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How to use begin_test method in Slash

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WeatherClassifier.py

Source:WeatherClassifier.py

1import numpy as np2import pandas3import argparse4import matplotlib.pyplot as plt5import datetime6from keras.models import Sequential7from keras.layers import Dense, Dropout, advanced_activations8def main():9    # Get input args10    args = parse_arguments()11    # Init random seed for reproducibility12    np.random.seed(0)13    # load the dataset14    dataframe = pandas.read_csv(args["data_path"], engine='python', parse_dates=['DATE'],15                                date_parser=lambda x: pandas.to_datetime(x, infer_datetime_format=True))16    # Define the training set using the input begin and end dates17    train_df= dataframe[(dataframe['DATE'] >= datetime.datetime(args["begin_train"],1,1)) &18                        (dataframe['DATE'] <= datetime.datetime(args["end_train"],12,31))]19    # Define the testing set using the input begin and end dates20    test_df = dataframe[(dataframe['DATE'] >= datetime.datetime(args["begin_test"],1,1)) &21                        (dataframe['DATE'] <= datetime.datetime(args["end_test"],12,31))]22    # Remove null and other invalid entries in the data23    train_data = np.nan_to_num(train_df['TAVG'].values.astype('float32'))24    test_data = np.nan_to_num(test_df['TAVG'].values.astype('float32'))25    # Combine the data to one array26    combined_data = np.append(train_data, test_data)27    # reshape dataset to window matrix28    look_back = 12  # This is the size of the window29    trainX, trainY = create_dataset(train_data, look_back)30    testX, testY = create_dataset(test_data, look_back)31    # Define and fit the model32    model = create_model(look_back=look_back)33    model.fit(trainX, trainY, epochs=500, batch_size=12, verbose=2)34    # Estimate model performance35    trainScore = model.evaluate(trainX, trainY, verbose=0)36    print('Train Score: %.2f MAE' % (trainScore))37    testScore = model.evaluate(testX, testY, verbose=0)38    print('Test Score: %.2f MAE' % (testScore))39    # generate predictions for training40    trainPredict = model.predict(trainX)41    testPredict = model.predict(testX)42    # shift train predictions for plotting43    trainPredictPlot = np.empty((len(combined_data), 1))44    trainPredictPlot[:] = np.nan45    trainPredictPlot[look_back:len(trainPredict) + look_back] = trainPredict46    # shift test predictions for plotting47    testPredictPlot = np.empty((len(combined_data), 1))48    testPredictPlot[:] = np.nan49    testPredictPlot[len(trainPredict) + (look_back * 2) + 1:len(combined_data) - 1] = testPredict50    # Combine the results51    combined_df = train_df.append(test_df)52    combined_dates = combined_df['DATE']53    # plot baseline and predictions54    plt.plot(combined_dates, combined_data, )55    plt.plot(combined_dates, trainPredictPlot)56    plt.plot(combined_dates, testPredictPlot)57    plt.minorticks_on()58    plt.show()59# Standard Model Creation60def create_model(look_back):61    # create and fit Multilayer Perceptron model62    model = Sequential()63    model.add(Dense(100, input_dim=look_back, activation='relu'))64    # model.add(Dense(50, activation='relu'))65    # model.add(Dense(25, activation='relu'))66    # model.add(Dense(10, activation='relu'))67    # model.add(Dense(5, activation='relu'))68    model.add(Dense(1))69    model.compile(loss='mean_absolute_error', optimizer='nadam')70    return model71# convert an array of values into a dataset matrix72def create_dataset(dataset, look_back=1):73    dataX, dataY = [], []74    for i in range(len(dataset) - look_back - 1):75        a = dataset[i:(i + look_back)]76        dataX.append(a)77        dataY.append(dataset[i + look_back])78    return np.array(dataX), np.array(dataY)79# Command Line Arguments are parsed here80def parse_arguments():81    parser = argparse.ArgumentParser()82    parser.add_argument("-dp", "--data_path", help="Data File Path")83    parser.add_argument("-ad", "--adverse", help="Turns on Adversarial Learning")84    parser.add_argument("-m", "--mode", help="Choose mode: full, grid")85    parser.add_argument("-e", "--epochs", help="Number of Epochs", type=int, nargs="*")86    parser.add_argument("-tr", "--train_ratio", nargs="*", type=int,87                        help="Set Train Ratios. Enter as a percent (20,40,60,80). Can be a list space delimited")88    parser.add_argument("-bs", "--batch_size", nargs="*", type=int,89                        help="Batch size. Can be a list space delimited")90    parser.add_argument("-n", "--neurons", nargs="*", type=int,91                        