update construct_graph (#393)

fastNLP/io/file_utils.py

@@ -103,6 +103,11 @@ DATASET_DIR = {
     "yelp-review-polarity": "yelp_review_polarity.tar.gz",
     "sst-2": "SST-2.zip",
     "sst": "SST.zip",
+    'mr': 'mr.zip',
+    "R8": "R8.zip",
+    "R52": "R52.zip",
+    "20ng": "20ng.zip",
+    "ohsumed": "ohsumed.zip",
 
     # Classification, Chinese
     "chn-senti-corp": "chn_senti_corp.zip",

fastNLP/io/pipe/__init__.py

@@ -23,15 +23,15 @@ __all__ = [
     "ChnSentiCorpPipe",
     "THUCNewsPipe",
     "WeiboSenti100kPipe",
-    "MRPipe", "R52Pipe", "R8Pipe", "OhsumedPipe", "NG20Loader",
-    
+    "MRPipe", "R52Pipe", "R8Pipe", "OhsumedPipe", "NG20Pipe",
+
     "Conll2003NERPipe",
     "OntoNotesNERPipe",
     "MsraNERPipe",
     "WeiboNERPipe",
     "PeopleDailyPipe",
     "Conll2003Pipe",
-    
+
     "MatchingBertPipe",
     "RTEBertPipe",
     "SNLIBertPipe",
@@ -53,14 +53,20 @@ __all__ = [
     "RenamePipe",
     "GranularizePipe",
     "MachingTruncatePipe",
-    
+
     "CoReferencePipe",
 
-    "CMRC2018BertPipe"
+    "CMRC2018BertPipe",
+
+    "R52PmiGraphPipe",
+    "R8PmiGraphPipe",
+    "OhsumedPmiGraphPipe",
+    "NG20PmiGraphPipe",
+    "MRPmiGraphPipe"
 ]
 
 from .classification import CLSBasePipe, YelpFullPipe, YelpPolarityPipe, SSTPipe, SST2Pipe, IMDBPipe, ChnSentiCorpPipe, THUCNewsPipe, \
-    WeiboSenti100kPipe, AGsNewsPipe, DBPediaPipe, MRPipe, R8Pipe, R52Pipe, OhsumedPipe, NG20Loader
+    WeiboSenti100kPipe, AGsNewsPipe, DBPediaPipe, MRPipe, R8Pipe, R52Pipe, OhsumedPipe, NG20Pipe
 from .conll import Conll2003NERPipe, OntoNotesNERPipe, MsraNERPipe, WeiboNERPipe, PeopleDailyPipe
 from .conll import Conll2003Pipe
 from .coreference import CoReferencePipe
@@ -70,3 +76,5 @@ from .matching import MatchingBertPipe, RTEBertPipe, SNLIBertPipe, QuoraBertPipe
     LCQMCPipe, BQCorpusPipe, LCQMCBertPipe, RenamePipe, GranularizePipe, MachingTruncatePipe
 from .pipe import Pipe
 from .qa import CMRC2018BertPipe
+
+from .construct_graph import MRPmiGraphPipe, R8PmiGraphPipe, R52PmiGraphPipe, NG20PmiGraphPipe, OhsumedPmiGraphPipe

