[Add] 클래스화한 Summarizer 추가

+import numpy as np
+from .rank import pagerank
+from .sentence import sent_graph
+from .word import word_graph
+
+
+class KeywordSummarizer:
+    """
+    Arguments
+    ---------
+    sents : list of str
+        Sentence list
+    tokenize : callable
+        Tokenize function: tokenize(str) = list of str
+    min_count : int
+        Minumum frequency of words will be used to construct sentence graph
+    window : int
+        Word cooccurrence window size. Default is -1.
+        '-1' means there is cooccurrence between two words if the words occur in a sentence
+    min_cooccurrence : int
+        Minimum cooccurrence frequency of two words
+    vocab_to_idx : dict or None
+        Vocabulary to index mapper
+    df : float
+        PageRank damping factor
+    max_iter : int
+        Number of PageRank iterations
+    verbose : Boolean
+        If True, it shows training progress
+    """
+    def __init__(self, sents=None, tokenize=None, min_count=2,
+        window=-1, min_cooccurrence=2, vocab_to_idx=None,
+        df=0.85, max_iter=30, verbose=False):
+
+        self.tokenize = tokenize
+        self.min_count = min_count
+        self.window = window
+        self.min_cooccurrence = min_cooccurrence
+        self.vocab_to_idx = vocab_to_idx
+        self.df = df
+        self.max_iter = max_iter
+        self.verbose = verbose
+
+        if sents is not None:
+            self.train_textrank(sents)
+
+    def train_textrank(self, sents, bias=None):
+        """
+        Arguments
+        ---------
+        sents : list of str
+            Sentence list
+        bias : None or numpy.ndarray
+            PageRank bias term
+        Returns
+        -------
+        None
+        """
+
+        g, self.idx_to_vocab = word_graph(sents,
+            self.tokenize, self.min_count,self.window,
+            self.min_cooccurrence, self.vocab_to_idx, self.verbose)
+        self.R = pagerank(g, self.df, self.max_iter, bias).reshape(-1)
+        if self.verbose:
+            print('trained TextRank. n words = {}'.format(self.R.shape[0]))
+
+    def keywords(self, topk=30):
+        """
+        Arguments
+        ---------
+        topk : int
+            Number of keywords selected from TextRank
+        Returns
+        -------
+        keywords : list of tuple
+            Each tuple stands for (word, rank)
+        """
+        if not hasattr(self, 'R'):
+            raise RuntimeError('Train textrank first or use summarize function')
+        idxs = self.R.argsort()[-topk:]
+        keywords = [(self.idx_to_vocab[idx], self.R[idx]) for idx in reversed(idxs)]
+        return keywords
+
+    def summarize(self, sents, topk=30):
+        """
+        Arguments
+        ---------
+        sents : list of str
+            Sentence list
+        topk : int
+            Number of keywords selected from TextRank
+        Returns
+        -------
+        keywords : list of tuple
+            Each tuple stands for (word, rank)
+        """
+        self.train_textrank(sents)
+        return self.keywords(topk)
+
+
+class KeysentenceSummarizer:
+    """
+    Arguments
+    ---------
+    sents : list of str
+        Sentence list
+    tokenize : callable
+        Tokenize function: tokenize(str) = list of str
+    min_count : int
+        Minumum frequency of words will be used to construct sentence graph
+    min_sim : float
+        Minimum similarity between sentences in sentence graph
+    similarity : str
+        available similarity = ['cosine', 'textrank']
+    vocab_to_idx : dict or None
+        Vocabulary to index mapper
+    df : float
+        PageRank damping factor
+    max_iter : int
+        Number of PageRank iterations
+    verbose : Boolean
+        If True, it shows training progress
+    """
+    def __init__(self, sents=None, tokenize=None, min_count=2,
+        min_sim=0.3, similarity=None, vocab_to_idx=None,
+        df=0.85, max_iter=30, verbose=False):
+
+        self.tokenize = tokenize
+        self.min_count = min_count
+        self.min_sim = min_sim
+        self.similarity = similarity
+        self.vocab_to_idx = vocab_to_idx
+        self.df = df
+        self.max_iter = max_iter
+        self.verbose = verbose
+
+        if sents is not None:
+            self.train_textrank(sents)
+
+    def train_textrank(self, sents, bias=None):
+        """
+        Arguments
+        ---------
+        sents : list of str
+            Sentence list
+        bias : None or numpy.ndarray
+            PageRank bias term
+            Shape must be (n_sents,)
+        Returns
+        -------
+        None
+        """
+        g = sent_graph(sents, self.tokenize, self.min_count,
+            self.min_sim, self.similarity, self.vocab_to_idx, self.verbose)
+        self.R = pagerank(g, self.df, self.max_iter, bias).reshape(-1)
+        if self.verbose:
+            print('trained TextRank. n sentences = {}'.format(self.R.shape[0]))
+
+    def summarize(self, sents, topk=30, bias=None):
+        """
+        Arguments
+        ---------
+        sents : list of str
+            Sentence list
+        topk : int
+            Number of key-sentences to be selected.
+        bias : None or numpy.ndarray
+            PageRank bias term
+            Shape must be (n_sents,)
+        Returns
+        -------
+        keysents : list of tuple
+            Each tuple stands for (sentence index, rank, sentence)
+        Usage
+        -----
+            >>> from textrank import KeysentenceSummarizer
+            >>> summarizer = KeysentenceSummarizer(tokenize = tokenizer, min_sim = 0.5)
+            >>> keysents = summarizer.summarize(texts, topk=30)
+        """
+        n_sents = len(sents)
+        if isinstance(bias, np.ndarray):
+            if bias.shape != (n_sents,):
+                raise ValueError('The shape of bias must be (n_sents,) but {}'.format(bias.shape))
+        elif bias is not None:
+            raise ValueError('The type of bias must be None or numpy.ndarray but the type is {}'.format(type(bias)))
+
+        self.train_textrank(sents, bias)
+        idxs = self.R.argsort()[-topk:]
+        keysents = [(idx, self.R[idx], sents[idx]) for idx in reversed(idxs)]
+        return keysents
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