How to use partial_str method in avocado

Best Python code snippet using avocado_python

statistics.py

Source:statistics.py

1from src.constants import PREPROCCESSED_DATA_DIR, STATISTICS_BASE_DIR2from src.data.statistics import statistics3from src.data.class_infos import Instance as classes_info4import csv5import os6def compute_statistics(preprocessed_base_dir, flags="abcdefghi", output_dir=STATISTICS_BASE_DIR):7    first_line = "=" * 10 + " Sentiment Emojizer Data Information " + "=" * 10 + "\n"8    log_str = "=" * 10 + " Sentiment Emojizer Data Information " + "=" * 10 + "\n"9    10    if ('a' in flags):11        data_count = statistics.count_data(preprocessed_base_dir)12        partial_str = "Label's rows count:\n"13        csv_columns = ['label', 'data_count']14        csv_data =[]15        for key in data_count:16            class_name = classes_info.get_class_name(key)17            partial_str += f"{class_name}: {data_count[key]}\n"18            data = {"label": class_name, 'data_count':data_count[key]}19            csv_data.append(data)20        if(output_dir is not None):21            save_csv(csv_columns, csv_data, output_dir, "DataCount")22        log_str += partial_str23        log_str += "=" * (len(first_line) - 1) + "\n"24    if 'b' in flags:25        tokens_count = statistics.count_tokens(preprocessed_base_dir)26        partial_str = "Label's tokens count:\n"27        csv_columns = ['label', 'token_count']28        csv_data =[]29        for key in tokens_count:30            class_name = classes_info.get_class_name(key)31            partial_str += f"{class_name}: {tokens_count[key]}\n"32            data = {"label": class_name, 'token_count': tokens_count[key]}33            csv_data.append(data)34        if(output_dir is not None):35            save_csv(csv_columns, csv_data, output_dir, "TokenCount")36        log_str += partial_str37        log_str += "=" * (len(first_line) - 1) + "\n" 38    if 'c' in flags:39        tokens_count = statistics.unique_tokens(preprocessed_base_dir)40        partial_str = "Label's unique tokens count:\n"41        csv_columns = ['label', 'token_count']42        csv_data =[]43        for key in tokens_count:44            class_name = classes_info.get_class_name(key)45            partial_str += f"{class_name}: {len(tokens_count[key])}\n"46            data = {"label": class_name, 'token_count':tokens_count[key]}47            csv_data.append(data)48        if(output_dir is not None):49            save_csv(csv_columns, csv_data, output_dir, "UniqueTokenCount")50        log_str += partial_str51        log_str += "=" * (len(first_line) - 1) + "\n"52    if 'd' in flags:53        common_tokens = statistics.common_tokens(preprocessed_base_dir)54        partial_str = "Label's common tokens count:\n"55        csv_columns = ['label', 'common_tokens']56        csv_data =[]57        for key in common_tokens:58            id1, id2 =key59            class_name1 = classes_info.get_class_name(id1)60            class_name2 = classes_info.get_class_name(id2)61            partial_str += f"{class_name1}-{class_name2}: {len(common_tokens[key])}\n"62            data = {"label" : f"{class_name1}-{class_name2}", "common_tokens": len(common_tokens[key])}63            csv_data.append(data)64        if(output_dir is not None):65            save_csv(csv_columns, csv_data, output_dir, "CommonTokensCount")66        log_str += partial_str67        log_str += "=" * (len(first_line) - 1) + "\n"68    if 'e' in flags:69        uncommon_tokens = statistics.uncommon_tokens(preprocessed_base_dir)70        partial_str = "Label's uncommon tokens count:\n"71        csv_columns = ['label', 'uncommon_tokens']72        csv_data =[]73        for key in uncommon_tokens:74            id1, id2 =key75            class_name1 = classes_info.get_class_name(id1)76            class_name2 = classes_info.get_class_name(id2)77            partial_str += f"{class_name1}-{class_name2}: {len(uncommon_tokens[key])}\n"78            data = {"label" : f"{class_name1}-{class_name2}", "uncommon_tokens": len(uncommon_tokens[key])}79            csv_data.append(data)80        if(output_dir is not None):81            save_csv(csv_columns, csv_data, output_dir, "UncommonTokensCount")82        log_str += partial_str83        log_str += "=" * (len(first_line) - 1) + "\n"84    if 'f' in flags:85        uncommon_tokens = statistics.most_repeated_uncommon_tokens(preprocessed_base_dir)86        partial_str = "Label's most repeated uncommon tokens: (word, repeated_count)\n"87        for key in uncommon_tokens:88            class_name = classes_info.get_class_name(key)89            partial_str += f"{class_name}: {uncommon_tokens[key][:10]}\n"90        log_str += partial_str91        log_str += "=" * (len(first_line) - 1) + "\n"92    if 'g' in flags:93        common_tokens = statistics.common_tokens_relfreq(preprocessed_base_dir)94        partial_str = "Label's common tokens sorted by RelativeNormalizeFreq: (word, relfreq)\n"95        csv_columns = ['token', 'relfreq']96        for key in common_tokens:97            id1, id2 =key98            class_name1 = classes_info.get_class_name(id1)99            class_name2 = classes_info.get_class_name(id2)100            partial_str += f"{class_name1}-{class_name2}: {common_tokens[key][:10]}\n"101            csv_data = []102            for word, relfreq in common_tokens[key][:10]:103                csv_data.append({"token": word, "relfreq": relfreq}) 104            if (output_dir is not None):105                save_csv(csv_columns, csv_data, output_dir, f"{class_name1}-{class_name2}_RelFreq")106        log_str += partial_str107        log_str += "=" * (len(first_line) - 1) + "\n"108    if 'h' in flags:109        tokens = statistics.sorted_words_tfidf(preprocessed_base_dir)110        partial_str = "Label's tokens sorted by TF-IDF: (word, tfidf)\n"111        csv_columns = ['token', 'tfidf']112        for key in tokens:113            class_name = classes_info.get_class_name(key)114            partial_str += f"{class_name}: {tokens[key][:10]}\n"115            csv_data =[]116            for word, tfidf in tokens[key][:10]:117                csv_data.append({"token": word, "tfidf": tfidf}) 118            if output_dir is not None:119                save_csv(csv_columns, csv_data, output_dir, f"{class_name}_TFIDF")120        log_str += partial_str121        log_str += "=" * (len(first_line) - 1) + "\n"122    if 'i' in flags:123        #TODO plot histogram and save it124        pass125    return log_str126# compute_statistics(PREPROCCESSED_DATA_DIR)127def save_csv(csv_columns, csv_data, base_dir, name):128    if not os.path.exists(base_dir):129        os.makedirs(base_dir, exist_ok=True)130    131    path = os.path.join(base_dir, f"{name}.csv")132    with open(path, 'w') as csvfile:133        writer = csv.DictWriter(csvfile, fieldnames=csv_columns)134        writer.writeheader()135        for data in csv_data:136            writer.writerow(data)137    138import argparse139import json140parser = argparse.ArgumentParser()141parser.add_argument("--flags", type=str ,default="abcdefghijkl")142parser.add_argument("--input", type=str, default=PREPROCCESSED_DATA_DIR)143parser.add_argument("--out", type=str, default=None)144args = parser.parse_args()145if __name__ == "__main__":146    preprocessed_base_dir = args.input147    flags = args.flags148    output_dir = args.out149    # print(output_dir)...

