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How to use _group_string method in autotest

Best Python code snippet using autotest_python

csv_encoder.py

Source:csv_encoder.py

...26class SpreadsheetCsvEncoder(CsvEncoder):27    def _total_index(self, group, num_columns):28        row_index, column_index = group['header_indices']29        return row_index * num_columns + column_index30    def _group_string(self, group):31        result = '%s / %s' % (group['pass_count'], group['complete_count'])32        if group['incomplete_count'] > 0:33            result +=  ' (%s incomplete)' % group['incomplete_count']34        if 'extra_info' in group:35            result = '\n'.join([result] + group['extra_info'])36        return result37    def _build_value_table(self):38        value_table = [''] * self._num_rows * self._num_columns39        for group in self._response['groups']:40            total_index = self._total_index(group, self._num_columns)41            value_table[total_index] = self._group_string(group)42        return value_table43    def _header_string(self, header_value):44        return '/'.join(header_value)45    def _process_value_table(self, value_table, row_headers):46        total_index = 047        for row_index in xrange(self._num_rows):48            row_header = self._header_string(row_headers[row_index])49            row_end_index = total_index + self._num_columns50            row_values = value_table[total_index:row_end_index]51            self._append_output_row([row_header] + row_values)52            total_index += self._num_columns53    def encode(self):54        header_values = self._response['header_values']55        assert len(header_values) == 2...

present.py

Source:present.py

...22                    res['model'] = model_name23                results += metrics24        self.results = results25    26    def _group_string(self, res):27        # create a string from the concatenation of values defined in the group28        s = '#'.join('%s=%s' %(key, str(res[key])) for key in self.group if key in res)29        return s30    31    def filter_and_group(self):32        # filter results according to condition33        results_filtered = [res for res in self.results if not False in 34                            [res[key] == val for key, val in self.condition.items()]]35        # find the posible value combinations to group by36        group_strings = set( self._group_string(res) for res in results_filtered)37        # create a dictionary with results for each group38        results_dic = {group_string: {'x': [], 'y': []} for group_string in group_strings}39        for res in results_filtered:40            res_string = self._group_string(res)41            results_dic[res_string]['x'].append(res[self.x])42            results_dic[res_string]['y'].append(res[self.y])43        # sort the results of the group according to x44        for res_string, res_group in results_dic.items():45            x = np.array(res_group['x'])46            y = np.array(res_group['y'])47            sort_indices = np.argsort(x)48            res_group['x'] = x[sort_indices]49            res_group['y'] = y[sort_indices]50        return results_dic51        52    def plot(self, xlim=(), ylim=(), legend_loc=0):53        # get results_dic54        results_dic = self.filter_and_group()...

problem_3.py

Source:problem_3.py

1import numpy as np2from scipy import sparse3from sklearn.covariance import EmpiricalCovariance4from sklearn.datasets import load_svmlight_file5from sklearn.linear_model import LogisticRegression6from sklearn.preprocessing import MultiLabelBinarizer7from skmultilearn.problem_transform import LabelPowerset8from FINALS.pb3.trainBR import get_instance_f19class GroupClassifier:10    def __init__(self, label_groups, no_of_labels) -> None:11        super().__init__()12        self.no_of_labels = no_of_labels13        self.label_groups = label_groups14        self.classifiers = []15    def fit(self, features, labels):16        for label_group in self.label_groups:17            temp_labels = np.zeros((features.shape[0], self.no_of_labels), dtype=int)18            for ix, x in enumerate(features):19                for _label in label_group:20                    temp_labels[ix, _label] = labels[ix, _label]21            _classifier = LabelPowerset(LogisticRegression(solver='liblinear', penalty="l1", C=0.1,22                                                           multi_class='ovr',23                                                           tol=1e-8, max_iter=100))24            _classifier.fit(features, sparse.csr_matrix(temp_labels))25            self.classifiers.append(_classifier)26    def predict(self, features):27        predictions = np.zeros((features.shape[0], self.no_of_labels))28        for _classifier in self.classifiers:29            temp_pred = ([list(line.nonzero()[1]) for line in _classifier.predict(features)])30            for ix, _pred in enumerate(temp_pred):31                if _pred:32                    predictions[ix, _pred] = 133        return predictions34def create_groups(y):35    label_covariance = EmpiricalCovariance().fit(y).covariance_36    _groups = []37    _groups_set = set()38    for i in range(10):39        _group = []40        for j in range(10):41            if i != j and j > i and label_covariance[i][j] >= 0.0024:42                _group.append(j)43        _group_string = str(sorted(_group))44        if _group and _group_string not in _groups_set:45            _groups.append(_group)46            _groups_set.add(_group_string)47    return _groups48x_train, y_train = load_svmlight_file("all_train.csv", multilabel=True, n_features=30, zero_based=True)49x_test, y_test = load_svmlight_file("all_test.csv", multilabel=True, n_features=30, zero_based=True)50tran = MultiLabelBinarizer()51y_train2 = tran.fit_transform(y_train)52y_test2 = tran.fit_transform(y_test)53groups = create_groups(np.append(y_train2, y_test2, axis=0))54groups = [[0, 1, 2], [3, 4, 5, 9], [6, 7, 8]]55print("Groups=>", groups)56group_classifier = GroupClassifier(groups, 10)57group_classifier.fit(x_train, y_train2)58y_train_pred = group_classifier.predict(x_train)59tr1, tr2 = get_instance_f1(y_train_pred, y_train)60print("datasets\taccuracy\tf1")61print("train\t" + str(tr1) + "\t" + str(tr2))62y_test_pred = group_classifier.predict(x_test)63test1, test2 = get_instance_f1(y_test_pred, y_test)...

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