fastNLP/io/pipe/construct_graph.py

@@ -0,0 +1,268 @@
+
+__all__ =[
+    'MRPmiGraphPipe',
+    'R8PmiGraphPipe',
+    'R52PmiGraphPipe',
+    'OhsumedPmiGraphPipe',
+    'NG20PmiGraphPipe'
+]
+try:
+    import networkx as nx
+    from sklearn.feature_extraction.text import CountVectorizer
+    from sklearn.feature_extraction.text import TfidfTransformer
+    from sklearn.pipeline import Pipeline
+except:
+    pass
+from collections import defaultdict
+import itertools
+import math
+from tqdm import tqdm
+import numpy as np
+
+from ..data_bundle import DataBundle
+from ...core.const import Const
+from ..loader.classification import MRLoader, OhsumedLoader, R52Loader, R8Loader, NG20Loader
+
+
+def _get_windows(content_lst: list, window_size:int):
+    r"""
+        滑动窗口处理文本，获取词频和共现词语的词频
+        :param content_lst:
+        :param window_size:
+        :return: 词频，共现词频，窗口化后文本段的数量
+    """
+    word_window_freq = defaultdict(int)  # w(i)  单词在窗口单位内出现的次数
+    word_pair_count = defaultdict(int)  # w(i, j)
+    windows_len = 0
+    for words in tqdm(content_lst, desc="Split by window"):
+        windows = list()
+
+        if isinstance(words, str):
+            words = words.split()
+        length = len(words)
+
+        if length <= window_size:
+            windows.append(words)
+        else:
+            for j in range(length - window_size + 1):
+                window = words[j: j + window_size]
+                windows.append(list(set(window)))
+
+        for window in windows:
+            for word in window:
+                word_window_freq[word] += 1
+
+            for word_pair in itertools.combinations(window, 2):
+                word_pair_count[word_pair] += 1
+
+        windows_len += len(windows)
+    return word_window_freq, word_pair_count, windows_len
+
+def _cal_pmi(W_ij, W, word_freq_i, word_freq_j):
+    r"""
+        params: w_ij:为词语i,j的共现词频
+                w:文本数量
+                word_freq_i: 词语i的词频
+                word_freq_j: 词语j的词频
+        return: 词语i，j的tfidf值
+    """
+    p_i = word_freq_i / W
+    p_j = word_freq_j / W
+    p_i_j = W_ij / W
+    pmi = math.log(p_i_j / (p_i * p_j))
+
+    return pmi
+
+def _count_pmi(windows_len, word_pair_count, word_window_freq, threshold):
+    r"""
+        params: windows_len: 文本段数量
+                word_pair_count: 词共现频率字典
+                word_window_freq: 词频率字典
+                threshold: 阈值
+        return 词语pmi的list列表,其中元素为[word1, word2, pmi]
+    """
+    word_pmi_lst = list()
+    for word_pair, W_i_j in tqdm(word_pair_count.items(), desc="Calculate pmi between words"):
+        word_freq_1 = word_window_freq[word_pair[0]]
+        word_freq_2 = word_window_freq[word_pair[1]]
+
+        pmi = _cal_pmi(W_i_j, windows_len, word_freq_1, word_freq_2)
+        if pmi <= threshold:
+            continue
+        word_pmi_lst.append([word_pair[0], word_pair[1], pmi])
+    return word_pmi_lst
+
+class GraphBuilderBase:
+    def __init__(self, graph_type='pmi', widow_size=10, threshold=0.):
+        self.graph = nx.Graph()
+        self.word2id = dict()
+        self.graph_type = graph_type
+        self.window_size = widow_size
+        self.doc_node_num = 0
+        self.tr_doc_index = None
+        self.te_doc_index = None
+        self.dev_doc_index = None
+        self.doc = None
+        self.threshold = threshold
+
+    def _get_doc_edge(self, data_bundle: DataBundle):
+        r'''
+            对输入的DataBundle进行处理，然后生成文档-单词的tfidf值
+            ：param: data_bundle中的文本若为英文，形式为[ 'This is the first document.'],若为中文则为['他 喜欢 吃 苹果']
+            : return 返回带有具有tfidf边文档-单词稀疏矩阵
+        '''
+        tr_doc = list(data_bundle.get_dataset("train").get_field(Const.RAW_WORD))
+        val_doc = list(data_bundle.get_dataset("dev").get_field(Const.RAW_WORD))
+        te_doc = list(data_bundle.get_dataset("test").get_field(Const.RAW_WORD))
+        doc = tr_doc + val_doc + te_doc
+        self.doc = doc
+        self.tr_doc_index = [ind for ind in range(len(tr_doc))]
+        self.dev_doc_index = [ind+len(tr_doc) for ind in range(len(val_doc))]
+        self.te_doc_index = [ind+len(tr_doc)+len(val_doc) for ind in range(len(te_doc))]
+        text_tfidf = Pipeline([('count', CountVectorizer(token_pattern=r'\S+', min_df=1, max_df=1.0)),
+                               ('tfidf', TfidfTransformer(norm=None, use_idf=True, smooth_idf=False, sublinear_tf=False))])
+
+        tfidf_vec = text_tfidf.fit_transform(doc)
+        self.doc_node_num = tfidf_vec.shape[0]
+        vocab_lst = text_tfidf['count'].get_feature_names()
+        for ind, word in enumerate(vocab_lst):
+            self.word2id[word] = ind
+        for ind, row in enumerate(tfidf_vec):
+            for col_index, value in zip(row.indices, row.data):
+                self.graph.add_edge(ind, self.doc_node_num+col_index, weight=value)
+        return nx.to_scipy_sparse_matrix(self.graph)
+
+    def _get_word_edge(self):
+        word_window_freq, word_pair_count, windows_len = _get_windows(self.doc, self.window_size)
+        pmi_edge_lst = _count_pmi(windows_len, word_pair_count, word_window_freq, self.threshold)
+        for edge_item in pmi_edge_lst:
+            word_indx1 = self.doc_node_num + self.word2id[edge_item[0]]
+            word_indx2 = self.doc_node_num + self.word2id[edge_item[1]]
+            if word_indx1 == word_indx2:
