{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8537cbba",
   "metadata": {},
   "source": [
    "# Evaluate Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99b70284",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d4d092",
   "metadata": {},
   "source": [
    "#### Load the Model and libaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd695375",
   "metadata": {
    "height": 115
   },
   "outputs": [],
   "source": [
    "from src.Language_Evaluation import llm_language_evaluation\n",
    "from src.data_analysis import run_analysis\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd2b8af",
   "metadata": {
    "height": 30
   },
   "source": [
    "#### Load the Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac659e2f",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "PATH = 'DATA PATH'\n",
    "MODEL = \"Llama-2-13b\"\n",
    "TEMPERATURE = 0\n",
    "N_REPETITIONS = 0\n",
    "REASONING = False\n",
    "LANGUAGES = ['spanish', 'tagalog', 'portuguese', 'english']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137bfc14",
   "metadata": {},
   "source": [
    "#### Run The Experiments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cf6751b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run evaluation:\n",
    "llm_language_evaluation(path=PATH, model=MODEL, temperature=TEMPERATURE, n_repetitions=N_REPETITIONS, reasoning=REASONING, languages=LANGUAGES)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edde4ac2",
   "metadata": {},
   "source": [
    "#### See the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8eef88dd",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "if N_REPETITIONS > 1:\n",
    "    df = pd.read_csv(f\"responses/{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}_{N_REPETITIONS}Repetitions.csv\")\n",
    "else:\n",
    "    df = pd.read_csv(f\"responses/{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}.csv\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b8f898",
   "metadata": {},
   "source": [
    "### Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e74454b",
   "metadata": {},
   "outputs": [],
   "source": [
    "TEMPERATURE = str(TEMPERATURE).replace('.', '_')\n",
    "\n",
    "run_analysis(model=MODEL, temperature=TEMPERATURE, n_repetitions=N_REPETITIONS, languages=LANGUAGES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "939ee357",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_REPETITIONS = 1 if N_REPETITIONS < 1 else N_REPETITIONS\n",
    "pd.read_csv(f'results/results_{MODEL}_Temperature{TEMPERATURE}_Repetitions{N_REPETITIONS}/matches_results_{MODEL}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59dc2a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_REPETITIONS = 1 if N_REPETITIONS < 1 else N_REPETITIONS\n",
    "pd.read_csv(f'results/results_{MODEL}_Temperature{TEMPERATURE}_Repetitions{N_REPETITIONS}/matches_by_theme_{MODEL}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "807f6fba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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