diff --git a/AUTHORS.md b/AUTHORS.md index 41423fa6..df16f050 100644 --- a/AUTHORS.md +++ b/AUTHORS.md @@ -16,5 +16,7 @@ [Meikai Bao](https://github.com/BAOOOOOM) +[Yuting Ning](https://github.com/nnnyt) + The stared contributors are the corresponding authors. diff --git a/EduNLP/Formula/Formula.py b/EduNLP/Formula/Formula.py index f7d7a542..6b0cc38d 100644 --- a/EduNLP/Formula/Formula.py +++ b/EduNLP/Formula/Formula.py @@ -15,6 +15,17 @@ class Formula(object): """ + + Parameters + ---------- + formula: str or List[Dict] + latex formula string or the parsed abstracted syntax tree + variable_standardization + const_mathord + init + args + kwargs + Examples -------- >>> f = Formula("x") @@ -34,21 +45,8 @@ class Formula(object): -------- the parsed abstracted syntax tree """ - def __init__(self, formula: (str, List[Dict]), variable_standardization=False, const_mathord=None, init=True, *args, **kwargs): - """ - - Parameters - ---------- - formula: str or List[Dict] - latex formula string or the parsed abstracted syntax tree - variable_standardization - const_mathord - init - args - kwargs - """ self._formula = formula self._ast = None if init is True: @@ -131,6 +129,15 @@ def resetable(self): class FormulaGroup(object): """ + + Parameters + ---------- + formula: str or List[Dict] or List[Formula] + latex formula string or the parsed abstracted syntax tree or a group of parsed abstracted syntax tree + variable_standardization + const_mathord + detach + Examples --------- >>> fg = FormulaGroup(["x + y", "y + x", "z + x"]) @@ -141,8 +148,9 @@ class FormulaGroup(object): ;;> >>> fg = FormulaGroup(["x", Formula("y"), "x"]) >>> fg.elements - [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'y', 'role': None},\ - {'id': 2, 'type': 'mathord', 'text': 'x', 'role': None}] + [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None}, \ +{'id': 1, 'type': 'mathord', 'text': 'y', 'role': None}, \ +{'id': 2, 'type': 'mathord', 'text': 'x', 'role': None}] >>> fg = FormulaGroup(["x", Formula("y"), "x"], variable_standardization=True) >>> fg.elements [{'id': 0, 'type': 'mathord', 'text': 'x', 'role': None, 'var': 0}, \ @@ -153,24 +161,12 @@ class FormulaGroup(object): -------- the parsed abstracted syntax forest """ - def __init__(self, formula_list: (list, List[str], List[Formula]), variable_standardization=False, const_mathord=None, detach=True ): - """ - - Parameters - ---------- - formula: str or List[Dict] or List[Formula] - latex formula string or the parsed abstracted syntax tree or a group of parsed abstracted syntax tree - variable_standardization - const_mathord - detach - - """ forest = [] self._formulas = [] for formula in formula_list: @@ -261,7 +257,8 @@ def link_formulas(*formula: Formula, link_vars=True, **kwargs): Parameters ---------- - formula:the parsed abstracted syntax tree + formula + the parsed abstracted syntax tree link_vars kwargs """ diff --git a/EduNLP/I2V/i2v.py b/EduNLP/I2V/i2v.py index 3152bb7a..3b58401f 100644 --- a/EduNLP/I2V/i2v.py +++ b/EduNLP/I2V/i2v.py @@ -16,7 +16,7 @@ class I2V(object): If you want to get vector from item, you can use other model like D2V and W2V. Parameters - ---------- + ----------- tokenizer: str the tokenizer name t2v: str @@ -26,8 +26,11 @@ class I2V(object): tokenizer_kwargs: dict the parameters passed to tokenizer pretrained_t2v: bool + True: use pretrained t2v model + False: use your own t2v model + kwargs: the parameters passed to t2v @@ -39,33 +42,13 @@ class I2V(object): >>> model_path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin" # doctest: +ELLIPSIS >>> i2v = D2V("text","d2v",filepath=model_path, pretrained_t2v = False) # doctest: +ELLIPSIS >>> i2v(item) # doctest: +ELLIPSIS - ([array([ ...dtype=float32)], None) + ([array([...dtype=float32)], None) Returns ------- i2v model: I2V """ - def __init__(self, tokenizer, t2v, *args, tokenizer_kwargs: dict = None, pretrained_t2v=False, **kwargs): - """ - - Parameters - ---------- - tokenizer: str - the tokenizer name - t2v: str - the name of token2vector model - args: - the parameters passed to t2v - tokenizer_kwargs: dict - the parameters passed to tokenizer - pretrained_t2v: bool - True: use pretrained t2v model - False: use your own t2v model - kwargs: - the parameters passed to t2v - - """ self.tokenizer: Tokenizer = get_tokenizer(tokenizer, **tokenizer_kwargs if tokenizer_kwargs is not None else {}) if pretrained_t2v: logger.info("Use pretrained t2v model %s" % t2v) @@ -125,8 +108,11 @@ def vector_size(self): class D2V(I2V): """ + + Bases: I2V + Parameters - ---------- + ----------- tokenizer: str the tokenizer name t2v: str @@ -142,7 +128,7 @@ class D2V(I2V): the parameters passed to t2v Examples - -------- + --------- >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \ ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\ ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"} @@ -160,7 +146,7 @@ def infer_vector(self, items, tokenize=True, indexing=False, padding=False, key= ''' Parameters - ---------- + ----------- items:str the text of question tokenize:bool @@ -175,7 +161,7 @@ def infer_vector(self, items, tokenize=True, indexing=False, padding=False, key= the parameters passed to t2v Returns - ------- + -------- vector:list ''' tokens = self.tokenize(items, return_token=True, key=key) if tokenize is True else items @@ -189,8 +175,11 @@ def from_pretrained(cls, name, model_dir=MODEL_DIR, *args, **kwargs): class W2V(I2V): """ + + Bases: I2V + Parameters - ---------- + ----------- tokenizer: str the tokenizer name t2v: str @@ -206,19 +195,40 @@ class W2V(I2V): the parameters passed to t2v Examples - -------- + --------- >>> i2v = get_pretrained_i2v("test_w2v", "examples/test_model/data/w2v") >>> item_vector, token_vector = i2v(["有学者认为：‘学习’，必须适应实际"]) - >>> item_vector - array([[...]], dtype=float32) + >>> item_vector # doctest: +ELLIPSIS + [array([...], dtype=float32)] Returns - ------- + -------- i2v model: W2V """ def infer_vector(self, items, tokenize=True, indexing=False, padding=False, key=lambda x: x, *args, **kwargs) -> tuple: + ''' + + Parameters + ----------- + items:str + the text of question + tokenize:bool + True: tokenize the item + indexing:bool + padding:bool + key: lambda function + the parameter passed to tokenizer, select the text to be processed + args: + the parameters passed to t2v + kwargs: + the parameters passed to t2v + + Returns + -------- + vector:list + ''' tokens = self.tokenize(items, return_token=True) if tokenize is True else items tokens = [token for token in tokens] return self.t2v(tokens, *args, **kwargs), self.t2v.infer_tokens(tokens, *args, **kwargs) @@ -244,18 +254,18 @@ def get_pretrained_i2v(name, model_dir=MODEL_DIR): """ Parameters - ---------- + ----------- name: str the name of item2vector model model_dir:str the path of model, default: MODEL_DIR = '~/.EduNLP/model' Returns - ------- + -------- i2v model: I2V Examples - -------- + --------- >>> item = {"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, \ ... 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，\ ... 此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$"} diff --git a/EduNLP/ModelZoo/rnn/rnn.py b/EduNLP/ModelZoo/rnn/rnn.py index c3359fa9..97585170 100644 --- a/EduNLP/ModelZoo/rnn/rnn.py +++ b/EduNLP/ModelZoo/rnn/rnn.py @@ -9,6 +9,20 @@ class LM(nn.Module): """ + + Parameters + ---------- + rnn_type：str + Legal types including RNN, LSTM, GRU,ELMO + vocab_size: int + embedding_dim: int + hidden_size: int + num_layers + bidirectional + embedding + model_params + kwargs + Examples -------- >>> import torch @@ -30,22 +44,6 @@ class LM(nn.Module): def __init__(self, rnn_type: str, vocab_size: int, embedding_dim: int, hidden_size: int, num_layers=1, bidirectional=False, embedding=None, model_params=None, **kwargs): - """ - - Parameters - ---------- - rnn_type：str - Legal types including RNN, LSTM, GRU,ELMO - vocab_size: int - embedding_dim: int - hidden_size: int - num_layers - bidirectional - embedding - model_params - kwargs - - """ super(LM, self).