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How to use test_keywords method in pyshould

Best Python code snippet using pyshould_python

real_fake.py

Source:real_fake.py

1import numpy as np 2import pandas as pd3# ====================================TRAINING========================================4# Importing training dataset5train = pd.read_csv('data/train.csv')6# Creating train_keywords, train_text, and train_targets arrays from training dataset7train_keywords = train.iloc[:, 1].values8train_text = train.iloc[:, 3].values9train_targets = train.iloc[:, -1].values10# Reshaping train_keywords to prepare for imputing11train_keywords = train_keywords.reshape(len(train_keywords), 1)12# Imputing train_keywords to get rid of NaN values13from sklearn.impute import SimpleImputer14imputer = SimpleImputer(missing_values=np.nan, strategy='constant', fill_value='no_keyword')15train_keywords = imputer.fit_transform(train_keywords)16# One Hot Encoding the train_keywords column17from sklearn.preprocessing import OneHotEncoder18from sklearn.compose import ColumnTransformer19encoder = OneHotEncoder(dtype=int, drop='first')20ct = ColumnTransformer([('encoding', encoder, [0])], remainder='passthrough')21train_keywords = ct.fit_transform(train_keywords)22# Cleaning the train_text array and creating a corpus23from nltk.corpus import stopwords24from nltk.stem.porter import PorterStemmer25import re26train_corpus = []27all_stopwords = stopwords.words('english')28all_stopwords.remove('not')29ps = PorterStemmer()30for i in range(0, len(train_text)):31    tweet = re.sub('[^a-zA-Z]', ' ', train_text[i])32    tweet = tweet.lower()33    tweet = tweet.split()34    tweet = [ps.stem(word) for word in tweet if word not in set(all_stopwords)]35    tweet = ' '.join(tweet)36    train_corpus.append(tweet)37# Count Vectorizing train_corpus to create the bag of words model38from sklearn.feature_extraction.text import CountVectorizer39cv = CountVectorizer()40train_corpus = cv.fit_transform(train_corpus)41# Transforming train_corpus into an array for concatenation42train_corpus = train_corpus.toarray()43# Reshaping train_keywords into an array for concatenation44train_keywords = train_keywords.toarray()45# Concatenating train_keywords to train_corpus to create x_train array46x_train = np.concatenate((train_keywords, train_corpus), axis=1)47# Creating and training the classifier48from sklearn.svm import SVC49classifier = SVC(kernel='rbf', random_state=0)50classifier.fit(x_train, train_targets)51# ====================================TESTING========================================52# Importing the testing dataset53test = pd.read_csv('data/test.csv')54# Creating ids, test_keywords, and test_text arrays from testing datsaet55test_keywords = test.iloc[:, 1].values56test_text = test.iloc[:, -1].values57ids = test.iloc[:, 0].values58# Reshaping test_keywords to prepare for imputing59test_keywords = test_keywords.reshape(len(test_keywords), 1)60# Imputing train_keywords to get rid of NaN values61test_keywords = imputer.transform(test_keywords)62# One Hot Encoding test_keywords63test_keywords = ct.transform(test_keywords)64# Cleaning the test_text array and creating a corpus65test_corpus = []66for i in range(0, len(test_text)):67    tweet = re.sub('[^a-zA-Z]', ' ', test_text[i])68    tweet = tweet.lower()69    tweet = tweet.split()70    tweet = [ps.stem(word) for word in tweet if word not in set(all_stopwords)]71    tweet = ' '.join(tweet)72    test_corpus.append(tweet)73# Count Vectorizing test_corpus to create the bag of words model74test_corpus = cv.transform(test_corpus)75# Transforming test_corpus into an array for concatenation76test_corpus = test_corpus.toarray()77# Transforming test_keywords into an array for concatenation78test_keywords = test_keywords.toarray()79# Concatenating test_corpus to test_corpus to create x_test array80x_test = np.concatenate((test_keywords, test_corpus), axis=1)81# Predicting with the classifier82predictions = classifier.predict(x_test)83# Reshaping ids for concatenation84ids = ids.reshape(len(ids), 1)85# Reshaping predictions for concatenation86predictions = predictions.reshape(len(predictions), 1)87# Concatenating ids and predictions88submission = np.concatenate((ids, predictions), axis=1)89# Saving submission to a .csv file...

