<commit_before># -*- coding: utf-8 -*-

"""
Tools for Model Accuracy Estimation
===================================

"""

from . import tools, EloModel, PFAModel, PFAWithSpacing


class Test(object):

    def __init__(self, data, model_kwargs=None, train=True):
        self.model_kwargs = model_kwargs or {}
        self.train_set, self.test_set = self.split_data(data)

        if train:
            self.model = self.train_model()

    def retrain_with(self, model_kwargs=None):
        self.model_kwargs = model_kwargs or {}
        self.model = self.train_model()

    def split_data(self, data):
        raise NotImplementedError()

    def rmse(self):
        y_true = self.test_set['correct']
        y_pred = self.test_set.apply(self.model.predict, axis=1)
        return tools.rmse(y_true, y_pred)

    def auc(self):
        raise NotImplementedError()


class EloTest(Test):

    def train_model(self):
        model = EloModel(self.train_set, **self.model_kwargs)
        model.train()
        return model

    def split_data(self, data):
        data = tools.shuffle_data(tools.prior_data(data))
        return tools.split_data(data)


class PFATest(Test):

    def train_model(self):
        model = PFAModel(self.train_set, **self.model_kwargs)
        model.train()
        return model


class PFAWithSpacingTest(Test):

    def train_model(self):
        model = PFAWithSpacing(self.train_set, **self.model_kwargs)
        model.train()
        return model<commit_msg>Implemented AUC and RMSE for testing accuracy.<commit_after># -*- coding: utf-8 -*-

"""
Tools for Model Accuracy Estimation
===================================

"""

from sklearn import metrics

from . import tools


class PrecisionResult(object):
    """Represents estimated accuracy of a model.

    :param value: Estimated accuracy.
    :type value: float
    :param method: Name of the used method (e.g. *RMES* or *AUC*).
    :type method: string
    :param size: Size of the training set.
    :type size: int
    """

    def __init__(self, value, method, size):
        self.value = value
        self.method = method
        self.size = size

    def __str__(self):
        return self.__repr__()

    def __repr__(self):
        return ('{self.method}: {self.value}\n'
                'Training Set Size: {self.size}').format(self=self)


class PrecisionTest(object):
    """Represents model precision test.

    :param model: Insance of the model to test.
    :type model: :class:`models.Model`
    :param data: Data to use for the test.
    :type data: :class:`pandas.DataFrame`
    """

    def __init__(self, model, data):
        self.data = data
        self.model = model

    def run(self):
        """Prepares training set, test set and trains the model.
        """
        train_set, test_set = self.model.split_data(self.data)
        self.model.train(train_set)

        self.y_true = test_set['is_correct']
        self.y_pred = test_set.apply(self.model.predict, axis=1)
        self.train_set_size = len(train_set)

    def rmse(self):
        """Estimates precision of the model using Root Mean Squared Error
        (RMSE) as metric.
        """
        result = tools.rmse(self.y_true, self.y_pred)
        return PrecisionResult(result, 'RMSE', self.train_set_size)

    def auc(self):
        """Estimates precision of the model using Area Under the Curve
        (AUC) as metric."""
        fpr, tpr, thresholds = \
            metrics.roc_curve(self.y_true, self.y_pred, pos_label=1)
        result = metrics.auc(fpr, tpr)
        return PrecisionResult(result, 'AUC', self.train_set_size)
{'fraction_non_alphanumeric': 0.13019390581717452, 'fraction_numerical': 0.0013850415512465374, 'mean_word_length': 2.71120246659815, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '4961677', 'n_tokens_mistral': 1148, 'n_tokens_neox': 1100, 'n_words': 286}