199 lines
6.6 KiB
Python
199 lines
6.6 KiB
Python
import torch
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import random
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from torch import nn
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from torch import optim
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import pandas
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import numpy as np
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import re
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import timeit
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from sklearn.metrics import precision_score, recall_score, accuracy_score
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from torch.utils.data import Dataset, DataLoader
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#10 features: 4 normal + 6 from domain_onehot
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model = nn.Sequential(
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nn.Linear(10, 16),
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nn.ReLU(),
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nn.Linear(16,1),
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nn.Sigmoid())
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criterion = nn.MSELoss()
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optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
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#optimizer = optim.Adam(model.parameters())
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minibatch_size = 5
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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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c2 = c / len(str(word))
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x_counts.append(c2)
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return x_counts
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def count_vowels(x):
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out = []
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for index,row in x.iteritems():
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vowel_len = len(re.findall(r'[aąeęioóuy]', str(row)))
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word_len = len(str(row))
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out.append(vowel_len / word_len) #RATE
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return out
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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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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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#Transforms df with categorical values to One Hot format
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def ToOneHot(df_col):
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out = []
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df_labels = pandas.unique(df_col)
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l_count = len(df_labels)
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for index, row in df_col.iteritems():
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blank_one_hot = np.full(l_count, 0)
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for i in range(0, l_count):
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if df_labels[i] == row:
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blank_one_hot[i] = 1
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out.append(blank_one_hot)
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out_df = pandas.DataFrame(out, columns=df_labels)
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return out_df, df_labels
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def ToOneHot_preproces(df_col, df_labels):
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out = []
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l_count = len(df_labels)
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for index, row in df_col.iteritems():
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blank_one_hot = np.full(l_count, 0)
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for i in range(0, l_count):
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if df_labels[i] == row:
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blank_one_hot[i] = 1
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out.append(blank_one_hot)
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out_df = pandas.DataFrame(out, columns=df_labels)
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return out_df
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class TrainDataset(Dataset):
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def __init__(self, X, y):
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self.X = X
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self.y = y
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def __len__(self):
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return len(self.X)
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def __getitem__(self, idx):
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return self.X[idx], self.y[idx]
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#Load data:
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#Train
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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(count_vowels(train_data['Word']), dtype=torch.float))
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domain_onehot, domain_labels = ToOneHot(train_data['Domain'])
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x3 = torch.tensor(domain_onehot.values, dtype=torch.float)
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x4 = Normalize(torch.tensor(count_polish_diacritics(train_data['Word']),dtype=torch.float))
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x5 = Normalize(torch.tensor(train_data['Word'].str.len(), dtype=torch.float))
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x_temp = torch.stack((x1,x2,x4, x5),0)
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x = torch.cat([x_temp.transpose(1,0), x3], 1)
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#debug_x = pandas.DataFrame(x.numpy())
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y = torch.tensor(train_data['Sane'], dtype=torch.float)
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#dev0
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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(count_vowels(dev_data['Word']), dtype=torch.float), x2)
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dev_x3 = torch.tensor(ToOneHot_preproces(dev_data['Domain'], domain_labels).values, 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_x5 = Normalize(torch.tensor(dev_data['Word'].str.len(), dtype=torch.float), x5)
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dev_x_temp = torch.stack((dev_x1, dev_x2, dev_x4, dev_x5), 0)
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dev_x = torch.cat([dev_x_temp.transpose(1,0), dev_x3], 1)
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#test-A
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testA_data = pandas.read_csv('test-A/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(count_vowels(testA_data['Word']), dtype=torch.float), x2)
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testA_x3 = torch.tensor(ToOneHot_preproces(testA_data['Domain'], domain_labels).values, 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_x5 = Normalize(torch.tensor(testA_data['Word'].str.len(), dtype=torch.float), x5)
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testA_x_temp = torch.stack((testA_x1,testA_x2,testA_x4, testA_x5),0)
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testA_x = torch.cat([testA_x_temp.transpose(1,0), testA_x3], 1)
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dataset_train = TrainDataset(x, y)
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trainloader=DataLoader(dataset=dataset_train,batch_size=5)
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for i in range(2):
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for xb, yb_expected in trainloader: # for each iteration a bach of samples is taken from loader(currently batch_size=5)
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yp = model(xb)
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# debug
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"""
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debug_xb = pandas.DataFrame(xb.numpy())
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debug_yb_expected = pandas.DataFrame(yb_expected.numpy())
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debug_yp = pandas.DataFrame(yp.detach().numpy())
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"""
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#print(torch.squeeze(yp))
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#print(yb_expected)
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loss = criterion(torch.squeeze(yp), yb_expected)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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#print(loss)
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#4 200
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#elapsed_time = timeit.timeit(train_loop, number=1)
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#print("Training time: ", elapsed_time, "seconds")
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#saving results:
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#dev0:
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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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dev_y = model(dev_x)
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file=open("dev-0/out.tsv","w")
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file2=open("dev-0/out_float.tsv","w")
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for i in range(0,11026):
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file2.write(str(dev_y[i].data.item()) + "\n")
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var = dev_y[i].data.item()
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if var < 0.5:
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file.write("0" + "\n")
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else:
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file.write("1" + "\n")
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file.close()
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file2.close()
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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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dev_y_pred = pandas.DataFrame(pandas.read_csv('dev-0/out.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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#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
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#testA:
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testA_y = model(testA_x)
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file=open("test-A/out.tsv","w+")
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file2=open("test-A/out_float.tsv","w")
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for i in range(0,11061):
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file2.write(str(testA_y[i].data.item()) + "\n")
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if testA_y[i].data.item() < 0.5:
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file.write("0\n")
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else:
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file.write("1\n")
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file.close()
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file2.close()
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file=open("results.txt","w")
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file.write("loss = " + str(loss.item())+ "\n")
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file.write("f1: " + str(score) + "\n" )
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file.close() |