{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **Coleridge Initiative - Show US the Data**\n","metadata":{}},{"cell_type":"markdown","source":"The goal of this competition is to extract out the references to various studies/dataset that is embedded in the content. \n\nThe approach followed is similar to that of a translation problem using an attention network. We provide the input text and the model has to provide an output text. In this case, the output text is the \"references\" in the text. \n\nThis notebook uses a pretrained BERTTokenizer to generate the encoded tokens for the provided text and then uses a simple transformer model to generate an output that is also a sequence of BERTTokens.\n\nTo acheive this, we are going to do the following\n\n* Preprocessing the data\n    * Read the relevant content for each id.\n    * chunk them into sizes of 500.\n    * clean these chunks and BERTTokenize them. This will be the input to the transformer.\n    * During training, we read the output dataset label\n    * clean these and BERTTokenize them.\n    * wrap these around [CLS] and [SEP] tokens. This will be the ouput for the transformer.\n* Model and training the model\n    * We create a simple pytorch network based on transformers.\n    * we train the network using the inputs and outputs that we generated during pretraining\n    * we use NLLLoss for backpropagation in the network.\n\n\nThis model provides a score of 0.532 during inference with the test data.\nhttps://www.kaggle.com/sreejaej/colleridge-attention-inference/ \n","metadata":{}},{"cell_type":"code","source":"##\n# Import the relevant libraries\n##\n\nimport pandas as pd\nimport numpy as np\nimport json\nimport re\nimport torch.nn as nn \nimport torch\nfrom transformers import BertTokenizer\nfrom nltk.corpus import stopwords\nfrom tqdm import tqdm\nimport random\n\n","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:18:16.388770Z","iopub.execute_input":"2021-06-14T07:18:16.389603Z","iopub.status.idle":"2021-06-14T07:18:17.625738Z","shell.execute_reply.started":"2021-06-14T07:18:16.389478Z","shell.execute_reply":"2021-06-14T07:18:17.624414Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##### # Set the constants that are used in the code \n##\n\nDATA_PROCESSING_MODE = \"PROCESS_TRAINING_DATA\"\n#DATA_PROCESSING_MODE = \"USE_PREPROCESSED_TRAINING_DATA\"\n\nTRAINING_MODE = \"TRAIN_FROM_SCRATCH\"\n#TRAINING_MODE = \"USE_TRAINED_MODEL\"\n\nTRAIN_PATH = '../input/coleridgeinitiative-show-us-the-data/train.csv'\nTRAIN_DATA_FOLDER = '../input/coleridgeinitiative-show-us-the-data/train'\nTRAINING_BATCH_END_INDEX = 5000\nVAL_BATCH_START_INDEX = 5000 \nVAL_BATCH_END_INDEX = 6000  \n\nPREPROCESSED_TRAIN_PATH = \"../input/colldata10-2/colleridge_processed_train-10.parquet\"\nPREPROCESSED_VAL_PATH =\"../input/colldata10-2/colleridge_processed_val-10.parquet\"\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nMAX_SEQ = 200\nOVERLAP=20\nEMBEDDING_DIM = 200 \nBATCH_SIZE = 8 \nLEARNING_RATE=1e-3\nEPOCHS = 15 ","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:18:17.627656Z","iopub.execute_input":"2021-06-14T07:18:17.628142Z","iopub.status.idle":"2021-06-14T07:18:17.636677Z","shell.execute_reply.started":"2021-06-14T07:18:17.628094Z","shell.execute_reply":"2021-06-14T07:18:17.635130Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##\n# Read the data \n##\ntrain = pd.read_csv(TRAIN_PATH)\ntrain","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:18:17.638816Z","iopub.execute_input":"2021-06-14T07:18:17.639141Z","iopub.status.idle":"2021-06-14T07:18:17.781206Z","shell.execute_reply.started":"2021-06-14T07:18:17.639110Z","shell.execute_reply":"2021-06-14T07:18:17.779816Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Preprocessing","metadata":{}},{"cell_type":"markdown","source":"![DataPreprocessing.jpg](attachment:5c54789f-f52f-48a2-8865-4b94b6c33e3b.jpg)","metadata":{},"attachments":{"5c54789f-f52f-48a2-8865-4b94b6c33e3b.jpg":{"image/jpeg":"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= train['cleaned_label'].apply(lambda x : x.strip())\n","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:18:17.783686Z","iopub.execute_input":"2021-06-14T07:18:17.784133Z","iopub.status.idle":"2021-06-14T07:18:17.797537Z","shell.execute_reply.started":"2021-06-14T07:18:17.784084Z","shell.execute_reply":"2021-06-14T07:18:17.796233Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"known_labels = np.unique( train['cleaned_label'] ) ","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:18:17.799212Z","iopub.execute_input":"2021-06-14T07:18:17.799517Z","iopub.status.idle":"2021-06-14T07:18:17.825167Z","shell.execute_reply.started":"2021-06-14T07:18:17.799487Z","shell.execute_reply":"2021-06-14T07:18:17.824316Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''for label in known_labels:\n    ignore=False;\n    for word in label.split():\n        if word in ['study', 'data', 'dataset', 'database', 'survey']:\n            ignore=True\n            break\n    if( ignore==False):\n        print(label)'''\n    ","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:18:17.826455Z","iopub.execute_input":"2021-06-14T07:18:17.826935Z","iopub.status.idle":"2021-06-14T07:18:17.840234Z","shell.execute_reply.started":"2021-06-14T07:18:17.826903Z","shell.execute_reply":"2021-06-14T07:18:17.838897Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''with open(TRAIN_DATA_FOLDER+'/'+ 'fd23e7e0-a5d2-4f98-992d-9209c85153bb' +'.json', 'r') as f:\n    #Load the json \n    paper = json.load(f)\n    print(paper)\n    #print( re.findall('\\([A-Z]{4,}', paper[0].get('text')) ) '''","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:18:17.841710Z","iopub.execute_input":"2021-06-14T07:18:17.842010Z","iopub.status.idle":"2021-06-14T07:18:17.854149Z","shell.execute_reply.started":"2021-06-14T07:18:17.841982Z","shell.execute_reply":"2021-06-14T07:18:17.853065Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"####\n#\n# This method is used to clean the input list of words.\n#\n####\nstopwords_set = set(stopwords.words('english')) \ndef clean_text(input_words):\n    #return [ re.sub(' +', ' ', re.sub('[^A-Za-z0-9\\[\\]]+', ' ', str(r).lower()).strip()) for r in input_words if not r.lower() in stopwords_set  ]\n    return [ re.sub(' +', ' ', re.sub('[^A-Za-z0-9\\[\\]]+', ' ', str(r).lower()).strip()) for r in input_words ]\n\n\n\n####\n#\n# This method takes a single chunk of 'input words', cleans and tokenizes them using a tokenizer.  It also compares the input \n# for a set of \"potential output string\". If any of them match, the matches string is also tokenized with a wraping \n# [cls] [sep]. The tokenized input and output array is returned as the output. \n#\n####\ndef tokenize_single_chunk(input_words, potential_output_strings, tokenizer):\n\n    successfully_matched_string = \"[cls] \"\n    \n    matched_list = [] \n    \n    for match_string in potential_output_strings:  \n        if \" \".join(clean_text(match_string.split())) not in \" \".join(clean_text(input_words)):\n            continue    \n        matched_list.append(match_string)\n\n    for match_string1 in matched_list:\n        '''ignore=False        \n        for match_string2 in matched_list:\n            if( (match_string1 != match_string2) and (match_string1 in match_string2 )):\n                ignore=True\n        if( ignore == False): '''  \n        successfully_matched_string = successfully_matched_string  +  match_string1    +  \" [sep] \"\n\n    if (successfully_matched_string == \"[cls] \" ):\n        successfully_matched_string = successfully_matched_string  +  \" [sep] \"\n        \n    #print(successfully_matched_string)\n    \n         \n    encoded_input_word_list = clean_text(input_words)\n    encoded_input_word_list = list(map(tokenizer.convert_tokens_to_ids, tokenizer.tokenize ( \" \".join(encoded_input_word_list)) ))\n    \n\n    if ( len(encoded_input_word_list) <= MAX_SEQ ):\n        encoded_input_word_list = encoded_input_word_list + list(np.zeros( MAX_SEQ - len(encoded_input_word_list)) )\n    else :\n        encoded_input_word_list = encoded_input_word_list[0:MAX_SEQ]\n    \n    \n    encoded_output_word_list = list(map(tokenizer.convert_tokens_to_ids, tokenizer.tokenize ( successfully_matched_string ) ))\n    \n\n    if ( len(encoded_output_word_list) < MAX_SEQ ):\n        encoded_output_word_list = encoded_output_word_list +  list(np.zeros( MAX_SEQ - len(encoded_output_word_list)) )\n    else:\n        encoded_output_word_list = encoded_output_word_list[:MAX_SEQ]\n        \n    \n    return encoded_input_word_list, encoded_output_word_list\n\n\n\n\n####\n#\n# This method takes a dataframe with each row representing an id and 'output labels'. The corresponding text in the json is read  \n# and converted into chunks of 450 (Tokenizer uses word piece that causes the count to increase so setting to 450. (Todo: Relook) ).