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How to use test_sets method in assertpy

Best Python code snippet using assertpy_python

linreg.py

1import csv2import numpy as np3import matplotlib.pyplot as plt4def read_data(filename):5	data = []6	with open(filename) as csvfile:7		reader = csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC)8		for row in reader:9			data.append(row)10	return np.array(data)11def mse(l, train, test, train_regs, test_regs):12	tp = np.matrix.transpose(train)13	i = np.identity(len(train[0]))14	train_t = np.matrix.transpose(train_regs)[0]15	test_t = np.matrix.transpose(test_regs)[0]16	w = np.dot(np.matmul(np.linalg.inv((l * i) + np.matmul(tp, train)), tp), train_t)17	return np.mean(np.square(np.matmul(test, w) - test_t))	18def task1(train_sets, train_regs, test_sets, test_regs):19	sets = ["100-10", "100-100", "1000-100", "forest_fire", "real_estate", "50(1000)-100", "100(1000)-100", "150(1000)-100"]20	print("Lambda\tMSE")21	for i in range(len(train_sets)):22		train_mse = []23		test_mse = []24		for l in range(0, 151):25			train_mse.append(mse(l, train_sets[i], train_sets[i], train_regs[i], train_regs[i]))26			test_mse.append(mse(l, train_sets[i], test_sets[i], train_regs[i], test_regs[i]))27		plt.plot(range(0, 151), train_mse, label = "Train")28		plt.plot(range(0, 151), test_mse, label = "Test")29		plt.legend()30		plt.ylabel("MSE")31		plt.xlabel("Lambda")32		plt.title(sets[i])33		plt.show()34		print(str(np.argmin(test_mse)) + "\t" + str(min(test_mse)))35def task2(train_sets, train_regs, test_sets, test_regs):36	print("Lambda\tMSE")37	for i in range(len(train_sets)):38		mse_folds = []39		for j in range (0, 10):40			test_nums = range(0, len(train_sets[i]))[j::10]41			train_nums = np.setdiff1d(range(0, len(train_sets[i])), test_nums)42			train = np.take(train_sets[i], train_nums, axis = 0)43			test = np.take(train_sets[i], test_nums, axis = 0)44			train_reg = np.take(train_regs[i], train_nums)45			test_reg = np.take(train_regs[i], test_nums)46			47			mses = []48			for l in range(0, 151):49				mses.append(mse(l, train, test, train_reg, test_reg))50			mse_folds.append(mses)51		opt_l = np.argmin(np.sum(mse_folds, axis = 0))52		test_mse = mse(l, train_sets[i], test_sets[i], train_regs[i], test_regs[i])53		print(str(opt_l) + "\t" + str(test_mse))54	return55def task3(train_sets, train_regs, test_sets, test_regs):56	print("Alpha\tBeta\tMSE")57	for i in range(0, 8):58		train = train_sets[i]59		tp = np.matrix.transpose(train)60		r = train_regs[i]61		l = np.linalg.eigvals(np.matmul(tp, train))62		m = 0	63		a = 164		b = 165		diff = 166		while (diff > .0000000001):67			s = np.linalg.inv(a * np.identity(len(train[0])) + b * np.matmul(tp, train))68			m = np.matrix.transpose(b * np.dot(np.matmul(s, tp), r))[0]69			c = np.sum(np.divide(b * l, (a + b * l)))70			newa = c / np.matmul(np.matrix.transpose(m), m)71			newb = 1.0/((1.0/(len(train) - c)) * np.sum(np.square(r - np.dot(m, tp))))72			diff = min(abs(a - newa), abs(b - newb))73			a = newa74			b = newb75		test_mse = np.mean(np.square(np.matmul(test_sets[i], m) - test_regs[i]))76		print(str(np.real(a)) + "\t" + str(np.real(b)) + "\t" + str(np.real(test_mse)))77	return78	79if __name__ == "__main__":80	train_sets = []81	train_sets.append(read_data("train-100-10.csv"))82	train_sets.append(read_data("train-100-100.csv"))	83	train_sets.append(read_data("train-1000-100.csv"))84	train_sets.append(read_data("train-forestfire.csv"))85	train_sets.append(read_data("train-realestate.csv"))86	train_sets.append(train_sets[2][:50])87	train_sets.append(train_sets[2][:100])88	train_sets.append(train_sets[2][:150])89	train_regs = []90	train_regs.append(read_data("trainR-100-10.csv"))91        train_regs.append(read_data("trainR-100-100.csv"))92        train_regs.append(read_data("trainR-1000-100.csv"))93        train_regs.append(read_data("trainR-forestfire.csv"))94        train_regs.append(read_data("trainR-realestate.csv"))95        train_regs.append(train_regs[2][:50])96        train_regs.append(train_regs[2][:100])97        train_regs.append(train_regs[2][:150])98	99	test_sets = []100	test_sets.append(read_data("test-100-10.csv"))101        test_sets.append(read_data("test-100-100.csv"))102        test_sets.append(read_data("test-1000-100.csv"))103        test_sets.append(read_data("test-forestfire.csv"))104        test_sets.append(read_data("test-realestate.csv"))105	test_sets.append(test_sets[2])106	test_sets.append(test_sets[2])107	test_sets.append(test_sets[2])108	test_regs = []109	test_regs.append(read_data("testR-100-10.csv"))110        test_regs.append(read_data("testR-100-100.csv"))111        test_regs.append(read_data("testR-1000-100.csv"))112        test_regs.append(read_data("testR-forestfire.csv"))113        test_regs.append(read_data("testR-realestate.csv"))114        test_regs.append(test_regs[2])115        test_regs.append(test_regs[2])116        test_regs.append(test_regs[2])117	task1(train_sets, train_regs, test_sets, test_regs)118	task2(train_sets, train_regs, test_sets, test_regs)...

