How to use test_start method in avocado

Best Python code snippet using avocado_python

importer.py

Source:importer.py

1import pandas as pd2import numpy as np3import statsmodels.api as sm4import statsmodels5import sklearn6import requests7import socket8from sklearn import preprocessing9import scipy10from scipy.stats import pearsonr11from math import sqrt12from pandas.plotting import register_matplotlib_converters13import os14dirname = os.path.dirname(__file__)15scaler = sklearn.preprocessing.MinMaxScaler()16# Define the path to CSV datasets here:17PATH_TO_DATASET_H = ""18PATH_TO_DATASET_M = ""19PATH_TO_DATASET_L = ""20def create_features(df, resolution):21    22    # Month sin and cos features:23    df['mnth_sin'] = np.sin((df.index.month-1)*(2.*np.pi/12))24    df['mnth_cos'] = np.cos((df.index.month-1)*(2.*np.pi/12))25    26    # Workday Feature -> True if not weekand and not swedish public holiday27    df["is_workday"] = (df.index.weekday < 5) & (np.logical_not(np.isin(df.index.date, sweden_holidays)))*128    29    months = [i for i in range(1, 13)]30    weekdays = [i for i in range(0, 7)]31    hours = [i for i in range(0, 24)]32    for month in months:33        df["month_" + str(month)] = (df.index.month == month)*1 # One hot encoding of current month34    for weekday in weekdays:35        df["weekday_" + str(weekday)] = (df.index.weekday == weekday)*1 # One hot encoding of current weekday36    if(not resolution == "1d"):37        # The hourly one hot encoded feature is only used for the 15min and 1h resolution, not for daily data:38        for hour in hours:39            df["hour_" + str(hour)] = (df.index.hour == hour)*140            df['hr_sin'] = np.sin((df.index.hour + (df.index.minute/60)) * (2.*np.pi/24))41            df['hr_cos'] = np.cos((df.index.hour + (df.index.minute/60)) * (2.*np.pi/24))42    return df43def load_h(resolution = "1h", full_dataset = False, scaler_only=False):44    45    # We import the values of the datasets and define the index46    df = pd.read_csv(os.path.join(dirname, PATH_TO_DATASET_H), delimiter=";", index_col="Time")47    df.index = pd.to_datetime(df.index, format="%Y-%m-%d %H:%M")48    df.index.freq = df.index.inferred_freq49    50    # Renaming of to english Feature Names:51    df["Windspeed"] = df["Vindhastighet"]52    df["Temperature"] = df["Lufttemperatur"]53 54    if(full_dataset):55        df = df["2017-01-24":"2019-03-07"]56    else:57        df = df["2017-01-24":"2018-12-07"]58        59    if(resolution=="15min"):60        # Down sampling to quarter-hourly resolution61        df = df.resample("15min").interpolate()62    if(resolution=="1d"):63        # Resampling to daily resolution64        df = df.resample("1d").mean()65        66    # Now we readd our newly generated features67    df = create_features(df, resolution)68    69    if(scaler_only==False):70        # Returns the scaled dataset71        df[["Load", "Windspeed","Temperature"]] = scaler.fit_transform(df[["Load", "Windspeed","Temperature"]].to_numpy())72        return df73    else:74        # Returns the scaler only -> used in economic part for inverse scaling back to original values75        return scaler.fit(df[["Load"]])76    77  78    79def load_m(resolution = "1h", full_dataset = False, scaler_only=False):80    df = pd.read_csv(PATH_TO_DATASET_M,index_col="Time")81    df.index = pd.to_datetime(df.index, format="%Y-%m-%d %H:%M")82    df.index.freq = df.index.inferred_freq83    84    if(full_dataset):85        df = df["2017-01-24":"2019-03-07"]86    else:87        df = df["2017-01-24":"2018-12-07"]88        89    if(resolution=="15min"):90        df = df.resample("15min").interpolate()91    if(resolution=="1d"):92        df = df.resample("1d").mean()93        94    # Now we readd the features95    df = create_features(df, resolution)96    97    if(scaler_only==False):98        df[["Load", "Windspeed","Temperature"]] = scaler.fit_transform(df[["Load", "Windspeed","Temperature"]].to_numpy())99        return df100    else:101        return scaler.fit(df[["Load"]])102    103    104    105    106    107def load_customer_l(resolution = "1h", full_dataset = False):108    df = pd.read_csv(os.path.join(dirname, PATH_TO_DATASET_L), usecols=["Time", "e507b", "Windspeed", "Temperature"], index_col="Time")109    df.index = pd.to_datetime(df.index)110    df.index.freq = df.index.inferred_freq111    if(full_dataset):112        df = df["2017-01-24":"2019-03-07"]113    else:114        df = df["2017-01-24":"2018-12-07"]115        116    if(resolution=="15min"):117        df = df.resample("15min").interpolate()118    if(resolution=="1d"):119        df = df.resample("1d").mean()120        121    # Now we readd the features122    df = create_features(df, resolution)123    124    df["Load"] = scaler.fit_transform(df[["Load"]].to_numpy())125    df["Windspeed"] = scaler.fit_transform(df[["Windspeed"]].to_numpy())126    df["Temperature"] = scaler.fit_transform(df[["Temperature"]].to_numpy())127    return df128    129    130    131sweden_holidays = ["2017-01-01", "2017-01-06", "2017-04-14", "2017-04-17", "2017-05-01", "2017-05-25", "2017-06-04", "2017-06-06", "2017-06-23", "2017-06-24", "2017-11-04", "2017-12-24", "2017-12-25", "2017-12-26", "2017-12-31", "2018-01-01", "2018-01-06", "2018-03-30", "2018-04-01", "2018-04-02", "2018-05-01", "2018-05-10", "2018-05-20", "2018-06-06", "2018-06-22", "2018-06-23", "2018-11-03", "2018-12-24", "2018-12-25", "2018-12-26", "2018-12-31", "2019-01-01", "2019-01-06"]132    133    134    135def get_datasets_with_config():136    return get_datasets_for_final_evaluation(full_dataset=False)137    138def get_datasets_for_final_evaluation(full_dataset=True):139    return [140        {   141            "name": "H@15min",142            "set": load_h(resolution="15min", full_dataset=True), 143            "horizon":4, 144            "seasonal": 96,145            "hw": {146                "smoothing_level": 1,147                "smoothing_trend": 0.99355,148                "smoothing_seasonal": 0.07195,149                "sample_size": 800,150                "trend": "add",151                "seasonal": "add",152                "seasonal_periods": 96 153            },154            "svr": {155                "C": 0.125,156                "epsilon": 0.03125157            },158            "rf": {159                "n_estimators": 1100,160                "max_features": 0.5,161                "min_samples_leaf": 50162            },163            "knn": {164                "n_neighbors": 5,165                "weights": "distance"166            },167            "arima": {168                "order": (5,0,2),169                "sample_size": 650170            },171            "arimax": {172              "features": ["Temperature", "Windspeed", "hr_cos", "hr_sin"]  173            },174            "ann": {175                "layers": 1,176                "learning_rate": 0.001,177                "neurons_per_layer": 16,178                "optimizer": "adam",179                "dropout": 0.1180            },181            "rnn": {182                "layers": 1,183                "learning_rate": 0.001,184                "neurons_per_layer": 16,185                "optimizer": "adam",186                "dropout": 0.1,187                "cell_type": "gru"188            }189        },190        { 191            "name": "M@15min",192            "set": load_m(resolution="15min", full_dataset=True),193            "horizon":4,194            "seasonal": 96,195            "hw": {196                "smoothing_level": 0.35716,197                "smoothing_trend": 0.62701,198                "smoothing_seasonal": 0.57316,199                "sample_size": 500,200                "trend": "add",201                "seasonal": "add",202                "seasonal_periods": 96 203            },204            "svr": {205                "C": 0.3125,206                "epsilon": 0.125207            },208            "rf": {209                "n_estimators": 800,210                "max_features": 0.33,211                "min_samples_leaf": 50212            },213            "knn": {214                "n_neighbors": 50,215                "weights": "uniform"216            },217            "arima": {218                "order": (5,0,3),219                "sample_size": 500220            },221            "arimax": {222              "features": ["Temperature", "Windspeed", "hr_cos", "mnth_cos", "mnth_sin", "is_workday"]  223            },224            "ann": {225                "layers": 2,226                "learning_rate": 0.001,227                "neurons_per_layer": 16,228                "optimizer": "adam",229                "dropout": 0.1230            },231            "rnn": {232                "layers": 1,233                "learning_rate": 0.001,234                "neurons_per_layer": 16,235                "optimizer": "adam",236                "dropout": 0.1,237                "cell_type": "gru"238            }239        },240        { 241            "set": load_l(resolution="15min", full_dataset=True),242            "name": "L@15min",243            "horizon":4,244            "seasonal": 96,245            "hw": {246                "smoothing_level": 0.16035,247                "smoothing_trend": 0.19047,248                "smoothing_seasonal": 0.58363,249                "sample_size": 500,250                "trend": "add",251                "seasonal": "add",252                "seasonal_periods": 96 253            },254            "svr": {255                "C": 0.125,256                "epsilon": 0.125257            },258            "rf": {259                "n_estimators": 1400,260                "max_features": 0.2,261                "min_samples_leaf": 50262            },263            "knn": {264                "n_neighbors": 150,265                "weights": "uniform"266            },267            "arima": {268                "order": (1,0,5),269                "sample_size": 650270            },271            "arimax": {272              "features": ["Temperature", "Windspeed", "hr_cos"]  273            },274            "ann": {275                "layers": 1,276                "learning_rate": 0.001,277                "neurons_per_layer": 16,278                "optimizer": "adam",279                "dropout": 0280            },281            "rnn": {282                "layers": 1,283                "learning_rate": 0.001,284                "neurons_per_layer": 16,285                "optimizer": "adam",286                "dropout": 0,287                "cell_type": "lstm"288            }289        },290        { 291            "name": "H@1h",292            "set": load_h(full_dataset=full_dataset),293            "horizon":24, 294            "seasonal": 24,295            "svr": {296                "C": 0.5,297                "epsilon": 0.03125298            },299            "rf": {300                "n_estimators": 800,301                "max_features": 0.5,302                "min_samples_leaf": 5303            },304            "knn": {305                "n_neighbors": 8,306                "weights": "uniform"307            },308            "arima": {309                "order": (4,0,5),310                "sample_size": 800311            },312            "arimax": {313              "features": ["Temperature", "Windspeed", "hr_cos", "mnth_cos", "mnth_sin"]  314            },315            "sarima": {316                "order": (1,0,0),317                "seasonal_order": (2,0,0,24),318                "sample_size": 800319            },320            "hw": {321                "smoothing_level": 0.00836,322                "smoothing_trend": 0.0145,323                "smoothing_seasonal": 0.01246,324                "sample_size": 850,325                "trend": "add",326                "seasonal": "add",327                "seasonal_periods": 24 328            },329            "ann": {330                "layers": 2,331                "learning_rate": 0.001,332                "neurons_per_layer": 16,333                "optimizer": "adam",334                "dropout": 0.1335            },336            "rnn": {337                "layers": 2,338                "learning_rate": 0.001,339                "neurons_per_layer": 16,340                "optimizer": "adam",341                "dropout": 0.1,342                "cell_type": "gru"343            }344        },345        { 346            "name": "M@1h",347            "set":load_m(full_dataset=full_dataset),348            "horizon":24,349            "seasonal": 24,350            "svr": {351                "C": 0.3125,352                "epsilon": 0.0625353            },354            "rf": {355                "n_estimators": 200,356                "max_features": 0.33,357                "min_samples_leaf": 25358            },359            "knn": {360                "n_neighbors": 15,361                "weights": "uniform"362            },363            "arima": {364                "order":(5,0,5),365                "sample_size": 900366            },367            "arimax": {368              "features": ["Temperature", "Windspeed", "hr_cos"]  369            },370            "sarima": {371                "order": (2,1,3),372                "seasonal_order": (2,0,1,24),373                "sample_size": 900374            },375            "hw": {376                "smoothing_level": 0.894132,377                "smoothing_trend": 0.31404,378                "smoothing_seasonal": 0.31394,379                "sample_size": 600,380                "trend": "add",381                "seasonal": "add",382                "seasonal_periods": 24 383            },384            "ann": {385                "layers": 2,386                "learning_rate": 0.001,387                "neurons_per_layer": 128,388                "optimizer": "adam",389                "dropout": 0.1390            },391            "rnn": {392                "layers": 1,393                "learning_rate": 0.001,394                "neurons_per_layer": 16,395                "optimizer": "adam",396                "dropout": 0.1,397                "cell_type": "gru"398            }399        },400        { 401            "name": "L@1h",402            "set": load_l(full_dataset=full_dataset),403            "horizon":24,404            "seasonal": 24,405            "svr": {406                "C": 0.5,407                "epsilon": 0.125408            },409            "rf": {410                "n_estimators": 500,411                "max_features": 0.5,412                "min_samples_leaf": 50413            },414            "knn": {415                "n_neighbors": 150,416                "weights": "uniform"417            },418            "arima": {419                "order": (2,1,1),420                "sample_size": 500421            },422            "arimax": {423              "features": ["Temperature", "Windspeed", "hr_cos", ]  424            },425            "sarima": {426                "order": (3,1,0),427                "seasonal_order": (2,0,0,24),428                "sample_size": 500429            },430            "hw": {431                "smoothing_level": 0.00154,432                "smoothing_trend": 0.17787,433                "smoothing_seasonal": 0.17986,434                "sample_size": 200,435                "trend": "add",436                "seasonal": "add",437                "seasonal_periods": 7438            },439            "ann": {440                "layers": 1,441                "learning_rate": 0.001,442                "neurons_per_layer": 32,443                "optimizer": "adam",444                "dropout": 0445            },446            "rnn": {447                "layers": 1,448                "learning_rate": 0.001,449                "neurons_per_layer": 32,450                "optimizer": "adam",451                "dropout": 0,452                "cell_type": "gru"453            }454        },455        {456            "name": "H@1d",457            "set": load_h(resolution="1d", full_dataset=full_dataset), 458            "horizon":30,459            "seasonal": 7,460            "svr": {461                "C": 2,462                "epsilon": 0.0078125463            },464            "rf": {465                "n_estimators": 1100,466                "max_features": 0.5,467                "min_samples_leaf": 5468            },469            "knn": {470                "n_neighbors": 70,471                "weights": "distance"472            },473            "arima": {474                "order": (5,1,3),475                "sample_size": 250476            },477            "arimax": {478              "features": ["Temperature", "Windspeed", "is_workday", ]  479            },480            "sarima": {481                "order": (0,1,1),482                "seasonal_order": (1,0,1,7),483                "sample_size": 450484            },485            "hw": {486                "smoothing_level": 0.03257,487                "smoothing_trend": 0.00125,488                "smoothing_seasonal": 0.00937,489                "sample_size": 500,490                "trend": "add",491                "seasonal": "add",492                "seasonal_periods": 7493            },494            "ann": {495                "layers": 2,496                "learning_rate": 0.001,497                "neurons_per_layer": 128,498                "optimizer": "adam",499                "dropout": 0.1500            },501            "rnn": {502                "layers": 2,503                "learning_rate": 0.001,504                "neurons_per_layer": 128,505                "optimizer": "adam",506                "dropout": 0,507                "cell_type": "gru"508            }509        },510        511        {512            "name": "M@1d",        513            "set": load_m(resolution="1d", full_dataset=full_dataset), 514            "horizon":30,515            "seasonal": 7,516            "svr": {517                "C": 2,518                "epsilon": 0.125519            },520            "rf": {521                "n_estimators": 1100,522                "max_features": 0.33,523                "min_samples_leaf": 5524            },525            "knn": {526                "n_neighbors": 70,527                "weights": "distance"528            },529            "arima": {530                "order": (4,1,2),531                "sample_size": 450532            },533            "arimax": {534              "features": ["Temperature", "Windspeed", "mnth_sin", "is_workday" ]  535            },536            "sarima": {537                "order": (5,0,2),538                "seasonal_order": (1,0,0,7),539                "sample_size": 450540            },541            "hw": {542                "smoothing_level": 0.00009,543                "smoothing_trend": 0.35317,544                "smoothing_seasonal": 0.00027,545                "sample_size": 450,546                "trend": "add",547                "seasonal": "add",548                "seasonal_periods": 7549            },550            "ann": {551                "layers": 1,552                "learning_rate": 0.001,553                "neurons_per_layer": 128,554                "optimizer": "adam",555                "dropout": 0556            },557            "rnn": {558                "layers": 2,559                "learning_rate": 0.001,560                "neurons_per_layer": 16,561                "optimizer": "adam",562                "dropout": 0.1,563                "cell_type": "gru"564            }565        },566       567        {568            "name": "L@1d",569            "set": load_l(resolution="1d", full_dataset=full_dataset), 570            "horizon":30,571            "seasonal": 7,572            "svr": {573                "C": 0.125,574                "epsilon": 0.125575            },576            "rf": {577                "n_estimators": 1100,578                "max_features": 0.5,579                "min_samples_leaf": 50580            },581            "knn": {582                "n_neighbors": 120,583                "weights": "uniform"584            },585            "arima": {586                "order": (5,0,5),587                "sample_size": 450588            },589            "arimax": {590              "features": ["Temperature", "Windspeed", "mnth_cos", "is_workday" ]  591            },592            "sarima": {593                "order": (1,1,2),594                "seasonal_order": (2,0,2,7),595                "sample_size": 450596            },597            "hw": {598                "smoothing_level": 0.048975,599                "smoothing_trend": 0.162792,600                "smoothing_seasonal": 0.16720,601                "sample_size": 500,602                "trend": "add",603                "seasonal": "add",604                "seasonal_periods": 7605            },606            "ann": {607                "layers": 2,608                "learning_rate": 0.001,609                "neurons_per_layer": 128,610                "optimizer": "rms",611                "dropout": 0612            },613            "rnn": {614                "layers": 1,615                "learning_rate": 0.001,616                "neurons_per_layer": 64,617                "optimizer": "rms",618                "dropout": 0.1,619                "cell_type": "lstm"620            }621        }622    ]623def get_test_windows(resolution="1h"):624    if(resolution=="15min"):625        return [{'id': 0, 'train_end': '2018-12-07 00:00:00', 'test_start': '2018-12-07 00:15:00'}, {'id': 1, 'train_end': '2018-12-07 00:45:00', 'test_start': '2018-12-07 01:00:00'}, {'id': 2, 'train_end': '2018-12-07 01:30:00', 'test_start': '2018-12-07 01:45:00'}, {'id': 3, 'train_end': '2018-12-07 02:15:00', 'test_start': '2018-12-07 02:30:00'}, {'id': 4, 'train_end': '2018-12-07 03:15:00', 'test_start': '2018-12-07 03:30:00'}, {'id': 5, 'train_end': '2018-12-07 04:00:00', 'test_start': '2018-12-07 04:15:00'}, {'id': 6, 'train_end': '2018-12-07 04:45:00', 'test_start': '2018-12-07 05:00:00'}, {'id': 7, 'train_end': '2018-12-07 05:45:00', 'test_start': '2018-12-07 06:00:00'}, {'id': 8, 'train_end': '2018-12-07 06:30:00', 'test_start': '2018-12-07 06:45:00'}, {'id': 9, 'train_end': '2018-12-07 07:15:00', 'test_start': '2018-12-07 07:30:00'}, {'id': 10, 'train_end': '2018-12-07 08:15:00', 'test_start': '2018-12-07 08:30:00'}, {'id': 11, 'train_end': '2018-12-07 09:00:00', 'test_start': '2018-12-07 09:15:00'}, {'id': 12, 'train_end': '2018-12-07 09:45:00', 'test_start': '2018-12-07 10:00:00'}, {'id': 13, 'train_end': '2018-12-07 10:45:00', 'test_start': '2018-12-07 11:00:00'}, {'id': 14, 'train_end': '2018-12-07 11:30:00', 'test_start': '2018-12-07 11:45:00'}, {'id': 15, 'train_end': '2018-12-07 12:15:00', 'test_start': '2018-12-07 12:30:00'}, {'id': 16, 'train_end': '2018-12-07 13:15:00', 'test_start': '2018-12-07 13:30:00'}, {'id': 17, 'train_end': '2018-12-07 14:00:00', 'test_start': '2018-12-07 14:15:00'}, {'id': 18, 'train_end': '2018-12-07 14:45:00', 'test_start': '2018-12-07 15:00:00'}, {'id': 19, 'train_end': '2018-12-07 15:45:00', 'test_start': '2018-12-07 16:00:00'}]626    if(resolution=="1h"):627        return [{'id': 0, 'train_end': '2018-12-07 00:00:00', 'test_start': '2018-12-07 01:00:00'}, {'id': 1, 'train_end': '2018-12-11 17:00:00', 'test_start': '2018-12-11 18:00:00'}, {'id': 2, 'train_end': '2018-12-16 10:00:00', 'test_start': '2018-12-16 11:00:00'}, {'id': 3, 'train_end': '2018-12-21 04:00:00', 'test_start': '2018-12-21 05:00:00'}, {'id': 4, 'train_end': '2018-12-25 21:00:00', 'test_start': '2018-12-25 22:00:00'}, {'id': 5, 'train_end': '2018-12-30 14:00:00', 'test_start': '2018-12-30 15:00:00'}, {'id': 6, 'train_end': '2019-01-04 08:00:00', 'test_start': '2019-01-04 09:00:00'}, {'id': 7, 'train_end': '2019-01-09 01:00:00', 'test_start': '2019-01-09 02:00:00'}, {'id': 8, 'train_end': '2019-01-13 18:00:00', 'test_start': '2019-01-13 19:00:00'}, {'id': 9, 'train_end': '2019-01-18 12:00:00', 'test_start': '2019-01-18 13:00:00'}, {'id': 10, 'train_end': '2019-01-23 05:00:00', 'test_start': '2019-01-23 06:00:00'}, {'id': 11, 'train_end': '2019-01-27 23:00:00', 'test_start': '2019-01-28 00:00:00'}, {'id': 12, 'train_end': '2019-02-01 16:00:00', 'test_start': '2019-02-01 17:00:00'}, {'id': 13, 'train_end': '2019-02-06 09:00:00', 'test_start': '2019-02-06 10:00:00'}, {'id': 14, 'train_end': '2019-02-11 03:00:00', 'test_start': '2019-02-11 04:00:00'}, {'id': 15, 'train_end': '2019-02-15 20:00:00', 'test_start': '2019-02-15 21:00:00'}, {'id': 16, 'train_end': '2019-02-20 13:00:00', 'test_start': '2019-02-20 14:00:00'}, {'id': 17, 'train_end': '2019-02-25 07:00:00', 'test_start': '2019-02-25 08:00:00'}, {'id': 18, 'train_end': '2019-03-02 00:00:00', 'test_start': '2019-03-02 01:00:00'}, {'id': 19, 'train_end': '2019-03-06 18:00:00', 'test_start': '2019-03-06 19:00:00'}]628    if(resolution=="1d"):629        return [{'id': 0, 'train_end': '2018-12-07 00:00:00', 'test_start': '2018-12-08 00:00:00'}, {'id': 1, 'train_end': '2018-12-10 00:00:00', 'test_start': '2018-12-11 00:00:00'}, {'id': 2, 'train_end': '2018-12-13 00:00:00', 'test_start': '2018-12-14 00:00:00'}, {'id': 3, 'train_end': '2018-12-16 00:00:00', 'test_start': '2018-12-17 00:00:00'}, {'id': 4, 'train_end': '2018-12-19 00:00:00', 'test_start': '2018-12-20 00:00:00'}, {'id': 5, 'train_end': '2018-12-23 00:00:00', 'test_start': '2018-12-24 00:00:00'}, {'id': 6, 'train_end': '2018-12-26 00:00:00', 'test_start': '2018-12-27 00:00:00'}, {'id': 7, 'train_end': '2018-12-29 00:00:00', 'test_start': '2018-12-30 00:00:00'}, {'id': 8, 'train_end': '2019-01-01 00:00:00', 'test_start': '2019-01-02 00:00:00'}, {'id': 9, 'train_end': '2019-01-04 00:00:00', 'test_start': '2019-01-05 00:00:00'}, {'id': 10, 'train_end': '2019-01-08 00:00:00', 'test_start': '2019-01-09 00:00:00'}, {'id': 11, 'train_end': '2019-01-11 00:00:00', 'test_start': '2019-01-12 00:00:00'}, {'id': 12, 'train_end': '2019-01-14 00:00:00', 'test_start': '2019-01-15 00:00:00'}, {'id': 13, 'train_end': '2019-01-17 00:00:00', 'test_start': '2019-01-18 00:00:00'}, {'id': 14, 'train_end': '2019-01-20 00:00:00', 'test_start': '2019-01-21 00:00:00'}, {'id': 15, 'train_end': '2019-01-24 00:00:00', 'test_start': '2019-01-25 00:00:00'}, {'id': 16, 'train_end': '2019-01-27 00:00:00', 'test_start': '2019-01-28 00:00:00'}, {'id': 17, 'train_end': '2019-01-30 00:00:00', 'test_start': '2019-01-31 00:00:00'}, {'id': 18, 'train_end': '2019-02-02 00:00:00', 'test_start': '2019-02-03 00:00:00'}, {'id': 19, 'train_end': '2019-02-06 00:00:00', 'test_start': '2019-02-07 00:00:00'}]630def return_pearson_r(x,y):...

