字典替换优化，表头识别初步模型

BiddingKG/dl/entityLink/entityLink.py

@@ -64,12 +64,19 @@ def link_entitys(list_entitys,on_value=0.8):
 
         # 2021/12/21 替换通过字典识别到的取长度最大的相似实体
         for _entity in range_entity:
+            used_linked_entitys = []
+            if not _entity.linked_entitys:
+                continue
+            _entity.linked_entitys.sort(key=lambda x: len(x.entity_text), reverse=True)
             for _ent in _entity.linked_entitys:
+                if _ent in used_linked_entitys:
+                    break
                 # print("_entity, _ent", _entity.entity_text, _ent.if_dict_match, _ent.entity_text)
-                if re.search("公司$", _ent.entity_text) is not None \
-                        and _ent.if_dict_match == 1:
+                if _ent.if_dict_match == 1:
                     if len(_ent.entity_text) > len(_entity.entity_text):
+                        # print("字典替换", _entity.entity_text, "->", _ent.entity_text)
                         _entity.entity_text = _ent.entity_text
+                        used_linked_entitys.append(_ent)
 
 
 def getEnterprisePath():

BiddingKG/dl/table_head/loss.py

@@ -0,0 +1,13 @@
+import tensorflow as tf
+import keras as K
+
+
+def focal_loss(gamma=2., alpha=.5):
+    def focal_loss_fixed(y_true, y_pred):
+        pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
+        pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
+        return - K.backend.sum(alpha * K.backend.pow(1. - pt_1, gamma)
+                               * K.backend.log(K.backend.epsilon()+pt_1))\
+               - K.backend.sum((1-alpha) * K.backend.pow(pt_0, gamma)
+                               * K.backend.log(1. - pt_0 + K.backend.epsilon()))
+    return focal_loss_fixed

BiddingKG/dl/table_head/models/model.py

@@ -0,0 +1,39 @@
+from keras import layers, models
+from keras.layers import Lambda
+
+from BiddingKG.dl.table_head.models.my_average_pooling import MyAveragePooling1D
+from BiddingKG.dl.table_head.models.self_attention import SeqSelfAttention
+
+
+def get_model(input_shape, output_shape):
+    # Input
+    input_1 = layers.Input(shape=input_shape[1:], dtype="float32")
+    input_2 = layers.Input(shape=input_shape[1:], dtype="float32")
+
+    # Embedding
+    embed_1 = layers.Embedding(input_dim=6624, output_dim=32,
+                               input_length=input_shape[1], mask_zero=True)(input_1)
+    embed_2 = layers.Embedding(input_dim=6624, output_dim=32,
+                               input_length=input_shape[1], mask_zero=True)(input_2)
+
+    # Bi-LSTM
+    bi_lstm_1 = layers.Bidirectional(layers.LSTM(16, return_sequences=True))(embed_1)
+    bi_lstm_2 = layers.Bidirectional(layers.LSTM(16, return_sequences=True))(embed_2)
+
+    # Self-Attention
+    self_attention_1 = SeqSelfAttention(attention_activation='sigmoid')(bi_lstm_1)
+    self_attention_2 = SeqSelfAttention(attention_activation='sigmoid')(bi_lstm_2)
+
+    # Concat
+    concat = layers.concatenate([self_attention_1, self_attention_2])
+
+    # Dense + Softmax
+    output = layers.Dense(output_shape[0], activation="softmax")(concat)
+
+    # mask mean pooling
+    output = MyAveragePooling1D(axis=1)(output)
+
+    model = models.Model(inputs=[input_1, input_2], outputs=output)
+
+    model.summary()
+    return model

