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How to use get_group_counts method in autotest

Best Python code snippet using autotest_python

featsel.py

Source:featsel.py

...9    N01 = np.sum(td[t,in_c] == 0)10    N10 = np.sum(td[t,not_in_c] > 0)11    N00 = np.sum(td[t,not_in_c] == 0)12    return (N11,N01,N10,N00)13def get_group_counts(N11,N01,N10,N00):14    N1_ = N10 + N1115    N_1 = N11 + N0116    N0_ = N01 + N0017    N_0 = N10 + N0018    N = N10 + N11 + N01 + N0019    return (N1_,N_1,N0_,N_0,N)20def mutual_information(td, delta):21    """22    td: a term-document V x |X| matrix23    delta: |X| x M matrix where delta(i,j) = 1 if document i belongs to class j24    output: a V x M matrix of scores I(t,c)25    """26    V,X = td.shape27    X_,M = delta.shape28    assert(X==X_)29    I = np.zeros((V,M), dtype=np.double)30    for t in range(V):31        for c in range(M):32            N11,N01,N10,N00 = get_counts(td, delta, t, c)33            N1_,N_1,N0_,N_0,N = get_group_counts(N11,N01,N10,N00)34            # FIXME: how to deal with log2(0) when the numerator is 0?35            I[t,c] = N11/N * log2((N*N11)/(N1_*N_1)) + \36                     N01/N * log2((N*N01)/(N0_*N_1)) + \37                     N10/N * log2((N*N10)/(N1_*N_0)) + \38                     N00/N * log2((N*N00)/(N0_*N_0))39    return I40def chi2(td, delta):41    """42    td: a term-document V x |X| matrix43    delta: |X| x M matrix where delta(i,j) = 1 if document i belongs to class j44    output: a V x M matrix of scores chi2(t,c)45    """46    V,X = td.shape47    X_,M = delta.shape48    assert(X==X_)49    chi2m = np.zeros((V,M), dtype=np.double)50    for t in range(V):51        for c in range(M):52            N11,N01,N10,N00 = get_counts(td, delta, t, c)53            N1_,N_1,N0_,N_0,N = get_group_counts(N11,N01,N10,N00)54            chi2m[t,c] = (N*(N11*N00-N10*N01)**2)/(N1_*N_1*N0_*N_0)55    return chi2m56def select_max(td, vocab, A, K):57    """58    Select the best K/M features for each of the M classes59    td: a term-document V x |X| matrix60    delta: |X| x M matrix where delta(i,j) = 1 if document i belongs to class j61    A: matrix returned by chi2 or mutual_information62    output: the new reduced term-document matrix and the new vocabulary dict63    """64    V, M = A.shape65    d = {}66    for m in range(M):67        k = 1...

GroupBasedMetric.py

Source:GroupBasedMetric.py

...8    def calculate(self, sensitive_attributes, df, debug=True):9        sum_ratio = 010        sum_difference = 011        for sensitive_attribute in sensitive_attributes:12            numerators = self.group_numerator.get_group_counts(sensitive_attribute, df)13            denominators = self.group_denominator.get_group_counts(sensitive_attribute, df)14            if 0 in numerators or 0 in denominators:15                sum_ratio += 016                sum_difference += 117            else:18                privileged = numerators[0] / (numerators[0] + denominators[0])19                unprivileged = numerators[1] / (numerators[1] + denominators[1])20                #print("{} - {}-{}".format(self.name, round(privileged, 2), round(unprivileged, 2)))21                sum_ratio += min(unprivileged / privileged, privileged / unprivileged)22                sum_difference += abs(privileged - unprivileged)23        ratio = round(sum_ratio / len(sensitive_attributes), 2)24        difference = round(sum_difference / len(sensitive_attributes), 2)25        self.ratios.append(ratio)26        self.differences.append(difference)27        if debug:28            print("{}_ratio - {}".format(self.name, ratio))29            print("{}_diff - {}".format(self.name, difference))30        return ratio, difference31class TP:32    @staticmethod33    def get_group_counts(sensitive_attribute, df):34        return [35            len(df[(df[sensitive_attribute] == 1) & (df["y"] == 1) & (df["y_pred"] == 1)]),36            len(df[(df[sensitive_attribute] == 0) & (df["y"] == 1) & (df["y_pred"] == 1)])37        ]38class FP:39    @staticmethod40    def get_group_counts(sensitive_attribute, df):41        return [42            len(df[(df[sensitive_attribute] == 1) & (df["y"] == 0) & (df["y_pred"] == 1)]),43            len(df[(df[sensitive_attribute] == 0) & (df["y"] == 0) & (df["y_pred"] == 1)])44        ]45class TN:46    @staticmethod47    def get_group_counts(sensitive_attribute, df):48        return [49            len(df[(df[sensitive_attribute] == 1) & (df["y"] == 0) & (df["y_pred"] == 0)]),50            len(df[(df[sensitive_attribute] == 0) & (df["y"] == 0) & (df["y_pred"] == 0)])51        ]52class FN:53    @staticmethod54    def get_group_counts(sensitive_attribute, df):55        return [56            len(df[(df[sensitive_attribute] == 1) & (df["y"] == 1) & (df["y_pred"] == 0)]),57            len(df[(df[sensitive_attribute] == 0) & (df["y"] == 1) & (df["y_pred"] == 0)])58        ]59class Sens:60    @staticmethod61    def get_group_counts(sensitive_attribute, df):62        return [63            len(df[df[sensitive_attribute] == 1]),64            len(df[df[sensitive_attribute] == 0])65        ]66class PosSens:67    @staticmethod68    def get_group_counts(sensitive_attribute, df):69        return [70            len(df[(df[sensitive_attribute] == 1) & (df["y_pred"] == 1)]),71            len(df[(df[sensitive_attribute] == 0) & (df["y_pred"] == 1)])...

solution.py

Source:solution.py

...9        )10        for group in groups11    ]12    return sum(intersections)13def get_group_counts():14    path = os.path.join(os.path.dirname(__file__), 'inputs.txt')15    with open(path, 'r') as file_handle:16        groups = file_handle.read().split('\n\n')17        return [18            get_unique_answers(groups),19            get_duplicate_answers(groups)20        ]21if __name__ == "__main__":22    unique, duplicate = get_group_counts()23    print(f'Solution to part a: {unique}')...

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