argo_clean.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Argoverse Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following tests contain the evaluation of Explainable Boosting Machines (EBM) on [Argoverse](https://www.argoverse.org/av2.html#forecasting-link) motion forcasting dataset. \n",
    "Interestingly, without dense road network data, the model give 3.88 m of final error for 6 modes (comparing with state-of-the-art of around 1 m).\n",
    "\n",
    "The code assume existance of argoverse dataset files extracted in a neighbour folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import glob,os\n",
    "from pathlib import Path\n",
    "from scipy import stats\n",
    "\n",
    "#from pygam import LinearGAM, s, f,l,te, ExpectileGAM\n",
    "\n",
    "from multiprocessing import Pool\n",
    "from av2.map.map_api import ArgoverseStaticMap\n",
    "\n",
    "from interpret import show\n",
    "from interpret.glassbox import ExplainableBoostingRegressor\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load files\n",
    "\n",
    "Format is\n",
    " 4 x, 4y, 5 in_vx, 5 in_vy, h,t, 4 ttc, 6 out_x, 6 out_y, (24*2: (xy pairs) map)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "XsYsval = np.load('XsYs_pet_gc_val_v1.npy') # X,84\n",
    "# 4 x, 4y, 5 in_vx, 5 in_vy, h,t, 4 ttc, 6 out_x, 6 out_y, (24*2: (xy pairs) map)\n",
    "Ys_all_val = XsYsval[:,24:36]\n",
    "Ys_val = Ys_all_val[:,[5,11]]\n",
    "Xs_val = XsYsval[:,:24] # no maps\n",
    "Xs_maps_val = XsYsval[:,36:] \n",
    "\n",
    "XsYs = np.load('XsYs_pet_gc_train_v1.npy') # X,84\n",
    "# 4 x, 4y, 5 in_vx, 5 in_vy, h,t, 4 ttc, 6 out_x, 6 out_y, (24*2: (xy pairs) map)\n",
    "Ys_all = XsYs[:,24:36]\n",
    "Ys = Ys_all[:,[5,11]]\n",
    "Xs = XsYs[:,:24] # no maps\n",
    "Xs_maps = XsYs[:,36:] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "maps_cnts = np.array([[-5.14150e+01,  2.00000e-02],\n",
    "       [-5.48700e+01, -2.30200e+01],\n",
    "       [-1.81400e+01,  5.16500e+01],\n",
    "       [ 1.21150e+01,  0.00000e+00],\n",
    "       [ 1.21150e+01, -3.26650e+01],\n",
    "       [ 1.21200e+01,  3.71950e+01],\n",
    "       [ 2.10650e+01,  0.00000e+00],\n",
    "       [ 2.10650e+01, -3.95800e+01],\n",
    "       [ 2.10650e+01,  3.58100e+01],\n",
    "       [ 3.11850e+01,  5.00000e-03],\n",
    "       [ 3.11850e+01, -3.08000e+01],\n",
    "       [ 3.11850e+01,  3.78950e+01],\n",
    "       [ 4.18200e+01, -1.00000e-02],\n",
    "       [ 4.18250e+01, -3.32950e+01],\n",
    "       [ 4.18250e+01,  3.52200e+01],\n",
    "       [ 5.26450e+01,  0.00000e+00],\n",
    "       [ 5.26350e+01, -3.17000e+01],\n",
    "       [ 5.26450e+01,  3.03650e+01],\n",
    "       [ 6.51250e+01, -5.00000e-03],\n",
    "       [ 6.51300e+01, -2.66250e+01],\n",
    "       [ 6.51300e+01,  2.76250e+01],\n",
    "       [ 1.11845e+02,  0.00000e+00],\n",
    "       [ 1.09440e+02, -3.22350e+01],\n",
    "       [ 1.05120e+02,  2.80750e+01]]).flatten()\n",
    "\n",
    "Xs_maps[:,:][(Xs_maps[:,:]==maps_cnts)] = 0\n",
    "Xs_maps_val[:,:][(Xs_maps_val[:,:]==maps_cnts)] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rotate all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "c, s_ = np.cos(Xs[:,18]), np.sin(Xs[:,18])\n",
    "R_mat = np.array([[c, s_], \n",
    "                  [-s_, c]]).transpose(2,0,1)\n",
    "#tranform\n",
    "Ys = (np.einsum('BNi,Bi ->BN', R_mat, Ys))\n",
    "for i in range(4):\n",
    "    Xs[:,[i,i+4]] = np.einsum('BNi,Bi ->BN', R_mat, Xs[:,[i,i+4]])\n",
    "    Xs[:,[i+8,i+13]] = np.einsum('BNi,Bi ->BN', R_mat, Xs[:,[i+8,i+13]])\n",
    "    \n",
    "Xs[:,[12,17]] = np.einsum('BNi,Bi ->BN', R_mat, Xs[:,[12,17]])\n",
    "\n",
    "\n",
    "c, s_ = np.cos(Xs_val[:,18]), np.sin(Xs_val[:,18])\n",
    "R_mat = np.array([[c, s_], [-s_, c]]).transpose(2,0,1)\n",
    "Ys_val = (np.einsum('BNi,Bi ->BN', R_mat, Ys_val))\n",
    "for i in [0,1,2,3]:\n",
    "    Xs_val[:,[i,i+4]] = np.einsum('BNi,Bi ->BN', R_mat, Xs_val[:,[i,i+4]])\n",
    "    Xs_val[:,[i+8,i+13]] = np.einsum('BNi,Bi ->BN', R_mat, Xs_val[:,[i+8,i+13]])\n",
    "# speed\n",
    "Xs_val[:,[12,17]] = np.einsum('BNi,Bi ->BN', R_mat, Xs_val[:,[12,17]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get vehicles only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vehciles percentage: 88.41066890769754%\n"
     ]
    }
   ],
   "source": [
    "#types_dict = {'VEHICLE':4,'PEDESTRIAN':0,'MOTORCYCLIST':2,'CYCLIST':1,'BUS':3}\n",
    "# {88.41, 6.42 :1.5, 1.3:4.5, 0.51:9, 3.2:8.13}\n",
    "type_ = 4\n",
    "mask_v = (Xs_val[:,19]==type_)\n",
    "XsYsval = XsYsval[mask_v]\n",
    "Ys_val = Ys_val[mask_v]\n",
    "Xs_val = Xs_val[mask_v] # no maps\n",
    "Xs_maps_val = Xs_maps_val[mask_v] \n",
    "\n",
    "\n",
    "mask_v = (Xs[:,19]==type_)\n",
    "print(f'vehciles percentage: {100*sum(mask_v)/XsYs.shape[0]}%')\n",
    "XsYs = XsYs[mask_v]\n",
    "Ys = Ys[mask_v]\n",
    "Xs = Xs[mask_v] # no maps\n",