BiddingKG/dl/table_head/models/my_average_pooling.py

@@ -0,0 +1,31 @@
+from keras import backend as K
+from keras.engine.topology import Layer
+import tensorflow as tf
+
+
+class MyAveragePooling1D(Layer):
+    def __init__(self, axis, **kwargs):
+        self.supports_masking = True
+        self.axis = axis
+        super(MyAveragePooling1D, self).__init__(**kwargs)
+
+    def compute_mask(self, input, input_mask=None):
+        # need not to pass the mask to next layers
+        return None
+
+    def call(self, x, mask=None):
+        if mask is not None:
+            mask = K.repeat(mask, x.shape[-1])
+            mask = tf.transpose(mask, [0,2,1])
+            mask = K.cast(mask, K.floatx())
+            x = x * mask
+            return K.sum(x, axis=self.axis) / K.sum(mask, axis=self.axis)
+        else:
+            return K.mean(x, axis=self.axis)
+
+    def compute_output_shape(self, input_shape):
+        output_shape = []
+        for i in range(len(input_shape)):
+            if i != self.axis:
+                output_shape.append(input_shape[i])
+        return tuple(output_shape)

BiddingKG/dl/table_head/models/self_attention.py

@@ -0,0 +1,240 @@
+import keras
+from keras import backend as K
+
+
+class SeqSelfAttention(keras.layers.Layer):
+
+    ATTENTION_TYPE_ADD = 'additive'
+    ATTENTION_TYPE_MUL = 'multiplicative'
+
+    def __init__(self,
+                 units=32,
+                 attention_width=None,
+                 attention_type=ATTENTION_TYPE_ADD,
+                 return_attention=False,
+                 history_only=False,
+                 kernel_initializer='glorot_normal',
+                 bias_initializer='zeros',
+                 kernel_regularizer=None,
+                 bias_regularizer=None,
+                 kernel_constraint=None,
+                 bias_constraint=None,
+                 use_additive_bias=True,
+                 use_attention_bias=True,
+                 attention_activation=None,
+                 attention_regularizer_weight=0.0,
+                 **kwargs):
+        """Layer initialization.
+
+        For additive attention, see: https://arxiv.org/pdf/1806.01264.pdf
+
+        :param units: The dimension of the vectors that used to calculate the attention weights.
+        :param attention_width: The width of local attention.
+        :param attention_type: 'additive' or 'multiplicative'.
+        :param return_attention: Whether to return the attention weights for visualization.
+        :param history_only: Only use historical pieces of data.
+        :param kernel_initializer: The initializer for weight matrices.
+        :param bias_initializer: The initializer for biases.
+        :param kernel_regularizer: The regularization for weight matrices.
+        :param bias_regularizer: The regularization for biases.
+        :param kernel_constraint: The constraint for weight matrices.
+        :param bias_constraint: The constraint for biases.
+        :param use_additive_bias: Whether to use bias while calculating the relevance of inputs features
+                                  in additive mode.
+        :param use_attention_bias: Whether to use bias while calculating the weights of attention.
+        :param attention_activation: The activation used for calculating the weights of attention.
+        :param attention_regularizer_weight: The weights of attention regularizer.
+        :param kwargs: Parameters for parent class.
+        """
+        super(SeqSelfAttention, self).__init__(**kwargs)
+        self.supports_masking = True
+        self.units = units
+        self.attention_width = attention_width
+        self.attention_type = attention_type
+        self.return_attention = return_attention
+        self.history_only = history_only
+        if history_only and attention_width is None:
+            self.attention_width = int(1e9)
+
+        self.use_additive_bias = use_additive_bias
+        self.use_attention_bias = use_attention_bias
+        self.kernel_initializer = keras.initializers.get(kernel_initializer)
+        self.bias_initializer = keras.initializers.get(bias_initializer)
+        self.kernel_regularizer = keras.regularizers.get(kernel_regularizer)
+        self.bias_regularizer = keras.regularizers.get(bias_regularizer)
+        self.kernel_constraint = keras.constraints.get(kernel_constraint)
