TimeSeries_Transform.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "from ipywidgets import FloatProgress\n",
    "from IPython.display import display\n",
    "\n",
    "from scipy.io import arff\n",
    "\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from collections import defaultdict\n",
    "\n",
    "\n",
    "from wildboar.tree import ShapeletTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "from wildboar._utils import print_tree\n",
    "\n",
    "from wildboar.distance import matches, distance\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "from tstransform.transform import NearestNeighbourLabelTransformer\n",
    "from tstransform.transform import IncrementalTreeLabelTransform\n",
    "from tstransform.transform import LockingIncrementalTreeLabelTransform\n",
    "\n",
    "from tstransform.evaluation import cost\n",
    "from tstransform.evaluation import differences\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rc('text', usetex=True)\n",
    "plt.style.use(['seaborn-white', 'seaborn-paper'])\n",
    "sns.set_palette(\"colorblind\")\n",
    "def set_size(fig, ax, width=4, height=1.9):\n",
    "    fig.set_size_inches(width, height)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    if isinstance(ax, list):\n",
    "        for a in ax:\n",
    "            sns.despine(ax=a, left=False)    \n",
    "    else:\n",
    "        sns.despine(ax=ax, left=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_labels(y):\n",
    "    tmp = defaultdict(list)\n",
    "    for i, label in enumerate(y):\n",
    "        tmp[label].append(i)\n",
    "\n",
    "    label_index = {label:np.array(arr) for label, arr in tmp.items()}\n",
    "    return dict(label_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_seed = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_arff_data(dataset_name, train_fraction=0.8, random_seed=10):\n",
    "    rnd = np.random.RandomState(random_seed)\n",
    "\n",
    "    data, meta = arff.loadarff(\n",
    "        \"TSC Problems/{0}/{0}.arff\".format(dataset_name))\n",
    "    df = pd.DataFrame(data)\n",
    "    x = df.iloc[:, :-1].values\n",
    "    y = df.iloc[:, -1].values.astype(int)\n",
    "\n",
    "    idx = np.arange(x.shape[0])\n",
    "    rnd.shuffle(idx)\n",
    "\n",
    "    train_size = round(x.shape[0] * train_fraction)\n",
    "\n",
    "    x_train = x[idx[:train_size], :]\n",
    "    y_train = y[idx[:train_size]]\n",
    "\n",
    "    x_test = x[idx[train_size:], :]\n",
    "    y_test = y[idx[train_size:]]\n",
    "\n",
    "    label_index = group_labels(y_test)\n",
    "    \n",
    "    return x_train, y_train, x_test, y_test, label_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train, x_test, y_test, label_index = load_arff_data(\"ECG200\", random_seed=random_seed, train_fraction=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_label = 1\n",
    "from_label = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = IncrementalTreeLabelTransform(\n",
    "    epsilon=1,\n",
    "    random_state=random_seed,\n",
    "    n_shapelets=100,\n",
    "    max_shapelet_size=0.3,\n",
    "    n_jobs=8,\n",
    "    batch_size=0.1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = NearestNeighbourLabelTransformer(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tstransform.transform.NearestNeighbourLabelTransformer at 0x7ff0820f5cc0>,\n",
       " KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "     n_clusters=46, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "     random_state=10, tol=0.0001, verbose=0))"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.fit(x_train, y_train, to_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 43, 31,  0, 18, 35, 13, 18, 25,  5,  0, 29, 11, 18,  6, 38,  2,\n",
       "       24,  4, 18, 39, 18, 42, 31, 26, 25, 15,  1, 19, 11, 10,  3, 42, 17,\n",
       "       42, 31,  1, 12,  3, 45,  8,  6, 22, 16,  1,  3, 16, 38,  5,  1, 12,\n",
       "       18, 15, 18, 20,  8, 18, 16, 32,  3, 12, 12, 26,  8,  1, 18,  5, 24,\n",
       "       18, 40, 19, 28, 32, 33,  8, 28,  1,  8,  8, 13,  9,  1,  3, 42, 17,\n",