CS535MLClassifier.py

Source:CS535MLClassifier.py

1__credits__ = ["Harsh Fatepuria","Rahul Agrawal"]2__email__ = "fatepuri@usc.edu, rahulagr@usc.edu"3'''4command to execute: 5	python CS535MLClassifier.py -svm -svm3 -svmrbf -nb -nn6note: needs sklearn, statistics and numpy python libraries7'''8import statistics9import math10import sys11import numpy as np12from sklearn import svm13from sklearn import metrics14from sklearn.naive_bayes import GaussianNB15from sklearn.model_selection import KFold, cross_val_score16from sklearn.neural_network import MLPClassifier17class CS535MLClassifier:18	def __init__(self,filename):19		self.dataset_file_name=filename20	def get_fold_test_and_train_only(self,typeOfTest,test_start,test_end):21		input_file=open(self.dataset_file_name , 'r')22		dataset=input_file.read().split("\n")23		if typeOfTest==1:24			# accoustic25			start_column=326			end_column=527		elif typeOfTest==0:28			# visual29			start_column=630			end_column=831		else:32			# multimodal33			start_column=334			end_column=835		test1_data=list()36		test1_label=list()37		train1_data=list()38		train1_label=list()39		for i in range(test_start,test_end+1):40			row=list()41			data=dataset[i].split(",")42			for j in range(start_column,end_column+1):43				row.append(float(data[j]))44			test1_data.append(row)45			test1_label.append(int(data[2]))46		for i in range(1,test_start):47			row=list()48			data=dataset[i].split(",")49			for j in range(start_column,end_column+1):50				row.append(float(data[j]))51			train1_data.append(row)52			train1_label.append(int(data[2]))53		for i in range(test_end+1,281):54			row=list()55			data=dataset[i].split(",")56			for j in range(start_column,end_column+1):57				row.append(float(data[j]))58			train1_data.append(row)59			train1_label.append(int(data[2]))60		return test1_data,test1_label,train1_data,train1_label61	def get_fold(self,typeOfTest,test_start,test_end,validate_start,validate_end,train_start,train_end):62		input_file=open(self.dataset_file_name , 'r')63		dataset=input_file.read().splitlines()64		if typeOfTest==1:65			# accoustic66			start_column=367			end_column=568		elif typeOfTest==0:69			# visual70			start_column=671			end_column=872		else:73			# multimodal74			start_column=375			end_column=876		test1_data=list()77		test1_label=list()78		validate1_data=list()79		validate1_label=list()80		train1_data=list()81		train1_label=list()82		for i in range(test_start,test_end+1):83			row=list()84			data=dataset[i].split(",")85			for j in range(start_column,end_column+1):86				row.append(float(data[j]))87			test1_data.append(row)88			test1_label.append(int(data[2]))89		for i in range(validate_start,validate_end+1):90			row=list()91			data=dataset[i].split(",")92			for j in range(start_column,end_column+1):93				row.append(float(data[j]))94			validate1_data.append(row)95			validate1_label.append(int(data[2]))96		for i in range(train_start,train_end+1):97			row=list()98			data=dataset[i].split(",")99			for j in range(start_column,end_column+1):100				row.append(float(data[j]))101			train1_data.append(row)102			train1_label.append(int(data[2]))103		return test1_data,test1_label,validate1_data,validate1_label,train1_data,train1_label104	def perform_naivebayes(self,typeOfTest,test_start,test_end):105		test1_data,test1_label,train1_data,train1_label = self.get_fold_test_and_train_only(typeOfTest,test_start,test_end)106		clf = GaussianNB()107		test_results=list()108		clf.fit(train1_data, train1_label)109		for line in test1_data:110			predicted = clf.predict([line])[0]111			test_results.append(predicted)112		print "Accuracy in test: ",metrics.accuracy_score(test1_label,test_results)113	def perform_svm(self,typeOfTest,test_start,test_end,validate_start,validate_end,train_start,train_end):114		test1_data,test1_label,validate1_data,validate1_label,train1_data,train1_label = self.get_fold(typeOfTest,test_start,test_end,validate_start,validate_end,train_start,train_end)115		clf = svm.LinearSVC()116		accuracy=list()117		for c in [0.001,0.01,0.1,1,10,100]:118			validation_result=list()119			clf.set_params(C=c)120			clf.fit(train1_data, train1_label)121			correct_classification = 0122			i=0123			for line in validate1_data:124				validation_result.append(clf.predict([line])[0])125			accuracy.append(metrics.accuracy_score(validate1_label,validation_result))126		print "Accuracy in validation (max): ",max(accuracy) 127		cMax=math.pow(10,(accuracy.index(max(accuracy))-3))128		print "Hyperparameter(c) for max accuracy = ",cMax129		clf = svm.LinearSVC()130		test_results=list()131		train_results=list()132		clf.set_params(C=cMax)133		clf.fit(train1_data, train1_label)134		for line in train1_data:135			train_results.append(clf.predict([line])[0])136		print "Accuracy in training: ",metrics.accuracy_score(train1_label,train_results)137		138		for line in test1_data:139			predicted = clf.predict([line])[0]140			test_results.append(predicted)141		print "\nAccuracy in test: ",metrics.accuracy_score(test1_label,test_results)142	def perform_svm_n_fold(self,num_folds,typeOfTest,test_start,test_end):143		test1_data,test1_label,train1_data,train1_label = self.get_fold_test_and_train_only(typeOfTest,test_start,test_end)144		k_fold = KFold(n_splits=num_folds)145		accuracy=list()146		for c in [0.001,0.01,0.1,1,10,100]:147			clf = svm.LinearSVC(C=c)148			accuracy.append(statistics.mean(cross_val_score(clf,train1_data,train1_label,cv=k_fold,n_jobs=-1)))149		print "Accuracy in validation(max): ",max(accuracy)150		print "Accuracy in validation(average): ",statistics.mean(accuracy)151		cMax=math.pow(10,(accuracy.index(max(accuracy))-3))152		print "Hyperparameter(c) for max accuracy = ",cMax153		clf = svm.LinearSVC()154		test_results=list()155		train_results=list()156		clf.set_params(C=cMax)157		