Plots.ipynb

{
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  {
   "cell_type": "code",
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   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams.update({'font.size': 15})\n",
    "matplotlib.rcParams.update({'font.weight': 'bold'})\n",
    "from matplotlib import ticker\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "from MainFiles.Windowing import *"
   ],
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T10:21:41.384640400Z",
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  },
  {
   "cell_type": "markdown",
   "source": [
    "# 0. Introduction\n",
    "\n",
    "In this notebook we will generate the plots which show the performance of the extrapolation techniques across the extrapolation settings"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 1. Load the data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "            Count\nsize_train       \n16            245\n23            246\n32            246\n40              1\n45            244\n...           ...\n1048576         2\n1482910         1\n1486391         1\n2097152         1\n2205023         1\n\n[201 rows x 1 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Count</th>\n    </tr>\n    <tr>\n      <th>size_train</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>16</th>\n      <td>245</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>246</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>246</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>244</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1048576</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1482910</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1486391</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2097152</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2205023</th>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>201 rows × 1 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "win = Windowing(aggCurves)\n",
    "win.set_points([16,   23,   32,   45,   64,   91,  128,  181,  256,  362,  512,\n",
    "        724, 1024, 1448, 2048, 2896, 4096])\n",
    "\n",
    "win.Last()\n",
    "win.MDS()\n",
    "win.MDS_low_budget_binary() # Uses for loop instead of broadcasting to save on memory\n",
    "win.MMF(steps = 4000, lr = 0.01)\n"
   ],
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T10:28:16.501654500Z",
     "start_time": "2024-02-08T10:22:00.980473700Z"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "# 2. Method\n",
    "\n",
    "We want to compare how good each extrapolation technique is in the binary context and regression context. To do this we have 3 different performance metrics. 2 for the binary case and 1 for the regression case.\n",
    "In the regression case we simply take the absolute error. For the binary case we take a binary error, here the error will be 1 if the prediction is wrong and 0 if it is correct (as to which learner wins in the binary problem).\n",
    "Secondly, we also calculate the regret / risk, this is just: if the wrong learning algorithm would be picked what is the decrease in performance you would get."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Binary\n",
    "We start off with calculating the binary error and then the regret / risk"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
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  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# We make function so that it is easier to do for multiple different windows/targets\n",
    "# This function will perform the binary comparison for the given data\n",
    "def Binary(data):\n",
    "    # Which learner would win in the binary problem\n",
    "    predictions = (data[:,:,None] > data[:,None]).astype(float)\n",
    "\n",
    "    # keep nans\n",
    "    nans = np.isnan(data[:,:,None]) + np.isnan(data[:,None])\n",
    "    predictions[nans] = np.nan\n",
    "\n",
    "    # Make ties 0.5\n",
    "    predictions[data[:,:,None] == data[:,None]] = 0.5\n",
    "\n",
    "    # Don't compete against same learner\n",
    "    np.einsum('ijj...->ij...', predictions)[...] = np.nan\n",
    "\n",
    "    return predictions"
   ],
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     "name": "#%%\n"
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    "ExecuteTime": {
     "end_time": "2024-02-08T10:34:15.150261400Z",
     "start_time": "2024-02-08T10:34:15.124237500Z"
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "# We get the binary predictions\n",
    "MDSbinary = win.results['MDS']['binary']\n",
    "MDSregression = Binary(win.results['MDS']['regression'])\n",
    "MMF = Binary(win.results['MMF']['regression'])\n",
    "Last = Binary(win.results['Last']['regression'])\n",
    "actual = Binary(win.target)"
   ],
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     "end_time": "2024-02-08T10:34:17.428334500Z",
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  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "# Tie means no learning curve crossing thus we predict the largest last anchor\n",
    "# this is the same as using last\n",
    "MDSbinary[MDSbinary == 0.5] = Last[MDSbinary == 0.5]\n",
    "MDSregression[MDSregression == 0.5] = Last[MDSregression == 0.5]\n",
    "MMF[MMF == 0.5] = Last[MMF == 0.5]\n",
    "actual[actual == 0.5] = Last[actual == 0.5]\n",
    "# Because we round down with int(0.5) later, right wins by default"
   ],
   "metadata": {
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     "name": "#%%\n"
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     "end_time": "2024-02-08T10:34:18.716507800Z",
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  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "# All true values, so we flip it around, now left is right and right is left. This is because we have set 1 as left and 0 as right\n",