\n# Process each of these chunks and 'output labels' to create the input and ouputs needed to train the network. \n# This returns a dataframe with encoded inputs and ouput as each row (for each chunk )\n# \n####\ndef process_data (df, tokenizer) :\n    max_word_count = 0 \n    text_array = [] \n    code_array=[]\n     \n    \n    df_final = pd.DataFrame(columns = ['Id', 'text', 'code'])\n\n    #words_in_cap=[]\n    \n    for index, id in tqdm ( enumerate ( df.index) )  :\n        with open(TRAIN_DATA_FOLDER+'/'+ id +'.json', 'r') as f:\n                #Load the json \n                paper = json.load(f)\n                #print(id)\n                #print(paper)\n                \n                #Convert the relevant sentences into a single paragraph\n                section_sentences =[]\n\n                for section_index in range ( 0, len(paper)):\n                    #words_in_cap = words_in_cap + re.findall('[A-Z]{4,}', paper[section_index].get('text'))\n                    section_sentences = section_sentences +  re.split('[.;\\n•]',paper[section_index].get('text'))   \n                section_sentences = \"[sep]\".join(section_sentences) \n                sentence_words = section_sentences.split() \n        \n               \n            \n                # Chunk the paragraph into size of 500. Todo:Relook at this \n                n = int(0.7*MAX_SEQ)\n                word_chunks = [sentence_words[i:i+n+OVERLAP]  for i in range(0, len(sentence_words), n)] \n                word_chunks[-1] = word_chunks[-1] + list(np.empty( MAX_SEQ - len(word_chunks[-1]), dtype=str) )\n \n                     \n                # Add the words in the labels to the output wordlist\n                labels  = df[index]\n\n                # Encode/Embed the words for processing\n                df_new = pd.DataFrame(columns = ['Id', 'text', 'code','rseq_no'])\n                chunk_count= len(word_chunks)\n                for i in range ( 0, len(word_chunks)):\n                    encoded_input_word_list, encoded_output_word_list = tokenize_single_chunk(word_chunks[i], labels, tokenizer )\n                    df_temp = pd.DataFrame ({\"Id\":id, \"text\":[encoded_input_word_list], \"code\": [encoded_output_word_list], 'rseq_no':chunk_count- i}, index=[i])\n                    df_new =df_new.append(df_temp, ignore_index=True)\n                    #print(df_new)\n\n        df_final = df_final.append(df_new, ignore_index=True)\n\n    df_final['word_count'] = list( map ( (lambda i : len ( [ x for x in df_final.iloc[i]['code'] if x not in [30522, 30523, 0.0] ] ) ) , range(0, df_final.shape[0])) )\n\n    '''words_in_cap = np.unique( words_in_cap)\n    for cap_word in words_in_cap:\n        print(cap_word)\n        tokenizer.add_tokens(cap_word)'''\n        \n    return df_final","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:18:17.855584Z","iopub.execute_input":"2021-06-14T07:18:17.856135Z","iopub.status.idle":"2021-06-14T07:18:17.883308Z","shell.execute_reply.started":"2021-06-14T07:18:17.856100Z","shell.execute_reply":"2021-06-14T07:18:17.881799Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##\n# Group the data to obtain all the output labels for an id \n##\n\ntrain['dataset_title'] = train['dataset_title'].apply ( lambda x: re.sub('[^A-Za-z0-9\\[\\]]+', ' ', str(x).lower()).strip() )\ntrain['dataset_label'] = train['dataset_label'].apply ( lambda x:  re.sub('[^A-Za-z0-9\\[\\]]+', ' ', str(x).lower()).strip() )\n\n'''train_new = train [['Id', 'dataset_title', 'dataset_label', 'cleaned_label']].groupby('Id').apply(lambda r: np.unique( list( r['dataset_label'].values) \n                                                                                +  list ( r['cleaned_label'].values) \n                                                                                + list ( r['dataset_title'].values )) )'''\n\ntrain_new = train [['Id', 'dataset_title', 'dataset_label', 'cleaned_label']].groupby('Id').apply(lambda r: np.unique( list ( r['cleaned_label'].values) \n                                                                                ))\ntrain_new","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:18:17.886784Z","iopub.execute_input":"2021-06-14T07:18:17.887243Z","iopub.status.idle":"2021-06-14T07:18:19.149605Z","shell.execute_reply.started":"2021-06-14T07:18:17.887181Z","shell.execute_reply":"2021-06-14T07:18:19.148603Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"temp = list(map( lambda x : list(x), train_new)) \nknown_labels = np.unique( [j for sub in temp for j in