132 lines
3.3 KiB
Python
132 lines
3.3 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# In[2]:
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import torch
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import gensim.downloader as downloader
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import pandas as pd
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import csv
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from nltk.tokenize import word_tokenize as tokenize
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import numpy as np
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# In[7]:
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class NeuralNetworkModel(torch.nn.Module):
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def __init__(self, input_size, hidden_size, num_classes):
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super(NeuralNetworkModel, self).__init__()
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self.fc1 = torch.nn.Linear(input_size,hidden_size)
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self.fc2 = torch.nn.Linear(hidden_size,num_classes)
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def forward(self,x):
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x = self.fc1(x)
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x = torch.relu(x)
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x = self.fc2(x)
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x = torch.sigmoid(x)
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return x
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# In[4]:
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w2v = downloader.load('word2vec-google-news-300')
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# In[9]:
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#model + settings
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nn = NeuralNetworkModel(300,300,1)
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crit = torch.nn.BCELoss()
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opti = torch.optim.SGD(nn.parameters(), lr=0.08)
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BATCH_SIZE = 5
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epochs = 5
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# In[12]:
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#trening
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#wczytanie danych
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train_data_in = pd.read_csv('train/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\t', nrows=3000)
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train_data_ex = pd.read_csv('train/expected.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\t', nrows=3000)
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#preprocessing
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train_in = train_data_in[0].str.lower()
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train_in = [tokenize(line) for line in train_in]
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train_in = [np.mean([w2v[x] for x in data if x in w2v] or [np.zeros(300)], axis=0) for data in train_in]
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train_ex = train_data_ex[0]
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for epoch in range(epochs):
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nn.train()
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for i in range(0,train_data_ex.shape[0],BATCH_SIZE):
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x = train_in[i:i + BATCH_SIZE]
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x = torch.tensor(x)
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y = train_ex[i:i + BATCH_SIZE]
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y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)
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opti.zero_grad()
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y_pred = nn(x.float())
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loss = crit(y_pred,y)
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loss.backward()
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opti.step()
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# In[27]:
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#dev-0 predict
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#wczytanie danych
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dev0_data = pd.read_csv('dev-0/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\t')
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dev0_data = dev0_data[0].str.lower()
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dev0_data = [tokenize(line) for line in dev0_data]
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dev0_data = [np.mean([w2v[x] for x in data if x in w2v] or [np.zeros(300)], axis=0) for data in dev0_data]
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dev0_y=[]
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nn.eval()
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with torch.no_grad():
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for i in range(0, len(dev0_data), BATCH_SIZE):
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x = dev0_data[i:i + BATCH_SIZE]
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x = torch.tensor(x)
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dev0_y_pred = nn(x.float())
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dev0_y_prediction = (dev0_y_pred > 0.5)
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dev0_y.extend(dev0_y_prediction)
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#zapis wyników
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np.asarray(dev0_y, dtype=np.int32).tofile('dev-0/out.tsv', sep='\n')
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# In[28]:
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#test-A predict
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#wczytanie danych
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testA_data = pd.read_csv('test-A/in.tsv.xz', compression='xz', header=None, quoting=csv.QUOTE_NONE, sep='\t')
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testA_data = testA_data[0].str.lower()
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testA_data = [tokenize(line) for line in testA_data]
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testA_data = [np.mean([w2v[x] for x in data if x in w2v] or [np.zeros(300)], axis=0) for data in testA_data]
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testA_y=[]
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nn.eval()
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with torch.no_grad():
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for i in range(0, len(testA_data), BATCH_SIZE):
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x = testA_data[i:i + BATCH_SIZE]
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x = torch.tensor(x)
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testA_y_pred = nn(x.float())
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testA_y_prediction = (testA_y_pred > 0.5)
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testA_y.extend(testA_y_prediction)
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#zapis wyników
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np.asarray(testA_y, dtype=np.int32).tofile('test-A/out.tsv', sep='\n')
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