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How to use test_start method in Testify
Best Python code snippet using Testify_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 });...
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