clf.fit(train1_data, train1_label)158		for line in train1_data:159			train_results.append(clf.predict([line])[0])160		print "Accuracy in training: ",metrics.accuracy_score(train1_label,train_results)161		for line in test1_data:162			test_results.append(clf.predict([line])[0])163		print "Accuracy in test: ",metrics.accuracy_score(test1_label,test_results)164	def perform_svm_rbf(self,typeOfTest,test_start,test_end,validate_start,validate_end,train_start,train_end):165		test1_data,test1_label,validate1_data,validate1_label,train1_data,train1_label = self.get_fold(typeOfTest,test_start,test_end,validate_start,validate_end,train_start,train_end)166		clf = svm.SVC()167		accuracy=list()168		for c in [0.001,0.01,0.1,1,10,100]:169			validation_result=list()170			clf.set_params(C=c,kernel="rbf")171			clf.fit(train1_data, train1_label)172			correct_classification = 0173			i=0174			for line in validate1_data:175				validation_result.append(clf.predict([line])[0])176			accuracy.append(metrics.accuracy_score(validate1_label,validation_result))177		cMax=math.pow(10,(accuracy.index(max(accuracy))-3))178		print "Hyperparameter(c) for max accuracy = ",cMax179		clf = svm.SVC(C=cMax,kernel="rbf")180		test_results=list()181		clf.fit(train1_data, train1_label)182		for line in test1_data:183			test_results.append(clf.predict([line])[0])184		print "Accuracy in test: ",metrics.accuracy_score(test1_label,test_results)185	def perform_nn(self,typeOfTest,test_start,test_end,validate_start,validate_end,train_start,train_end):		186		test1_data,test1_label,validate1_data,validate1_label,train1_data,train1_label = self.get_fold(typeOfTest,test_start,test_end,validate_start,validate_end,train_start,train_end)187		accuracy=list()188		hidden_layers=list()189		for ii in range(1,11):190			hidden_layers.append((ii,))191		for hidden_layer in hidden_layers:192			clf = MLPClassifier(solver = 'lbfgs',alpha = 1, hidden_layer_sizes = hidden_layer, random_state = 1)193			clf.fit(train1_data, train1_label)194			195			correct_classification=0196			i=0197			for line in validate1_data:198				if clf.predict([line])[0] == validate1_label[i] :199					correct_classification = correct_classification + 1200				i=i+1201			accuracy.append(correct_classification/float(len(validate1_label)))202		hidden_layer_for_highest_accuracy=hidden_layers[accuracy.index(max(accuracy))]203		print "Hyperparameter(c) for max accuracy = ",hidden_layer_for_highest_accuracy204		clf = MLPClassifier(solver = 'lbfgs',alpha = 1, hidden_layer_sizes = hidden_layer_for_highest_accuracy, random_state = 1)205		clf.fit(train1_data, train1_label)206		test_results=list()207		for line in test1_data:208			test_results.append(clf.predict([line])[0])209		print "Accuracy in test: ",metrics.accuracy_score(test1_label,test_results)210def naive_bayes(a):211	print "\n\n\nNAIVE BAYES"212	print "-------------------"213	print "\nExperiment 1: "214	print "-----------------"215	test_start=1216	test_end=67217	print "Multimodal:"218	a.perform_naivebayes(2,test_start,test_end)219	print "\nAudio:"220	a.perform_naivebayes(1,test_start,test_end)221	print "\nVisual:"222	a.perform_naivebayes(0,test_start,test_end)223	print "\n\nExperiment 2: "224	print "-----------------"225	test_start=212226	test_end=280227	print "Multimodal:"228	a.perform_naivebayes(2,test_start,test_end)229	print "\nAudio:"230	a.perform_naivebayes(1,test_start,test_end)231	print "\nVisual:"232	a.perform_naivebayes(0,test_start,test_end)233	print "\n\nExperiment 3: "234	print "-----------------"235	test_start=141236	test_end=211237	print "Multimodal:"238	a.perform_naivebayes(2,test_start,test_end)239	print "\nAudio:"240	a.perform_naivebayes(1,test_start,test_end)241	print "\nVisual:"242	a.perform_naivebayes(0,test_start,test_end)243	print "\n\nExperiment 4: "244	print "-----------------"245	test_start=68246	test_end=140247	print "Multimodal:"248	a.perform_naivebayes(2,test_start,test_end)249	print "\nAudio:"250	a.perform_naivebayes(1,test_start,test_end)251	print "\nVisual:"252	a.perform_naivebayes(0,test_start,test_end)253def neural_net(a):254	print "\n\n\nNEURAL NETWORK"255	print "------------------"256	print "\nExperiment 1: "257	print "-----------------"258	test_start=1259	test_end=67260	validate_start=68261	validate_end=140262	train_start=141263	train_end=280264	print "Multimodal:"265	a.perform_nn(2,test_start,test_end,validate_start,validate_end,train_start,train_end)266	print "\nAudio:"267	a.perform_nn(1,test_start,test_end,validate_start,validate_end,train_start,train_end)268	print "\nVisual:"269	a.perform_nn(0,test_start,test_end,validate_start,validate_end,train_start,train_end)270	print "\n\nExperiment 2: "271	print "-----------------"272	test_start=212273	test_end=280274	validate_start=141275	validate_end=211276	train_start=1277	train_end=140278	print "Multimodal:"279	a.perform_nn(2,test_start,test_end,validate_start,validate_end,train_start,train_end)280	print "\nAudio:"281	a.perform_nn(1,test_start,test_end,validate_start,validate_end,train_start,train_end)282	print "\nVisual:"283	a.perform_nn(0,test_start,test_end,validate_start,validate_end,train_start,train_end)284	print "\n\nExperiment 3: "285	print "-----------------"286	test_start=141287	test_end=211288	validate_start=212289	validate_end=280290	train_start=1291	train_end=140292	print "Multimodal:"293	a.perform_nn(2,test_start,test_end,validate_start,validate_end,train_start,train_end)294	print "\nAudio:"295	a.perform_nn(1,test_start,test_end,validate_start,validate_end,train_start,train_end)296	print "\nVisual:"297	a.perform_nn(0,test_start,test_end,validate_start,validate_end,train_start,train_end)298	print "\n\nExperiment 4: "299	print "-----------------"300	test_start=68301	test_end=140302	validate_start=1303	validate_end=67304	train_start=141305	train_end=280306	