00', '2015-01-26 01:00:00',
                   '2015-01-27 01:00:00', '2015-01-28 01:00:00',
                   '2015-01-29 01:00:00', '2015-01-31 01:00:00',
                   '2015-02-01 01:00:00', '2015-02-02 01:00:00',
                   '2015-02-03 01:00:00', '2015-02-04 01:00:00',
                   '2015-02-05 01:00:00', '2015-02-07 01:00:00',
                   '2015-02-08 01:00:00', '2015-02-09 01:00:00',
                   '2015-02-10 01:00:00', '2015-02-11 01:00:00',
                   '2015-02-12 01:00:00', '2015-02-14 01:00:00',
                   '2015-02-15 01:00:00', '2015-02-16 01:00:00',
                   '2015-02-17 01:00:00', '2015-02-18 01:00:00',
                   '2015-02-19 01:00:00', '2015-02-22 01:00:00',
                   '2015-02-23 01:00:00', '2015-02-24 01:00:00',
                   '2015-02-25 01:00:00', '2015-02-26 01:00:00',
                   '2015-02-28 01:00:00', '2015-03-01 01:00:00',
                   '2015-03-02 01:00:00', '2015-03-03 01:00:00',
                   '2015-03-04 01:00:00', '2015-03-05 01:00:00',
                   '2015-03-07 01:00:00', '2015-03-08 01:00:00',
                   '2015-03-09 01:00:00', '2015-03-10 01:00:00',
                   '2015-03-11 01:00:00'],
                  dtype='datetime64[ns]', name='time_id', freq=None))

  • ts_id
    PandasIndex

    PandasIndex(Index(['finishing_1', 'finishing_10', 'finishing_11', 'finishing_12',
           'finishing_2', 'finishing_3', 'finishing_4', 'finishing_5',
           'finishing_6', 'finishing_7', 'finishing_8', 'finishing_9', 'sweing_1',
           'sweing_10', 'sweing_11', 'sweing_12', 'sweing_2', 'sweing_3',
           'sweing_4', 'sweing_5', 'sweing_6', 'sweing_7', 'sweing_8', 'sweing_9'],
          dtype='object', name='ts_id'))

• Attributes: (7)

  _fixed_at :

  2024-12-04T21:50:44.408790-12:00

  _is_fixed :

  True

  author :

  ['NA']

  configs :

  {'default': {'task': 'classification', 'split': 'split', 'target': 'productivity_binary'}, 'regression': {'task': 'regression', 'split': 'split', 'target': 'productivity_numerical'}}

  license :

  CC BY 4.0

  source :

  https://archive.ics.uci.edu/dataset/597/productivity+prediction+of+garment+employees

  title :

  Productivity Prediction of Garment Employees

# the shape is (n_time_series, n_channels, n_timestamps)
da.shape

(24, 9, 59)

# the array is stored as a sparse array
da.data

Format	coo
Data Type	float64
Shape	(24, 9, 59)
nnz	10267
Density	0.8056340238543629
Read-only	True
Size	320.8K
Storage ratio	3.22

# dimensions contain the time series ids, signal ids and timestamps
da.dims

('ts_id', 'signal_id', 'time_id')

# e.g., these are the time series ids
da["ts_id"].data

array(['finishing_1', 'finishing_10', 'finishing_11', 'finishing_12',
       'finishing_2', 'finishing_3', 'finishing_4', 'finishing_5',
       'finishing_6', 'finishing_7', 'finishing_8', 'finishing_9',
       'sweing_1', 'sweing_10', 'sweing_11', 'sweing_12', 'sweing_2',
       'sweing_3', 'sweing_4', 'sweing_5', 'sweing_6', 'sweing_7',
       'sweing_8', 'sweing_9'], dtype='<U12')

# there are also static variables, such as the class
da["productivity_binary"].data

array([1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
       0, 1], dtype=int32)

