{
    "info": {
        "author": "znwang25",
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        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: Chinese (Simplified)",
            "Natural Language :: Chinese (Traditional)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Text Processing",
            "Topic :: Text Processing :: Linguistic"
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        "description": "fuzzychinese\n=====\n\u5f62\u8fd1\u8bcd\u4e2d\u6587\u6a21\u7cca\u5339\u914d\n\nA simple tool to fuzzy match chinese words, particular useful for proper name matching and address matching. \n\n\u4e00\u4e2a\u53ef\u4ee5\u6a21\u7cca\u5339\u914d\u5f62\u8fd1\u5b57\u8bcd\u7684\u5c0f\u5de5\u5177\u3002\u5bf9\u4e8e\u4e13\u6709\u540d\u8bcd\uff0c\u5730\u5740\u7684\u5339\u914d\u5c24\u5176\u6709\u7528\u3002\n\n## \u5b89\u88c5\u8bf4\u660e\n```\npip install fuzzychinese\n```\n\n## \u4f7f\u7528\u8bf4\u660e\n\u9996\u5148\u4f7f\u7528\u60f3\u8981\u5339\u914d\u7684\u5b57\u5178\u5bf9\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u3002\n\n\u7136\u540e\u7528`FuzzyChineseMatch.transform(raw_words, n)` \u6765\u5feb\u901f\u67e5\u627e\u4e0e`raw_words`\u7684\u8bcd\u6700\u76f8\u8fd1\u7684\u524dn\u4e2a\u8bcd\u3002\n\n\u8bad\u7ec3\u6a21\u578b\u65f6\u6709\u4e09\u79cd\u5206\u6790\u65b9\u5f0f\u53ef\u4ee5\u9009\u62e9\uff0c\u7b14\u5212\u5206\u6790(`stroke`)\uff0c\u90e8\u9996\u5206\u6790(`radical`)\uff0c\u548c\u5355\u5b57\u5206\u6790(`char`)\u3002\u4e5f\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574`ngram_range`\u7684\u503c\u6765\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\u3002\n\n\u5339\u914d\u5b8c\u6210\u540e\u8fd4\u56de\u76f8\u4f3c\u5ea6\u5206\u6570\uff0c\u5339\u914d\u7684\u76f8\u8fd1\u8bcd\u8bed\u53ca\u5176\u539f\u6709\u7d22\u5f15\u53f7\u3002\n\n```python\n    import pandas as pd\n    from fuzzychinese import FuzzyChineseMatch\n    test_dict =  pd.Series(['\u957f\u767d\u671d\u9c9c\u65cf\u81ea\u6cbb\u53bf','\u957f\u9633\u571f\u5bb6\u65cf\u81ea\u6cbb\u53bf','\u57ce\u6b65\u82d7\u65cf\u81ea\u6cbb\u53bf','\u8fbe\u5c14\u7f55\u8302\u660e\u5b89\u8054\u5408\u65d7','\u6c68\u7f57\u5e02'])\n    raw_word = pd.Series(['\u8fbe\u8302\u8054\u5408\u65d7','\u957f\u9633\u53bf','\u6c69\u7f57\u5e02'])\n    assert('\u6c69\u7f57\u5e02'!='\u6c68\u7f57\u5e02') # They are not the same!\n\n    fcm = FuzzyChineseMatch(ngram_range=(3, 3), analyzer='stroke')\n    fcm.fit(test_dict)\n    top2_similar = fcm.transform(raw_word, n=2)\n    res = pd.concat([\n        raw_word,\n        pd.DataFrame(top2_similar, columns=['top1', 'top2']),\n        pd.DataFrame(\n            fcm.get_similarity_score(),\n            columns=['top1_score', 'top2_score']),\n        pd.DataFrame(\n            fcm.get_index(),\n            columns=['top1_index', 'top2_index'])],\n                    axis=1)\n```\n\n|            | top1               | top2             | top1_score | top2_score | top1_index | top2_index |\n| ---------- | ------------------ | ---------------- | ---------- | ---------- | ---------- | ---------- |\n| \u8fbe\u8302\u8054\u5408\u65d7 | \u8fbe\u5c14\u7f55\u8302\u660e\u5b89\u8054\u5408\u65d7 | \u957f\u767d\u671d\u9c9c\u65cf\u81ea\u6cbb\u53bf | 0.824751   | 0.287237   | 3          | 0          |\n| \u957f\u9633\u53bf     | \u957f\u9633\u571f\u5bb6\u65cf\u81ea\u6cbb\u53bf   | \u957f\u767d\u671d\u9c9c\u65cf\u81ea\u6cbb\u53bf | 0.610285   | 0.475000   | 1          | 0          |\n| \u6c69\u7f57\u5e02     | \u6c68\u7f57\u5e02             | \u957f\u767d\u671d\u9c9c\u65cf\u81ea\u6cbb\u53bf | 1.000000   | 0.152093   | 4          | 0          |\n\n## \u5176\u4ed6\u529f\u80fd\n+ \u76f4\u63a5\u4f7f\u7528`Stroke`, `Radical`\u8fdb\u884c\u6c49\u5b57\u5206\u89e3\u3002\n    ```python\n    stroke = Stroke()\n    radical = Radical()\n    print(\"\u50cf\", stroke.get_stroke(\"\u50cf\"))\n    print(\"\u50cf\", radical.get_radical(\"\u50cf\"))\n    ```\n    ```\n    \u50cf \u31d2\u3021\u31d2\u31c7\u3021\u31d5\u4e00\u31d2\u31c1\u31d2\u31d2\u31d2\u31cf\n    \u50cf \u4eba\u8c61\n    ```\n+ \u4f7f\u7528`FuzzyChineseMatch.compare_two_columns(X, Y)`\u5bf9\u6bcf\u4e00\u884c\u7684\u4e24\u4e2a\u8bcd\u8fdb\u884c\u6bd4\u8f83\uff0c\u83b7\u5f97\u76f8\u4f3c\u5ea6\u5206\u6570\u3002\n\n+ \u8be6\u60c5\u8bf7\u53c2\u89c1[\u8bf4\u660e\u6587\u6863](http://znwang.me/fuzzychinese/).