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task2_02.py
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task2_02.py
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import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch import nn
import sklearn.model_selection
from sklearn.feature_extraction import text
from sklearn.metrics import f1_score
import math
func_act_names = {
0: "Sigmoid",
1: "Tanh",
2: "ReLU",
3: "ELU"
}
optim_names = {
0: "SGD",
1: "SGD with momentum",
2: "RMSprop",
3: "Adam"
}
def accuracy(y_pred, y):
return (torch.round(y_pred) == y).float().sum() / len(y_pred)
def f1(y_pred, y):
return f1_score(y.detach().numpy(), y_pred.round().detach().numpy(), average='macro')
def kill_random():
seed = 1
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
torch.use_deterministic_algorithms(True)
def prepare(data):
data.pop("severe_toxic")
data.pop("obscene")
data.pop("threat")
data.pop("insult")
data.pop("identity_hate")
teach_data, test_data = sklearn.model_selection.train_test_split(data, test_size=0.2, random_state=5)
vectorizer = text.CountVectorizer(lowercase=True, ngram_range=(1, 1), strip_accents='unicode',
stop_words={'english'}, analyzer='word')
vectorizer.fit(data['comment_text'])
X = vectorizer.transform(teach_data["comment_text"]).toarray()
y_tr = teach_data['toxic'].to_numpy()
y = torch.tensor(y_tr).float()
y = torch.Tensor(np.array([np.array([y_s]) for y_s in y]))
X_test = vectorizer.transform(test_data["comment_text"]).toarray()
y_te = test_data['toxic'].to_numpy()
y_test = torch.tensor(y_te).float()
y_test = torch.Tensor(np.array([np.array([y_s]) for y_s in y_test]))
return X, y, X_test, y_test
def create_model(amount_of_batches, layers=1, func=2, optim_type=2, has_batch=False, dropout_prob=None):
func_activ = {
0: nn.Sigmoid(),
1: nn.Tanh(),
2: nn.ReLU(),
3: nn.ELU()
}
model = nn.Sequential()
if has_batch:
model.add_module('b1', nn.BatchNorm1d(amount_of_batches, True))
for i in range(1, layers):
model.add_module(f'l{i}',
nn.Linear(in_features=amount_of_batches, out_features=(math.ceil(amount_of_batches / 10))))
model.add_module(f'a{i}', func_activ.get(func))
amount_of_batches = math.ceil(amount_of_batches / 10)
model.add_module(f'l{layers}', nn.Linear(in_features=amount_of_batches, out_features=1))
model.add_module(f'a{layers}', nn.Sigmoid())
if dropout_prob is not None:
model.add_module('d1', nn.Dropout(p=dropout_prob))
optims = {
0: torch.optim.SGD(model.parameters(), lr=0.01, momentum=0),
1: torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5),
2: torch.optim.RMSprop(model.parameters(), lr=0.01, alpha=0.5, momentum=0),
3: torch.optim.Adam(model.parameters())
}
return model, optims.get(optim_type)
def training(X, y, model, optim):
loss = nn.BCELoss()
for epoch in range(1000):
optim.zero_grad()
model.train()
y_pred = model.forward(torch.tensor(X).float())
loss_f = loss(y_pred, y)
loss_f.backward()
optim.step()
return loss(model.forward(torch.tensor(X).float()), y).item(), \
accuracy(model.forward(torch.tensor(X).float()), y), \
f1(model.forward(torch.tensor(X).float()), y)
def predicting(X, y, model):
loss = nn.BCELoss()
return loss(model.forward(torch.tensor(X).float()), y).item(), \
accuracy(model.forward(torch.tensor(X).float()), y), \
f1(model.forward(torch.tensor(X).float()), y)
def print_graph(loss, loss_test, acc, acc_test):
plt.plot(loss, 'ob', label="train")
plt.plot(loss_test, 'or', label="test")
