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How to use set_combined method in tappy

Best Python code snippet using tappy_python

__init__.py

Source:__init__.py

1from functools import reduce2from operator import eq, ne, or_, and_3from typing import Dict, Union, Sequence, Iterable4import numpy as np5import torch6from sklearn.metrics import roc_auc_score, roc_curve7from sklearn.preprocessing import label_binarize8# todo wrap the class-mapping from multi_class_roc_auc9def is_array_class(array):10    return isinstance(array, np.ndarray) or isinstance(array, torch.Tensor)11def validate_array_class(array: Union[torch.Tensor, np.ndarray]):12    assert is_array_class(array), f"Type disagree: expect" \13        f"np.ndarray or torch.Tensor, got: {type(array)}"14def array_to_numpy(array: Union[torch.Tensor, np.ndarray]):15    validate_array_class(array), f"not ndarray or tensor. {type(array)}"16    if isinstance(array, np.ndarray):17        return array18    return array.cpu().detach().numpy()19def to_long(array: Union[torch.Tensor, np.ndarray]):20    validate_array_class(array)21    if not isinstance(array, torch.Tensor):22        type_field = 'astype'23        dtype = np.int6424    else:25        type_field = 'type'26        dtype = torch.LongTensor27    return getattr(array, type_field)(dtype)28def is_sequence_alike(obj):29    return isinstance(obj, Sequence) or isinstance(obj, Iterable) or is_array_class(obj)30def validate_parititon_structure(label_group):31    assert is_sequence_alike(label_group), f"1st level structure must be sequence-alike. Got{type(label_group)}"32    for x in label_group:33        assert is_sequence_alike(x), f"2nd level must be sequence-alike. Got{type(x)}"34def label_group_elements(label_group: Sequence[Sequence[int]]):35    set_list = [set(x) for x in label_group]36    set_combined = reduce(or_, set_list)37    return set_list, set_combined38def is_disjoint_partition(label_group: Sequence[Sequence[int]]):39    validate_parititon_structure(label_group)40    set_list, set_combined = label_group_elements(label_group)41    sum_len = np.asarray([len(set_obj) for set_obj in set_list]).sum()42    length_combined = len(set_combined)43    # if length of combined set mismatch sum_len, then there are repetitive elements44    return length_combined == sum_len45def validate_disjoint_partition(label_group: Sequence[Sequence[int]]):46    assert is_disjoint_partition(label_group), f"Not Disjoint: {label_group}"47def is_label_defined(labels: Union[np.ndarray, torch.Tensor], label_group: Sequence[Sequence[int]]):48    set_list, set_combined = label_group_elements(label_group)49    labels = array_to_numpy(labels)50    check_result = np.asarray([label_value in set_combined for label_value in labels])51    bool_all_defined = check_result.all()52    set_undefined_labels = set(labels[~check_result])53    return bool_all_defined, set_undefined_labels54def validate_label_definition(labels: Union[np.ndarray, torch.Tensor], label_group: Sequence[Sequence[int]]):55    is_all_defined, set_undefined_labels = is_label_defined(labels, label_group)56    assert is_all_defined, f"Unexpected Label Encounted: {set_undefined_labels}"57def label_encode_by_partition(labels: Union[np.ndarray, torch.Tensor], label_group: Sequence[Sequence[int]]):58    # precondition_1: disjoint59    validate_disjoint_partition(label_group)60    # precondition_2: all labels must be defined in label_group61    validate_label_definition(labels, label_group)62    # x in labels as 0/1 assignment. idx as the target encoded label value defined by the order of label_group63    # if pack/partition in label_group are disjoint, and there are no unexpected labels64    # that are not defined in the label_group then the corresponding65    # idx * (x in pack) across each pack (column) in label_group should have exactly one non-zero value66    # use sum to get the final encoded label.67    labels = array_to_numpy(labels)68    final_label = np.asarray([[idx * (x in pack) for x in labels] for idx, pack in enumerate(label_group)])\69        .sum(axis=0)70    return final_label71def label_binarize_group(labels: Union[np.ndarray, torch.Tensor], anchor_group, anchor_positive: bool):72    if not isinstance(anchor_group, np.ndarray) or not isinstance(anchor_group, torch.Tensor) \73            or not isinstance(anchor_group, Sequence):74        anchor_group = np.atleast_1d(anchor_group)75    if isinstance(labels, torch.Tensor):76        anchor_group = torch.from_numpy(anchor_group).type("torch.LongTensor")77    labels = to_long(labels)78    anchor_group = to_long(anchor_group)79    if not anchor_positive:80        connector = and_81        comparator = ne82    else:83        connector = or_84        comparator = eq85    # mask array is a tuple of arrays, where each array performs the comparison86    # between label and anchor element87    # for positive anchor --> comparator is eq, as any label == anchor yields 1, and 0 otherwise.88    # for negative anchor --> use ne: label != anchor yields 1, and 0 otherwise.89    # connector: for positive anchor, label is mapped to 1 if it matches any of the anchor element90    # for negative anchor: not [or] ==> and.91    # not ( label == anchor1 or label== anchor2... ) ==> label != anchor1 and label!= anchor2 ...92    mask_array = tuple(comparator(labels, x) for x in anchor_group)93    labels = reduce(connector, mask_array)94    return labels95    # if anchor_positive, then True=Positive, otherwise, True=Neg96def array_copy_heper(array: Union[torch.Tensor, np.ndarray]):97    if not isinstance(array, torch.Tensor):98        copy_func = array.copy99    else:100        copy_func = array.clone101    return copy_func()102# buggy. There appears to be an existing implementation in sklearn/metrics/ranking/label_binarize103def label_binarize_vs_rest(labels: Union[np.ndarray, torch.Tensor],104                           anchor_class: int, anchor_positive: bool = True):105    assert isinstance(labels, np.ndarray) or isinstance(labels, torch.Tensor), f"Type disagree: expect" \106        f"np.ndarray or torch.Tensor, got: {type(labels)}"107    labels = array_copy_heper(labels)108    offset_subtract = 1 if anchor_positive else -1109    comparator = eq if anchor_positive else ne110    labels[labels != anchor_class] = anchor_class - offset_subtract111    # assumption that labels here contains two unique values are false112    # labels = (labels == labels.max())113    labels = comparator(labels, anchor_class)114    labels = to_long(labels)115    return labels116def roc_auc_result_dict(auc, fpr, tpr, threshold):117    return {118        "auc": auc,119        "fpr": fpr,120        "tpr": tpr,121        "thresholds": threshold122    }123def roc_auc_helper(y_true, y_pred, original_class_id, roc_auc_dict=None):124    if roc_auc_dict is None:125        roc_auc_dict = dict()126    auc = roc_auc_score(y_true, y_pred)127    fpr, tpr, thresholds = roc_curve(y_true, y_pred)128    pk_data = roc_auc_result_dict(auc, fpr, tpr, thresholds)129    roc_auc_dict[original_class_id] = pk_data130    return roc_auc_dict131def multi_class_roc_auc_vs_all(y_true: Union[np.ndarray, Sequence[int]],132                               y_pred_all_classes: Union[np.ndarray, Sequence[int]],133                               positive_classes: Union[np.ndarray, Sequence[int]]134                               ) -> Dict[int, Dict[str, Union[float, np.ndarray]]]:135    roc_auc_dict: Dict[int, Dict] = dict()136    y_true = np.atleast_1d(y_true)137    y_pred_all_classes = np.atleast_2d(y_pred_all_classes)138    assert y_pred_all_classes.shape[0] == y_true.size, f"# of data point disagree." \139        f" {y_pred_all_classes.shape[0]} vs. {y_true.size}"140    if y_pred_all_classes.shape[0] == 1:141        raise ValueError(f"y_pred_all_classes has only one row. Expected to be >=2")142    label_binary_array = label_binarize(positive_classes, y_true)143    score_merged = y_pred_all_classes.shape[1] <= 2144    for original_class, y_true in zip(positive_classes, label_binary_array):145        if not score_merged:146            y_score = np.atleast_1d(y_pred_all_classes[:, original_class]).squeeze()147        else:148            y_score = np.atleast_1d(y_pred_all_classes[:, -1]).squeeze()149        roc_auc_dict = roc_auc_helper(y_true, y_score, original_class, roc_auc_dict=roc_auc_dict)...