+        self.bias_constraint = keras.constraints.get(bias_constraint)
+        self.attention_activation = keras.activations.get(attention_activation)
+        self.attention_regularizer_weight = attention_regularizer_weight
+        self._backend = keras.backend.backend()
+
+        if attention_type == SeqSelfAttention.ATTENTION_TYPE_ADD:
+            self.Wx, self.Wt, self.bh = None, None, None
+            self.Wa, self.ba = None, None
+        elif attention_type == SeqSelfAttention.ATTENTION_TYPE_MUL:
+            self.Wa, self.ba = None, None
+        else:
+            raise NotImplementedError('No implementation for attention type : ' + attention_type)
+
+    def get_config(self):
+        config = {
+            'units': self.units,
+            'attention_width': self.attention_width,
+            'attention_type': self.attention_type,
+            'return_attention': self.return_attention,
+            'history_only': self.history_only,
+            'use_additive_bias': self.use_additive_bias,
+            'use_attention_bias': self.use_attention_bias,
+            'kernel_initializer': keras.initializers.serialize(self.kernel_initializer),
+            'bias_initializer': keras.initializers.serialize(self.bias_initializer),
+            'kernel_regularizer': keras.regularizers.serialize(self.kernel_regularizer),
+            'bias_regularizer': keras.regularizers.serialize(self.bias_regularizer),
+            'kernel_constraint': keras.constraints.serialize(self.kernel_constraint),
+            'bias_constraint': keras.constraints.serialize(self.bias_constraint),
+            'attention_activation': keras.activations.serialize(self.attention_activation),
+            'attention_regularizer_weight': self.attention_regularizer_weight,
+        }
+        base_config = super(SeqSelfAttention, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    def build(self, input_shape):
+        if self.attention_type == SeqSelfAttention.ATTENTION_TYPE_ADD:
+            self._build_additive_attention(input_shape)
+        elif self.attention_type == SeqSelfAttention.ATTENTION_TYPE_MUL:
+            self._build_multiplicative_attention(input_shape)
+        super(SeqSelfAttention, self).build(input_shape)
+
+    def _build_additive_attention(self, input_shape):
+        feature_dim = int(input_shape[2])
+
+        self.Wt = self.add_weight(shape=(feature_dim, self.units),
+                                  name='{}_Add_Wt'.format(self.name),
+                                  initializer=self.kernel_initializer,
+                                  regularizer=self.kernel_regularizer,
+                                  constraint=self.kernel_constraint)
+        self.Wx = self.add_weight(shape=(feature_dim, self.units),
+                                  name='{}_Add_Wx'.format(self.name),
+                                  initializer=self.kernel_initializer,
+                                  regularizer=self.kernel_regularizer,
+                                  constraint=self.kernel_constraint)
+        if self.use_additive_bias:
+            self.bh = self.add_weight(shape=(self.units,),
+                                      name='{}_Add_bh'.format(self.name),
+                                      initializer=self.bias_initializer,
+                                      regularizer=self.bias_regularizer,
+                                      constraint=self.bias_constraint)
+
+        self.Wa = self.add_weight(shape=(self.units, 1),
+                                  name='{}_Add_Wa'.format(self.name),
+                                  initializer=self.kernel_initializer,
+                                  regularizer=self.kernel_regularizer,
+                                  constraint=self.kernel_constraint)
+        if self.use_attention_bias:
+            self.ba = self.add_weight(shape=(1,),
+                                      name='{}_Add_ba'.format(self.name),
+                                      initializer=self.bias_initializer,
+                                      regularizer=self.bias_regularizer,
+                                      constraint=self.bias_constraint)
+
+    def _build_multiplicative_attention(self, input_shape):
+        feature_dim = int(input_shape[2])
+
+        self.Wa = self.add_weight(shape=(feature_dim, feature_dim),