# the train/test split
da["split"].data

array(['train', 'train', 'test', 'train', 'train', 'test', 'train',
       'train', 'train', 'test', 'train', 'train', 'test', 'train',
       'train', 'test', 'train', 'train', 'train', 'train', 'test',
       'train', 'train', 'train'], dtype='<U5')

# all the coordinates can be accessed via the `coords` variable
da.coords

Coordinates:
    day                     (time_id) <U9 2kB 'Thursday' ... 'Wednesday'
    department              (ts_id) <U9 864B 'finishing' ... 'sweing'
    productivity_binary     (ts_id) int32 96B 1 0 1 1 1 1 1 1 ... 1 1 0 0 0 0 1
    productivity_class      (ts_id) <U4 384B 'high' 'low' ... 'low' 'high'
    productivity_numerical  (ts_id) float32 96B 0.8126 0.6283 ... 0.7005 0.7503
    quarter                 (time_id) <U8 2kB 'Quarter1' ... 'Quarter2'
  * signal_id               (signal_id) <U21 756B 'idle_men' ... 'wip'
    split                   (ts_id) <U5 480B 'train' 'train' ... 'train' 'train'
    team                    (ts_id) int32 96B 1 10 11 12 2 3 4 ... 3 4 5 6 7 8 9
  * time_id                 (time_id) datetime64[ns] 472B 2015-01-01T01:00:00...
  * ts_id                   (ts_id) <U12 1kB 'finishing_1' ... 'sweing_9'

# metadata contains informations about the datasets and tasks
da.attrs

{'_fixed_at': '2024-12-04T21:50:44.408790-12:00',
 '_is_fixed': True,
 'author': ['NA'],
 'configs': {'default': {'task': 'classification',
   'split': 'split',
   'target': 'productivity_binary'},
  'regression': {'task': 'regression',
   'split': 'split',
   'target': 'productivity_numerical'}},
 'license': 'CC BY 4.0',
 'source': 'https://archive.ics.uci.edu/dataset/597/productivity+prediction+of+garment+employees',
 'title': 'Productivity Prediction of Garment Employees'}

Data Handling and Plotting

Data can be accessed with standard xarray methods.

import matplotlib.pyplot as plt
import numpy as np

# the first time series
da[0]

<xarray.DataArray 'data' (signal_id: 9, time_id: 59)> Size: 9kB
<COO: shape=(9, 59), dtype=float64, nnz=392, fill_value=nan>
Coordinates:
    day                     (time_id) <U9 2kB 'Thursday' ... 'Wednesday'
    department              <U9 36B 'finishing'
    productivity_binary     int32 4B 1
    productivity_class      <U4 16B 'high'
    productivity_numerical  float32 4B 0.8126
    quarter                 (time_id) <U8 2kB 'Quarter1' ... 'Quarter2'
  * signal_id               (signal_id) <U21 756B 'idle_men' ... 'wip'
    split                   <U5 20B 'train'
    team                    int32 4B 1
  * time_id                 (time_id) datetime64[ns] 472B 2015-01-01T01:00:00...
    ts_id                   <U12 48B 'finishing_1'
Attributes:
    _fixed_at:  2024-12-04T21:50:44.408790-12:00
    _is_fixed:  True
    author:     ['NA']
    configs:    {'default': {'task': 'classification', 'split': 'split', 'tar...
    license:    CC BY 4.0
    source:     https://archive.ics.uci.edu/dataset/597/productivity+predicti...
    title:      Productivity Prediction of Garment Employees

xarray.DataArray

'data'

• signal_id: 9
• time_id: 59

• <COO: nnz=392, fill_value=nan>

  Format	coo
  Data Type	float64
  Shape	(9, 59)
  nnz	392
  Density	0.7382297551789078
  Read-only	True
  Size	9.2K
  Storage ratio	2.21

• Coordinates: (11)
  • day
    (time_id)
    <U9
    'Thursday' ... 'Wednesday'

    array(['Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday',
           'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday',
           'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday'],
          dtype='<U9')

