[
  {
    "path": ".github/FUNDING.yml",
    "content": "# These are supported funding model platforms\n\ngithub: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]\npatreon: # Replace with a single Patreon username\nopen_collective: # Replace with a single Open Collective username\nko_fi: # Replace with a single Ko-fi username\ntidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel\ncommunity_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry\nliberapay: # Replace with a single Liberapay username\nissuehunt: # Replace with a single IssueHunt username\notechie: # Replace with a single Otechie username\ncustom: [\"https://github.com/RandolphVI/Hierarchical-Multi-Label-Text-Classification/blob/master/.github/Wechat.jpeg\", \"https://github.com/RandolphVI/Hierarchical-Multi-Label-Text-Classification/blob/master/.github/Alipay.jpeg\"]\n"
  },
  {
    "path": ".gitignore",
    "content": "### Compiled source ###\n*.com\n*.class\n*.dll\n*.exe\n*.o\n*.so\n\n### Packages ###\n# it's better to unpack these files and commit the raw source\n# git has its own built in compression methods\n*.7z\n*.dmg\n*.gz\n*.iso\n*.jar\n*.rar\n*.tar\n*.zip\n\n### Logs and databases ###\n*.log\n*.sql\n*.sqlite\n\n### Mac OS generated files ###\n.DS_Store\n.DS_Store?\n._*\n.Spotlight-V100\n.Trashes\nehthumbs.db\nThumbs.db\n\n### JetBrain config files ###\n.idea\n\n### Python ###\n# Byte-compiled / optimized / DLL files\n*.npy\n__pycache__/\n*.py[cod]\n*$py.class\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n\n# Translations\n*.mo\n*.pot\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n### IPythonNotebook ###\n# Temporary data\n.ipynb_checkpoints/\n\n### Current Project ###\n# Data File\n*.txt\n*.tsv\n*.csv\n*.json\n*.jpg\n*.png\n*.html\n*.pickle\n*.kv\n*.pdf\n!/data\n!/data/train_sample.json\n!/data/validation_sample.json\n!/data/test_sample.json\n\n# Project File\n/HMC-LMLP\n/HMCN\n/SVM\n\n# Model File\n*.model\n*.pb\nruns/\ngraph/\n\n# Analysis File\nData Analysis.md\n\n# Log File\nlogs/\n\n# Related Code\ntemp.py\n\n### Else ###\nrandolph/\nIcon?\n*.graffle"
  },
  {
    "path": ".travis.yml",
    "content": "language: python\n\nmatrix:\n  include:\n    - python: 3.6\n\ninstall:\n  - pip install -r requirements.txt\n  - pip install coveralls\n\nbefore_script:\n  - export PYTHONPATH=$PWD\n\nscript:\n  - true # add other tests here\n  - coveralls"
  },
  {
    "path": "HARNN/test_harnn.py",
    "content": "# -*- coding:utf-8 -*-\n__author__ = 'Randolph'\n\nimport os\nimport sys\nimport time\nimport logging\nimport numpy as np\n\nsys.path.append('../')\nlogging.getLogger('tensorflow').disabled = True\n\nimport tensorflow as tf\nfrom utils import checkmate as cm\nfrom utils import data_helpers as dh\nfrom utils import param_parser as parser\nfrom sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, average_precision_score\n\nargs = parser.parameter_parser()\nMODEL = dh.get_model_name()\nlogger = dh.logger_fn(\"tflog\", \"logs/Test-{0}.log\".format(time.asctime()))\n\nCPT_DIR = 'runs/' + MODEL + '/checkpoints/'\nBEST_CPT_DIR = 'runs/' + MODEL + '/bestcheckpoints/'\nSAVE_DIR = 'output/' + MODEL\n\n\ndef create_input_data(data: dict):\n    return zip(data['pad_seqs'], data['section'], data['subsection'], data['group'],\n               data['subgroup'], data['onehot_labels'], data['labels'])\n\n\ndef test_harnn():\n    \"\"\"Test HARNN model.\"\"\"\n    # Print parameters used for the model\n    dh.tab_printer(args, logger)\n\n    # Load word2vec model\n    word2idx, embedding_matrix = dh.load_word2vec_matrix(args.word2vec_file)\n\n    # Load data\n    logger.info(\"Loading data...\")\n    logger.info(\"Data processing...\")\n    test_data = dh.load_data_and_labels(args, args.test_file, word2idx)\n\n    # Load harnn model\n    OPTION = dh._option(pattern=1)\n    if OPTION == 'B':\n        logger.info(\"Loading best model...\")\n        checkpoint_file = cm.get_best_checkpoint(BEST_CPT_DIR, select_maximum_value=True)\n    else:\n        logger.info(\"Loading latest model...\")\n        checkpoint_file = tf.train.latest_checkpoint(CPT_DIR)\n    logger.info(checkpoint_file)\n\n    graph = tf.Graph()\n    with graph.as_default():\n        session_conf = tf.ConfigProto(\n            allow_soft_placement=args.allow_soft_placement,\n            log_device_placement=args.log_device_placement)\n        session_conf.gpu_options.allow_growth = args.gpu_options_allow_growth\n        sess = tf.Session(config=session_conf)\n        with sess.as_default():\n            # Load the saved meta graph and restore variables\n            saver = tf.train.import_meta_graph(\"{0}.meta\".format(checkpoint_file))\n            saver.restore(sess, checkpoint_file)\n\n            # Get the placeholders from the graph by name\n            input_x = graph.get_operation_by_name(\"input_x\").outputs[0]\n            input_y_first = graph.get_operation_by_name(\"input_y_first\").outputs[0]\n            input_y_second = graph.get_operation_by_name(\"input_y_second\").outputs[0]\n            input_y_third = graph.get_operation_by_name(\"input_y_third\").outputs[0]\n            input_y_fourth = graph.get_operation_by_name(\"input_y_fourth\").outputs[0]\n            input_y = graph.get_operation_by_name(\"input_y\").outputs[0]\n            dropout_keep_prob = graph.get_operation_by_name(\"dropout_keep_prob\").outputs[0]\n            alpha = graph.get_operation_by_name(\"alpha\").outputs[0]\n            is_training = graph.get_operation_by_name(\"is_training\").outputs[0]\n\n            # Tensors we want to evaluate\n            first_scores = graph.get_operation_by_name(\"first-output/scores\").outputs[0]\n            second_scores = graph.get_operation_by_name(\"second-output/scores\").outputs[0]\n            third_scores = graph.get_operation_by_name(\"third-output/scores\").outputs[0]\n            fourth_scores = graph.get_operation_by_name(\"fourth-output/scores\").outputs[0]\n            scores = graph.get_operation_by_name(\"output/scores\").outputs[0]\n\n            # Split the output nodes name by '|' if you have several output nodes\n            output_node_names = \"first-output/scores|second-output/scores|third-output/scores|fourth-output/scores|output/scores\"\n\n            # Save the .pb model file\n            output_graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def,\n                                                                            output_node_names.split(\"|\"))\n            tf.train.write_graph(output_graph_def, \"graph\", \"graph-harnn-{0}.pb\".format(MODEL), as_text=False)\n\n            # Generate batches for one epoch\n            batches = dh.batch_iter(list(create_input_data(test_data)), args.batch_size, 1, shuffle=False)\n\n            # Collect the predictions here\n            true_labels = []\n            predicted_labels = []\n            predicted_scores = []\n\n            # Collect for calculating metrics\n            true_onehot_labels = [[], [], [], [], []]\n            predicted_onehot_scores = [[], [], [], [], []]\n            predicted_onehot_labels = [[], [], [], [], []]\n\n            for batch_test in batches:\n                x, sec, subsec, group, subgroup, y_onehot, y = zip(*batch_test)\n\n                y_batch_test_list = [y_onehot, sec, subsec, group, subgroup]\n\n                feed_dict = {\n                    input_x: x,\n                    input_y_first: sec,\n                    input_y_second: subsec,\n                    input_y_third: group,\n                    input_y_fourth: subgroup,\n                    input_y: y_onehot,\n                    dropout_keep_prob: 1.0,\n                    alpha: args.alpha,\n                    is_training: False\n                }\n                batch_global_scores, batch_first_scores, batch_second_scores, batch_third_scores, batch_fourth_scores = \\\n                    sess.run([scores, first_scores, second_scores, third_scores, fourth_scores], feed_dict)\n\n                batch_scores = [batch_global_scores, batch_first_scores, batch_second_scores,\n                                batch_third_scores, batch_fourth_scores]\n\n                # Get the predicted labels by threshold\n                batch_predicted_labels_ts, batch_predicted_scores_ts = \\\n                    dh.get_label_threshold(scores=batch_scores[0], threshold=args.threshold)\n\n                # Add results to collection\n                for labels in y:\n                    true_labels.append(labels)\n                for labels in batch_predicted_labels_ts:\n                    predicted_labels.append(labels)\n                for values in batch_predicted_scores_ts:\n                    predicted_scores.append(values)\n\n                for index in range(len(predicted_onehot_scores)):\n                    for onehot_labels in y_batch_test_list[index]:\n                        true_onehot_labels[index].append(onehot_labels)\n                    for onehot_scores in batch_scores[index]:\n                        predicted_onehot_scores[index].append(onehot_scores)\n                    # Get one-hot prediction by threshold\n                    predicted_onehot_labels_ts = \\\n                        dh.get_onehot_label_threshold(scores=batch_scores[index], threshold=args.threshold)\n                    for onehot_labels in predicted_onehot_labels_ts:\n                        predicted_onehot_labels[index].append(onehot_labels)\n\n            # Calculate Precision & Recall & F1\n            for index in range(len(predicted_onehot_scores)):\n                test_pre = precision_score(y_true=np.array(true_onehot_labels[index]),\n                                           y_pred=np.array(predicted_onehot_labels[index]), average='micro')\n                test_rec = recall_score(y_true=np.array(true_onehot_labels[index]),\n                                        y_pred=np.array(predicted_onehot_labels[index]), average='micro')\n                test_F1 = f1_score(y_true=np.array(true_onehot_labels[index]),\n                                   y_pred=np.array(predicted_onehot_labels[index]), average='micro')\n                test_auc = roc_auc_score(y_true=np.array(true_onehot_labels[index]),\n                                         y_score=np.array(predicted_onehot_scores[index]), average='micro')\n                test_prc = average_precision_score(y_true=np.array(true_onehot_labels[index]),\n                                                   y_score=np.array(predicted_onehot_scores[index]), average=\"micro\")\n                if index == 0:\n                    logger.info(\"[Global] Predict by threshold: Precision {0:g}, Recall {1:g}, \"\n                                \"F1 {2:g}, AUC {3:g}, AUPRC {4:g}\"\n                                .format(test_pre, test_rec, test_F1, test_auc, test_prc))\n                else:\n                    logger.info(\"[Local] Predict by threshold in Level-{0}: Precision {1:g}, Recall {2:g}, \"\n                                \"F1 {3:g}, AUPRC {4:g}\".format(index, test_pre, test_rec, test_F1, test_prc))\n\n            # Save the prediction result\n            if not os.path.exists(SAVE_DIR):\n                os.makedirs(SAVE_DIR)\n            dh.create_prediction_file(output_file=SAVE_DIR + \"/predictions.json\", data_id=test_data['id'],\n                                      true_labels=true_labels, predict_labels=predicted_labels,\n                                      predict_scores=predicted_scores)\n    logger.info(\"All Done.\")\n\n\nif __name__ == '__main__':\n    test_harnn()\n"
  },
  {
    "path": "HARNN/text_harnn.py",
    "content": "# -*- coding:utf-8 -*-\n__author__ = 'Randolph'\n\nimport tensorflow as tf\n\n\nclass TextHARNN(object):\n    \"\"\"A HARNN for text classification.\"\"\"\n\n    def __init__(\n            self, sequence_length, vocab_size, embedding_type, embedding_size, lstm_hidden_size, attention_unit_size,\n            fc_hidden_size, num_classes_list, total_classes, l2_reg_lambda=0.0, pretrained_embedding=None):\n\n        # Placeholders for input, output, dropout_prob and training_tag\n        self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name=\"input_x\")\n        self.input_y_first = tf.placeholder(tf.float32, [None, num_classes_list[0]], name=\"input_y_first\")\n        self.input_y_second = tf.placeholder(tf.float32, [None, num_classes_list[1]], name=\"input_y_second\")\n        self.input_y_third = tf.placeholder(tf.float32, [None, num_classes_list[2]], name=\"input_y_third\")\n        self.input_y_fourth = tf.placeholder(tf.float32, [None, num_classes_list[3]], name=\"input_y_fourth\")\n        self.input_y = tf.placeholder(tf.float32, [None, total_classes], name=\"input_y\")\n        self.dropout_keep_prob = tf.placeholder(tf.float32, name=\"dropout_keep_prob\")\n        self.alpha = tf.placeholder(tf.float32, name=\"alpha\")\n        self.is_training = tf.placeholder(tf.bool, name=\"is_training\")\n\n        self.global_step = tf.Variable(0, trainable=False, name=\"Global_Step\")\n\n        def _attention(input_x, num_classes, name=\"\"):\n            \"\"\"\n            Attention Layer.\n\n            Args:\n                input_x: [batch_size, sequence_length, lstm_hidden_size * 2]\n                num_classes: The number of i th level classes.\n                name: Scope name.