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How to use test_sets method in assertpy
Best Python code snippet using assertpy_python
linreg.py
Source: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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