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214
Feed-Forward.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "308eb052",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import gensim.downloader as downloader\n",
"import pandas as pd\n",
"import csv\n",
"from nltk.tokenize import word_tokenize as tokenize\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "212c0f2c",
"metadata": {},
"outputs": [],
"source": [
"class NeuralNetworkModel(torch.nn.Module):\n",
" \n",
" def __init__(self, input_size, hidden_size, num_classes):\n",
" super(NeuralNetworkModel, self).__init__()\n",
" self.fc1 = torch.nn.Linear(input_size,hidden_size)\n",
" self.fc2 = torch.nn.Linear(hidden_size,num_classes)\n",
" \n",
" def forward(self,x):\n",
" x = self.fc1(x)\n",
" x = torch.relu(x)\n",
" x = self.fc2(x)\n",
" x = torch.sigmoid(x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "afd13d8c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"
]
}
],
"source": [
"w2v = downloader.load('word2vec-google-news-300')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f68b0a7c",
"metadata": {},
"outputs": [],
"source": [
"#model + settings\n",
"\n",
"nn = NeuralNetworkModel(300,300,1)\n",
"crit = torch.nn.BCELoss()\n",
"opti = torch.optim.SGD(nn.parameters(), lr=0.08)\n",
"BATCH_SIZE = 5\n",
"epochs = 5"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f0713eab",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#trening\n",
"\n",
"#wczytanie danych\n",
"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)\n",
"train_data_ex = pd.read_csv('train/expected.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t', nrows=3000)\n",
"\n",
"#preprocessing\n",
"train_in = train_data_in[0].str.lower()\n",
"train_in = [tokenize(line) for line in train_in]\n",
"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]\n",
"train_ex = train_data_ex[0]\n",
"\n",
"for epoch in range(epochs):\n",
" nn.train()\n",
" for i in range(0,train_data_ex.shape[0],BATCH_SIZE):\n",
" x = train_in[i:i + BATCH_SIZE]\n",
" x = torch.tensor(x)\n",
" y = train_ex[i:i + BATCH_SIZE]\n",
" y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)\n",
" \n",
" opti.zero_grad()\n",
" y_pred = nn(x.float())\n",
" loss = crit(y_pred,y)\n",
" loss.backward()\n",
" opti.step()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "1ab1dce0",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-27-b22b834acfd4>:21: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
" np.asarray(dev0_y, dtype=np.int32).tofile('dev-0/out.tsv', sep='\\n')\n",
"<ipython-input-27-b22b834acfd4>:21: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n",
" np.asarray(dev0_y, dtype=np.int32).tofile('dev-0/out.tsv', sep='\\n')\n"
]
}
],
"source": [
"#dev-0 predict\n",
"\n",
"#wczytanie danych\n",
"dev0_data = pd.read_csv('dev-0/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
"dev0_data = dev0_data[0].str.lower()\n",
"dev0_data = [tokenize(line) for line in dev0_data]\n",
"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]\n",
"\n",
"dev0_y=[]\n",
"nn.eval()\n",
"with torch.no_grad():\n",
" for i in range(0, len(dev0_data), BATCH_SIZE):\n",
" x = dev0_data[i:i + BATCH_SIZE]\n",
" x = torch.tensor(x)\n",
" dev0_y_pred = nn(x.float())\n",
"\n",
" dev0_y_prediction = (dev0_y_pred > 0.5)\n",
" dev0_y.extend(dev0_y_prediction)\n",
" \n",
"#zapis wyników\n",
"np.asarray(dev0_y, dtype=np.int32).tofile('dev-0/out.tsv', sep='\\n')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "22941828",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-28-ed4376367760>:21: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
" np.asarray(testA_y, dtype=np.int32).tofile('test-A/out.tsv', sep='\\n')\n",
"<ipython-input-28-ed4376367760>:21: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n",
