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pulls train and eval code into functions in utils #9

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31aa95b
comments out nltk as it was throwing some error and we don't need it …
coopslarhette Dec 12, 2020
36a5d34
extracts training and evaluation to two functions in utils to keep co…
coopslarhette Dec 12, 2020
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15 changes: 9 additions & 6 deletions data_loader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,12 +9,15 @@
import argparse

# only need to run once for synonym generator
import nltk
nltk.download('wordnet')

# The domesticviolence and survivorsofabuse subreddits will be class 0, critical; these are personal stories, calls for help, requests for advice
# The abuseInterrupted subreddit will be class 1, noncritical; it mostly contains empty text, links to articles, general statements about abuse, etc.
# Everything else will be class 2, general/unrelated
# note: don't need this unless we augment again and it throws errors
# import nltk
# nltk.download('wordnet')

'''
The domesticviolence and survivorsofabuse subreddits will be class 0, critical; these are personal stories, calls for help, requests for advice
The abuseInterrupted subreddit will be class 1, noncritical; it mostly contains empty text, links to articles, general statements about abuse, etc.
Everything else will be class 2, general/unrelated
'''

CLASSES = {
'relationship_advice': 2, # 5874 samples
Expand Down
80 changes: 6 additions & 74 deletions gru.py
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Expand Up @@ -2,12 +2,10 @@
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from tqdm import trange

from data_loader import load_data
from utils import build_vocab, make_minibatch, strings_to_tensors
from utils import build_vocab, strings_to_tensors, train_model, evaluate_model


class GRU(nn.Module):
Expand Down Expand Up @@ -36,7 +34,6 @@ def forward(self, input_seq):


if __name__ == "__main__":

# If there's an available GPU, lets train on it
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

Expand Down Expand Up @@ -77,73 +74,8 @@ def forward(self, input_seq):
loss_func = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)

"""
Train the model
"""
epochs = trange(1, n_epochs + 1)
for epoch in epochs:
model.train()

# Accumulate loss for all samples in this epoch
running_loss = 0
num_batches = 0
num_samples = len(train_data)

shuffled_indices = torch.randperm(num_samples)

# Gradient descent algorithm for each data sample
batches = trange(0, num_samples - bs, bs, leave=False)
for count in batches:
# Extract minibatches and send to device
indices = shuffled_indices[count: count + bs]
minibatch_data, minibatch_label = make_minibatch(
indices, train_data, train_labels)
minibatch_data, minibatch_label = minibatch_data.to(
device), minibatch_label.to(device)

# Make predictions on the training data
scores = model(minibatch_data)

# Backpropagation
loss = loss_func(scores, minibatch_label)
loss.backward()
optimizer.step()
optimizer.zero_grad()

# Compute metrics
with torch.no_grad():
num_batches += 1
running_loss += loss.item()

epochs.set_description(
f'Epoch {epoch} / {n_epochs} | Loss: {running_loss/num_batches}')

"""
Evaluate the model
"""
with torch.no_grad():
model.eval()
predicted_labels = []

# cycle through test set in batches
batches = trange(0, len(test_data) - bs, bs,
desc='evaluating on test set', leave=False)
for i in batches:
# extract minibatch
indices = torch.arange(i, i + bs)
minibatch_data, _ = make_minibatch(indices, test_data, test_labels)
minibatch_data = minibatch_data.to(device)

# make and score predictions
scores = model(minibatch_data)
predicted_labels.extend(scores.argmax(dim=1).tolist())

# evaluate remaining samples
indices = torch.arange(len(predicted_labels), len(test_labels))
minibatch_data, _ = make_minibatch(indices, test_data, test_labels)
minibatch_data = minibatch_data.to(device)

scores = model(minibatch_data)
predicted_labels.extend(scores.argmax(dim=1).tolist())

print(classification_report(y_true=test_labels, y_pred=predicted_labels))
train_model(model=model, n_epochs=n_epochs, train_data=train_data, train_labels=train_labels,
bs=bs, device=device, loss_func=loss_func, optimizer=optimizer)

evaluate_model(model=model, test_data=test_data, test_labels=test_labels,
bs=bs, device=device)
86 changes: 8 additions & 78 deletions lstm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,12 +2,10 @@
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from tqdm import trange

from data_loader import load_data
from utils import build_vocab, make_minibatch, strings_to_tensors
from utils import build_vocab, strings_to_tensors, train_model, evaluate_model


class LSTM(nn.Module):
Expand Down Expand Up @@ -46,17 +44,16 @@ def forward(self, input_seq):


if __name__ == "__main__":