    "Xs_maps = Xs_maps[mask_v] \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show label data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "mask_outliers = (Ys[:,1]<75)\n",
    "Ys = Ys[mask_outliers]\n",
    "Xs = Xs[mask_outliers]\n",
    "Xs_maps = Xs_maps[mask_outliers]\n",
    "#Ys_p = Ys[kmeans.predict(Ys)==4]\n",
    "plt.scatter(Ys[:,0],Ys[:,1],marker='.',linewidths=0.01)\n",
    "plt.show()\n",
    "# Dense modes X: [0,100]\n",
    "# Dense modes Y: [-50,50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single GAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ys_masked = Ys.copy()\n",
    "Xs_masked = np.hstack((Xs,Xs_maps))\n",
    "Ys_val_masked = Ys_val.copy()\n",
    "Xs_val_masked = np.hstack((Xs_val,Xs_maps_val))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "use_kmeans = False\n",
    "n_modes = 24\n",
    "if use_kmeans:\n",
    "\n",
    "    clusterer = KMeans(n_clusters=n_modes,random_state=42).fit(Ys_masked[:,:2])#,random_state=42,n_init=10\n",
    "    sub_modes = []\n",
    "    for i in range(n_modes):\n",
    "        cluster_i = (clusterer.labels_==i)\n",
    "        #print(sum(cluster_i))\n",
    "        #if (sum(cluster_i))>1000:\n",
    "        sub_modes.append(Ys_masked[cluster_i])\n",
    "        color = np.array([[np.random.rand() for _  in range(3)]])\n",
    "        plt.scatter(Ys_masked[cluster_i][:,0],Ys_masked[cluster_i][:,1],c=color,marker='.',linewidths=0.001)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_x = np.array([20.775646305106598,\n",
    " 20.93104028609399,\n",
    " 21.05146860312531,\n",
    " 21.843791411066185,\n",
    " 21.074766417950574,\n",
    " 21.016570779069,\n",
    " 21.917447223980943,\n",
    " 20.708498358123844,\n",
    " 21.086716388645584,\n",
    " 21.70619309361271,\n",
    " 21.285708965509258,\n",
    " 21.584203288536738,\n",
    " 21.44216998170091,\n",
    " 21.986344089961726,\n",
    " 21.929598997667892,\n",
    " 20.694559735173268,\n",
    " 22.24303021435951,\n",
    " 21.83491598861273,\n",
    " 19.1960089011812,\n",
    " 22.08437113394659,\n",
    " 21.235146703698785,\n",
    " 17.55908379095713,\n",
    " 19.95352114876765,\n",
    " 19.059908422509988])\n",
    "\n",
    "map_x -= map_x.min()\n",
    "map_x /= map_x.max()\n",
    "map_y = np.array([4.68457705, 4.62759123, 4.69970082, 4.48792953, 4.65558691,\n",
    "       4.6761963 , 4.41020033, 4.65050579, 4.57087074, 4.3774534 ,\n",
    "       4.57045138, 4.47114705, 4.36377177, 4.54395602, 4.44304642,\n",
    "       4.35202836, 4.49518266, 4.40383455, 4.34288804, 4.45750773,\n",
    "       4.37139527, 4.36040509, 4.47675049, 4.36771985])\n",
    "map_y -= map_y.min()\n",
    "map_y /= map_y.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yasin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\interpret\\glassbox\\_ebm\\_json.py:407: UserWarning:\n",
      "\n",
      "JSON formats are in beta. The JSON format may change in a future version without compatibility between releases.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "f_names = ['x0','x1','x2','x3','y0','y1','y2','y3',\n",
    "           'vx0','vx1','vx2','vx3','vx4','vy0','vy1','vy2','vy3','vy4',\n",
    "           'heading','type','poc_left','poc_front','poc_right','poc_back']\n",
    "\n",
    "for i in range(24):\n",
    "    f_names.extend([f'mode{i}x',f'mode{i}y'])\n",
    "\n",
    "file_name_x = 'gam_allx'\n",
    "file_name_y = 'gam_ally'\n",
    "if type_!=4:\n",
    "    file_name_x = f'gam_allx_{type_}'\n",
    "    file_name_y = f'gam_ally_{type_}'\n",
    "\n",
    "if os.path.isfile(file_name_x)  :\n",
    "\n",
    "    gam_allx = ExplainableBoostingRegressor()._from_json(file_name_x)\n",
    "    gam_ally = ExplainableBoostingRegressor()._from_json(file_name_y)\n",
    "else:\n",
    "    # 4 x, 4y, 5 in_vx, 5 in_vy, h,t, 4 ttc, 6 out_x, 6 out_y, (24*2: (xy pairs) map)\n",
    "    gam_allx = ExplainableBoostingRegressor(feature_names=f_names).fit(Xs_masked[:,:],(Ys_masked[:,0]))\n",
    "    print('done training on x axis')\n",
    "    gam_allx.to_json(file_name_x)\n",
    "    gam_ally = ExplainableBoostingRegressor(feature_names=f_names).fit(Xs_masked[:,:],(Ys_masked[:,1]))\n",
    "    print('done training on y axis')\n",
    "    gam_ally.to_json(file_name_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Maps only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    f_names = []\n",
    "    for i in range(24):\n",
    "        f_names.extend([f'mode{i}x',f'mode{i}y'])\n",
    "        \n",
    "    file_name_x = 'gam_allx_maps'\n",
    "    file_name_y = 'gam_ally_maps'\n",
    "    if type_!=4:\n",
    "        file_name_x = f'gam_allx_maps_{type_}'\n",
    "        file_name_y = f'gam_ally_maps_{type_}'\n",
    "\n",
    "    if os.path.isfile(file_name_x):\n",
    "\n",
    "        gam_allx_maps = ExplainableBoostingRegressor()._from_json(file_name_x)\n",
    "        gam_ally_maps = ExplainableBoostingRegressor()._from_json(file_name_y)\n",
