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How to use query_dictionary method in pyresttest

Best Python code snippet using pyresttest_python

main.py

Source:main.py

1# order of functions2# 1. collectDocuments(path) -> Collect the documents to be indexed.3#       1.01 tokenization(document) -> Tokenize the text.4#   1.1 Index the documents that each term occurs in5# 2. processingQuery(query) -> taking query input6#       2.02 postfixQuery(query_stream) -> converting infix boolean query to postfix7#       2.03 postfixEvaluation()8#           2.03.1 intersection and union of postings lists9import os10import glob11import re12from contractions import contractions13from porterstemmer import PorterStemmer14from stack import Stack15sizes_of_stemmed_lists = []16term_document_dictionary = {}17list_of_documentID = []18query_dictionary = {}19#collecting documents20def collectDocuments(path):21    # opening files one by one in the directory22    for filename in glob.glob(os.path.join(path, '*.txt')):23        with open(os.path.join(os.getcwd(), filename), 'r') as f:24            #handling the encoding of the text files25            f = open(filename, "r", encoding="utf8")26            #Reading from file27            file_contents = f.read()28            f.close()29            # sending the document for tokenization30            final_tokenized_terms = []31            final_tokenized_terms = tokenization(file_contents)32            #extracting documentid from the path33            path_split = os.path.split(filename)34            document_id = path_split[1][:-4]35            integer_document_id = int(document_id)36            #saving document id37            list_of_documentID.append(integer_document_id)38            #maintaining dictionary with posting list39            for word in final_tokenized_terms:40                if word in term_document_dictionary.keys():41                    #if key exists then append the document list42                    term_document_dictionary[word].append(integer_document_id)43                    #sorting the postings list after a new posting is entered44                    term_document_dictionary[word].sort()45                else:46                    #if key does not exist then create a document list for the key47                    term_document_dictionary[word] = [integer_document_id]48#tokenizing49def tokenization(document):50    #CASEFOLDING51    # casefolding or lower casing the whole document52    document_to_work = document.casefold()53    #OPENING CONTRACTIONS54    # making a list for contractions processing55    list_terms = document_to_work.split()56    list_terms_to_work = list()57    for word in list_terms:58        if word in contractions:59            #using an imported dictionary for contractions60            list_terms_to_work.append(contractions[word])61        else:62            list_terms_to_work.append(word)63    #REMOVING PUNCTUATIONS64    document_to_work = ' '.join(list_terms_to_work)65    document_to_work = re.sub(r'[^\w\s]', '', document_to_work)66    #REMOVING FINAL LEFT OVER WHITESPACES67    finalised_terms_with_stopwords = document_to_work.split()68    # REMOVING STOP WORDS AND DUPLICATE WORDS69    # opening stopwords file70    f = open('dataset/Stopword-List.txt', 'r', encoding='utf8')71    stop_words = f.read()72    stop_list = stop_words.split()73    f.close()74    #removing stop words75    finalised_terms_without_stopwords = list(set(finalised_terms_with_stopwords)^set(stop_list))76    finalised_terms_without_stopwords = list(finalised_terms_without_stopwords)77    #STEMMING78    # stemmer = PorterStemmer()79    # stem_list = list()80    # stem_list = [stemmer.stem(word) for word in finalised_terms_without_stopwords]81    # #removing stemmed duplicates82    # stem_list = set(stem_list)83    # stem_list = list(stem_list)84    #SORTING THE TERMS85    # stem_list.sort()86    # sizes_of_stemmed_lists.append(len(stem_list))87    return finalised_terms_without_stopwords88#Query processing89def processingQuery(query):90    answer = []91    postfixed_query = list()92    #LOWER CASE HANDLING AND LISTING TERMS IN A QUERY93    query_stream = query.lower().split()94    # print(query_stream)95    #not a proximity query96    if len(query_stream) == 1:97        if query_stream[0] in term_document_dictionary.keys():98            answer = term_document_dictionary[query_stream[0]]99        return answer100    elif len(query_stream) > 1:101        if "/" not in query:102            #postfixing an infix boolean query103            postfixed_query = postfixQuery(query_stream)104            # print(postfixed_query)105            answer = evaluatePostfixQuery(postfixed_query)106        else:107            #handling proximity query108            if "and" not in query_stream:109                query=" + ".join(query_stream)110                # print(query)111                query_stream_positional=query.split()112            proximity = query_stream_positional.pop()113            query_stream_positional.pop()114            # print(query_stream_positional)115            proximity = proximity.replace("/","")116            proximity_len = int(proximity) + 1117            answer = evaluatePostfixQuery(query_stream_positional)118            query_stream.pop()119            prox_answer = []120            for document in answer:121                #checking proximity122                path = 'dataset/ShortStories/' + str(document)+'.txt'123                f = open(path, "r", encoding="utf8")124                # Reading from file125                file_contents = f.read()126                f.close()127                #fixing punctuations128                file_contents = re.sub(r'[^\w\s]', '', file_contents)129                #lower casing130                terms = file_contents.split()131                terms = [i.lower() for i in terms]132                # 2 term proximity query133                if len(query_stream) == 2:134                    index_1 = []135                    index_2 = []136                    count_1 = int(0)137                    for item in terms:138                        if item == query_stream[0]:139                            index_1.append(count_1)140                        if item == query_stream[1]:141                            index_2.append(count_1)142                        count_1 += 1143                    for item in index_1:144                        i = int(1)145                        while i != proximity_len + 1:146                            if item + i in index_2:147                                