03_Fasttext.ipynb

{
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   "execution_count": 1,
   "id": "d096c47e-eb0c-4017-8f14-0f97ead02a3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5f10c5ba-08c8-4081-89ac-e796d49220bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import fasttext\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "import sys\n",
    "sys.path.append('DialogueActClassification')\n",
    "\n",
    "from dialogue_classification.dataset_utils import load_swda_corpus_data\n",
    "from dialogue_classification.train_test_split import train_set_idx, valid_set_idx, test_set_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d006a41-8661-4107-a075-ca1065f65fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b9855bf7-0300-457e-8ef1-1c64fcb7925c",
   "metadata": {},
   "outputs": [],
   "source": [
    "!gzip -dk cc.en.300.bin.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5480ba6c-dac4-4735-b527-b645c34741c6",
   "metadata": {},
   "source": [
    "## Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4881211d-55b1-4bbd-92bc-46f9e994ebaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading SwDA Corpus...\n",
      "\n",
      "Found 43 different utterance tags.\n",
      "\n",
      "Loaded SwDA Corpus.\n"
     ]
    }
   ],
   "source": [
    "talks, talk_names, tag_indices, tag_occurances = load_swda_corpus_data('swda/swda/swda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bab26076-0890-48f0-be06-c912a40b2c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1155, 43)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(talks), len(tag_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "861d5c56-0736-462c-9765-ca464db7af99",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def preprocess(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub(\"[,']+\", '', text)\n",
    "    text = re.sub('\\.', ' . ', text)\n",
    "    text = re.sub('\\?', ' ? ', text)\n",
    "    text = re.sub('!', ' ! ', text)\n",
    "    text = re.sub('\\s+', ' ', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4839c599-1cd2-474c-9482-731ac3496a22",
   "metadata": {},
   "outputs": [],
   "source": [
    "texts, labels = [], []\n",
    "for text, label in talks:\n",
    "    assert len(text) == len(label)\n",
    "    text = [' '.join(utterance) for utterance in text]\n",
    "    texts.append(text)\n",
    "    labels.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "885f429a-767b-4b6b-b77f-6c29887e2543",
   "metadata": {},
   "outputs": [],
   "source": [
    "texts = [[preprocess(utt) for utt in text] for text in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d051c5a3-13ba-443c-a3c8-26f1246411ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_idx = np.isin(talk_names, train_set_idx)\n",
    "valid_idx = np.isin(talk_names, valid_set_idx)\n",
    "test_idx = np.isin(talk_names, test_set_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7cc4e42f-d115-47f0-9b68-a25d9dadcf4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/pk/0hw9m8496w7gxnl1pfsssfdddh310s/T/ipykernel_33254/1925198038.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  texts, labels = np.array(texts), np.array(labels)\n"
     ]
    }
   ],
   "source": [
    "texts, labels = np.array(texts), np.array(labels)\n",
    "\n",
    "train_texts, train_labels = texts[train_idx], labels[train_idx]\n",
    "valid_texts, valid_labels = texts[valid_idx], labels[valid_idx]\n",
    "test_texts, test_labels = texts[test_idx], labels[test_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "22703259-27de-4db0-ab4d-48f5c6beea05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1003, 112, 19)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_texts), len(valid_texts), len(test_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00285eb3-e298-4203-8dfc-b2f82a15f21d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# flatten\n",
    "train_texts = sum(train_texts.tolist(), [])\n",
    "valid_texts = sum(valid_texts.tolist(), [])\n",
    "test_texts = sum(test_texts.tolist(), [])\n",
    "\n",
    "train_labels = np.array(sum(train_labels.tolist(), []))\n",
    "valid_labels = np.array(sum(valid_labels.tolist(), []))\n",
    "test_labels = np.array(sum(test_labels.tolist(), []))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e481f213-0f0c-4733-9319-2a77b084dddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['so ive been concerned about crime lately . ',\n",
       " 'uh-huh . ',\n",
       " 'uh its really scary to listen to the news every night and --',\n",
       " 'uh-huh . ',\n",
       " '-- to hear about all the problems . ',\n",
       " 'i wondered if you were taking any special precautions in your neighborhood ? ',\n",
       " 'well i i think we have a neighborhood watch <laughter> . ',\n",
       " 'uh-huh . ',\n",
       " 'i think . ',\n",
       " '<laughter> . ']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_texts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dd892414-033a-444e-acb5-638f7f84546a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([38, 11, 39, 11,  1, 37, 25, 11, 38, 42])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec2179dd-34c3-4ac1-9b55-a055fc0084f0",
   "metadata": {},
   "source": [
    "## Fasttext embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "62526bf1-1671-4c67-bb61-cb1301817b69",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n"
     ]
    }
   ],
   "source": [
    "fasttext_model = fasttext.load_model(\"cc.en.300.bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "08eef5ba-ce5e-44d6-a143-bc785aa3f147",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 193325/193325 [00:01<00:00, 108718.93it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20218/20218 [00:00<00:00, 107278.91it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4514/4514 [00:00<00:00, 107568.25it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "X_train = [fasttext_model.get_sentence_vector(text) for text in tqdm(train_texts)]\n",
    "X_valid = [fasttext_model.get_sentence_vector(text) for text in tqdm(valid_texts)]\n",
    "X_test = [fasttext_model.get_sentence_vector(text) for text in tqdm(test_texts)]\n",
    "\n",
    "X_train = np.stack(X_train)\n",
    "X_valid = np.stack(X_valid)\n",
    "X_test = np.stack(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1546e2a5-bb3d-41a3-acca-e197aaa7da7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193325, 300)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad2316ab-75c8-4094-ad89-c477d39031d2",
   "metadata": {},
   "source": [
    "## Train & Eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "80c92aaf-3345-4c5a-8920-79729ff58562",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/e.trufanova/.pyenv/versions/3.8.6/lib/python3.8/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/Users/e.trufanova/.pyenv/versions/3.8.6/lib/python3.8/site-packages/lightgbm/sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50]\tvalid_0's multi_logloss: 1.24505\n",
      "[100]\tvalid_0's multi_logloss: 1.09023\n",
      "[150]\tvalid_0's multi_logloss: 1.04074\n",
      "[200]\tvalid_0's multi_logloss: 1.02112\n",
      "[250]\tvalid_0's multi_logloss: 1.01343\n",
      "[300]\tvalid_0's multi_logloss: 1.01141\n",
      "[350]\tvalid_0's multi_logloss: 1.01238\n"
     ]
    },
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       "               random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(learning_rate=0.02, n_estimators=1000, objective=&#x27;multiclass&#x27;,\n",
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      ],
      "text/plain": [
       "LGBMClassifier(learning_rate=0.02, n_estimators=1000, objective='multiclass',\n",
       "               random_state=42)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LGBMClassifier(n_estimators=1000, objective='multiclass', n_jobs=-1, random_state=42, learning_rate=0.02)\n",
    "\n",
    "model.fit(\n",
    "    X_train, \n",
    "    train_labels, \n",
    "    eval_metric='multi_logloss', \n",
    "    eval_set=[(X_valid, valid_labels)],\n",
    "    early_stopping_rounds=50, \n",
    "    verbose=50,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "05a4dcc1-f5cd-4836-8905-91ef9b3a84b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6657066902968543"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(test_labels, model.predict(X_test), average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5164f3f5-dfc1-478d-9b31-3b0f61744e73",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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