plt.legend(loc="upper left")
plt.show()
plt.plot(acc, 'ob', label="train")
plt.plot(acc_test, 'or', label="test")
plt.legend(loc="upper left")
plt.show()
def start():
print("Read data from csv")
data = pd.read_csv("train.csv")
data = data.iloc[0:1000]
print("Prepare data")
X, y, X_test, y_test = prepare(data)
amount_of_batches = len(X[0])
loss = []
loss_test = []
acc = []
acc_test = []
f1_test = []
# ----------------------------------------------------------------------
print("Task 2.3")
loss_test.clear()
loss.clear()
acc_test.clear()
acc.clear()
f1_test.clear()
kill_random()
for i in range(1, 5):
print(f'amount of layers: {i}')
model, optim = create_model(amount_of_batches, layers=i)
loss_t, acc_t, f1_t = training(X, y, model, optim)
loss_p, acc_p, f1_p = predicting(X_test, y_test, model)
loss.append(loss_t)
acc.append(acc_t)
loss_test.append(loss_p)
acc_test.append(acc_p)
f1_test.append(f1_p)
print(f1_p)
print(f1_test)
print_graph(loss, loss_test, acc, acc_test)
# ----------------------------------------------------------------------
print('\n', '-' * 20)
print("Task 2.4")
loss_test.clear()
loss.clear()
acc_test.clear()
acc.clear()
f1_test.clear()
kill_random()
for i in range(4):
print(f'type of activation function: {func_act_names.get(i)}')
model, optim = create_model(amount_of_batches, layers=4, func=i)
loss_t, acc_t, f1_t = training(X, y, model, optim)
loss_p, acc_p, f1_p = predicting(X_test, y_test, model)
loss.append(loss_t)
acc.append(acc_t)
loss_test.append(loss_p)
acc_test.append(acc_p)
f1_test.append(f1_p)
print(f1_p)
print(f1_test)
print_graph(loss, loss_test, acc, acc_test)
# ----------------------------------------------------------------------
print('\n', '-' * 20)
print("Task 2.5")
loss_test.clear()
loss.clear()
acc_test.clear()
acc.clear()
f1_test.clear()
kill_random()
for i in range(4):
print(f'type of optimizer: {optim_names.get(i)}')
model, optim = create_model(amount_of_batches, layers=4, func=2, optim_type=i)
loss_t, acc_t, f1_t = training(X, y, model, optim)
loss_p, acc_p, f1_p = predicting(X_test, y_test, model)
loss.append(loss_t)
acc.append(acc_t)
loss_test.append(loss_p)
acc_test.append(acc_p)
f1_test.append(f1_p)
print(f1_p)
print(f1_test)
print_graph(loss, loss_test, acc, acc_test)
# ----------------------------------------------------------------------
print('\n', '-' * 20)
print("Task 2.6")
loss_test.clear()
loss.clear()
acc_test.clear()
acc.clear()
f1_test.clear()
kill_random()
for i in [None, 0.2, 0.5]:
for j in [False, True]:
print(f'has batchNorm: {j}, probability of dropout: {i}')
model, optim = create_model(amount_of_batches, layers=4, func=2, optim_type=2, has_batch=j, dropout_prob=i)
loss_t, acc_t, f1_t = training(X, y, model, optim)
loss_p, acc_p, f1_p = predicting(X_test, y_test, model)
loss.append(loss_t)
acc.append(acc_t)
loss_test.append(loss_p)
acc_test.append(acc_p)
f1_test.append(f1_p)
print(f1_p)
print(f1_test)
print_graph(loss, loss_test, acc, acc_test)
# ----------------------------------------------------------------------
print('\n', '-' * 20)
print("Task 2.7")
loss_test.clear()
loss.clear()
acc_test.clear()
acc.clear()
f1_test.clear()
kill_random()
model, optim = create_model(amount_of_batches, layers=4, func=2, optim_type=2, has_batch=False, dropout_prob=None)
loss_t, acc_t, f1_t = training(X, y, model, optim)
loss_p, acc_p, f1_p = predicting(X_test, y_test, model)
print(f1_p)
if __name__ == '__main__':
start()