+                                  name='{}_Mul_Wa'.format(self.name),
+                                  initializer=self.kernel_initializer,
+                                  regularizer=self.kernel_regularizer,
+                                  constraint=self.kernel_constraint)
+        if self.use_attention_bias:
+            self.ba = self.add_weight(shape=(1,),
+                                      name='{}_Mul_ba'.format(self.name),
+                                      initializer=self.bias_initializer,
+                                      regularizer=self.bias_regularizer,
+                                      constraint=self.bias_constraint)
+
+    def call(self, inputs, mask=None, **kwargs):
+        input_len = K.shape(inputs)[1]
+
+        if self.attention_type == SeqSelfAttention.ATTENTION_TYPE_ADD:
+            e = self._call_additive_emission(inputs)
+        elif self.attention_type == SeqSelfAttention.ATTENTION_TYPE_MUL:
+            e = self._call_multiplicative_emission(inputs)
+
+        if self.attention_activation is not None:
+            e = self.attention_activation(e)
+        if self.attention_width is not None:
+            if self.history_only:
+                lower = K.arange(0, input_len) - (self.attention_width - 1)
+            else:
+                lower = K.arange(0, input_len) - self.attention_width // 2
+            lower = K.expand_dims(lower, axis=-1)
+            upper = lower + self.attention_width
+            indices = K.expand_dims(K.arange(0, input_len), axis=0)
+            e -= 10000.0 * (1.0 - K.cast(lower <= indices, K.floatx()) * K.cast(indices < upper, K.floatx()))
+        if mask is not None:
+            mask = K.expand_dims(K.cast(mask, K.floatx()), axis=-1)
+            e -= 10000.0 * ((1.0 - mask) * (1.0 - K.permute_dimensions(mask, (0, 2, 1))))
+
+        # a_{t} = \text{softmax}(e_t)
+        e = K.exp(e - K.max(e, axis=-1, keepdims=True))
+        a = e / K.sum(e, axis=-1, keepdims=True)
+
+        # l_t = \sum_{t'} a_{t, t'} x_{t'}
+        v = K.batch_dot(a, inputs)
+        if self.attention_regularizer_weight > 0.0:
+            self.add_loss(self._attention_regularizer(a))
+
+        if self.return_attention:
+            return [v, a]
+        return v
+
+    def _call_additive_emission(self, inputs):
+        input_shape = K.shape(inputs)
+        batch_size, input_len = input_shape[0], input_shape[1]
+
+        # h_{t, t'} = \tanh(x_t^T W_t + x_{t'}^T W_x + b_h)
+        q = K.expand_dims(K.dot(inputs, self.Wt), 2)
+        k = K.expand_dims(K.dot(inputs, self.Wx), 1)
+        if self.use_additive_bias:
+            h = K.tanh(q + k + self.bh)
+        else:
+            h = K.tanh(q + k)
+
+        # e_{t, t'} = W_a h_{t, t'} + b_a
+        if self.use_attention_bias:
+            e = K.reshape(K.dot(h, self.Wa) + self.ba, (batch_size, input_len, input_len))
+        else:
+            e = K.reshape(K.dot(h, self.Wa), (batch_size, input_len, input_len))
+        return e
+
+    def _call_multiplicative_emission(self, inputs):
+        # e_{t, t'} = x_t^T W_a x_{t'} + b_a
+        e = K.batch_dot(K.dot(inputs, self.Wa), K.permute_dimensions(inputs, (0, 2, 1)))
+        if self.use_attention_bias:
+            e += self.ba[0]
+        return e
+
+    def compute_output_shape(self, input_shape):
+        output_shape = input_shape
+        if self.return_attention:
+            attention_shape = (input_shape[0], output_shape[1], input_shape[1])
+            return [output_shape, attention_shape]
+        return output_shape
+
+    def compute_mask(self, inputs, mask=None):
+        if self.return_attention:
+            return [mask, None]
+        return mask
+
+    def _attention_regularizer(self, attention):
+        batch_size = K.cast(K.shape(attention)[0], K.floatx())
+        input_len = K.shape(attention)[-1]
+        indices = K.expand_dims(K.arange(0, input_len), axis=0)
+        diagonal = K.expand_dims(K.arange(0, input_len), axis=-1)
+        eye = K.cast(K.equal(indices, diagonal), K.floatx())
+        return self.attention_regularizer_weight * K.sum(K.square(K.batch_dot(
+            attention,
+            K.permute_dimensions(attention, (0, 2, 1))) - eye)) / batch_size
+
+    @staticmethod
+    def get_custom_objects():
+        return {'SeqSelfAttention': SeqSelfAttention}