    "# in the binary setting, thus we flip it around here as numpy sees 1 as right and 0 as left\n",
    "true = np.stack([win.target[:,None] +0*win.target[:,:,None], win.target[:,None]*0 +win.target[:,:,None]])\n",
    "\n",
    "def regret_calc(dat):\n",
    "    # Indexes for the predictions\n",
    "    temp = np.copy(dat)\n",
    "    nans = np.isnan(temp)\n",
    "    temp[nans] = 0\n",
    "    temp = temp.astype(int)\n",
    "\n",
    "    # Indexes for the true values\n",
    "    temp2 = np.copy(actual)\n",
    "    nans2 = np.isnan(temp2)\n",
    "    temp2[nans2] = 0\n",
    "    temp2 = temp2.astype(int)\n",
    "\n",
    "    # regret\n",
    "    regret = np.squeeze(np.take_along_axis(true, temp2[None], axis=0) - np.take_along_axis(true, temp[None], axis=0))\n",
    "    regret[nans] = np.nan\n",
    "    regret[nans2] = np.nan\n",
    "\n",
    "    return regret"
   ],
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     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T10:34:21.116690700Z",
     "start_time": "2024-02-08T10:34:20.942532500Z"
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "# Regret for each method\n",
    "MDSregret = regret_calc(MDSbinary)\n",
    "MMFregret = regret_calc(MMF)\n",
    "Lastregret = regret_calc(Last)"
   ],
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     "name": "#%%\n"
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    "ExecuteTime": {
     "end_time": "2024-02-08T10:34:26.633712200Z",
     "start_time": "2024-02-08T10:34:22.973381500Z"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "## Regression\n",
    "We take the absolute error for the regression prediction objective"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "# since we don't have a nice heatmap function, we will do this by hand\n",
    "kMMF = win.results['MMF']['test error']\n",
    "kMDS = win.results['MDS']['test error']\n",
    "kLast = win.results['Last']['test error']"
   ],
   "metadata": {
    "collapsed": false,
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     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T10:34:31.596227800Z",
     "start_time": "2024-02-08T10:34:31.571205400Z"
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   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 3. Results"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Extrapolation on singular curve (used as example in paper)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "def PlotCurveAndPrediction(dataset, learner, window, target, scaled=True, legend=True):\n",
    "    '''\n",
    "    Given the learner, dataset and window this will return a scatter plot of the\n",
    "    actual curve, the MMF predicted curve, the k-nearest curves (scaled) and the\n",
    "    MDS prediction. It even plots the missing anchor points that are not included\n",
    "    when tensorizing the calculations.\n",
    "    '''\n",
    "    fig, ax = plt.subplots(1, 1, figsize=[6.4, 4.8])\n",
    "    datasetID, learnerID, windowID, targetID = win._returnIDs(dataset, learner, window, target)\n",
    "\n",
    "    if np.isnan(win.label[datasetID, learnerID, targetID]):\n",
    "        warnings.warn(\"Given curve has no value at target point. Predictions can still be made and shown \"\n",
    "                      \"but are not used in the error calculations.\", UserWarning)\n",
    "\n",
    "    stopID = np.where(win.points == win.target_anchors[targetID])[0][0]\n",
    "    startID = np.where(win.points == win.train_anchors[windowID])[0][0]\n",
    "    #ax.scatter(win.points[startID+1:stopID], win.dataframe[datasetID, learnerID, startID+1:stopID], color='blue', marker='x',\n",
    "    #           label='')\n",
    "\n",
    "    # Last One prediction\n",
    "    x = np.linspace(win.train_anchors[0], win.target_anchors[targetID], 10000)\n",
    "    ax.plot(x, np.full(x.shape, win.segment[datasetID, learnerID, windowID]), color='green',\n",
    "            label='Last')\n",
    "\n",
    "    # MMF prediction plot\n",
    "    a, b, c, d = win.results['MMF']['parameters'][datasetID, learnerID, 0, windowID]\n",
    "    fun = lambda x: (a * b + c * x ** d) / (b + x ** d)\n",
    "    ax.plot(x, fun(x), label='MMF')\n",
    "\n",
    "    # MDS regression k-nearest neighbors, also put the regularised points back in\n",
    "    knn = win.results['MDS']['k closest curves ID'][datasetID, :, learnerID, windowID, targetID]\n",
    "\n",
    "    for k in knn:\n",
    "        if scaled:\n",
    "            c = win.results['MDS']['scalar'][datasetID, k, learnerID, windowID]\n",
    "            ax.scatter(win.points[:stopID + 1], c * win.dataframe[k, learnerID, :stopID + 1], s=5)\n",
    "        else:\n",
    "            ax.scatter(win.points[:stopID + 1], win.dataframe[k, learnerID, :stopID + 1], s=5)\n",
    "\n",
    "    # MDS regression prediction\n",
    "    ax.scatter(win.target_anchors[targetID],\n",
    "               win.results['MDS']['regression'][datasetID, learnerID, windowID, targetID], color='red', marker='x',\n",
    "               label='MDS')\n",
    "\n",
    "    # set y axis and x axis name\n",
    "    ax.set_ylabel('Accuracy', fontsize=15, fontweight='bold')\n",
    "    ax.set_xlabel('Train set size', fontsize=15, fontweight='bold')\n",
    "\n",
    "    # Curve points, we also take the regularised points and put them back in to see\n",
    "    ax.scatter(win.train_anchors[:windowID + 1], win.segment[datasetID, learnerID][:windowID + 1], color='blue',\n",
    "               marker='o', label='Anchors in curve segment')\n",
    "    ax.scatter(win.target_anchors[targetID], win.label[datasetID, learnerID, targetID], color='red', label='Target anchor')\n",
    "\n",
    "    # make a title\n",
