{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: Scoring the Reconstruction\n",
    "\n",
    "Notebook Code: [![License: MIT](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) Notebook Prose: [![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After estimation and subtraction of the baseline, detection of peaks, splitting into windows, and fitting\n",
    "the peaks to a phenomenological function, we are left with the irritating problem \n",
    "of assessing how well we have done. Consider the following chromatogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Performing baseline correction: 100%|██████████| 299/299 [00:00<00:00, 3630.80it/s]\n",
      "Deconvolving mixture: 100%|██████████| 2/2 [00:08<00:00,  4.23s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<Figure size 640x480 with 1 Axes>,\n",
       " <Axes: xlabel='time_min', ylabel='intensity_mV (baseline corrected)'>]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from hplc.quant import Chromatogram\n",
    "import pandas as pd \n",
    "\n",
    "# Load the sample chromatogram and fit the peaks using default parameters.\n",
    "df = pd.read_csv('data/sample_chromatogram.txt')\n",
    "chrom = Chromatogram(df, cols={'time':'time_min', 'signal':'intensity_mV'}, \n",
    "                     time_window=[10, 20])\n",
    "peaks = chrom.fit_peaks()\n",
    "chrom.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By eye, this looks like a very good reconstruction of the chromatogram, but \n",
    "it would be nice to have a quantitative measure. \n",
    "\n",
    "## The Reconstruction Score\n",
    "Quantifying concentrations from HPLC data requires a measure of the **A**rea **U**nder\n",
    "the **C**urve (AUC), or more correctly stated, the integrated signal over a \n",
    "given time interval.  A perfect reconstruction of the chromatogram, resulting from \n",
    "summing over all constituent peaks in a mixture, would yield an identical AUC\n",
    "over any given time interval as the integrated signal of the original chromatogram. \n",
    "This can be defined mathematically as \n",
    "$$\n",
    "\\frac{\\sum\\limits_i^{N_\\text{peaks}} \\sum\\limits_{t=0}^{t_\\text{max}}S_i(t)}{\\sum\\limits_{t=0}^{t_\\text{max}}S(t)^\\text{(observed)}} = \\frac{\\text{AUC}^\\text{(inferred)}}{\\text{AUC}^\\text{(observed)}} = 1, \\tag{1}\n",
    "$$\n",
    "where $i$ represents the $i$-th component signal, $N_\\text{peaks}$ denotes the number of \n",
    "peaks in a given peak window, and $t$ denotes the discrete time point.\n",
    "In peak windows where the constituent signal is very small $S_{i}^\\text{(observed)} \\rightarrow 0$,\n",
    "even small deviations between the inferred mixture and the observed signal can cause \n",
    "this quantity to be much larger or much smaller than one, even if the total integrated \n",
    "signal difference is small. \n",
    "\n",
    "To account for this fact, we can modify Eq. 1 as \n",
    "\n",
    "$$\n",
    "R = \\frac{1 + \\text{AUC}^\\text{(inferred)}}{1 + \\text{AUC}^{(observed)}}, \\tag{2}\n",
    "$$\n",
    "which we term a *reconstruction score* or $R$-score for short. \n",
    "\n",
    "In practice, you'll never get an $R$-score of exactly 1, but you can get close. \n",
    "For example, an $R$-score can be computed for the chromatogram reconstruction \n",
    "shown above by calling the `_score_reconstruction` method of a `Chromatogram` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>window_type</th>\n",
       "      <th>window_id</th>\n",
       "      <th>reconstruction_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>peak</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>peak</td>\n",
       "      <td>2</td>\n",
       "      <td>0.995222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>interpeak</td>\n",
       "      <td>1</td>\n",
       "      <td>0.390153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>interpeak</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>interpeak</td>\n",
       "      <td>3</td>\n",
       "      <td>0.084916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  window_type  window_id  reconstruction_score\n",
       "0        peak          1              0.998430\n",
       "1        peak          2              0.995222\n",