+                continue
+            self.graph.add_edge(word_indx1, word_indx2, weight=edge_item[2])
+
+    def build_graph(self, data_bundle: DataBundle):
+        r"""
+            对输入的DataBundle进行处理，然后返回该scipy_sparse_matrix类型的邻接矩阵。
+
+            :param ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象
+            :return:
+        """
+        raise NotImplementedError
+
+    def build_graph_from_file(self, path: str):
+        r"""
+            传入文件路径，生成处理好的scipy_sparse_matrix对象。paths支持的路径形式可以参考 ：:meth:`fastNLP.io.Loader.load()`
+
+            :param paths:
+            :return: scipy_sparse_matrix
+        """
+        raise NotImplementedError
+
+
+class MRPmiGraphPipe(GraphBuilderBase):
+
+    def __init__(self, graph_type='pmi', widow_size=10, threshold=0.):
+        super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold)
+
+    def build_graph(self, data_bundle: DataBundle):
+        r'''
+            params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象.
+            return 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index.
+        '''
+        self._get_doc_edge(data_bundle)
+        self._get_word_edge()
+        return nx.to_scipy_sparse_matrix(self.graph,
+                                         nodelist=list(range(self.graph.number_of_nodes())),
+                                         weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index)
+
+    def build_graph_from_file(self, path: str):
+        data_bundle = MRLoader().load(path)
+        return self.build_graph(data_bundle)
+
+class R8PmiGraphPipe(GraphBuilderBase):
+
+    def __init__(self, graph_type='pmi', widow_size=10, threshold=0.):
+        super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold)
+
+    def build_graph(self, data_bundle: DataBundle):
+        r'''
+            params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象.
+            return 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index.
+        '''
+        self._get_doc_edge(data_bundle)
+        self._get_word_edge()
+        return nx.to_scipy_sparse_matrix(self.graph,
+                                         nodelist=list(range(self.graph.number_of_nodes())),
+                                         weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index)
+
+    def build_graph_from_file(self, path: str):
+        data_bundle = R8Loader().load(path)
+        return self.build_graph(data_bundle)
+
+class R52PmiGraphPipe(GraphBuilderBase):
+
+    def __init__(self, graph_type='pmi', widow_size=10, threshold=0.):
+        super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold)
+
+    def build_graph(self, data_bundle: DataBundle):
+        r'''
+            params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象.
+            return 返回csr类型的稀疏矩阵;训练集，验证集，测试集，在图中的index.
+        '''
+        self._get_doc_edge(data_bundle)
+        self._get_word_edge()
+        return nx.to_scipy_sparse_matrix(self.graph,
+                                         nodelist=list(range(self.graph.number_of_nodes())),
+                                         weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index)
+
+    def build_graph_from_file(self, path: str):
+        data_bundle = R52Loader().load(path)
+        return self.build_graph(data_bundle)
+
+class OhsumedPmiGraphPipe(GraphBuilderBase):
+
+    def __init__(self, graph_type='pmi', widow_size=10, threshold=0.):
+        super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold)
+
+    def build_graph(self, data_bundle: DataBundle):
+        r'''
+            params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象.
+            return 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index.
+        '''
+        self._get_doc_edge(data_bundle)
+        self._get_word_edge()
+        return nx.to_scipy_sparse_matrix(self.graph,
+                                         nodelist=list(range(self.graph.number_of_nodes())),
+                                         weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index)
+
+    def build_graph_from_file(self, path: str):
+        data_bundle = OhsumedLoader().load(path)
+        return self.build_graph(data_bundle)
+
+
+class NG20PmiGraphPipe(GraphBuilderBase):
+
+    def __init__(self, graph_type='pmi', widow_size=10, threshold=0.):
+        super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold)
+
+    def build_graph(self, data_bundle: DataBundle):
+        r'''
+            params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象.
+            return 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index.
+        '''
+        self._get_doc_edge(data_bundle)
+        self._get_word_edge()
+        return nx.to_scipy_sparse_matrix(self.graph,
+                                         nodelist=list(range(self.graph.number_of_nodes())),
+                                         weight='weight', dtype=np.float32, format='csr'), (
+               self.tr_doc_index, self.dev_doc_index, self.te_doc_index)
+
+    def build_graph_from_file(self, path: str):
+        r'''
+            param: path->数据集的路径.
+            return: 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index.
+        '''
+        data_bundle = NG20Loader().load(path)
+        return self.build_graph(data_bundle)