{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time series interpolating with aeon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose we have a set of time series with different lengths, i.e. different number\n",
    "of time points. Currently, most of aeon's functionality requires equal-length time series, so to use aeon, we need to first converted our data into equal-length time series. In this tutorial, you will learn how to use the `TSInterpolator` to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-19T14:31:58.456171Z",
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     "iopub.status.idle": "2020-12-19T14:31:59.189497Z",
     "shell.execute_reply": "2020-12-19T14:31:59.190005Z"
    }
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   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from aeon.classification.convolution_based import RocketClassifier\n",
    "from aeon.datasets import load_basic_motions\n",
    "from aeon.transformations.compose import ColumnConcatenator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ordinary situation\n",
    "\n",
    "Here is a normal situation, when all time series have same length. We load an example\n",
    " data set from aeon and train a classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-19T14:31:59.194445Z",
     "iopub.status.busy": "2020-12-19T14:31:59.193903Z",
     "iopub.status.idle": "2020-12-19T14:32:01.019896Z",
     "shell.execute_reply": "2020-12-19T14:32:01.020463Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.25"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = load_basic_motions(return_type=\"nested_univ\")\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "\n",
    "steps = [\n",
    "    (\"concatenate\", ColumnConcatenator()),\n",
    "    (\"classify\", RocketClassifier()),\n",
    "]\n",
    "clf = Pipeline(steps)\n",
    "clf.fit(X_train, y_train)\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## If time series are unequal length, aeon's algorithm may raise an error\n",
    "\n",
    "Now we are going to spoil the data set a little bit by randomly cutting the time series. This leads to unequal-length time series. Consequently, we have an error while attempt to train a classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-19T14:32:01.026183Z",
     "iopub.status.busy": "2020-12-19T14:32:01.025650Z",
     "iopub.status.idle": "2020-12-19T14:32:01.239714Z",
     "shell.execute_reply": "2020-12-19T14:32:01.240542Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IndexError: Data seen by RocketClassifier instance has unequal length series, but this RocketClassifier instance cannot handle unequal length series. Calls with unequal length series may result in error or unreliable results.\n"
     ]
    }
   ],
   "source": [
    "from aeon.datasets import load_plaid\n",
    "\n",
    "X, y = load_plaid(return_type=\"nested_univ\")\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "\n",
    "try:\n",
    "    clf = RocketClassifier()\n",
    "    clf = Pipeline(steps)\n",
    "    clf.fit(X_train, y_train)\n",
    "    clf.score(X_test, y_test)\n",
    "except ValueError as e:\n",
    "    print(f\"ValueError: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now the interpolator enters\n",
    "Now we use our interpolator to resize time series of different lengths to user-defined length. Internally, it uses linear interpolation from scipy and draws equidistant samples on the user-defined number of points.\n",
    "\n",
    "After interpolating the data, the classifier works again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-19T14:32:01.245270Z",
     "iopub.status.busy": "2020-12-19T14:32:01.244733Z",
     "iopub.status.idle": "2020-12-19T14:32:02.911970Z",
     "shell.execute_reply": "2020-12-19T14:32:02.912833Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.7732342007434945"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from aeon.transformations.collection.interpolate import TSInterpolator\n",
    "\n",
    "steps = [\n",
    "    (\"transform\", TSInterpolator(50)),\n",
    "    (\"classify\", RocketClassifier()),\n",
    "]\n",
    "clf = Pipeline(steps)\n",
    "clf.fit(X_train, y_train)\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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