  • department
    ()
    <U9
    'finishing'

    array('finishing', dtype='<U9')

  • productivity_binary
    ()
    int32
    1

    array(1, dtype=int32)

  • productivity_class
    ()
    <U4
    'high'

    array('high', dtype='<U4')

  • productivity_numerical
    ()
    float32
    0.8126

    array(0.812625, dtype=float32)

  • quarter
    (time_id)
    <U8
    'Quarter1' ... 'Quarter2'

    array(['Quarter1', 'Quarter1', 'Quarter1', 'Quarter1', 'Quarter1',
           'Quarter1', 'Quarter2', 'Quarter2', 'Quarter2', 'Quarter2',
           'Quarter2', 'Quarter2', 'Quarter3', 'Quarter3', 'Quarter3',
           'Quarter3', 'Quarter3', 'Quarter3', 'Quarter4', 'Quarter4',
           'Quarter4', 'Quarter4', 'Quarter4', 'Quarter4', 'Quarter5',
           'Quarter5', 'Quarter1', 'Quarter1', 'Quarter1', 'Quarter1',
           'Quarter1', 'Quarter1', 'Quarter2', 'Quarter2', 'Quarter2',
           'Quarter2', 'Quarter2', 'Quarter2', 'Quarter3', 'Quarter3',
           'Quarter3', 'Quarter3', 'Quarter3', 'Quarter4', 'Quarter4',
           'Quarter4', 'Quarter4', 'Quarter4', 'Quarter4', 'Quarter1',
           'Quarter1', 'Quarter1', 'Quarter1', 'Quarter1', 'Quarter1',
           'Quarter2', 'Quarter2', 'Quarter2', 'Quarter2'], dtype='<U8')

  • signal_id
    (signal_id)
    <U21
    'idle_men' 'idle_time' ... 'wip'

    array(['idle_men', 'idle_time', 'incentive', 'no_of_style_change',
           'no_of_workers', 'over_time', 'smv', 'targeted_productivity', 'wip'],
          dtype='<U21')

  • split
    ()
    <U5
    'train'

    array('train', dtype='<U5')

  • team
    ()
    int32
    1

    array(1, dtype=int32)

  • time_id
    (time_id)
    datetime64[ns]
    2015-01-01T01:00:00 ... 2015-03-...

    array(['2015-01-01T01:00:00.000000000', '2015-01-03T01:00:00.000000000',
           '2015-01-04T01:00:00.000000000', '2015-01-05T01:00:00.000000000',
           '2015-01-06T01:00:00.000000000', '2015-01-07T01:00:00.000000000',
           '2015-01-08T01:00:00.000000000', '2015-01-10T01:00:00.000000000',
           '2015-01-11T01:00:00.000000000', '2015-01-12T01:00:00.000000000',
           '2015-01-13T01:00:00.000000000', '2015-01-14T01:00:00.000000000',
           '2015-01-15T01:00:00.000000000', '2015-01-17T01:00:00.000000000',
           '2015-01-18T01:00:00.000000000', '2015-01-19T01:00:00.000000000',
           '2015-01-20T01:00:00.000000000', '2015-01-21T01:00:00.000000000',
           '2015-01-22T01:00:00.000000000', '2015-01-24T01:00:00.000000000',
           '2015-01-25T01:00:00.000000000', '2015-01-26T01:00:00.000000000',
           '2015-01-27T01:00:00.000000000', '2015-01-28T01:00:00.000000000',
           '2015-01-29T01:00:00.000000000', '2015-01-31T01:00:00.000000000',
           '2015-02-01T01:00:00.000000000', '2015-02-02T01:00:00.000000000',
           '2015-02-03T01:00:00.000000000', '2015-02-04T01:00:00.000000000',
           '2015-02-05T01:00:00.000000000', '2015-02-07T01:00:00.000000000',
           '2015-02-08T01:00:00.000000000', '2015-02-09T01:00:00.000000000',
           '2015-02-10T01:00:00.000000000', '2015-02-11T01:00:00.000000000',
           '2015-02-12T01:00:00.000000000', '2015-02-14T01:00:00.000000000',
           '2015-02-15T01:00:00.000000000', '2015-02-16T01:00:00.000000000',
           '2015-02-17T01:00:00.000000000', '2015-02-18T01:00:00.000000000',
           '2015-02-19T01:00:00.000000000', '2015-02-22T01:00:00.000000000',
           '2015-02-23T01:00:00.000000000', '2015-02-24T01:00:00.000000000',
           '2015-02-25T01:00:00.000000000', '2015-02-26T01:00:00.000000000',
           '2015-02-28T01:00:00.000000000', '2015-03-01T01:00:00.000000000',
           '2015-03-02T01:00:00.000000000', '2015-03-03T01:00:00.000000000',
           '2015-03-04T01:00:00.000000000', '2015-03-05T01:00:00.000000000',
           '2015-03-07T01:00:00.000000000', '2015-03-08T01:00:00.000000000',
           '2015-03-09T01:00:00.000000000', '2015-03-10T01:00:00.000000000',
           '2015-03-11T01:00:00.000000000'], dtype='datetime64[ns]')

