survey.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['Q1', 'Q2', 'Q3', 'Q4']\n",
    "models = ['gan', 'transformer_1', 'transformer_2']\n",
    "gan_path = 'gan_by_emotion/'\n",
    "transformer_1_path = 'transformer_1_by_emotion/'\n",
    "transformer_2_path = 'transformer_2_by_emotion/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "gan_list = []\n",
    "transformer_1_list = []\n",
    "transformer_2_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the file names for each emotion\n",
    "for label in labels:\n",
    "    for model in models:\n",
    "        if model == 'gan':\n",
    "            for file in os.listdir(os.path.join(gan_path, label)):\n",
    "                gan_list.append(model + '_' + label + '_' + file)\n",
    "        elif model == 'transformer_1':\n",
    "            for file in os.listdir(os.path.join(transformer_1_path, label)):\n",
    "                transformer_1_list.append(model + '_' + label + '_' + file)\n",
    "        elif model == 'transformer_2':\n",
    "            for file in os.listdir(os.path.join(transformer_2_path, label)):\n",
    "                transformer_2_list.append(model + '_' + label + '_' + file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the file names to a df\n",
    "gan_df = pd.DataFrame(gan_list, columns=['file_name'])\n",
    "transformer_1_df = pd.DataFrame(transformer_1_list, columns=['file_name'])\n",
    "transformer_2_df = pd.DataFrame(transformer_2_list, columns=['file_name'])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 40,
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   "outputs": [
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       "                       file_name\n",
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       "2      gan_Q1_transformer336.mid\n",
       "3     gan_Q1_transformer1262.mid\n",
       "4      gan_Q1_transformer322.mid\n",
       "...                          ...\n",
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       "5056   gan_Q4_transformer307.mid\n",
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       "\n",
       "[5059 rows x 1 columns]"
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     "execution_count": 40,
     "metadata": {},
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   "source": [
    "gan_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the dfS to csv\n",
    "# gan_df.to_csv('gan_file_names.csv', index=False)\n",
    "# transformer_1_df.to_csv('transformer_1_file_names.csv', index=False)\n",
    "# transformer_2_df.to_csv('transformer_2_file_names.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "gan_sample = gan_df.sample(n=12, random_state=7)\n",
    "transformer_1_sample = transformer_1_df.sample(n=12, random_state=8)\n",
    "transformer_2_sample = transformer_2_df.sample(n=12, random_state=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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       "      <th>78</th>\n",
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       "      <th>387</th>\n",
       "      <td>gan_Q1_transformer1228.mid</td>\n",
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       "      <th>1090</th>\n",
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       "      <th>4957</th>\n",
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       "      <th>4063</th>\n",
       "      <td>gan_Q4_transformer903.mid</td>\n",
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       "      <th>1915</th>\n",
       "      <td>gan_Q2_transformer384.mid</td>\n",
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       "      <th>2933</th>\n",
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    "gan_sample"
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   "cell_type": "code",
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