How to use _get_data_from_file method in avocado

Best Python code snippet using avocado_python

dataset.py

Source:dataset.py

1import numpy as np2import mixture_multivariateBernoulli.config as config3def _get_data_from_file(args):4    with np.load(args['data_file']) as parameters:5        all_outcomes = parameters['all_outcomes']6        prob_of_outcomes = parameters['prob_of_outcomes']7        data = parameters['train_data']8        data_probs = parameters['train_data_probs']9        if config.random_data:10            rnd_prm = np.random.permutation(len(data))11            data = data[rnd_prm]12            # if not(data_probs == None):13            # if (data_probs is not None):14            if len(data_probs.shape) > 0:15                data_probs = data_probs[rnd_prm]16        train_data = data[0:args['train_size']]17        valid_data = data[args['train_size']:(args['train_size'] + args['valid_size'])]18        # if not(data_probs == None):19        # if data_probs is not None:20        if len(data_probs.shape) > 0:21            train_data_probs = data_probs[0:args['train_size']]22            valid_data_probs = data_probs[args['train_size']:(args['train_size'] + args['valid_size'])]23        else:24            train_data_probs = None25            valid_data_probs = None26        if args['test_size'] == 'FULL_TEST':27            test_data = all_outcomes28            test_data_probs = prob_of_outcomes29        else:30            data = parameters['test_data']31            data_probs = parameters['test_data_probs']32            if config.random_data:33                rnd_prm = np.random.permutation(len(data))34                data = data[rnd_prm]35                # if not(data_probs == None):36                # if data_probs is not None:37                if len(data_probs.shape) > 0:38                    data_probs = data_probs[rnd_prm]39            test_data = data[0:args['test_size']]40            # if not(data_probs == None):41            # if data_probs is not None:42            if len(data_probs.shape) > 0:43                test_data_probs = data_probs[0:args['test_size']]44            else:45                test_data_probs = None46    return {'train_data': train_data,47            'train_data_probs': train_data_probs,48            'valid_data': valid_data,49            'valid_data_probs': valid_data_probs,50            'test_data': test_data,51            'test_data_probs': test_data_probs}52def get_data(args):53    if args['data_name'] == 'grid':54        args['data_file'] = 'datasets/grid' + str(args['height']) + 'by' + str(args['width']) + '.npz'55        return _get_data_from_file(args)56    elif args['data_name'] == 'Boltzmann':57        args['data_file'] = 'datasets/Boltzman_' + str(args['n']) + '&' + str(args['m']) + '.npz'58        return _get_data_from_file(args)59    elif args['data_name'].startswith('mnist'):60        if args['digit'] == 'All':61            tr = args['train_size']62            va = args['valid_size']63            te = args['test_size']64            args['train_size'] = args['train_size'] // 1065            args['valid_size'] = args['valid_size'] // 1066            args['test_size'] = args['test_size'] // 1067            args['data_file'] = 'datasets/binary_mnist_' + str(0) + '.npz'68            res = _get_data_from_file(args)69            for d in range(1, 10):70                args['data_file'] = 'datasets/binary_mnist_' + str(d) + '.npz'71                tmp = _get_data_from_file(args)72                res['train_data'] = np.concatenate([res['train_data'], tmp['train_data']], axis=0)73                res['valid_data'] = np.concatenate([res['valid_data'], tmp['valid_data']], axis=0)74                res['test_data'] = np.concatenate([res['test_data'], tmp['test_data']], axis=0)75            np.random.shuffle(res['train_data'])76            np.random.shuffle(res['valid_data'])77            np.random.shuffle(res['test_data'])78            args['train_size'] = tr79            args['valid_size'] = va80            args['test_size'] = te81            return res82        else:83            raise Exception("ERROR: mnist should not be run for just one digit")84            # args['data_file'] = 'datasets/binary_mnist_'+ str(args['digit']) + '.npz'85            # return _get_data_from_file(args)86    elif args['data_name'].startswith('ocr'):87        tr = args['train_size']88        va = args['valid_size']89        te = args['test_size']90        args['train_size'] = tr // 2091        args['valid_size'] = va // 2092        args['test_size'] = te // 2093        args['data_file'] = 'datasets/ocr_' + str(config.ocr_characters[0]) + '.npz'94        res = _get_data_from_file(args)95        for d in range(1, 20):96            args['train_size'] = tr // 2097            args['valid_size'] = va // 2098            args['test_size'] = te // 2099            args['data_file'] = 'datasets/ocr_' + str(config.ocr_characters[d]) + '.npz'100            tmp = _get_data_from_file(args)101            res['train_data'] = np.concatenate([res['train_data'], tmp['train_data']], axis=0)102            res['valid_data'] = np.concatenate([res['valid_data'], tmp['valid_data']], axis=0)103            res['test_data'] = np.concatenate([res['test_data'], tmp['test_data']], axis=0)104        np.random.shuffle(res['train_data'])105        np.random.shuffle(res['valid_data'])106        np.random.shuffle(res['test_data'])107        #################### in baayad avaz beshe!!!108        args['train_size'] = tr109        args['valid_size'] = va110        args['test_size'] = te111        return res112    elif args['data_name'] == 'k_sparse':113        args['data_file'] = 'datasets/k_sparse_' + str(args['n']) + '_' + str(args['sparsity_degree']) + '.npz'114        return _get_data_from_file(args)115    elif args['data_name'] == 'rcv1':116        args['data_file'] = 'datasets/rcv1.npz'117        return _get_data_from_file(args)118    elif args['data_name'] == 'BayesNet':119        args['data_file'] = 'datasets/BayesNet_' + str(args['n']) + '_' + str(args['par_num']) + '.npz'120        return _get_data_from_file(args)121    else:...