\n            Returns:\n                attention_weight: [batch_size, num_classes, sequence_length]\n                attention_out: [batch_size, lstm_hidden_size * 2]\n            \"\"\"\n            num_units = input_x.get_shape().as_list()[-1]\n            with tf.name_scope(name + \"attention\"):\n                W_s1 = tf.Variable(tf.truncated_normal(shape=[attention_unit_size, num_units],\n                                                       stddev=0.1, dtype=tf.float32), name=\"W_s1\")\n                W_s2 = tf.Variable(tf.truncated_normal(shape=[num_classes, attention_unit_size],\n                                                       stddev=0.1, dtype=tf.float32), name=\"W_s2\")\n                # attention_matrix: [batch_size, num_classes, sequence_length]\n                attention_matrix = tf.map_fn(\n                    fn=lambda x: tf.matmul(W_s2, x),\n                    elems=tf.tanh(\n                        tf.map_fn(\n                            fn=lambda x: tf.matmul(W_s1, tf.transpose(x)),\n                            elems=input_x,\n                            dtype=tf.float32\n                        )\n                    )\n                )\n                attention_weight = tf.nn.softmax(attention_matrix, name=\"attention\")\n                attention_out = tf.matmul(attention_weight, input_x)\n                attention_out = tf.reduce_mean(attention_out, axis=1)\n            return attention_weight, attention_out\n\n        def _fc_layer(input_x, name=\"\"):\n            \"\"\"\n            Fully Connected Layer.\n\n            Args:\n                input_x: [batch_size, *]\n                name: Scope name.\n            Returns:\n                fc_out: [batch_size, fc_hidden_size]\n            \"\"\"\n            with tf.name_scope(name + \"fc\"):\n                num_units = input_x.get_shape().as_list()[-1]\n                W = tf.Variable(tf.truncated_normal(shape=[num_units, fc_hidden_size],\n                                                    stddev=0.1, dtype=tf.float32), name=\"W\")\n                b = tf.Variable(tf.constant(value=0.1, shape=[fc_hidden_size], dtype=tf.float32), name=\"b\")\n                fc = tf.nn.xw_plus_b(input_x, W, b)\n                fc_out = tf.nn.relu(fc)\n            return fc_out\n\n        def _local_layer(input_x, input_att_weight, num_classes, name=\"\"):\n            \"\"\"\n            Local Layer.\n\n            Args:\n                input_x: [batch_size, fc_hidden_size]\n                input_att_weight: [batch_size, num_classes, sequence_length]\n                num_classes: Number of classes.\n                name: Scope name.\n            Returns:\n                logits: [batch_size, num_classes]\n                scores: [batch_size, num_classes]\n                visual: [batch_size, sequence_length]\n            \"\"\"\n            with tf.name_scope(name + \"output\"):\n                num_units = input_x.get_shape().as_list()[-1]\n                W = tf.Variable(tf.truncated_normal(shape=[num_units, num_classes],\n                                                    stddev=0.1, dtype=tf.float32), name=\"W\")\n                b = tf.Variable(tf.constant(value=0.1, shape=[num_classes], dtype=tf.float32), name=\"b\")\n                logits = tf.nn.xw_plus_b(input_x, W, b, name=\"logits\")\n                scores = tf.sigmoid(logits, name=\"scores\")\n\n                # shape of visual: [batch_size, sequence_length]\n                visual = tf.multiply(input_att_weight, tf.expand_dims(scores, -1))\n                visual = tf.nn.softmax(visual)\n                visual = tf.reduce_mean(visual, axis=1, name=\"visual\")\n            return logits, scores, visual\n\n        def _linear(input_, output_size, initializer=None, scope=\"SimpleLinear\"):\n            \"\"\"\n            Linear map: output[k] = sum_i(Matrix[k, i] * args[i] ) + Bias[k].\n\n            Args:\n                input_: a tensor or a list of 2D, batch x n, Tensors.\n                output_size: int, second dimension of W[i].\n                initializer: The initializer.\n                scope: VariableScope for the created subgraph; defaults to \"SimpleLinear\".\n            Returns:\n                A 2D Tensor with shape [batch x output_size] equal to\n                sum_i(args[i] * W[i]), where W[i]s are newly created matrices.\n            Raises:\n                ValueError: if some of the arguments has unspecified or wrong shape.\n            \"\"\"\n\n            shape = input_.get_shape().as_list()\n            if len(shape) != 2:\n                raise ValueError(\"Linear is expecting 2D arguments: {0}\".format(str(shape)))\n            if not shape[1]:\n                raise ValueError(\"Linear expects shape[1] of arguments: {0}\".format(str(shape)))\n            input_size = shape[1]\n\n            # Now the computation.\n            with tf.variable_scope(scope):\n                W = tf.get_variable(\"W\", [input_size, output_size], dtype=input_.dtype)\n                b = tf.get_variable(\"b\", [output_size], dtype=input_.dtype, initializer=initializer)\n\n            return tf.nn.xw_plus_b(input_, W, b)\n\n        def _highway_layer(input_, size, num_layers=1, bias=-2.0):\n            \"\"\"\n            Highway Network (cf. http://arxiv.org/abs/1505.00387).\n            t = sigmoid(Wx + b); h = relu(W'x + b')\n            z = t * h + (1 - t) * x\n            where t is transform gate, and (1 - t) is carry gate.\n            \"\"\"\n\n            for idx in range(num_layers):\n                h = tf.nn.relu(_linear(input_, size, scope=(\"highway_h_{0}\".format(idx))))\n                t = tf.sigmoid(_linear(input_, size, initializer=tf.constant_initializer(bias),\n                                       scope=(\"highway_t_{0}\".format(idx))))\n                output = t * h + (1. - t) * input_\n                input_ = output\n\n            return output\n\n        # Embedding Layer\n        with tf.device(\"/cpu:0\"), tf.name_scope(\"embedding\"):\n            # Use random generated the word vector by default\n            # Can also be obtained through our own word vectors trained by our corpus\n            if pretrained_embedding is None:\n                self.embedding = tf.Variable(tf.random_uniform([vocab_size, embedding_size], minval=-1.0, maxval=1.0,\n                                                               dtype=tf.float32), trainable=True, name=\"embedding\")\n            else:\n                if embedding_type == 0:\n                    self.embedding = tf.constant(pretrained_embedding, dtype=tf.float32, name=\"embedding\")\n                if embedding_type == 1:\n                    self.embedding = tf.Variable(pretrained_embedding, trainable=True,\n                                                 dtype=tf.float32, name=\"embedding\")\n            self.embedded_sentence = tf.nn.embedding_lookup(self.embedding, self.input_x)\n            # Average Vectors\n            # [batch_size, embedding_size]\n            self.embedded_sentence_average = tf.reduce_mean(self.embedded_sentence, axis=1)\n\n        # Bi-LSTM Layer\n        with tf.name_scope(\"Bi-lstm\"):\n            lstm_fw_cell = tf.nn.rnn_cell.LSTMCell(lstm_hidden_size)  # forward direction cell\n            lstm_bw_cell = tf.nn.rnn_cell.LSTMCell(lstm_hidden_size)  # backward direction cell\n            if self.dropout_keep_prob is not None:\n                lstm_fw_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_fw_cell, output_keep_prob=self.dropout_keep_prob)\n                lstm_bw_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_bw_cell, output_keep_prob=self.dropout_keep_prob)\n\n            # Creates a dynamic bidirectional recurrent neural network\n            # shape of `outputs`: tuple -> (outputs_fw, outputs_bw)\n            # shape of `outputs_fw`: [batch_size, sequence_length, lstm_hidden_size]\n\n            # shape of `state`: tuple -> (outputs_state_fw, output_state_bw)\n            # shape of `outputs_state_fw`: tuple -> (c, h) c: memory cell; h: hidden state\n            outputs, state = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell, lstm_bw_cell,\n                                                             self.embedded_sentence, dtype=tf.float32)\n            # Concat output\n            self.lstm_out = tf.concat(outputs, axis=2)  # [batch_size, sequence_length, lstm_hidden_size * 2]\n            self.lstm_out_pool = tf.reduce_mean(self.lstm_out, axis=1)  # [batch_size, lstm_hidden_size * 2]\n\n        # First Level\n        self.first_att_weight, self.first_att_out = _attention(self.lstm_out, num_classes_list[0], name=\"first-\")\n        self.first_local_input = tf.concat([self.lstm_out_pool, self.first_att_out], axis=1)\n        self.first_local_fc_out = _fc_layer(self.first_local_input, name=\"first-local-\")\n        self.first_logits, self.first_scores, self.first_visual = _local_layer(\n            self.first_local_fc_out, self.first_att_weight, num_classes_list[0], name=\"first-\")\n\n        # Second Level\n        self.second_att_input = tf.multiply(self.lstm_out, tf.expand_dims(self.first_visual, -1))\n        self.second_att_weight, self.second_att_out = _attention(\n            self.second_att_input, num_classes_list[1], name=\"second-\")\n        self.second_local_input = tf.concat([self.lstm_out_pool, self.second_att_out], axis=1)\n        self.second_local_fc_out = _fc_layer(self.second_local_input, name=\"second-local-\")\n        self.second_logits, self.second_scores, self.second_visual = _local_layer(\n            self.second_local_fc_out, self.second_att_weight, num_classes_list[1], name=\"second-\")\n\n        # Third Level\n        self.third_att_input = tf.multiply(self.lstm_out, tf.expand_dims(self.second_visual, -1))\n        self.third_att_weight, self.third_att_out = _attention(\n            self.third_att_input, num_classes_list[2], name=\"third-\")\n        self.third_local_input = tf.concat([self.lstm_out_pool, self.third_att_out], axis=1)\n        self.third_local_fc_out = _fc_layer(self.third_local_input, name=\"third-local-\")\n        self.third_logits, self.third_scores, self.third_visual = _local_layer(\n            self.third_local_fc_out, self.third_att_weight, num_classes_list[2], name=\"third-\")\n\n        # Fourth Level\n        self.fourth_att_input = tf.multiply(self.lstm_out, tf.expand_dims(self.third_visual, -1))\n        self.fourth_att_weight, self.fourth_att_out = _attention(\n            self.fourth_att_input, num_classes_list[3], name=\"fourth-\")\n        self.fourth_local_input = tf.concat([self.lstm_out_pool, self.fourth_att_out], axis=1)\n        self.fourth_local_fc_out = _fc_layer(self.fourth_local_input, name=\"fourth-local-\")\n        self.fourth_logits, self.fourth_scores, self.fourth_visual = _local_layer(\n            self.fourth_local_fc_out, self.fourth_att_weight, num_classes_list[3], name=\"fourth-\")\n\n        # Concat\n        # shape of ham_out: [batch_size, fc_hidden_size * 4]\n        self.ham_out = tf.concat([self.first_local_fc_out, self.second_local_fc_out,\n                                  self.third_local_fc_out, self.fourth_local_fc_out], axis=1)\n\n        # Fully Connected Layer\n        self.fc_out = _fc_layer(self.ham_out)\n\n        # Highway Layer\n        with tf.name_scope(\"highway\"):\n            self.highway = _highway_layer(self.fc_out, self.fc_out.get_shape()[1], num_layers=1, bias=0)\n\n        # Add dropout\n        with tf.name_scope(\"dropout\"):\n            self.h_drop = tf.nn.dropout(self.highway, self.dropout_keep_prob)\n\n        # Global scores\n        with tf.name_scope(\"global-output\"):\n            num_units = self.h_drop.get_shape().as_list()[-1]\n            W = tf.Variable(tf.truncated_normal(shape=[num_units, total_classes],\n                                                stddev=0.1, dtype=tf.float32), name=\"W\")\n            b = tf.Variable(tf.constant(value=0.1, shape=[total_classes], dtype=tf.float32), name=\"b\")\n            self.global_logits = tf.nn.xw_plus_b(self.h_drop, W, b, name=\"logits\")\n            self.global_scores = tf.sigmoid(self.global_logits, name=\"scores\")\n\n        with tf.name_scope(\"output\"):\n            self.local_scores = tf.concat([self.first_scores, self.second_scores,\n                                           self.third_scores, self.fourth_scores], axis=1)\n            self.scores = tf.add(self.alpha * self.global_scores, (1 - self.alpha) * self.local_scores, name=\"scores\")\n\n        # Calculate mean cross-entropy loss, L2 loss\n        with tf.name_scope(\"loss\"):\n            def cal_loss(labels, logits, name):\n                losses = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits)\n                losses = tf.reduce_mean(tf.reduce_sum(losses, axis=1), name=name + \"losses\")\n                return losses\n\n            # Local Loss\n            losses_1 = cal_loss(labels=self.input_y_first, logits=self.first_logits, name=\"first_\")\n            losses_2 = cal_loss(labels=self.input_y_second, logits=self.second_logits, name=\"second_\")\n            losses_3 = cal_loss(labels=self.input_y_third, logits=self.third_logits, name=\"third_\")\n            losses_4 = cal_loss(labels=self.input_y_fourth, logits=self.fourth_logits, name=\"fourth_\")\n            local_losses = tf.add_n([losses_1, losses_2, losses_3, losses_4], name=\"local_losses\")\n\n            # Global Loss\n            global_losses = cal_loss(labels=self.input_y, logits=self.global_logits, name=\"global_\")\n\n            # L2 Loss\n            l2_losses = tf.add_n([tf.nn.l2_loss(tf.cast(v, tf.float32)) for v in tf.trainable_variables()],\n                                 name=\"l2_losses\") * l2_reg_lambda\n            self.loss = tf.add_n([local_losses, global_losses, l2_losses], name=\"loss\")"
  },
  {
    "path": "HARNN/train_harnn.py",
