CURIE_act_1400.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import openai\n",
    "import json\n",
    "\n",
    "openai.organization = 'YOUR ORGANIZATION ID'\n",
    "openai.api_key = 'YOUR API KEY'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read and format data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/fine_tune_activity.csv')\n",
    "\n",
    "# Training set\n",
    "df_train = df[df['Set'] == 'training']\n",
    "df_train = df_train[['Sequence', 'activity']]\n",
    "df_train.columns = ['prompt', 'completion']\n",
    "df_train = df_train.sample(n=1400, random_state=0)\n",
    "df_train.to_json('data/activity_seq_train_1400.jsonl', orient='records', lines=True)\n",
    "\n",
    "# Test set\n",
    "df_test = df[df['Set'] == 'test']\n",
    "df_test = df_test[['Sequence', 'activity']]\n",
    "df_test.columns = ['prompt', 'completion']\n",
    "df_test.to_json('data/activity_seq_test.jsonl', orient='records', lines=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare data for submission with OpenAI API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# openai tools fine_tunes.prepare_data -f activity_seq_train_1400.jsonl     "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# openai --api-key 'YOUR API KEY' api fine_tunes.create -t \"activity_seq_train_1400_prepared.jsonl\" -v \"activity_seq_test_prepared.jsonl\" --compute_classification_metrics --classification_positive_class \" 1\" --model curie --suffix 'activity_1400' --n_epochs 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# openai --api-key 'YOUR API KEY' api fine_tunes.follow -i ft-rTqLjfIfPl3MMgGw3L4ghHvW"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check results of classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# openai --api-key 'YOUR API KEY' api fine_tunes.results -i ft-rTqLjfIfPl3MMgGw3L4ghHvW > curie_result_activity_1400.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>elapsed_tokens</th>\n",
       "      <th>elapsed_examples</th>\n",
       "      <th>training_loss</th>\n",
       "      <th>training_sequence_accuracy</th>\n",
       "      <th>training_token_accuracy</th>\n",
       "      <th>validation_loss</th>\n",
       "      <th>validation_sequence_accuracy</th>\n",
       "      <th>validation_token_accuracy</th>\n",
       "      <th>classification/accuracy</th>\n",
       "      <th>classification/precision</th>\n",
       "      <th>classification/recall</th>\n",
       "      <th>classification/auroc</th>\n",
       "      <th>classification/auprc</th>\n",
       "      <th>classification/f1.0</th>\n",
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       "      <th>7001</th>\n",
       "      <td>7002</td>\n",
       "      <td>283892</td>\n",
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       "      <td>0.00402</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "      step  elapsed_tokens  elapsed_examples  training_loss  \\\n",
       "7001  7002          283892             14004        0.00402   \n",
       "\n",
       "      training_sequence_accuracy  training_token_accuracy  validation_loss  \\\n",
       "7001                         1.0                      1.0              NaN   \n",
       "\n",
       "      validation_sequence_accuracy  validation_token_accuracy  \\\n",
       "7001                           NaN                        NaN   \n",
       "\n",
       "      classification/accuracy  classification/precision  \\\n",
       "7001                 0.707286                  0.707809   \n",
       "\n",
       "      classification/recall  classification/auroc  classification/auprc  \\\n",
       "7001                0.70603              0.761541               0.77176   \n",
       "\n",
       "      classification/f1.0  \n",
       "7001             0.706918  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.read_csv('data/curie_result_activity_1400.csv')\n",
    "results.tail(1)"
   ]
  }
 ],
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