loss_histograms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 76\u001b[0m\n\u001b[1;32m     74\u001b[0m hist_df \u001b[38;5;241m=\u001b[39m load_histogram_data()\n\u001b[1;32m     75\u001b[0m hist_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhops\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m---> 76\u001b[0m plot_loss_histograms(hist_df)\n",
      "Cell \u001b[0;32mIn[5], line 60\u001b[0m, in \u001b[0;36mplot_loss_histograms\u001b[0;34m(hist_df)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;66;03m# Second grouping with subjects\u001b[39;00m\n\u001b[1;32m     52\u001b[0m g \u001b[38;5;241m=\u001b[39m sns\u001b[38;5;241m.\u001b[39mFacetGrid(\n\u001b[1;32m     53\u001b[0m     plot_df,\n\u001b[1;32m     54\u001b[0m     col\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mN_profiles\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     58\u001b[0m     aspect\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.5\u001b[39m\n\u001b[1;32m     59\u001b[0m )\n\u001b[0;32m---> 60\u001b[0m g\u001b[38;5;241m.\u001b[39mmap_dataframe(\n\u001b[1;32m     61\u001b[0m     sns\u001b[38;5;241m.\u001b[39mhistplot,\n\u001b[1;32m     62\u001b[0m     x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     63\u001b[0m     multiple\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlayer\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     64\u001b[0m     alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m\n\u001b[1;32m     65\u001b[0m )\n\u001b[1;32m     66\u001b[0m g\u001b[38;5;241m.\u001b[39madd_legend()\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m g\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39mravel():\n",
      "File \u001b[0;32m~/.local/lib/python3.11/site-packages/seaborn/axisgrid.py:825\u001b[0m, in \u001b[0;36mFacetGrid.map_dataframe\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    822\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m data_ijk\n\u001b[1;32m    824\u001b[0m     \u001b[38;5;66;03m# Draw the plot\u001b[39;00m\n\u001b[0;32m--> 825\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_facet_plot(func, ax, args, kwargs)\n\u001b[1;32m    827\u001b[0m \u001b[38;5;66;03m# For axis labels, prefer to use positional args for backcompat\u001b[39;00m\n\u001b[1;32m    828\u001b[0m \u001b[38;5;66;03m# but also extract the x/y kwargs and use if no corresponding arg\u001b[39;00m\n\u001b[1;32m    829\u001b[0m axis_labels \u001b[38;5;241m=\u001b[39m [kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m), kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)]\n",
      "File \u001b[0;32m~/.local/lib/python3.11/site-packages/seaborn/axisgrid.py:854\u001b[0m, in \u001b[0;36mFacetGrid._facet_plot\u001b[0;34m(self, func, ax, plot_args, plot_kwargs)\u001b[0m\n\u001b[1;32m    852\u001b[0m     plot_args \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    853\u001b[0m     plot_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m ax\n\u001b[0;32m--> 854\u001b[0m func(\u001b[38;5;241m*\u001b[39mplot_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mplot_kwargs)\n\u001b[1;32m    856\u001b[0m \u001b[38;5;66;03m# Sort out the supporting information\u001b[39;00m\n\u001b[1;32m    857\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_legend_data(ax)\n",
      "File \u001b[0;32m~/.local/lib/python3.11/site-packages/seaborn/distributions.py:1416\u001b[0m, in \u001b[0;36mhistplot\u001b[0;34m(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1405\u001b[0m estimate_kws \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m   1406\u001b[0m     stat\u001b[38;5;241m=\u001b[39mstat,\n\u001b[1;32m   1407\u001b[0m     bins\u001b[38;5;241m=\u001b[39mbins,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1411\u001b[0m     cumulative\u001b[38;5;241m=\u001b[39mcumulative,\n\u001b[1;32m   1412\u001b[0m )\n\u001b[1;32m   1414\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m p\u001b[38;5;241m.\u001b[39munivariate:\n\u001b[0;32m-> 1416\u001b[0m     p\u001b[38;5;241m.\u001b[39mplot_univariate_histogram(\n\u001b[1;32m   1417\u001b[0m         multiple\u001b[38;5;241m=\u001b[39mmultiple,\n\u001b[1;32m   1418\u001b[0m         element\u001b[38;5;241m=\u001b[39melement,\n\u001b[1;32m   1419\u001b[0m         fill\u001b[38;5;241m=\u001b[39mfill,\n\u001b[1;32m   1420\u001b[0m         shrink\u001b[38;5;241m=\u001b[39mshrink,\n\u001b[1;32m   1421\u001b[0m         common_norm\u001b[38;5;241m=\u001b[39mcommon_norm,\n\u001b[1;32m   1422\u001b[0m         common_bins\u001b[38;5;241m=\u001b[39mcommon_bins,\n\u001b[1;32m   1423\u001b[0m         kde\u001b[38;5;241m=\u001b[39mkde,\n\u001b[1;32m   1424\u001b[0m         kde_kws\u001b[38;5;241m=\u001b[39mkde_kws,\n\u001b[1;32m   1425\u001b[0m         color\u001b[38;5;241m=\u001b[39mcolor,\n\u001b[1;32m   1426\u001b[0m         legend\u001b[38;5;241m=\u001b[39mlegend,\n\u001b[1;32m   1427\u001b[0m         estimate_kws\u001b[38;5;241m=\u001b[39mestimate_kws,\n\u001b[1;32m   1428\u001b[0m         line_kws\u001b[38;5;241m=\u001b[39mline_kws,\n\u001b[1;32m   1429\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   1430\u001b[0m     )\n\u001b[1;32m   1432\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1434\u001b[0m     p\u001b[38;5;241m.