{ "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": [ "
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setevalmodetracked_postracked_negtracked_pos_protracked_neg_pro
0controlopen37110.7708330.229167
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" ], "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": [ "
" ] }, "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": [ "
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idf1idpidrrecallprecisionnum_unique_objectsmostly_trackedpartially_trackedmostly_lostnum_false_positivesnum_missesnum_switchesnum_fragmentationsmotamotpnum_transfernum_ascendnum_migrate
set
control0.7738280.9438570.6557080.6734740.96943086.041.015.030.0104.01599.09.031.00.6503980.0393700.09.00.0
surprise0.7766200.9421860.6605450.6789110.96838333.016.04.013.035.0507.04.012.00.6542120.0394682.02.00.0
\n", "
" ], "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 }