  • ts_id
    ()
    <U12
    'finishing_1'

    array('finishing_1', dtype='<U12')

• Indexes: (2)
  • signal_id
    PandasIndex

    PandasIndex(Index(['idle_men', 'idle_time', 'incentive', 'no_of_style_change',
           'no_of_workers', 'over_time', 'smv', 'targeted_productivity', 'wip'],
          dtype='object', name='signal_id'))

  • time_id
    PandasIndex

    PandasIndex(DatetimeIndex(['2015-01-01 01:00:00', '2015-01-03 01:00:00',
                   '2015-01-04 01:00:00', '2015-01-05 01:00:00',
                   '2015-01-06 01:00:00', '2015-01-07 01:00:00',
                   '2015-01-08 01:00:00', '2015-01-10 01:00:00',
                   '2015-01-11 01:00:00', '2015-01-12 01:00:00',
                   '2015-01-13 01:00:00', '2015-01-14 01:00:00',
                   '2015-01-15 01:00:00', '2015-01-17 01:00:00',
                   '2015-01-18 01:00:00', '2015-01-19 01:00:00',
                   '2015-01-20 01:00:00', '2015-01-21 01:00:00',
                   '2015-01-22 01:00:00', '2015-01-24 01:00:00',
                   '2015-01-25 01:00:00', '2015-01-26 01:00:00',
                   '2015-01-27 01:00:00', '2015-01-28 01:00:00',
                   '2015-01-29 01:00:00', '2015-01-31 01:00:00',
                   '2015-02-01 01:00:00', '2015-02-02 01:00:00',
                   '2015-02-03 01:00:00', '2015-02-04 01:00:00',
                   '2015-02-05 01:00:00', '2015-02-07 01:00:00',
                   '2015-02-08 01:00:00', '2015-02-09 01:00:00',
                   '2015-02-10 01:00:00', '2015-02-11 01:00:00',
                   '2015-02-12 01:00:00', '2015-02-14 01:00:00',
                   '2015-02-15 01:00:00', '2015-02-16 01:00:00',
                   '2015-02-17 01:00:00', '2015-02-18 01:00:00',
                   '2015-02-19 01:00:00', '2015-02-22 01:00:00',
                   '2015-02-23 01:00:00', '2015-02-24 01:00:00',
                   '2015-02-25 01:00:00', '2015-02-26 01:00:00',
                   '2015-02-28 01:00:00', '2015-03-01 01:00:00',
                   '2015-03-02 01:00:00', '2015-03-03 01:00:00',
                   '2015-03-04 01:00:00', '2015-03-05 01:00:00',
                   '2015-03-07 01:00:00', '2015-03-08 01:00:00',
                   '2015-03-09 01:00:00', '2015-03-10 01:00:00',
                   '2015-03-11 01:00:00'],
                  dtype='datetime64[ns]', name='time_id', freq=None))