sub] )","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:18:19.151393Z","iopub.execute_input":"2021-06-14T07:18:19.151725Z","iopub.status.idle":"2021-06-14T07:18:19.277577Z","shell.execute_reply.started":"2021-06-14T07:18:19.151684Z","shell.execute_reply":"2021-06-14T07:18:19.276350Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##\n# Intialize a BERT Tokenizer and Process the input data and make it ready for training. \n# Create train and validataion ( Todo : do this using Cross Validation )\n##\n\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)\ntokenizer.add_tokens('[cls]')\ntokenizer.add_tokens('[sep]')\n\nif ( DATA_PROCESSING_MODE == 'PROCESS_TRAINING_DATA' ):\n    df_train = process_data(train_new[:TRAINING_BATCH_END_INDEX], tokenizer)  \n    df_train.to_parquet(\"colleridge_processed_train.parquet\")\n\n    df_val = process_data(train_new[VAL_BATCH_START_INDEX:VAL_BATCH_END_INDEX], tokenizer)  \n    df_val.to_parquet(\"colleridge_processed_val.parquet\")\n    \nelse :\n    df_train = pd.read_parquet (PREPROCESSED_TRAIN_PATH)\n    df_val = pd.read_parquet(PREPROCESSED_VAL_PATH)\n    df_train = df_train.append(df_val)\n\n    ","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:18:19.279235Z","iopub.execute_input":"2021-06-14T07:18:19.279557Z","iopub.status.idle":"2021-06-14T07:19:10.522520Z","shell.execute_reply.started":"2021-06-14T07:18:19.279527Z","shell.execute_reply":"2021-06-14T07:19:10.521639Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"vocab_len= 30522 + len( tokenizer.get_added_vocab() )\nvocab_len","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:19:10.524116Z","iopub.execute_input":"2021-06-14T07:19:10.524658Z","iopub.status.idle":"2021-06-14T07:19:10.530889Z","shell.execute_reply.started":"2021-06-14T07:19:10.524622Z","shell.execute_reply":"2021-06-14T07:19:10.529757Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#df_tx = df_train.groupby('Id').apply( lambda row: sum(row['word_count'])) \n#train_new[train_new.index == '01ab57bd-b4ad-4440-a99a-40f1f810f4cd']['01ab57bd-b4ad-4440-a99a-40f1f810f4cd']\n#process_data ( train_new[train_new.index == 'd67cdcda-a6fc-41b5-9ee0-016edabe4aec'] , tokenizer ) ","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:19:10.532170Z","iopub.execute_input":"2021-06-14T07:19:10.532447Z","iopub.status.idle":"2021-06-14T07:19:10.548869Z","shell.execute_reply.started":"2021-06-14T07:19:10.532420Z","shell.execute_reply":"2021-06-14T07:19:10.548074Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train = df_train.reset_index()\ndf_train","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:19:10.549880Z","iopub.execute_input":"2021-06-14T07:19:10.550324Z","iopub.status.idle":"2021-06-14T07:19:10.619528Z","shell.execute_reply.started":"2021-06-14T07:19:10.550284Z","shell.execute_reply":"2021-06-14T07:19:10.618298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train[df_train['Id']=='630de001-fa5b-4973-ba3f-52f6f1f4dc2d']","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:19:10.620935Z","iopub.execute_input":"2021-06-14T07:19:10.621310Z","iopub.status.idle":"2021-06-14T07:19:10.634999Z","shell.execute_reply.started":"2021-06-14T07:19:10.621279Z","shell.execute_reply":"2021-06-14T07:19:10.634102Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Training","metadata":{}},{"cell_type":"markdown","source":"![Train.jpg](attachment:2f931856-1b98-4fdb-b3b7-40f478cb76ef.jpg)","metadata":{},"attachments":{"2f931856-1b98-4fdb-b3b7-40f478cb76ef.jpg":{"image/jpeg":"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image taken from : https://arxiv.org/abs/1706.03762","metadata":{}},{"cell_type":"code","source":"##\n# Define the transformer model \n##\n\nimport torch.nn as nn \n\nclass FFN(nn.Module):\n    def __init__(self, state_size=EMBEDDING_DIM):\n        super(FFN, self).__init__()\n        self.state_size = state_size\n        self.lr1 = nn.Linear(state_size, state_size)\n        self.relu = nn.ReLU()\n        self.lr2 = nn.Linear(state_size, state_size)\n        self.dropout = nn.Dropout(0.2)\n    \n    def forward(self, x):\n        x = self.lr1(x)\n        x = self.relu(x)\n        x = self.lr2(x)\n        return self.dropout(x)\n\ndef future_mask(seq_length):\n    future_mask = np.triu(np.ones((seq_length, seq_length)), k=1).astype('bool')\n    return torch.from_numpy(future_mask)\n\nclass ColleridgeTransformerModel(nn.Module):\n\n    def __init__(self, input_vocab_len , output_vocab_len,embed_dim = EMBEDDING_DIM, max_seq=MAX_SEQ): \n        super(ColleridgeTransformerModel, self).