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        "author": "Hsin-Min Lu, Yu-Lun Li, Chi-Yu Lin",
        "author_email": "luim@ntu.edu.tw",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "namesex_light\n-------------\n\nNamesex_light is a lighweight package that predicts the gender tendency of Chinese given names. This module comes with a L2 regularized logistic regression trained on 10,730 Chinese given names (in traditional Chinese) with reliable gender lables collected from public data. The predict() function takes a list of names and output predicted gender tendency (1 for male and 0 for female) or probability of being a male name. Namesex_light has a sister project, namesex, that performs similar tasks with higher accuracy.\n\nAdditional information about namesex and namesex_light can be found `in another document (in Chinese) <https://github.com/hsinmin/namesex/blob/master/vignettee_namesex_exp1.ipynb>`_.\n\nThe prediction performance evaluated by ten-fold cross validation is:\n\n========= =========== =====================\nMetric    Performance Performance Std. Dev.\n--------- ----------- ---------------------\nAccuracy  0.8957      0.007327\nF1        0.8920      0.007873\nPrecision 0.8852      0.012238\nRecall    0.8991      0.008936\nLogloss   114.35      6.413972\n========= =========== =====================\n\n\nUse pip/pip3 to install namesex_light.::\n\n    pip install namesex_light\n\nTo use namesex_light, pass in an array or list of given names to predict(). For each element in the input list, predict() returns 1 or 0 for male or female prediction. Set \"predprob = True\" to return probability of being a male name. The following is a simple sample code.::\n\n\n    >>> import namesex_light\n    >>> nsl = namesex_light.namesex_light()\n    >>> nsl.predict(['\u6c11\u8c6a', '\u611b\u9e97', '\u5fd7\u660e'])\n    array([1, 0, 1])\n    >>> nsl.predict(['\u6c11\u8c6a', '\u611b\u9e97', '\u5fd7\u660e'], predprob=True)\n    array([0.99968932, 0.00530066, 0.9938986 ])\n\nNote that namesex_light was trained using Chinese given names only. However, it may be used to classifier translated names as well::\n\n    >>> nsl.predict(['\u963f\u6ce2\u7f85', '\u963f\u6ce2\u7f85', '\u96f7', '\u827e\u7f8e', '\u5e03\u862d\u59ae', '\u963f\u66fc\u9054'])\n    array([1, 1, 1, 0, 0, 1])\n\nThis module is intended for a quick plug-and-play. The original training dataset is not included.\n\nTesting Dataset\n---------------\n\nThis package comes with a small testing dataset that was not used for model training. The following sample code illustrate a simple usage.::\n\n    >>> testdata = namesex_light.testdata()\n    >>> nsl = namesex_light.namesex_light()\n    >>> pred = nsl.predict(testdata.gname)\n    >>> print(\"The first 5 given names are: {}\".format(testdata.gname[0:5]))\n    The first 5 given names are: ['\u7fca\u5982', '\u59a4\u5ead', '\u8ac6\u748b', '\u5927\u958e', '\u548c\u7dad']\n    >>> print(\"    and their sex:          {}\".format(testdata.sex[0:5]))\n        and their sex:          [0, 0, 1, 1, 1]\n    >>> print(\"    and their predicted sex:{}\".format(pred[0:5]))\n        and their predicted sex:[0 0 1 1 1]\n    >>> accuracy = np.sum(pred == testdata.sex) / len(pred)\n    >>> print(\" Prediction accuracy = {}\".format(accuracy))\n     Prediction accuracy = 0.8627450980392157\n\nNote that the accuracy is slightly lower compared to the accuracy of ten-fold cross valudation. I guess this is normal since this testset is collected from a source that is different from the training dataset.\n\n",
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        "keywords": "classify_sex Chinese_given_name",
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        "name": "namesex-light",
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