" np.asarray(testA_y, dtype=np.int32).tofile('test-A/out.tsv', sep='\\n')\n"
]
}
],
"source": [
"#test-A predict\n",
"\n",
"#wczytanie danych\n",
"testA_data = pd.read_csv('test-A/in.tsv.xz', compression='xz', header=None, quoting=csv.QUOTE_NONE, sep='\\t')\n",
"testA_data = testA_data[0].str.lower()\n",
"testA_data = [tokenize(line) for line in testA_data]\n",
"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]\n",
"\n",
"testA_y=[]\n",
"nn.eval()\n",
"with torch.no_grad():\n",
" for i in range(0, len(testA_data), BATCH_SIZE):\n",
" x = testA_data[i:i + BATCH_SIZE]\n",
" x = torch.tensor(x)\n",
" testA_y_pred = nn(x.float())\n",
"\n",
" testA_y_prediction = (testA_y_pred > 0.5)\n",
" testA_y.extend(testA_y_prediction)\n",
" \n",
"#zapis wyników\n",
"np.asarray(testA_y, dtype=np.int32).tofile('test-A/out.tsv', sep='\\n')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

131
Feed-Forward.py
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#!/usr/bin/env python
# coding: utf-8
# In[2]:
import torch
import gensim.downloader as downloader
import pandas as pd
import csv
from nltk.tokenize import word_tokenize as tokenize
import numpy as np
# In[7]:
class NeuralNetworkModel(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetworkModel, self).__init__()
self.fc1 = torch.nn.Linear(input_size,hidden_size)
self.fc2 = torch.nn.Linear(hidden_size,num_classes)
def forward(self,x):
x = self.fc1(x)
x = torch.relu(x)
x = self.fc2(x)
x = torch.sigmoid(x)
return x
# In[4]:
w2v = downloader.load('word2vec-google-news-300')
# In[9]:
#model + settings
nn = NeuralNetworkModel(300,300,1)
crit = torch.nn.BCELoss()
opti = torch.optim.SGD(nn.parameters(), lr=0.08)
BATCH_SIZE = 5
epochs = 5
# In[12]:
#trening
#wczytanie danych
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)
train_data_ex = pd.read_csv('train/expected.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\t', nrows=3000)
#preprocessing
train_in = train_data_in[0].str.lower()
train_in = [tokenize(line) for line in train_in]
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]
train_ex = train_data_ex[0]
for epoch in range(epochs):
nn.train()
for i in range(0,train_data_ex.shape[0],BATCH_SIZE):
x = train_in[i:i + BATCH_SIZE]
x = torch.tensor(x)
y = train_ex[i:i + BATCH_SIZE]
y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)
opti.zero_grad()
y_pred = nn(x.float())
loss = crit(y_pred,y)
loss.backward()
opti.step()
# In[27]:
#dev-0 predict
#wczytanie danych
dev0_data = pd.read_csv('dev-0/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\t')
dev0_data = dev0_data[0].str.lower()
dev0_data = [tokenize(line) for line in dev0_data]
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]
dev0_y=[]
nn.eval()
with torch.no_grad():
for i in range(0, len(dev0_data), BATCH_SIZE):
x = dev0_data[i:i + BATCH_SIZE]
x = torch.tensor(x)
dev0_y_pred = nn(x.float())
dev0_y_prediction = (dev0_y_pred > 0.5)
dev0_y.extend(dev0_y_prediction)
#zapis wyników
np.asarray(dev0_y, dtype=np.int32).tofile('dev-0/out.tsv', sep='\n')
# In[28]:
#test-A predict
#wczytanie danych
testA_data = pd.read_csv('test-A/in.tsv.xz', compression='xz', header=None, quoting=csv.QUOTE_NONE, sep='\t')
testA_data = testA_data[0].str.lower()
testA_data = [tokenize(line) for line in testA_data]
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]
testA_y=[]
nn.eval()
with torch.no_grad():
for i in range(0, len(testA_data), BATCH_SIZE):
x = testA_data[i:i + BATCH_SIZE]
x = torch.tensor(x)
testA_y_pred = nn(x.float())
testA_y_prediction = (testA_y_pred > 0.5)
testA_y.extend(testA_y_prediction)
#zapis wyników
np.asarray(testA_y, dtype=np.int32).tofile('test-A/out.tsv', sep='\n')

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