    "    ax.set_title('Dataset: {}, Learner: {}, Last anchor in curve segment: {}, Target anchor: {}'.format('kr-vs-kp',\n",
    "                                                                                     win.Slearners[learnerID],\n",
    "                                                                                          win.train_anchors[\n",
    "                                                                                              windowID],\n",
    "                                                                                          win.target_anchors[\n",
    "                                                                                              targetID]), fontweight='bold')\n",
    "\n",
    "    if legend:\n",
    "        ax.legend()"
   ],
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   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 460.8x345.6 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotCurveAndPrediction(0,3,8,12, legend = True)\n",
    "#plt.savefig('../Plots/Paper/predictions.pdf', dpi=300, bbox_inches='tight')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T10:34:41.447193100Z",
     "start_time": "2024-02-08T10:34:40.907701800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Aggregation 1 (Fixed curve segment and fixed target)\n",
    "We show this only for the binary prediction objective and only for the binary error."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\1501730530.py:9: RuntimeWarning: Mean of empty slice\n",
      "  MDS_vs_MMF = np.nanmean(np.abs(MDS_Agg1 - actual_Agg1) - np.abs(MMF_Agg1 - actual_Agg1), axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\1501730530.py:10: RuntimeWarning: Mean of empty slice\n",
      "  MDS_vs_Last = np.nanmean(np.abs(MDS_Agg1 - actual_Agg1) - np.abs(Last_Agg1 - actual_Agg1), axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\1501730530.py:11: RuntimeWarning: Mean of empty slice\n",
      "  MMF_vs_Last = np.nanmean(np.abs(MMF_Agg1 - actual_Agg1) - np.abs(Last_Agg1 - actual_Agg1), axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\1501730530.py:68: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 720x691.2 with 5 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Win = -5\n",
    "Target = -1\n",
    "\n",
    "MDS_Agg1 = MDSbinary[:,:,:,Win,Target]\n",
    "MMF_Agg1 = MMF[:,:,:,Win,Target]\n",
    "Last_Agg1 = Last[:,:,:,Win,Target]\n",
    "actual_Agg1 = actual[:,:,:,Win,Target]\n",
    "\n",
    "MDS_vs_MMF = np.nanmean(np.abs(MDS_Agg1 - actual_Agg1) - np.abs(MMF_Agg1 - actual_Agg1), axis=0)\n",
    "MDS_vs_Last = np.nanmean(np.abs(MDS_Agg1 - actual_Agg1) - np.abs(Last_Agg1 - actual_Agg1), axis=0)\n",
    "MMF_vs_Last = np.nanmean(np.abs(MMF_Agg1 - actual_Agg1) - np.abs(Last_Agg1 - actual_Agg1), axis=0)\n",
    "\n",
    "#diagonals to 0\n",
    "np.fill_diagonal(MDS_vs_MMF, 0)\n",
    "np.fill_diagonal(MDS_vs_Last, 0)\n",
    "np.fill_diagonal(MMF_vs_Last, 0)\n",
    "\n",
    "\n",
    "title = ('Last anchor in curve segment: {}, Target anchor: {}'.format(win.train_anchors[Win],win.target_anchors[Target]))\n",
    "fig, ax = plt.subplots(2, 2, figsize = (5*2,4.8*2))\n",
    "\n",
    "# make a new colorbar axis\n",
    "cbar_ax = fig.add_axes([1, .3, .03, .4])\n",
    "\n",
    "\n",
    "sns.heatmap(MDS_vs_MMF, vmin=-1, vmax=1, cmap='seismic', ax = ax[0,0], cbar_ax = None, cbar = False)\n",
    "ax[0,0].set_title('MDS vs MMF', fontsize=15, fontweight='bold')\n",
    "ax[0,0].tick_params(left=True, bottom=True)\n",
    "\n",
    "sns.heatmap(MDS_vs_Last, vmin=-1, vmax=1, cmap='seismic', ax = ax[0,1], cbar_ax = None, cbar = False)\n",
    "ax[0,1].set_title('MDS vs Last', fontsize=15, fontweight='bold')\n",
    "ax[0,1].tick_params(left=True, bottom=True)\n",
    "\n",
    "sns.heatmap(MMF_vs_Last, vmin=-1, vmax=1, cmap='seismic', ax = ax[1,0], cbar_ax=cbar_ax, cbar_kws={'label': 'Relative binary error', 'orientation': 'vertical', 'ticks': [-1, 0, 1]})\n",
    "ax[1,0].set_title('MMF vs Last', fontsize=15, fontweight='bold')\n",
    "ax[1,0].tick_params(left=True, bottom=True)\n",
    "cbar_ax.yaxis.label.set_size(15)\n",
    "cbar_ax.yaxis.label.set_weight('bold')\n",
    "cbar_ax.yaxis.label.set_rotation(270)\n",
    "\n",
    "# set tick labels\n",
    "ticks = np.arange(len(Slearners)) + 0.5\n",
    "#index = [np.arange(0, len(Slearners), 3).astype(int), np.arange(1, len(Slearners), 3), np.arange(2, len(Slearners), 3)]\n",
    "#index = [np.arange(0, len(Slearners),2), np.arange(1, len(Slearners), 2)]\n",
    "Slearners = np.array(Slearners)\n",
    "for i,id in enumerate([(0,0), (0,1), (1,0)]):\n",
    "    # ax[id].set_xticks(ticks[index[0]])\n",
    "    # ax[id].set_yticks(ticks[index[1]])\n",
    "    ax[id].set_yticks(ticks)\n",
    "    ax[id].set_xticks(ticks)\n",
    "    # ax[id].set_yticklabels(Slearners[index[0]], rotation=0, fontsize=12, fontweight='bold')\n",
    "    # ax[id].set_xticklabels(Slearners[index[1]], rotation=45, fontsize=12, fontweight='bold', ha='right')\n",
    "    ax[id].set_yticklabels(Slearners, rotation=0, fontsize=12, fontweight='bold')\n",
    "    ax[id].set_xticklabels(Slearners, rotation=45, fontsize=12, fontweight='bold', ha='right')\n",
    "\n",
    "\n",
    "bplots = [MDS_vs_MMF[~np.isnan(MDS_vs_MMF)].ravel(), MDS_vs_Last[~np.isnan(MDS_vs_Last)].ravel(), MMF_vs_Last[~np.isnan(MMF_vs_Last)].ravel()]\n",
    "ax[1,1].boxplot(bplots, labels=['MDS vs MMF', 'MDS vs Last', 'MMF vs Last'], showmeans=True, meanline=True)\n",
    "bp = ax[1,1].boxplot(bplots, labels=['MDS vs MMF', 'MDS vs Last', 'MMF vs Last'], showmeans=True, meanline=True)\n",
    "ax[1,1].axhline(0,linestyle='--')\n",
    "ax[1,1].set_ylim([-0.1,0.15])\n",
    "ax[1,1].set_title('Boxplots of mean relative binary errors', fontsize=15, fontweight='bold')\n",
    "\n",
