{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "027adfa9-5e64-474b-9a95-12e5c28c90a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import random\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9fff288-d062-4c27-b9dc-db8579bbd3cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(83607)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f85d8d2c-eca9-481d-b848-4d43a072b5fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use this as v1 of our dataset\n",
    "revision = \"0ecb2228e6c290dd22836024f32e559cc9b9711e\"\n",
    "original_dataset_file = \"gold_standard_v1.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c94cf8c-2185-4ca5-9191-02dd06c2fa0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download it - the simple way\n",
    "url = f\"https://raw.githubusercontent.com/lucijakrusic/SentiAnno/{revision}/gold_standard.csv\"\n",
    "r = requests.get(url, allow_redirects=True)\n",
    "\n",
    "if r:\n",
    "    with open(original_dataset_file, \"wb\") as f_out:\n",
    "        f_out.write(r.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1457fbb4-aeab-4c2e-a283-bcc12406ef3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# E.g.\n",
    "# {\"negative\": [...], \"positive\": [...]\n",
    "label_sentences_mapping = {}\n",
    "\n",
    "num_examples = 0\n",
    "\n",
    "with open(original_dataset_file, \"rt\") as csv_file:\n",
    "    csv_reader = csv.reader(csv_file, delimiter=',')\n",
    "\n",
    "    # Skip header\n",
    "    next(csv_reader, None)\n",
    "\n",
    "    for line in csv_reader:\n",
    "        assert len(line) == 5\n",
    "\n",
    "        sentence = line[2]\n",
    "        label = line[-1]\n",
    "\n",
    "        current_example = [label, sentence]\n",
    "        \n",
    "        if label in label_sentences_mapping:\n",
    "            label_sentences_mapping[label].append(current_example)\n",
    "        else:\n",
    "            label_sentences_mapping[label] = [current_example]\n",
    "\n",
    "        num_examples += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be5eda4e-bc2b-4f24-a584-7d7b34987f73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label negative has 447 sentences\n",
      "Label mixed has 56 sentences\n",
      "Label positive has 81 sentences\n",
      "Label neutral has 345 sentences\n"
     ]
    }
   ],
   "source": [
    "for label, sentences in label_sentences_mapping.items():\n",
    "    print(\"Label\", label, \"has\", len(sentences), \"sentences\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e218106a-8f23-41b0-96e5-b57a7a83fc5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create 80 / 10 / 10 splits, but for each label (to avoid over/under-representing labels\n",
    "\n",
    "train_examples = []\n",
    "dev_examples = []\n",
    "test_examples = []\n",
    "\n",
    "for _, sentences in label_sentences_mapping.items():\n",
    "    random.shuffle(sentences)\n",
    "\n",
    "    split_1 = int(0.8 * len(sentences))\n",
    "    split_2 = int(0.9 * len(sentences))\n",
    "\n",
    "    current_train_examples = sentences[:split_1]\n",
    "    current_dev_examples = sentences[split_1:split_2]\n",
    "    current_test_examples = sentences[split_2:]\n",
    "\n",
    "    train_examples += current_train_examples\n",
    "    dev_examples += current_dev_examples\n",
    "    test_examples += current_test_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e1fc73f-1b9b-41eb-946b-872a1308712d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 741\n",
      "Number of development examples: 93\n",
      "Number of test examples: 95\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of training examples:\", len(train_examples))\n",
    "print(\"Number of development examples:\", len(dev_examples))\n",
    "print(\"Number of test examples:\", len(test_examples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5f359476-ecd5-4502-86ba-fee8bd8d3dcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert num_examples == (len(train_examples) + len(dev_examples) + len(test_examples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29e1f3c9-3c3b-4541-840d-3db304966762",
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_examples(examples: str, split_name: str):\n",
    "    # Shuffle again for more fun ;)\n",
    "    random.shuffle(examples)\n",
    "    with open(f\"{split_name}.txt\", \"wt\") as f_out:\n",
    "        for example in examples:\n",
    "            label, sentence = example\n",
    "\n",
    "            # Fix!\n",
    "            sentence = sentence.replace(\"\\n\", \" \")\n",
    "            \n",
    "            # We stick to Flair format for classification tasks, which is basically FastText inspired ;)\n",
    "            new_label = \"__label__\" + label\n",
    "            f_out.write(f\"{new_label} {sentence}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "502b3865-3efe-4730-9be8-ea675fd3feec",
   "metadata": {},
   "outputs": [],
   "source": [
    "write_examples(train_examples, \"train\")\n",
    "write_examples(dev_examples, \"dev\")\n",
    "write_examples(test_examples, \"test\")"
   ]
  }
 ],
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