1 files changed, 469 insertions, 0 deletions
diff --git a/evaluation/adept_ablation/lambda/evaluation_loci_looped_abl3.ipynb b/evaluation/adept_ablation/lambda/evaluation_loci_looped_abl3.ipynb
new file mode 100644
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+++ b/evaluation/adept_ablation/lambda/evaluation_loci_looped_abl3.ipynb
@@ -0,0 +1,469 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "import scipy.stats as stats\n",
+    "import os\n",
+    "\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+    "pd.options.mode.chained_assignment = None \n",
+    "plt.style.use('ggplot')\n",
+    "sns.color_palette(\"Paired\");\n",
+    "sns.set_theme();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# setting path to results folder\n",
+    "root_path = '../../../out/pretrained/adept_ablations/lambda'\n",
+    "\n",
+    "# list all folders in root path that don't stat with a dot\n",
+    "nets = ['adept_level1_ablation_lambda.run511']\n",
+    "\n",
+    "# read pickle file\n",
+    "tf = pd.DataFrame()\n",
+    "sf = pd.DataFrame()\n",
+    "af = pd.DataFrame()\n",
+    "\n",
+    "# load statistics files from nets\n",
+    "for net in nets:\n",
+    "    path = os.path.join(root_path, net, 'results')\n",
+    "    with open(os.path.join(path, 'trialframe.csv'), 'rb') as f:\n",
+    "        tf_temp = pd.read_csv(f, index_col=0)\n",
+    "    tf_temp['net'] = net\n",
+    "    tf = pd.concat([tf,tf_temp])\n",
+    "\n",
+    "    with open(os.path.join(path, 'slotframe.csv'), 'rb') as f:\n",
+    "        sf_temp = pd.read_csv(f, index_col=0)\n",
+    "    sf_temp['net'] = net\n",
+    "    sf = pd.concat([sf,sf_temp])\n",
+    "\n",
+    "    with open(os.path.join(path, 'accframe.csv'), 'rb') as f:\n",
+    "        af_temp = pd.read_csv(f, index_col=0)\n",
+    "    af_temp['net'] = net\n",
+    "    af = pd.concat([af,af_temp])\n",
+    "\n",
+    "# cast variables\n",
+    "sf['visible'] = sf['visible'].astype(bool)\n",
+    "sf['bound'] = sf['bound'].astype(bool)\n",
+    "sf['occluder'] = sf['occluder'].astype(bool)\n",
+    "sf['inimage'] = sf['inimage'].astype(bool)\n",
+    "sf['vanishing'] = sf['vanishing'].astype(bool)\n",
+    "sf['alpha_pos'] = 1-sf['alpha_pos']\n",
+    "sf['alpha_ges'] = 1-sf['alpha_ges']\n",
+    "\n",
+    "# scale to percentage\n",
+    "sf['TE'] = sf['TE'] * 100\n",
+    "\n",
+    "# add surprise as dummy code\n",
+    "tf['control'] = [('control' in set) for set in tf['set']]\n",
+    "sf['control'] = [('control' in set)  for set in sf['set']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculate Tracking Error (TE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tracking Error when visible: M: 3.34 , STD: 4.22, Count: 1873\n",
+      "Tracking Error when occluded: M: 2.78 , STD: 1.66, Count: 492\n"
+     ]
+    }
+   ],
+   "source": [
+    "grouping = (sf.inimage & sf.bound & ~sf.occluder & sf.control)\n",
+    "\n",
+    "def get_stats(col):\n",
+    "    return f' M: {col.mean():.3} , STD: {col.std():.3}, Count: {col.count()}'\n",
+    "\n",
+    "# When Visible\n",
+    "temp = sf[grouping & sf.visible]\n",
+    "print(f'Tracking Error when visible:' + get_stats(temp['TE']))\n",
+    "\n",
+    "# When Occluded\n",
+    "temp = sf[grouping & ~sf.visible]\n",
+    "print(f'Tracking Error when occluded:' + get_stats(temp['TE']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculate Succesfull Trackings (TE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>set</th>\n",
+       "      <th>evalmode</th>\n",
+       "      <th>tracked_pos</th>\n",
+       "      <th>tracked_neg</th>\n",
+       "      <th>tracked_pos_pro</th>\n",
+       "      <th>tracked_neg_pro</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>control</td>\n",
+       "      <td>open</td>\n",
+       "      <td>37</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.770833</td>\n",
+       "      <td>0.229167</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       set evalmode  tracked_pos  tracked_neg  tracked_pos_pro  \\\n",
+       "0  control     open           37           11         0.770833   \n",
+       "\n",
+       "   tracked_neg_pro  \n",
+       "0         0.229167  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# succesfull trackings: In the last visible moment of the target, the slot was less than 10% away from the target\n",
+    "# determine last visible frame numeric\n",
+    "grouping_factors = ['net','set','evalmode','scene','slot']\n",
+    "ff = sf[sf.visible & sf.bound & sf.inimage].groupby(grouping_factors).max()\n",
+    "ff.rename(columns = {'frame':'last_visible'}, inplace = True)\n",
+    "sf = sf.merge(ff[['last_visible']], on=grouping_factors, how='left')\n",
+    "\n",
+    "# same for first bound frame\n",
+    "ff = sf[sf.visible & sf.bound & sf.inimage].groupby(grouping_factors).min()\n",
+    "ff.rename(columns = {'frame':'first_visible'}, inplace = True)\n",
+    "sf = sf.merge(ff[['first_visible']], on=grouping_factors, how='left')\n",
+    "\n",
+    "# add dummy variable to sf\n",
+    "sf['last_visible'] = (sf['last_visible'] == sf['frame'])\n",
+    "\n",
+    "# extract the trials where the target was last visible and threshold the TE\n",
+    "ff = sf[sf['last_visible']] \n",
+    "ff['tracked_pos'] = (ff['TE'] < 10)\n",
+    "ff['tracked_neg'] = (ff['TE'] >= 10)\n",