    "# legend\n",
    "ax[1,1].legend([bp['medians'][0], bp['means'][0]], ['median', 'mean'], loc = (0.54,0.7))\n",
    "\n",
    "plt.suptitle(title, fontsize=20, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.savefig('Plots/Paper/Agg1.png', dpi=300, bbox_inches='tight')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T10:40:28.788986500Z",
     "start_time": "2024-02-08T10:40:26.119556900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Aggregation 2 (Moving curve segment and fixed target)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\2578957212.py:2: RuntimeWarning: Mean of empty slice\n",
      "  MDS_error = np.nanmean(np.abs(MDSbinary - actual),axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\2578957212.py:3: RuntimeWarning: Mean of empty slice\n",
      "  MMF_error = np.nanmean(np.abs(MMF - actual), axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\2578957212.py:4: RuntimeWarning: Mean of empty slice\n",
      "  Last_error = np.nanmean(np.abs(Last - actual), axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\2578957212.py:7: RuntimeWarning: Mean of empty slice\n",
      "  Mean_MDSregret = np.nanmean(MDSregret, axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\2578957212.py:8: RuntimeWarning: Mean of empty slice\n",
      "  Mean_MMFregret = np.nanmean(MMFregret, axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\2578957212.py:9: RuntimeWarning: Mean of empty slice\n",
      "  Mean_Lastregret = np.nanmean(Lastregret, axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\2578957212.py:12: RuntimeWarning: Mean of empty slice\n",
      "  KaveragesMMF = np.nanmean(kMMF, axis = 0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\2578957212.py:13: RuntimeWarning: Mean of empty slice\n",
      "  KaveragesMDS = np.nanmean(kMDS, axis = 0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\2578957212.py:14: RuntimeWarning: Mean of empty slice\n",
      "  KaveragesLast = np.nanmean(kLast, axis = 0)\n"
     ]
    }
   ],
   "source": [
    "# Get individual errors\n",
    "MDS_error = np.nanmean(np.abs(MDSbinary - actual),axis=0)\n",
    "MMF_error = np.nanmean(np.abs(MMF - actual), axis=0)\n",
    "Last_error = np.nanmean(np.abs(Last - actual), axis=0)\n",
    "\n",
    "# Same for regret / risk\n",
    "Mean_MDSregret = np.nanmean(MDSregret, axis=0)\n",
    "Mean_MMFregret = np.nanmean(MMFregret, axis=0)\n",
    "Mean_Lastregret = np.nanmean(Lastregret, axis=0)\n",
    "\n",
    "# Same for regression, absolute error\n",
    "KaveragesMMF = np.nanmean(kMMF, axis = 0)\n",
    "KaveragesMDS = np.nanmean(kMDS, axis = 0)\n",
    "KaveragesLast = np.nanmean(kLast, axis = 0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T10:40:51.376946Z",
     "start_time": "2024-02-08T10:40:50.062750600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\3996366466.py:2: RuntimeWarning: Mean of empty slice\n",
      "  MDSvsMMF = np.nanmean(np.abs(MDSbinary - actual) - np.abs(MMF - actual), axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\3996366466.py:3: RuntimeWarning: Mean of empty slice\n",
      "  MMFvsLast = np.nanmean(np.abs(MMF - actual) - np.abs(Last - actual), axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\3996366466.py:4: RuntimeWarning: Mean of empty slice\n",
      "  MDSvsLast = np.nanmean(np.abs(MDSbinary - actual) - np.abs(Last - actual), axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\3996366466.py:7: RuntimeWarning: Mean of empty slice\n",
      "  MDSregretvsMMFregret = np.nanmean(MDSregret - MMFregret, axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\3996366466.py:8: RuntimeWarning: Mean of empty slice\n",
      "  MMFregretvsLastregret = np.nanmean(MMFregret - Lastregret, axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\3996366466.py:9: RuntimeWarning: Mean of empty slice\n",
      "  MDSregretvsLastregret = np.nanmean(MDSregret - Lastregret, axis=0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\3996366466.py:13: RuntimeWarning: Mean of empty slice\n",
      "  KaveragesMDSvsMMF = np.nanmean(kMDS - kMMF, axis = 0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\3996366466.py:14: RuntimeWarning: Mean of empty slice\n",
      "  KaveragesMDSvsLast = np.nanmean(kMDS - kLast, axis = 0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_1832\\3996366466.py:15: RuntimeWarning: Mean of empty slice\n",
      "  KaveragesMMFvsLast = np.nanmean(kMMF - kLast, axis = 0)\n"
     ]
    }
   ],
   "source": [
    "# Get relative errors\n",
    "MDSvsMMF = np.nanmean(np.abs(MDSbinary - actual) - np.abs(MMF - actual), axis=0)\n",
    "MMFvsLast = np.nanmean(np.abs(MMF - actual) - np.abs(Last - actual), axis=0)\n",
    "MDSvsLast = np.nanmean(np.abs(MDSbinary - actual) - np.abs(Last - actual), axis=0)\n",
    "\n",
    "# Regret / risk\n",
    "MDSregretvsMMFregret = np.nanmean(MDSregret - MMFregret, axis=0)\n",
    "MMFregretvsLastregret = np.nanmean(MMFregret - Lastregret, axis=0)\n",
    "MDSregretvsLastregret = np.nanmean(MDSregret - Lastregret, axis=0)\n",
    "\n",
    "\n",
    "# Regression, absolute error\n",
    "KaveragesMDSvsMMF = np.nanmean(kMDS - kMMF, axis = 0)\n",
    "KaveragesMDSvsLast = np.nanmean(kMDS - kLast, axis = 0)\n",
    "KaveragesMMFvsLast = np.nanmean(kMMF - kLast, axis = 0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T10:40:54.022353800Z",
     "start_time": "2024-02-08T10:40:51.380950100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1036.8x259.2 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots = {}\n",
    "Target = -1\n",
    "if Target != -1:\n",
    "    ind = range(len(win.train_anchors))[:Target + 1]\n",
    "else:\n",
    "    ind = range(len(win.train_anchors))\n",
    "plots['MDS'] = {str(win.train_anchors[i]): Mean_MDSregret[..., i, Target] for i in ind}\n",