__init__() rnn_type = rnn_type.upper() self.embedding = torch.nn.Embedding(vocab_size, embedding_dim) if embedding is None else embedding diff --git a/EduNLP/ModelZoo/utils/masker.py b/EduNLP/ModelZoo/utils/masker.py index 4e56d738..18e5369b 100644 --- a/EduNLP/ModelZoo/utils/masker.py +++ b/EduNLP/ModelZoo/utils/masker.py @@ -7,6 +7,13 @@ class Masker(object): """ + + Parameters + ---------- + mask: int, str + per + seed + Examples ------- >>> masker = Masker(per=0.5, seed=10) @@ -29,35 +36,17 @@ class Masker(object): [['a', '[MASK]', 'c'], ['d', '[PAD]', '[PAD]'], ['hello', '[MASK]', '[PAD]']] >>> mask_label [[0, 1, 0], [0, 0, 0], [0, 1, 0]] - """ + Returns + ---------- + list:list of masked_seq and list of masked_list + """ def __init__(self, mask: (int, str, ...) = 0, per=0.2, seed=None): - """ - - Parameters - ---------- - mask: int, str - per - seed - """ self.seed = np.random.default_rng(seed) self.per = per self.mask = mask def __call__(self, seqs, length=None, *args, **kwargs) -> tuple: - """ - - Parameters - ---------- - seqs:list - length - args - kwargs - - Returns - ---------- - list:list of masked_seq and list of masked_list - """ seqs = deepcopy(seqs) masked_list = [] if length is None: diff --git a/EduNLP/ModelZoo/utils/padder.py b/EduNLP/ModelZoo/utils/padder.py index 52a662ee..443eb374 100644 --- a/EduNLP/ModelZoo/utils/padder.py +++ b/EduNLP/ModelZoo/utils/padder.py @@ -5,7 +5,8 @@ class PadSequence(object): - """Pad the sequence. + """ + Pad the sequence. Pad the sequence to the given `length` by inserting `pad_val`. If `clip` is set, sequence that has length larger than `length` will be clipped. @@ -17,24 +18,17 @@ class PadSequence(object): pad_val : number The pad value. Default 0 clip : bool - """ + Returns + ------- + ret : list of number + """ def __init__(self, length, pad_val=0, clip=True): self._length = length self._pad_val = pad_val self._clip = clip def __call__(self, sample: list): - """ - - Parameters - ---------- - sample : list of number - - Returns - ------- - ret : list of number - """ sample_length = len(sample) if sample_length >= self._length: if self._clip and sample_length > self._length: diff --git a/EduNLP/SIF/segment/segment.py b/EduNLP/SIF/segment/segment.py index 6d6550d0..f93efbf0 100644 --- a/EduNLP/SIF/segment/segment.py +++ b/EduNLP/SIF/segment/segment.py @@ -80,24 +80,24 @@ class SepSegment(str): class SegmentList(object): - def __init__(self, item, figures: dict = None): - """ + """ - Parameters - ---------- - item - figures:dict + Parameters + ---------- + item + figures:dict - Returns - ---------- - list:tokenizated item + Returns + ---------- + list:tokenizated item - Examples - -------- - >>> test_item = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$" - >>> SegmentList(test_item) - ['如图所示，则三角形', 'ABC', '的面积是', '\\\\SIFBlank', '。', \\FigureID{1}] - """ + Examples + -------- + >>> test_item = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$" + >>> SegmentList(test_item) + ['如图所示，则三角形', 'ABC', '的面积是', '\\\\SIFBlank', '。', \\FigureID{1}] + """ + def __init__(self, item, figures: dict = None): self._segments = [] self._text_segments = [] self._formula_segments = [] diff --git a/EduNLP/Tokenizer/tokenizer.py b/EduNLP/Tokenizer/tokenizer.py index 45b50f1f..761514fa 100644 --- a/EduNLP/Tokenizer/tokenizer.py +++ b/EduNLP/Tokenizer/tokenizer.py @@ -15,6 +15,7 @@ def __call__(self, *args, **kwargs): class PureTextTokenizer(Tokenizer): r""" + Examples -------- >>> tokenizer = PureTextTokenizer() @@ -39,7 +40,6 @@ class PureTextTokenizer(Tokenizer): '0', '\\right', '\\}', ',', '\\quad', 'B', '=', '\\{', '-', '4', ',', '1', ',', '3', ',', '5', '\\}', ',', '\\quad', 'A', '\\cap', 'B', '='] """ - def __init__(self, *args, **kwargs): self.tokenization_params = { "formula_params": { @@ -79,8 +79,13 @@ class TextTokenizer(Tokenizer): >>> tokens = tokenizer(items) >>> next(tokens)[:10] ['公式', '[FORMULA]', '如图', '[FIGURE]', 'x', ',', 'y', '约束条件', '公式', '[FORMULA]'] + >>> items = ["$\\SIFTag{stem_begin}$若复数$z=1+2 i+i^{3}$，则$|z|=$$\\SIFTag{stem_end}$\ + ... $\\SIFTag{options_begin}$$\\SIFTag{list_0}$0$\\SIFTag{list_1}$1$\\SIFTag{list_2}$$\\sqrt{2}$\ + ... $\\SIFTag{list_3}$2$\\SIFTag{options_end}$"] + >>> tokens = tokenizer(items) + >>> next(tokens)[:10] + ['[TAG]', '复数', 'z', '=', '1', '+', '2', 'i', '+', 'i'] """ - def __init__(self, *args, **kwargs): self.tokenization_params = { "formula_params": { diff --git a/EduNLP/Vector/gensim_vec.py b/EduNLP/Vector/gensim_vec.py index 13c5600a..ece3a077 100644 --- a/EduNLP/Vector/gensim_vec.py +++ b/EduNLP/Vector/gensim_vec.py @@ -11,16 +11,18 @@ class W2V(Vector): + """ + + Parameters + ---------- + filepath: + path to the pretrained model file + method: str + fasttext + other(Word2Vec) + binary + """ def __init__(self, filepath, method=None, binary=None): - """ - - Parameters - ---------- - filepath: - path to the pretrained model file - method - binary - """ fp = PurePath(filepath) self.binary = binary if binary is not None else (True if fp.suffix == ".bin" else False) if self.binary is True: @@ -62,14 +64,30 @@ def __getitem__(self, item): return self.wv[item] if item not in self.constants else np.zeros((self.vector_size,)) def infer_vector(self, items, agg="mean", *args, **kwargs) -> np.ndarray: - tokens = self.infer_tokens(items, *args, **kwargs) - return eval("np.%s" % agg)(tokens, axis=1) + token_vectors = self.infer_tokens(items, *args, **kwargs) + return [eval("np.%s" % agg)(item, axis=0) for item in token_vectors] def infer_tokens(self, items, *args, **kwargs) -> list: return [list(self(*item)) for item in items] class BowLoader(object): + """ + Using doc2bow model, which has a lot of effects. + + Convert document (a list of words) into the bag-of-words format = list of \ + (token_id, token_count) 2-tuples. Each word is assumed to be a \ + tokenized and normalized string (either unicode or utf8-encoded). \ + No further preprocessing is done on the words in document;\ + apply tokenization, stemming etc. before calling this method. + + If allow_update is set, then also update dictionary in the process: \ + create ids for new words. At the same time, update document frequencies – \ + for each word appearing in this document, increase its document frequency (self.dfs) by one. + + If allow_update is not set, this function is const, \ + aka read-only. + """ def __init__(self, filepath): self.dictionary = corpora.Dictionary.load(filepath) @@ -88,6 +106,11 @@ def vector_size(self): class TfidfLoader(object): + """ + This module implements functionality related to the Term Frequency - \ + Inverse Document Frequency \ + vector space bag-of-words models. + """ def __init__(self, filepath): self.tfidf_model = TfidfModel.load(filepath) # 'tfidf' model shold be used based on 'bow' model @@ -111,6 +134,22 @@ def vector_size(self): class D2V(Vector): + """ + It is a collection which include d2v, bow, tfidf method. + + Parameters + ----------- + filepath + method: str + d2v + bow + tfidf + item + + Returns + --------- + D2V + """ def __init__(self, filepath, method="d2v"): self._method = method self._filepath = filepath diff --git a/EduNLP/Vector/t2v.py b/EduNLP/Vector/t2v.py index 9adfe5e5..b2133587 100644 --- a/EduNLP/Vector/t2v.py +++ b/EduNLP/Vector/t2v.py @@ -21,6 +21,7 @@ class T2V(object): """ + Parameters ---------- model: str diff --git "a/docs/source/_static/\346\265\201\347\250\213\345\233\276.jpg" "b/docs/source/_static/\346\265\201\347\250\213\345\233\276.jpg" new file mode 100644 index 00000000..f765d295 Binary files /dev/null and "b/docs/source/_static/\346\265\201\347\250\213\345\233\276.jpg" differ diff --git a/docs/source/api/i2v.rst b/docs/source/api/i2v.rst index 29d40879..d8306edb 100644 --- a/docs/source/api/i2v.rst +++ b/docs/source/api/i2v.rst @@ -1,24 +1,6 @@ EduNLP.I2V ============ -.. automodule:: EduNLP.I2V.i2v.I2V - :members: - :imported-members: - -EduNLP.I2V.D2V -------------------- -.. automodule:: EduNLP.I2V.i2v.D2V - :members: - :imported-members: - -EduNLP.I2V.W2V -------------------- -.. automodule:: EduNLP.I2V.i2v.W2V - :members: - :imported-members: - -EduNLP.I2V.get_pretrained_i2v ---------------------------------------- -.. automodule:: EduNLP.I2V.i2v.get_pretrained_i2v +.. automodule:: EduNLP.I2V.i2v :members: :imported-members: diff --git a/docs/source/api/sif.rst b/docs/source/api/sif.rst index 4931807c..95f355fb 100644 --- a/docs/source/api/sif.rst +++ b/docs/source/api/sif.rst @@ -17,7 +17,7 @@ Segment Parser -------- -.. automodule:: EduNLP.SIF.parser.parser.Parser +.. automodule:: EduNLP.SIF.parser.parser :members: :imported-members: diff --git a/docs/source/tutorial/zh/index.rst b/docs/source/tutorial/zh/index.rst index 11a5cfc4..ef452a79 100644 --- a/docs/source/tutorial/zh/index.rst +++ b/docs/source/tutorial/zh/index.rst @@ -13,6 +13,10 @@ * `向量化 `_ +主要流程 +---------- + +.. figure:: ../../_static/流程图.jpg 示例 -------- diff --git a/docs/source/tutorial/zh/parse.rst b/docs/source/tutorial/zh/parse.rst index 3758dcb5..6c1dfe6a 100644 --- a/docs/source/tutorial/zh/parse.rst +++ b/docs/source/tutorial/zh/parse.rst @@ -120,7 +120,7 @@ item为str 或 List[Dict]类型，具体内容为latex 公式 或 公式经解 >>> plt.show() -.. figure:: ../../../_static/formula.png +.. figure:: ../../_static/formula.png 变量标准化 @@ -206,7 +206,7 @@ FormulaGroup [{'id': 0, 'type': 'supsub', 'text': '\\supsub', 'role': None}, {'id': 1, 'type': 'mathord', 'text': 'x', 'role': 'base', 'var': 0}, {'id': 2, 'type': 'textord', 'text': '2', 'role': 'sup'}, {'id': 3, 'type': 'rel', 'text': '=', 'role': None}, {'id': 4, 'type': 'mathord', 'text': 'y', 'role': None, 'var': 1}] >>> ForestPlotter().export(fs.ast_graph, root_list=[node["val"]["id"] for node in fs.ast if node["structure"]["father"] is None],) -.. figure:: ../../../_static/formulagroup.png +.. figure:: ../../_static/formulagroup.png 文本语法结构解析 diff --git a/docs/source/tutorial/zh/tokenize.rst b/docs/source/tutorial/zh/tokenize.rst index 9bac75b7..fa54a870 100644 --- a/docs/source/tutorial/zh/tokenize.rst +++ b/docs/source/tutorial/zh/tokenize.rst @@ -62,10 +62,10 @@ Examples： ------- 即综合解析，将带公式的句子切分为若干标记的过程。每个标记为一个“令牌”（token）。 -此功能对应的实现函数为tokenize，将已经经过结构成分分解后的item传入其中即可得到所需结果 +此功能对应的实现函数为tokenize，将已经经过结构成分分解后的item传入其中即可得到所需结果。 - :: + from EduNLP.Tokenizer import get_tokenizer >>> items = "如图所示，则三角形$ABC$的面积是$\\SIFBlank$。$\\FigureID{1}$" >>> tokenize(SegmentList(items)) @@ -75,7 +75,7 @@ Examples： -我们提供了多种已经封装好的令牌化器供用户便捷调用，通过查看 ``./EduNLP/Tokenizer/tokenizer.py`` 及 ``./EduNLP/Pretrain/gensim_vec.py`` 可以查看更多令牌化器，下面是一个完整的令牌化器列表 +我们提供了多种已经封装好的令牌化器供用户便捷调用，通过查看 ``./EduNLP/Tokenizer/tokenizer.py`` 及 ``./EduNLP/Pretrain/gensim_vec.py`` 可以查看更多令牌化器，下面是一个完整的令牌化器列表: - TextTokenizer diff --git a/examples/vectorization/i2v.ipynb b/examples/vectorization/i2v.ipynb index 3122fbce..f38ae4fe 100644 --- a/examples/vectorization/i2v.ipynb +++ b/examples/vectorization/i2v.ipynb @@ -1,192 +1,8856 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# I2V\n", - "\n", - "## 概述\n", - "\n", - "使用自己提供的任一预训练模型（给出模型存放路径即可）将给定的题目文本转成向量。\n", - "\n", - "- 优点：可以使用自己的模型，另可调整训练参数，灵活性强。" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 导入类" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 1, - "source": [ - "from EduNLP.I2V import D2V" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 输入\n", - "\n", - "类型：str \n", - "内容：题目文本 （text）" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 37, - "source": [ - "item = {\n", - "\"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\n", - "}" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 输出" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 34, - "source": [ - "model_path = \"../test_model/test_gensim_luna_stem_tf_d2v_256.bin\"\n", - "i2v = D2V(\"text\",\"d2v\",filepath=model_path, pretrained_t2v = False)\n", - "i2v " - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 34 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 35, - "source": [ - "i2v(item)" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "([array([ 4.76559885e-02, -1.60574958e-01, 1.94614579e-03, 2.40295693e-01,\n", - " 2.24517003e-01, -3.24351490e-02, 4.35789041e-02, -1.65670961e-02,\n", - " -7.77302235e-02, 4.23757173e-02, 4.62658405e-02, 7.54115507e-02,\n", - " -4.54682261e-02, -1.82153687e-01, 5.55203669e-02, 4.23391759e-02,\n", - " 8.86691213e-02, 6.97413310e-02, -2.47167766e-01, 2.54209518e-01,\n", - " -3.76413465e-02, 3.58376503e-02, -1.39907554e-01, -8.55517760e-02,\n", - " -1.62535697e-01, -4.44540828e-02, -3.99694731e-03, 1.83905549e-02,\n", - " -8.03738683e-02, -9.05910060e-02, 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-2.53734970e-03, -1.69801563e-01,\n", - " -2.28750743e-02, -2.55512260e-02, 1.70693725e-01, 2.35232189e-01,\n", - " -2.71384805e-01, -1.84327438e-01, 4.16823551e-02, 8.70332569e-02,\n", - " 1.82847306e-01, 2.76729286e-01, -4.31840494e-02, -1.38212308e-01,\n", - " -3.26297544e-02, -4.25132550e-02, -1.62892416e-01, 1.91870285e-03,\n", - " 1.52552709e-01, -1.01523520e-02, -9.16219354e-02, -5.46490997e-02,\n", - " 6.06994517e-02, -6.42470419e-02, 7.96310753e-02, -5.70830703e-02,\n", - " -8.82780831e-03, -3.94574478e-02, 9.63162258e-02, 1.54309124e-01,\n", - " 1.81100428e-01, 8.63620341e-02, 1.56518817e-02, -4.08006124e-02,\n", - " 5.20652272e-02, 8.38029310e-02, -1.55516326e-01, 3.57730500e-03,\n", - " -1.50946556e-02, 2.84812655e-02, 1.37905419e-01, 8.77659023e-02,\n", - " 8.23542774e-02, -1.04377635e-01, 4.80731949e-03, 1.18891411e-02,\n", - " 9.32120830e-02, 7.88019150e-02, -1.44494563e-01, -7.53350407e-02,\n", - " -1.13602541e-01, 5.43805361e-02, 1.64935380e-01, -2.00515296e-02,\n", - " 1.92917317e-01, -4.35359031e-02, 8.92477036e-02, -4.37481068e-02,\n", - " 4.01461311e-02, -2.59898454e-01, -1.11872263e-01, -1.25746787e-01,\n", - " -2.34577611e-01, -6.69524372e-02, 5.55978045e-02, -1.91931397e-01,\n", - " 5.87355606e-02, 1.01886272e-01, -2.64038593e-01, -2.05450356e-02,\n", - " -1.97510555e-01, 9.13371146e-02, 1.49546817e-01, -3.91026959e-02,\n", - " 5.94646595e-02, 1.29657034e-02, -3.72891256e-04, 5.56622408e-02,\n", - " 1.61776438e-01, 2.29037628e-02, -1.94774106e-01, -5.02247922e-02,\n", - " -5.45939505e-02, 5.31783216e-02, 1.26433298e-01, -1.23263724e-01,\n", - " 8.53074417e-02, -1.41412809e-01, -7.71067888e-02, 1.21865064e-01,\n", - " 4.73318882e-02, 7.20091909e-02, -9.83269960e-02, 1.99413914e-02,\n", - " -1.88907124e-02, -2.14710683e-02, -4.93260436e-02, 1.64937660e-01,\n", - " -1.07827298e-01, -7.75848776e-02, -6.23578345e-03, -1.05760902e-01,\n", - " -4.14819457e-02, 5.95730543e-02, 4.11023498e-02, -2.18305327e-02,\n", - " -2.30057724e-02, -3.34391668e-02, 1.30382255e-01, 5.10290638e-02,\n", - " -1.21569566e-01, -1.23630039e-01, -1.83883369e-01, 1.10945016e-01,\n", - " -1.05633408e-01, -8.24846700e-02, -3.76710802e-01, -4.50239740e-02],\n", - " dtype=float32)],\n", - " None)" - ] - }, - "metadata": {}, - "execution_count": 35 - } - ], - "metadata": {} - } - ], - "metadata": { - "orig_nbformat": 4, - "language_info": { - "name": "python", - "version": "3.8.5", - "mimetype": "text/x-python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "pygments_lexer": "ipython3", - "nbconvert_exporter": "python", - "file_extension": ".py" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3.8.5 64-bit" - }, - "interpreter": { - "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# I2V\r\n", + "\r\n", + "## 概述\r\n", + "\r\n", + "使用自己提供的任一预训练模型（给出模型存放路径即可）将给定的题目文本转成向量。\r\n", + "\r\n", + "- 优点：可以使用自己的模型，另可调整训练参数，灵活性强。" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# D2V" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## 导入类" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "source": [ + "from EduNLP.I2V import D2V" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## 输入\r\n", + "\r\n", + "类型：str \r\n", + "内容：题目文本 （text）" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 10, + "source": [ + "items = [\r\n", + "r\"1如图几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\",\r\n", + "r\"2如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$\"\r\n", + "]" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## 输出" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 11, + "source": [ + "model_path = \"../../data/d2v/test_d2v_256.bin\"\r\n", + "i2v = D2V(\"pure_text\",\"d2v\",filepath=model_path, pretrained_t2v = False)\r\n", + "\r\n", + "item_vectors, token_vectors = i2v(items)\r\n", + "print(item_vectors, token_vectors)\r\n", + "print(len(item_vectors), item_vectors[0].shape) # For d2v, token_vector is None" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[array([ 0.10634826, -0.10273553, -0.04942605, 0.15323296, 0.25780037,\n", + " -0.06630539, -0.02834828, 0.0065832 , -0.07495898, 0.06529831,\n", + " 0.07989643, 0.14697047, -0.06513839, -0.23898382, 0.04266138,\n", + " 0.15211952, 0.17704317, 0.02768603, -0.1854407 , 0.21742067,\n", + " 0.00984387, 0.06774732, -0.02058181, -0.13266353, -0.20851782,\n", + " 0.00558438, 0.00460878, -0.05139542, -0.06485009, -0.04847892,\n", + " 0.09366203, 0.13947366, -0.07552954, 0.05977268, -0.09442269,\n", + " 0.12721013, 0.13273577, -0.02567345, 0.09778284, -0.08030444,\n", + " -0.05697263, -0.02538129, -0.04187621, -0.03123644, 0.11242954,\n", + " -0.00650479, 0.06117244, 0.06449873, 0.05163544, 0.01810673,\n", + " 0.03808153, 0.03746097, 0.1275719 , 0.07381074, -0.14620851,\n", + " 0.05757713, 0.02838781, -0.12927914, 0.07510903, 0.08147711,\n", + " -0.06523656, -0.01486957, 0.02304654, 0.04361376, 0.21421443,\n", + " -0.02614699, -0.03873423, 0.21355043, -0.09329057, -0.09335907,\n", + " 0.08157952, 0.00625934, -0.05051626, -0.07981606, -0.03226125,\n", + " 0.08958097, -0.04725425, -0.06789905, 0.06730124, 0.06633109,\n", + " -0.07370041, -0.13176298, -0.01786646, 0.11614659, -0.04729063,\n", + " 0.06670643, 0.12553535, -0.1768527 , -0.09982615, -0.07682986,\n", + " 0.06889272, -0.04036296, 0.03164692, -0.1446157 , 0.03707608,\n", + " 0.13580611, -0.07748953, -0.02646814, -0.01086655, -0.09684405,\n", + " -0.15440914, -0.1067182 , 0.0349819 , 0.10395218, -0.00877615,\n", + " 0.05640977, -0.01675119, 0.03014355, 0.12512377, 0.01414524,\n", + " 0.0211464 , -0.06591589, 0.08297148, 0.07012929, -0.10675234,\n", + " -0.04061191, 0.01302199, 0.11621079, 0.00885058, -0.0304758 ,\n", + " -0.01553952, -0.12865704, 0.17576885, -0.02950172, -0.01326492,\n", + " 0.01504841, 0.09476334, -0.03984889, 0.00763261, -0.01630022,\n", + " -0.03672133, 0.01365909, 0.05986133, -0.10593513, -0.08282372,\n", + " -0.08374579, -0.04833878, 0.0489895 , 0.00875692, -0.03651298,\n", + " -0.01757672, -0.01623743, 0.23217827, 0.15424854, -0.26954046,\n", + " -0.04381514, 0.03540273, 0.06735364, 0.09081568, 