\u001b[39mplot_bivariate_histogram(\n\u001b[1;32m   1435\u001b[0m         common_bins\u001b[38;5;241m=\u001b[39mcommon_bins,\n\u001b[1;32m   1436\u001b[0m         common_norm\u001b[38;5;241m=\u001b[39mcommon_norm,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1446\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   1447\u001b[0m     )\n",
      "File \u001b[0;32m~/.local/lib/python3.11/site-packages/seaborn/distributions.py:571\u001b[0m, in \u001b[0;36m_DistributionPlotter.plot_univariate_histogram\u001b[0;34m(self, multiple, element, fill, common_norm, common_bins, shrink, kde, kde_kws, color, legend, line_kws, estimate_kws, **plot_kws)\u001b[0m\n\u001b[1;32m    566\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m element \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbars\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    567\u001b[0m \n\u001b[1;32m    568\u001b[0m     \u001b[38;5;66;03m# Use matplotlib bar plotting\u001b[39;00m\n\u001b[1;32m    570\u001b[0m     plot_func \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mbar \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_variable \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mbarh\n\u001b[0;32m--> 571\u001b[0m     artists \u001b[38;5;241m=\u001b[39m plot_func(\n\u001b[1;32m    572\u001b[0m         hist[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medges\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    573\u001b[0m         hist[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheights\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m-\u001b[39m bottom,\n\u001b[1;32m    574\u001b[0m         hist[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwidths\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    575\u001b[0m         bottom,\n\u001b[1;32m    576\u001b[0m         align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medge\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    577\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39martist_kws,\n\u001b[1;32m    578\u001b[0m     )\n\u001b[1;32m    580\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m bar \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[1;32m    581\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_variable \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m~/.local/lib/python3.11/site-packages/matplotlib/__init__.py:1473\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1470\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m   1471\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   1472\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1473\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(\n\u001b[1;32m   1474\u001b[0m             ax,\n\u001b[1;32m   1475\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(sanitize_sequence, args),\n\u001b[1;32m   1476\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{k: sanitize_sequence(v) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems()})\n\u001b[1;32m   1478\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1479\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m   1480\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[0;32m~/.local/lib/python3.11/site-packages/matplotlib/axes/_axes.py:2551\u001b[0m, in \u001b[0;36mAxes.bar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2548\u001b[0m     edgecolor \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mrepeat(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m   2549\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2550\u001b[0m     edgecolor \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mchain(\n\u001b[0;32m-> 2551\u001b[0m         itertools\u001b[38;5;241m.\u001b[39mcycle(mcolors\u001b[38;5;241m.\u001b[39mto_rgba_array(edgecolor)),\n\u001b[1;32m   2552\u001b[0m         \u001b[38;5;66;03m# Fallback if edgecolor == \"none\".\u001b[39;00m\n\u001b[1;32m   2553\u001b[0m         itertools\u001b[38;5;241m.\u001b[39mrepeat(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m   2555\u001b[0m \u001b[38;5;66;03m# We will now resolve the alignment and really have\u001b[39;00m\n\u001b[1;32m   2556\u001b[0m \u001b[38;5;66;03m# left, bottom, width, height vectors\u001b[39;00m\n\u001b[1;32m   2557\u001b[0m _api\u001b[38;5;241m.\u001b[39mcheck_in_list([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124medge\u001b[39m\u001b[38;5;124m'\u001b[39m], align\u001b[38;5;241m=\u001b[39malign)\n",
