14.TSLearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tslearn.utils import to_time_series_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = to_time_series_dataset([[1, 2, 3, 4], [1, 2, 3], [2, 5, 6, 7, 8, 9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = [0, 0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1.],\n",
       "        [ 2.],\n",
       "        [ 3.],\n",
       "        [ 4.],\n",
       "        [nan],\n",
       "        [nan]],\n",
       "\n",
       "       [[ 1.],\n",
       "        [ 2.],\n",
       "        [ 3.],\n",
       "        [nan],\n",
       "        [nan],\n",
       "        [nan]],\n",
       "\n",
       "       [[ 2.],\n",
       "        [ 5.],\n",
       "        [ 6.],\n",
       "        [ 7.],\n",
       "        [ 8.],\n",
       "        [ 9.]]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 1]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tslearn.clustering import GlobalAlignmentKernelKMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gak_km = GlobalAlignmentKernelKMeans(n_clusters=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resumed because of empty cluster\n",
      "2.000 --> 2.000 --> \n"
     ]
    }
   ],
   "source": [
    "labels_gak = gak_km.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_gak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tslearn.clustering import TimeSeriesKMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = TimeSeriesKMeans(n_clusters=2, metric=\"dtw\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resumed because of empty cluster\n",
      "Resumed because of empty cluster\n",
      "Resumed because of empty cluster\n",
      "5.000 --> 1.056 --> 1.056 --> \n"
     ]
    }
   ],
   "source": [
    "labels = km.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "km_bis = TimeSeriesKMeans(n_clusters=2, metric=\"softdtw\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resumed because of empty cluster\n",
      "Resumed because of empty cluster\n",
      "Resumed because of empty cluster\n",
      "Resumed because of empty cluster\n",
      "Resumed because of empty cluster\n",
      "Resumed because of empty cluster\n",
      "58.996 --> 1.956 --> 1.956 --> 1.956 --> 1.956 --> \n"
     ]
    }
   ],
   "source": [
    "labels_bis = km_bis.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tslearn.clustering import TimeSeriesKMeans, silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = TimeSeriesKMeans(n_clusters=2, metric=\"dtw\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.000 --> 1.056 --> 1.056 --> \n"
     ]
    }
   ],
   "source": [
    "labels = km.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5875727602071013"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(X, labels, metric=\"dtw\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tslearn.barycenters import dtw_barycenter_averaging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar = dtw_barycenter_averaging(X, barycenter_size=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tslearn.barycenters import softdtw_barycenter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tslearn.utils import ts_zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_barycenter = ts_zeros(sz=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar = softdtw_barycenter(X, init=initial_barycenter)"
   ]
  }
 ],
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