    "content": "# -*- coding:utf-8 -*-\n__author__ = 'Randolph'\n\nimport os\nimport sys\nimport time\nimport logging\n\nsys.path.append('../')\nlogging.getLogger('tensorflow').disabled = True\n\nimport numpy as np\nimport tensorflow as tf\nfrom text_harnn import TextHARNN\nfrom utils import checkmate as cm\nfrom utils import data_helpers as dh\nfrom utils import param_parser as parser\nfrom sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, average_precision_score\n\nargs = parser.parameter_parser()\nOPTION = dh._option(pattern=0)\nlogger = dh.logger_fn(\"tflog\", \"logs/{0}-{1}.log\".format('Train' if OPTION == 'T' else 'Restore', time.asctime()))\n\n\ndef create_input_data(data: dict):\n    return zip(data['pad_seqs'], data['section'], data['subsection'],\n               data['group'], data['subgroup'], data['onehot_labels'])\n\n\ndef train_harnn():\n    \"\"\"Training HARNN model.\"\"\"\n    # Print parameters used for the model\n    dh.tab_printer(args, logger)\n\n    # Load word2vec model\n    word2idx, embedding_matrix = dh.load_word2vec_matrix(args.word2vec_file)\n\n    # Load sentences, labels, and training parameters\n    logger.info(\"Loading data...\")\n    logger.info(\"Data processing...\")\n    train_data = dh.load_data_and_labels(args, args.train_file, word2idx)\n    val_data = dh.load_data_and_labels(args, args.validation_file, word2idx)\n\n    # Build a graph and harnn object\n    with tf.Graph().as_default():\n        session_conf = tf.ConfigProto(\n            allow_soft_placement=args.allow_soft_placement,\n            log_device_placement=args.log_device_placement)\n        session_conf.gpu_options.allow_growth = args.gpu_options_allow_growth\n        sess = tf.Session(config=session_conf)\n        with sess.as_default():\n            harnn = TextHARNN(\n                sequence_length=args.pad_seq_len,\n                vocab_size=len(word2idx),\n                embedding_type=args.embedding_type,\n                embedding_size=args.embedding_dim,\n                lstm_hidden_size=args.lstm_dim,\n                attention_unit_size=args.attention_dim,\n                fc_hidden_size=args.fc_dim,\n                num_classes_list=args.num_classes_list,\n                total_classes=args.total_classes,\n                l2_reg_lambda=args.l2_lambda,\n                pretrained_embedding=embedding_matrix)\n\n            # Define training procedure\n            with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n                learning_rate = tf.train.exponential_decay(learning_rate=args.learning_rate,\n                                                           global_step=harnn.global_step,\n                                                           decay_steps=args.decay_steps,\n                                                           decay_rate=args.decay_rate,\n                                                           staircase=True)\n                optimizer = tf.train.AdamOptimizer(learning_rate)\n                grads, vars = zip(*optimizer.compute_gradients(harnn.loss))\n                grads, _ = tf.clip_by_global_norm(grads, clip_norm=args.norm_ratio)\n                train_op = optimizer.apply_gradients(zip(grads, vars), global_step=harnn.global_step, name=\"train_op\")\n\n            # Keep track of gradient values and sparsity (optional)\n            grad_summaries = []\n            for g, v in zip(grads, vars):\n                if g is not None:\n                    grad_hist_summary = tf.summary.histogram(\"{0}/grad/hist\".format(v.name), g)\n                    sparsity_summary = tf.summary.scalar(\"{0}/grad/sparsity\".format(v.name), tf.nn.zero_fraction(g))\n                    grad_summaries.append(grad_hist_summary)\n                    grad_summaries.append(sparsity_summary)\n            grad_summaries_merged = tf.summary.merge(grad_summaries)\n\n            # Output directory for models and summaries\n            out_dir = dh.get_out_dir(OPTION, logger)\n            checkpoint_dir = os.path.abspath(os.path.join(out_dir, \"checkpoints\"))\n            best_checkpoint_dir = os.path.abspath(os.path.join(out_dir, \"bestcheckpoints\"))\n\n            # Summaries for loss\n            loss_summary = tf.summary.scalar(\"loss\", harnn.loss)\n\n            # Train summaries\n            train_summary_op = tf.summary.merge([loss_summary, grad_summaries_merged])\n            train_summary_dir = os.path.join(out_dir, \"summaries\", \"train\")\n            train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)\n\n            # Validation summaries\n            validation_summary_op = tf.summary.merge([loss_summary])\n            validation_summary_dir = os.path.join(out_dir, \"summaries\", \"validation\")\n            validation_summary_writer = tf.summary.FileWriter(validation_summary_dir, sess.graph)\n\n            saver = tf.train.Saver(tf.global_variables(), max_to_keep=args.num_checkpoints)\n            best_saver = cm.BestCheckpointSaver(save_dir=best_checkpoint_dir, num_to_keep=3, maximize=True)\n\n            if OPTION == 'R':\n                # Load harnn model\n                logger.info(\"Loading model...\")\n                checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)\n                logger.info(checkpoint_file)\n\n                # Load the saved meta graph and restore variables\n                saver = tf.train.import_meta_graph(\"{0}.meta\".format(checkpoint_file))\n                saver.restore(sess, checkpoint_file)\n            if OPTION == 'T':\n                if not os.path.exists(checkpoint_dir):\n                    os.makedirs(checkpoint_dir)\n                sess.run(tf.global_variables_initializer())\n                sess.run(tf.local_variables_initializer())\n\n                # Save the embedding visualization\n                saver.save(sess, os.path.join(out_dir, \"embedding\", \"embedding.ckpt\"))\n\n            current_step = sess.run(harnn.global_step)\n\n            def train_step(batch_data):\n                \"\"\"A single training step.\"\"\"\n                x, sec, subsec, group, subgroup, y_onehot = zip(*batch_data)\n\n                feed_dict = {\n                    harnn.input_x: x,\n                    harnn.input_y_first: sec,\n                    harnn.input_y_second: subsec,\n                    harnn.input_y_third: group,\n                    harnn.input_y_fourth: subgroup,\n                    harnn.input_y: y_onehot,\n                    harnn.dropout_keep_prob: args.dropout_rate,\n                    harnn.alpha: args.alpha,\n                    harnn.is_training: True\n                }\n                _, step, summaries, loss = sess.run(\n                    [train_op, harnn.global_step, train_summary_op, harnn.loss], feed_dict)\n                logger.info(\"step {0}: loss {1:g}\".format(step, loss))\n                train_summary_writer.add_summary(summaries, step)\n\n            def validation_step(val_loader, writer=None):\n                \"\"\"Evaluates model on a validation set.\"\"\"\n                batches_validation = dh.batch_iter(list(create_input_data(val_loader)), args.batch_size, 1)\n\n                # Predict classes by threshold or topk ('ts': threshold; 'tk': topk)\n                eval_counter, eval_loss = 0, 0.0\n                eval_pre_tk = [0.0] * args.topK\n                eval_rec_tk = [0.0] * args.topK\n                eval_F1_tk = [0.0] * args.topK\n\n                true_onehot_labels = []\n                predicted_onehot_scores = []\n                predicted_onehot_labels_ts = []\n                predicted_onehot_labels_tk = [[] for _ in range(args.topK)]\n\n                for batch_validation in batches_validation:\n                    x, sec, subsec, group, subgroup, y_onehot = zip(*batch_validation)\n                    feed_dict = {\n                        harnn.input_x: x,\n                        harnn.input_y_first: sec,\n                        harnn.input_y_second: subsec,\n                        harnn.input_y_third: group,\n                        harnn.input_y_fourth: subgroup,\n                        harnn.input_y: y_onehot,\n                        harnn.dropout_keep_prob: 1.0,\n                        harnn.alpha: args.alpha,\n                        harnn.is_training: False\n                    }\n                    step, summaries, scores, cur_loss = sess.run(\n                        [harnn.global_step, validation_summary_op, harnn.scores, harnn.loss], feed_dict)\n\n                    # Prepare for calculating metrics\n                    for i in y_onehot:\n                        true_onehot_labels.append(i)\n                    for j in scores:\n                        predicted_onehot_scores.append(j)\n\n                    # Predict by threshold\n                    batch_predicted_onehot_labels_ts = \\\n                        dh.get_onehot_label_threshold(scores=scores, threshold=args.threshold)\n                    for k in batch_predicted_onehot_labels_ts:\n                        predicted_onehot_labels_ts.append(k)\n\n                    # Predict by topK\n                    for top_num in range(args.topK):\n                        batch_predicted_onehot_labels_tk = dh.get_onehot_label_topk(scores=scores, top_num=top_num+1)\n                        for i in batch_predicted_onehot_labels_tk:\n                            predicted_onehot_labels_tk[top_num].append(i)\n\n                    eval_loss = eval_loss + cur_loss\n                    eval_counter = eval_counter + 1\n\n                    if writer:\n                        writer.add_summary(summaries, step)\n\n                eval_loss = float(eval_loss / eval_counter)\n\n                # Calculate Precision & Recall & F1\n                eval_pre_ts = precision_score(y_true=np.array(true_onehot_labels),\n                                              y_pred=np.array(predicted_onehot_labels_ts), average='micro')\n                eval_rec_ts = recall_score(y_true=np.array(true_onehot_labels),\n                                           y_pred=np.array(predicted_onehot_labels_ts), average='micro')\n                eval_F1_ts = f1_score(y_true=np.array(true_onehot_labels),\n                                      y_pred=np.array(predicted_onehot_labels_ts), average='micro')\n\n                for top_num in range(args.topK):\n                    eval_pre_tk[top_num] = precision_score(y_true=np.array(true_onehot_labels),\n                                                           y_pred=np.array(predicted_onehot_labels_tk[top_num]),\n                                                           average='micro')\n                    eval_rec_tk[top_num] = recall_score(y_true=np.array(true_onehot_labels),\n                                                        y_pred=np.array(predicted_onehot_labels_tk[top_num]),\n                                                        average='micro')\n                    eval_F1_tk[top_num] = f1_score(y_true=np.array(true_onehot_labels),\n                                                   y_pred=np.array(predicted_onehot_labels_tk[top_num]),\n                                                   average='micro')\n\n                # Calculate the average AUC\n                eval_auc = roc_auc_score(y_true=np.array(true_onehot_labels),\n                                         y_score=np.array(predicted_onehot_scores), average='micro')\n                # Calculate the average PR\n                eval_prc = average_precision_score(y_true=np.array(true_onehot_labels),\n                                                   y_score=np.array(predicted_onehot_scores), average='micro')\n\n                return eval_loss, eval_auc, eval_prc, eval_pre_ts, eval_rec_ts, eval_F1_ts, \\\n                       eval_pre_tk, eval_rec_tk, eval_F1_tk\n\n            # Generate batches\n            batches_train = dh.batch_iter(list(create_input_data(train_data)), args.batch_size, args.epochs)\n            num_batches_per_epoch = int((len(train_data['pad_seqs']) - 1) / args.batch_size) + 1\n\n            # Training loop. For each batch...\n            for batch_train in batches_train:\n                train_step(batch_train)\n                current_step = tf.train.global_step(sess, harnn.global_step)\n\n                if current_step % args.evaluate_steps == 0:\n                    logger.info(\"\\nEvaluation:\")\n                    eval_loss, eval_auc, eval_prc, \\\n                    eval_pre_ts, eval_rec_ts, eval_F1_ts, eval_pre_tk, eval_rec_tk, eval_F1_tk = \\\n                        validation_step(val_data, writer=validation_summary_writer)\n                    logger.info(\"All Validation set: Loss {0:g} | AUC {1:g} | AUPRC {2:g}\"\n                                .format(eval_loss, eval_auc, eval_prc))\n                    # Predict by threshold\n                    logger.info(\"Predict by threshold: Precision {0:g}, Recall {1:g}, F1 {2:g}\"\n                                .format(eval_pre_ts, eval_rec_ts, eval_F1_ts))\n                    # Predict by topK\n                    logger.info(\"Predict by topK:\")\n                    for top_num in range(args.topK):\n                        logger.info(\"Top{0}: Precision {1:g}, Recall {2:g}, F1 {3:g}\"\n                                    .format(top_num+1, eval_pre_tk[top_num], eval_rec_tk[top_num], eval_F1_tk[top_num]))\n                    best_saver.handle(eval_prc, sess, current_step)\n                if current_step % args.checkpoint_steps == 0:\n                    checkpoint_prefix = os.path.join(checkpoint_dir, \"model\")\n                    path = saver.save(sess, checkpoint_prefix, global_step=current_step)\n                    logger.info(\"Saved model checkpoint to {0}\\n\".format(path))\n                if current_step % num_batches_per_epoch == 0:\n                    current_epoch = current_step // num_batches_per_epoch\n                    logger.info(\"Epoch {0} has finished!\".format(current_epoch))\n\n    logger.info(\"All Done.\")\n\n\nif __name__ == '__main__':\n    train_harnn()"
  },
  {
    "path": "HARNN/visualization.py",