    "plots['MMF'] = {str(win.train_anchors[i]): Mean_MMFregret[..., i, Target] for i in ind}\n",
    "plots['Last'] = {str(win.train_anchors[i]): Mean_Lastregret[..., i, Target] for i in ind}\n",
    "\n",
    "# plots2 = {}\n",
    "# if Target != -1:\n",
    "#     ind = range(len(win.train_anchors))[:Target + 1]\n",
    "# else:\n",
    "#     ind = range(len(win.train_anchors))\n",
    "# plots2['MDS vs MMF'] = {str(win.train_anchors[i]): MDSregretvsMMFregret[..., i, Target] for i in ind}\n",
    "# plots2['MDS vs Last'] = {str(win.train_anchors[i]): MDSregretvsLastregret[..., i, Target] for i in ind}\n",
    "# plots2['MMF vs Last'] = {str(win.train_anchors[i]): MMFregretvsLastregret[..., i, Target] for i in ind}\n",
    "\n",
    "# ylim2 = (-0.02, 0.01)\n",
    "# hline2 = 0\n",
    "# legend_loc2 = 'lower right'\n",
    "\n",
    "title = 'Target anchor: {}'.format(win.target_anchors[Target])\n",
    "ylim = (-0.001, 0.03)\n",
    "figsize = (1.5*1.5, 1*1.5)\n",
    "xlabel = 'Last anchor in window'\n",
    "ylabel1 = 'Mean risk'\n",
    "Q1Q3 = True\n",
    "median = True\n",
    "save = 'Plots\\Paper\\Agg2.pdf'\n",
    "dpi = 300\n",
    "xrot = False\n",
    "col = ['black', 'red', 'blue', 'green']\n",
    "legend_loc = 'upper right'\n",
    "Q1Q3 = True\n",
    "median = True\n",
    "ls = '--'\n",
    "hline = 'N'\n",
    "\n",
    "plotlabels = list(plots.keys())\n",
    "# plotlabels2 = list(plots2.keys())\n",
    "w, h = *figsize,\n",
    "fig, ax = plt.subplots(1, 3, figsize=(6.4 * w, 2.4 * h))\n",
    "if ylim:\n",
    "    ax[0].set_ylim(*ylim)\n",
    "    # ax[1][0].set_ylim(*ylim2)\n",
    "#if ylabel:\n",
    "#    ax[0][0].set_ylabel(ylabel, fontsize=20, fontweight='bold')\n",
    "#    ax[1][0].set_ylabel(ylabel, fontsize=20, fontweight='bold')\n",
    "#if xlabel:\n",
    "#    ax[1][0].set_xlabel(xlabel, fontsize=20, fontweight='bold')\n",
    "if hline != 'N':\n",
    "    plt.axhline(hline, linestyle=ls)\n",
    "\n",
    "# ax[1][0].axhline(hline2, linestyle=ls)\n",
    "\n",
    "if title:\n",
    "    fig.suptitle(title, y=1, fontsize=20, fontweight='bold')\n",
    "\n",
    "n_dicts = len(plots)\n",
    "if type(plots[plotlabels[0]]) != dict:\n",
    "    n_dicts = 1\n",
    "for i in range(n_dicts):\n",
    "    c = col[i]\n",
    "    if n_dicts == 1:\n",
    "        labels, data = [*zip(*plots.items())]\n",
    "        # labels2, data2 = [*zip(*plots2.items())]\n",
    "    else:\n",
    "        labels, data = [*zip(*plots[plotlabels[i]].items())]\n",
    "        # labels2, data2 = [*zip(*plots2[plotlabels2[i]].items())]\n",
    "    data = [np.array(dat)[~np.isnan(np.array(dat))] for dat in data]\n",
    "    means = [np.mean(dat) for dat in data]\n",
    "\n",
    "    # data2 = [np.array(dat)[~np.isnan(np.array(dat))] for dat in data2]\n",
    "    # means2 = [np.mean(dat) for dat in data2]\n",
    "\n",
    "    ax[0].plot(means, c=c, label=plotlabels[i])\n",
    "    # ax[1][0].plot(means2, c=c, label=plotlabels2[i])\n",
    "    #dotted line\n",
    "    if median:\n",
    "        medians = [np.quantile(dat, 0.5) for dat in data]\n",
    "        # medians2 = [np.quantile(dat, 0.5) for dat in data2]\n",
    "\n",
    "        ax[0].plot(medians, c=c, linestyle='dotted')\n",
    "        # ax[1][0].plot(medians2, c=c, linestyle='dotted')\n",
    "    if Q1Q3:\n",
    "        q1s = [np.quantile(dat, 0.25) for dat in data]\n",
    "        q3s = [np.quantile(dat, 0.75) for dat in data]\n",
    "        # q1s2 = [np.quantile(dat, 0.25) for dat in data2]\n",
    "        # q3s2 = [np.quantile(dat, 0.75) for dat in data2]\n",
    "\n",
    "        ax[0].fill_between(range(len(labels)), q1s, q3s, alpha=0.2, color=c)\n",
    "        # ax[1][0].fill_between(range(len(labels)), q1s2, q3s2, alpha=0.2, color=c)\n",
    "pos = np.arange(0, len(labels))\n",
    "ax[0].set_xticks(pos)\n",
    "# ax[1][0].set_xticks(pos[1::2])\n",
    "if xrot:\n",
    "    ax.set_xticklabels(labels, rotation=45, ha='right')\n",
    "else:\n",
    "    ax[0].set_xticklabels(labels, rotation=90)\n",
    "    # ax[1][0].set_xticklabels(labels[1::2], rotation=45, ha='right')\n",
    "    ax[0].tick_params(left=True, bottom=True)\n",
    "    # ax[1][0].tick_params(left=True, bottom=True)\n",
    "if n_dicts != 1:\n",
    "    ax[0].legend(loc=legend_loc)\n",
    "    # ax[1][0].legend(loc=legend_loc2)\n",
    "\n",
    "\n",
    "#Plot 2\n",
    "plots = {}\n",
    "if Target != -1:\n",
    "    ind = range(len(win.train_anchors))[:Target+1]\n",
    "else:\n",
    "    ind = range(len(win.train_anchors))\n",
    "plots['MDS'] = {str(win.train_anchors[i]): MDS_error[...,i, Target] for i in ind}\n",
    "plots['MMF'] = {str(win.train_anchors[i]): MMF_error[...,i, Target] for i in ind}\n",
    "plots['Last'] = {str(win.train_anchors[i]): Last_error[...,i, Target] for i in ind}\n",
    "\n",
    "\n",
    "# plots2 = {}\n",
    "# if Target != -1:\n",
    "#     ind = range(len(win.train_anchors))[:Target+1]\n",
    "# else:\n",
    "#     ind = range(len(win.train_anchors))\n",
    "# plots2['MDS vs MMF'] = {str(win.train_anchors[i]): MDSvsMMF[...,i, Target] for i in ind}\n",
    "# plots2['MDS vs Last'] = {str(win.train_anchors[i]): MDSvsLast[...,i, Target] for i in ind}\n",
    "# plots2['MMF vs Last'] = {str(win.train_anchors[i]): MMFvsLast[...,i, Target] for i in ind}\n",
    "\n",
    "ylim = (-0.001, 0.5)\n",
    "# ylim2 = (-0.2,0.15)\n",
    "\n",
    "\n",
    "if ylim:\n",
    "    ax[1].set_ylim(*ylim)\n",
    "    # ax[1][1].set_ylim(*ylim2)\n",
    "#if ylabel:\n",
    "#    ax[0][0].set_ylabel(ylabel, fontsize=20, fontweight='bold')\n",
    "#    ax[1][0].set_ylabel(ylabel, fontsize=20, fontweight='bold')\n",
    "#if xlabel:\n",
    "#    ax[1][0].set_xlabel(xlabel, fontsize=20, fontweight='bold')\n",
    "if hline != 'N':\n",