data_getter.py

Source:data_getter.py

...5    TOWN_DATA = CURRENT_DIR + "/../../preprocessing_data/towns.txt"6    CSV_TRAIN_DATA = CURRENT_DIR + "/../../data/Test_rev1.csv"7    UNIQUE_JOB_DATA = CURRENT_DIR + "/../../preprocessing_data/job_roles_unique.txt"8    @staticmethod9    def _get_data_from_file(filename, lowercase=True):10        with open(filename, 'r') as f:11            read_data = []12            for row in f.readlines():13                row_data = row.lower() if lowercase else row14                read_data.append(row_data.replace("\n", ""))15            return read_data16    @classmethod17    def get_towns(cls, lowercase=True):18        """19        get list of towns above 1000020        :return:list of towns21        """22        return DataGetter._get_data_from_file(cls.TOWN_DATA, lowercase)23    @classmethod24    def get_stop_word_inc_cities(cls, lowercase=True):25        """26        get list of towns above 10000 pop + stop words27        :return:list of towns28        """29        return DataGetter._get_data_from_file(cls.STOP_WORDS_WITH_CITIES, lowercase)30    @classmethod31    def get_unique_job_roles(cls, lowercase=True):32        """33        Get list of unique jobs as specifed by british achieve34        """...

cpc6128.py

Source:cpc6128.py

...4from memory import Memory, RomChunk, UpperRomChunk5from instruction import Cpu6import cpcio7import screen8def _get_data_from_file(fname):9    with open(fname, 'rb') as fin:10        data = fin.read()11    return data12def _build_memory(screen):13    """ Initialize CPC464 memory map """14    memory = Memory(screen)15    memory.add_chunk(0x0000, RomChunk(_get_data_from_file('6128L.rom')))16    memory.add_chunk(0xC000, UpperRomChunk(0, _get_data_from_file('6128U-basic.rom')))17    memory.apply_ram_map(0)18    memory.dump_map()19    return memory20def _build_cpu(memory_map):21    return Cpu(memory_map)22class Cpc6128(object):23    def __init__(self):24        self.screen = scr = screen.Screen()25        self.memory = _build_memory(self.screen)26        self.cpu = _build_cpu(self.memory)27        self.gatearray = cpcio.GateArray(self.cpu, self.memory, scr)28        self.ppi8255 = cpcio.PPI8255(self.cpu)29        self.printer = cpcio.PrinterPort(self.cpu)30        self.crtc = cpcio.Crtc(self.cpu, scr)...

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