    "content": "# -*- coding:utf-8 -*-\n__author__ = 'Randolph'\n\nimport sys\nimport time\nimport logging\n\nsys.path.append('../')\nlogging.getLogger('tensorflow').disabled = True\n\nimport tensorflow as tf\nfrom utils import checkmate as cm\nfrom utils import data_helpers as dh\nfrom utils import param_parser as parser\n\nargs = parser.parameter_parser()\nMODEL = dh.get_model_name()\nlogger = dh.logger_fn(\"tflog\", \"logs/Test-{0}.log\".format(time.asctime()))\n\nCPT_DIR = 'runs/' + MODEL + '/checkpoints/'\nBEST_CPT_DIR = 'runs/' + MODEL + '/bestcheckpoints/'\nSAVE_DIR = 'output/' + MODEL\n\n\ndef create_input_data(data: dict):\n    return zip(data['pad_seqs'], data['content'], data['section'], data['subsection'], data['group'],\n               data['subgroup'], data['onehot_labels'])\n\n\ndef normalization(visual_list, visual_len, epsilon=1e-12):\n    min_weight = min(visual_list[:visual_len])\n    max_weight = max(visual_list[:visual_len])\n    margin = max_weight - min_weight\n\n    result = []\n    for i in range(visual_len):\n        value = (visual_list[i] - min_weight) / (margin + epsilon)\n        result.append(value)\n    return result\n\n\ndef create_visual_file(input_x, visual_list: list, seq_len):\n    f = open('attention.html', 'w')\n    f.write('<html style=\"margin:0;padding:0;\"><body style=\"margin:0;padding:0;\">\\n')\n    f.write('<div style=\"margin:25px;\">\\n')\n    for visual in visual_list:\n        f.write('<p style=\"margin:10px;\">\\n')\n        for i in range(seq_len):\n            alpha = \"{:.2f}\".format(visual[i])\n            word = input_x[0][i]\n            f.write('\\t<span style=\"margin-left:3px;background-color:rgba(255,0,0,{0})\">{1}</span>\\n'\n                    .format(alpha, word))\n        f.write('</p>\\n')\n    f.write('</div>\\n')\n    f.write('</body></html>')\n    f.close()\n\n\ndef visualize():\n    \"\"\"Visualize HARNN model.\"\"\"\n\n    # Load word2vec model\n    word2idx, embedding_matrix = dh.load_word2vec_matrix(args.word2vec_file)\n\n    # Load data\n    logger.info(\"Loading data...\")\n    logger.info(\"Data processing...\")\n    test_data = dh.load_data_and_labels(args, args.test_file, word2idx)\n\n    # Load harnn model\n    OPTION = dh._option(pattern=1)\n    if OPTION == 'B':\n        logger.info(\"Loading best model...\")\n        checkpoint_file = cm.get_best_checkpoint(BEST_CPT_DIR, select_maximum_value=True)\n    else:\n        logger.info(\"Loading latest model...\")\n        checkpoint_file = tf.train.latest_checkpoint(CPT_DIR)\n    logger.info(checkpoint_file)\n\n    graph = tf.Graph()\n    with graph.as_default():\n        session_conf = tf.ConfigProto(\n            allow_soft_placement=args.allow_soft_placement,\n            log_device_placement=args.log_device_placement)\n        session_conf.gpu_options.allow_growth = args.gpu_options_allow_growth\n        sess = tf.Session(config=session_conf)\n        with sess.as_default():\n            # Load the saved meta graph and restore variables\n            saver = tf.train.import_meta_graph(\"{0}.meta\".format(checkpoint_file))\n            saver.restore(sess, checkpoint_file)\n\n            # Get the placeholders from the graph by name\n            input_x = graph.get_operation_by_name(\"input_x\").outputs[0]\n            input_y_first = graph.get_operation_by_name(\"input_y_first\").outputs[0]\n            input_y_second = graph.get_operation_by_name(\"input_y_second\").outputs[0]\n            input_y_third = graph.get_operation_by_name(\"input_y_third\").outputs[0]\n            input_y_fourth = graph.get_operation_by_name(\"input_y_fourth\").outputs[0]\n            input_y = graph.get_operation_by_name(\"input_y\").outputs[0]\n            dropout_keep_prob = graph.get_operation_by_name(\"dropout_keep_prob\").outputs[0]\n            alpha = graph.get_operation_by_name(\"alpha\").outputs[0]\n            is_training = graph.get_operation_by_name(\"is_training\").outputs[0]\n\n            # Tensors we want to evaluate\n            first_visual = graph.get_operation_by_name(\"first-output/visual\").outputs[0]\n            second_visual = graph.get_operation_by_name(\"second-output/visual\").outputs[0]\n            third_visual = graph.get_operation_by_name(\"third-output/visual\").outputs[0]\n            fourth_visual = graph.get_operation_by_name(\"fourth-output/visual\").outputs[0]\n\n            # Split the output nodes name by '|' if you have several output nodes\n            output_node_names = \"first-output/visual|second-output/visual|third-output/visual|fourth-output/visual|output/scores\"\n\n            # Save the .pb model file\n            output_graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def,\n                                                                            output_node_names.split(\"|\"))\n            tf.train.write_graph(output_graph_def, \"graph\", \"graph-harnn-{0}.pb\".format(MODEL), as_text=False)\n\n            # Generate batches for one epoch\n            batches = dh.batch_iter(list(create_input_data(test_data)), args.batch_size, 1, shuffle=False)\n\n            for batch_test in batches:\n                x, x_content, sec, subsec, group, subgroup, y_onehot = zip(*batch_test)\n\n                feed_dict = {\n                    input_x: x,\n                    input_y_first: sec,\n                    input_y_second: subsec,\n                    input_y_third: group,\n                    input_y_fourth: subgroup,\n                    input_y: y_onehot,\n                    dropout_keep_prob: 1.0,\n                    alpha: args.alpha,\n                    is_training: False\n                }\n                batch_first_visual, batch_second_visual, batch_third_visual, batch_fourth_visual = \\\n                    sess.run([first_visual, second_visual, third_visual, fourth_visual], feed_dict)\n\n                batch_visual = [batch_first_visual, batch_second_visual, batch_third_visual, batch_fourth_visual]\n\n                seq_len = len(x_content[0])\n                pad_len = len(batch_first_visual[0])\n                length = (pad_len if seq_len >= pad_len else seq_len)\n                visual_list = []\n\n                for visual in batch_visual:\n                    visual_list.append(normalization(visual[0].tolist(), length))\n\n                create_visual_file(x_content, visual_list, seq_len)\n    logger.info(\"Done.\")\n\n\nif __name__ == '__main__':\n    visualize()\n"
  },
  {
    "path": "LICENSE",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. 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  },
  {
    "path": "README.md",
    "content": "# Hierarchical Multi-Label Text Classification\n\n[![Python Version](https://img.shields.io/badge/language-python3.6-blue.svg)](https://www.python.org/downloads/) [![Build Status](https://travis-ci.org/RandolphVI/Hierarchical-Multi-Label-Text-Classification.svg?branch=master)](https://travis-ci.org/RandolphVI/Hierarchical-Multi-Label-Text-Classification)[![Codacy Badge](https://api.codacy.com/project/badge/Grade/80fe0da5f16146219a5d0a66f8c8ed70)](https://www.codacy.com/manual/chinawolfman/Hierarchical-Multi-Label-Text-Classification?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=RandolphVI/Hierarchical-Multi-Label-Text-Classification&amp;utm_campaign=Badge_Grade)[![License](https://img.shields.io/github/license/RandolphVI/Hierarchical-Multi-Label-Text-Classification.svg)](https://www.apache.org/licenses/LICENSE-2.0) \n\nThis repository is my research project, which has been accepted by CIKM'19. The [paper](https://dl.acm.org/citation.cfm?id=3357384.3357885) is already published.\n\nThe main objective of the project is to solve the hierarchical multi-label text classification (**HMTC**) problem. Different from the multi-label text classification, HMTC assigns each instance (object) into multiple categories and these categories are stored in a hierarchy structure, is a fundamental but challenging task of numerous applications.\n\n## Requirements\n\n- Python 3.6\n- Tensorflow 1.15.0\n- Tensorboard 1.15.0\n- Sklearn 0.19.1\n- Numpy 1.16.2\n- Gensim 3.8.3\n- Tqdm 4.49.0\n\n## Introduction\n\nMany real-world applications organize data in a hierarchical structure, where classes are specialized into subclasses or grouped into superclasses. For example, an electronic document (e.g. web-pages, digital libraries, patents and e-mails) is associated with multiple categories and all these categories are stored hierarchically in a **tree** or **Direct Acyclic Graph (DAG)**. \n\nIt provides an elegant way to show the characteristics of data and a multi-dimensional perspective to tackle the classification problem via hierarchy structure. \n\n![](https://farm8.staticflickr.com/7806/31717892987_e2e851eaaf_o.png)\n\nThe Figure shows an example of predefined labels in hierarchical multi-label classification of documents in patent texts. \n\n- Documents are shown as colored rectangles, labels as rounded rectangles. \n- Circles in the rounded rectangles indicate that the corresponding document has been assigned the label. \n- Arrows indicate a hierarchical structure between labels.\n\n## Project\n\nThe project structure is below:\n\n```text\n.\n├── HARNN\n│   ├── train.py\n│   ├── layers.py\n│   ├── ham.py\n│   ├── test.py\n│   └── visualization.py\n├── utils\n│   ├── checkmate.py\n│   ├── param_parser.py\n│   └── data_helpers.py\n├── data\n│   ├── word2vec_100.model.* [Need Download]\n│   ├── Test_sample.json\n│   ├── Train_sample.json\n│   └── Validation_sample.json\n├── LICENSE\n├── README.md\n└── requirements.txt\n```\n\n## Data\n\nYou can download the [Patent Dataset](https://drive.google.com/open?id=1So3unr5p_vlYq31gE0Ly07Z2XTvD5QlM) used in the paper. And the [Word2vec model file](https://drive.google.com/file/d/1tZ9WPXkoJmWwtcnOU8S_KGPMp8wnYohR/view?usp=sharing) (dim=100) is also uploaded. **Make sure they are under the `/data` folder.**\n\n:warning: As for **Education Dataset**, they may be subject to copyright protection under Chinese law. Thus, detailed information is not provided.\n\n### :octocat: Text Segment\n\n1. You can use `nltk` package if you are going to deal with the English text data.\n\n2. You can use `jieba` package if you are going to deal with the Chinese text data.\n\n### :octocat: Data Format\n\nSee data format in `/data` folder which including the data sample files. For example:\n\n```\n{\"id\": \"3930316\", \n\"title\": [\"sighting\", \"firearm\"], \n\"abstract\": [\"rear\", \"sight\", \"firearm\", \"ha\", \"peephole\", \"device\", \"formed\", \"hollow\", \"tube\", \"end\", ...], \n\"section\": [5], \"subsection\": [104], \"group\": [512], \"subgroup\": [6535], \n\"labels\": [5, 113, 649, 7333]}\n```\n\n- `id`: just the id.\n- `title` & `abstract`: it's the word segment (after cleaning stopwords).\n- `section` / `subsection` / `group` / `subgroup`: it's the first / second / third / fourth level category index.\n- `labels`: it's the total category which add the index offset. (I will explain that later)\n\n### :octocat: How to construct the data?\n\nUse the sample of the Patent Dataset as an example. I will explain how to construct the label index. \nFor patent dataset, the class number for each level is: [9, 128, 661, 8364].\n\n**Step 1:** For the first level, Patent dataset has 9 classes. You should index these 9 classes first, like:\n\n```\n{\"Chemistry\": 0, \"Physics\": 1, \"Electricity\": 2, \"XXX\": 3, ..., \"XXX\": 8}\n```\n\n**Step 2**: Next, you index the next level (total **128** classes), like:\n\n```\n{\"Inorganic Chemistry\": 0, \"Organic Chemistry\": 1, \"Nuclear Physics\": 2, \"XXX\": 3, ..., \"XXX\": 127}\n```\n\n**Step 3**: Then, you index the third level (total **661** classes), like:\n\n```\n{\"Steroids\": 0, \"Peptides\": 1, \"Heterocyclic Compounds\": 2, ..., \"XXX\": 660}\n```\n\n**Step 4**: If you have the fourth level or deeper level, index them.\n\n**Step 5**: Now suppose you have one record (**id: 3930316** mentioned before):\n\n```\n{\"id\": \"3930316\", \n\"title\": [\"sighting\", \"firearm\"], \n\"abstract\": [\"rear\", \"sight\", \"firearm\", \"ha\", \"peephole\", \"device\", \"formed\", \"hollow\", \"tube\", \"end\", ...], \n\"section\": [5], \"subsection\": [104], \"group\": [512], \"subgroup\": [6535],\n\"labels\": [5, 104+9, 512+9+128, 6535+9+128+661]}\n```\n\nThus, the record should be construed as follows:\n\n```\n{\"id\": \"3930316\", \n\"title\": [\"sighting\", \"firearm\"], \n\"abstract\": [\"rear\", \"sight\", \"firearm\", \"ha\", \"peephole\", \"device\", \"formed\", \"hollow\", \"tube\", \"end\", ...], \n\"section\": [5], \"subsection\": [104], \"group\": [512], \"subgroup\": [6535], \n\"labels\": [5, 113, 649, 7333]}\n```\n\nThis repository can be used in other datasets (text classification) in two ways:\n1. Modify your datasets into the same format of [the sample](https://github.com/RandolphVI/Hierarchical-Multi-Label-Text-Classification/tree/master/data).\n2. Modify the data preprocess code in `data_helpers.py`.\n\nAnyway, it should depend on what your data and task are.\n\n### :octocat: Pre-trained Word Vectors\n\nYou can pre-training your word vectors(based on your corpus) in many ways:\n- Use `gensim` package to pre-train data.\n- Use `glove` tools to pre-train data.\n- Even can use `bert` to pre-train data.\n\n## Usage\n\nSee [Usage](https://github.com/RandolphVI/Hierarchical-Multi-Label-Text-Classification/blob/master/Usage.md).\n\n## Network Structure\n\n![](https://live.staticflickr.com/65535/48647692206_2e5e6e7f13_o.png)\n\n## Reference\n\n**If you want to follow the paper or utilize the code, please note the following info in your work:** \n\n```bibtex\n@inproceedings{huang2019hierarchical,\n  author    = {Wei Huang and\n               Enhong Chen and\n               Qi Liu and\n               Yuying Chen and\n               Zai Huang and\n               Yang Liu and\n               Zhou Zhao and\n               Dan Zhang and\n               Shijin Wang},\n  title     = {Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network Approach},\n  booktitle = {Proceedings of the 28th {ACM} {CIKM} International Conference on Information and Knowledge Management, {CIKM} 2019, Beijing, CHINA, Nov 3-7, 2019},\n  pages     = {1051--1060},\n  year      = {2019},\n}\n```\n---\n\n## About Me\n\n黄威，Randolph\n\nSCU SE Bachelor; USTC CS Ph.D.\n\nEmail: chinawolfman@hotmail.com\n\nMy Blog: [randolph.pro](http://randolph.pro)\n\nLinkedIn: [randolph's linkedin](https://www.linkedin.com/in/randolph-%E9%BB%84%E5%A8%81/)\n"