• Attributes: (7)

  _fixed_at :

  2024-12-04T21:50:44.408790-12:00

  _is_fixed :

  True

  author :

  ['NA']

  configs :

  {'default': {'task': 'classification', 'split': 'split', 'target': 'productivity_binary'}, 'regression': {'task': 'regression', 'split': 'split', 'target': 'productivity_numerical'}}

  license :

  CC BY 4.0

  source :

  https://archive.ics.uci.edu/dataset/597/productivity+prediction+of+garment+employees

  title :

  Productivity Prediction of Garment Employees

# the first channel of the first time series
da[0, 0]

<xarray.DataArray 'data' (time_id: 59)> Size: 784B
<COO: shape=(59,), dtype=float64, nnz=49, fill_value=nan>
Coordinates:
    day                     (time_id) <U9 2kB 'Thursday' ... 'Wednesday'
    department              <U9 36B 'finishing'
    productivity_binary     int32 4B 1
    productivity_class      <U4 16B 'high'
    productivity_numerical  float32 4B 0.8126
    quarter                 (time_id) <U8 2kB 'Quarter1' ... 'Quarter2'
    signal_id               <U21 84B 'idle_men'
    split                   <U5 20B 'train'
    team                    int32 4B 1
  * time_id                 (time_id) datetime64[ns] 472B 2015-01-01T01:00:00...
    ts_id                   <U12 48B 'finishing_1'
Attributes:
    _fixed_at:  2024-12-04T21:50:44.408790-12:00
    _is_fixed:  True
    author:     ['NA']
    configs:    {'default': {'task': 'classification', 'split': 'split', 'tar...
    license:    CC BY 4.0
    source:     https://archive.ics.uci.edu/dataset/597/productivity+predicti...
    title:      Productivity Prediction of Garment Employees

xarray.DataArray

'data'

• time_id: 59

• <COO: nnz=49, fill_value=nan>

  Format	coo
  Data Type	float64
  Shape	(59,)
  nnz	49
  Density	0.8305084745762712
  Read-only	True
  Size	784
  Storage ratio	1.66

• Coordinates: (11)
  • day
    (time_id)
    <U9
    'Thursday' ... 'Wednesday'

    array(['Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
           'Thursday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday',
           'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday',
           'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday'],
          dtype='<U9')

  • department
    ()
    <U9
    'finishing'

    array('finishing', dtype='<U9')

  • productivity_binary
    ()
    int32
    1

    array(1, dtype=int32)

  • productivity_class
    ()
    <U4
    'high'

    array('high', dtype='<U4')

  • productivity_numerical
    ()
    float32
    0.8126

    array(0.812625, dtype=float32)

  • quarter
    (time_id)
    <U8
    'Quarter1' ... 'Quarter2'

    array(['Quarter1', 'Quarter1', 'Quarter1', 'Quarter1', 'Quarter1',
           'Quarter1', 'Quarter2', 'Quarter2', 'Quarter2', 'Quarter2',
           'Quarter2', 'Quarter2', 'Quarter3', 'Quarter3', 'Quarter3',
           'Quarter3', 'Quarter3', 'Quarter3', 'Quarter4', 'Quarter4',
           'Quarter4', 'Quarter4', 'Quarter4', 'Quarter4', 'Quarter5',
           'Quarter5', 'Quarter1', 'Quarter1', 'Quarter1', 'Quarter1',
           'Quarter1', 'Quarter1', 'Quarter2', 'Quarter2', 'Quarter2',
           'Quarter2', 'Quarter2', 'Quarter2', 'Quarter3', 'Quarter3',
           'Quarter3', 'Quarter3', 'Quarter3', 'Quarter4', 'Quarter4',
           'Quarter4', 'Quarter4', 'Quarter4', 'Quarter4', 'Quarter1',
           'Quarter1', 'Quarter1', 'Quarter1', 'Quarter1', 'Quarter1',
           'Quarter2', 'Quarter2', 'Quarter2', 'Quarter2'], dtype='<U8')