__init__()\n        self.embed_dim = embed_dim\n        self.embedding = nn.Embedding(input_vocab_len, embed_dim)\n        self.pos_embedding = nn.Embedding(max_seq, embed_dim)\n        self.e_embedding = nn.Embedding(output_vocab_len, embed_dim)\n        self.multi_att = nn.MultiheadAttention(embed_dim=embed_dim, num_heads=8, dropout=0.2)\n        self.dropout = nn.Dropout(0.2)\n        self.layer_normal = nn.LayerNorm(embed_dim) \n        self.ffn = FFN(embed_dim)\n        self.pred = nn.Linear(embed_dim, output_vocab_len)\n        self.softmax = nn.LogSoftmax(dim=2)\n    \n    def forward(self, x, generated_words):\n        device = x.device       \n        \n        # Embed the token\n        x = self.embedding(x)\n        \n        # Calculate the position id and embed it\n        pos_id = torch.arange(x.size(1)).unsqueeze(0).to(device)\n        \n        #Add it to the poisition id\n        pos_x = self.pos_embedding(pos_id)\n        x = x + pos_x\n        \n        # This is the target_id/query ( right shifted input )\n        e = self.e_embedding(generated_words) \n        pos_e_id = torch.arange(e.size(1)).unsqueeze(0).to(device)\n        pos_e = self.pos_embedding(pos_e_id)\n        e = e + pos_e\n         \n        # Send the query key and value to the multi attention network \n        x = x.permute(1, 0, 2) # x: [bs, s_len, embed] => [s_len, bs, embed]\n        att_output, att_weight = self.multi_att(x, x, x)\n        x = self.layer_normal(att_output + x)\n        \n        \n        for i in range ( 0, 1):\n            \n            e = e.permute(1, 0, 2)\n              \n            att_mask = future_mask(e.size(0)).to(device) \n\n            # Send the query key and value to the multi attention network \n            att_output, att_weight = self.multi_att(e, e, e, attn_mask=att_mask)\n\n            e = self.layer_normal(att_output + e)\n\n            # Send the query key and value to the multi attention network \n            att_output, att_weight = self.multi_att(e, x, x)\n\n            # Normalization \n            att_output = self.layer_normal(att_output + e)\n\n            # Reshape the output\n            att_output = att_output.permute(1, 0, 2) # att_output: [s_len, bs, embed] => [bs, s_len, embed]\n\n            # Send the output to FFN.\n            e = self.ffn(att_output)\n\n            # Normalize\n            e = self.layer_normal(e + att_output)\n        \n        # predict the output string \n        e = self.pred(e)\n        # Softmax the output string\n        e = self.softmax(e)\n        e = e.permute(0, 2, 1)\n        return e, att_weight","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:19:10.636334Z","iopub.execute_input":"2021-06-14T07:19:10.636936Z","iopub.status.idle":"2021-06-14T07:19:10.659356Z","shell.execute_reply.started":"2021-06-14T07:19:10.636889Z","shell.execute_reply":"2021-06-14T07:19:10.658465Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##\n# Create the dataset class that will be used in the training \n##\n\nfrom torch.utils.data import Dataset, DataLoader\n\nclass ColleridgeDataset(Dataset):\n    def __init__(self, df, max_seq=MAX_SEQ):\n        super(ColleridgeDataset, self).__init__()\n        self.max_seq = max_seq\n        self.samples = df\n\n    def __len__(self):\n        #print(len(self.samples.index) )\n        return len(self.samples.index)\n    \n    def __pair_to_tensor__(self, pair):\n        input_tensor = torch.tensor(pair[0] , dtype=torch.int, device=device).view(-1, 1) \n        target_tensor = torch.tensor(pair[1], dtype=torch.int, device=device).view(-1, 1) \n        return (input_tensor, target_tensor)\n    \n    def __getitem__(self, index):\n        x, y = self.__pair_to_tensor__( (self.samples.iloc[index]['text'], self.samples.iloc[index]['code']) )\n        target_id = y[:-1]\n        label = y[1:]\n        #print(target_id.squeeze(-1)[0:10])\n        return  x.squeeze(-1), target_id.squeeze(-1), label\n##\n# This method trains the model with the iterator data for ONE epoch. \n##\nfrom sklearn.metrics import roc_auc_score\ndef train_epoch(model, train_iterator, optim, criterion, device=\"cpu\"):\n    model.train()\n\n    train_loss = []\n    num_corrects = 0\n    num_total = 0\n    labels = []\n    outs = []\n\n    tbar = tqdm(train_iterator)\n    for item in tbar:\n        x = item[0].to(device).long()\n        target_id = item[1].to(device).long()\n        label = item[2].to(device).long() \n\n        # Reset the gradients before invoking the 'forward' again\n        optim.zero_grad()\n        \n        # Invoke the 'forward' of the model\n        output, atten_weight = model(x, target_id)\n        \n        # compute the loss with the actual and computed value\n        loss = criterion(output , label.squeeze(-1) )\n\n        # when we call loss.backward(), the whole graph is \n        # differentiated w.r.t. the loss, and all Variables in the graph will have their \n        # .grad Variable accumulated with the gradient.