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bfarzin/IMDB_data tokenize LM build SpaCy (default).ipynb

Created March 11, 2019 16:30
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IMDB Data Example SpaCy and SentencePiece
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IMDB_data tokenize LM build SpaCy (default).ipynb
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IMDB_data tokenize LM build.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from fastai import *\n",
"from fastai.text import *\n",
"import re\n",
"import sentencepiece as spm #https://github.com/google/sentencepiece\n",
"\n",
"np.random.seed(20180311)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sentencepiece tokenizer & LM Build"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data processing to DataBunch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get IMDB Data and build DF"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PosixPath('/home/farzin/.fastai/data/imdb')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"IMDB_PATH = untar_data(URLs.IMDB)\n",
"IMDB_PATH"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(75000, 25000)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## raw data does not have the unsup in the train directory. Just move it there\n",
"# ~/.fastai/data/imdb$ mv unsup/ ./train/\n",
"CLASSES = ['neg', 'pos', 'unsup']\n",
"\n",
"def get_texts(path):\n",
" texts,labels = [],[]\n",
" for idx,label in enumerate(CLASSES):\n",
" for fname in (path/label).glob('*.*'):\n",
" texts.append(fname.open('r', encoding='utf8').read())\n",
" labels.append([0]) ## all unsupervised case\n",
"\n",
" return np.array(texts),np.array(labels)\n",
"\n",
"train_texts,train_labels = get_texts(IMDB_PATH/'train')\n",
"valid_texts,valid_labels = get_texts(IMDB_PATH/'test')\n",
"\n",
"len(train_texts),len(valid_texts)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"## we can combine the \"train\" and \"valid\" for the LM step\n",
"all_texts = np.concatenate([train_texts,valid_texts])\n",
"all_texts_df = pd.DataFrame({'text':all_texts, 'labels':[0]*len(all_texts)}, columns=['labels','text'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Default Rules:\n",
" ['fix_html', 'replace_rep', 'replace_wrep', 'spec_add_spaces', 'rm_useless_spaces'] \n",
"\n",
"\n",
"<function fix_html at 0x7f1058e22ea0>\n",
"<function replace_rep at 0x7f1058e22d90>\n",
"<function replace_wrep at 0x7f1058e22e18>\n",
"<function spec_add_spaces at 0x7f105dbc2730>\n",
"<function rm_useless_spaces at 0x7f1058e22d08>\n"
]
}
],
"source": [
"raw_text = all_texts_df.loc[:,'text']\n",
"\n",
"print(\"Default Rules:\\n\",[x.__name__ for x in defaults.text_pre_rules],\"\\n\\n\")\n",
"\n",
"for rule in defaults.text_pre_rules:\n",
" print(rule)\n",
" raw_text = raw_text.apply(lambda x: rule(str(x)))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 <s>\"Fred Claus\" somehow avoids becoming this y...\n",
"1 <s>Firstly I would like to point out that I on...\n",
"2 <s>I finally got to have a look at this experi...\n",
"3 <s>After having red the overwhelming reviews t...\n",
"4 <s>About three minutes into this thing I start...\n",
"Name: new_text, dtype: object"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## SentencePiece expects <s> ... </s> so we add that here:\n",
"all_texts_df['new_text'] = '<s>' + raw_text + '</s>'\n",
"all_texts_df['new_text'].head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"## save as a file for SP\n",
"formatted_text_file = 'tmp_IMDB_SP_example'\n",
"all_texts_df['new_text'].to_frame().to_csv(formatted_text_file, header=False,index=False,quotechar=' ')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SP Tokenizer SWIG wrapper"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# m.model and m.vocab created in the pwd\n",
"vocab_size = 500\n",
"model_prefix = 'wk103m_example'\n",
"\n",
"spm.SentencePieceTrainer.Train(f'--input={formatted_text_file}'\\\n",
" f' --model_prefix={model_prefix}'\\\n",
" f' --vocab_size={vocab_size}')\n",
"# f'--unk_piece={UNK} --bos_piece={BOS} --eos_id=-1 --pad_piece={PAD}')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1\t<unk>\t0\r\n",
" 2\t<s>\t0\r\n",
" 3\t</s>\t0\r\n",
" 4\t▁\t-2.84395\r\n",
" 5\ts\t-2.97\r\n",
" 6\tt\t-3.53742\r\n",
" 7\t▁the\t-3.7748\r\n",
" 8\t.\t-3.86454\r\n",
" 9\t,\t-3.91818\r\n",
" 10\t▁a\t-4.07048\r\n",
" 11\tn\t-4.18876\r\n",
" 12\ta\t-4.24687\r\n",
" 13\te\t-4.25059\r\n",
" 14\to\t-4.27973\r\n",
" 15\ty\t-4.44583\r\n",
" 16\t▁to\t-4.48563\r\n",
" 17\ting\t-4.50046\r\n",
" 18\t▁and\t-4.50961\r\n",
" 19\t▁of\t-4.5516\r\n",
" 20\t'\t-4.64682\r\n",
" 21\td\t-4.69638\r\n",
" 22\t▁in\t-4.76258\r\n",
" 23\ti\t-4.77796\r\n",
" 24\ted\t-4.78844\r\n",
" 25\tc\t-4.81265\r\n",
" 26\tr\t-4.84313\r\n",
" 27\t▁is\t-4.84642\r\n",
" 28\tre\t-4.896\r\n",
" 29\tp\t-4.94282\r\n",
" 30\t▁I\t-4.97998\r\n",
" 31\ter\t-4.9895\r\n",