       "       37, 18,  6, 21, 38, 25, 38,  0, 12,  1, 42, 10,  3, 12, 12, 38,  3,\n",
       "       27, 21, 26, 15, 31, 34,  3, 41, 12, 30, 34, 11,  7, 23, 26, 32,  3,\n",
       "       44, 41,  8, 18,  6, 42, 31, 15,  2, 18, 31,  8, 25, 14, 36, 26,  3,\n",
       "       12,  4, 22, 12], dtype=int32)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, -1,  1,  1,  1, -1, -1,  1, -1, -1,  1, -1,  1,  1, -1, -1,  1,\n",
       "       -1, -1,  1,  1,  1,  1,  1, -1, -1,  1, -1, -1,  1, -1,  1,  1, -1,\n",
       "        1,  1, -1,  1,  1, -1,  1, -1,  1,  1, -1,  1,  1,  1, -1,  1,  1,\n",
       "        1,  1,  1, -1,  1,  1,  1, -1,  1,  1,  1,  1,  1, -1,  1, -1, -1,\n",
       "        1, -1, -1,  1, -1, -1,  1,  1, -1,  1,  1, -1,  1, -1,  1,  1, -1,\n",
       "       -1,  1, -1, -1,  1, -1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,\n",
       "       -1, -1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1, -1,  1, -1, -1,  1,\n",
       "       -1,  1,  1,  1, -1,  1,  1, -1,  1,  1,  1,  1, -1, -1,  1, -1,  1,\n",
       "        1, -1, -1,  1])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff08251bb70>]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x316.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(b.cluster_centers_[8,:])\n",
    "plt.plot(x_train[0,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x316.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trf = t.transform(x_train[6, :].reshape(1, -1)).reshape(-1)\n",
    "plt.plot(trf)\n",
    "plt.plot(x_train[6, :])\n",
    "t.predict(trf.reshape(1, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = t.predict(x_test) == from_label\n",
    "x_from = x_test[cond]\n",
    "y_from = y_test[cond]\n",
    "x_prime = t.transform(x_from)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_from_ir = x_from\n",
    "x_prime_ir = x_prime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='auto', leaf_size=30, metric='euclidean',\n",
       "         metric_params=None, n_jobs=None, n_neighbors=1, p=2, radius=1.0)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh = NearestNeighbors(1, metric=\"euclidean\")\n",
    "neigh.fit(x_train[y_train == to_label])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n",
      "[[48]]\n",
      "Transformation cost    : 5.5306363902273485\n",
      "Transformation fraction: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x136.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx=np.random.randint(0, x_prime.shape[0], size=1)#\n",
    "print(idx)\n",
    "def plot_example(ax, x_i, min_x, closest, x_train, y_train, to_label, from_label, nn_x=None):\n",
    "    ax.plot(min_x, color=sns.xkcd_rgb[\"amber\"], label=\"$f(T')$={}\".format(\"Normal\"))\n",
    "    ax.plot(x_i, color=sns.xkcd_rgb[\"windows blue\"], label=\"$f(T)$={}\".format(\"Abnormal\"))\n",
    "    if nn_x is not None:\n",
    "        ax.plot(nn_x, \n",
    "                c=\"gray\", \n",
    "                dashes=[2, 2], \n",
    "                label=\"$NN_1(T,  D_{y=\" + str(\"Normal\") + \"})$\",\n",
    "        )\n",
    "\n",
    "closest = neigh.kneighbors(x_from[idx], return_distance=False)\n",
    "print(closest)\n",
    "nn_x = x_train[y_train == to_label][closest.reshape(-1)].reshape(-1)\n",
    "fig, ax = plt.subplots(1)\n",
    "plot_example(ax, \n",
    "             x_from[idx].reshape(-1), \n",
    "             x_prime[idx].reshape(-1), \n",
    "             closest, \n",
    "             x_train, \n",
    "             y_train, \n",
    "             to_label, \n",
    "             from_label, \n",
    "             None)\n",
    "#plot_example(ax, x_from[idx], nn_x_prime[idx], closest, x_train, y_train, to_label, from_label)\n",
    "plt.legend(loc=1)\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Voltage\")\n",
    "#plt.ylim([-3, 6])\n",
    "set_size(fig, ax)\n",
    "#plt.title(\"SE transform (cost: {:.2f}, %-change: {:.2f})\")\n",
    "plt.savefig(\"figure/ecg_200_motivation.pdf\")\n",
    "print(\"Transformation cost    :\", cost(x_prime[idx].reshape(1, -1), x_from[idx].reshape(1, -1)))\n",
