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+0.8967489004135132 +0.8560936450958252 +0.5613073706626892 +0.7645094990730286 diff --git a/solution2.py b/solution2.py index 2f0c022..8f918db 100644 --- a/solution2.py +++ b/solution2.py @@ -2,135 +2,160 @@ import torch import pandas import re import numpy as np -from sklearn.preprocessing import LabelEncoder from sklearn.metrics import precision_score, recall_score, accuracy_score -learning_rate = torch.tensor(0.00001, dtype=torch.float) +learning_rate = torch.tensor(0.00005, dtype=torch.float) def f1_score(y_true, y_pred): precision = precision_score(y_true, y_pred, average='micro') recall = recall_score(y_true, y_pred, average='micro') F1 = 2 * (precision * recall) / (precision + recall) return F1 -W = torch.rand([4,16],dtype=torch.float, requires_grad=True) -b = torch.rand(16,dtype=torch.float, requires_grad=True) -U = torch.rand(16,dtype=torch.float, requires_grad=True) -c = torch.rand(1,dtype=torch.float, requires_grad=True) +W1 = torch.rand([5,16],dtype=torch.float, requires_grad=True) +b1 = torch.rand(16,dtype=torch.float, requires_grad=True) +W2 = torch.rand(16,dtype=torch.float, requires_grad=True) +b2 = torch.rand(1,dtype=torch.float, requires_grad=True) + + def count_polish_diacritics(x): x_counts = [] for i, word in x.iteritems(): c = len(re.findall(r'[ąćęłńóśźż]', str(word))) - x_counts.append(c) + c2 = c / len(str(word)) + x_counts.append(c2) return x_counts - +def count_vowels(x): + out = [] + for index,row in x.iteritems(): + vowel_len = len(re.findall(r'[aąeęioóuy]', str(row))) + word_len = len(str(row)) + out.append(vowel_len / word_len) #RATE + return out def Normalize(data, d = None): if (d is None): d = data r = data - d.min() return r/(d.max() - d.min()) +def model(data_x): + h1=torch.relu(data_x.transpose(1,0) @ W1 + b1) + m_y = torch.sigmoid(h1 @ W2 + b2) + return m_y + train_data = pandas.read_csv('train/train.tsv', sep='\t', names=['Sane', 'Domain', 'Word', 'Frequency'], header=None) x1 = Normalize(torch.tensor(train_data['Frequency'], dtype=torch.float)) -x2 = Normalize(torch.tensor(train_data['Word'].str.len(), dtype=torch.float)) -le = LabelEncoder() -le.fit(train_data['Domain']) -encoded_domain_col= le.transform(train_data['Domain']) -x3 = torch.tensor(encoded_domain_col, dtype=torch.float) +x2 = Normalize(torch.tensor(count_vowels(train_data['Word']), dtype=torch.float)) +x3 = torch.tensor(train_data['Domain'].astype('category').cat.codes, dtype=torch.float) + x4 = Normalize(torch.tensor(count_polish_diacritics(train_data['Word']),dtype=torch.float)) -x = torch.stack((x1,x2,x3,x4),0) +x5 = Normalize(torch.tensor(train_data['Word'].str.len(), dtype=torch.float)) +x = torch.stack((x1,x2,x3,x4, x5),0) y = torch.tensor(train_data['Sane'], dtype=torch.float) +count=1 +for index, row in train_data['Sane'].iteritems(): + if row > 0: + count += 1 + +print(count) +print(y) + +print("Training...") +criterion = torch.nn.MSELoss(reduction='sum') +for i in range(80): + for j in range(1000): + y_predicted = model(x) + cost = criterion(y_predicted, y) + cost.backward() + #print(str(i), " ; ", cost) + if (cost.item() < 40000): + learning_rate = torch.tensor(0.00001, dtype=torch.float) + #if (cost.item() < 1614): + # learning_rate = torch.tensor(0.000001, dtype=torch.float) + with