BiddingKG/dl/table_head/pre_process.py

@@ -0,0 +1,189 @@
+import sys
+import os
+sys.path.append(os.path.abspath("../.."))
+import psycopg2
+import numpy as np
+
+
+def get_sentence_index_list(sentence, dict_path='utils/ppocr_keys_v1.txt'):
+    with open(dict_path, 'r') as f:
+        character_list = f.readlines()
+    for i in range(len(character_list)):
+        character_list[i] = character_list[i][:-1]
+
+    index_list = []
+    for character in sentence:
+        if character == '':
+            index_list.append(0)
+        elif character in character_list:
+            _index = character_list.index(character) + 1
+            index_list.append(_index)
+        else:
+            index_list.append(0)
+    return index_list
+
+
+def postgresql_util(sql, limit):
+    conn = psycopg2.connect(dbname="table_head_label", user="postgres", password="postgres",
+                            host="192.168.2.103")
+    cursor = conn.cursor()
+    cursor.execute(sql)
+    print(sql)
+
+    rows = cursor.fetchmany(1000)
+    cnt = 0
+    all_rows = []
+    while rows:
+        if cnt >= limit:
+            break
+        all_rows += rows
+        cnt += len(rows)
+        rows = cursor.fetchmany(1000)
+    return all_rows
+
+
+def get_data_from_sql(dim=10):
+    sql = """
+    select table_text, pre_label, post_label, id
+    from label_table_head_info 
+    where update_user <> 'test27' and table_box_cnt >= 4 and table_box_cnt <= 200 
+    limit 1000;
+    """
+    # sql = """
+    # select table_text, pre_label, post_label, id
+    # from label_table_head_info
+    # where id = 843
+    # """
+    result_list = postgresql_util(sql, limit=10000)
+
+    all_data_list = []
+    all_data_label_list = []
+    i = 0
+    # 一行就是一篇表格
+    for table in result_list:
+        i += 1
+        if i % 100 == 0:
+            print("Loop", i)
+
+        pre_label = eval(table[1])
+        post_label = eval(table[2])
+        _id = table[3]
+
+        # table_text需要特殊处理
+        try:
+            table_text = table[0]
+            if table_text[0] == '"':
+                table_text = eval(table_text)
+            else:
+                table_text = table_text
+            table_text = table_text.replace('\\', '/')
+            table_text = eval(table_text)
+        except:
+            print("无法识别table_text", _id)
+            continue
+
+        # 只有一行的也不要
+        if len(post_label) >= 2:
+            data_list, data_label_list = table_process(table_text, post_label, _id)
+        elif len(pre_label) >= 2:
+            data_list, data_label_list = table_process(table_text, pre_label, _id)
+        else:
+            data_list, data_label_list = [], []
+
+        for data in data_list:
+            # 中文字符映射为index
+            data[0] = get_sentence_index_list(data[0])
+            data[1] = get_sentence_index_list(data[1])
+
+            # 维度不够，填充掩码0
+            if len(data[0]) < dim:
+                data[0] = data[0] + [0]*(dim-len(data[0]))
+            elif len(data[0]) > dim:
+                data[0] = data[0][:dim]
+            if len(data[1]) < dim:
+                data[1] = data[1] + [0]*(dim-len(data[1]))
+            elif len(data[1]) > dim:
+                data[1] = data[1][:dim]
+        all_data_list += data_list
+        all_data_label_list += data_label_list
+    return all_data_list, all_data_label_list
+
+
+def table_process(text_list, label_list, _id):
+    if len(text_list) != len(label_list):
+        print("文字单元格与标注单元格数量不匹配！", _id)
+        print("len(text_list)", len(text_list), "len(label_list)", len(label_list))
+        return [], []
+
+    data_list = []
+    data_label_list = []
+    for i in range(len(text_list)):
+        row = text_list[i]
+        row_label = label_list[i]
+
+        if i < len(text_list) - 1:
+            next_row = text_list[i+1]
+            next_row_label = label_list[i+1]
+        else:
+            next_row = []
+            next_row_label = []
+
+        for j in range(len(row)):
+            col = row[j]
+            col_label = row_label[j]
+
+            if j < len(row) - 1:
+                next_col = row[j+1]
+                next_col_label = row_label[j+1]
+            else:
+                next_col = ""
+                next_col_label = ""
+
+            if next_row:
+                next_row_col = next_row[j]
+                next_row_col_label = next_row_label[j]
+            else:
+                next_row_col = ""
+                next_row_col_label = ""
+
+            if next_col:
+                if col != next_col:
+                    data_list.append([col, next_col])
+                    data_label_list.append([int(col_label), int(next_col_label)])
+            if next_row_col:
+                if col != next_row_col:
+                    data_list.append([col, next_row_col])
+                    data_label_list.append([int(col_label), int(next_row_col_label)])
+
+    return data_list, data_label_list
+
+
+def get_data_from_file():
+    data_path = 'train_data/data.txt'
+    data_label_path = 'train_data/data_label.txt'
+
+    with open(data_path, 'r') as f:
+        data_list = f.readlines()
+    with open(data_label_path, 'r') as f:
+        data_label_list = f.readlines()
+
+    for i in range(len(data_list)):
+        data_list[i] = eval(data_list[i][:-1])
+        data_label_list[i] = eval(data_label_list[i][:-1])
+
+    print(len(data_list))
+    return data_list, data_label_list
+
+
+def processed_save_to_txt():
+    list1, list2 = get_data_from_sql()
+    with open('train_data/data.txt', 'w') as f:
+        for line in list1:
+            f.write(str(line) + "\n")
+    with open('train_data/data_label.txt', 'w') as f:
+        for line in list2:
+            f.write(str(line) + "\n")
+
+
+if __name__ == '__main__':
+    get_data_from_file()