    "print(\"Transformation fraction:\", differences(x_prime[idx], x_from[idx], axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation cost    : 1.0190504504710192\n",
      "Transformation fraction: 0.15625\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx=1 # change me\n",
    "closest = neigh.kneighbors(x_from[idx].reshape(1, -1), return_distance=False)\n",
    "fig, [ax_1, ax_2, ax_3] = plt.subplots(3, sharex=True)\n",
    "plot_example(ax_2, x_from[idx], x_prime[idx], closest, x_train, y_train, to_label, from_label)\n",
    "plot_example(ax_1, x_from_ir[idx], x_prime_ir[idx], closest, x_train, y_train, to_label, from_label)\n",
    "#plot_example(ax, x_from[idx], nn_x_prime[idx], closest, x_train, y_train, to_label, from_label)\n",
    "ax_1.legend()\n",
    "ax_2.legend()\n",
    "#ax_1.set_xlabel(\"Time\")\n",
    "#ax_1.set_ylabel(\"Spectral density\")\n",
    "ax_1.set_title(\"Reversible tweaking ($\\tau_{RT}$)\")\n",
    "ax_2.set_title(\"Irreversible tweaking ($\\tau_{IRT}$)\")\n",
    "ax_3.plot(np.mean(x_test[y_test==to_label], axis=0), color=sns.xkcd_rgb[\"amber\"], label=\"$\\\\mu_{y=\"+str(to_label)+\"}$\")\n",
    "ax_3.plot(np.mean(x_test[y_test==from_label], axis=0),  color=sns.xkcd_rgb[\"windows blue\"], label=\"$\\\\mu_{y=\"+str(from_label)+\"}$\")\n",
    "#ax_3.plot(np.mean(x_prime_ir, axis=0),  color=sns.xkcd_rgb[\"blue\"], label=\"$\\\\mu_{y=\"+str(from_label)+\"}$\")\n",
    "#ax_3.plot(np.nanmean(x_prime, axis=0),  color=sns.xkcd_rgb[\"green\"], label=\"$\\\\mu_{y=\"+str(from_label)+\"}$\")\n",
    "ax_3.set_title(\"Average per class\")\n",
    "ax_3.set_xlabel(\"Time\")\n",
    "ax_3.legend()\n",
    "#ax_2.set_ylabel(\"Spectral density\")\n",
    "set_size(fig, [ax_1, ax_2, ax_3], height=3)\n",
    "#plt.title(\"SE transform (cost: {:.2f}, %-change: {:.2f})\")\n",
    "plt.savefig(\"figure/example_transform_insect.pdf\")\n",
    "print(\"Transformation cost    :\", cost(x_prime[idx].reshape(1, -1), x_from[idx].reshape(1, -1)))\n",
    "print(\"Transformation fraction:\", differences(x_prime[idx], x_from[idx], axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "query data dimension must match training data dimension",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-b4050ba8c875>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m         )\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mclosest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mneigh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkneighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_prime\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_distance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mplot_example\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_from\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_prime\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrom_label\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/tests/lib/python3.7/site-packages/sklearn/neighbors/base.py\u001b[0m in \u001b[0;36mkneighbors\u001b[0;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m    441\u001b[0m                 delayed_query(\n\u001b[1;32m    442\u001b[0m                     X[s], n_neighbors, return_distance)\n\u001b[0;32m--> 443\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen_even_slices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    444\u001b[0m             )\n\u001b[1;32m    445\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/tests/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    981\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 983\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    984\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/tests/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    823\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 825\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    826\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/tests/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 782\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    