print "Multimodal:"307	a.perform_nn(2,test_start,test_end,validate_start,validate_end,train_start,train_end)308	print "\nAudio:"309	a.perform_nn(1,test_start,test_end,validate_start,validate_end,train_start,train_end)310	print "\nVisual:"311	a.perform_nn(0,test_start,test_end,validate_start,validate_end,train_start,train_end)312def svm_n_folds(a,numFolds):313	print "\n\n\nSVM with ",numFolds," folds and not respecting speaker independence"314	print "---------------------------------------------------------------------------"315	print "\nExperiment 1: "316	print "-----------------"317	test_start=1318	test_end=67319	a.perform_svm_n_fold(numFolds,2,test_start,test_end)320	print "\n\nExperiment 2: "321	print "-----------------"322	test_start=212323	test_end=280324	a.perform_svm_n_fold(numFolds,2,test_start,test_end)325	print "\n\nExperiment 3: "326	print "-----------------"327	test_start=141328	test_end=211329	a.perform_svm_n_fold(numFolds,2,test_start,test_end)330	print "\n\nExperiment 4: "331	print "-----------------"332	test_start=68333	test_end=140334	a.perform_svm_n_fold(numFolds,2,test_start,test_end)335def svm_rbf(a):336	print "\n\n\nSVM with RBF Kernel"337	print "---------------------------"338	print "\nExperiment 1: "339	print "-----------------"340	test_start=1341	test_end=67342	validate_start=68343	validate_end=140344	train_start=141345	train_end=280346	print "Multimodal:"347	a.perform_svm_rbf(2,test_start,test_end,validate_start,validate_end,train_start,train_end)348	print "\nAudio:"349	a.perform_svm_rbf(1,test_start,test_end,validate_start,validate_end,train_start,train_end)350	print "\nVisual:"351	a.perform_svm_rbf(0,test_start,test_end,validate_start,validate_end,train_start,train_end)352	print "\n\nExperiment 2: "353	print "-----------------"354	test_start=212355	test_end=280356	validate_start=141357	validate_end=211358	train_start=1359	train_end=140360	print "Multimodal:"361	a.perform_svm_rbf(2,test_start,test_end,validate_start,validate_end,train_start,train_end)362	print "\nAudio:"363	a.perform_svm_rbf(1,test_start,test_end,validate_start,validate_end,train_start,train_end)364	print "\nVisual:"365	a.perform_svm_rbf(0,test_start,test_end,validate_start,validate_end,train_start,train_end)366	print "\n\nExperiment 3: "367	print "-----------------"368	test_start=141369	test_end=211370	validate_start=212371	validate_end=280372	train_start=1373	train_end=140374	print "Multimodal:"375	a.perform_svm_rbf(2,test_start,test_end,validate_start,validate_end,train_start,train_end)376	print "\nAudio:"377	a.perform_svm_rbf(1,test_start,test_end,validate_start,validate_end,train_start,train_end)378	print "\nVisual:"379	a.perform_svm_rbf(0,test_start,test_end,validate_start,validate_end,train_start,train_end)380	print "\n\nExperiment 4: "381	print "-----------------"382	test_start=68383	test_end=140384	validate_start=1385	validate_end=67386	train_start=141387	train_end=280388	print "Multimodal:"389	a.perform_svm_rbf(2,test_start,test_end,validate_start,validate_end,train_start,train_end)390	print "\nAudio:"391	a.perform_svm_rbf(1,test_start,test_end,validate_start,validate_end,train_start,train_end)392	print "\nVisual:"393	a.perform_svm_rbf(0,test_start,test_end,validate_start,validate_end,train_start,train_end)394def svm_linear(a):395	print "\n\n\nLinear SVM"396	print "--------------------"397	print "\nExperiment 1: "398	print "-----------------"399	test_start=1400	test_end=67401	validate_start=68402	validate_end=140403	train_start=141404	train_end=280405	print "Multimodal:"406	a.perform_svm(2,test_start,test_end,validate_start,validate_end,train_start,train_end)407	print "\nAudio:"408	a.perform_svm(1,test_start,test_end,validate_start,validate_end,train_start,train_end)409	print "\nVisual:"410	a.perform_svm(0,test_start,test_end,validate_start,validate_end,train_start,train_end)411	print "\n\nExperiment 2: "412	print "-----------------"413	test_start=212414	test_end=280415	validate_start=141416	validate_end=211417	train_start=1418	train_end=140419	print "Multimodal:"420	a.perform_svm(2,test_start,test_end,validate_start,validate_end,train_start,train_end)421	print "\nAudio:"422	a.perform_svm(1,test_start,test_end,validate_start,validate_end,train_start,train_end)423	print "\nVisual:"424	a.perform_svm(0,test_start,test_end,validate_start,validate_end,train_start,train_end)425	print "\n\nExperiment 3: "426	print "-----------------"427	test_start=141428	test_end=211429	validate_start=212430	validate_end=280431	train_start=1432	train_end=140433	print "Multimodal:"434	a.perform_svm(2,test_start,test_end,validate_start,validate_end,train_start,train_end)435	print "\nAudio:"436	a.perform_svm(1,test_start,test_end,validate_start,validate_end,train_start,train_end)437	print "\nVisual:"438	a.perform_svm(0,test_start,test_end,validate_start,validate_end,train_start,train_end)439	print "\n\nExperiment 4: "440	print "-----------------"441	test_start=68442	test_end=140443	validate_start=1444	validate_end=67445	train_start=141446	train_end=280447	print "Multimodal:"448	a.perform_svm(2,test_start,test_end,validate_start,validate_end,train_start,train_end)449	print "\nAudio:"450	a.perform_svm(1,test_start,test_end,validate_start,validate_end,train_start,train_end)451	print "\nVisual:"452	a.perform_svm(0,test_start,test_end,validate_start,validate_end,train_start,train_end)453if len(sys.argv) == 1:454	print "Please specifiy the classifier(s) to execute...\n"455else:456	a=CS535MLClassifier('dataset.csv')457	args=sys.argv[1:]458	args=list(set(args))459	for arg in args:460		if arg=="-nb":461			naive_bayes(a)462		if arg=="-svm":463			svm_linear(a)464		if arg=="-svm3":465			svm_n_folds(a,3)466		if arg=="-svm4":467			svm_n_folds(a,4)468		if arg=="-nn":469			neural_net(a)470		if arg=="-svmrbf":...