+    "\n",
+    "# fill NaN with 0\n",
+    "sf = sf.merge(ff[grouping_factors + ['tracked_pos', 'tracked_neg']], on=grouping_factors, how='left')\n",
+    "sf['tracked_pos'].fillna(False, inplace=True)\n",
+    "sf['tracked_neg'].fillna(False, inplace=True)\n",
+    "\n",
+    "# Aggreagte over all scenes\n",
+    "temp = sf[(sf['frame']== 1) & ~sf.occluder & sf.control & (sf.first_visible < 20)]\n",
+    "temp = temp.groupby(['set', 'evalmode']).sum()\n",
+    "temp = temp[['tracked_pos', 'tracked_neg']]\n",
+    "temp = temp.reset_index()\n",
+    "\n",
+    "temp['tracked_pos_pro'] = temp['tracked_pos'] / (temp['tracked_pos'] + temp['tracked_neg'])\n",
+    "temp['tracked_neg_pro'] = temp['tracked_neg'] / (temp['tracked_pos'] + temp['tracked_neg'])\n",
+    "\n",
+    "temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mostly Tracked stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temp = af[af.index == 'OVERALL']\n",
+    "temp['mostly_tracked'] = temp['mostly_tracked'] / temp['num_unique_objects']\n",
+    "temp['partially_tracked'] = temp['partially_tracked'] / temp['num_unique_objects']\n",
+    "temp['mostly_lost'] = temp['mostly_lost'] / temp['num_unique_objects']\n",
+    "g = temp[['mostly_tracked', 'partially_tracked', 'mostly_lost','set']].set_index(['set']).groupby(['set']).mean().plot(kind='bar', stacked=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MOTA "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>idf1</th>\n",
+       "      <th>idp</th>\n",
+       "      <th>idr</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>num_unique_objects</th>\n",
+       "      <th>mostly_tracked</th>\n",
+       "      <th>partially_tracked</th>\n",
+       "      <th>mostly_lost</th>\n",
+       "      <th>num_false_positives</th>\n",
+       "      <th>num_misses</th>\n",
+       "      <th>num_switches</th>\n",
+       "      <th>num_fragmentations</th>\n",
+       "      <th>mota</th>\n",
+       "      <th>motp</th>\n",
+       "      <th>num_transfer</th>\n",
+       "      <th>num_ascend</th>\n",
+       "      <th>num_migrate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>set</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>control</th>\n",
+       "      <td>0.773828</td>\n",
+       "      <td>0.943857</td>\n",
+       "      <td>0.655708</td>\n",
+       "      <td>0.673474</td>\n",
+       "      <td>0.969430</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>1599.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>0.650398</td>\n",
+       "      <td>0.039370</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surprise</th>\n",
+       "      <td>0.776620</td>\n",
+       "      <td>0.942186</td>\n",
+       "      <td>0.660545</td>\n",
+       "      <td>0.678911</td>\n",
+       "      <td>0.968383</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>507.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.654212</td>\n",
+       "      <td>0.039468</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              idf1       idp       idr    recall  precision  \\\n",
+       "set                                                           \n",
+       "control   0.773828  0.943857  0.655708  0.673474   0.969430   \n",
+       "surprise  0.776620  0.942186  0.660545  0.678911   0.968383   \n",
+       "\n",
+       "          num_unique_objects  mostly_tracked  partially_tracked  mostly_lost  \\\n",
+       "set                                                                            \n",
+       "control                 86.0            41.0               15.0         30.0   \n",
+       "surprise                33.0            16.0                4.0         13.0   \n",
+       "\n",
+       "          num_false_positives  num_misses  num_switches  num_fragmentations  \\\n",
+       "set                                                                           \n",
+       "control                 104.0      1599.0           9.0                31.0   \n",
+       "surprise                 35.0       507.0           4.0                12.0   \n",
+       "\n",
+       "              mota      motp  num_transfer  num_ascend  num_migrate  \n",
+       "set                                                                  \n",
+       "control   0.650398  0.039370           0.0         9.0          0.0  \n",
+       "surprise  0.654212  0.039468           2.0         2.0          0.0  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "af[af.index == 'OVERALL'].groupby(['set']).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gate Openings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percept gate openings when visible: M: 0.132 , STD: 0.145, Count: 1873\n",
+      "Percept gate openings when occluded: M: 0.00809 , STD: 0.0444, Count: 492\n"
+     ]
+    }
+   ],
+   "source": [
+    "grouping = (sf.inimage & sf.bound & ~sf.occluder & sf.control)\n",
+    "temp = sf[grouping & sf.visible]\n",
+    "print(f'Percept gate openings when visible:' + get_stats(temp['alpha_pos'] + temp['alpha_ges']))\n",
+    "temp = sf[grouping & ~sf.visible]\n",
+    "print(f'Percept gate openings when occluded:' + get_stats(temp['alpha_pos'] + temp['alpha_ges']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "loci23",
+   "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}