  },
  {
    "path": "Usage.md",
    "content": "# Usage\n\n## Options\n\n### Input and output options\n\n```\n  --train-file              STR    Training file.      \t\tDefault is `data/Train_sample.json`.\n  --validation-file         STR    Validation file.      \tDefault is `data/Validation_sample.json`.\n  --test-file               STR    Testing file.       \t\tDefault is `data/Test_sample.json`.\n  --word2vec-file           STR    Word2vec model file.\t\tDefault is `data/word2vec_100.model`.\n```\n\n### Model option\n\n```\n  --pad-seq-len             INT     Padding Sequence length of data.                    Depends on data.\n  --embedding-type          INT     The embedding type.                                 Default is 1.\n  --embedding-dim           INT     Dim of character embedding.                         Default is 100.\n  --lstm-dim                INT     Dim of LSTM neurons.                                Default is 256.\n  --lstm-layers             INT     Number of LSTM layers.                              Defatul is 1.\n  --attention-dim           INT     Dim of Attention neurons.                           Default is 200.\n  --attention-penalization  BOOL    Use attention penalization or not.                  Default is True.\n  --fc-dim                  INT     Dim of FC neurons.                                  Default is 512.\n  --dropout-rate            FLOAT   Dropout keep probability.                           Default is 0.5.\n  --alpha                   FLOAT   Weight of global part in loss cal.                  Default is 0.5.\n  --num-classes-list        LIST    Each number of labels in hierarchical structure.    Depends on data.\n  --total-classes           INT     Total number of labels.                             Depends on data.\n  --topK                    INT     Number of top K prediction classes.                 Default is 5.\n  --threshold               FLOAT   Threshold for prediction classes.                   Default is 0.5.\n```\n\n### Training option\n\n```\n  --epochs                  INT     Number of epochs.                       Default is 20.\n  --batch-size              INT     Batch size.                             Default is 32.\n  --learning-rate           FLOAT   Adam learning rate.                     Default is 0.001.\n  --decay-rate              FLOAT   Rate of decay for learning rate.        Default is 0.95.\n  --decay-steps             INT     How many steps before decy lr.          Default is 500.\n  --evaluate-steps          INT     How many steps to evluate val set.      Default is 50.\n  --l2-lambda               FLOAT   L2 regularization lambda.               Default is 0.0.\n  --checkpoint-steps        INT     How many steps to save model.           Default is 50.\n  --num-checkpoints         INT     Number of checkpoints to store.         Default is 10.\n```\n\n## Training\n\nThe following commands train the model.\n\n```bash\n$ python3 train_harnn.py\n```\n\nTraining a model for a 30 epochs and set batch size as 256.\n\n```bash\n$ python3 train_harnn.py --epochs 30 --batch-size 256\n```\n\nIn the beginning, you will see the program shows:\n\n![](https://live.staticflickr.com/65535/49737484641_a1fca341c6_o.png)\n\n**You need to choose Training or Restore. (T for Training and R for Restore)**\n\nAfter training, you will get the `/log` and  `/run` folder.\n\n- `/log` folder saves the log info file.\n- `/run` folder saves the checkpoints.\n\nIt should be like this:\n\n```text\n.\n├── logs\n├── runs\n│   └── 1586077936 [a 10-digital format]\n│       ├── bestcheckpoints\n│       ├── checkpoints\n│       ├── embedding\n│       └── summaries\n├── test_harnn.py\n├── text_harnn.py\n└── train_harnn.py\n```\n\n**The programs name and identify the model by using the asctime (It should be 10-digital number, like 1586077936).** \n\n## Restore\n\nWhen your model stops training for some reason and you want to restore training, you can:\n\nIn the beginning, you will see the program shows:\n\n![](https://live.staticflickr.com/65535/49737999667_b6cd3e0f94_o.png)\n\n**And you need to input R for restore.**\n\nThen you will be asked to give the model name (a 10-digital format, like 1586077936):\n\n![](https://live.staticflickr.com/65535/49737156823_a5945fa958_o.png)\n\nAnd the model will continue training from the last time.\n\n## Test\n\nThe following commands test the model.\n\n```bash\n$ python3 test_harnn.py\n```\n\nThen you will be asked to give the model name (a 10-digital format, like 1586077936):\n\n![](https://live.staticflickr.com/65535/49737165843_56b8a25363_o.png)\n\nAnd you can choose to use the best model or the latest model **(B for Best, L for Latest)**:\n\n![](https://live.staticflickr.com/65535/49737168723_08a512aea8_o.png)\n\nFinally, you can get the `predictions.json` file under the `/outputs`  folder, it should be like:\n\n```text\n.\n├── graph\n├── logs\n├── output\n│   └── 1586077936\n│       └── predictions.json\n├── runs\n│   └── 1586077936\n│       ├── bestcheckpoints\n│       ├── checkpoints\n│       ├── embedding\n│       └── summaries\n├── test_harnn.py\n├── text_harnn.py\n└── train_harnn.py\n```\n\n"
  },
  {
    "path": "data/Test_sample.json",
    "content": "{\"id\": \"5973818\", \"title\": [\"method\", \"apparatus\", \"controlling\", \"electrochromic\", \"device\"], \"abstract\": [\"method\", \"concomitant\", \"apparatus\", \"electrochromic\", \"control\", \"system\", \"component\", \"causing\", \"charging\", \"discharging\", \"electrochromic\", \"device\", \"subject\", \"drift\", \"error\", \"error\", \"case\", \"error\", \"actual\", \"programming\", \"level\", \"desired\", \"programming\", \"level\", \"count\", \"diverge\", \"invention\", \"periodically\", \"determines\", \"actual\", \"programming\", \"level\", \"electrochromic\", \"device\", \"responsively\", \"adjusts\", \"parameter\", \"representative\", \"desired\", \"programming\", \"level\"], \"section\": [6], \"subsection\": [107], \"group\": [538], \"subgroup\": [6846], \"labels\": [6, 116, 675, 7644]}\n{\"id\": \"5973822\", \"title\": [\"acousto-optic\", \"tunable\", \"filter\", \"method\", \"calculating\", \"equivalence\", \"incident\", \"angle\"], \"abstract\": [\"non-collinear\", \"type\", \"acousto-optic\", \"tunable\", \"filter\", \"incident\", \"angle\", \"source\", \"light\", \"beam\", \"radiated\", \"light\", \"source\", \"acoustic\", \"medium\", \"set\", \"equivalence\", \"incident\", \"angle\", \"wavelength\", \"lambda\", \"\", \"diffracted\", \"ordinary\", \"ray\", \"wavelength\", \"lambda\", \"\", \"\", \"diffracted\", \"extraordinary\", \"ray\", \"approximately\", \"identical\", \"diffracted\", \"ordinary\", \"ray\", \"diffracted\", \"extraordinary\", \"ray\", \"approximately\", \"identical\", \"wavelength\", \"superposed\", \"intensity\", \"superposed\", \"ray\", \"detected\", \"spectrometry\", \"performed\", \"based\", \"superposed\", \"diffracted\", \"ray\", \"intensity\", \"sharp\", \"waveform\", \"accurate\", \"spectroscopy\", \"made\", \"intensity\", \"source\", \"light\", \"beam\", \"low\"], \"section\": [6], \"subsection\": [107], \"group\": [538], \"subgroup\": [6846], \"labels\": [6, 116, 675, 7644]}\n{\"id\": \"5974213\", \"title\": [\"transparent\", \"light\", \"guide\", \"member\"], \"abstract\": [\"light\", \"guide\", \"member\", \"guiding\", \"beam\", \"light\", \"light\", \"emitting\", \"element\", \"fixedly\", \"mounted\", \"printed\", \"circuit\", \"board\", \"display\", \"window\", \"defined\", \"casing\", \"encloses\", \"light\", \"emitting\", \"element\", \"printed\", \"circuit\", \"board\", \"light\", \"guide\", \"member\", \"includes\", \"transparent\", \"rod\", \"reflecting\", \"surface\", \"defined\", \"location\", \"length\", \"transparent\", \"rod\", \"transparent\", \"rod\", \"designed\", \"beam\", \"light\", \"travel\", \"dimension\", \"entire\", \"length\", \"transparent\", \"rod\", \"light\", \"receiving\", \"face\", \"defined\", \"end\", \"transparent\", \"rod\", \"light\", \"emitting\", \"face\", \"defined\", \"opposite\", \"end\", \"transparent\", \"rod\"], \"section\": [6], \"subsection\": [107], \"group\": [536], \"subgroup\": [6834], \"labels\": [6, 116, 673, 7632]}\n{\"id\": \"5974232\", \"title\": [\"image\", \"processing\", \"apparatus\", \"executes\", \"abortion\", \"image\", \"processing\", \"method\", \"resuming\", \"aborted\", \"image\", \"processing\"], \"abstract\": [\"image\", \"processing\", \"apparatus\", \"includes\", \"input\", \"inputting\", \"image\", \"data\", \"subjected\", \"image\", \"process\", \"storage\", \"storing\", \"image\", \"data\", \"inputted\", \"input\", \"processing\", \"reading\", \"image\", \"data\", \"stored\", \"storage\", \"subjecting\", \"read\", \"image\", \"data\", \"instructed\", \"image\", \"process\", \"abortion\", \"aborting\", \"image\", \"process\", \"image\", \"data\", \"performed\", \"processing\", \"hold\", \"instructing\", \"giving\", \"instruction\", \"image\", \"data\", \"process\", \"ha\", \"aborted\", \"abortion\", \"continuously\", \"held\", \"storage\", \"hold\", \"continuously\", \"holding\", \"image\", \"data\", \"storage\", \"based\", \"instruction\", \"hold\", 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\"processing\", \"transmitted\", \"voice\", \"signal\", \"include\", \"centralized\", \"frame\", \"controller\", \"providing\", \"boundary\", \"control\", \"signal\", \"voice\", \"processing\", \"block\", \"controlling\", \"operation\", \"voice\", \"processing\", \"block\", \"transmitted\", \"voice\", \"signal\", \"based\", \"boundary\", \"control\", \"signal\"], \"section\": [6, 7], \"subsection\": [123, 115], \"group\": [638, 586, 633], \"subgroup\": [7278, 7273, 7867, 7279, 7271, 7934, 7276], \"labels\": [6, 7, 132, 124, 775, 723, 770, 8076, 8071, 8665, 8077, 8069, 8732, 8074]}\n{\"id\": \"6522849\", \"title\": [\"rotation\", \"stabilizing\", \"device\", \"including\", \"inertia\", \"member\", \"connected\", \"rotary\", \"center\", \"axis\", \"rotary\", \"member\", \"viscoelastic\", \"member\"], \"abstract\": [\"rotation\", \"stabilizing\", \"device\", \"includes\", \"rotary\", \"member\", \"rotated\", \"rotating\", \"center\", \"axis\", \"inertia\", \"member\", \"vibrating\", \"accordance\", \"change\", \"rotating\", \"speed\", \"rotary\", \"member\", \"viscoelastic\", \"connector\", \"detachably\", \"connecting\", \"inertia\", \"member\", \"rotary\", \"member\", \"viscoelastic\", \"connector\", \"ha\", \"viscosity\", \"elasticity\", \"change\", \"form\", \"accordance\", \"vibration\", \"inertia\", \"member\", \"inertia\", \"member\", \"connected\", \"outer\", \"side\", \"side\", \"rotary\", \"member\", \"phase\", \"direction\", \"rotating\", \"center\", \"axis\", \"rotary\", \"member\", \"viscoelastic\", \"connector\"], \"section\": [6, 7, 5], \"subsection\": [123, 94, 108], \"group\": [543, 639, 450], \"subgroup\": [7945, 6910, 5847, 7935], \"labels\": [6, 7, 5, 132, 103, 117, 680, 776, 587, 8743, 7708, 6645, 8733]}\n{\"id\": \"6523044\", \"title\": [\"collecting\", \"storing\", \"retrieving\", \"knowledge\", \"organization\"], \"abstract\": [\"method\", \"collecting\", \"storing\", \"retrieving\", \"knowledge\", \"includes\", \"step\", \"collecting\", \"knowledge\", \"information\", \"information-source\", \"terminal\", \"network\", \"knowledge\", \"information\", \"including\", \"information\", \"research\", \"development\", \"problem\", \"solution\", \"problem\", \"storing\", \"collected\", \"knowledge\", \"information\", \"library\", \"classifying\", \"collected\", \"knowledge\", \"information\", \"based\", \"type\", \"attribute\", \"collected\", \"knowledge\", \"information\", \"organizing\", \"stored\", \"knowledge\", \"information\", \"based\", \"importance\", \"frequency\", \"thereof\", \"storing\", \"organized\", \"knowledge\", \"information\", \"library\", \"standardizing\", \"organized\", \"knowledge\", \"information\", \"usable\", \"retrieving\", \"standardized\", \"knowledge\", \"information\", \"library\", \"response\", \"retrieval\", \"request\", \"provide\", \"retrieved\", \"information\", \"requesting\", \"party\", \"made\", \"retrieval\", \"request\"], \"section\": [6, 8], \"subsection\": [127, 111], \"group\": [558, 659], \"subgroup\": [8319, 7038], \"labels\": [6, 8, 136, 120, 695, 796, 9117, 7836]}\n{\"id\": \"6523143\", \"title\": [\"memory\", \"testing\", \"apparatus\"], \"abstract\": [\"memory\", \"testing\", \"apparatus\", \"capable\", \"reducing\", \"time\", \"transmitting\", \"address\", \"signal\", \"pattern\", \"generator\", \"failure\", \"analysis\", \"memory\", \"memory\", \"testing\", \"apparatus\", \"includes\", \"pattern\", \"generator\", \"output\", \"input\", \"address\", \"signal\", \"input\", \"test\", \"signal\", \"including\", \"input\", \"data\", \"signal\", \"expectation\", \"data\", \"signal\", \"signal\", \"input-output\", \"unit\", \"comparator\", \"compare\", \"output\", \"data\", \"signal\", \"expectation\", \"data\", \"output\", \"defect-indicating\", \"data\", \"defective\", \"data\", \"storing\", \"memory\", \"failure\", \"history\", \"storing\", \"memory\", \"pattern\", \"generator\", \"includes\", \"sequence\", \"control\", \"unit\", \"generates\", \"sequence\", \"control\", \"signal\", \"control\", \"address\", \"generator\", \"data\", \"generator\", \"control\", \"signal\", \"generator\", \"connected\", \"thereto\", \"address\", \"generator\", \"output\", \"address\", \"signal\", \"address\", \"signal\", \"therefrom\", \"address\", \"generator\", \"connected\", \"separate\", \"transmission\", \"line\", \"defective\", \"data\", \"storing\", \"memory\", \"failure\", \"history\", \"storing\", \"memory\", \"address\", \"signal\", \"address\", \"signal\", \"independently\", \"separately\", \"supplied\", \"defective\", \"data\", \"storing\", \"unit\", \"failure\", \"history\", \"storing\", \"memory\"], \"section\": [6], \"subsection\": [116], \"group\": [588], \"subgroup\": [7318], \"labels\": [6, 