solution.py

Source:solution.py

1import timeit2import numpy as np3from collections  import defaultdict4DO_TEST = 05def read_file(filename):6    data = []7    with open(filename, 'r') as file:8        data_string = file.read()9    10    for line in data_string.split('\n'):11        combs =  line.split('|')[0]12        outs = line.split('|')[1]13        data.append((combs, outs))14    15    return data16def do_part1(file):17    data = read_file(file)18    one = 019    four = 020    seven = 021    eight = 022    for line in data:23        for out in line[1].strip().split():24            if len(out) == 2: one += 125            if len(out) == 4 : four += 126            if len(out) == 3 : seven += 127            if len(out) == 7 : eight += 1 28    # print(one, four, seven, eight)29    result = one + four + seven + eight30    print(f"part 1 = {result}")31    32def do_part2(file):33    data = read_file(file)34    unknown = []35    sum = 036    for line in data:37        # get the combinations38        digits = defaultdict(str)39        for combs in line[0].strip().split():40            if len(combs) == 2:41                digits[1] = combs42            elif len(combs) == 3:43                digits[7] = combs44            elif len(combs) == 4:45                digits[4]= combs46            elif len(combs) == 7:47                digits[8] = combs48            else:49                unknown.append(combs)50        # detect unknown51        for combs in unknown:52            if len(combs) == 6:53                combined = combs + digits[4]54                # set_combined = remove_dups(combined)55                if count_unique(combined) == 2:56                    digits[9] = combs57                else:58                    combined = combs + digits[4] + digits[7]59                    # set_combined = list(set(combined))60                    if count_unique(combined) == 2:61                        digits[6] = combs62                    else:63                        digits[0] = combs64            if len(combs) == 5:65                combined = combs + digits[1]             66                # set_combined = list(set(combined))67                if count_unique(combined)== 3:68                    digits[3]= combs69                else:70                    combined = combs +digits[4]71                    # set_combined = list(set(combined))72                    if count_unique(combined) == 3:73                        digits[5] = combs74                    else:75                        digits[2] = combs76        # convert77        str_num = ""78        for out in  line[1].strip().split():79            str_num += str(get_key(digits,out))80        81        sum += int(str_num)82                    83        84    result = sum85    print(f"part 2 = {result}")86def get_key(dict, val):87    for key, value in dict.items():88        combined = value + val89        if count_unique(combined) == 0:90             return key91    return -192def count_unique(str_val):93    single = []94    double = []95    for let in str_val:96        if let in single:97            if let not in double:98                double.append(let)99        else:100            single.append(let)101    for d in double:102        single.remove(d)103    return len(single)104# main105test = ("test.txt")106real = ("input.txt")107data = real108if DO_TEST == 1:109    data = test110print('results : ')111starttime_1 = timeit.default_timer()112do_part1(data)113endtime_1 = timeit.default_timer()114starttime_2 = timeit.default_timer()115do_part2(data)116endtime_2 = timeit.default_timer()117print('performance :')118print('part1 = ', (endtime_1-starttime_1), " sec")...

core.py

Source:core.py

...41        return Criteria(42            f"({str(self)} or {str(criteria)})",43            lambda *kargs, **kwargs: self.fn(*kargs, **kwargs)44            or criteria.fn(*kargs, **kwargs),45        ).set_combined()46    def __and__(self, criteria):47        return Criteria(48            f"({str(self)} and {str(criteria)})",49            lambda *kargs, **kwargs: self.fn(*kargs, **kwargs)50            and criteria.fn(*kargs, **kwargs),51        ).set_combined()52    def __rshift__(self, ref: Storage):53        self.ref = ref54        return self55    def __invert__(self):56        return Criteria(57            f"(not {str(self)})", lambda *kargs, **kwargs: not self.fn(*kargs, **kwargs)58        ).set_combined()59    def set_combined(self):60        self.combined = True61        return self62def fn_to_criteria(fn):63    def fn_to_criteria_wrapper(*kargs, **kwargs):64        return Criteria(fn.__name__, fn(*kargs, **kwargs), *kargs, **kwargs)65    return fn_to_criteria_wrapper66@fn_to_criteria67def expected(x, optional):...

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