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How to use test_split method in yandex-tank

Best Python code snippet using yandex-tank

create_stats.py

Source:create_stats.py

1import os2from pathlib import Path3import numpy as np4def remove_zero_entries(d: dict):5    new_d = {k: v for k, v in d.items() if v != 0}6    return new_d7class DatasetSplit:8    def __init__(self, data_folder: Path,9                 examples_file_name: str,10                 definitions_file_name: str,11                 hypernyms_file_name: str,12                 labels_file_name: str,13                 domains_file_name: str = None):14        self.domain_dict = {15            'wnt': ["0"],16            'msh': ["1"],17            'ctl': ["2"],18            'cps': ["3"],19            'domains': ["1", "2", "3"]20        }21        self.examples = [line.split('\t') for line in22                         (data_folder / examples_file_name).read_text().strip('\n').split('\n')]23        self.defs = [line for line in24                     (data_folder / definitions_file_name).read_text().strip('\n').split('\n')]25        self.hyps = [line.split('\t') for line in26                     (data_folder / hypernyms_file_name).read_text().strip('\n').split('\n')]27        self.labels = [line for line in28                       (data_folder / labels_file_name).read_text().strip('\n').split('\n')]29        if domains_file_name is not None:30            self.domains = [line for line in31                            (data_folder / domains_file_name).read_text().strip('\n').split('\n')]32        else:33            self.domains = None34        self.targets = ([l[0] for l in self.examples])35    def num_total_examples(self, domain=None):36        if domain is not None:37            return len(list(np.where(np.isin(self.domains, self.domain_dict[domain]))[0]))38        return len(self.examples)39    def num_unique_senses(self, domain=None):40        if domain is not None:41            indices = list(np.where(np.isin(self.domains, self.domain_dict[domain]))[0])42            return len(set(list(np.array(self.defs)[indices])))43        return len(set(self.defs))44    def num_unique_targets(self, domain):45        if domain is not None:46            indices = list(np.where(np.isin(self.domains, self.domain_dict[domain]))[0])47            return len(set(list(np.array(self.targets)[indices])))48        return len(set(self.targets))49    def examples_per_sense(self, domain=None):50        """51        creates a list of number of examples per sense provided in the dataset52        outcome: ordered list by numbers of used examples per sense53        example: {1:10, 2:5, 4:15} means that for one sense 10 examples have been provided,54        for 2 senses 5 examples have been provided, for 4 senses have been provided and so on55        """56        if domain is not None:57            indices = list(np.where(np.isin(self.domains, self.domain_dict[domain]))[0])58            domain_defs = list(np.array(self.defs)[indices])59            examples_per_sense = [domain_defs.count(x) for x in set(domain_defs)]60        else:61            examples_per_sense = [self.defs.count(x) for x in set(self.defs)]62        count_examples_per_sense = remove_zero_entries({i: examples_per_sense.count(i) for i in range(1, 100)})63        return count_examples_per_sense64    def examples_per_target(self, domain: list = None):65        if domain is not None:66            indices = list(np.where(np.isin(self.domains, self.domain_dict[domain]))[0])67            domain_tgts = list(np.array(self.targets)[indices])68            nof_examples_per_target = [domain_tgts.count(x) for x in set(domain_tgts)]69        else:70            nof_examples_per_target = [self.targets.count(x) for x in set(self.targets)]71        count_examples_per_target = remove_zero_entries({i: nof_examples_per_target.count(i) for i in range(1, 100)})72        return count_examples_per_target73    def percent_positive_examples(self, domain=None):74        if domain is not None:75            indices = list(np.where(np.isin(self.domains, self.domain_dict[domain]))[0])76            return list(np.array(self.labels)[indices]).count('T') / len(indices)77        return self.labels.count('T') / len(self.labels)78    def target_overlap(self, overlap_dataset, second_dataset=None, domain=None):79        if domain is not None:80            indices = list(np.where(np.isin(self.domains, self.domain_dict[domain]))[0])81        else:82            indices = list(range(len(self.targets)))83        if second_dataset is not None:84            unique_targets_a = set(np.array(self.targets)[indices]) | set(second_dataset.targets)85        else:86            unique_targets_a = set(np.array(self.targets)[indices])87        overlap_targets = unique_targets_a & set(overlap_dataset.targets)88        return len(overlap_targets) / len(overlap_dataset.targets)89    def sense_overlap(self, overlap_dataset, second_dataset=None, domain=None):90        if domain is not None:91            indices = list(np.where(np.isin(self.domains, self.domain_dict[domain]))[0])92        else:93            indices = list(range(len(self.defs)))94        if second_dataset is not None:95            unique_defs_a = set(np.array(self.defs)[indices]) | set(second_dataset.defs)96        else:97            unique_defs_a = set(np.array(self.defs)[indices])98        overlap_defs = unique_defs_a & set(overlap_dataset.defs)99        return len(overlap_defs) / len(overlap_dataset.defs)100    def instance_overlap(self, overlap_dataset, second_dataset=None):101        instances_self = set([e + d for e, d in zip(self.examples, self.defs)])102        instances_overlap = set([e + d for e, d in zip(overlap_dataset.examples, overlap_dataset.defs)])103        if second_dataset is not None:104            instances_second = set([e + d for e, d in zip(second_dataset.examples, second_dataset.defs)])105            unique_instances_a = instances_self | instances_second106        else:107            unique_instances_a = instances_self108        overlap_defs = unique_instances_a & set(instances_overlap)109        return len(overlap_defs) / len(instances_overlap)110def get_pos_label_percent(folder: str, prefix: str, label_indices: list = None):111    labels = open(os.path.join(folder, prefix + 'labels.txt')).read().split("\n")112    if label_indices is not None:113        labels = list(np.array(labels)[label_indices])114    return labels.count('T') / len(labels)115def get_unique_senses(folder: str, prefix: str, definition_indices: list = None):116    definitions = open(os.path.join(folder, prefix + 'definitions.txt')).read().split("\n")117    if definition_indices is not None:118        definitions = list(np.array(definitions)[definition_indices])119    return len(set(definitions))120def get_total_number(folder: str, prefix: str, example_indices: list = None):121    examples = open(os.path.join(folder, prefix + 'definitions.txt')).read().split("\n")122    if example_indices is not None:123        examples = list(np.array(examples)[example_indices])124    return len(examples)125def print_stats(dataset: DatasetSplit, domain=None):126    print('total number', dataset.num_total_examples(domain=domain))127    print('number unique senses', dataset.num_unique_senses(domain=domain))128    print('number unique targets', dataset.num_unique_targets(domain=domain))129    print('percent positive labels', dataset.percent_positive_examples(domain=domain))130if __name__ == '__main__':131    location = Path(__file__).parent.parent132    train_folder = location / 'data' /'en' / 'Training'133    dev_folder = location / 'data' /'en' /'Development'134    test_folder = location / 'data' /'en' / 'Test'135    train_split = DatasetSplit(data_folder=Path(train_folder),136                               examples_file_name='train_examples.txt',137                               definitions_file_name='train_definitions.txt',138                               hypernyms_file_name='train_hypernyms.txt',139                               labels_file_name='train_labels.txt')140    dev_split = DatasetSplit(data_folder=Path(dev_folder),141                             examples_file_name='dev_examples.txt',142                             definitions_file_name='dev_definitions.txt',143                             hypernyms_file_name='dev_hypernyms.txt',144                             labels_file_name='dev_labels.txt')145    # test_split = DatasetSplit(data_folder=Path(test_folder),146    #                          examples_file_name='test_examples.txt',147    #                          definitions_file_name='test_definitions.txt',148    #                          hypernyms_file_name='test_hypernyms.txt',149    #                          labels_file_name='test_labels.txt',150    #                          domains_file_name='test_domains.txt')151    print(train_split.target_overlap(dev_split))152    # print(train_split.target_overlap(test_split))153    # print(train_split.target_overlap(test_split, dev_split))154    print()155    # print(test_split.target_overlap(train_split))156    # print(test_split.target_overlap(train_split, domain='wnt'))157    # print(test_split.target_overlap(train_split, domain='msh'))158    # print(test_split.target_overlap(train_split, domain='ctl'))159    # print(test_split.target_overlap(train_split, domain='cps'))160    print()161    print(train_split.sense_overlap(dev_split))162    # print(train_split.sense_overlap(test_split))163    # print(train_split.sense_overlap(test_split, dev_split))164    print()165    # print(test_split.sense_overlap(train_split))166    # print(test_split.sense_overlap(train_split, domain='wnt'))167    # print(test_split.sense_overlap(train_split, domain='msh'))168    # print(test_split.sense_overlap(train_split, domain='ctl'))169    # print(test_split.sense_overlap(train_split, domain='cps'))170    print('\n##### train #####')171    print_stats(train_split)172    print('\n##### dev #####')173    print_stats(dev_split)174    # print('\n##### test #####')175    # print_stats(test_split)176    #177    # print('\n##### wnt')178    # print_stats(test_split, 'wnt')179    #180    # print('\n##### msh')181    # print_stats(test_split, 'msh')182    #183    # print('\n##### ctl')184    # print_stats(test_split, 'ctl')185    #186    # print('\n##### cps')187    # print_stats(test_split, 'cps')188    #189    # print('\n##### all domains')...

boston_housing.py

Source:boston_housing.py

1"""Boston housing price regression dataset.2"""3from __future__ import absolute_import4from __future__ import division5from __future__ import print_function6from ..utils.data_utils import get_file7import numpy as np8def load_data(path='boston_housing.npz', test_split=0.2, seed=113):9    """Loads the Boston Housing dataset.10    # Arguments11        path: path where to cache the dataset locally12            (relative to ~/.keras/datasets).13        test_split: fraction of the data to reserve as test set.14        seed: Random seed for shuffling the data15            before computing the test split.16    # Returns17        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.18    """19    assert 0 <= test_split < 120    path = get_file(path,21                    origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz',22                    file_hash='f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')23    f = np.load(path)24    x = f['x']25    y = f['y']26    f.close()27    np.random.seed(seed)28    indices = np.arange(len(x))29    np.random.shuffle(indices)30    x = x[indices]31    y = y[indices]32    x_train = np.array(x[:int(len(x) * (1 - test_split))])33    y_train = np.array(y[:int(len(x) * (1 - test_split))])34    x_test = np.array(x[int(len(x) * (1 - test_split)):])35    y_test = np.array(y[int(len(x) * (1 - test_split)):])...

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