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ksanu
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11
.idea/TAU_21_sane_words.iml
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.idea/TAU_21_sane_words.iml
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23
README.md
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23
README.md
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Sane words challenge
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======================
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Guess if a given word is a correct Polish word in a given domain. Additionally, you have the information on reported frequency of the word in source texts.
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Each entry in training data set is of the form: __Sane (0 or 1), Domain, Word, Frequency__.
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Evaluation metric is F2-score.
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Directory structure
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-------------------
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* `README.md` — this file
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* `config.txt` — configuration file
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* `train/` — directory with training data
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* `train/train.tsv` — train set
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* `dev-0/` — directory with dev (test) data
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* `dev-0/in.tsv` — input data for the dev set
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* `dev-0/expected.tsv` — expected (reference) data for the dev set
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* `test-A` — directory with test data
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* `test-A/in.tsv` — input data for the test set
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* `test-A/expected.tsv` — expected (reference) data for the test set
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1
config.txt
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1
config.txt
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--metric F2 --precision 4
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11026
dev-0/expected.tsv
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11026
dev-0/expected.tsv
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11026
dev-0/in.tsv
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11026
dev-0/in.tsv
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11026
dev-0/out.tsv
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dev-0/out.tsv
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11026
dev-0/out_float.tsv
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11026
dev-0/out_float.tsv
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136
solution2.py
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136
solution2.py
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import torch
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import pandas
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import re
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import numpy as np
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from sklearn.preprocessing import LabelEncoder
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from sklearn.metrics import precision_score, recall_score, accuracy_score
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learning_rate = torch.tensor(0.00001, dtype=torch.float)
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def f1_score(y_true, y_pred):
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precision = precision_score(y_true, y_pred, average='micro')
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recall = recall_score(y_true, y_pred, average='micro')
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F1 = 2 * (precision * recall) / (precision + recall)
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return F1
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W = torch.rand([4,16],dtype=torch.float, requires_grad=True)
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b = torch.rand(16,dtype=torch.float, requires_grad=True)
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U = torch.rand(16,dtype=torch.float, requires_grad=True)
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c = torch.rand(1,dtype=torch.float, requires_grad=True)
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def count_polish_diacritics(x):
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x_counts = []
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for i, word in x.iteritems():
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c = len(re.findall(r'[ąćęłńóśźż]', str(word)))
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x_counts.append(c)
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return x_counts
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def Normalize(data, d = None):
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if (d is None):
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d = data
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r = data - d.min()
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return r/(d.max() - d.min())
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train_data = pandas.read_csv('train/train.tsv', sep='\t', names=['Sane', 'Domain', 'Word', 'Frequency'], header=None)
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x1 = Normalize(torch.tensor(train_data['Frequency'], dtype=torch.float))
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x2 = Normalize(torch.tensor(train_data['Word'].str.len(), dtype=torch.float))
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le = LabelEncoder()
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le.fit(train_data['Domain'])
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encoded_domain_col= le.transform(train_data['Domain'])
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x3 = torch.tensor(encoded_domain_col, dtype=torch.float)
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x4 = Normalize(torch.tensor(count_polish_diacritics(train_data['Word']),dtype=torch.float))
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x = torch.stack((x1,x2,x3,x4),0)
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y = torch.tensor(train_data['Sane'], dtype=torch.float)
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#dev data:
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dev_data = pandas.read_csv('dev-0/in.tsv', sep='\t', names=['Domain', 'Word', 'Frequency'], header=None)
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dev_x1 = Normalize(torch.tensor(dev_data['Frequency'], dtype=torch.float), x1)
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dev_x2 = Normalize(torch.tensor(dev_data['Word'].str.len(), dtype=torch.float), x2)
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dev_encoded_domain_col = le.transform(dev_data['Domain'])
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dev_x3 = torch.tensor(dev_encoded_domain_col, dtype=torch.float)
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dev_x4 = Normalize(torch.tensor(count_polish_diacritics(dev_data['Word']), dtype=torch.float), x4)
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dev_x = torch.stack((dev_x1, dev_x2, dev_x3, dev_x4), 0)
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dev_y_test = pandas.DataFrame(pandas.read_csv('dev-0/expected.tsv', encoding="utf-8", delimiter='\t', header=None))
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print("Training...")