    "    else:\n",
    "        # 4 x, 4y, 5 in_vx, 5 in_vy, h,t, 4 ttc, 6 out_x, 6 out_y, (24*2: (xy pairs) map)\n",
    "        gam_allx_maps = ExplainableBoostingRegressor(feature_names=f_names).fit(Xs_masked[:,24:],(Ys_masked[:,0]))\n",
    "        print('done training on x axis')\n",
    "        gam_allx_maps.to_json(file_name_x)\n",
    "        gam_ally_maps = ExplainableBoostingRegressor(feature_names=f_names).fit(Xs_masked[:,24:],(Ys_masked[:,1]))\n",
    "        print('done training on y axis')\n",
    "        gam_ally_maps.to_json(file_name_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!-- http://127.0.0.1:7188/2073503201216/ -->\n",
       "<a href=\"http://127.0.0.1:7188/2073503201216/\" target=\"_new\">Open in new window</a><iframe src=\"http://127.0.0.1:7188/2073503201216/\" width=100% height=800 frameBorder=\"0\"></iframe>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "global_ex_x  = gam_allx.explain_global()\n",
    "global_ex_y  = gam_ally.explain_global()\n",
    "\n",
    "\n",
    "local_ex_x = gam_allx.explain_local(Xs_masked[:,:])\n",
    "local_ex_y = gam_ally.explain_local(Xs_masked[:,:])\n",
    "\n",
    "show([global_ex_x,local_ex_x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.174310134624231\n",
      "3.963797898207976\n",
      "final error: 9.136847529997924\n"
     ]
    }
   ],
   "source": [
    "errorsx_esm = abs((gam_allx.predict(Xs_val_masked[:,:]))-(Ys_val_masked[:,0]))\n",
    "errorsx_esmy = abs((gam_ally.predict(Xs_val_masked[:,:]))-(Ys_val_masked[:,1]))\n",
    "print(errorsx_esm.mean())\n",
    "print(errorsx_esmy.mean())\n",
    "\n",
    "print(f'final error: {np.linalg.norm(np.vstack((errorsx_esm,errorsx_esmy)).T,axis=1).mean()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global Explaination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = global_ex_x.data()['scores'][0:]\n",
    "names = global_ex_x.data()['names'][0:]\n",
    "\n",
    "plt.figure(figsize=(28,6))\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.bar(range(0,len(scores)*10,10),scores,width=7,align='center')\n",
    "plt.xticks(np.arange(0,len(scores)*10,10),names,rotation=90)\n",
    "plt.xlim(0,len(scores)*10)\n",
    "plt.yticks(rotation=45)\n",
    "plt.ylabel('Feature Mean Importance for x')\n",
    "\n",
    "scores = global_ex_y.data()['scores'][0:]\n",
    "names = global_ex_y.data()['names'][0:]\n",
    "plt.subplot(212)\n",
    "plt.bar(range(0,len(scores)*10,10),scores,width=7,align='center')\n",
    "plt.xticks(np.arange(0,len(scores)*10,10),names,rotation=90)\n",
    "plt.xlim(0,len(scores)*10)\n",
    "plt.yticks(rotation=45)\n",
    "plt.ylabel('Feature Mean Importance for y')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best 6 Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "names = global_ex_x.data()['names']\n",
    "scores = global_ex_x.data()['scores']\n",
    "\n",
    "best_6_mask = scores>(np.sort(scores)[-7])\n",
    "p_in = 1\n",
    "plt.figure(figsize=(18,8))\n",
    "plt.title('Best six features dependence graphs for X model')\n",
    "sigma_factor = 3\n",
    "for k_i in np.arange(len(scores))[best_6_mask]:\n",
    "    f_dict = global_ex_x.data(key=k_i)\n",
    "    if f_dict['type']=='interaction':\n",
    "        ax = plt.subplot(2,3,p_in)\n",
    "        p_in += 1\n",
    "        s_data = f_dict['scores'].T\n",
    "        plt.imshow(s_data)\n",
    "        ay_l,ax_l = names[k_i].split('&')\n",
    "        plt.ylabel(ax_l,labelpad=0.5)\n",
    "        plt.xlabel(ay_l,labelpad=0.1)\n",
    "        plt.xticks(ticks=np.arange(len(f_dict['left_names'])),labels=np.round(f_dict['left_names'],2),rotation=90)\n",
    "        plt.yticks(ticks=np.arange(len(f_dict['right_names'])),labels=np.round(f_dict['right_names'],1))\n",
    "        # TODO heatmap \n",
    "    else:\n",
    "        x_data = f_dict['names'][:-1]\n",
    "        y_data = f_dict['scores']\n",
    "        up_data = f_dict['upper_bounds']\n",
    "        up_data_m = sigma_factor*(up_data-y_data) + y_data\n",
    "        low_data = f_dict['lower_bounds']\n",
    "        low_data_m = sigma_factor*(low_data-y_data) + y_data\n",
    "        ax = plt.subplot(2,3,p_in)\n",
    "        p_in += 1\n",
    "        plt.plot(x_data,y_data)\n",
    "        #plt.plot(x_data,up_data_m ,c='r',ls='--')\n",
    "        #plt.plot(x_data,low_data_m,c='r',ls='--')\n",
    "        plt.xlabel(names[k_i],labelpad=0.5)\n",
    "        plt.ylabel(\"scores\",labelpad=0.1)\n",
    "        \n",
    "        ax.plot(x_data, low_data_m, color='tab:green', alpha=0.1)\n",
    "        ax.plot(x_data, up_data_m, color='tab:green', alpha=0.1)\n",
    "        ax.fill_between(x_data, low_data_m, up_data_m,color='tab:green', alpha=0.2)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cut to equal train batches (On X):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "YsXs = np.hstack((Ys_masked[:,:],Xs_masked[:,:]))\n",