    "    plt.axhline(hline, linestyle=ls)\n",
    "\n",
    "# ax[1][1].axhline(hline2, linestyle=ls)\n",
    "\n",
    "if title:\n",
    "    fig.suptitle(title, y=0.9, fontsize=20, fontweight='bold')\n",
    "\n",
    "n_dicts = len(plots)\n",
    "if type(plots[plotlabels[0]]) != dict:\n",
    "    n_dicts = 1\n",
    "for i in range(n_dicts):\n",
    "    c = col[i]\n",
    "    if n_dicts == 1:\n",
    "        labels, data = [*zip(*plots.items())]\n",
    "        # labels2, data2 = [*zip(*plots2.items())]\n",
    "    else:\n",
    "        labels, data = [*zip(*plots[plotlabels[i]].items())]\n",
    "        # labels2, data2 = [*zip(*plots2[plotlabels2[i]].items())]\n",
    "    data = [np.array(dat)[~np.isnan(np.array(dat))] for dat in data]\n",
    "    means = [np.mean(dat) for dat in data]\n",
    "\n",
    "    # data2 = [np.array(dat)[~np.isnan(np.array(dat))] for dat in data2]\n",
    "    # means2 = [np.mean(dat) for dat in data2]\n",
    "\n",
    "    ax[1].plot(means, c=c, label=plotlabels[i])\n",
    "    # ax[1][1].plot(means2, c=c, label=plotlabels2[i])\n",
    "    #dotted line\n",
    "    if median:\n",
    "        medians = [np.quantile(dat, 0.5) for dat in data]\n",
    "        # medians2 = [np.quantile(dat, 0.5) for dat in data2]\n",
    "\n",
    "        ax[1].plot(medians, c=c, linestyle='dotted')\n",
    "        # ax[1][1].plot(medians2, c=c, linestyle='dotted')\n",
    "    if Q1Q3:\n",
    "        q1s = [np.quantile(dat, 0.25) for dat in data]\n",
    "        q3s = [np.quantile(dat, 0.75) for dat in data]\n",
    "        # q1s2 = [np.quantile(dat, 0.25) for dat in data2]\n",
    "        # q3s2 = [np.quantile(dat, 0.75) for dat in data2]\n",
    "\n",
    "        ax[1].fill_between(range(len(labels)), q1s, q3s, alpha=0.2, color=c)\n",
    "        # ax[1][1].fill_between(range(len(labels)), q1s2, q3s2, alpha=0.2, color=c)\n",
    "pos = np.arange(0, len(labels))\n",
    "ax[1].set_xticks(pos)\n",
    "# ax[1][1].set_xticks(pos[1::2])\n",
    "if xrot:\n",
    "    ax.set_xticklabels(labels, rotation=45, ha='right')\n",
    "else:\n",
    "    ax[1].set_xticklabels(labels, rotation=90)\n",
    "    # ax[1][1].set_xticklabels(labels[1::2], rotation=45, ha='right')\n",
    "    # enable tick lines\n",
    "    ax[1].tick_params(left=True, bottom=True)\n",
    "    # ax[1][1].tick_params(left=True, bottom=True)\n",
    "if n_dicts != 1:\n",
    "    ax[1].legend(loc=legend_loc)\n",
    "    # ax[1][1].legend(loc=legend_loc2)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#Plot 3\n",
    "Target = -1\n",
    "\n",
    "plots = {}\n",
    "if Target != -1:\n",
    "    ind = range(len(win.train_anchors))[:Target+1]\n",
    "else:\n",
    "    ind = range(len(win.train_anchors))\n",
    "\n",
    "plots['MDS'] = {str(win.train_anchors[i]): KaveragesMDS[:,i,Target] for i in ind}\n",
    "plots['MMF'] = {str(win.train_anchors[i]): KaveragesMMF[:,i,Target] for i in ind}\n",
    "plots['Last'] = {str(win.train_anchors[i]): KaveragesLast[:,i,Target] for i in ind}\n",
    "\n",
    "\n",
    "# plots2 = {}\n",
    "# if Target != -1:\n",
    "#     ind = range(len(win.train_anchors))[:Target+1]\n",
    "# else:\n",
    "#     ind = range(len(win.train_anchors))\n",
    "# \n",
    "# plots2['MDS vs MMF'] = {str(win.train_anchors[i]): KaveragesMDSvsMMF[:,i,Target] for i in ind}\n",
    "# plots2['MDS vs Last'] = {str(win.train_anchors[i]): KaveragesMDSvsLast[:,i,Target] for i in ind}\n",
    "# plots2['MMF vs Last'] = {str(win.train_anchors[i]): KaveragesMMFvsLast[:,i,Target] for i in ind}\n",
    "\n",
    "\n",
    "# ylim2 = (-0.12,0.05)\n",
    "# hline2 = 0\n",
    "# legend_loc2 = 'lower right'\n",
    "\n",
    "ylim = (-0.01,0.3)\n",
    "xlabel='Last anchor in curve segment'\n",
    "ylabel2='Mean difference in absolute error'\n",
    "Q1Q3=True\n",
    "median=True\n",
    "legend_loc='upper right'\n",
    "ls='--'\n",
    "hline = 'N'\n",
    "\n",
    "\n",
    "plotlabels = list(plots.keys())\n",
    "# plotlabels2 = list(plots2.keys())\n",
    "if ylim:\n",
    "    ax[2].set_ylim(*ylim)\n",
    "    # ax[1][2].set_ylim(*ylim2)\n",
    "#if ylabel:\n",
    "#    ax[0][1].set_ylabel(ylabel, fontsize=15, fontweight='bold')\n",
    "#    ax[1][1].set_ylabel(ylabel, fontsize=15, fontweight='bold')\n",
    "#if xlabel:\n",
    "#    ax[1][1].set_xlabel(xlabel, fontsize=15, fontweight='bold')\n",
    "if hline != 'N':\n",
    "    plt.axhline(hline, linestyle=ls)\n",
    "\n",
    "# ax[1][2].axhline(hline2, linestyle=ls)\n",
    "\n",
    "n_dicts = len(plots)\n",
    "if type(plots[plotlabels[0]]) != dict:\n",
    "    n_dicts = 1\n",
    "for i in range(n_dicts):\n",
    "    c = col[i]\n",
    "    if n_dicts == 1:\n",
    "        labels, data = [*zip(*plots.items())]\n",
    "        # labels2, data2 = [*zip(*plots2.items())]\n",
    "    else:\n",
    "        labels, data = [*zip(*plots[plotlabels[i]].items())]\n",
    "        # labels2, data2 = [*zip(*plots2[plotlabels2[i]].items())]\n",
    "    data = [np.array(dat)[~np.isnan(np.array(dat))] for dat in data]\n",
    "    means = [np.mean(dat) for dat in data]\n",
    "\n",
    "    # data2 = [np.array(dat)[~np.isnan(np.array(dat))] for dat in data2]\n",
    "    # means2 = [np.mean(dat) for dat in data2]\n",
    "\n",
    "    ax[2].plot(means, c=c, label=plotlabels[i])\n",
    "    # ax[1][2].plot(means2, c=c, label=plotlabels2[i])\n",
    "    #dotted line\n",
    "    if median:\n",
    "        medians = [np.quantile(dat, 0.5) for dat in data]\n",
    "        # medians2 = [np.quantile(dat, 0.5) for dat in data2]\n",
    "\n",
    "        ax[2].plot(medians, c=c, linestyle='dotted')\n",
    "        # ax[1][2].plot(medians2, c=c, linestyle='dotted')\n",
    "    if Q1Q3:\n",
    "        q1s = [np.quantile(dat, 0.25) for dat in data]\n",