prox_answer.append(document)148                            elif item - i in index_2:149                                prox_answer.append(document)150                            i += 1151                # 3 term proximity query152                else:153                    index_1 = []154                    index_2 = []155                    index_3 = []156                    count_1 = int(0)157                    for item in terms:158                        if item == query_stream[0]:159                            index_1.append(count_1)160                        if item == query_stream[1]:161                            index_2.append(count_1)162                        if item == query_stream[2]:163                            index_3.append(count_1)164                        count_1 += 1165                    for item in index_1:166                        i = int(0)167                        while i != proximity_len + 1:168                            if item + i in index_2:169                                prox_answer.append(document)170                            elif item - i in index_2:171                                prox_answer.append(document)172                            i += 1173                answer = prox_answer174        return answer175#converting logical infix to postfix176def postfixQuery(query_stream):177    temp_stream = []178    for i in query_stream:179        if i == "not":180            temp_stream.append(i.replace("not", "-"))181        elif i == "and":182            temp_stream.append(i.replace("and", "*"))183        elif i == "or":184            temp_stream.append(i.replace("or", "+"))185        else:186            temp_stream.append(i)187    query_stream = temp_stream188    precedence = {"-": 3, "*": 2, "+": 1}189    postfix_query = []190    stack = Stack()191    for term_token in query_stream:192        if term_token.isidentifier():193            postfix_query.append(term_token)194        elif term_token == '(':195            stack.push(term_token)196        elif term_token == ')':197            topToken = stack.pop()198            while topToken != '(':199                postfix_query.append(topToken)200                topToken = stack.pop()201        else:  # must be operator202            if not stack.empty():203                while not stack.empty() and precedence[stack.peek()] >= precedence[term_token]:204                    postfix_query.append(stack.pop())205            stack.push(term_token)206    while not stack.empty():207        postfix_query.append(stack.pop())208    return postfix_query209#evaluating the postfix expression210def evaluatePostfixQuery(postfixed_query):211    stack = Stack()212    operators = ["*","+","-"]213    for token in postfixed_query:214        if token not in operators:215            stack.push(token)216        else:217            if token == "-":218                term_1 = stack.pop()219                # print(term_1)220                if term_1 in term_document_dictionary.keys():221                    not_term_1 = list(set(list_of_documentID)^set(term_document_dictionary[term_1]))222                    query_dictionary[term_1] = not_term_1223                else:224                    query_dictionary[term_1] = []225                stack.push(term_1)226            elif token == "*":227                term_2 = stack.pop()228                term_1 = stack.pop()229                # print(term_1, term_2)230                if term_1 not in query_dictionary.keys():231                    if term_1 in term_document_dictionary.keys():232                        query_dictionary[term_1] = term_document_dictionary[term_1]233                    else:234                        query_dictionary[term_1] = []235                if term_2 not in query_dictionary.keys():236                    if term_2 in term_document_dictionary.keys():237                        query_dictionary[term_2] = term_document_dictionary[term_2]238                    else:239                        query_dictionary[term_2] = []240                query_dictionary["answer"] = intersect(term_1,term_2)241                stack.push("answer")242            elif token == "+":243                term_2 = stack.pop()244                term_1 = stack.pop()245                # print(term_1, term_2)246                if term_1 not in query_dictionary.keys():247                    if term_1 in term_document_dictionary.keys():248                        query_dictionary[term_1] = term_document_dictionary[term_1]249                    else:250                        query_dictionary[term_1] = []251                if term_2 not in query_dictionary.keys():252                    if term_2 in term_document_dictionary.keys():253                        query_dictionary[term_2] = term_document_dictionary[term_2]254                    else:255                        query_dictionary[term_2] = []256                query_dictionary["answer"] = union(term_1, term_2)257                stack.push("answer")258    return query_dictionary["answer"]259#intersection of two postings lists260def intersect(term_1,term_2):261    answer = []262    postings_term_1 = query_dictionary[term_1]263    postings_term_2 = query_dictionary[term_2]264    len_p1 = len(postings_term_1)265    len_p2 = len(postings_term_2)266    index_p1 = int(0)267    index_p2 = int(0)268    while len_p1 != 0 and len_p2 != 0:269        if index_p1 <len(postings_term_1) and index_p2<len(postings_term_2):270            if postings_term_1[index_p1] == postings_term_2[index_p2]:271                answer.append(postings_term_1[index_p1])272                index_p1 += 1273                len_p1 -= 1274                index_p2 += 1275                len_p2 -= 1276            else:277                if postings_term_1[index_p1] < postings_term_2[index_p2]:278                    index_p1 += 1279                    len_p1 -= 1280                else:281                    index_p2 += 1282                    len_p2 -= 1283    return answer284#union of two postings lists285def union(term_1,term_2):286    answer = []287    postings_term_1 = query_dictionary[term_1]288    postings_term_2 = query_dictionary[term_2]289    answer = [term for term in postings_term_1]290    answer = [term for term in postings_term_2] + answer291    answer = set(answer)292    answer = list(answer)293    answer.sort()294    return answer295if __name__ == '__main__':296    collectDocuments('dataset/ShortStories')297    print("Size of the dictionary: "+ str(len(term_document_dictionary)))298    q = "god and man and love"299    print("Query: ",q)...