" 32\tu\t-4.99602\r\n",
" 33\t▁it\t-5.11542\r\n",
" 34\tor\t-5.14418\r\n",
" 35\tb\t-5.17535\r\n",
" 36\tar\t-5.22629\r\n",
" 37\tm\t-5.2283\r\n",
" 38\tle\t-5.23756\r\n",
" 39\t▁f\t-5.23919\r\n",
" 40\tw\t-5.28417\r\n",
" 41\tf\t-5.28488\r\n",
" 42\tly\t-5.29174\r\n",
" 43\t▁that\t-5.29407\r\n",
" 44\tal\t-5.30172\r\n",
" 45\tg\t-5.30389\r\n",
" 46\tl\t-5.37982\r\n",
" 47\tin\t-5.40889\r\n",
" 48\t▁this\t-5.4378\r\n",
" 49\t-\t-5.48409\r\n",
" 50\tk\t-5.5797\r\n"
]
}
],
"source": [
"## Vocab in order of frequency\n",
"!head -n50 {model_prefix}.vocab | nl"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"## load up the Processor\n",
"sp = spm.SentencePieceProcessor()\n",
"sp.load(f'{model_prefix}.model')\n",
"\n",
"## itos from m.vocab file: just read directly and populate the dictionary\n",
"itos = {}\n",
"with open(f'{model_prefix}.vocab','r') as f:\n",
" for line_num,line in enumerate(f):\n",
" itos[line_num] = line.split(\"\\t\")[0]\n",
" \n",
"stoi = {v:k for k,v in itos.items()}"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#Based on Tokenizer() class: https://github.com/fastai/fastai/blob/master/fastai/text/transform.py#L87\n",
"class CustomTokenizer():\n",
" '''Wrapper for SentencePiece toeknizer to fit into Fast.ai V1'''\n",
" def __init__(self,sp_processor,pre_rules:ListRules=None,post_rules:ListRules=None):\n",
" self.sp = sp_processor\n",
" self.pre_rules = ifnone(pre_rules, defaults.text_pre_rules )\n",
" \n",
" def __repr__(self) -> str:\n",
" return \"Custom Tokenizer\"\n",
"\n",
" def process_text(self, t:str) -> List[str]:\n",
" \"Processe one text `t` with tokenizer `tok`.\"\n",
" for rule in self.pre_rules: t = rule(t) \n",
" toks = sp.EncodeAsIds(t)\n",
" \n",
" return toks \n",
" \n",
" def _process_all_1(self,texts:Collection[str]) -> List[List[str]]:\n",
" 'Process a list of `texts` in one process'\n",
" return [self.process_text(t) for t in texts]\n",
" \n",
" def process_all(self, texts:Collection[str]) -> List[List[str]]: \n",
" \"Process a list of `texts`.\" \n",
" return self._process_all_1(texts)\n",
" \n",
"mycust_tok = CustomTokenizer(sp)\n",
"#setup Vocab object for use in LM \n",
"# Vocab source: https://github.com/fastai/fastai/blob/master/fastai/text/transform.py#L122\n",
"sp_vocab = Vocab(itos)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>labels</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>Detective Frank Keller(Al Pacino, in a perfect...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>This is a VERY good movie. I give it a 10.&lt;br ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>I can't say I enjoyed this as much as \"The Big...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>I sat last night to see this film being played...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>First of all, I agree that the plot left somet...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" labels text\n",
"0 0 Detective Frank Keller(Al Pacino, in a perfect...\n",
"1 0 This is a VERY good movie. I give it a 10.<br ...\n",
"2 0 I can't say I enjoyed this as much as \"The Big...\n",
"3 0 I sat last night to see this film being played...\n",
"4 0 First of all, I agree that the plot left somet..."
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idx = np.random.permutation(len(all_texts))\n",
"cut = int(0.1 * len(idx))\n",
"LM_train_df = pd.DataFrame({'text':all_texts[idx[cut:]], 'labels':[0] * (len(all_texts)-cut)}, columns=['labels','text'])\n",
"LM_valid_df = pd.DataFrame({'text':all_texts[idx[:cut]], 'labels':[0] * cut}, columns=['labels','text'])\n",
"LM_train_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"PATH = Path(\"./tmp_IMDB_example\")\n",
"\n",
"data = TextLMDataBunch.from_df(PATH, LM_train_df, LM_valid_df, \n",
" tokenizer=mycust_tok, vocab=sp_vocab, \n",
" text_cols='text', label_cols='labels')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LM Learner"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"learn = language_model_learner(data, AWD_LSTM, drop_mult=0.5)\n",
"learn.freeze()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LM Train"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"lr_find(learn)\n",
"learn.recorder.plot()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Total time: 14:24 <p><table style='width:375px; margin-bottom:10px'>\n",
" <tr>\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <th>3.747901</th>\n",
" <th>3.465941</th>\n",
" <th>0.260205</th>\n",
" <th>14:24</th>\n",
" </tr>\n",
"</table>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learn.fit_one_cycle(1,1e-2)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"learn.recorder.plot_losses()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.7 fasta.ai1 DEV",
"language": "python",
"name": "fastai1_dev"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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