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for _ in range(500):
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W.requires_grad_(True)
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b.requires_grad_(True)
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c.requires_grad_(True)
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U.requires_grad_(True)
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for _ in range(1000):
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h = torch.sigmoid(x.transpose(1, 0) @ W + b)
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y_predicted = torch.sigmoid(h @ U + c)
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cost = torch.sum((y_predicted - y) ** 2)
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cost.backward()
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with torch.no_grad():
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W = W - learning_rate * W.grad
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b = b - learning_rate * b.grad
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c = c - learning_rate * c.grad
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U = U - learning_rate * U.grad
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W.requires_grad_(True)
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b.requires_grad_(True)
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c.requires_grad_(True)
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U.requires_grad_(True)
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W.requires_grad_(False)
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b.requires_grad_(False)
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c.requires_grad_(False)
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U.requires_grad_(False)
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print("Dev0 pred...")
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# dev
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dev_h = torch.sigmoid(dev_x.transpose(1, 0) @ W + b)
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dev_y = torch.sigmoid(dev_h @ U + c)
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dev_y = dev_y.numpy()
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dev_y_pred = np.where(dev_y > 0.5, 1, 0)
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score = f1_score(dev_y_test, dev_y_pred)
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print("f1_score_dev0 within training: ", score, "\nAcc: ", accuracy_score(dev_y_test, dev_y_pred))
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W.requires_grad_(False)
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b.requires_grad_(False)
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c.requires_grad_(False)
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U.requires_grad_(False)
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print("Dev0 pred...")
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#dev
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dev_h = torch.sigmoid(dev_x.transpose(1, 0) @ W + b)
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dev_y = torch.sigmoid(dev_h @ U + c)
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dev_y = dev_y.numpy()
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dev_y_pred = np.where(dev_y > 0.5, 1, 0)
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#np.savetxt(f'./dev-0/out_float.tsv', dev_y, '%.f')
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with open('dev-0/out.tsv', 'w') as output_file:
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for out in dev_y_pred:
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print('%s' % out, file=output_file)
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with open('dev-0/out_float.tsv', 'w') as output_file:
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for out in dev_y:
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print('%s' % out, file=output_file)
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y_test = pandas.DataFrame(pandas.read_csv('dev-0/expected.tsv', encoding="utf-8", delimiter='\t', header=None))
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score = f1_score(y_test, dev_y_pred)
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print("f1_score_dev0 after training: ", score,"\nAcc: ", accuracy_score(dev_y_test, dev_y_pred))
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print("TestA pred...")
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#test-A
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testA_data = pandas.read_csv('dev-0/in.tsv', sep='\t', names=['Domain', 'Word', 'Frequency'], header=None)
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testA_x1 = Normalize(torch.tensor(testA_data['Frequency'], dtype=torch.float), x1)
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testA_x2 = Normalize(torch.tensor(testA_data['Word'].str.len(), dtype=torch.float), x2)
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testA_encoded_domain_col= le.transform(testA_data['Domain'])
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testA_x3 = torch.tensor(testA_encoded_domain_col, dtype=torch.float)
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testA_x4 = Normalize(torch.tensor(count_polish_diacritics(testA_data['Word']),dtype=torch.float), x4)
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testA_x = torch.stack((testA_x1,testA_x2,testA_x3,testA_x4),0)
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testA_h = torch.sigmoid(testA_x.transpose(1, 0) @ W + b)
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testA_y = torch.sigmoid(testA_h @ U + c)
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testA_y = testA_y.numpy()
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testA_y_pred = np.where(testA_y > 0.5, 1, 0)
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np.savetxt(f'./test-A/out_float.tsv', testA_y)
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with open('test-A/out.tsv', 'w') as output_file:
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for out in testA_y_pred:
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print('%s' % out, file=output_file)
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with open('test-A/out_float.tsv', 'w') as output_file:
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for out in testA_y:
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print('%s' % out, file=output_file)
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11061
test-A/in.tsv
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11061
test-A/in.tsv
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11026
test-A/out.tsv
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11026
test-A/out.tsv
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11026
test-A/out_float.tsv
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11026
test-A/out_float.tsv
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44344
train/train.tsv
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44344
train/train.tsv
Normal file
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Load Diff
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