torch.no_grad(): + W1 = W1 - learning_rate * W1.grad + b1 = b1 - learning_rate * b1.grad + W2 = W2 - learning_rate * W2.grad + b2 = b2 - learning_rate * b2.grad + + + W1.requires_grad_(True) + b1.requires_grad_(True) + W2.requires_grad_(True) + b2.requires_grad_(True) + + + + if (cost.item() < 1700): + break + #print("Dev0 pred...") + # dev + + + +print("Dev0 pred...") #dev data: dev_data = pandas.read_csv('dev-0/in.tsv', sep='\t', names=['Domain', 'Word', 'Frequency'], header=None) dev_x1 = Normalize(torch.tensor(dev_data['Frequency'], dtype=torch.float), x1) -dev_x2 = Normalize(torch.tensor(dev_data['Word'].str.len(), dtype=torch.float), x2) +dev_x2 = Normalize(torch.tensor(count_vowels(dev_data['Word']), dtype=torch.float), x2) -dev_encoded_domain_col = le.transform(dev_data['Domain']) -dev_x3 = torch.tensor(dev_encoded_domain_col, dtype=torch.float) +dev_x3 = Normalize(torch.tensor(dev_data['Domain'].astype('category').cat.codes, dtype=torch.float), x3) dev_x4 = Normalize(torch.tensor(count_polish_diacritics(dev_data['Word']), dtype=torch.float), x4) -dev_x = torch.stack((dev_x1, dev_x2, dev_x3, dev_x4), 0) +dev_x5 = Normalize(torch.tensor(dev_data['Word'].str.len(), dtype=torch.float), x5) + +dev_x = torch.stack((dev_x1, dev_x2, dev_x3, dev_x4, dev_x5), 0) dev_y_test = pandas.DataFrame(pandas.read_csv('dev-0/expected.tsv', encoding="utf-8", delimiter='\t', header=None)) -print("Training...") +dev_y = model(dev_x) +#dev_y_pred = np.where(dev_y > 0.5, 1, 0) +#np.savetxt(f'./dev-0/out.tsv', dev_y_pred, '%d') -for _ in range(500): - W.requires_grad_(True) - b.requires_grad_(True) - c.requires_grad_(True) - U.requires_grad_(True) - for _ in range(1000): - h = torch.sigmoid(x.transpose(1, 0) @ W + b) - y_predicted = torch.sigmoid(h @ U + c) - cost = torch.sum((y_predicted - y) ** 2) - cost.backward() - with torch.no_grad(): - W = W - learning_rate * W.grad - b = b - learning_rate * b.grad - c = c - learning_rate * c.grad - U = U - learning_rate * U.grad - W.requires_grad_(True) - b.requires_grad_(True) - c.requires_grad_(True) - U.requires_grad_(True) - W.requires_grad_(False) - b.requires_grad_(False) - c.requires_grad_(False) - U.requires_grad_(False) - print("Dev0 pred...") - # dev - dev_h = torch.sigmoid(dev_x.transpose(1, 0) @ W + b) - dev_y = torch.sigmoid(dev_h @ U + c) - dev_y = dev_y.numpy() - dev_y_pred = np.where(dev_y > 0.5, 1, 0) - score = f1_score(dev_y_test, dev_y_pred) - print("f1_score_dev0 within training: ", score, "\nAcc: ", accuracy_score(dev_y_test, dev_y_pred)) +file=open("dev-0/out.tsv","w") +file2=open("dev-0/out_float.tsv","w") -W.requires_grad_(False) -b.requires_grad_(False) -c.requires_grad_(False) -U.requires_grad_(False) - -print("Dev0 pred...") -#dev +for i in range(0,11026): + file2.write(str(dev_y[i].data.item()) + "\n") + var = dev_y[i].data.item() + if var < 0.5: + file.write("0" + "\n") + else: + file.write("1" + "\n") +file.close() +file2.close() -dev_h = torch.sigmoid(dev_x.transpose(1, 0) @ W + b) -dev_y = torch.sigmoid(dev_h @ U + c) -dev_y = dev_y.numpy() -dev_y_pred = np.where(dev_y > 0.5, 1, 0) -#np.savetxt(f'./dev-0/out_float.tsv', dev_y, '%.f') -with open('dev-0/out.tsv', 'w') as output_file: - for out in dev_y_pred: - print('%s' % out, file=output_file) -with open('dev-0/out_float.tsv', 