      "File \u001b[0;32m~/.local/lib/python3.11/site-packages/matplotlib/colors.py:485\u001b[0m, in \u001b[0;36mto_rgba_array\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    483\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mzeros((\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m4\u001b[39m), \u001b[38;5;28mfloat\u001b[39m)\n\u001b[1;32m    484\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 485\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(alpha):\n\u001b[1;32m    486\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray([to_rgba(c, a) \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m alpha], \u001b[38;5;28mfloat\u001b[39m)\n\u001b[1;32m    487\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/numpy/lib/function_base.py:348\u001b[0m, in \u001b[0;36miterable\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    344\u001b[0m         indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(indexer)\n\u001b[1;32m    345\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m m[indexer]\n\u001b[0;32m--> 348\u001b[0m \u001b[38;5;129m@set_module\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    349\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21miterable\u001b[39m(y):\n\u001b[1;32m    350\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    351\u001b[0m \u001b[38;5;124;03m    Check whether or not an object can be iterated over.\u001b[39;00m\n\u001b[1;32m    352\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    384\u001b[0m \n\u001b[1;32m    385\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    386\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2700x2400 with 48 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob\n",
    "from pathlib import Path\n",
    "\n",
    "def load_histogram_data():\n",
    "    # Load batched histogram files\n",
    "    batched_files = glob(\"results/loss_over_time_histograms_*_batched.csv\")\n",
    "    \n",
    "    # If no batched files, try individual files\n",
    "    if not batched_files:\n",
    "        histogram_files = glob(\"results/*/eval_results_histograms.csv\")\n",
    "        if not histogram_files:\n",
    "            raise FileNotFoundError(\"No histogram files found\")\n",
    "        dfs = [pd.read_csv(f) for f in histogram_files]\n",
    "        hist_df = pd.concat(dfs, ignore_index=True)\n",
    "    else:\n",
    "        dfs = [pd.read_csv(f) for f in batched_files]\n",
    "        hist_df = pd.concat(dfs, ignore_index=True)\n",
    "    \n",
    "    return hist_df\n",
    "\n",
    "\n",
    "def plot_loss_histograms(hist_df):\n",
    "    hist_df = hist_df[::1000]\n",
    "    # First grouping\n",
    "    g = sns.FacetGrid(\n",
    "        hist_df,\n",
    "        col='max_train_hops',\n",
    "        row='hops',\n",
    "        hue='n_params',\n",
    "        height=4,\n",
    "        aspect=1.5\n",
    "    )\n",
    "    g.map_dataframe(\n",
    "        sns.histplot,\n",
    "        x='loss',\n",
    "        multiple=\"layer\",\n",
    "        alpha=0.5,\n",
    "        stat='density',\n",
    "        bins='auto'\n",
    "    )\n",
    "    g.add_legend()\n",
    "    for ax in g.axes.ravel():\n",
    "        ax.set_xscale('log')\n",
    "    \n",
    "    plt.savefig('results/plots/loss_hist_by_config.png', bbox_inches='tight')\n",
    "    plt.close()\n",
    "\n",
    "    for max_train_hops in hist_df['max_train_hops'].unique():\n",
    "        plot_df = hist_df[(hist_df['max_train_hops'] == max_train_hops) & (hist_df['hops'] == max_train_hops)]\n",
    "        # Second grouping with subjects\n",
    "        g = sns.FacetGrid(\n",
    "            plot_df,\n",
    "            col='N_profiles',\n",
    "            row='subject',\n",
    "            hue='n_params',\n",
    "            height=3,\n",
    "            aspect=1.5\n",
    "        )\n",
    "        g.map_dataframe(\n",
    "            sns.histplot,\n",
    "            x='loss',\n",
    "            multiple=\"layer\",\n",
    "            alpha=0.5,\n",
    "            stat='density',\n",
    "            bins='auto'\n",
    "        )\n",
    "        g.add_legend()\n",
    "        for ax in g.axes.ravel():\n",
    "            ax.set_xscale('log')\n",
    "        \n",
    "        plt.savefig(f'results/plots/loss_hist_by_subject_omax{max_train_hops}.png', bbox_inches='tight')\n",
    "        plt.close()\n",
    "\n",
    "# Usage:\n",
    "hist_df = load_histogram_data()\n",
    "hist_df['hops'] += 2\n",
    "plot_loss_histograms(hist_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "995689"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hist_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9957"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hist_df[::100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}