\n        loss.backward()\n        \n        # move forward the optimizer\n        optim.step()\n        \n        # For display :\n        train_loss.append(loss.item())\n\n        tbar.set_description('loss - {:.4f}'.format(loss))\n\n    loss = np.mean(train_loss)\n\n    return loss","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:19:10.660713Z","iopub.execute_input":"2021-06-14T07:19:10.661317Z","iopub.status.idle":"2021-06-14T07:19:10.680115Z","shell.execute_reply.started":"2021-06-14T07:19:10.661270Z","shell.execute_reply":"2021-06-14T07:19:10.679291Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## #  Create a dataset that will provide the data for validation. Please note the 'target', initially, contains only the encoding for [cls] token \n#  Each generated ouput token in the previous cycle will be added to the target for the next prediction cycle.\n# \n##\n\nfrom torch.utils.data import Dataset, DataLoader\nclass ColleridgeDatasetEval(Dataset):\n    def __init__(self, df, max_seq=MAX_SEQ):\n        super(ColleridgeDatasetEval, self).__init__()\n        self.max_seq = max_seq\n        self.samples = df\n\n    def __len__(self):\n        return len(self.samples.index)\n    \n    def __getitem__(self, index):\n        x = torch.tensor(self.samples.iloc[index]['text'], dtype=torch.int, device=device).view(-1, 1)\n        y = torch.tensor(np.zeros(MAX_SEQ), dtype=torch.int, device=device).view(-1, 1)\n        y[0] = 30522\n        target_id = y[:-1]\n        \n        #label = torch.tensor(self.samples.iloc[index]['code'], device=device).view(1,-1).tolist()\n        return  x.squeeze(-1), target_id.squeeze(-1), np.array( self.samples.iloc[index]['code'] )  , self.samples.iloc[index]['Id']\n    \n##\n# Validate the model with unseen data and return the fbeta score \n##\n\ndef validate_model (model,dataloader,device ):\n\n    num=0\n\n    df_eval = pd.DataFrame(columns = ['id', 'predicted', 'truth'])\n    model.eval()\n\n    for item in tqdm(dataloader):\n        stop = False \n        i = 0\n        while ( i < MAX_SEQ-2 ):\n            output,_ = model( item[0].to(device).long(),item[1].to(device).long()  )\n            y = torch.argmax( output , dim=1)\n            z = y[0][i]\n            item[1][0][i+1]=z\n            i = i+1\n            if ( z == 0):\n                break\n        y = [30522] + y.tolist()[0]\n\n        pred_string = tokenizer.convert_tokens_to_string (tokenizer.convert_ids_to_tokens([int(z) for z in y if z not in [30522, 0, 0.0, 0 ]]))\n        text = tokenizer.convert_tokens_to_string (tokenizer.convert_ids_to_tokens([z for z in item[0].tolist()[0] if z not in [30522, 0, 0.0, 0 ]]))\n\n        for index, pred_label in enumerate ( pred_string.split('[sep]') )   : \n            if ( text.find(pred_label) != -1 ):\n                df_temp = pd.DataFrame ({\"id\":item[3],  \"predicted\": [pred_label], 'truth': item[2].tolist() }, index=[num])\n                df_eval =df_eval.append(df_temp, ignore_index=True)\n            else:\n                df_temp = pd.DataFrame ({\"id\":item[3],  \"predicted\": '', 'truth': item[2].tolist()  }, index=[num])\n                df_eval =df_eval.append(df_temp, ignore_index=True)\n            \n        num = num+1\n        #print(df_eval.shape)\n \n    \n    #df_eval = df_eval.reset_index()\n    #print(df_eval)\n\n    df_eval ['truth_string'] = df_eval['truth'].apply(lambda r: \"|\".join(tokenizer.convert_tokens_to_string \\\n                        (tokenizer.convert_ids_to_tokens([int(z) for z in r if z not in [30522, 0.0, 0 ]] )).split('[sep]')) )\n    #df_eval ['predicted_string'] = df_eval['predicted'].apply(lambda r: \"|\".join(tokenizer.convert_tokens_to_string (tokenizer.convert_ids_to_tokens( [int(z) for z in r if z not in [30522, 0.0, 0]] ) ).split('[sep]')))\n    df_result=df_eval.groupby('id').apply(lambda row: (  \"|\".join(np.unique(\"|\".join(row['truth_string'].values).split(\"|\"))), \\\n                                                       \"|\".join( np.unique( \"|\".join(row['predicted'].values).split(\"|\")) ) ) )\n        \n        \n    df_final_result = pd.DataFrame(columns = ['id', 'predicted', 'truth'])\n    df_final_result['id'] = df_result.index\n    df_final_result['truth'] = list(map(lambda row: row[0][1:],  df_result.values ))\n    df_final_result['predicted'] =list(map(lambda row: row[1][1:],  df_result.values ))\n    '''df_final_result['truth'] = list(map(lambda row: row[0],  df_result.values ))\n    df_final_result['predicted'] =list(map(lambda row: row[1],  df_result.values ))'''\n\n\n\n    return ( compute_fbeta(df_final_result['truth'].apply(lambda x: x.split('|')), \n                           df_final_result['predicted'].apply(lambda x: x.split('|')),\\\n                           0.5) )\n\n\n\n","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:19:10.681213Z","iopub.execute_input":"2021-06-14T07:19:10.681627Z","iopub.status.idle":"2021-06-14T07:19:10.725177Z","shell.execute_reply.started":"2021-06-14T07:19:10.681597Z","shell.execute_reply":"2021-06-14T07:19:10.723799Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\"\"\"Compute the Jaccard-based micro FBeta score.\n\n    References\n    ----------\n    - https://www.kaggle.com/c/coleridgeinitiative-show-us-the-data/overview/evaluation\n    - https://www.kaggle.com/armandmorin/matching-baseline-mlm-w-evaluation-metric\n\"\"\"\ndef compute_fbeta(y_true,\n                  y_pred,\n                  beta) -> float:\n    \"\"\"Compute the Jaccard-based micro FBeta score.\n\n    References\n    ----------\n    - https://www.kaggle.com/c/coleridgeinitiative-show-us-the-data/overview/evaluation\n    \"\"\"\n\n    def _jaccard_similarity(str1 , str2 ) :\n        #print(str1+\"XXXX\"+str2)\n        a = set(str1.split()) \n        b = set(str2.split())\n        c = a.intersection(b)\n        if ( len(a) + len(b) +len(c) == 0) :\n            return 0.0\n        else:\n            return float(len(c)) / (len(a) + len(b) - len(c)+1)\n\n    tp = 0.000001  # true positive\n    fp = 0.000001  # false positive\n    fn = 0.000001  # false negative\n    for ground_truth_list, predicted_string_list in zip(y_true, y_pred):\n        \n        #print(predicted_string_list)\n        #print(ground_truth_list)\n        #print(\"XXX\")\n           \n        predicted_string_list_sorted = sorted(predicted_string_list)\n        for ground_truth in sorted(ground_truth_list):     \n\n            if len(predicted_string_list_sorted) == 0:\n                fn += 1\n            else:\n                similarity_scores = [\n                    _jaccard_similarity(ground_truth, predicted_string)\n                    for predicted_string in predicted_string_list_sorted\n                ]\n                matched_idx = np.argmax(similarity_scores)\n                if similarity_scores[matched_idx] >= 0.5:\n                    predicted_string_list_sorted.pop(matched_idx)\n                    tp += 1\n                else:\n                    #print(\"false negative\")\n                    fn += 1\n        fp += len(predicted_string_list_sorted)\n\n    \n\n    tp *= (1 + beta ** 2)\n    fn *= beta ** 2\n    fbeta_score = tp / (tp + fp + fn)\n    return fbeta_score","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:19:10.727211Z","iopub.execute_input":"2021-06-14T07:19:10.727526Z","iopub.status.idle":"2021-06-14T07:19:10.745019Z","shell.execute_reply.started":"2021-06-14T07:19:10.727496Z","shell.execute_reply":"2021-06-14T07:19:10.743802Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''tokenizer.convert_tokens_to_string (tokenizer.convert_ids_to_tokens( \\\n                        df_train_new[ ((df_train_new['word_count']==0) & (df_train_new['rseq_no']< 5)) == True ].iloc[7]['text']))'''","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:19:33.809623Z","iopub.execute_input":"2021-06-14T07:19:33.810043Z","iopub.status.idle":"2021-06-14T07:19:33.816462Z","shell.execute_reply.started":"2021-06-14T07:19:33.810008Z","shell.execute_reply":"2021-06-14T07:19:33.815748Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##\n# Training and Validating the model\n##\n\nfrom tqdm import tqdm \nfrom sklearn.model_selection import KFold\n\n\nunique_ids = df_train['Id'].unique()\nkfold = KFold(6, shuffle=True, random_state=1)\ncurr_best_cv_score = -1\ncv_score_sum=0\n\ncv_iter=0\n\nif ( TRAINING_MODE ==  \"TRAIN_FROM_SCRATCH\" ):\n        \n    for train, test in kfold.split(range(0,130)):\n\n        #train_ids = [unique_ids[x] for x in train]\n        train_ids = [idx for idx