    "YsXs_test = np.hstack((Ys_val_masked[:,:],Xs_val_masked[:,:]))\n",
    "\n",
    "x_ranges = 8\n",
    "yx_sorted_x = sorted(YsXs,key=lambda x: x[0])\n",
    "single_len = (len(yx_sorted_x)//x_ranges)#+(x_ranges-1)\n",
    "sub_modes_x = [yx_sorted_x[(i*single_len):(i+1)*single_len] for i in range(x_ranges)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_sub_modes_x = []\n",
    "acc_mode = []\n",
    "acc = 0\n",
    "for i,mode_x in enumerate(sub_modes_x):\n",
    "    acc += (np.array(mode_x)[:,0].max()-np.array(mode_x)[:,0].min())\n",
    "    acc_mode.extend(mode_x)\n",
    "    #print(i,acc)\n",
    "    if acc>2:\n",
    "        new_sub_modes_x.append(acc_mode)\n",
    "        acc_mode = []\n",
    "        acc = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alternative (equal Ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21043, 74)\n",
      "(533, 74)\n",
      "(516, 74)\n",
      "(15082, 74)\n",
      "(3726, 74)\n",
      "(3284, 74)\n",
      "(14917, 74)\n",
      "(3077, 74)\n",
      "(4098, 74)\n",
      "(17048, 74)\n",
      "(2265, 74)\n",
      "(2779, 74)\n",
      "(18740, 74)\n",
      "(1566, 74)\n",
      "(1786, 74)\n",
      "(19837, 74)\n",
      "(1142, 74)\n",
      "(1113, 74)\n",
      "(20578, 74)\n",
      "(754, 74)\n",
      "(760, 74)\n",
      "(20653, 74)\n",
      "(797, 74)\n",
      "(642, 74)\n"
     ]
    }
   ],
   "source": [
    "y_ranges = 3\n",
    "edge_len = 8\n",
    "sub_modes = []\n",
    "modes_index = []\n",
    "min_percent = 1/(y_ranges*1)\n",
    "\n",
    "for ix,sub_mode_x in enumerate(new_sub_modes_x):\n",
    "    sub_mode_x_arr = np.array(sub_mode_x)\n",
    "    sub_ys = sub_mode_x_arr[:,1]\n",
    "    #assume mean 0\n",
    "    center_mode = sub_mode_x_arr[(sub_ys<edge_len)*(sub_ys>-edge_len)]\n",
    "    print(center_mode.shape) \n",
    "    sub_modes.append(center_mode)\n",
    "    modes_index.append(center_mode[:,:2].mean(axis=0))\n",
    "    \n",
    "    c_mode  = sub_mode_x_arr[(sub_ys<-edge_len)]\n",
    "    print(c_mode.shape) \n",
    "    sub_modes.append(c_mode)\n",
    "    modes_index.append(c_mode[:,:2].mean(axis=0))\n",
    "    \n",
    "    c_mode  = sub_mode_x_arr[(sub_ys>edge_len)] \n",
    "    print(c_mode.shape) \n",
    "    sub_modes.append(c_mode)\n",
    "    modes_index.append(c_mode[:,:2].mean(axis=0))\n",
    "\n",
    "    \n",
    "modes_index = np.array(modes_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show the culsters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "modes_ranges = []\n",
    "for mode in sub_modes:\n",
    "    if len(mode):\n",
    "        modes_ranges.append([(mode[:,0].min(),mode[:,0].max()),(mode[:,1].min(),mode[:,1].max())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Ys[:,0],Ys[:,1],marker='.',linewidths=0.01)\n",
    "yx_range = [-150,150]\n",
    "\n",
    "for i,sub_mode_x in enumerate(new_sub_modes_x):#x\n",
    "    sub_mode = np.array(sub_mode_x)\n",
    "    xmin,xmax = sub_mode[:,0].min(),sub_mode[:,0].max()\n",
    "    ymin,ymax = sub_mode[:,1].min(),sub_mode[:,1].max()\n",
    "    \n",
    "    \n",
    "    if i == 0:\n",
    "        plt.plot([xmin,xmin],[ymin,ymax],'g',linewidth=1)\n",
    "    plt.plot([xmax,xmax],[ymin,ymax],'g',linewidth=1)\n",
    "    offset = i*y_ranges\n",
    "    for j in range(y_ranges):\n",
    "        mode = sub_modes[offset+j]\n",
    "        #print(len(mode))\n",
    "        if len(mode)==0:\n",
    "            continue\n",
    "        ymin,ymax = mode[:,1].min(),mode[:,1].max()\n",
    "        #xmin,xmax = mode[:,0].min(),mode[:,0].max()\n",
    "        #if j==(y_ranges-1):\n",
    "        plt.plot([xmin,xmax],[ymin,ymin],'g',linewidth=1)\n",
    "        plt.plot([xmin,xmax],[ymax,ymax],'g',linewidth=1)\n",
    "        #plt.fill_between([xmin,xmax],y1=ymin,y2=ymax,color=[1-map_y[offset+j],0,0],cmap='hot',alpha=0.7)\n",
    "        plt.text(x=(xmin+xmax-1)/2,y=(ymin+ymax-1)/2,s=str(offset+j),color='black')\n",
    "        #print(len(mode))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_ranges = 12\n",
    "yx_sorted_x = sorted(YsXs_test,key=lambda x: x[0])\n",
    "single_len = (len(yx_sorted_x)//x_ranges)#+(x_ranges-1)\n",
    "sub_modes_x_val = [yx_sorted_x[(i*single_len):(i+1)*single_len] for i in range(x_ranges)]\n",
    "new_sub_modes_x_val = []\n",
    "acc_mode = []\n",
    "acc = 0\n",
    "for i,mode_x in enumerate(sub_modes_x_val):\n",
    "    acc += (np.array(mode_x)[:,0].max()-np.array(mode_x)[:,0].min())\n",
    "    acc_mode.extend(mode_x)\n",
    "    #print(i,acc)\n",
    "    if acc>2:\n",
    "        new_sub_modes_x_val.append(acc_mode)\n",
    "        acc_mode = []\n",
    "        acc = 0\n",
    "        \n",
    "#y_ranges = 15\n",
    "sub_modes_val = []\n",
    "modes_index = []\n",
    "for ix,sub_mode_x in enumerate(new_sub_modes_x_val):\n",
    "    yx_sorted_y = sorted(sub_mode_x,key=lambda x: x[1])\n",