constants.py

Source: constants.py

1from pathlib import Path2# Paths3PACKAGE_DIR = Path(__file__).resolve().parent.parent4RESOURCES_DIR = PACKAGE_DIR / "resources"5TOOLS_DIR = RESOURCES_DIR / "tools"6DATA_DIR = RESOURCES_DIR / "data"7STANFORD_CORENLP_DIR = TOOLS_DIR / "stanford-corenlp-full-2018-10-05"8UCCA_DIR = TOOLS_DIR / "ucca-bilstm-1.3.10"9UCCA_PARSER_PATH = UCCA_DIR / "models/ucca-bilstm"10TEST_SETS_PATHS = {11    ('asset_test', 'orig'): DATA_DIR / f'test_sets/asset/asset.test.orig',12    ('asset_test', 'refs'): [DATA_DIR / f'test_sets/asset/asset.test.simp.{i}' for i in range(10)],13    ('asset_valid', 'orig'): DATA_DIR / f'test_sets/asset/asset.valid.orig',14    ('asset_valid', 'refs'): [DATA_DIR / f'test_sets/asset/asset.valid.simp.{i}' for i in range(10)],15    ('turkcorpus_test', 'orig'): DATA_DIR / f'test_sets/turkcorpus/test.truecase.detok.orig',16    ('turkcorpus_test', 'refs'): [DATA_DIR / f'test_sets/turkcorpus/test.truecase.detok.simp.{i}' for i in range(8)],17    ('turkcorpus_valid', 'orig'): DATA_DIR / f'test_sets/turkcorpus/tune.truecase.detok.orig',18    ('turkcorpus_valid', 'refs'): [DATA_DIR / f'test_sets/turkcorpus/tune.truecase.detok.simp.{i}' for i in range(8)],19    ('turkcorpus_test_legacy', 'orig'): DATA_DIR / f'test_sets/turkcorpus/legacy/test.8turkers.tok.norm',20    ('turkcorpus_test_legacy', 'refs'): [21        DATA_DIR / f'test_sets/turkcorpus/legacy/test.8turkers.tok.turk.{i}' for i in range(8)22    ],23    ('turkcorpus_valid_legacy', 'orig'): DATA_DIR / f'test_sets/turkcorpus/legacy/tune.8turkers.tok.norm',24    ('turkcorpus_valid_legacy', 'refs'): [25        DATA_DIR / f'test_sets/turkcorpus/legacy/tune.8turkers.tok.turk.{i}' for i in range(8)26    ],27    ('pwkp_test', 'orig'): DATA_DIR / f'test_sets/pwkp/pwkp.test.orig',28    ('pwkp_test', 'refs'): [DATA_DIR / f'test_sets/pwkp/pwkp.test.simp'],29    ('pwkp_valid', 'orig'): DATA_DIR / f'test_sets/pwkp/pwkp.valid.orig',30    ('pwkp_valid', 'refs'): [DATA_DIR / f'test_sets/pwkp/pwkp.valid.simp'],31    ('hsplit_test', 'orig'): DATA_DIR / f'test_sets/hsplit/hsplit.tok.src',32    ('hsplit_test', 'refs'): [DATA_DIR / f'test_sets/hsplit/hsplit.tok.{i+1}' for i in range(4)],33    ('wikisplit_test', 'orig'): DATA_DIR / f'test_sets/wikisplit/wikisplit.test.untok.orig',34    ('wikisplit_test', 'refs'): [DATA_DIR / f'test_sets/wikisplit/wikisplit.test.untok.split'],35    ('wikisplit_valid', 'orig'): DATA_DIR / f'test_sets/wikisplit/wikisplit.valid.untok.orig',36    ('wikisplit_valid', 'refs'): [DATA_DIR / f'test_sets/wikisplit/wikisplit.valid.untok.split'],37    ('googlecomp_test', 'orig'): DATA_DIR / f'test_sets/googlecomp/googlecomp.test.orig',38    ('googlecomp_test', 'refs'): [DATA_DIR / f'test_sets/googlecomp/googlecomp.test.comp'],39    ('googlecomp_valid', 'orig'): DATA_DIR / f'test_sets/googlecomp/googlecomp.valid.orig',40    ('googlecomp_valid', 'refs'): [DATA_DIR / f'test_sets/googlecomp/googlecomp.valid.comp'],41    ('qats_test', 'orig'): DATA_DIR / f'test_sets/qats/qats.test.orig',42    ('qats_test', 'refs'): [DATA_DIR / f'test_sets/qats/qats.test.simp'],43}44SYSTEM_OUTPUTS_DIR = DATA_DIR / "system_outputs"45SYSTEM_OUTPUTS_DIRS_MAP = {46    "turkcorpus_test": SYSTEM_OUTPUTS_DIR / "turkcorpus/test",47    "turkcorpus_valid": SYSTEM_OUTPUTS_DIR / "turkcorpus/valid",48    "pwkp_test": SYSTEM_OUTPUTS_DIR / "pwkp/test",49}50# Constants51VALID_TEST_SETS = list(set([test_set for test_set, language in TEST_SETS_PATHS.keys()])) + ['custom']52VALID_METRICS = [53    'bleu',54    'sari',55    'samsa',56    'fkgl',57    'sent_bleu',58    'f1_token',59    'sari_legacy',60    'sari_by_operation',61    'bertscore',62]...