in train_new.index for truth in train_new[idx] if truth  in known_labels[train]  ]\n        df_train_new =  df_train[df_train['Id'].isin(train_ids) ].copy()\n        \n        #val_ids = [unique_ids[x] for x in test]\n        val_ids = [idx for idx in train_new.index for truth in train_new[idx] if truth  in known_labels[test]  ]\n        df_val_new = df_train[df_train['Id'].isin(val_ids)].copy()\n        df_val_new = df_val_new.reset_index()\n        print(df_val_new.shape)\n\n        xfrmer_model = ColleridgeTransformerModel( vocab_len ,vocab_len )\n        xfrmer_model.to(device)\n        \n        # Ensure that the data is balanced with rows which has study and the ones without. \n        pos_samples = df_train_new[ df_train_new['word_count']!=0 ] \n        neg_samples = df_train_new[ ((df_train_new['word_count']==0) & (df_train_new['rseq_no']< 7)) == True ] \n        neg_samples = neg_samples [:pos_samples.shape[0]]\n        print(pos_samples.shape[0], neg_samples.shape[0])\n        df_train_new =  pos_samples.append ( neg_samples ) \n        df_train_new = df_train_new.reset_index()\n        #print(df_train_new.shape)\n        \n        #print(df_train_new.index)\n\n        train_dataset = ColleridgeDataset(df_train_new)\n        train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n\n        val_dataset = ColleridgeDatasetEval(df_val_new)\n        val_dataloader = DataLoader(val_dataset, batch_size=1, shuffle=False)\n\n        optimizer = torch.optim.Adam(xfrmer_model.parameters(), lr=LEARNING_RATE)\n        criterion = nn.NLLLoss(reduction=\"sum\")\n        criterion.to(device)\n        \n\n        for epoch in range(EPOCHS):\n            loss = train_epoch(xfrmer_model, train_dataloader, optimizer, criterion, device)\n            print(\"epoch - {} train_loss - {:.2f} \".format(epoch, loss))\n\n        cv_score = validate_model (xfrmer_model,val_dataloader, device)\n        print(cv_score)\n        cv_score_sum = cv_score_sum + cv_score\n        print(cv_score_sum)\n\n        if( cv_score > curr_best_cv_score):\n            curr_best_cv_score = cv_score\n            torch.save(xfrmer_model.state_dict(), \"xform-17.model\")\n            \n        cv_iter = cv_iter+1\n        if(cv_iter>0):\n            break\n\n\n    print (\"average score = \", cv_score_sum/1)  \n    print( \"best score = \", cv_score)","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:25:27.570840Z","iopub.execute_input":"2021-06-14T07:25:27.571262Z","iopub.status.idle":"2021-06-14T07:25:47.345551Z","shell.execute_reply.started":"2021-06-14T07:25:27.571222Z","shell.execute_reply":"2021-06-14T07:25:47.343620Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Prediction","metadata":{}},{"cell_type":"markdown","source":"For Prediction - the input work tokens along with target ( just encoded [cls] token ) is provided to the trained network. This will generate the next output token. This is then appeneded and provided as target in the next iteration. This process is repeated till no more new tokens are generated.","metadata":{}},{"cell_type":"markdown","source":"![Predict.jpg](attachment:9a1094e0-9932-4d9b-8f4e-2db330dbf059.jpg)","metadata":{},"attachments":{"9a1094e0-9932-4d9b-8f4e-2db330dbf059.jpg":{"image/jpeg":"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= [idx for idx in train_new.index for truth in train_new[idx] if truth  in known_labels[100:]  ]\ndf_test = df_train[df_train['Id'].isin(test_ids)].copy()\ndf_test = df_test.reset_index()'''","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:25:24.793553Z","iopub.status.idle":"2021-06-14T07:25:24.793981Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''xfrmer_model = ColleridgeTransformerModel( vocab_len ,vocab_len )\nxfrmer_model.to(device)\nxfrmer_model.load_state_dict(torch.load('../input/model16-3/xform-16-3.model', map_location=device))\nvalidate_model (xfrmer_model,test_dataloader, device)'''","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:25:24.795629Z","iopub.status.idle":"2021-06-14T07:25:24.796086Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''xfrmer_model = ColleridgeTransformerModel( 30523 ,30523 )\nxfrmer_model.to(device)\nxfrmer_model.load_state_dict(torch.load('../input/model16/xform-16.model', map_location=device))\nvalidate_model (xfrmer_model,test_dataloader, device)'''","metadata":{"execution":{"iopub.status.busy":"2021-06-14T07:25:24.797166Z","iopub.status.idle":"2021-06-14T07:25:24.797608Z"},"trusted":true},"execution_count":null,"outputs":[]}]}