    "    single_len = (len(yx_sorted_y)//y_ranges)+(y_ranges-1)\n",
    "    #print(len(yx_sorted_y))\n",
    "    for iy in range(y_ranges):\n",
    "        c_mode = np.array(yx_sorted_y[(iy*single_len):(iy+1)*single_len])\n",
    "        if len(c_mode.shape)<2:\n",
    "            c_mode = c_mode[:,None]\n",
    "        #print(ix,iy,c_mode.shape)\n",
    "        sub_modes_val.append(c_mode)\n",
    "        modes_index.append(c_mode[:,:2].mean(axis=0))\n",
    "\n",
    "    #sub_modes.extend([np.array(yx_sorted_y[(i*single_len):(i+1)*single_len]) for i in range(y_ranges)])\n",
    "    \n",
    "#modes_index = np.array(modes_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_kmeans:\n",
    "    sub_modes = []\n",
    "    for i in range(35):\n",
    "        cluster_i = (clusterer.labels_==i)\n",
    "        #print(sum(cluster_i))\n",
    "        #if (sum(cluster_i))>1000:\n",
    "        sub_modes.append(YsXs[cluster_i])\n",
    "        color = np.array([[np.random.rand() for _  in range(3)]])\n",
    "        plt.scatter(YsXs[cluster_i][:,0],YsXs[cluster_i][:,1],c=color,marker='.',linewidths=0.001)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_kmeans:\n",
    "    sub_modes_val = []\n",
    "\n",
    "    val_modes = clusterer.predict(YsXs_test[:,:2])\n",
    "\n",
    "    for m in range(24):\n",
    "        mask_val = (val_modes==m)\n",
    "        sub_modes_val.append(YsXs_test[mask_val])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(YsXs_test[:,0],YsXs_test[:,1],marker='.',linewidths=0.01)\n",
    "yx_range = [-150,150]\n",
    "\n",
    "for i,sub_mode_x in enumerate(new_sub_modes_x_val):#x\n",
    "    sub_mode = np.array(sub_mode_x)\n",
    "    xmin,xmax = sub_mode[:,0].min(),sub_mode[:,0].max()\n",
    "    \n",
    "    \n",
    "    if i == 0:\n",
    "        plt.plot([xmin,xmin],yx_range,'k',linewidth=1)\n",
    "    plt.plot([xmax,xmax],yx_range,'k',linewidth=1)\n",
    "    offset = i*y_ranges\n",
    "    for j in range(y_ranges):\n",
    "        mode = sub_modes[offset+j]\n",
    "        #print(len(mode))\n",
    "        if len(mode)==0:\n",
    "            continue\n",
    "        ymin,ymax = mode[:,1].min(),mode[:,1].max()\n",
    "        #if j==(y_ranges-1):\n",
    "        plt.plot([xmin,xmax],[ymin,ymin],'k',linewidth=1)\n",
    "        plt.plot([xmin,xmax],[ymax,ymax],'k',linewidth=1)\n",
    "        #print(len(mode))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train modes models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model 0: 1.6785368406594057\n",
      "model 1: 3.158821580295527\n",
      "model 2: 3.1386429509397518\n",
      "model 3: 1.838653746238972\n",
      "model 4: 2.4589739964987496\n",
      "model 5: 1.9161843244385315\n",
      "model 6: 2.244602214180677\n",
      "model 7: 2.6175549809782304\n",
      "model 8: 2.115481834108718\n",
      "model 9: 2.4923207245538577\n",
      "model 10: 3.0365457951756336\n",
      "model 11: 2.6400496515595493\n",
      "model 12: 2.51208544425756\n",
      "model 13: 3.0406487162757645\n",
      "model 14: 2.7033495525297973\n",
      "model 15: 2.661252999245684\n",
      "model 16: 2.7803342437443446\n",
      "model 17: 2.8473356093570974\n",
      "model 18: 2.9700641408417456\n",
      "model 19: 3.2558960499490706\n",
      "model 20: 3.0976022181599583\n",
      "model 21: 4.6174382208652505\n",
      "model 22: 6.822539608383038\n",
      "model 23: 5.739443099372413\n",
      "==========\n",
      "3.0160149392753888\n"
     ]
    }
   ],
   "source": [
    "\n",
    "GAMs_x = []\n",
    "all_errs = []\n",
    "for m,mode,mode_val in zip(range(24),sub_modes,sub_modes_val):#y_ranges*x_ranges\n",
    "    if len(mode)==0: continue\n",
    "\n",
    "    # TODO test how every mode does with different x data point\n",
    "\n",
    "    gam = ExplainableBoostingRegressor(feature_names=f_names).fit(mode[:,2:],mode[:,0])\n",
    "    \n",
    "    errs = abs(gam.predict(mode_val[:,2:])-mode_val[:,0])#26+(m*2),27+(m*2)\n",
    "\n",
    "    print(f'model {m}: {errs.mean()}')\n",
    "    all_errs.append(errs.mean())\n",
    "\n",
    "    GAMs_x.append(gam)\n",
    "print('==========')\n",
    "print(np.mean(all_errs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mode 0: 2.196427008734265\n",
      "mode 1: 14.356406580680263\n",
      "mode 2: 13.129632827154676\n",
      "mode 3: 10.565754692904433\n",
      "mode 4: 11.145513657345923\n",
      "mode 5: 8.928353868060466\n",
      "mode 6: 8.403327020792593\n",
      "mode 7: 11.334722227189854\n",
      "mode 8: 7.2897051258263\n",
      "mode 9: 6.824494831064757\n",
      "mode 10: 11.744756943693849\n",
      "mode 11: 7.9212095669670335\n",
      "mode 12: 4.112948314321309\n",
      "mode 13: 10.754329662459714\n",
      "mode 14: 8.675575557720478\n",
      "mode 15: 2.9342305076277544\n",
      "mode 16: 12.66223301065723\n",
      "mode 17: 7.551084244359179\n",
