How to use in_each method in Sure

Best Python code snippet using sure_python

convert.py

Source:convert.py

...32                else:33                    setattr(tgt, key, src[key])34        else:35            if recursive:36                tgt = util.tool.in_each(src, lambda x : Common.to_args(x, recursive))37            else:38                tgt = src39        return tgt4041class List(object):42    def to_bert_msk_and_idx(src, source_len, max_len, bias):43        idx = util.tool.in_each(src, lambda x : [(1, 1)] + x[1 :])44        idx = util.tool.in_each(idx, lambda x : util.tool.idx_extender(x, max_len, 0, bias = bias))45        msk = util.tool.in_each(source_len, lambda x : [1] * x + [0] * (max_len - x))46        return idx, msk4748    def to_str(src):49        if isinstance(src, str):50            return src51        out = ""52        for ele in src:53            out += str(ele)54        return out5556    def to_bert_token_idx(src, tokener, max_len = 256):57        x = tokener.convert_tokens_to_ids(tokener.tokenize(util.convert.List.to_str(src)))58        # space maybe convert into anything59        if src == " ":60            x = [100]61        return x[:min(len(x),max_len)]6263    def to_bert_info(inputs, tokener, pad, cls, device, max_len = 256):64        raw_source = util.tool.in_each(inputs, lambda x : util.convert.List.to_bert_token_idx(x, tokener, max_len))65        source_len = util.tool.in_each(raw_source, lambda x : len(x) + 1)66        source, pad_idx = util.tool.pad([[[cls]] * len(inputs), raw_source], pad)67        if source == []:68            mmax_len = 069        else:70            mmax_len = len(source[0])71        source_idx, source_msk = util.convert.List.to_bert_msk_and_idx(pad_idx, source_len, mmax_len, -1)72        return (torch.Tensor(source).long().to(device), torch.Tensor(source_idx).long().to(device), torch.Tensor(source_msk).long().to(device)), source_len7374    def to_xlm_info(inputs, tokener, pad, cls, device, max_len = 256):75        raw_source = util.tool.in_each(inputs, lambda x : util.convert.List.to_bert_token_idx(x, tokener, max_len))76        source_len = util.tool.in_each(raw_source, lambda x : len(x) + 1)77        source, pad_idx = util.tool.pad([[[cls]] * len(inputs), raw_source], pad)78        if source == []:79            mmax_len = 080        else:81            mmax_len = len(source[0])82        source_idx, source_msk = util.convert.List.to_bert_msk_and_idx(pad_idx, source_len, mmax_len, -1)83        return torch.Tensor(source).long().to(device), torch.Tensor(source_msk).long().to(device)8485    def to_bert_info2(inputs1, inputs2, tokener, pad, cls, sep, device, max_len = 256):86        raw_source1 = util.tool.in_each(inputs1, lambda x : util.convert.List.to_bert_token_idx(x, tokener, max_len))87        raw_source2 = util.tool.in_each(inputs2, lambda x : util.convert.List.to_bert_token_idx(x, tokener, max_len))88        raw_source = util.tool.in_each(zip(raw_source1, raw_source2), lambda x : x[0] + [sep] + x[1] + [sep])89        source_len = util.tool.in_each(raw_source, lambda x : len(x) + 1)90        source, pad_idx = util.tool.pad([[[cls]] * len(raw_source), raw_source], pad)91        if source == []:92            max_len = 093        else:94            max_len = len(source[0])95        source_idx, source_msk = util.convert.List.to_bert_msk_and_idx(pad_idx, source_len, max_len, -1)
...

identify_confused.py

Source:identify_confused.py

1import pandas as pd2from xlwt import Workbook3import os4current_run_folder = "resnet50_0.001/"5resnet_files_list = os.listdir(current_run_folder)6df_dict = {}7for each in resnet_files_list:8    data_frame = pd.read_csv(current_run_folder + each)9    data_frame = data_frame.drop(index=80)10    data_frame = data_frame.drop("other", axis=1)11    classes = data_frame['Ground_Truth'].tolist()12    row_list = []13    class_confusion_rate = {}14    for out_each in classes:15        for in_each in range(len(classes)):16            if out_each != classes[in_each] and data_frame[out_each][in_each] > 0:17                dict_val = {18                    'predicted_class': out_each,19                    'actual_class': classes[in_each],20                    'value': data_frame[out_each][in_each]21                }22                row_list.append(dict_val)23                if out_each not in class_confusion_rate:24                    class_confusion_rate[out_each] = 025                if classes[in_each] not in class_confusion_rate:26                    class_confusion_rate[classes[in_each]] = 027                class_confusion_rate[out_each] += data_frame[out_each][in_each]28                class_confusion_rate[classes[in_each]] += data_frame[out_each][in_each]29                print(out_each, classes[in_each], data_frame[out_each][in_each])30    output_dataframe = pd.DataFrame(row_list, columns=['actual_class', 'predicted_class', 'value'])31    output_dataframe = output_dataframe.sort_values(by=['value'], ascending=False)32    output_dataframe = output_dataframe.reset_index(drop=True)33    max_val, min_val = output_dataframe["value"].max(), output_dataframe["value"].min()34    output_dataframe["normalized"] = (output_dataframe["value"] - min_val) / (max_val - min_val)35    output_dataframe["normalized"] = output_dataframe["normalized"].round(decimals=2)36    # output_dataframe.to_csv("confused_pairs.csv", index=False)37    output_dataframe1 = pd.DataFrame.from_dict(class_confusion_rate, orient='index', columns=["confusions_involved"])38    output_dataframe1 = output_dataframe1.sort_values(by=['confusions_involved'], ascending=False)39    # output_dataframe1 = output_dataframe1.reset_index(drop=True)40    df_dict[each.replace("confusion_matrix-", "")] = output_dataframe41    df_dict[each.replace("confusion_matrix-", "") + "_confusion_rates"] = output_dataframe142    print(f'finished {each}')43with pd.ExcelWriter(current_run_folder.replace("/", "") + '.xlsx', engine="openpyxl", mode='w') as writer:44    for key, value in df_dict.items():...

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