controversy.py

Source: controversy.py

1from cpath import data_path2from data_generator.tokenizer_b import FullTokenizerWarpper3from evaluation import *4from models.cnn_predictor import CNNPredictor5from models.controversy import *6def eval_all_contrv():7    ams_X, ams_Y = amsterdam.get_dev_data(False)8    clue_X, clue_Y = controversy.load_clueweb_testset()9    guardian_X, guardian_Y = controversy.load_guardian()10    models = []11    #models.append(("CNN/Wiki", CNNPredictor("WikiContrvCNN")))12    models.append(("CNN/Wiki", CNNPredictor("WikiContrvCNN_sigmoid", "WikiContrvCNN")))13    #models.append(("tlm/wiki", get_wiki_doc_lm()))14    #models.append(("Bert/Wiki", BertPredictor("WikiContrv2009")))15    #models.append(("Bert/Wiki", BertPredictor("WikiContrv2009_only_wiki")))16    #models.append(("tlm/dbpedia", get_dbpedia_contrv_lm()))17    #models.append(("tlm/Guardian", get_guardian16_lm()))18    #models.append(("yw_may", get_yw_may()))19    #models.append(("Guardian2", get_guardian_selective_lm()))20    test_sets = []21    #test_sets.append(("Ams18", [ams_X, ams_Y]))22    test_sets.append(("Clueweb" ,[clue_X, clue_Y]))23    #test_sets.append(("Guardian", [guardian_X, guardian_Y]))24    for set_name, test_set in test_sets:25        dev_X, dev_Y = test_set26        print(set_name)27        for name, model in models:28            scores = model.score(dev_X)29            auc = compute_pr_auc(scores, dev_Y)30            #auc = compute_auc(scores, dev_Y)31            acc = compute_opt_acc(scores, dev_Y)32            prec = compute_opt_prec(scores, dev_Y)33            recall = compute_opt_recall(scores, dev_Y)34            f1 = compute_opt_f1(scores, dev_Y)35            print("{0}\t{1:.03f}\t{2:.03f}\t{3:.03f}\t{4:.03f}\t{5:.03f}".format(name, auc, prec, recall, f1, acc))36def dataset_stat():37    ams_X, ams_Y = amsterdam.get_dev_data(False)38    clue_X, clue_Y = controversy.load_clueweb_testset()39    guardian_X, guardian_Y = controversy.load_guardian()40    vocab_size = 3052241    vocab_filename = "bert_voca.txt"42    voca_path = os.path.join(data_path, vocab_filename)43    encoder = FullTokenizerWarpper(voca_path)44    test_sets = []45    test_sets.append(("Ams18", [ams_X, ams_Y]))46    test_sets.append(("Clueweb" ,[clue_X, clue_Y]))47    test_sets.append(("Guardian", [guardian_X, guardian_Y]))48    for set_name, test_set in test_sets:49        dev_X, dev_Y = test_set50        num_over_size = 051        length_list = []52        for doc in dev_X:53            tokens = encoder.encode(doc)54            if len(tokens) > 200:55                num_over_size += 156            length_list.append(len(tokens))57        print("{0} {1:.03f} {2:.03f}".format(set_name, num_over_size / len(dev_X), average(length_list)))58if __name__ == '__main__':...

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