{ "cells": [ { "metadata": {}, "cell_type": "markdown", "source": "# Quick Start", "id": "9b3f8543b9d4a3ce" }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:06:23.707144Z", "start_time": "2025-05-05T15:06:23.293217Z" } }, "cell_type": "code", "source": "import xarray as xr", "id": "46db229d4c472926", "outputs": [], "execution_count": 1 }, { "metadata": {}, "cell_type": "markdown", "source": [ "## List available datasets\n", "To view available datasets, you can use the `list_datasets` function." ], "id": "d30e771c7c18ce94" }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:06:24.055433Z", "start_time": "2025-05-05T15:06:23.713804Z" } }, "cell_type": "code", "source": "from pyrregular import list_datasets", "id": "251025e4ba81e271", "outputs": [], "execution_count": 2 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:06:24.211006Z", "start_time": "2025-05-05T15:06:24.207307Z" } }, "cell_type": "code", "source": "print(list_datasets())", "id": "3770e2014d896c60", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['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']\n" ] } ], "execution_count": 3 }, { "metadata": {}, "cell_type": "markdown", "source": [ "## Loading the dataset from the online repository\n", "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." ], "id": "1207ea0ffeae29ec" }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:06:26.312970Z", "start_time": "2025-05-05T15:06:26.310443Z" } }, "cell_type": "code", "source": "from pyrregular import load_dataset", "id": "666b003649891eba", "outputs": [], "execution_count": 4 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:30:49.945704Z", "start_time": "2025-05-05T15:30:49.929853Z" } }, "cell_type": "code", "source": "ds = load_dataset(\"Garment.h5\")", "id": "972890b77353b268", "outputs": [], "execution_count": 64 }, { "metadata": {}, "cell_type": "markdown", "source": [ "The dataset is loaded as an xarray dataset.\n", "The dataset is saved in the default os cache directory, which can be found with:\n", "\n", "```python\n", "import pooch\n", "print(pooch.os_cache(\"pyrregular\"))\n", "```\n", "\n", "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.\n", "\n", "```python\n", "import xarray as xr\n", "ds = xr.load_dataset(\"path/to/file.h5\", engine=\"pyrregular\")\n", "```\n", "\n" ], "id": "e7d12a9637707a87" }, { "metadata": {}, "cell_type": "markdown", "source": "You can view the underlying DataArray by calling the `data` variable.", "id": "1c214e0d010e0821" }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:30:51.688292Z", "start_time": "2025-05-05T15:30:51.685839Z" } }, "cell_type": "code", "source": "da = ds.data", "id": "2a12f6098be40359", "outputs": [], "execution_count": 65 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:30:52.664383Z", "start_time": "2025-05-05T15:30:52.648724Z" } }, "cell_type": "code", "source": "da", "id": "65ddef336a16c0ef", "outputs": [ { "data": { "text/plain": [ " Size: 329kB\n", "\n", "Coordinates:\n", " day (time_id) \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'data' (ts_id: 24, signal_id: 9, time_id: 59)> Size: 329kB\n",
       "<COO: shape=(24, 9, 59), dtype=float64, nnz=10267, fill_value=nan>\n",
       "Coordinates:\n",
       "    day                     (time_id) <U9 2kB 'Thursday' ... 'Wednesday'\n",
       "    department              (ts_id) <U9 864B 'finishing' ... 'sweing'\n",
       "    productivity_binary     (ts_id) int32 96B 1 0 1 1 1 1 1 1 ... 1 1 0 0 0 0 1\n",
       "    productivity_class      (ts_id) <U4 384B 'high' 'low' ... 'low' 'high'\n",
       "    productivity_numerical  (ts_id) float32 96B 0.8126 0.6283 ... 0.7005 0.7503\n",
       "    quarter                 (time_id) <U8 2kB 'Quarter1' ... 'Quarter2'\n",
       "  * signal_id               (signal_id) <U21 756B 'idle_men' ... 'wip'\n",
       "    split                   (ts_id) <U5 480B 'train' 'train' ... 'train' 'train'\n",
       "    team                    (ts_id) int32 96B 1 10 11 12 2 3 4 ... 3 4 5 6 7 8 9\n",
       "  * time_id                 (time_id) datetime64[ns] 472B 2015-01-01T01:00:00...\n",
       "  * ts_id                   (ts_id) <U12 1kB 'finishing_1' ... 'sweing_9'\n",
       "Attributes:\n",
       "    _fixed_at:  2024-12-04T21:50:44.408790-12:00\n",
       "    _is_fixed:  True\n",
       "    author:     ['NA']\n",
       "    configs:    {'default': {'task': 'classification', 'split': 'split', 'tar...\n",
       "    license:    CC BY 4.0\n",
       "    source:     https://archive.ics.uci.edu/dataset/597/productivity+predicti...\n",
       "    title:      Productivity Prediction of Garment Employees
" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 66 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:30:55.609759Z", "start_time": "2025-05-05T15:30:55.605976Z" } }, "cell_type": "code", "source": [ "# the shape is (n_time_series, n_channels, n_timestamps)\n", "da.shape" ], "id": "29d6bad61e924b83", "outputs": [ { "data": { "text/plain": [ "(24, 9, 59)" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 67 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:30:57.379056Z", "start_time": "2025-05-05T15:30:57.374345Z" } }, "cell_type": "code", "source": [ "# the array is stored as a sparse array\n", "da.data" ], "id": "80a574b5d17f1af0", "outputs": [ { "data": { "text/plain": [ "" ], "text/html": [ "
Formatcoo
Data Typefloat64
Shape(24, 9, 59)
nnz10267
Density0.8056340238543629
Read-onlyTrue
Size320.8K
Storage ratio3.22
" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 68 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:30:59.239778Z", "start_time": "2025-05-05T15:30:59.236030Z" } }, "cell_type": "code", "source": [ "# dimensions contain the time series ids, signal ids and timestamps\n", "da.dims" ], "id": "5f8b1ca22c239c5d", "outputs": [ { "data": { "text/plain": [ "('ts_id', 'signal_id', 'time_id')" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 69 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:31:00.578375Z", "start_time": "2025-05-05T15:31:00.575140Z" } }, "cell_type": "code", "source": [ "# e.g., these are the time series ids\n", "da[\"ts_id\"].data" ], "id": "705e32262f1f5709", "outputs": [ { "data": { "text/plain": [ "array(['finishing_1', 'finishing_10', 'finishing_11', 'finishing_12',\n", " 'finishing_2', 'finishing_3', 'finishing_4', 'finishing_5',\n", " 'finishing_6', 'finishing_7', 'finishing_8', 'finishing_9',\n", " 'sweing_1', 'sweing_10', 'sweing_11', 'sweing_12', 'sweing_2',\n", " 'sweing_3', 'sweing_4', 'sweing_5', 'sweing_6', 'sweing_7',\n", " 'sweing_8', 'sweing_9'], dtype=' Size: 9kB\n", "\n", "Coordinates:\n", " day (time_id) \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'data' (signal_id: 9, time_id: 59)> Size: 9kB\n",
       "<COO: shape=(9, 59), dtype=float64, nnz=392, fill_value=nan>\n",
       "Coordinates:\n",
       "    day                     (time_id) <U9 2kB 'Thursday' ... 'Wednesday'\n",
       "    department              <U9 36B 'finishing'\n",
       "    productivity_binary     int32 4B 1\n",
       "    productivity_class      <U4 16B 'high'\n",
       "    productivity_numerical  float32 4B 0.8126\n",
       "    quarter                 (time_id) <U8 2kB 'Quarter1' ... 'Quarter2'\n",
       "  * signal_id               (signal_id) <U21 756B 'idle_men' ... 'wip'\n",
       "    split                   <U5 20B 'train'\n",
       "    team                    int32 4B 1\n",
       "  * time_id                 (time_id) datetime64[ns] 472B 2015-01-01T01:00:00...\n",
       "    ts_id                   <U12 48B 'finishing_1'\n",
       "Attributes:\n",
       "    _fixed_at:  2024-12-04T21:50:44.408790-12:00\n",
       "    _is_fixed:  True\n",
       "    author:     ['NA']\n",
       "    configs:    {'default': {'task': 'classification', 'split': 'split', 'tar...\n",
       "    license:    CC BY 4.0\n",
       "    source:     https://archive.ics.uci.edu/dataset/597/productivity+predicti...\n",
       "    title:      Productivity Prediction of Garment Employees
" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 78 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:32:01.830191Z", "start_time": "2025-05-05T15:32:01.820733Z" } }, "cell_type": "code", "source": [ "# the first channel of the first time series\n", "da[0, 0]" ], "id": "378a7cff0f986ab", "outputs": [ { "data": { "text/plain": [ " Size: 784B\n", "\n", "Coordinates:\n", " day (time_id) \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'data' (time_id: 59)> Size: 784B\n",
       "<COO: shape=(59,), dtype=float64, nnz=49, fill_value=nan>\n",
       "Coordinates:\n",
       "    day                     (time_id) <U9 2kB 'Thursday' ... 'Wednesday'\n",
       "    department              <U9 36B 'finishing'\n",
       "    productivity_binary     int32 4B 1\n",
       "    productivity_class      <U4 16B 'high'\n",
       "    productivity_numerical  float32 4B 0.8126\n",
       "    quarter                 (time_id) <U8 2kB 'Quarter1' ... 'Quarter2'\n",
       "    signal_id               <U21 84B 'idle_men'\n",
       "    split                   <U5 20B 'train'\n",
       "    team                    int32 4B 1\n",
       "  * time_id                 (time_id) datetime64[ns] 472B 2015-01-01T01:00:00...\n",