       "0   interpeak          1              0.390153\n",
       "1   interpeak          2              0.000200\n",
       "2   interpeak          3              0.084916"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the R_score for the above chromatogram\n",
    "scores = chrom._score_reconstruction()\n",
    "scores[['window_type', 'window_id', 'reconstruction_score']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the two peak windows (rows 1 & 2), the $R$-score is very close to one, within 0.01.\n",
    "Whether that is sufficient for your case or not is up to you, dear reader. My job is just \n",
    "to give you that number. \n",
    "\n",
    "## Scoring the regions between peaks\n",
    "But what about the interpeak regions? These windows correspond to the chromatogram \n",
    "signal that lies outside of peak windows -- thus, an $R$-score is a measure of \n",
    "how well you are reconstructing the subtracted baseline. As there will almost always \n",
    "be 0 inferred signal in this region, your $R$-scores will typically be terrible and \n",
    "close to 0. \n",
    "\n",
    "While this will *usually* mean you are just not reconstructing the signal noise, \n",
    "a terrible $R$-score in an interpeak region may mean that there are peaks present, \n",
    "but your choice of a prominence filter is not detecting them. In this case, \n",
    "it is better to have a measure of what the noise-to-signal ratio is in these regions. \n",
    "\n",
    "Mathematically, we can compute this as the [Fano factor](https://en.wikipedia.org/wiki/Fano_factor) of the region,\n",
    "which can be thought of as a measure of the \"predictability\" of the signal in this \n",
    "sequence. This can be computed as \n",
    "\n",
    "$$\n",
    "F = \\frac{\\langle S^2 \\rangle - \\langle S \\rangle^2}{\\langle S \\rangle}, \\tag{3}\n",
    "$$\n",
    "where $S$ is the signal within a peak window. If the Fano factor is small, then \n",
    "the region is likely background noise whereas a large Fano factor would indicate \n",
    "there may be a peak present and you need to adjust your peak detection criteria. \n",
    "\n",
    "But what determines if it's big or small? As all chromatograms have a peak (why \n",
    "else would you be using `hplc-py`?), we can compare the Fano factor of the interpeak \n",
    "regions  to the average Fano factors of the regions where we know there is signal. \n",
    "If this quantity, which term the *Fano ratio*, is close to zero, then it is likely \n",
    "the interpeak region is just noise and you are not missing anything substantive. However,\n",
    "if the Fano ratio is *not* close to zero, there may be a peak present. Again, \n",
    "what determines \"close\" to zero is arbitrary. \n",
    "\n",
    "\n",
    "## Generating a chromatogram report card\n",
    "In `hplc-py`, you can automatically generate \"report\" cards by calling the \n",
    "`assess_fit` method of a Chromatogram object. For the chromatogram above, the\n",
    "report card looks pretty good!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------Chromatogram Reconstruction Report Card----------------------\n",
      "\n",
      "Reconstruction of Peaks\n",
      "=======================\n",
      "\n",
      "\u001b[1m\u001b[42m\u001b[30mA+, Success:  Peak Window 1 (t: 10.558 - 11.758) R-Score = 0.9984\u001b[0m\n",
      "\u001b[1m\u001b[42m\u001b[30mA+, Success:  Peak Window 2 (t: 12.117 - 19.817) R-Score = 0.9952\u001b[0m\n",
      "\n",
      "Signal Reconstruction of Interpeak Windows\n",
      "==========================================\n",
      "                  \n",
      "\u001b[1m\u001b[43m\u001b[30mC-, Needs Review:  Interpeak Window 1 (t: 10.000 - 10.550) R-Score = 0.3902 & Fano Ratio = 0.0024\u001b[0m\n",
      "Interpeak window 1 is not well reconstructed by mixture, but has a small Fano factor\n",
      "compared to peak region(s). This is likely acceptable, but visually check this region.\n",
      "\n",
      "\u001b[1m\u001b[43m\u001b[30mC-, Needs Review:  Interpeak Window 2 (t: 11.767 - 12.108) R-Score = 10^-3 & Fano Ratio = 0.0012\u001b[0m\n",
      "Interpeak window 2 is not well reconstructed by mixture, but has a small Fano factor\n",
      "compared to peak region(s). This is likely acceptable, but visually check this region.\n",