create_queries.py

Source:create_queries.py

1'''2    Authors:3    Valentina Guerrero4    Aishwarya Varma 5'''6def create_movie_table_query(items_dictionary):7    query_dictionary = {}8    language_string = ''9    numbers = ['rating', 'revenue', 'runtime', 'release_year', 'budget']10    for item in items_dictionary:11        if items_dictionary[item]:12            if item not in numbers:13                query_dictionary[item] = "LIKE '%{}%'".format(items_dictionary[item])14            else:15                if item in ['rating', 'revenue', 'budget']:16                    query_dictionary[item] = ">= '{}' AND movies.{} > 0".format(items_dictionary[item], item)17                elif item == 'runtime':18                    query_dictionary[item] = "<= '{}' AND movies.runtime > 0 ".format(items_dictionary[item])19                else:20                    query_dictionary[item] = "= '{}'".format(items_dictionary[item])21        else:22            query_dictionary[item] = 'IS NOT NULL'23    query_skeleton = '''24        movies.title {}25        AND26        movies.rating {}27        AND28        movies.revenue {}29        AND 30        movies.runtime {}31        AND32        movies.release_year {}33        AND 34        movies.budget {}35        AND36        languages.lang_full {}37        AND 38        countries.country_name {}39        AND40        companies.company_name {}41        AND42        genres.genre {}43        AND44        movie_langs.movie_id = movies.id45        AND 46        movie_langs.lang_id = languages.id47        AND48        movie_countries.movie_id = movies.id49        AND50        movie_countries.country_id = countries.id51        AND52        movie_companies.movie_id = movies.id53        AND54        movie_companies.company_id = companies.id55        AND56        movies.id = movie_genres.movie_id57        AND58        movie_genres.genre_id = genres.id59        60    '''.format(query_dictionary['title'], query_dictionary['rating'], query_dictionary['revenue'], query_dictionary['runtime'], query_dictionary['release_year'], query_dictionary['budget'],query_dictionary['language'], query_dictionary['country'], query_dictionary['company'], query_dictionary['genre'] )61    return query_skeleton62def create_genres_table_query(movie_id):63    query = '''64    SELECT genres.genre65    FROM 66    movies,67    genres,68    movie_genres69    WHERE70    movies.id = '{}'71    72    ;'''.format(movie_id)73    return query74def create_search_all_query(search_string):75    if search_string.isnumeric():76        int_keyword = "= '{}'".format(search_string)77        string_keyword = "LIKE '%{}%'".format(search_string)78        query_skeleton = '''79            release_year {int_key}80            OR81            rating {int_key}82            OR83            runtime {int_key}84            OR85            title {string_key}86        '''.format(int_key=  int_keyword, string_key = string_keyword)87    else:88        string_keyword = "LIKE '%{}%'".format(search_string)89        query_skeleton = '''90            title {string_key}91        '''.format(string_key = string_keyword)92    return query_skeleton93def create_genre_query(keyword):94    genre_query = ''' 95    SELECT 96    movies.id, movies.title, movies.release_year, movies.rating, movies.poster_path97    FROM 98    movies,99    genres,100    movie_genres101    WHERE102    genres.genre LIKE '%{}%'103    AND104    movies.id = movie_genres.movie_id105    AND106    movie_genres.genre_id = genres.id107    '''.format(keyword)...

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