      "mode 18: 2.0233112504307442\n",
      "mode 19: 10.381829662696651\n",
      "mode 20: 8.251823831050816\n",
      "mode 21: 2.068406876924452\n",
      "mode 22: 9.728963861479004\n",
      "mode 23: 8.289558097320048\n",
      "==========\n",
      "8.386441634477574\n"
     ]
    }
   ],
   "source": [
    "\n",
    "GAMs_y = []\n",
    "all_errs = []\n",
    "#[x0,x1,x2,x3,y4,y5,y6,y7,vx8,vy9,t10,c11,pet_n12,pet_n13,cx14,cy15] + [cx16,...cx24]\n",
    "for m,mode,mode_val in zip(range(24),sub_modes,sub_modes_val):#x_ranges*y_ranges\n",
    "    if len(mode)==0: continue\n",
    "\n",
    "    gam = ExplainableBoostingRegressor(feature_names=f_names).fit(mode[:,2:],mode[:,1])#,lam=lams)#gridsearch(mode[:,2:],mode[:,0],lam=lams)#\n",
    "    \n",
    "    errs = abs(gam.predict(mode_val[:,2:])-mode_val[:,1])#,27+(m*2),26+(m*2)\n",
    "    #gam.to_json(f'ebm_modes\\gam_y_{type_}_{m}')\n",
    "    print(f'mode {m}: {errs.mean()}')\n",
    "\n",
    "    all_errs.append(errs.mean())\n",
    "\n",
    "    GAMs_y.append(gam)\n",
    "print('==========')\n",
    "print(np.mean(all_errs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best 6 Modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    " \n",
    "def get_std(model):\n",
    "    all_devs = 0\n",
    "    for m in model.standard_deviations_:\n",
    "        if len(m.shape)>1:\n",
    "            all_devs += m.mean(axis=0).mean(axis=0)\n",
    "        else:\n",
    "            all_devs += m.mean(axis=0)\n",
    "    return all_devs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========\n"
     ]
    }
   ],
   "source": [
    "loglikelihoods = []\n",
    "probs = []\n",
    "all_preds = []\n",
    "global_preds = []\n",
    "global_std = get_std(gam_allx)\n",
    "for m,gam in enumerate(GAMs_x):\n",
    "    X_input = np.nan_to_num(YsXs_test[:,2:])\n",
    "    #likely = gam_allx.loglikelihood(X_input,gam.predict(X_input))\n",
    "    predsx = gam.predict(YsXs_test[:,2:])\n",
    "    all_preds.append(predsx)\n",
    "    if len(global_preds)==0:\n",
    "        global_preds  =gam_allx.predict(X_input[:,:])\n",
    "    likely = stats.norm.logpdf(predsx,loc=global_preds,scale=global_std)\n",
    "    likely += stats.norm.logpdf(predsx,loc=predsx,scale=get_std(gam))/4\n",
    "    \n",
    "    loglikelihoods.append(likely)\n",
    "\n",
    "print('==========')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "loglikelihoodsy = []\n",
    "all_predsy = []\n",
    "probs_y = []\n",
    "global_preds = []\n",
    "global_std = get_std(gam_ally)\n",
    "for m,gam in enumerate(GAMs_y):\n",
    "    X_input = np.clip(YsXs_test[:,2:],-1000,1000)\n",
    "    predsy = gam.predict(YsXs_test[:,2:])\n",
    "    all_predsy.append(predsy)\n",
    "    \n",
    "    if len(global_preds)==0:\n",
    "        global_preds  =gam_ally.predict(X_input[:,:])\n",
    "    likely = stats.norm.logpdf(predsy,loc=global_preds,scale=global_std)\n",
    "    likely += stats.norm.logpdf(predsy,loc=predsy,scale=get_std(gam))/1\n",
    "    \n",
    "    loglikelihoodsy.append(likely)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yasin\\AppData\\Local\\Temp\\ipykernel_98664\\3849121465.py:2: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.array(loglikelihoodsy).shape\n",
    "roads = np.log(abs(X_input[:,-48::2]).astype(bool)).T\n",
    "n_hoods = (np.array(loglikelihoodsy)+np.array(loglikelihoods)*4)+roads\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Y error: 1.3518439425312059\n",
      " X error: 2.298989425439527\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.884891637934661"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_6y = []\n",
    "n_mode_f = 6\n",
    "for j in range(n_mode_f):\n",
    "    A = np.sort(n_hoods.T,axis=1)[:,-1-j]\n",
    "    pred = np.array(all_predsy).T[(n_hoods.T == A[:,None])]\n",
    "    top_6y.append(pred[:A.shape[0]])\n",
    "    \n",
    "print(f' Y error: {np.sort(abs(np.array(top_6y).T-YsXs_test[:,1:2]),axis=1)[:,0].mean()}')\n",
    "\n",
    "top_6 = []\n",
    "for j in range(n_mode_f):\n",
    "    A = np.sort(n_hoods.T,axis=1)[:,-1-j]\n",
    "    pred = np.array(all_preds).T[(n_hoods.T == A[:,None])]\n",
    "    top_6.append(pred[:A.shape[0]])\n",
    "    \n",
    "print(f' X error: {np.sort(abs(np.array(top_6).T-YsXs_test[:,:1]),axis=1)[:,0].mean()}')\n",
    "\n",
    "FDE_errors = np.sqrt((np.array(top_6).T - YsXs_test[:,:1])**2 + (np.array(top_6y).T - YsXs_test[:,1:2])**2)\n",
    "np.sort(FDE_errors,axis=1)[:,0].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cars: 3.88\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Local Explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_modes(input_arr):\n",
    "    \n",
    "    out = []\n",
    "    m = 0\n",
    "    for gamx,gamy in zip(GAMs_x,GAMs_y):\n",
    "        x = input_arr[:,2:]\n",
    "        y = input_arr[:,2:]\n",
    "        out.append([gamx.predict(x)[0],gamy.predict(y)[0]])\n",