783\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    784\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/tests/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/tests/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    543\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/tests/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 261\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/tests/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 261\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32msklearn/neighbors/binary_tree.pxi\u001b[0m in \u001b[0;36msklearn.neighbors.kd_tree.BinaryTree.query\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: query data dimension must match training data dimension"
     ]
    }
   ],
   "source": [
    "def plot_example(ax, x_i, min_x, closest, x_train, y_train, to_label, from_label):\n",
    "    ax.plot(min_x, color=sns.xkcd_rgb[\"amber\"], label=\"$f(T')={}$\".format(to_label))\n",
    "    ax.plot(x_i, color=sns.xkcd_rgb[\"windows blue\"], label=\"$f(T)={}$\".format(from_label))\n",
    "    for i in range(closest.shape[1]):\n",
    "        ax.plot(x_train[y_train == to_label][closest[0, i]], \n",
    "                c=\"gray\", \n",
    "                dashes=[2, 2], \n",
    "                label=\"$NN_1(T',  D_{y=\" + str(to_label) + \"})$\",\n",
    "        )\n",
    "\n",
    "closest = neigh.kneighbors(x_prime[idx].reshape(1, -1), return_distance=False)\n",
    "fig, ax = plt.subplots(1)\n",
    "plot_example(ax, x_from[idx], x_prime[idx], closest, x_train, y_train, to_label, from_label)\n",
    "plt.legend()\n",
    "set_size(fig, ax)\n",
    "#plt.title(\"SE transform (cost: {:.2f}, %-change: {:.2f})\")\n",
    "plt.savefig(\"figure/gun_point_example.pdf\")\n",
    "print(\"Transformation cost    :\", cost(x_prime[idx].reshape(1, -1), x_from[idx].reshape(1, -1)))\n",
    "print(\"Transformation fraction:\", differences(x_prime[idx], x_from[idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shape_transform_(s, x, i, theta):\n",
    "    \"\"\"\n",
    "    :param s: the shapelet\n",
    "    :param x: the time series\n",
    "    :param i: the matching location\n",
    "    :param theta: the desired distance\n",
    "    :returns: the shapelet with `theta`-distance\n",
    "    \"\"\"\n",
    "    x_match = x[i:(i + s.shape[0])]\n",
    "    v = (x_match - s) \n",
    "    norm_v = np.linalg.norm(v)\n",
    "    if norm_v == 0:\n",
    "        x_match = np.random.uniform(s.shape)\n",
    "        v = (x_match - s)\n",
    "        norm_v = np.linalg.norm(v)\n",
    "\n",
    "    return s + v / norm_v * theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x158.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def circ(r):\n",
    "    x = np.linspace(0, 2*np.pi, 100)\n",
    "    return r*np.cos(x), r*np.sin(x)\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "#if s.shape[0] == 2:\n",
    "plt.plot(*(np.vstack(circ(theta))+s.reshape(-1, 1)), color=sns.xkcd_rgb[\"warm grey\"], dashes=[2,2], label=\"$\\\\theta$\")\n",
    "#elif s.shape[0] == 1:\n",
    "#plt.plot(np.arange(-theta, theta), np.zeros(theta*2))\n",
    "#else:\n",
    "#    raise ValueError()\n",
    "plt.scatter(*s, label=\"$S$\", color=sns.xkcd_rgb[\"amber\"])\n",
    "plt.annotate(\"$S=[{}]$\".format(\",\".join(map(str, s))), xy=s+0.05)\n",
    "plt.scatter(*t, label=\"$\\\\mathbf{S}$\", color=sns.xkcd_rgb[\"windows blue\"])\n",
    "plt.annotate(\"$\\\\mathbf{{S}}=[{}]$\".format(\",\".join(map(str, t))), xy=t+0.05)\n",
    "plt.scatter(*p, label=\"$T'$\", color=sns.xkcd_rgb[\"faded green\"])\n",
    "plt.annotate(\"$\\\\mathcal{{S}}'=[{}]$\".format(\",\".join(map(lambda ss: \"{:.2f}\".format(ss), p))), xy=p+0.05)\n",
    "plt.legend(loc=4)\n",
    "plt.ylim([-1, 1.5])\n",
    "plt.xlim([0.3, 3.2])\n",
    "set_size(fig, ax, width=2.5, height=2.2)\n",
    "sns.despine(fig, ax, top=False, right=False)\n",
    "plt.savefig(\"figure/example_move.pdf\")\n",
    "#plt.xlim([-theta*2, theta*2])\n",
    "#plt.ylim([-theta*2, theta*2])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}