test_gen.py

Source:test_gen.py

1import os2import sys3import re4import ast 5from os import listdir6from os.path import isfile, join7def check_keywords(word, keyword_list):    8    for keyword in keyword_list:        9        if re.search(keyword, word):10            #print("\t" + word + " matched with \t" + keyword)11            return True12    return False13def read_dictionary(data):            14    # reconstructing the data as a dictionary 15    d = ast.literal_eval(data) 16    17    print("attributes dictionary : ", type(d)) 18    print(d) 19    return d20def generate_test(content, keyword_list, test_model, test_keywords, classToBeTested, output_package, attributes_dictionary):21    words = content.split()22    attributes = list()23    print("file content: \n")24    print(words)  25    print("attributes found: \n")26    for word in words:27        #check_keywords(word, keyword_list)        28        if not check_keywords(word, keyword_list):29            print(word)30            attributes.append(word)31    32    setters = ""33    assertions = ""34    #builds setters and assertions35    for attribute in attributes:      36      if(attribute != classToBeTested):37          value_to_set = None38          if attribute[:-1] in attributes_dictionary:39              value_to_set = attributes_dictionary[attribute[:-1]]40          attrList = list(attribute)41          attrList[0] = attrList[0].upper()42          attribute = ''.join(attrList)43          attribute = attribute[:-1]44          if value_to_set == None:45              setters = setters + "out.set" + attribute + "();\n"46          else:47              setters = setters + "out.set" + attribute + "("+value_to_set+");\n"48          assertions = assertions + "assertNotNull("+"out.get"+attribute+"());\n"49    test_model = test_model.replace("[classToBeTested]",classToBeTested)50    test_model = test_model.replace("[setters]", setters)51    test_model = test_model.replace("[assertions]", assertions)52    test_model = test_model.replace("[outputPackage]", output_package)53    54    print("generated test:\n")55    print(test_model)56    return test_model57if len(sys.argv) > 4:58    print('You have specified too many arguments')59    sys.exit()60if len(sys.argv) < 4:61    print('You need to specify the path to be listed and the output package')62    sys.exit()63input_path = sys.argv[1]64output_path = sys.argv[2]65output_package = sys.argv[3]66keys_file = open(".\special_keywords.txt", "r")67special_keywords = keys_file.read().split()68keys_file.close()69test_model_file = open(".\\test_model.txt", "r")70test_model = test_model_file.read()71test_model_file.close()72test_keywords_file = open(".\\test_keywords.txt", "r")73test_keywords = test_keywords_file.read()74test_keywords_file.close()75field_values_file = open(".\\field_values.json", "r")76field_values = field_values_file.read()77field_values_file.close()78attributes_dictionary = read_dictionary(field_values)79print("skipping these patterns: \n")80print(special_keywords)81if not os.path.isdir(input_path):82    print('The path specified does not exist')83    sys.exit()84print('reading data from: \t' + input_path + '\n')85print('writing data into: \t' + output_path + '\n')86onlyfiles = [f for f in listdir(input_path) if isfile(join(input_path, f))]87print("files to be processed: ")88print(onlyfiles)89print("\n")90for element in onlyfiles:91    f = open(input_path+"\\"+element, "r")92    print(".... generating test for class: \t" + element + "....")93    splittedFileName = element.split(".")94    classToBeTested = splittedFileName[0]95    fwr = open(output_path+"\\"+ classToBeTested + "Test.java", "w")96    fwr.write(generate_test(f.read(), special_keywords, test_model, test_keywords, classToBeTested, output_package, attributes_dictionary))97    fwr.close()...

test_data_utils.py

Source:test_data_utils.py

1"""2Suite of tests for library 'data' utilities submodule.3"""4# Package imports5from aylluiot.utils.data_utils import parse_inputs6def test_parse_inputs() -> None:7    """8    Suite of tests for parsing_inputs.9    """10    test_keywords = ['var_1', 'var_2']11    test_kwargs = {'var_1': 'abc', 'var_2': 123}12    test_args_one = ('abc', 123)13    test_args_two = (test_kwargs, )14    output_1_a, output_1_b = parse_inputs(keywords=test_keywords,15                                          _args=test_args_one)16    output_2_a, output_2_b = parse_inputs(keywords=test_keywords,17                                          _args=test_args_two)18    output_1_c, output_1_d = parse_inputs(keywords=test_keywords,19                                          _kwargs=test_kwargs)20    assert output_1_a == output_2_a21    assert output_1_a == output_1_c22    assert output_1_b == output_2_b...

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