    "        q3s = [np.quantile(dat, 0.75) for dat in data]\n",
    "        # q1s2 = [np.quantile(dat, 0.25) for dat in data2]\n",
    "        # q3s2 = [np.quantile(dat, 0.75) for dat in data2]\n",
    "\n",
    "        ax[2].fill_between(range(len(labels)), q1s, q3s, alpha=0.2, color=c)\n",
    "        # ax[1][2].fill_between(range(len(labels)), q1s2, q3s2, alpha=0.2, color=c)\n",
    "pos = np.arange(0, len(labels))\n",
    "ax[2].set_xticks(pos)\n",
    "# ax[1][2].set_xticks(pos[1::2])\n",
    "if xrot:\n",
    "    ax.set_xticklabels(labels, rotation=45, ha='right')\n",
    "else:\n",
    "    ax[2].set_xticklabels(labels, rotation=90)\n",
    "    # ax[1][2].set_xticklabels(labels[1::2], rotation=45, ha='right')\n",
    "    #enable tick lines\n",
    "    ax[2].tick_params(bottom=True, left = True)\n",
    "    # ax[1][2].tick_params(bottom=True, left = True)\n",
    "if n_dicts != 1:\n",
    "    ax[2].legend(loc=legend_loc)\n",
    "    # ax[1][2].legend(loc=legend_loc2)\n",
    "\n",
    "\n",
    "#if save:\n",
    "fig.suptitle(title, y=0.90, fontsize=25, fontweight='bold')\n",
    "# ax[1][0].yaxis.set_major_locator(plt.MaxNLocator(3))\n",
    "\n",
    "# Common labels\n",
    "fig.text(0.5, -0.01, xlabel, ha='center', fontsize=20, fontweight='bold')\n",
    "#fig.text(0, 0.5, ylabel1, va='center', rotation='vertical', fontsize=20, fontweight='bold')\n",
    "#fig.text(0.99, 0.5, ylabel2, va='center', rotation=270, fontsize=20, fontweight='bold')\n",
    "\n",
    "# per plot labels\n",
    "ax[1].set_ylabel('Binary error', fontsize=20, fontweight='bold')\n",
    "# ax[1][1].set_ylabel('Relative binary error', fontsize=20, fontweight='bold')\n",
    "ax[0].set_ylabel('Risk', fontsize=20, fontweight='bold')\n",
    "# ax[1][0].set_ylabel('Relative risk', fontsize=20, fontweight='bold')\n",
    "ax[2].set_ylabel('Absolute error', fontsize=20, fontweight='bold')\n",
    "# ax[1][2].set_ylabel('Relative absolute error', fontsize=20, fontweight='bold')\n",
    "\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "# set yaxis labels to be 2 decimals\n",
    "ax[2].yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "if title:\n",
    "    fig.suptitle(title, y=0.93, fontsize=20, fontweight='bold')\n",
    "\n",
    "plt.savefig(save, dpi=dpi, bbox_inches='tight')\n",
    "\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T10:58:29.874537100Z",
     "start_time": "2024-02-08T10:58:28.538321700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Aggregation 3 (Moving curve segments and targets)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_19816\\109722519.py:2: RuntimeWarning: Mean of empty slice\n",
      "  averagesMDSvsMMF = np.nanmean(MDSvsMMF, axis=(0,1))\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_19816\\109722519.py:3: RuntimeWarning: Mean of empty slice\n",
      "  averagesMMFvsLast = np.nanmean(MMFvsLast, axis=(0,1))\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_19816\\109722519.py:4: RuntimeWarning: Mean of empty slice\n",
      "  averagesMDSvsLast = np.nanmean(MDSvsLast, axis=(0,1))\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_19816\\109722519.py:7: RuntimeWarning: Mean of empty slice\n",
      "  regretAveragesMDSvsMMF = np.nanmean(MDSregretvsMMFregret, axis=(0,1))\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_19816\\109722519.py:8: RuntimeWarning: Mean of empty slice\n",
      "  regretAveragesMDSvsLast = np.nanmean(MDSregretvsLastregret, axis=(0,1))\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_19816\\109722519.py:9: RuntimeWarning: Mean of empty slice\n",
      "  regretAveragesMMFvsLast = np.nanmean(MMFregretvsLastregret, axis=(0,1))\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_19816\\109722519.py:12: RuntimeWarning: Mean of empty slice\n",
      "  KmeanaveragesMDSvsMMF = np.nanmean(KaveragesMDSvsMMF, axis = 0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_19816\\109722519.py:13: RuntimeWarning: Mean of empty slice\n",
      "  KmeanaverageMDSvsLast = np.nanmean(KaveragesMDSvsLast, axis = 0)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_19816\\109722519.py:14: RuntimeWarning: Mean of empty slice\n",
      "  KmeanaveragesMMFvsLast = np.nanmean(KaveragesMMFvsLast, axis = 0)\n"
     ]
    }
   ],
   "source": [
    "# Averages for heatmap\n",
    "averagesMDSvsMMF = np.nanmean(MDSvsMMF, axis=(0,1))\n",
    "averagesMMFvsLast = np.nanmean(MMFvsLast, axis=(0,1))\n",
    "averagesMDSvsLast = np.nanmean(MDSvsLast, axis=(0,1))\n",
    "\n",
    "#For regret\n",
    "regretAveragesMDSvsMMF = np.nanmean(MDSregretvsMMFregret, axis=(0,1))\n",
    "regretAveragesMDSvsLast = np.nanmean(MDSregretvsLastregret, axis=(0,1))\n",
    "regretAveragesMMFvsLast = np.nanmean(MMFregretvsLastregret, axis=(0,1))\n",
    "\n",
    "# Regression, absolute error\n",
    "KmeanaveragesMDSvsMMF = np.nanmean(KaveragesMDSvsMMF, axis = 0)\n",
    "KmeanaverageMDSvsLast = np.nanmean(KaveragesMDSvsLast, axis = 0)\n",
    "KmeanaveragesMMFvsLast = np.nanmean(KaveragesMMFvsLast, axis = 0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_19816\\4248893756.py:102: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
      "  cb = fig.colorbar(ax[0][0].imshow(regretAveragesMDSvsMMF, cmap = 'coolwarm', vmin = -0.01, vmax=0.01, aspect='auto'), cax=cbar_ax)\n",
      "C:\\Users\\lkiel\\AppData\\Local\\Temp\\ipykernel_19816\\4248893756.py:121: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 691.2x691.2 with 11 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# w = 2\n",
    "# h = 3.5\n",
    "w, h = (1.5, 2)\n",
    "fig, ax = plt.subplots(3,3, figsize=(6.4 * w, 4.8 * h))\n",
    "\n",
    "\n",
    "a = ax[0][0].imshow(regretAveragesMDSvsMMF, cmap = 'coolwarm', vmin = -0.01, vmax = 0.01, aspect='auto')\n",