'w') as output_file: - for out in dev_y: - print('%s' % out, file=output_file) y_test = pandas.DataFrame(pandas.read_csv('dev-0/expected.tsv', encoding="utf-8", delimiter='\t', header=None)) +dev_y_pred = pandas.DataFrame(pandas.read_csv('dev-0/out.tsv', encoding="utf-8", delimiter='\t', header=None)) score = f1_score(y_test, dev_y_pred) print("f1_score_dev0 after training: ", score,"\nAcc: ", accuracy_score(dev_y_test, dev_y_pred)) print("TestA pred...") #test-A -testA_data = pandas.read_csv('dev-0/in.tsv', sep='\t', names=['Domain', 'Word', 'Frequency'], header=None) +testA_data = pandas.read_csv('test-A/in.tsv', sep='\t', names=['Domain', 'Word', 'Frequency'], header=None) testA_x1 = Normalize(torch.tensor(testA_data['Frequency'], dtype=torch.float), x1) -testA_x2 = Normalize(torch.tensor(testA_data['Word'].str.len(), dtype=torch.float), x2) - -testA_encoded_domain_col= le.transform(testA_data['Domain']) -testA_x3 = torch.tensor(testA_encoded_domain_col, dtype=torch.float) +testA_x2 = Normalize(torch.tensor(count_vowels(testA_data['Word']), dtype=torch.float), x2) +testA_x3 = Normalize(torch.tensor(testA_data['Domain'].astype('category').cat.codes, dtype=torch.float), x3) testA_x4 = Normalize(torch.tensor(count_polish_diacritics(testA_data['Word']),dtype=torch.float), x4) -testA_x = torch.stack((testA_x1,testA_x2,testA_x3,testA_x4),0) +testA_x5 = Normalize(torch.tensor(testA_data['Word'].str.len(), dtype=torch.float), x5) -testA_h = torch.sigmoid(testA_x.transpose(1, 0) @ W + b) -testA_y = torch.sigmoid(testA_h @ U + c) -testA_y = testA_y.numpy() -testA_y_pred = np.where(testA_y > 0.5, 1, 0) -np.savetxt(f'./test-A/out_float.tsv', testA_y) -with open('test-A/out.tsv', 'w') as output_file: - for out in testA_y_pred: - print('%s' % out, file=output_file) -with open('test-A/out_float.tsv', 'w') as output_file: - for out in testA_y: - print('%s' % out, file=output_file) +testA_x = torch.stack((testA_x1,testA_x2,testA_x3,testA_x4, testA_x5),0) + +testA_y = model(testA_x) + +#np.savetxt(f'./test-A/out.tsv', testA_y_pred, '%d') + + +file=open("test-A/out.tsv","w") +file2=open("test-A/out_float.tsv","w") + +for i in range(0,11061): + file2.write(str(testA_y[i].data.item()) + "\n") + if testA_y[i].data.item() < 0.5: + file.write("0" + "\n") + else: + file.write("1" + "\n") +file.close() +file2.close() \ No newline at end of file diff --git a/test-A/out.tsv b/test-A/out.tsv index 27986fa..6c42091 100644 --- a/test-A/out.tsv +++ b/test-A/out.tsv @@ -1,9940 +1,7865 @@ 0 +1 +1 +1 0 0 0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 0 +1 +1 0 +1 +1 +1 +1 +1 +1 0 0 +1 +1 +1 0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 0 0 +1 +1 +1 +1 +1 0 0 +1 +1 +1 +1 +1 +1 +1 0 +1 0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 0 +1 +1 0 +1 +1 0 0 +1 +1 +1 0 +1 +1 +1 +1 +1 +1 +1 +1 0 +1 +1 +1 +1 +1 +1 +1 +1 0 0 0 +1 +1 0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 0 0 +1 +1 +1 0 +1 0 +1 +1 +1 +1 +1 +1 +1 +1 0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 0 +1 +1 +1 +1 +1 +1 0 +1 +1 +1 +1 +1 +1 0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 0 +1 +1 0 +1 0 0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 0 +1 +1 +1 +1 +1 +1 0 +1 +1 +1 +1 +1 +1 +1 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