BiddingKG/dl/table_head/predict.py

@@ -0,0 +1,12 @@
+def predict():
+    """
+    @summary: 预测数据
+    """
+    test_x,doc_id,ent_id,sen,ent_text,dianhua = get_data()
+    model = models.load_model("model_person.model",
+                              custom_objects={'precision': precision,
+                                              'recall': recall,
+                                              'f1_score': f1_score}
+                              )
+    predict_y = model.predict([test_x[0], test_x[1]])
+    label = [np.argmax(y) for y in predict_y]

BiddingKG/dl/table_head/train.py

@@ -0,0 +1,64 @@
+import sys
+import os
+sys.path.append(os.path.abspath("../.."))
+from keras import optimizers
+from tensorflow.contrib.metrics import f1_score
+from tensorflow.python.ops.metrics_impl import precision, recall
+from BiddingKG.dl.table_head.models.model import get_model
+from BiddingKG.dl.table_head.loss import focal_loss
+from keras.callbacks import ModelCheckpoint
+from BiddingKG.dl.table_head.pre_process import get_data_from_file
+import numpy as np
+
+
+input_shape = (2, 10)
+output_shape = (2,)
+pretrained_path = ""
+checkpoint_path = "checkpoints/"
+PRETRAINED = False
+CHECKPOINT = False
+
+
+def train():
+    # Data
+    data_x, data_y = get_data_from_file()
+    data_x = np.array(data_x)
+    data_y = np.array(data_y)
+
+    # Split -> Train, Test
+    split_size = int(len(data_x)*0.1)
+    test_x, test_y = data_x[:split_size], data_y[:split_size]
+    train_x, train_y = data_x[split_size:], data_y[split_size:]
+
+    # (table_num, 2 sentences, dim characters) -> (2, table_num, dim)
+    train_x = np.transpose(train_x, (1, 0, 2))
+    test_x = np.transpose(test_x, (1, 0, 2))
+
+    # Model
+    model = get_model(input_shape, output_shape)
+    if PRETRAINED:
+        model.load_weights(pretrained_path)
+        print("read pretrained model", pretrained_path)
+    else:
+        print("no pretrained")
+    if CHECKPOINT:
+        model.load_weights(checkpoint_path)
+        print("read checkpoint model", checkpoint_path)
+    else:
+        print("no checkpoint")
+
+    filepath = '{epoch:02d}-{val_loss:.2f}.h5'
+    checkpoint = ModelCheckpoint(checkpoint_path+filepath+".hdf5", monitor=focal_loss(),
+                                 verbose=1, save_best_only=True, mode='min')
+    model.compile(optimizer=optimizers.Adam(lr=0.0005), loss=focal_loss(),
+                  metrics=[focal_loss()])
+
+    print(train_x.shape, train_y.shape)
+    model.fit(x=[train_x[0], train_x[1]], y=train_y,
+              validation_data=([test_x[0], test_x[1]], test_y),
+              epochs=100, batch_size=128, shuffle=True,
+              callbacks=[checkpoint])
+
+
+if __name__ == '__main__':
+    train()

BiddingKG/dl/table_head/utils/ppocr_keys_v1.txt

@@ -0,0 +1,6623 @@
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