       "    ts_id                   <U12 48B 'finishing_1'\n",
       "Attributes:\n",
       "    _fixed_at:  2024-12-04T21:50:44.408790-12:00\n",
       "    _is_fixed:  True\n",
       "    author:     ['NA']\n",
       "    configs:    {'default': {'task': 'classification', 'split': 'split', 'tar...\n",
       "    license:    CC BY 4.0\n",
       "    source:     https://archive.ics.uci.edu/dataset/597/productivity+predicti...\n",
       "    title:      Productivity Prediction of Garment Employees
" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 79 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:32:06.274606Z", "start_time": "2025-05-05T15:32:06.268922Z" } }, "cell_type": "code", "source": [ "# to access the underlying sparse vector\n", "da[0, 0].data" ], "id": "d5f4ad9345fced98", "outputs": [ { "data": { "text/plain": [ "" ], "text/html": [ "
Formatcoo
Data Typefloat64
Shape(59,)
nnz49
Density0.8305084745762712
Read-onlyTrue
Size784
Storage ratio1.66
" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 80 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:32:32.476604Z", "start_time": "2025-05-05T15:32:32.473314Z" } }, "cell_type": "code", "source": [ "# to access the underlying dense vector\n", "da[0, 4].data.todense()" ], "id": "912726f21a20f5f3", "outputs": [ { "data": { "text/plain": [ "array([ 8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 2., 8., 8.,\n", " 8., nan, nan, nan, 8., 25., 8., 8., 10., 10., 10., 10., 15.,\n", " 19., 19., 10., 10., 12., 10., 10., 10., 12., 12., 12., 12., 8.,\n", " nan, nan, nan, nan, 12., nan, nan, nan, 8., 8., 8., 8., 8.,\n", " 8., 8., 8., 8., 8., 8., 8.])" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 87 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:32:37.154529Z", "start_time": "2025-05-05T15:32:37.151122Z" } }, "cell_type": "code", "source": [ "# this vector contains a lot of nans, which are the padding necessary to have shared timestamps w.r.t. the whole dataset\n", "np.isnan(da[0, 4].data.todense()).sum()" ], "id": "12a92568c4cba4cd", "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 89 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:32:41.970140Z", "start_time": "2025-05-05T15:32:41.913616Z" } }, "cell_type": "code", "source": [ "plt.plot(da[0, 4][\"time_id\"], da[0, 4], marker=\"o\")" ], "id": "57819f56de6f42de", "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 90 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:32:56.872173Z", "start_time": "2025-05-05T15:32:56.868779Z" } }, "cell_type": "code", "source": [ "# using the custom \".irr\" accessor, we can filter out the nans to the minimum amount possible due to raggedness\n", "np.isnan(da.irr[0, 4].data.todense()).sum()" ], "id": "5b9d4d465f7093d3", "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 92 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:33:00.161976Z", "start_time": "2025-05-05T15:33:00.105243Z" } }, "cell_type": "code", "source": [ "plt.plot(da.irr[0, 4][\"time_id\"], da.irr[0, 4], marker=\"o\")" ], "id": "ca8f6610f5eac81", "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 93 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:33:11.580675Z", "start_time": "2025-05-05T15:33:11.460982Z" } }, "cell_type": "code", "source": [ "# the fourth channel first 10 time series of the dataset, as a heatmap\n", "da.irr[:10, 4].plot()" ], "id": "dab8dc82f6be60d2", "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 94 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:34:39.784115Z", "start_time": "2025-05-05T15:34:39.693654Z" } }, "cell_type": "code", "source": [ "# plotting some channels\n", "da.irr[0, 2].plot(label=da.coords[\"signal_id\"][2].item())\n", "da.irr[0, 4].plot(label=da.coords[\"signal_id\"][4].item())\n", "da.irr[0, 5].plot(label=da.coords[\"signal_id\"][5].item())\n", "plt.legend()" ], "id": "b08ace5b6257e125", "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {}, "output_type": "display_data" } ], "execution_count": 103 }, { "metadata": {}, "cell_type": "markdown", "source": [ "## Downstream Tasks\n", "The xarray is nice, but not supported by basically any downstream library. Thus, we can convert it into a numpy array." ], "id": "f5e8e382d95b1bd" }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:37:32.467872Z", "start_time": "2025-05-05T15:37:30.128386Z" } }, "cell_type": "code", "source": [ "%%time\n", "# time series data, timestamps\n", "X, T = da.irr.to_dense(\n", " normalize_time=True, # normalize the time index to [0, 1]\n", ")" ], "id": "27123ef5b12c1806", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.23 s, sys: 79 ms, total: 