  • signal_id
    ()
    <U21
    'idle_men'

    array('idle_men', dtype='<U21')

  • split
    ()
    <U5
    'train'

    array('train', dtype='<U5')

  • team
    ()
    int32
    1

    array(1, dtype=int32)

  • time_id
    (time_id)
    datetime64[ns]
    2015-01-01T01:00:00 ... 2015-03-...

    array(['2015-01-01T01:00:00.000000000', '2015-01-03T01:00:00.000000000',
           '2015-01-04T01:00:00.000000000', '2015-01-05T01:00:00.000000000',
           '2015-01-06T01:00:00.000000000', '2015-01-07T01:00:00.000000000',
           '2015-01-08T01:00:00.000000000', '2015-01-10T01:00:00.000000000',
           '2015-01-11T01:00:00.000000000', '2015-01-12T01:00:00.000000000',
           '2015-01-13T01:00:00.000000000', '2015-01-14T01:00:00.000000000',
           '2015-01-15T01:00:00.000000000', '2015-01-17T01:00:00.000000000',
           '2015-01-18T01:00:00.000000000', '2015-01-19T01:00:00.000000000',
           '2015-01-20T01:00:00.000000000', '2015-01-21T01:00:00.000000000',
           '2015-01-22T01:00:00.000000000', '2015-01-24T01:00:00.000000000',
           '2015-01-25T01:00:00.000000000', '2015-01-26T01:00:00.000000000',
           '2015-01-27T01:00:00.000000000', '2015-01-28T01:00:00.000000000',
           '2015-01-29T01:00:00.000000000', '2015-01-31T01:00:00.000000000',
           '2015-02-01T01:00:00.000000000', '2015-02-02T01:00:00.000000000',
           '2015-02-03T01:00:00.000000000', '2015-02-04T01:00:00.000000000',
           '2015-02-05T01:00:00.000000000', '2015-02-07T01:00:00.000000000',
           '2015-02-08T01:00:00.000000000', '2015-02-09T01:00:00.000000000',
           '2015-02-10T01:00:00.000000000', '2015-02-11T01:00:00.000000000',
           '2015-02-12T01:00:00.000000000', '2015-02-14T01:00:00.000000000',
           '2015-02-15T01:00:00.000000000', '2015-02-16T01:00:00.000000000',
           '2015-02-17T01:00:00.000000000', '2015-02-18T01:00:00.000000000',
           '2015-02-19T01:00:00.000000000', '2015-02-22T01:00:00.000000000',
           '2015-02-23T01:00:00.000000000', '2015-02-24T01:00:00.000000000',
           '2015-02-25T01:00:00.000000000', '2015-02-26T01:00:00.000000000',
           '2015-02-28T01:00:00.000000000', '2015-03-01T01:00:00.000000000',
           '2015-03-02T01:00:00.000000000', '2015-03-03T01:00:00.000000000',
           '2015-03-04T01:00:00.000000000', '2015-03-05T01:00:00.000000000',
           '2015-03-07T01:00:00.000000000', '2015-03-08T01:00:00.000000000',
           '2015-03-09T01:00:00.000000000', '2015-03-10T01:00:00.000000000',
           '2015-03-11T01:00:00.000000000'], dtype='datetime64[ns]')

  • ts_id
    ()
    <U12
    'finishing_1'

    array('finishing_1', dtype='<U12')