help="Number of Neurons. Can be a list space delimited")92    parser.add_argument("-o", "--optimizer", nargs="*",93                        help="Optimizers. Can be a list space delimited")94    parser.add_argument("-w", "--weight_constraint", nargs="*", type=int,95                        help="Weight Constraint. Can be a list space delimited")96    parser.add_argument("-d", "--dropout", nargs="*", type=int,97                        help="Dropout. Enter as percent (10,20,30,40...). Can be a list space delimited.")98    parser.add_argument("-model", "--model", help="Select which model to run: all, one_layer, four_decr, four_same")99    parser.add_argument("-s", "--splits", help="Number of Splits for SSS", type=int)100    parser.add_argument("-btr", "--begin_train", help="Year to begin training (1940-2016)", type=int)101    parser.add_argument("-etr", "--end_train", help="Year to end training. Should be higher than begin & <=2017", type=int)102    parser.add_argument("-bts", "--begin_test", help="Year to begin testing (1940-2017)", type=int)103    parser.add_argument("-ets", "--end_test", help="Year to end testing. Should be higher than begin test.", type=int)104    args = parser.parse_args()105    arguments = {}106    if args.data_path:107        arguments["data_path"] = args.data_path108    else:109        print("Default Data Path: ../Data/BWIMonthly1939.csv")110        arguments["data_path"] = "../Data/BWIMonthly1939.csv"111    if args.adverse:112        adverse = True113    else:114        adverse = False115    arguments["adverse"] = adverse116    if args.mode == "grid":117        mode = "grid"118        print("Mode is %s" % mode)119    else:120        mode = "full"121        print("Mode is %s" % mode)122    arguments["mode"] = mode123    if args.model == "all":124        model = ["oneLayer", "fourDecr", "fourSame"]125    elif args.model in ["oneLayer", "fourDecr", "fourSame"]:126        model = [args.model]127    else:128        print("Defaulting to All models")129        model = ["oneLayer", "fourDecr", "fourSame"]130    arguments["model"] = model131    if args.epochs:132        epochs = args.epochs133    else:134        print("Defaulting to 16 epochs")135        epochs = 16136    arguments["epochs"] = epochs137    if args.train_ratio:138        train_ratio = args.train_ratio139    else:140        print("Defaulting to testing all ratios")141        train_ratio = [20, 40, 60, 80]142    arguments["train_ratio"] = train_ratio143    if args.batch_size:144        batch_size = args.batch_size145    else:146        print("Defaulting to Batch Size 10")147        batch_size = 10148    arguments["batch_size"] = batch_size149    if args.neurons:150        neurons = args.neurons151    else:152        print("Defaulting to 45 Neurons")153        neurons = 45154    arguments["neurons"] = neurons155    if args.optimizer:156        optimizer = args.optimizer157    else:158        print("Defaulting to NADAM Optimizer")159        optimizer = "Nadam"160    arguments["optimizer"] = optimizer161    if args.weight_constraint:162        weight_constraint = args.weight_constraint163    else:164        print("Defaulting to weight constraint 5")165        weight_constraint = 5166    arguments["weight_constraint"] = weight_constraint167    if args.dropout:168        dropout = args.dropout169    else:170        print("Defaulting to dropout of 10%")171        dropout = 10172    arguments["dropout"] = dropout173    if args.splits:174        splits = args.splits175    else:176        print("Defaulting to 1 SSS Split")177        splits = 1178    arguments["splits"] = splits179    if args.begin_train:180        begin_train = args.begin_train181    else:182        print("Default begin training is 1940")183        begin_train = 1940184    arguments["begin_train"] = begin_train185    if args.end_train:186        end_train = args.end_train187    else:188        print("Defult end training is 1980")189        end_train = 1980190    if end_train < begin_train:191        print("End_Train should be bigger than Begin_Train")192        exit(1)193    arguments["end_train"] = end_train194    if args.begin_test:195        begin_test = args.begin_test196    else:197        print("Default begin test is 1981")198        begin_test = 1981199    arguments["begin_test"] = begin_test200    if args.end_test:201        end_test = args.end_test202    else:203        print("Default end test is 2017")204        end_test = 2017205    if end_test < begin_test:206        print("End_Test should be bigger than Begin_Test")207        exit(1)208    arguments["end_test"] = end_test209    return arguments210if __name__ == "__main__":...