2.31 s\n", "Wall time: 2.34 s\n" ] } ], "execution_count": 104 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:37:45.422413Z", "start_time": "2025-05-05T15:37:45.418693Z" } }, "cell_type": "code", "source": [ "# 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\n", "X.shape, T.shape" ], "id": "8a0c87ccc42574ca", "outputs": [ { "data": { "text/plain": [ "((24, 9, 59), (24, 1, 59))" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 106 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:39:10.490130Z", "start_time": "2025-05-05T15:39:10.480235Z" } }, "cell_type": "code", "source": [ "# static variables\n", "Z = da.coords.to_dataset()[[\"split\", \"productivity_binary\"]].to_pandas()\n", "Z.head()" ], "id": "4dea0f23ccd1472d", "outputs": [ { "data": { "text/plain": [ " split productivity_binary department productivity_class \\\n", "ts_id \n", "finishing_1 train 1 finishing high \n", "finishing_10 train 0 finishing low \n", "finishing_11 test 1 finishing high \n", "finishing_12 train 1 finishing high \n", "finishing_2 train 1 finishing high \n", "\n", " productivity_numerical team \n", "ts_id \n", "finishing_1 0.812625 1 \n", "finishing_10 0.628333 10 \n", "finishing_11 0.874028 11 \n", "finishing_12 0.922840 12 \n", "finishing_2 0.819271 2 " ], "text/html": [ "
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splitproductivity_binarydepartmentproductivity_classproductivity_numericalteam
ts_id
finishing_1train1finishinghigh0.8126251
finishing_10train0finishinglow0.62833310
finishing_11test1finishinghigh0.87402811
finishing_12train1finishinghigh0.92284012
finishing_2train1finishinghigh0.8192712
\n", "
" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 107 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:39:14.702934Z", "start_time": "2025-05-05T15:39:14.700725Z" } }, "cell_type": "code", "source": [ "# target and split\n", "y, split = da.irr.get_task_target_and_split()" ], "id": "a43081e226cd2d5f", "outputs": [], "execution_count": 108 }, { "metadata": {}, "cell_type": "markdown", "source": "### Train-test split", "id": "241c7df51ad080be" }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:39:35.137375Z", "start_time": "2025-05-05T15:39:35.133270Z" } }, "cell_type": "code", "source": [ "X_train, X_test = X[split != \"test\"], X[split == \"test\"]\n", "y_train, y_test = y[split != \"test\"], y[split == \"test\"]\n", "X_train.shape, y_train.shape, X_test.shape, y_test.shape" ], "id": "f595181ed24af7b5", "outputs": [ { "data": { "text/plain": [ "((18, 9, 59), (18,), (6, 9, 59), (6,))" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 111 }, { "metadata": {}, "cell_type": "markdown", "source": [ "### Classification\n", "We have several ready-to-use classifiers in the `pyrregular` package. Be sure to install the required dependencies." ], "id": "729c5e348732b2c4" }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:43:13.588046Z", "start_time": "2025-05-05T15:43:13.585632Z" } }, "cell_type": "code", "source": "from pyrregular.models.rocket import rocket_pipeline", "id": "6ef40431b350e5ca", "outputs": [], "execution_count": 118 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-05T15:43:13.724642Z", "start_time": "2025-05-05T15:43:13.623585Z" } }, "cell_type": "code", "source": [ "%%time\n", "model = rocket_pipeline\n", "model.fit(X_train, y_train)\n", "model.score(X_test, y_test)" ], "id": "7ab8394bfc75e620", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[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.\n", "[LightGBM] [Info] Number of positive: 11, number of negative: 7\n", "[LightGBM] [Info] Total Bins 0\n", "[LightGBM] [Info] Number of data points in the train set: 18, number of used features: 0\n", "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.611111 -> initscore=0.451985\n", "[LightGBM] [Info] Start training from score 0.451985\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", "CPU times: user 93.1 ms, sys: 4.02 ms, total: 97.1 ms\n", "Wall time: 98.3 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/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.\n", " warn(msg, obj=self)\n", "/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.\n", " self._fit(X=X_inner, y=y_inner)\n", "/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.\n", " warnings.warn(\n", "/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.\n", " warn(msg, obj=self)\n", "/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.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "0.6666666666666666" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 119 } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }