Metadata-Version: 2.1
Name: tags2vec
Version: 0.0.0
Summary: A tool that converts information such as tags in multiple strings into features in the form of vectors with 1s and 0s as elements
Home-page: https://github.co.jp/
Author: bib_inf
Author-email: contact.bibinf@gmail.com
License: CC0 v1.0
Description: # json_stock
        
        下の方に日本語の説明があります
        
        ## Overview
        - A tool that converts information such as tags in multiple strings into features in the form of vectors with 1s and 0s as elements
        
        ## Usage
        ```python
        import tags2vec
        
        # Convert training data
        train_tags = [
        	["Spicy", "Red", "Delicious"],
        	["Sweet", "Green"]
        ]
        # Convert training data Tag info -> Vector (training) [tags2vec]
        train_x, tags_info = tags2vec.train_tr(train_tags)
        """
        train_x (numpy array):
        [[1. 1. 1. 0. 0.]
         [0. 0. 0. 1. 1.]]
        
        tags_info: ["Spicy", "Red", "Delicious", "Sweet", "Green"]
        """
        
        # Convert test data
        test_tags = [
        	["Sweet", "Red", "Delicious"],
        	["Spicy", "Yellow"],
        ]
        # Tag info -> Vector (prediction) [tags2vec]
        test_x = tags2vec.pred_tr(test_tags, tags_info)
        """
        test_x (numpy array):
        [[0. 1. 1. 1. 0.]
         [1. 0. 0. 0. 0.]]
        """
        ```
        
        ## detailed explanation
        - This tool is designed for pre-processing of supervised learning.
        	- The vector output is therefore a numpy matrix.
        - During the training phase, a list of tag information is output in the form of tags_info variables
        - During the prediction phase, a vector is generated based on the tags_info information (list of tags and their order)
        	- This ensures that prediction is consistent with the training phase
        - If a tag appears during prediction that was not present during training, it is ignored
        
        
        
        ## 概要
        - タグ情報をベクトル特徴量に変換するツール
        - 具体的には、複数の文字列のタグのような情報を、1と0を要素として持つベクトルの形の特徴量に変換するツール
        
        ## 使用例
        ```python
        import tags2vec
        
        # 学習データの変換
        train_tags = [
        	["Spicy", "Red", "Delicious"],
        	["Sweet", "Green"]
        ]
        # タグ情報 -> ベクトル (学習時) [tags2vec]
        train_x, tags_info = tags2vec.train_tr(train_tags)
        """
        train_x (numpy array):
        [[1. 1. 1. 0. 0.]
         [0. 0. 0. 1. 1.]]
        
        tags_info: ["Spicy", "Red", "Delicious", "Sweet", "Green"]
        """
        
        # 推論データの変換
        test_tags = [
        	["Sweet", "Red", "Delicious"],
        	["Spicy", "Yellow"],
        ]
        # タグ情報 -> ベクトル (推論時) [tags2vec]
        test_x = tags2vec.pred_tr(test_tags, tags_info)
        """
        test_x (numpy array):
        [[0. 1. 1. 1. 0.]
         [1. 0. 0. 0. 0.]]
        """
        ```
        
        ## 詳細説明
        - このツールは教師あり学習の前処理を想定して作られています
        	- そのため、ベクトルの出力はnumpy行列で出力されます
        - 学習のフェーズでタグ情報の一覧がtags_info変数の形で出力されます
        - 推論フェーズでは、tags_infoの情報 (タグ一覧とその順序) にもとづいてベクトルが生成されます
        	- これによって、学習時と一貫した推論を行うことができます
        - 学習時に存在しなかったタグが推論時に現れた場合は、そのタグは無視されます
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Description-Content-Type: text/markdown