test_formatters.py

Source:test_formatters.py

...135    fmt = formatters[name](**opts)136    logger = StructuredLogger('test_logger')137    logger.add_handler(StreamHandler(buf, fmt))138    logger.suite_start(['test_foo', 'test_bar', 'test_baz'])139    logger.test_start('test_foo')140    logger.test_end('test_foo', 'OK')141    logger.test_start('test_bar')142    logger.test_status('test_bar', 'a subtest', 'PASS')143    logger.test_end('test_bar', 'OK')144    logger.test_start('test_baz')145    logger.test_end('test_baz', 'FAIL', 'FAIL', 'expected 0 got 1')146    logger.suite_end()147    result = buf.getvalue()148    print("Dumping result for copy/paste:")149    print(result)150    assert result == expected151@pytest.mark.parametrize("name,opts,expected", FORMATS['FAIL'],152                         ids=ids('FAIL'))153def test_fail(name, opts, expected):154    stack = """155    SimpleTest.is@SimpleTest/SimpleTest.js:312:5156    @caps/tests/mochitest/test_bug246699.html:53:1157""".strip('\n')158    buf = BytesIO()159    fmt = formatters[name](**opts)160    logger = StructuredLogger('test_logger')161    logger.add_handler(StreamHandler(buf, fmt))162    logger.suite_start(['test_foo', 'test_bar', 'test_baz'])163    logger.test_start('test_foo')164    logger.test_end('test_foo', 'FAIL', 'PASS', 'expected 0 got 1')165    logger.test_start('test_bar')166    logger.test_status('test_bar', 'a subtest', 'FAIL', 'PASS', 'expected 0 got 1', stack)167    logger.test_status('test_bar', 'another subtest', 'TIMEOUT')168    logger.test_end('test_bar', 'OK')169    logger.test_start('test_baz')170    logger.test_end('test_baz', 'PASS', 'FAIL')171    logger.suite_end()172    result = buf.getvalue()173    print("Dumping result for copy/paste:")174    print(result)175    assert result == expected176if __name__ == '__main__':...

peakcache.spec.js

Source:peakcache.spec.js

1describe('peakcache', function() {2    var peakcache;3    var test_length = 200;4    var test_length2 = 300;5    var test_start = 50;6    var test_end = 100;7    var test_start2 = 100;8    var test_end2 = 120;9    var test_start3 = 120;10    var test_end3 = 150;11    var window_size = 20;12    function __createPeakCache() {13        peakcache = Object.create(WaveSurfer.PeakCache);14        peakcache.init();15    }16    beforeEach(function (done) {17        __createPeakCache();18        done();19    });20    it('empty cache returns full range', function() {21        var newranges = peakcache.addRangeToPeakCache(test_length, test_start, test_end);22        expect(newranges.length).toEqual(1);23        expect(newranges[0][0]).toEqual(test_start);24        expect(newranges[0][1]).toEqual(test_end);25    });26    it('different length clears cache', function() {27        peakcache.addRangeToPeakCache(test_length, test_start, test_end);28        var newranges = peakcache.addRangeToPeakCache(test_length2, test_start, test_end);29        expect(newranges.length).toEqual(1);30        expect(newranges[0][0]).toEqual(test_start);31        expect(newranges[0][1]).toEqual(test_end);32    });33    it('consecutive calls return no ranges', function() {34        peakcache.addRangeToPeakCache(test_length, test_start, test_end);35        var newranges = peakcache.addRangeToPeakCache(test_length, test_start, test_end);36        expect(newranges.length).toEqual(0);37    });38    it('sliding window returns window sized range', function() {39        var newranges = peakcache.addRangeToPeakCache(test_length, test_start, test_end);40        expect(newranges.length).toEqual(1);41        expect(newranges[0][0]).toEqual(test_start);42        expect(newranges[0][1]).toEqual(test_end);43        var newranges = peakcache.addRangeToPeakCache(test_length, test_start + window_size, test_end + window_size);44        expect(newranges.length).toEqual(1);45        expect(newranges[0][0]).toEqual(test_end);46        expect(newranges[0][1]).toEqual(test_end + window_size);47        var newranges = peakcache.addRangeToPeakCache(test_length, test_start + window_size * 2, test_end + window_size * 2);48        expect(newranges.length).toEqual(1);49        expect(newranges[0][0]).toEqual(test_end + window_size);50        expect(newranges[0][1]).toEqual(test_end + window_size * 2);51    });52    it('disjoint set creates two ranges', function() {53        peakcache.addRangeToPeakCache(test_length, test_start, test_end);54        peakcache.addRangeToPeakCache(test_length, test_start3, test_end3);55        var ranges = peakcache.getCacheRanges();56        expect(ranges.length).toEqual(2);57        expect(ranges[0][0]).toEqual(test_start);58        expect(ranges[0][1]).toEqual(test_end);59        expect(ranges[1][0]).toEqual(test_start3);60        expect(ranges[1][1]).toEqual(test_end3);61    });62    it('filling in disjoint sets coalesces', function() {63        peakcache.addRangeToPeakCache(test_length, test_start, test_end);64        peakcache.addRangeToPeakCache(test_length, test_start3, test_end3);65        var newranges = peakcache.addRangeToPeakCache(test_length, test_start, test_end3);66        expect(newranges.length).toEqual(1);67        expect(newranges[0][0]).toEqual(test_end);68        expect(newranges[0][1]).toEqual(test_start3);69        var ranges = peakcache.getCacheRanges();70        expect(ranges.length).toEqual(1);71        expect(ranges[0][0]).toEqual(test_start);72        expect(ranges[0][1]).toEqual(test_end3);73    });74    it('filling in disjoint sets coalesces / edge cases', function() {75        peakcache.addRangeToPeakCache(test_length, test_start, test_end);76        peakcache.addRangeToPeakCache(test_length, test_start3, test_end3);77        var newranges = peakcache.addRangeToPeakCache(test_length, test_start2, test_end2);78        expect(newranges.length).toEqual(1);79        expect(newranges[0][0]).toEqual(test_end);80        expect(newranges[0][1]).toEqual(test_start3);81        var ranges = peakcache.getCacheRanges();82        expect(ranges.length).toEqual(1);83        expect(ranges[0][0]).toEqual(test_start);84        expect(ranges[0][1]).toEqual(test_end3);85    });...

Automation Testing Tutorials

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