test_zebraParser.py

Source:test_zebraParser.py

...23              '--checkargs']24full_cmd_5 = ['--input', 'somefile.zbtex',25              '--texmfhome', '/home/mancilla/development/Zebrackets/src/test',26              '--checkargs']27def begin_test(name):28    print()29    print(70*'=')30    print("On test: " + name)31    print()32class TestZebraParser(unittest.TestCase):33    def setUp(self):34        pass35    ## Checking the argparse parametrization, no errors in command36    def test_zebraparser_cmd1(self):37        begin_test("test_zebraparser_cmd1")38        self.assertEqual(39            zebraParser.zebraParserParser(full_cmd_1), 40            'Error: Invalid input file, zbtex extension required.')41    ## Checking the argparse parametrization, checking for file extensions42    def test_zebraparser_cmd2(self):43        begin_test("test_zebraparser_cmd2")44        try:45          self.assertIn(zebraParser.zebraParserParser(full_cmd_2), None)46        except:47            pass48    def test_zebraparser_cmd3(self):49        begin_test("test_zebraparser_cmd3")50        self.assertEqual(51            zebraParser.zebraParserParser(full_cmd_3),52            "Error: Invalid texmf, path is not a directory.")53    def test_zebraparser_cmd4(self):54        begin_test("test_zebraparser_cmd4")55        self.assertEqual(56            zebraParser.zebraParserParser(full_cmd_4),57            "Error: TEXMFHOME environment variable is not set.")58    def test_zebraparser_cmd5(self):59        begin_test("test_zebraparser_cmd5")60        self.assertEqual(zebraParser.zebraParserParser(full_cmd_5), None)61    ## Checking the argparse parametrization, with --h62    # Argparse sends an error exit code, so we need to catch here63    def test_zebraparser_cmd0(self):64        begin_test("test_zebraparser_cmd0")65        try:66            self.assertEqual(zebraParser.zebraParserParser(['--h']), None)67        except:68            pass69#            print(sys.exc_info()[0])70#            print(sys.exc_info())71    ## Checking the argparse parametrization, with no values72    # Argparse raises an exception so we need to catch it here73    def test_zebraparser_noargs(self):74        begin_test("test_zebraparser_noargs")75        try:76            zebraParser.zebraParserParser()77        except:78            pass79if __name__ == '__main__':...

main.py

Source:main.py

1import time2import requests345website = 'https://random-word-api.herokuapp.com/word?number='67# ask user to begin test, else end testing8print('The test will begin after the first word starts.')9begin_test = input('Would you like to start test? y/n: ').lower()1011while begin_test != 'n':12    if begin_test.startswith('y'):13        # ask how many words to test14        num_words = input('How many words would you like to test against? Enter a number: ')1516        # send get request to get random json of words and save resulting json as list of words to test17        word_list = requests.get(website + num_words).json()18        print(f'Here is the full list of words:\n')19        print(word_list)20        # start clock21        test_start = time.time()2223        for count,word in enumerate(word_list):24            word_to_test = ''25            while word_to_test != word_list[count]:26                print(word_list[count])27                word_to_test = input(':')28        test_end = time.time()29        average_speed = int(num_words) / (test_end - test_start)*6030        print(f'Your results are:\n{average_speed} words per minute')31        begin_test = input('Would you like to go again? y/n: ')32    elif begin_test.startswith('n'):33        print('Test ended. See you next time!')34        begin_test = 'n'3536    else:
...

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