• Indexes: (1)
  • time_id
    PandasIndex

    PandasIndex(DatetimeIndex(['2015-01-01 01:00:00', '2015-01-03 01:00:00',
                   '2015-01-04 01:00:00', '2015-01-05 01:00:00',
                   '2015-01-06 01:00:00', '2015-01-07 01:00:00',
                   '2015-01-08 01:00:00', '2015-01-10 01:00:00',
                   '2015-01-11 01:00:00', '2015-01-12 01:00:00',
                   '2015-01-13 01:00:00', '2015-01-14 01:00:00',
                   '2015-01-15 01:00:00', '2015-01-17 01:00:00',
                   '2015-01-18 01:00:00', '2015-01-19 01:00:00',
                   '2015-01-20 01:00:00', '2015-01-21 01:00:00',
                   '2015-01-22 01:00:00', '2015-01-24 01:00:00',
                   '2015-01-25 01:00:00', '2015-01-26 01:00:00',
                   '2015-01-27 01:00:00', '2015-01-28 01:00:00',
                   '2015-01-29 01:00:00', '2015-01-31 01:00:00',
                   '2015-02-01 01:00:00', '2015-02-02 01:00:00',
                   '2015-02-03 01:00:00', '2015-02-04 01:00:00',
                   '2015-02-05 01:00:00', '2015-02-07 01:00:00',
                   '2015-02-08 01:00:00', '2015-02-09 01:00:00',
                   '2015-02-10 01:00:00', '2015-02-11 01:00:00',
                   '2015-02-12 01:00:00', '2015-02-14 01:00:00',
                   '2015-02-15 01:00:00', '2015-02-16 01:00:00',
                   '2015-02-17 01:00:00', '2015-02-18 01:00:00',
                   '2015-02-19 01:00:00', '2015-02-22 01:00:00',
                   '2015-02-23 01:00:00', '2015-02-24 01:00:00',
                   '2015-02-25 01:00:00', '2015-02-26 01:00:00',
                   '2015-02-28 01:00:00', '2015-03-01 01:00:00',
                   '2015-03-02 01:00:00', '2015-03-03 01:00:00',
                   '2015-03-04 01:00:00', '2015-03-05 01:00:00',
                   '2015-03-07 01:00:00', '2015-03-08 01:00:00',
                   '2015-03-09 01:00:00', '2015-03-10 01:00:00',
                   '2015-03-11 01:00:00'],
                  dtype='datetime64[ns]', name='time_id', freq=None))

• Attributes: (7)

  _fixed_at :

  2024-12-04T21:50:44.408790-12:00

  _is_fixed :

  True

  author :

  ['NA']

  configs :

  {'default': {'task': 'classification', 'split': 'split', 'target': 'productivity_binary'}, 'regression': {'task': 'regression', 'split': 'split', 'target': 'productivity_numerical'}}

  license :

  CC BY 4.0

  source :

  https://archive.ics.uci.edu/dataset/597/productivity+prediction+of+garment+employees

  title :

  Productivity Prediction of Garment Employees

# to access the underlying sparse vector
da[0, 0].data

Format	coo
Data Type	float64
Shape	(59,)
nnz	49
Density	0.8305084745762712
Read-only	True
Size	784
Storage ratio	1.66

# to access the underlying dense vector
da[0, 4].data.todense()

array([ 8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  2.,  8.,  8.,
        8., nan, nan, nan,  8., 25.,  8.,  8., 10., 10., 10., 10., 15.,
       19., 19., 10., 10., 12., 10., 10., 10., 12., 12., 12., 12.,  8.,
       nan, nan, nan, nan, 12., nan, nan, nan,  8.,  8.,  8.,  8.,  8.,
        8.,  8.,  8.,  8.,  8.,  8.,  8.])

# this vector contains a lot of nans, which are the padding necessary to have shared timestamps w.r.t. the whole dataset
np.isnan(da[0, 4].data.todense()).sum()

10

plt.plot(da[0, 4]["time_id"], da[0, 4], marker="o")

[<matplotlib.lines.Line2D at 0x14eb06990>]

# using the custom ".irr" accessor, we can filter out the nans to the minimum amount possible due to raggedness
np.isnan(da.irr[0, 4].data.todense()).sum()

0

plt.plot(da.irr[0, 4]["time_id"], da.irr[0, 4], marker="o")

[<matplotlib.lines.Line2D at 0x14eb6b230>]

# the fourth channel first 10 time series of the dataset, as a heatmap
da.irr[:10, 4].plot()

<matplotlib.collections.QuadMesh at 0x14dcf3680>

# plotting some channels
da.irr[0, 2].plot(label=da.coords["signal_id"][2].item())
da.irr[0, 4].plot(label=da.coords["signal_id"][4].item())
da.irr[0, 5].plot(label=da.coords["signal_id"][5].item())
plt.legend()

<matplotlib.legend.Legend at 0x16ea32870>

Downstream Tasks

The xarray is nice, but not supported by basically any downstream library. Thus, we can convert it into a numpy array.

%%time
# time series data, timestamps
X, T = da.irr.to_dense(
    normalize_time=True,  # normalize the time index to [0, 1]
)

CPU times: user 2.23 s, sys: 79 ms, total: 2.31 s
Wall time: 2.34 s

# the shape is (n_time_series, n_channels, n_timestamps), timestamps are returned as a separate channel, for downstream methods that are able to use them
X.shape, T.shape

((24, 9, 59), (24, 1, 59))

# static variables
Z = da.coords.to_dataset()[["split", "productivity_binary"]].to_pandas()
Z.head()

	split	productivity_binary	department	productivity_class	productivity_numerical	team
ts_id
finishing_1	train	1	finishing	high	0.812625	1
finishing_10	train	0	finishing	low	0.628333	10
finishing_11	test	1	finishing	high	0.874028	11
finishing_12	train	1	finishing	high	0.922840	12
finishing_2	train	1	finishing	high	0.819271	2

# target and split
y, split = da.irr.get_task_target_and_split()

Train-test split

X_train, X_test = X[split != "test"], X[split == "test"]
y_train, y_test = y[split != "test"], y[split == "test"]
X_train.shape, y_train.shape, X_test.shape, y_test.shape

((18, 9, 59), (18,), (6, 9, 59), (6,))

Classification

We have several ready-to-use classifiers in the pyrregular package. Be sure to install the required dependencies.

from pyrregular.models.rocket import rocket_pipeline

%%time
model = rocket_pipeline
model.fit(X_train, y_train)
model.score(X_test, y_test)

[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.
[LightGBM] [Info] Number of positive: 11, number of negative: 7
[LightGBM] [Info] Total Bins 0
[LightGBM] [Info] Number of data points in the train set: 18, number of used features: 0
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.611111 -> initscore=0.451985
[LightGBM] [Info] Start training from score 0.451985
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
CPU times: user 93.1 ms, sys: 4.02 ms, total: 97.1 ms
Wall time: 98.3 ms

/Users/francesco/miniforge3/envs/timeseries_dl/lib/python3.12/site-packages/sktime/base/_base_panel.py:307: UserWarning: Data seen by SklearnClassifierPipeline instance has missing values, but this SklearnClassifierPipeline instance cannot handle missing values. Calls with missing values may result in error or unreliable results.
  warn(msg, obj=self)
/Users/francesco/miniforge3/envs/timeseries_dl/lib/python3.12/site-packages/sktime/transformations/base.py:512: UserWarning: X is of equal length, consider using MiniRocketMultivariate for speedup and stability instead.
  self._fit(X=X_inner, y=y_inner)
/Users/francesco/miniforge3/envs/timeseries_dl/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
  warnings.warn(
/Users/francesco/miniforge3/envs/timeseries_dl/lib/python3.12/site-packages/sktime/base/_base_panel.py:307: UserWarning: Data seen by SklearnClassifierPipeline instance has missing values, but this SklearnClassifierPipeline instance cannot handle missing values. Calls with missing values may result in error or unreliable results.
  warn(msg, obj=self)
/Users/francesco/miniforge3/envs/timeseries_dl/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
  warnings.warn(

0.6666666666666666