125, 725, 8116]}\n{\"id\": \"6523365\", \"title\": [\"accumulator\", \"internal\", \"heat\", \"exchanger\"], \"abstract\": [\"accumulator\", \"internal\", \"heat\", \"exchanger\", \"air\", \"conditioning\", \"refrigeration\", \"system\", \"compressor\", \"condenser\", \"expansion\", \"device\", \"evaporator\", \"disclosed\", \"operation\", \"accumulator\", \"system\", \"high\", \"pressure\", \"high\", \"temperature\", \"refrigerant\", \"flowing\", \"condenser\", \"low\", \"pressure\", \"low\", \"temperature\", \"refrigerant\", \"flowing\", \"evaporator\", \"simultaneously\", \"enter\", \"flow\", \"heat\", \"exchanger\", \"disposed\", \"accumulator\", \"low\", \"pressure\", \"low\", \"temperature\", \"refrigerant\", \"absorbs\", \"heat\", \"cool\", \"high\", \"pressure\", \"high\", \"temperature\", \"refrigerant\", \"embodiment\", \"heat\", \"exchanger\", \"comprises\", \"tube\", \"high\", \"temperature\", \"channel\", \"low\", \"temperature\", \"channel\", \"extending\", \"interior\", \"tube\", \"embodiment\", \"heat\", \"exchanger\", \"comprises\", \"single\", \"spirally\", \"wound\", \"coaxial\", \"tube\", \"outer\", \"tube\", \"tube\", \"positioned\", \"outer\", \"tube\", \"embodiment\", \"heat\", \"exchanger\", \"comprises\", \"plurality\", \"coaxial\", \"tube\", \"coaxial\", \"tube\", \"outer\", \"tube\", \"tube\", \"positioned\", \"outer\", \"tube\", \"tube\", \"fluidly\", \"connected\"], \"section\": [5], \"subsection\": [100, 103], \"group\": [495, 505, 506], \"subgroup\": [6312, 6486, 6335, 6468, 6337, 6344, 6321], \"labels\": [5, 109, 112, 632, 642, 643, 7110, 7284, 7133, 7266, 7135, 7142, 7119]}\n{\"id\": \"6523673\", \"title\": [\"method\", \"apparatus\", \"replacing\", \"pallet\", \"car\", \"traveling\", \"grate\", \"machine\"], \"abstract\": [\"apparatus\", \"method\", \"changing\", \"pallet\", \"car\", \"on-the-fly\", \"traveling\", \"grate\", \"machine\", \"includes\", \"spaced-apart\", \"drive\", \"sprocket\", \"positioned\", \"end\", \"intermediate\", \"curved\", \"end\", \"portion\", \"engaging\", \"driving\", \"supporting\", \"roller\", \"pallet\", \"car\", \"causing\", \"movement\", \"pallet\", \"car\", \"top\", \"bottom\", \"strand\", \"machine\", \"apparatus\", \"changing\", \"pallet\", \"car\", \"includes\", \"hinged\", \"section\", \"outer\", \"guide\", \"rail\", \"located\", \"drive\", \"sprocket\", \"adjacent\", \"bottom\", \"strand\", \"guide\", \"rail\", \"section\", \"hinged\", \"guide\", \"rail\", \"moved\", \"locked\", \"position\", \"open\", \"position\", \"open\", \"position\", \"selected\", \"pallet\", \"car\", \"requiring\", \"replacement\", \"removed\", \"drive\", \"sprocket\", \"outer\", \"guide\", \"rail\", \"drive\", \"sprocket\", \"continues\", \"turn\", \"hinged\", \"section\", \"outer\", \"guide\", \"rail\", \"arc\", \"provided\", \"top\", \"drive\", \"sprocket\", \"adjacent\", \"upper\", \"strand\", \"guide\", \"rail\", \"hinged\", \"section\", \"outer\", \"guide\", \"rail\", \"selectively\", \"moved\", \"locked\", \"position\", \"open\", \"position\", \"open\", \"position\", \"replacement\", \"pallet\", \"car\", \"inserted\", \"sprocket\", \"guide\", \"rail\", \"position\", \"previously\", \"occupied\", \"aforementioned\", \"pallet\", \"car\", \"drive\", \"sprocket\", \"rotates\", \"hinged\", \"section\", \"outer\", \"guide\", \"rail\", \"moved\", \"back\", \"locked\", \"position\", \"procedure\", \"repeated\", \"replace\", \"damaged\", \"worn\", \"pallet\", \"car\", \"requiring\", \"maintenance\"], \"section\": [1, 5], \"subsection\": [46, 102], \"group\": [501, 239, 500], \"subgroup\": [6440, 6423, 3068, 6419], \"labels\": [1, 5, 55, 111, 638, 376, 637, 7238, 7221, 3866, 7217]}\n{\"id\": \"6523674\", \"title\": [\"belt\", \"conveyor\", \"transition\", \"vacuum\", \"stabilization\"], \"abstract\": [\"transition\", \"section\", \"belt\", \"conveyor\", \"transfer\", \"series\", \"object\", \"conveyed\", \"ending\", \"section\", \"conveyor\", \"pair\", \"guide\", \"rail\", \"laterally\", \"beginning\", \"section\", \"conveyor\", \"source\", \"vacuum\", \"positioned\", \"ending\", \"section\", \"conveyor\", \"beginning\", \"section\", \"conveyor\", \"hold\", \"conveyed\", \"object\", \"conveyor\", \"surface\", \"object\", \"transferred\", \"surface\", \"eliminates\", \"problem\", \"conveyed\", \"object\", \"falling\", \"transferred\", \"conveyor\", \"surface\", \"conveyor\", \"surface\"], \"section\": [1], \"subsection\": [46], \"group\": [239], \"subgroup\": [3055, 3056, 3074], \"labels\": [1, 55, 376, 3853, 3854, 3872]}\n{\"id\": \"6523758\", \"title\": [\"fuel\", \"injector\", \"needle\", \"lower\", \"guide\", \"disk\"], \"abstract\": [\"fuel\", \"injector\", \"provided\", \"fuel\", \"injector\", \"includes\", \"fuel\", \"metering\", \"member\", \"end\", \"disposed\", \"longitudinal\", \"axis\", \"seat\", \"located\", \"fuel\", \"metering\", \"member\", \"proximate\", \"end\", \"needle\", \"reciprocably\", \"disposed\", \"fuel\", \"metering\", \"member\", \"needle\", \"ha\", \"longitudinal\", \"needle\", \"axis\", \"fuel\", \"injector\", \"includes\", \"guide\", \"disposed\", \"fuel\", \"metering\", \"member\", \"proximate\", \"seat\", \"guide\", \"includes\", \"generally\", \"planar\", \"disk\", \"surface\", \"surface\", \"outer\", \"perimeter\", \"guide\", \"includes\", \"generally\", \"concentric\", \"central\", \"opening\", \"extending\", \"therethrough\", \"central\", \"opening\", \"sized\", \"reciprocating\", \"element\", \"reciprocate\", \"guide\", \"includes\", \"opening\", \"extending\", \"surface\", \"central\", \"opening\", \"outer\", \"perimeter\", \"opening\", \"extends\", \"generally\", \"parallel\", \"longitudinal\", \"axis\", \"method\", \"evacuating\", \"vapor\", \"bubble\", \"proximate\", \"valve\", \"seat\", \"fuel\", \"injector\", \"provided\"], \"section\": [5], \"subsection\": [89], \"group\": [429], \"subgroup\": [5526, 5528, 5531], \"labels\": [5, 98, 566, 6324, 6326, 6329]}\n{\"id\": \"6523830\", \"title\": [\"casino\", \"game\"], \"abstract\": [\"casino\", \"game\", \"dealer\", \"player\", \"deck\", \"card\", \"consisting\", \"essentially\", \"forty\", \"card\", \"ace\", \"ten\", \"suit\", \"card\", \"equal\", \"face\", \"card\", \"ace\", \"valued\", \"player\", \"make\", \"base\", \"wager\", \"dealer\", \"deal\", \"card\", \"player\", \"card\", \"dealer\", \"player\", \"form\", \"final\", \"hand\", \"standing\", \"receiving\", \"additional\", \"card\", \"player\", \"receives\", \"card\", \"player\", \"hand\", \"sum\", \"exceed\", \"ten\", \"player\", \"automatically\", \"loses\", \"base\", \"wager\", \"collected\", \"alternatively\", \"player\", \"form\", \"final\", \"hand\", \"consisting\", \"essentially\", \"ace\", \"player\", \"automatically\", \"win\", \"player\", \"rewarded\", \"optional\", \"embodiment\", \"dealer\", \"receives\", \"hand\", \"ace\", \"player\", \"optionally\", \"increase\", \"base\", \"wager\", \"initial\", \"card\", \"dealt\", \"ace\", \"dealer\", \"form\", \"final\", \"hand\", \"drawing\", \"additional\", \"card\", \"standing\", \"optionally\", \"house\", \"rule\", \"base\", \"wager\", \"resolved\", \"summing\", \"card\", \"dealer\", \"final\", \"hand\", \"declaring\", \"final\", \"hand\", \"sum\", \"closer\", \"ten\", \"exceeding\", \"ten\", \"winning\", \"hand\", \"player\", \"winning\", \"hand\", \"rewarded\", \"wager\", \"collected\", \"player\", \"losing\", \"hand\", \"event\", \"push\", \"player\", \"base\", \"wager\", \"returned\"], \"section\": [0], \"subsection\": [14], \"group\": [80], \"subgroup\": [1061], \"labels\": [0, 23, 217, 1859]}\n{\"id\": \"6524320\", \"title\": [\"cannula\", \"receiving\", \"surgical\", \"instrument\"], \"abstract\": [\"cannula\", \"receiving\", \"surgical\", \"instrument\", \"performing\", \"surgical\", \"procedure\", \"body\", \"includes\", \"tubular\", \"structure\", \"defining\", \"passage\", \"surgical\", \"instrument\", \"inserted\", \"body\", \"tubular\", \"structure\", \"includes\", \"expandable\", \"portion\", \"enabling\", \"increase\", \"cross-sectional\", \"area\", \"passage\", \"expandable\", \"portion\", \"tubular\", \"structure\", \"ha\", \"slot\", \"guide\", \"member\", \"disposed\", \"slot\", \"guide\", \"member\", \"movable\", \"end\", \"slot\", \"end\", \"slot\", \"enable\", \"cross-sectional\", \"area\", \"passage\", \"increase\", \"expandable\", \"portion\", \"ha\", \"stop\", \"end\", \"slot\", \"engageable\", \"guide\", \"member\", \"retain\", \"guide\", \"member\", \"position\", \"relative\", \"slot\", \"resist\", \"movement\", \"guide\", \"member\", \"position\", \"relative\", \"slot\"], \"section\": [0], \"subsection\": [12], \"group\": [61], \"subgroup\": [700], \"labels\": [0, 21, 198, 1498]}\n{\"id\": \"6524813\", \"title\": [\"polynucleotide\", \"encoding\", \"staphylococcus\", \"aureus\"], \"abstract\": [\"invention\", \"polypeptide\", \"polynucleotides\", \"encoding\", \"polypeptide\", \"method\", \"producing\", \"polypeptide\", \"recombinant\", \"technique\", \"provided\", \"method\", \"utilizing\", \"polypeptide\", \"screen\", \"antibacterial\", \"compound\"], \"section\": [2, 0], \"subsection\": [58, 12], \"group\": [282, 68], \"subgroup\": [844, 843, 834, 3713], \"labels\": [2, 0, 67, 21, 419, 205, 1642, 1641, 1632, 4511]}\n{\"id\": \"6525130\", \"title\": [\"polymerization\", \"silicone\", \"surfactant\", \"medium\"], \"abstract\": [\"process\", \"producing\", \"polymerized\", \"silicone\", \"fluid\", \"comprises\", \"step\", \"charging\", \"reaction\", \"vessel\", \"hydroxy-terminated\", \"silicone\", \"fluid\", \"ethoxy-or\", \"methoxy-terminated\", \"functional\", \"silane\", \"basic\", \"catalyst\", \"surfactant\", \"package\", \"cloud\", \"point\", \"deg\", \"reaction\", \"temperature\", \"needed\", \"react\", \"functional\", \"silane\", \"hydroxy-terminated\", \"silicone\", \"fluid\", \"heating\", \"content\", \"reaction\", \"vessel\", \"temperature\", \"deg\", \"cloud\", \"point\", \"surfactant\", \"package\", \"copolymerize\", \"silicone\", \"fluid\", \"functional\", \"silane\", \"terminating\", \"copolymerization\", \"reaction\", \"diluting\", \"product\", \"water\", \"form\", \"emulsion\"], \"section\": [2, 1], \"subsection\": [15, 59], \"group\": [87, 286], \"subgroup\": [1181, 3858], \"labels\": [2, 1, 24, 68, 224, 423, 1979, 4656]}\n{\"id\": \"6525176\", \"title\": [\"purification\", \"proteinase\", \"inhibitor\"], \"abstract\": [\"method\", \"purifying\", \"human\", \"alpha-l\", \"proteinase\", \"inhibitor\", \"agr\", \"-\", \"pi\", \"solution\", \"derived\", \"milk\", \"transgenic\", \"animal\", \"expressing\", \"agr\", \"-\", \"pi\", \"comprises\", \"contacting\", \"solution\", \"cation\", \"exchange\", \"substrate\", \"condition\", \"sufficient\", \"bind\", \"non-tg\", \"-\", \"agr\", \"-\", \"pi\", \"contaminant\", \"substrate\", \"substantially\", \"binding\", \"tg\", \"agr\", \"-\", \"pi\", \"substrate\", \"preferred\", \"embodiment\", \"purified\", \"tg\", \"agr\", \"-\", \"pi\", \"pg\", \"-\", \"agr\", \"-\", \"pi-whey\", \"protein\", \"mg\", \"total\", \"protein\"], \"section\": [2, 8], \"subsection\": [58, 127], \"group\": [659, 282], \"subgroup\": [8297, 3713], \"labels\": [2, 8, 67, 136, 796, 419, 9095, 4511]}\n{\"id\": \"6525579\", \"title\": [\"pulse\", \"translational\", \"circuit\"], \"abstract\": [\"circuit\", \"producing\", \"output\", \"pulse\", \"correspond\", \"pre-selected\", \"voltage\", \"pulse\", \"occurring\", \"input\", \"signal\", \"train\", \"connected\", \"derive\", \"input\", \"signal\", \"train\", \"succession\", \"unidirectional\", \"pulse\", \"pulse\", \"occur\", \"time\", \"coincidence\", \"leading\", \"trailing\", \"edge\", \"pre-selected\", \"voltage\", \"pulse\", \"including\", \"one-shot\", \"multi-vibrator\", \"connected\", \"receive\", \"unidirectional\", \"pulse\", \"produce\", \"series\", \"pulse\", \"uniform\", \"duration\", \"le\", \"duration\", \"pre-selected\", \"voltage\", \"pulse\", \"including\", \"bi-stable\", \"multi-vibrator\", \"connected\", \"receive\", \"produce\", \"series\", \"pulse\", \"series\", \"pulse\", \"equal\", \"half\", \"number\", \"pulse\", \"series\", \"occurring\", \"time\", \"interval\", \"including\", \"discriminator\", \"circuit\", \"connected\", \"receive\", \"series\", \"pulse\", \"derive\", \"series\", \"pulse\", \"corresponds\", \"pre-selected\", \"voltage\", \"pulse\", \"occurring\", \"input\", \"signal\", \"train\"], \"section\": [7], \"subsection\": [122], \"group\": [630], \"subgroup\": [7839], \"labels\": [7, 131, 767, 8637]}\n{\"id\": \"6525611\", \"title\": [\"power\", \"amplifier\", \"protection\"], \"abstract\": [\"present\", \"invention\", \"circuitry\", \"detecting\", \"excessive\", \"voltage\", \"output\", \"power\", \"amplifier\", \"reducing\", \"bias\", \"provided\", \"amplifier\", \"detecting\", \"excessive\", \"voltage\", \"reducing\", \"amplifier\", \"bias\", \"amplifier\", \"gain\", \"reduced\", \"effectively\", \"suppress\", \"output\", \"voltage\", \"excessive\", \"voltage\", \"condition\", \"removed\", \"peak\", \"detection\", \"circuitry\", \"adapted\", \"monitor\", \"voltage\", \"radio\", \"frequency\", \"rf\", \"signal\", \"device\", \"output\", \"voltage\", \"rf\", \"output\", \"signal\", \"exceed\", \"predefined\", \"threshold\", \"peak\", \"detection\", \"circuitry\", \"provide\", \"bias\", \"control\", \"signal\", \"bias\", \"network\", \"amplifier\", \"circuitry\", \"bias\", \"network\", \"preferably\", \"configured\", \"respond\", \"bias\", \"control\", \"signal\", \"reducing\", \"bias\", \"provided\", \"amplifier\", \"circuitry\", \"reducing\", \"gain\", \"amplifier\", \"circuitry\"], \"section\": [7], \"subsection\": [122], \"group\": [626], \"subgroup\": [7786], \"labels\": [7, 131, 763, 