    "        m += 1\n",
    "    return np.array(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[181], line 7\u001b[0m\n\u001b[0;32m      5\u001b[0m log_ids \u001b[38;5;241m=\u001b[39m glob\u001b[38;5;241m.\u001b[39mglob(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdataroot\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/*\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      6\u001b[0m filenr \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m15\u001b[39m\n\u001b[1;32m----> 7\u001b[0m log_dir_ \u001b[38;5;241m=\u001b[39m (\u001b[43mlog_ids\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfilenr\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m      8\u001b[0m argoverse_scenario_dir \u001b[38;5;241m=\u001b[39m Path(log_ids[filenr])\u001b[38;5;66;03m#/ log_id\u001b[39;00m\n\u001b[0;32m      9\u001b[0m static_map \u001b[38;5;241m=\u001b[39m ArgoverseStaticMap\u001b[38;5;241m.\u001b[39mfrom_json(argoverse_scenario_dir\u001b[38;5;241m/\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_map_archive_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlog_dir_\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.json\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "import av2.utils.raster as raster_utils\n",
    "\n",
    "data_split = \"val\"\n",
    "dataroot =  f\"{data_split}\"\n",
    "log_ids = glob.glob(f\"{dataroot}/*\")\n",
    "filenr = 15\n",
    "log_dir_ = (log_ids[filenr].split('\\\\')[-1])\n",
    "argoverse_scenario_dir = Path(log_ids[filenr])#/ log_id\n",
    "static_map = ArgoverseStaticMap.from_json(argoverse_scenario_dir/f\"log_map_archive_{log_dir_}.json\")\n",
    "\n",
    "scenario_path = argoverse_scenario_dir.rglob(\"*.parquet\")\n",
    "data = pd.read_parquet(next(scenario_path))\n",
    "\n",
    "focal_track_id = (data['focal_track_id'][0])\n",
    "main_data = data.loc[data['track_id'] == focal_track_id].sort_values('timestep')\n",
    "h = main_data.iloc[49]['heading']\n",
    "c_, s_ = np.cos(h), np.sin(h)\n",
    "R_ = np.array([[c_, s_], \n",
    "                [-s_, c_]])#.transpose(2,0,1)\n",
    "xyz_org = np.array([main_data.iloc[49]['position_x'],main_data.iloc[49]['position_y'],0])\n",
    "lane_ploys = [(R_@(np.vstack((lane.right_lane_boundary.xyz,lane.left_lane_boundary.xyz[::-1],lane.right_lane_boundary.xyz[0]))-xyz_org).T[:2,:]).T\n",
    "            for lane in static_map.get_scenario_lane_segments()]# if lane.lane_type=='VEHICLE']\n",
    "\n",
    "x_min, y_min, x_max, y_max = -200,-100,150,100\n",
    "\n",
    "# The resolution of the rasterization will affect image dimensions.\n",
    "array_s_city = 1\n",
    "img_h = int((y_max - y_min + 1) * array_s_city)\n",
    "img_w = int((x_max - x_min + 1) * array_s_city)\n",
    "def poly2map(polys):\n",
    "    # scale determines the resolution of the raster DA layer.\n",
    "    #array_Sim2_city = Sim2(R=R_mat, t=np.array([200,100]), s=array_s_city)\n",
    "\n",
    "    # convert vertices for each polygon from a 3d array in city coordinates, to a 2d array\n",
    "    # in image/array coordinates.\n",
    "    da_polygons_img = [np.round((\n",
    "        dapc[:, :2]+np.array([-x_min,-y_min]))*array_s_city).astype(np.int32) for dapc in polys]\n",
    "\n",
    "    return raster_utils.get_mask_from_polygons(da_polygons_img, img_h, img_w)\n",
    "\n",
    "raster_arr = poly2map(lane_ploys)\n",
    "\n",
    "plt.imshow(raster_arr[::-1,:],cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(1-raster_arr[::-1,:],cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.title('Global map importance (red for x and green for y)')\n",
    "\n",
    "\n",
    "\n",
    "scores = global_ex_x.data()['scores'][0:]\n",
    "scores_y = global_ex_y.data()['scores'][0:]\n",
    "names = global_ex_y.data()['names'][0:]\n",
    "\n",
    "    \n",
    "for i,sub_mode_x in enumerate(new_sub_modes_x):#x\n",
    "    sub_mode = np.array(sub_mode_x)\n",
    "    xmin,xmax = sub_mode[:,0].min(),sub_mode[:,0].max()\n",
    "    ymin,ymax = sub_mode[:,1].min(),sub_mode[:,1].max()\n",
    "    \n",
    "    \n",
    "    #if i == 0:\n",
    "    #    plt.plot([0,0],[0,ymax-ymin],'g',linewidth=1)\n",
    "    #plt.plot([xmax-x_min,xmax-x_min],[0,ymax-y_min],'g',linewidth=1)\n",
    "    offset = i*y_ranges\n",
    "    for j in range(y_ranges):\n",
    "        mode = sub_modes[offset+j]\n",
    "        #print(len(mode))\n",
    "        if len(mode)==0:\n",
    "            continue\n",
    "        ymin,ymax = mode[:,1].min(),mode[:,1].max()\n",
    "        #print(xmin,xmax,ymin,ymax)\n",
    "        #xmin,xmax = mode[:,0].min(),mode[:,0].max()\n",
    "        #if j==(y_ranges-1):\n",
    "        try:\n",
    "            mode_indx = names.index(f'mode{j+(y_ranges*i)}x')\n",
    "            clr_red = (scores[mode_indx]+scores[mode_indx+1])/3\n",
    "            clr_green = (scores_y[mode_indx]+scores_y[mode_indx+1])/3\n",
    "        except ValueError:\n",
