# pyFTS.benchmarks.Tests — pyFTS 1.6 documentation

# Source code for pyFTS.benchmarks.Tests

    import numpy as np
    import pandas as pd

    from pyFTS.benchmarks.Measures import acf

    def BoxPierceStatistic(data, h):
        """
        Q Statistic for Box-Pierce test

        :param data:
        :param h:
        :return:
        """
        n = len(data)
        s = 0
        for k in np.arange(1, h + 1):
            r = acf(data, k)
            s += r ** 2
        return n * s

    def BoxLjungStatistic(data, h):
        """
        Q Statistic for Ljung–Box test

        :param data:
        :param h:
        :return:
        """
        n = len(data)
        s = 0
        for k in np.arange(1, h + 1):
            r = acf(data, k)
            s += r ** 2 / (n - k)
        return n * (n - 2) * s

    def format_experiment_table(df, exclude=[], replace={}, csv=True, std=False):
        rows = []
        columns = []
        datasets = df.Dataset.unique()
        models = df.Model.unique()
        for model in models:
            test = np.any([model.rfind(k) != -1 for k in exclude]) if len(exclude) > 0 else False
            if not test:
                columns.append(model)

        for dataset in datasets:
            row = [dataset]
            if std:
                row_std = [dataset]
            for model in columns:
                avg = np.nanmin(df[(df.Dataset == dataset) & (df.Model == model)]["AVG"].values)
                row.append(round(avg, 3))
                if std:
                    _std = np.nanmin(df[(df.Dataset == dataset) & (df.Model == model)]["STD"].values)
                    row_std.append("(" + str(round(_std, 3)) + ")")

            rows.append(row)
            if std:
                rows.append(row_std)

        for k in range(len(columns)):
            if columns[k] in replace:
                columns[k] = replace[columns[k]]

        columns.insert(0, "dataset")

        if csv:
            header = ""
            for k in range(len(columns)):
                if k > 0:
                    header += ","
                header += columns[k]

            body = ""
            for k in range(len(rows)):
                row = ""
                for w in range(len(rows[k])):
                    if w > 0:
                        row += ","
                    row += str(rows[k][w])
                body += '\n{}'.format(row)

            return header + body
        else:
            ret = pd.DataFrame(rows, columns=columns)
            return ret

    def test_mean_equality(tests, alpha=.05, method='friedman'):
        """
        Test for the equality of the means, with alpha confidence level.

        H_0: There's no significant difference between the means
        H_1: There is at least one significant difference between the means

        :param tests:
        :param alpha:
        :param method:
        :return:
        """
        from stac.stac import nonparametric_tests as npt

        methods = tests.columns[1:]

        values = []
        for k in methods:
            values.append(tests[k].values)

        if method=='quade':
            f_value, p_value, rankings, pivots = npt.quade_test(*values)
        elif method=='friedman':
            f_value, p_value, rankings, pivots = npt.friedman_aligned_ranks_test(*values)
        else:
            raise Exception('Unknown test method!')

        print("F-Value: {} \tp-Value: {}".format(f_value, p_value))

        if p_value < alpha:
            print("\nH0 is rejected!\n")
        else:
            print("\nH0 is accepted!\n")

        post_hoc = {}
        rows = []
        for k in np.arange(0, len(methods)):
            rows.append([methods[k], rankings[k]])
            post_hoc[methods[k]] = pivots[k]

        return [pd.DataFrame(rows, columns=['METHOD', 'RANK']).sort_values(['RANK']), post_hoc]

    def post_hoc_tests(post_hoc, control_method, alpha=.05, method='finner'):
        '''
        Finner paired post-hoc test with NSFTS as control method.

        $H_0$: There is no significant difference between the means

        $H_1$: There is a significant difference between the means

        :param post_hoc:
        :param control_method:
        :param alpha:
        :param method:
        :return:
        '''
        from stac.stac import nonparametric_tests as npt

        if method == 'bonferroni_dunn':
            comparisons, z_values, p_values, adj_p_values = npt.bonferroni_dunn_test(post_hoc,control_method)
        elif method == 'holm':
            comparisons, z_values, p_values, adj_p_values = npt.holm_test(post_hoc,control_method)
        elif  method == 'finner':
            comparisons, z_values, p_values, adj_p_values = npt.finner_test(post_hoc, control_method)
        else:
            raise Exception('Unknown test method!')

        rows = []
        for k in np.arange(len(comparisons)):
            test = 'H0 Accepted' if adj_p_values[k] > alpha else 'H0 Rejected'
            rows.append([comparisons[k], z_values[k], p_values[k], adj_p_values[k], test])

        return pd.DataFrame(rows, columns=['COMPARISON', 'Z-VALUE', 'P-VALUE', 'ADJUSTED P-VALUE', 'Result'])