UCR_Time_Series_Classification_Univariate_Datasets.ipynb

Multivariate Time Series Classification - LSST.ipynb

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      "source": "# UEA & UCR Time Series Classification Multivariate Datasets*: LSST\n\n*A. Bagnall, J. Lines, W. Vickers and E. Keogh, The UEA & UCR Time Series Classification Repository,\nwww.timeseriesclassification.com"
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    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Import libraries"
    },
    {
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      "cell_type": "code",
      "source": "%reload_ext autoreload\n%autoreload 2\n%matplotlib inline",
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          "end_time": "2018-11-28T12:05:13.972801Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from fastai import *\nfrom fastai.vision import *\nimport fastai\nfastai.__version__",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "'1.0.28'"
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          "metadata": {}
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          "end_time": "2018-11-28T12:05:13.998554Z"
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        "trusted": true
      },
      "cell_type": "code",
      "source": "import warnings\nwarnings.filterwarnings(\"ignore\")",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2018-11-28T12:05:14.001000Z",
          "end_time": "2018-11-28T12:05:14.069727Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from tslearn.datasets import extract_from_zip_url\nfrom scipy.io import arff",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Prepare time series data"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2018-11-28T12:05:15.165526Z",
          "end_time": "2018-11-28T12:05:15.191580Z"
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        "trusted": true
      },
      "cell_type": "code",
      "source": "source_dir = 'http://www.timeseriesclassification.com/Downloads/'\ntarget_dir='my_data/Downloads/'\nSEL_DATASET = 'LSST'",
      "execution_count": 5,
      "outputs": []
    },
    {
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          "start_time": "2018-11-28T12:05:16.133809Z",
          "end_time": "2018-11-28T12:05:19.558812Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "extract_from_zip_url(\n    source_dir + SEL_DATASET + '.zip',\n    target_dir=target_dir + SEL_DATASET,\n    verbose=True)",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Successfully extracted file /tmp/tmp6t_001ja/LSST.zip to path my_data/Downloads/LSST\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "'my_data/Downloads/LSST'"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2018-11-28T11:44:35.863131Z",
          "end_time": "2018-11-28T11:44:35.885674Z"
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      },
      "cell_type": "markdown",
      "source": "There are 6 time series per sample"
    },
    {
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          "start_time": "2018-11-28T12:05:26.229914Z",
          "end_time": "2018-11-28T12:05:26.830825Z"
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      },
      "cell_type": "code",
      "source": "train_data_dict = {}\ntest_data_dict = {}\nfor i in range(6):\n    train_data_dict[i] = pd.DataFrame(\n        arff.loadarff('my_data/Downloads/LSST/LSSTDimension' + str(i + 1) +\n                      '_TRAIN.arff')[0])\n    test_data_dict[i] = pd.DataFrame(\n        arff.loadarff('my_data/Downloads/LSST/LSSTDimension' + str(i + 1) +\n                      '_TEST.arff')[0])",
      "execution_count": 7,
      "outputs": []
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      },
      "cell_type": "code",
      "source": "classes = np.sort(np.unique(train_data_dict[0].iloc[:, -1].values.astype(int)))\nnb_classes = len(classes)\nnb_classes, classes",
      "execution_count": 9,
      "outputs": [
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          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "(14, array([ 6, 15, 16, 42, 52, 53, 62, 64, 65, 67, 88, 90, 92, 95]))"
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