    "            clr_red = 1\n",
    "            clr_green = 1\n",
    "        plt.fill_between([xmin+200,xmax+200],y1=100-ymin,y2=100-ymax,color=[clr_red,clr_green,0],cmap='hot',alpha=0.6)#color=[1-map_y[offset+j],0,0]\n",
    "#print(raster_arr.shape)\n",
    "plt.xlim([50,370])\n",
    "plt.ylim([0,175])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yasin\\AppData\\Local\\Temp\\ipykernel_2096\\4205754764.py:65: DeprecationWarning:\n",
      "\n",
      "In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "\n",
      "C:\\Users\\yasin\\AppData\\Local\\Temp\\ipykernel_2096\\4205754764.py:79: DeprecationWarning:\n",
      "\n",
      "In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "key_ = filenr-1\n",
    "\n",
    "pred_20 = get_all_modes(YsXs_test[key_:key_+1,:])\n",
    "best_mode = np.linalg.norm(pred_20 - YsXs_test[key_,:2],axis=1).argmin()\n",
    "local_ex_x_mode = GAMs_x[best_mode].explain_local(YsXs_test[:key_+2,2:])\n",
    "local_ex_y_mode = GAMs_y[best_mode].explain_local(YsXs_test[:key_+2,2:])\n",
    "\n",
    "data_x = local_ex_x_mode.data(key=key_)\n",
    "data_y = local_ex_y_mode.data(key=key_)\n",
    "names = data_x['names']\n",
    "input_data = [[],[]]\n",
    "\n",
    "# draw input \n",
    "input_data = [YsXs_test[key_,2:6]+200,YsXs_test[key_,6:10]+100]\n",
    "#for s_ in range(0):\n",
    "#    f_ind_x = names.index(f'x{s_}')\n",
    "#    f_ind_y = names.index(f'y{s_}')\n",
    "#    input_data[0].append(data_x['values'][f_ind_x])\n",
    "#    input_data[1].append(data_x['values'][f_ind_y])\n",
    "    \n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title(f'global models ex {key_}')\n",
    "plt.subplot(131)\n",
    "plt.plot(input_data[0],input_data[1],color='b',label='input')\n",
    "#draw output\n",
    "scores = np.array(data_x['scores'])\n",
    "scores_y = np.array(data_y['scores'])\n",
    "#plt.plot(data_x['perf']['predicted'],data_y['perf']['predicted'],marker='x',color='r',label='predicted')\n",
    "plt.plot(data_x['perf']['predicted']+200,data_y['perf']['predicted']+100,marker='x',color='r',label='best predicted')\n",
    "plt.plot(YsXs_test[key_,0]+200,YsXs_test[key_,1]+100,marker='x',color='k',label='ground truth')\n",
    "plt.imshow(raster_arr[::-1,:],cmap='gray',alpha=0.5)\n",
    "#plt.axis('off')\n",
    "for i,sub_mode_x in enumerate(new_sub_modes_x):#x       \n",
    "    sub_mode = np.array(sub_mode_x)\n",
    "    xmin,xmax = sub_mode[:,0].min(),sub_mode[:,0].max()\n",
    "    offset = i*y_ranges\n",
    "    for j in range(y_ranges):\n",
    "        mode = sub_modes[offset+j]\n",
    "        if len(mode)==0:continue\n",
    "        ymin,ymax = mode[:,1].min(),mode[:,1].max()\n",
    "        try:\n",
    "            mode_indx = names.index(f'mode{j+(y_ranges*i)}x')\n",
    "            clr_red = scores[mode_indx]+scores[mode_indx+1]\n",
    "            #clr_red = scores_y[mode_indx]+scores_y[mode_indx+1]\n",
    "            clr_red *= 20\n",
    "        except ValueError:\n",
    "            clr_red = 1\n",
    "        if clr_red>0: clr=[0,0,min(clr_red,0.99)]\n",
    "        else: clr=[min(abs(clr_red),0.99),0,0]\n",
    "        #print(clr_red)\n",
    "        plt.fill_between([xmin+200,xmax+200],y1=100-ymin,y2=100-ymax,color=clr,cmap='hot',alpha=0.25)\n",
    "        \n",
    "plt.legend()\n",
    "#plt.ylim([-1,1])\n",
    "plt.title('Input data and the closest mode prediction')\n",
    "#plt.axis('square')\n",
    "\n",
    "#plt.show()\n",
    "\n",
    "plt.subplot(132)\n",
    "#draw best 10 amounts to x\n",
    "\n",
    "mask_best_6 = np.sort(np.abs(scores))[-11]<np.abs(scores)\n",
    "\n",
    "plt.bar(range(0,len(scores[mask_best_6])*10,10),scores[mask_best_6],width=5,align='center',color=[['r','b'][x>0] for x in scores[mask_best_6]])\n",
    "plt.xticks(np.arange(0,len(scores[mask_best_6])*10,10),np.array(names)[mask_best_6],rotation=80)\n",
    "plt.xlim(0,len(scores[mask_best_6])*10)\n",
    "plt.yticks(rotation=45)\n",
    "plt.ylabel('Feature Mean Importance for x')\n",
    "plt.title(f'Feature weight for x (Intercept {np.round(data_x[\"extra\"][\"scores\"][0],decimals=2)})')\n",
    "#plt.ylim([-1,1])\n",
    "#plt.show()\n",
    "\n",
    "plt.subplot(133)\n",
    "#draw best 10 amounts to y\n",
    "scores = np.array(data_y['scores'])\n",
    "mask_best_6 = np.sort(np.abs(scores))[-11]<np.abs(scores)\n",
    "\n",
    "plt.bar(range(0,len(scores[mask_best_6])*10,10),scores[mask_best_6],width=5,align='center',color=[['r','b'][x>0] for x in scores[mask_best_6]])\n",
    "plt.xticks(np.arange(0,len(scores[mask_best_6])*10,10),np.array(names)[mask_best_6],rotation=80)\n",
    "plt.xlim(0,len(scores[mask_best_6])*10)\n",
    "plt.yticks(rotation=45)\n",
    "plt.ylabel('Feature Mean Importance for y ')\n",
    "plt.title(f'Features weight for y (Intercept {np.round(data_y[\"extra\"][\"scores\"][-1],decimals=2)})')\n",
    "#plt.ylim([-1,1])\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}