8584]}\n{\"id\": \"6525628\", \"title\": [\"surface\", \"mount\", \"rc\", \"array\", \"narrow\", \"tab\", \"portion\", \"electrode\", \"plate\"], \"abstract\": [\"discrete\", \"array\", \"rc\", \"component\", \"cofireable\", \"resistive\", \"material\", \"part\", \"internal\", \"electrode\", \"device\", \"device\", \"include\", \"sintered\", \"body\", \"multilayer\", \"ceramic\", \"material\", \"multiple\", \"electrode\", \"layer\", \"stacked\", \"layer\", \"comprises\", \"resistive\", \"electrode\", \"pattern\", \"extending\", \"sintered\", \"body\", \"respective\", \"pair\", \"termination\", \"layer\", \"comprise\", \"electrode\", \"pattern\", \"extending\", \"transverse\", \"resistive\", \"electrode\", \"pattern\", \"end\", \"termination\", \"embodiment\", \"opposing\", \"side\", \"electrode\", \"serve\", \"input\", \"output\", \"terminal\", \"respective\", \"feedthrough\", \"filter\", \"feedthrough\", \"arrangement\", \"terminal\", \"provided\", \"end\", \"terminal\", \"invention\", \"describes\", \"improved\", \"termination\", \"structure\", \"including\", \"layer\", \"made\", \"metal\", \"oxide\", \"material\"], \"section\": [7], \"subsection\": [120, 124, 122], \"group\": [649, 628, 603, 601], \"subgroup\": [7382, 7807, 7801, 8056, 7415], \"labels\": [7, 129, 133, 131, 786, 765, 740, 738, 8180, 8605, 8599, 8854, 8213]}\n{\"id\": \"6525875\", \"title\": [\"microscope\", \"generating\", \"three-dimensional\", \"representation\", \"object\", \"image\", \"generated\", \"microscope\"], \"abstract\": [\"microscope\", \"disclosed\", \"determines\", \"complex\", \"three-dimensional\", \"representation\", \"object\", \"based\", \"series\", \"recording\", \"light\", \"wave\", \"diffracted\", \"object\", \"direction\", \"wave\", \"lighting\", \"object\", \"varies\", \"succesive\", \"recording\", \"diffracted\", \"wave\", \"interferes\", \"reference\", \"wave\", \"receiving\", \"surface\", \"frequency\", \"representation\", \"diffracted\", \"wave\", \"computed\", \"interference\", \"pattern\", \"received\", \"receiving\", \"surface\", \"plurality\", \"frequency\", \"representation\", \"diffracted\", \"wave\", \"superimposed\", \"yielding\", \"frequency\", \"representation\", \"object\", \"phase\", \"frequency\", \"representation\", \"diffracted\", \"wave\", \"shifted\", \"order\", \"compensate\", \"variation\", \"phase\", \"difference\", \"reference\", \"wave\", \"wave\", \"lighting\", \"object\"], \"section\": [6], \"subsection\": [108], \"group\": [544], \"subgroup\": [6922, 6924, 6928, 6921], \"labels\": [6, 117, 681, 7720, 7722, 7726, 7719]}"
  },
  {
    "path": "utils/checkmate.py",
    "content": "import os\nimport glob\nimport json\nimport numpy as np\nimport tensorflow as tf\n\n\nclass BestCheckpointSaver(object):\n    \"\"\"Maintains a directory containing only the best n checkpoints.\n    Inside the directory is a best_checkpoints JSON file containing a dictionary\n    mapping of the best checkpoint filepaths to the values by which the checkpoints\n    are compared.  Only the best n checkpoints are contained in the directory and JSON file.\n    This is a light-weight wrapper class only intended to work in simple,\n    non-distributed settings.  It is not intended to work with the tf.Estimator\n    framework.\n    \"\"\"\n    def __init__(self, save_dir, num_to_keep=1, maximize=True, saver=None):\n        \"\"\"Creates a `BestCheckpointSaver`.\n        `BestCheckpointSaver` acts as a wrapper class around a `tf.train.Saver`.\n\n        Args:\n            save_dir: The directory in which the checkpoint files will be saved.\n            num_to_keep: The number of best checkpoint files to retain.\n            maximize: Define 'best' values to be the highest values.  For example,\n              set this to True if selecting for the checkpoints with the highest\n              given accuracy.  Or set to False to select for checkpoints with the\n              lowest given error rate.\n            saver: A `tf.train.Saver` to use for saving checkpoints.  A default\n              `tf.train.Saver` will be created if none is provided.\n        \"\"\"\n        self._num_to_keep = num_to_keep\n        self._save_dir = save_dir\n        self._save_path = os.path.join(save_dir, 'model')\n        self._maximize = maximize\n        self._saver = saver if saver else tf.train.Saver(\n            max_to_keep=None,\n            save_relative_paths=True\n        )\n\n        if not os.path.exists(save_dir):\n            os.makedirs(save_dir)\n        self.best_checkpoints_file = os.path.join(save_dir, 'best_checkpoints')\n\n    def handle(self, value, sess, global_step):\n        \"\"\"Updates the set of best checkpoints based on the given result.\n\n        Args:\n            value: The value by which to rank the checkpoint.\n            sess: A tf.Session to use to save the checkpoint.\n            global_step: The global step.\n        \"\"\"\n        current_ckpt = 'model-{}'.format(global_step)\n        value = float(value)\n        if not os.path.exists(self.best_checkpoints_file):\n            self._save_best_checkpoints_file({current_ckpt: value})\n            self._saver.save(sess, self._save_path, global_step)\n            return\n\n        best_checkpoints = self._load_best_checkpoints_file()\n\n        if len(best_checkpoints) < self._num_to_keep:\n            best_checkpoints[current_ckpt] = value\n            self._save_best_checkpoints_file(best_checkpoints)\n            self._saver.save(sess, self._save_path, global_step)\n            return\n\n        if self._maximize:\n            should_save = not all(current_best >= value\n                                  for current_best in best_checkpoints.values())\n        else:\n            should_save = not all(current_best <= value\n                                  for current_best in best_checkpoints.values())\n        if should_save:\n            best_checkpoint_list = self._sort(best_checkpoints)\n\n            worst_checkpoint = os.path.join(self._save_dir,\n                                            best_checkpoint_list.pop(-1)[0])\n            self._remove_outdated_checkpoint_files(worst_checkpoint)\n            self._update_internal_saver_state(best_checkpoint_list)\n\n            best_checkpoints = dict(best_checkpoint_list)\n            best_checkpoints[current_ckpt] = value\n            self._save_best_checkpoints_file(best_checkpoints)\n\n            self._saver.save(sess, self._save_path, global_step)\n\n    def _save_best_checkpoints_file(self, updated_best_checkpoints):\n        with open(self.best_checkpoints_file, 'w') as f:\n            json.dump(updated_best_checkpoints, f, indent=3)\n\n    def _remove_outdated_checkpoint_files(self, worst_checkpoint):\n        os.remove(os.path.join(self._save_dir, 'checkpoint'))\n        for ckpt_file in glob.glob(worst_checkpoint + '.*'):\n            os.remove(ckpt_file)\n\n    def _update_internal_saver_state(self, best_checkpoint_list):\n        best_checkpoint_files = [\n            (ckpt[0], np.inf)  # TODO: Try to use actual file timestamp\n            for ckpt in best_checkpoint_list\n        ]\n        self._saver.set_last_checkpoints_with_time(best_checkpoint_files)\n\n    def _load_best_checkpoints_file(self):\n        with open(self.best_checkpoints_file, 'r') as f:\n            best_checkpoints = json.load(f)\n        return best_checkpoints\n\n    def _sort(self, best_checkpoints):\n        best_checkpoints = [\n            (ckpt, best_checkpoints[ckpt])\n            for ckpt in sorted(best_checkpoints,\n                               key=best_checkpoints.get,\n                               reverse=self._maximize)\n        ]\n        return best_checkpoints\n\n\ndef get_best_checkpoint(best_checkpoint_dir, select_maximum_value=True):\n    \"\"\"Returns filepath to the best checkpoint.\n    Reads the best_checkpoints file in the best_checkpoint_dir directory.\n    Returns the filepath in the best_checkpoints file associated with\n    the highest value if select_maximum_value is True, or the filepath\n    associated with the lowest value if select_maximum_value is False.\n\n    Args:\n        best_checkpoint_dir: Directory containing best_checkpoints JSON file.\n        select_maximum_value: If True, select the filepath associated\n          with the highest value.  Otherwise, select the filepath associated\n          with the lowest value.\n    Returns:\n        The full path to the best checkpoint file.\n    \"\"\"\n    best_checkpoints_file = os.path.join(best_checkpoint_dir, 'best_checkpoints')\n    assert os.path.exists(best_checkpoints_file)\n    with open(best_checkpoints_file, 'r') as f:\n        best_checkpoints = json.load(f)\n    best_checkpoints = [\n        ckpt for ckpt in sorted(best_checkpoints,\n                                key=best_checkpoints.get,\n                                reverse=select_maximum_value)\n    ]\n    return os.path.join(os.path.abspath(best_checkpoint_dir),  best_checkpoints[0])\n"
  },
  {
    "path": "utils/data_helpers.py",
    "content": "# -*- coding:utf-8 -*-\n__author__ = 'Randolph'\n\nimport os\nimport time\nimport heapq\nimport gensim\nimport logging\nimport json\nimport numpy as np\nfrom collections import OrderedDict\nfrom pylab import *\nfrom texttable import Texttable\nfrom gensim.models import KeyedVectors\nfrom tflearn.data_utils import pad_sequences\n\n\ndef _option(pattern):\n    \"\"\"\n    Get the option according to the pattern.\n    pattern 0: Choose training or restore.\n    pattern 1: Choose best or latest checkpoint.\n\n    Args:\n        pattern: 0 for training step. 1 for testing step.\n    Returns:\n        The OPTION.\n    \"\"\"\n    if pattern == 0:\n        OPTION = input(\"[Input] Train or Restore? (T/R): \")\n        while not (OPTION.upper() in ['T', 'R']):\n            OPTION = input(\"[Warning] The format of your input is illegal, please re-input: \")\n    if pattern == 1:\n        OPTION = input(\"Load Best or Latest Model? (B/L): \")\n        while not (OPTION.isalpha() and OPTION.upper() in ['B', 'L']):\n            OPTION = input(\"[Warning] The format of your input is illegal, please re-input: \")\n    return OPTION.upper()\n\n\ndef logger_fn(name, input_file, level=logging.INFO):\n    \"\"\"\n    The Logger.\n\n    Args:\n        name: The name of the logger.\n        input_file: The logger file path.\n        level: The logger level.\n    Returns:\n        The logger.\n    \"\"\"\n    logger = logging.getLogger(name)\n    logger.setLevel(level)\n    log_dir = os.path.dirname(input_file)\n    if not os.path.exists(log_dir):\n        os.makedirs(log_dir)\n    formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\n\n    # File Handler\n    fh = logging.FileHandler(input_file, mode='w')\n    fh.setFormatter(formatter)\n    logger.addHandler(fh)\n\n    # stream Handler\n    sh = logging.StreamHandler()\n    sh.setFormatter(formatter)\n    sh.setLevel(logging.INFO)\n    logger.addHandler(sh)\n    return logger\n\n\ndef tab_printer(args, logger):\n    \"\"\"\n    Function to print the logs in a nice tabular format.\n\n    Args:\n        args: Parameters used for the model.\n        logger: The logger.\n    \"\"\"\n    args = vars(args)\n    keys = sorted(args.keys())\n    t = Texttable()\n    t.add_rows([[k.replace(\"_\", \" \").capitalize(), args[k]] for k in keys])\n    t.add_rows([[\"Parameter\", \"Value\"]])\n    logger.info('\\n' + t.draw())\n\n\ndef get_out_dir(option, logger):\n    \"\"\"\n    Get the out dir for saving model checkpoints.\n\n    Args:\n        option: Train or Restore.\n        logger: The logger.\n    Returns:\n        The output dir for model checkpoints.\n    \"\"\"\n    if option == 'T':\n        timestamp = str(int(time.time()))\n        out_dir = os.path.abspath(os.path.join(os.path.curdir, \"runs\", timestamp))\n        logger.info(\"Writing to {0}\\n\".format(out_dir))\n    if option == 'R':\n        MODEL = input(\"[Input] Please input the checkpoints model you want to restore, \"\n                      \"it should be like (1490175368): \")  # The model you want to restore\n\n        while not (MODEL.isdigit() and len(MODEL) == 10):\n            MODEL = input(\"[Warning] The format of your input is illegal, please re-input: \")\n        out_dir = os.path.abspath(os.path.join(os.path.curdir, \"runs\", MODEL))\n        logger.info(\"Writing to {0}\\n\".format(out_dir))\n    return out_dir\n\n\ndef get_model_name():\n    \"\"\"\n    Get the model name used for test.\n\n    Returns:\n        The model name.\n    \"\"\"\n    MODEL = input(\"[Input] Please input the model file you want to test, it should be like (1490175368): \")\n\n    while not (MODEL.isdigit() and len(MODEL) == 10):\n        MODEL = input(\"[Warning] The format of your input is illegal, \"\n                      \"it should be like (1490175368), please re-input: \")\n    return MODEL\n\n\ndef create_prediction_file(output_file, data_id, true_labels, predict_labels, predict_scores):\n    \"\"\"\n    Create the prediction file.\n\n    Args:\n        output_file: The all classes predicted results provided by network.\n        data_id: The data record id info provided by dict <Data>.\n        true_labels: The all true labels.\n        predict_labels: The all predict labels by threshold.\n        predict_scores: The all predict scores by threshold.\n    Raises:\n        IOError: If the prediction file is not a .json file.\n    \"\"\"\n    if not output_file.endswith('.json'):\n        raise IOError(\"[Error] The prediction file is not a json file.\"\n                      \"Please make sure the prediction data is a json file.\")\n    with open(output_file, 'w') as fout:\n        data_size = len(predict_labels)\n        for i in range(data_size):\n            data_record = OrderedDict([\n                ('id', data_id[i]),\n                ('labels', [int(i) for i in true_labels[i]]),\n                ('predict_labels', [int(i) for i in predict_labels[i]]),\n                ('predict_scores', [round(i, 4) for i in predict_scores[i]])\n            ])\n            fout.write(json.dumps(data_record, ensure_ascii=False) + '\\n')\n\n\ndef get_onehot_label_threshold(scores, threshold=0.5):\n    \"\"\"\n    Get the predicted one-hot labels based on the threshold.