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 "cells": [
  {
   "cell_type": "markdown",
   "id": "specific-efficiency",
   "metadata": {},
   "source": [
    "# Análisis de resultados Smina\n",
    "\n",
    "Importa las librerías necesarias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "light-reflection",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "liable-shock",
   "metadata": {},
   "source": [
    "Carga los resultados del scrining. Asumiendo que tu archivo lleva por nombe `vs_results.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "grave-citizenship",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carga de los resultados de vina\n",
    "dk_res = pd.read_csv('./vs_results.csv')\n",
    "dk_res.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exceptional-amsterdam",
   "metadata": {},
   "source": [
    "Carga el archivo con las etiquetas de actividad de las moléculas:\n",
    "- 1 = molécula activa para CDK2 (*true binder*)\n",
    "- 0 = molécula señuelo para CDK2 (*inactive/decoy*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "developing-jamaica",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carga de las actividades reales de los ligandos\n",
    "df_act = pd.read_csv('./cdk2_activity_mols.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hidden-disclosure",
   "metadata": {},
   "source": [
    "Utiliza el comando `merge` de *[pandas](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html)* para unir la tabla de resultados con la de etiquetas de actividad. El parámetro `inner` permite conservar sólo las filas con valores en común en ambas tablas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "modified-timeline",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unión de ambas tablas\n",
    "df = dk_res.merge(df_act, how='inner')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "integral-radiation",
   "metadata": {},
   "source": [
    "## Visualización de los resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "genetic-insertion",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run plot_metrics.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "enhanced-wisdom",
   "metadata": {},
   "source": [
    "### Ranking de moléculas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "unsigned-motorcycle",
   "metadata": {},
   "outputs": [],
   "source": [
    "vs_res = PlotMetric(y_true = df.actividad, \n",
    "                    y_pred_dict = {'smina': df.score})\n",
    "vs_res.plot_actives_distribution(add_to_title='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fatal-mirror",
   "metadata": {},
   "source": [
    "### Curva ROC y Área bajo la curva ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "japanese-browser",
   "metadata": {},
   "outputs": [],
   "source": [
    "vs_res.plot_roc_auc(title='VS results',\n",
    "                    fontsize='x-small', \n",
    "                    show_by_itself = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "opening-letter",
   "metadata": {},
   "source": [
    "### Distribución de los scores entre Activos e Inactivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "progressive-shame",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "compare_two_distributions(df)"
   ]
  }
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