      "\n",
      "\u001b[1m\u001b[43m\u001b[30mC-, Needs Review:  Interpeak Window 3 (t: 19.825 - 20.000) R-Score = 10^-1 & Fano Ratio = 0.0000\u001b[0m\n",
      "Interpeak window 3 is not well reconstructed by mixture, but has a small Fano factor\n",
      "compared to peak region(s). This is likely acceptable, but visually check this region.\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Generate a report card with the default tolerances\n",
    "scores = chrom.assess_fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This report card is telling you that the two peak windows seem to be really well \n",
    "reconstructed, whereas the interpeak regions *may* be poorly reconstructed and \n",
    "you should take a look.  If you have a sense of what the relative tolerances \n",
    "should be (meaning, you have made a subjective decision of how close or far from 1.0\n",
    "you deem to be successful), you can pass different tolerances to `assess_fit`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------Chromatogram Reconstruction Report Card----------------------\n",
      "\n",
      "Reconstruction of Peaks\n",
      "=======================\n",
      "\n",
      "\u001b[1m\u001b[41m\u001b[30mF, Failed:  Peak Window 1 (t: 10.558 - 11.758) R-Score = 0.9984\u001b[0m\n",
      "Peak mixture poorly reconstructs signal. You many need to adjust parameter bounds\n",
      "or add manual peak positions (if you have a shouldered pair, for example). If\n",
      "you have a very noisy signal, you may need to increase the reconstruction\n",
      "tolerance `rtol`.\n",
      "\u001b[1m\u001b[41m\u001b[30mF, Failed:  Peak Window 2 (t: 12.117 - 19.817) R-Score = 0.9952\u001b[0m\n",
      "Peak mixture poorly reconstructs signal. You many need to adjust parameter bounds\n",
      "or add manual peak positions (if you have a shouldered pair, for example). If\n",
      "you have a very noisy signal, you may need to increase the reconstruction\n",
      "tolerance `rtol`.\n",
      "\n",
      "Signal Reconstruction of Interpeak Windows\n",
      "==========================================\n",
      "                  \n",
      "\u001b[1m\u001b[43m\u001b[30mC-, Needs Review:  Interpeak Window 1 (t: 10.000 - 10.550) R-Score = 0.3902 & Fano Ratio = 0.0024\u001b[0m\n",
      "Interpeak window 1 is not well reconstructed by mixture, but has a small Fano factor\n",
      "compared to peak region(s). This is likely acceptable, but visually check this region.\n",
      "\n",
      "\u001b[1m\u001b[43m\u001b[30mC-, Needs Review:  Interpeak Window 2 (t: 11.767 - 12.108) R-Score = 10^-3 & Fano Ratio = 0.0012\u001b[0m\n",
      "Interpeak window 2 is not well reconstructed by mixture, but has a small Fano factor\n",
      "compared to peak region(s). This is likely acceptable, but visually check this region.\n",
      "\n",
      "\u001b[1m\u001b[43m\u001b[30mC-, Needs Review:  Interpeak Window 3 (t: 19.825 - 20.000) R-Score = 10^-1 & Fano Ratio = 0.0000\u001b[0m\n",
      "Interpeak window 3 is not well reconstructed by mixture, but has a small Fano factor\n",
      "compared to peak region(s). This is likely acceptable, but visually check this region.\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Assess the fit, but with different tolerances.\n",
    "scores = chrom.assess_fit(rtol=1E-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In either case, `assess_fit` will still print out the $R$-scores and Fano ratios \n",
    "for you to make your own call on what is good or bad. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessing the fit ≠ computing uncertainty \n",
    "\n",
    "If you take one thing away from this page, please let it be that an $R$-score \n",
    "is **not** a measure of the uncertainty in your reconstruction. It is solely \n",
    "to be used as discriminator for you to make a judgement call of whether \n",
    "you are properly reconstructing the signal.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<small> © Griffin Chure, 2024. This notebook and the code within are released under a \n",
    "[Creative-Commons CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) and \n",
    "[GPLv3](https://www.gnu.org/licenses/gpl-3.0) license, respectively.</small>\n"
   ]
  }
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