00017_letter_combination_of_a_phone_number.py

Source:00017_letter_combination_of_a_phone_number.py

1from typing import *2class Solution:3    def letterCombinations(self, digits: str) -> List[str]:4        d = {5            "2": "abc",6            "3": "def",7            "4": "ghi",8            "5": "jkl",9            "6": "mno",10            "7": "pqrs",11            "8": "tuv",12            "9": "wxyz",13        }14        n = len(digits)15        result = []16        def rec(i, partial_str):17            nonlocal digits18            nonlocal n19            nonlocal result20            if i >= n:21                if len(partial_str) > 0:22                    result.append(partial_str)23                return24            digit = digits[i]25            candidates = d[digit]26            for char in candidates:27                rec(i + 1, partial_str + char)28        rec(0, "")29        return result30s = Solution()...

BOJ. 16916.py

Source:BOJ. 16916.py

1full_str = input()2partial_str = input()3# print(1 if partial_str in full_str else 0)4start_idx = 05end_idx = start_idx + len(partial_str) - 1 # idxëê¹ -1ì í´ì¤ì¼ í¨6str_to_check = full_str[start_idx: end_idx + 1]7for _ in range(len(full_str)-len(partial_str)+1): #8    if str_to_check == partial_str:9        print(1)10        break11    end_idx += 112    if end_idx == len(full_str):13        continue14    else:15        str_to_check = str_to_check[1:] + full_str[end_idx]16else:...

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