\n\n## \u81f4\u8c22\n\n\u62c6\u5b57\u6570\u636e\u6765\u81ea\u4e8e [\u6f22\u8a9e\u62c6\u5b57\u5b57\u5178](https://github.com/kfcd/chaizi) by [\u958b\u653e\u8a5e\u5178\u7db2](http://kaifangcidian.com/)\u3002\n\n## Installation\n```\npip install fuzzychinese\n```\n\n## Quickstart\n\nFirst train a model with the target list of words you want to match to. \n\nThen use `FuzzyChineseMatch.transform(raw_words, n)` to find top n most similar words in the target for your `raw_words` . \n\nThere are three analyzers to choose from when training a model: `stroke`, `radical`, and `char`. You can also change `ngram_range` to fine-tune the model.\n\nAfter the matching, similarity score, matched words and its corresponding index are returned. \n\n```python\n    from fuzzychinese import FuzzyChineseMatch\n    test_dict =  pd.Series(['\u957f\u767d\u671d\u9c9c\u65cf\u81ea\u6cbb\u53bf','\u957f\u9633\u571f\u5bb6\u65cf\u81ea\u6cbb\u53bf','\u57ce\u6b65\u82d7\u65cf\u81ea\u6cbb\u53bf','\u8fbe\u5c14\u7f55\u8302\u660e\u5b89\u8054\u5408\u65d7','\u6c68\u7f57\u5e02'])\n    raw_word = pd.Series(['\u8fbe\u8302\u8054\u5408\u65d7','\u957f\u9633\u53bf','\u6c69\u7f57\u5e02'])\n    assert('\u6c69\u7f57\u5e02'!='\u6c68\u7f57\u5e02') # They are not the same!\n\n    fcm = FuzzyChineseMatch(ngram_range=(3, 3), analyzer='stroke')\n    fcm.fit(test_dict)\n    top2_similar = fcm.transform(raw_word, n=2)\n    res = pd.concat([\n        raw_word,\n        pd.DataFrame(top2_similar, columns=['top1', 'top2']),\n        pd.DataFrame(\n            fcm.get_similarity_score(),\n            columns=['top1_score', 'top2_score']),\n        pd.DataFrame(\n            fcm.get_index(),\n            columns=['top1_index', 'top2_index'])],\n                    axis=1)\n```\n\n|            | top1               | top2             | top1_score | top2_score | top1_index | top2_index |\n| ---------- | ------------------ | ---------------- | ---------- | ---------- | ---------- | ---------- |\n| \u8fbe\u8302\u8054\u5408\u65d7 | \u8fbe\u5c14\u7f55\u8302\u660e\u5b89\u8054\u5408\u65d7 | \u957f\u767d\u671d\u9c9c\u65cf\u81ea\u6cbb\u53bf | 0.824751   | 0.287237   | 3          | 0          |\n| \u957f\u9633\u53bf     | \u957f\u9633\u571f\u5bb6\u65cf\u81ea\u6cbb\u53bf   | \u957f\u767d\u671d\u9c9c\u65cf\u81ea\u6cbb\u53bf | 0.610285   | 0.475000   | 1          | 0          |\n| \u6c69\u7f57\u5e02     | \u6c68\u7f57\u5e02             | \u957f\u767d\u671d\u9c9c\u65cf\u81ea\u6cbb\u53bf | 1.000000   | 0.152093   | 4          | 0          |\n\n## Other use\n+ Directly use `Stroke`, `Radical` to decompose Chinese character into strokes or radicals.\n    ```python\n    stroke = Stroke()\n    radical = Radical()\n    print(\"\u50cf\", stroke.get_stroke(\"\u50cf\"))\n    print(\"\u50cf\", radical.get_radical(\"\u50cf\"))\n    ```\n    ```\n    \u50cf \u31d2\u3021\u31d2\u31c7\u3021\u31d5\u4e00\u31d2\u31c1\u31d2\u31d2\u31d2\u31cf\n    \u50cf \u4eba\u8c61\n    ```\n+ Use `FuzzyChineseMatch.compare_two_columns(X, Y)` to compare the pair of words in each row to get similarity score.\n\n+ See [documentation](http://znwang.me/fuzzychinese/) for details.\n\n\n## Credits\n\nData for Chinese radicals are from [\u6f22\u8a9e\u62c6\u5b57\u5b57\u5178](https://github.com/kfcd/chaizi) by [\u958b\u653e\u8a5e\u5178\u7db2](http://kaifangcidian.com/).\n\n",
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