0.07033931,\n", + " -0.14287043, -0.1028204 , -0.04575491, -0.01768357, -0.08948888,\n", + " 0.07118583, 0.1542743 , 0.09116074, -0.08839961, 0.01807798,\n", + " 0.12579061, -0.03509037, 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4.44654636e-02, -1.71815068e-01, 5.64131141e-02, -1.30362645e-01,\n", + " -2.15643197e-01, -6.48207739e-02, 1.80758730e-01, 6.18097410e-02,\n", + " -6.83161020e-02, 1.69224367e-01, -2.53407449e-01, -4.33427393e-02,\n", + " -8.90076980e-02, 7.33624548e-02, 5.79761080e-02, -3.76918130e-02,\n", + " 8.55930522e-02, 2.99188751e-03, 1.56438854e-02, 2.43693665e-02,\n", + " 2.17681393e-01, 6.79032505e-02, -1.83962315e-01, -1.40863275e-02,\n", + " -1.53916925e-01, 8.76621082e-02, 9.75017548e-02, -6.90853447e-02,\n", + " 3.22550870e-02, -1.22331316e-02, 8.18267651e-03, 3.01490221e-02,\n", + " -1.40115758e-02, 1.17691020e-02, -2.29610443e-01, -1.88834459e-01,\n", + " -4.37003709e-02, -3.97654139e-02, 2.05663759e-02, 9.48096588e-02,\n", + " 4.75249775e-02, 6.99462742e-02, 9.24514905e-02, -1.96493998e-01,\n", + " 3.27189267e-02, 4.80476059e-02, 3.26564163e-02, -1.90033496e-03,\n", + " 3.73094343e-03, -5.62442988e-02, 5.95186092e-02, -2.72717867e-02,\n", + " -8.34571719e-02, -2.22789161e-02, -1.91354036e-01, 1.27723783e-01,\n", + " -1.69917479e-01, -3.11680529e-02, -2.62214512e-01, -2.82380339e-02],\n", + " dtype=float32)] None\n", + "2 (256,)\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# W2V" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 12, + "source": [ + "from EduNLP.I2V import W2V\r\n", + "\r\n", + "model_path = \"../../data/w2v/general_literal_300/general_literal_300.kv\"\r\n", + "i2v = W2V(\"pure_text\",\"w2v\",filepath=model_path, pretrained_t2v = False)\r\n", + "try:\r\n", + " item_vectors, token_vectors = i2v(items)\r\n", + " print(item_vectors) # 每个句子vector 为np.ndarray\r\n", + " print(token_vectors) # 每个单词vector list\r\n", + " print(len(item_vectors), item_vectors[0].shape,len(token_vectors), len(token_vectors[0]),len(token_vectors[0][0]))\r\n", + "except Exception as e:\r\n", + " print(e)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + 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"python3", + "display_name": "Python 3.6.13 64-bit ('data': conda)" + }, + "interpreter": { + "hash": "776957673adb719a00031a24ed5efd2fa5ce8a13405e5193f8d278edd3805d55" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/tests/test_sif/test_tokenization.py b/tests/test_sif/test_tokenization.py index b904a71b..43dfde02 100644 --- a/tests/test_sif/test_tokenization.py +++ b/tests/test_sif/test_tokenization.py @@ -3,7 +3,7 @@ import pytest from EduNLP.SIF.constants import Symbol -from EduNLP.SIF.segment.segment import SegmentList +from EduNLP.SIF.segment.segment import SegmentList, LatexFormulaSegment from EduNLP.SIF.tokenization import text from EduNLP.SIF.tokenization import formula from EduNLP.SIF.tokenization.tokenization import TokenList @@ -32,3 +32,5 @@ def test_tokenization(): with pytest.raises(TypeError): tl.append("[Unknown]") + + tl.append(LatexFormulaSegment('x+y'), False) diff --git a/tests/test_vec/test_vec.py b/tests/test_vec/test_vec.py index d97210d8..bc7194d9 100644 --- a/tests/test_vec/test_vec.py +++ b/tests/test_vec/test_vec.py @@ -110,7 +110,7 @@ def test_w2v_i2v(stem_text_tokens, tmpdir, stem_data): ) i2v = I_W2V("pure_text", "w2v", filepath) - i_vec, t_vec = i2v(stem_data[:1]) + i_vec, t_vec = i2v(stem_data[:2]) assert len(i_vec[0]) == i2v.vector_size assert len(t_vec[0][0]) == i2v.vector_size @@ -187,7 +187,7 @@ def test_d2v(stem_text_tokens, tmpdir, stem_data): assert len(t2v(stem_text_tokens[:1])[0]) == t2v.vector_size i2v = I_D2V("pure_text", "d2v", filepath) - i_vec, t_vec = i2v(stem_data[:1]) + i_vec, t_vec = i2v(stem_data[:2]) assert len(i_vec[0]) == i2v.vector_size assert t_vec is None