    "ax[1][0].imshow(regretAveragesMDSvsLast, cmap = 'coolwarm', vmin = -0.01, vmax = 0.01, aspect='auto')\n",
    "ax[2][0].imshow(regretAveragesMMFvsLast, cmap = 'coolwarm', vmin = -0.01, vmax = 0.01, aspect='auto')\n",
    "\n",
    "\n",
    "b = ax[0][1].imshow(averagesMDSvsMMF, cmap = 'coolwarm', vmin = -0.1, vmax = 0.1)\n",
    "ax[2][1].imshow(averagesMMFvsLast, cmap = 'coolwarm', vmin = -0.1, vmax = 0.1)\n",
    "ax[1][1].imshow(averagesMDSvsLast, cmap = 'coolwarm', vmin = -0.1, vmax = 0.1)\n",
    "\n",
    "# set title\n",
    "ax[0][0].set_title('MDS vs MMF', weight = 'bold', loc='right')\n",
    "ax[2][0].set_title('MMF vs Last', weight = 'bold', loc='right')\n",
    "ax[1][0].set_title('MDS vs Last', weight = 'bold', loc='right')\n",
    "\n",
    "ax[0][1].set_title('MDS vs MMF', weight = 'bold', loc='right')\n",
    "ax[2][1].set_title('MMF vs Last', weight = 'bold', loc='right')\n",
    "ax[1][1].set_title('MDS vs Last', weight = 'bold', loc='right')\n",
    "\n",
    "ax[0][2].set_title('MDS vs MMF', weight = 'bold', loc='right')\n",
    "ax[2][2].set_title('MMF vs Last', weight = 'bold', loc='right')\n",
    "ax[1][2].set_title('MDS vs Last', weight = 'bold', loc='right')\n",
    "\n",
    "c = ax[0][2].imshow(KmeanaveragesMDSvsMMF, cmap = 'coolwarm', vmin = -0.1, vmax = 0.1, aspect='auto')\n",
    "ax[1][2].imshow(KmeanaverageMDSvsLast, cmap = 'coolwarm', vmin = -0.1, vmax = 0.1, aspect='auto')\n",
    "ax[2][2].imshow(KmeanaveragesMMFvsLast, cmap = 'coolwarm', vmin = -0.1, vmax = 0.1, aspect='auto')\n",
    "\n",
    "# set (tick) labela\n",
    "sl = slice(0, len(win.target_anchors), 2)\n",
    "sl2 = slice(1, len(win.target_anchors), 2)\n",
    "\n",
    "for i in range(3):\n",
    "    ax[i][0].set_xticks(np.arange(len(win.target_anchors))[sl])\n",
    "    ax[i][0].set_yticks(np.arange(len(win.train_anchors))[sl])\n",
    "    ax[i][0].set_xticklabels(win.target_anchors[sl])\n",
    "    ax[i][0].set_yticklabels(win.train_anchors[sl])\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax[i][0].get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    ax[i][1].set_xticks(np.arange(len(win.target_anchors))[sl])\n",
    "    ax[i][1].set_yticks(np.arange(len(win.train_anchors))[sl])\n",
    "    ax[i][1].set_xticklabels(win.target_anchors[sl])\n",
    "    ax[i][1].set_yticklabels(win.train_anchors[sl])\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax[i][1].get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "\n",
    "    ax[i][2].set_xticks(np.arange(len(win.target_anchors))[sl2])\n",
    "    ax[i][2].set_yticks(np.arange(len(win.train_anchors))[sl])\n",
    "    ax[i][2].set_xticklabels(win.target_anchors[sl2])\n",
    "    ax[i][2].set_yticklabels(win.train_anchors[sl])\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax[i][2].get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "    # if i is 1 or 2\n",
    "    if i == 1 or i == 0:\n",
    "        ax[i][0].xaxis.set_ticklabels([])\n",
    "        ax[i][1].xaxis.set_ticklabels([])\n",
    "        ax[i][2].xaxis.set_ticklabels([])\n",
    "\n",
    "\n",
    "    if i == 1:\n",
    "        ax[i][2].set_yticks(np.arange(len(win.train_anchors))[sl2])\n",
    "        ax[i][2].set_yticklabels(win.train_anchors[sl2])\n",
    "        ax[i][0].set_yticks(np.arange(len(win.train_anchors))[sl2])\n",
    "        ax[i][0].set_yticklabels(win.train_anchors[sl2])\n",
    "        ax[i][1].set_yticks(np.arange(len(win.train_anchors))[sl2])\n",
    "        ax[i][1].set_yticklabels(win.train_anchors[sl2])\n",
    "\n",
    "    ax[i][1].yaxis.set_ticklabels([])\n",
    "    ax[i][2].yaxis.set_ticklabels([])\n",
    "\n",
    "# set colorbar for all subplots the same\n",
    "#fig.subplots_adjust(right=0.8)\n",
    "#cbar_ax = fig.add_axes([1.05, 0.15, 0.05, 0.7])\n",
    "#cb = fig.colorbar(ax[0][0].imshow(averagesMDSvsMMF, cmap = 'coolwarm', vmin = -0.01, vmax=0.01, aspect='auto'), cax=cbar_ax)\n",
    "#cb.set_label(label = 'Mean difference in binary / absolute error', weight = 'bold', labelpad=-90, size = 25)\n",
    "\n",
    "# dividers\n",
    "plots = [a,b,c]\n",
    "# for i in range(3):\n",
    "#     divider = make_axes_locatable(ax[1][i])\n",
    "#     cax = divider.append_axes('right', size='5%')\n",
    "#     cb = fig.colorbar(plots[i], cax=cax, orientation='vertical')\n",
    "#     tick_locator = ticker.MaxNLocator(nbins=4)\n",
    "#     cb.locator = tick_locator\n",
    "#     cb.formatter.set_powerlimits((0, 0))\n",
    "#     cb.update_ticks()\n",
    "\n",
    "fig.subplots_adjust(right=0.85)\n",
    "cbar_ax = fig.add_axes([1.1, 0.15, 0.05, 0.7])\n",
    "cb = fig.colorbar(ax[0][0].imshow(regretAveragesMDSvsMMF, cmap = 'coolwarm', vmin = -0.01, vmax=0.01, aspect='auto'), cax=cbar_ax)\n",
    "cb.set_label(label = 'Mean relative risk', weight = 'bold', labelpad=-90, size = 25)\n",
    "\n",
    "pos = cb.ax.get_position()\n",
    "cb.ax.set_aspect('auto')\n",
    "ax2 = cb.ax.twinx()\n",
    "ax2.set_ylim([-0.01*1000, 0.01*1000])\n",
    "ax2.yaxis.set_major_locator(plt.MaxNLocator(4))\n",
    "#ax2.yaxis.set_major_formatter(plt.set_powerlimits((0, 0)))\n",
    "\n",
    "tick_locator = ticker.MaxNLocator(nbins=4)\n",
    "cb.locator = tick_locator\n",
    "cb.update_ticks()\n",
    "\n",
    "cb.formatter.set_powerlimits((0, 0))\n",
    "\n",
    "fig.text(0.5, 0, \"Target anchor\", ha='center', fontsize=20, fontweight='bold')\n",
    "fig.text(0, 0.5, \"Last anchor in curve segment\", va='center', rotation='vertical', fontsize=20, fontweight='bold')\n",
    "fig.text(1.2, 0.5, \"Mean relative absolute / binary error\", va='center', rotation=90, fontsize=25, fontweight='bold')\n",
    "fig.tight_layout()\n",
    "path = '..\\Plots\\Paper\\Agg3.pdf'\n",
    "#plt.savefig(path, dpi=300, bbox_inches='tight')"
   ],
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