\n    If there is no predict score greater than threshold, then choose the label which has the max predict score.\n\n    Args:\n        scores: The all classes predicted scores provided by network.\n        threshold: The threshold (default: 0.5).\n    Returns:\n        predicted_onehot_labels: The predicted labels (one-hot).\n    \"\"\"\n    predicted_onehot_labels = []\n    scores = np.ndarray.tolist(scores)\n    for score in scores:\n        count = 0\n        onehot_labels_list = [0] * len(score)\n        for index, predict_score in enumerate(score):\n            if predict_score >= threshold:\n                onehot_labels_list[index] = 1\n                count += 1\n        if count == 0:\n            max_score_index = score.index(max(score))\n            onehot_labels_list[max_score_index] = 1\n        predicted_onehot_labels.append(onehot_labels_list)\n    return predicted_onehot_labels\n\n\ndef get_onehot_label_topk(scores, top_num=1):\n    \"\"\"\n    Get the predicted one-hot labels based on the topK.\n\n    Args:\n        scores: The all classes predicted scores provided by network.\n        top_num: The max topK number (default: 5).\n    Returns:\n        predicted_onehot_labels: The predicted labels (one-hot).\n    \"\"\"\n    predicted_onehot_labels = []\n    scores = np.ndarray.tolist(scores)\n    for score in scores:\n        onehot_labels_list = [0] * len(score)\n        max_num_index_list = list(map(score.index, heapq.nlargest(top_num, score)))\n        for i in max_num_index_list:\n            onehot_labels_list[i] = 1\n        predicted_onehot_labels.append(onehot_labels_list)\n    return predicted_onehot_labels\n\n\ndef get_label_threshold(scores, threshold=0.5):\n    \"\"\"\n    Get the predicted labels based on the threshold.\n    If there is no predict score greater than threshold, then choose the label which has the max predict score.\n    Note: Only Used in `test_model.py`\n\n    Args:\n        scores: The all classes predicted scores provided by network.\n        threshold: The threshold (default: 0.5).\n    Returns:\n        predicted_labels: The predicted labels.\n        predicted_scores: The predicted scores.\n    \"\"\"\n    predicted_labels = []\n    predicted_scores = []\n    scores = np.ndarray.tolist(scores)\n    for score in scores:\n        count = 0\n        index_list = []\n        score_list = []\n        for index, predict_score in enumerate(score):\n            if predict_score >= threshold:\n                index_list.append(index)\n                score_list.append(predict_score)\n                count += 1\n        if count == 0:\n            index_list.append(score.index(max(score)))\n            score_list.append(max(score))\n        predicted_labels.append(index_list)\n        predicted_scores.append(score_list)\n    return predicted_labels, predicted_scores\n\n\ndef get_label_topk(scores, top_num=1):\n    \"\"\"\n    Get the predicted labels based on the topK.\n    Note: Only Used in `test_model.py`\n\n    Args:\n        scores: The all classes predicted scores provided by network.\n        top_num: The max topK number (default: 5).\n    Returns:\n        The predicted labels.\n    \"\"\"\n    predicted_labels = []\n    predicted_scores = []\n    scores = np.ndarray.tolist(scores)\n    for score in scores:\n        score_list = []\n        index_list = np.argsort(score)[-top_num:]\n        index_list = index_list[::-1]\n        for index in index_list:\n            score_list.append(score[index])\n        predicted_labels.append(np.ndarray.tolist(index_list))\n        predicted_scores.append(score_list)\n    return predicted_labels, predicted_scores\n\n\ndef create_metadata_file(word2vec_file, output_file):\n    \"\"\"\n    Create the metadata file based on the corpus file (Used for the Embedding Visualization later).\n\n    Args:\n        word2vec_file: The word2vec file.\n        output_file: The metadata file path.\n    Raises:\n        IOError: If word2vec model file doesn't exist.\n    \"\"\"\n    if not os.path.isfile(word2vec_file):\n        raise IOError(\"[Error] The word2vec file doesn't exist.\")\n\n    wv = KeyedVectors.load(word2vec_file, mmap='r')\n    word2idx = dict([(k, v.index) for k, v in wv.vocab.items()])\n    word2idx_sorted = [(k, word2idx[k]) for k in sorted(word2idx, key=word2idx.get, reverse=False)]\n\n    with open(output_file, 'w+') as fout:\n        for word in word2idx_sorted:\n            if word[0] is None:\n                print(\"[Warning] Empty Line, should replaced by any thing else, or will cause a bug of tensorboard\")\n                fout.write('<Empty Line>' + '\\n')\n            else:\n                fout.write(word[0] + '\\n')\n\n\ndef load_word2vec_matrix(word2vec_file):\n    \"\"\"\n    Get the word2idx dict and embedding matrix.\n\n    Args:\n        word2vec_file: The word2vec file.\n    Returns:\n        word2idx: The word2idx dict.\n        embedding_matrix: The word2vec model matrix.\n    Raises:\n        IOError: If word2vec model file doesn't exist.\n    \"\"\"\n    if not os.path.isfile(word2vec_file):\n        raise IOError(\"[Error] The word2vec file doesn't exist. \")\n\n    wv = KeyedVectors.load(word2vec_file, mmap='r')\n\n    word2idx = OrderedDict({\"_UNK\": 0})\n    embedding_size = wv.vector_size\n    for k, v in wv.vocab.items():\n        word2idx[k] = v.index + 1\n    vocab_size = len(word2idx)\n\n    embedding_matrix = np.zeros([vocab_size, embedding_size])\n    for key, value in word2idx.items():\n        if key == \"_UNK\":\n            embedding_matrix[value] = [0. for _ in range(embedding_size)]\n        else:\n            embedding_matrix[value] = wv[key]\n    return word2idx, embedding_matrix\n\n\ndef load_data_and_labels(args, input_file, word2idx: dict):\n    \"\"\"\n    Load research data from files, padding sentences and generate one-hot labels.\n\n    Args:\n        args: The arguments.\n        input_file: The research record.\n        word2idx: The word2idx dict.\n    Returns:\n        The dict <Data> (includes the record tokenindex and record labels)\n    Raises:\n        IOError: If word2vec model file doesn't exist\n    \"\"\"\n    if not input_file.endswith('.json'):\n        raise IOError(\"[Error] The research record is not a json file. \"\n                      \"Please preprocess the research record into the json file.\")\n\n    def _token_to_index(x: list):\n        result = []\n        for item in x:\n            if item not in word2idx.keys():\n                result.append(word2idx['_UNK'])\n            else:\n                word_idx = word2idx[item]\n                result.append(word_idx)\n        return result\n\n    def _create_onehot_labels(labels_index, num_labels):\n        label = [0] * num_labels\n        for item in labels_index:\n            label[int(item)] = 1\n        return label\n\n    Data = dict()\n    with open(input_file) as fin:\n        Data['id'] = []\n        Data['content_index'] = []\n        Data['content'] = []\n        Data['section'] = []\n        Data['subsection'] = []\n        Data['group'] = []\n        Data['subgroup'] = []\n        Data['onehot_labels'] = []\n        Data['labels'] = []\n\n        for eachline in fin:\n            record = json.loads(eachline)\n            id = record['id']\n            content = record['abstract']\n            section = record['section']\n            subsection = record['subsection']\n            group = record['group']\n            subgroup = record['subgroup']\n            labels = record['labels']\n\n            Data['id'].append(id)\n            Data['content_index'].append(_token_to_index(content))\n            Data['content'].append(content)\n            Data['section'].append(_create_onehot_labels(section, args.num_classes_list[0]))\n            Data['subsection'].append(_create_onehot_labels(subsection, args.num_classes_list[1]))\n            Data['group'].append(_create_onehot_labels(group, args.num_classes_list[2]))\n            Data['subgroup'].append(_create_onehot_labels(subgroup, args.num_classes_list[3]))\n            Data['onehot_labels'].append(_create_onehot_labels(labels, args.total_classes))\n            Data['labels'].append(labels)\n        Data['pad_seqs'] = pad_sequences(Data['content_index'], maxlen=args.pad_seq_len, value=0.)\n    return Data\n\n\ndef batch_iter(data, batch_size, num_epochs, shuffle=True):\n    \"\"\"\n    含有 yield 说明不是一个普通函数，是一个 Generator.\n    函数效果：对 data，一共分成 num_epochs 个阶段（epoch），在每个 epoch 内，如果 shuffle=True，就将 data 重新洗牌，\n    批量生成 (yield) 一批一批的重洗过的 data，每批大小是 batch_size，一共生成 int(len(data)/batch_size)+1 批。\n\n    Args:\n        data: The data.\n        batch_size: The size of the data batch.\n        num_epochs: The number of epochs.\n        shuffle: Shuffle or not (default: True).\n    Returns:\n        A batch iterator for data set.\n    \"\"\"\n    data = np.array(data)\n    data_size = len(data)\n    num_batches_per_epoch = int((data_size - 1) / batch_size) + 1\n    for epoch in range(num_epochs):\n        # Shuffle the data at each epoch\n        if shuffle:\n            shuffle_indices = np.random.permutation(np.arange(data_size))\n            shuffled_data = data[shuffle_indices]\n        else:\n            shuffled_data = data\n        for batch_num in range(num_batches_per_epoch):\n            start_index = batch_num * batch_size\n            end_index = min((batch_num + 1) * batch_size, data_size)\n            yield shuffled_data[start_index:end_index]\n"
  },
  {
    "path": "utils/param_parser.py",
    "content": "import argparse\n\n\ndef parameter_parser():\n    \"\"\"\n    A method to parse up command line parameters.\n    The default hyperparameters give good results without cross-validation.\n    \"\"\"\n    parser = argparse.ArgumentParser(description=\"Run HARNN.\")\n\n    # Data Parameters\n    parser.add_argument(\"--train-file\", nargs=\"?\", default=\"../data/Train_sample.json\", help=\"Training data.\")\n    parser.add_argument(\"--validation-file\", nargs=\"?\", default=\"../data/Validation_sample.json\", help=\"Validation data.\")\n    parser.add_argument(\"--test-file\", nargs=\"?\", default=\"../data/Test_sample.json\", help=\"Testing data.\")\n    parser.add_argument(\"--metadata-file\", nargs=\"?\", default=\"../data/metadata.tsv\",\n                        help=\"Metadata file for embedding visualization.\")\n    parser.add_argument(\"--word2vec-file\", nargs=\"?\", default=\"../data/word2vec_100.kv\",\n                        help=\"Word2vec file for embedding characters (the dim need to be the same as embedding dim).\")\n\n    # Model Hyperparameters\n    parser.add_argument(\"--pad-seq-len\", type=int, default=150, help=\"Padding sequence length. (depends on the data)\")\n    parser.add_argument(\"--embedding-type\", type=int, default=1, help=\"The embedding type.\")\n    parser.add_argument(\"--embedding-dim\", type=int, default=100, help=\"Dimensionality of character embedding.\")\n    parser.add_argument(\"--lstm-dim\", type=int, default=256, help=\"Dimensionality of LSTM neurons.\")\n    parser.add_argument(\"--lstm-layers\", type=int, default=1, help=\"Number of LSTM layers.\")\n    parser.add_argument(\"--attention-dim\", type=int, default=200, help=\"Dimensionality of Attention neurons.\")\n    parser.add_argument(\"--attention-penalization\", type=bool, default=True, help=\"Use attention penalization or not.\")\n    parser.add_argument(\"--fc-dim\", type=int, default=512, help=\"Dimensionality for FC neurons.\")\n    parser.add_argument(\"--dropout-rate\", type=float, default=0.5, help=\"Dropout keep probability.\")\n    parser.add_argument(\"--alpha\", type=float, default=0.5, help=\"Weight of global part in scores cal.\")\n    parser.add_argument(\"--num-classes-list\", type=list, default=[9, 128, 661, 8364],\n                        help=\"Each number of labels in hierarchical structure. (depends on the task)\")\n    parser.add_argument(\"--total-classes\", type=int, default=9162, help=\"Total number of labels. (depends on the task)\")\n    parser.add_argument(\"--topK\", type=int, default=5, help=\"Number of top K prediction classes.\")\n    parser.add_argument(\"--threshold\", type=float, default=0.5, help=\"Threshold for prediction classes.\")\n\n    # Training Parameters\n    parser.add_argument(\"--epochs\", type=int, default=20, help=\"Number of training epochs.\")\n    parser.add_argument(\"--batch-size\", type=int, default=32, help=\"Batch Size.\")\n    parser.add_argument(\"--learning-rate\", type=float, default=0.001, help=\"Learning rate.\")\n    parser.add_argument(\"--decay-rate\", type=float, default=0.95, help=\"Rate of decay for learning rate.\")\n    parser.add_argument(\"--decay-steps\", type=int, default=500, help=\"How many steps before decay learning rate.\")\n    parser.add_argument(\"--evaluate-steps\", type=int, default=10, help=\"Evaluate model on val set after how many steps.\")\n    parser.add_argument(\"--norm-ratio\", type=float, default=1.25,\n                        help=\"The ratio of the sum of gradients norms of trainable variable.\")\n    parser.add_argument(\"--l2-lambda\", type=float, default=0.0, help=\"L2 regularization lambda.\")\n    parser.add_argument(\"--checkpoint-steps\", type=int, default=10, help=\"Save model after how many steps.\")\n    parser.add_argument(\"--num-checkpoints\", type=int, default=5, help=\"Number of checkpoints to store.\")\n\n    # Misc Parameters\n    parser.add_argument(\"--allow-soft-placement\", type=bool, default=True, help=\"Allow device soft device placement.\")\n    parser.add_argument(\"--log-device-placement\", type=bool, default=False, help=\"Log placement of ops on devices.\")\n    parser.add_argument(\"--gpu-options-allow-growth\", type=bool, default=True, help=\"Allow gpu options growth.\")\n\n    return parser.parse_args()"
  }
]