# If there's an available GPU, lets train on it
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

"""
Load the training, validation, and test data
"""
posts, labels = load_data(
og_file_path='data/reddit_submissions.csv',
aug_file_path='data/synonym_augmented_reddit_submissions.csv',
include_og=True,
og_file_path='data/reddit_submissions.csv',
aug_file_path='data/synonym_augmented_reddit_submissions.csv',
include_og=True,
include_aug=True)

posts_train, posts_test, train_labels, test_labels = train_test_split(
Expand Down Expand Up @@ -91,78 +88,11 @@ def forward(self, input_seq):
loss_func = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)

"""
Train the model
"""
epochs = trange(1, n_epochs + 1)
for epoch in epochs:
model.train()

# Accumulate loss for all samples in this epoch
running_loss = 0
num_batches = 0
num_samples = len(train_data)

shuffled_indices = torch.randperm(num_samples)

# Gradient descent algorithm for each data sample
batches = trange(0, num_samples - bs, bs, leave=False)
for count in batches:
# Extract minibatches and send to GPU
indices = shuffled_indices[count : count + bs]
minibatch_data, minibatch_label = make_minibatch(
indices, train_data, train_labels)
minibatch_data, minibatch_label = minibatch_data.to(
device), minibatch_label.to(device)

# Make a predictions on the training data
scores = model(minibatch_data)

# Backpropagation
loss = loss_func(scores, minibatch_label)
loss.backward()
optimizer.step()
optimizer.zero_grad()

# Compute metrics
with torch.no_grad():
num_batches += 1
running_loss += loss.item()

epochs.set_description(
f'Epoch {epoch}/{n_epochs} | Loss: {running_loss/num_batches}')

"""
Evaluate the model
"""
with torch.no_grad():
model.eval()
predicted_labels = []

# cycle through test set in batches
batches = trange(0, len(test_data) - bs, bs,
desc='evaluating on test set', leave=False)
for i in batches:
# extract minibatch
indices = torch.arange(i, i + bs)
minibatch_data, _ = make_minibatch(indices, test_data, test_labels)
minibatch_data = minibatch_data.to(device)

# make and score predictions
scores = model(minibatch_data)
predicted_labels.extend(scores.argmax(dim=1).tolist())

# evaluate remaining samples
indices = torch.arange(len(predicted_labels), len(test_labels))
minibatch_data, _ = make_minibatch(indices, test_data, test_labels)
minibatch_data = minibatch_data.to(device)

scores = model(minibatch_data)
predicted_labels.extend(scores.argmax(dim=1).tolist())

print(classification_report(y_true=test_labels, y_pred=predicted_labels))

train_model(model=model, n_epochs=n_epochs, train_data=train_data, train_labels=train_labels,
bs=bs, device=device, loss_func=loss_func, optimizer=optimizer)

evaluate_model(model=model, test_data=test_data, test_labels=test_labels,
bs=bs, device=device)

"""
Output:
Expand Down
80 changes: 6 additions & 74 deletions rnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,12 +2,10 @@
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from tqdm import trange

from data_loader import load_data
from utils import build_vocab, make_minibatch, strings_to_tensors
from utils import build_vocab, strings_to_tensors, train_model, evaluate_model


class RNN(nn.Module):
Expand Down Expand Up @@ -38,7 +36,6 @@ def forward(self, input_seq):


if __name__ == "__main__":

# If there's an available GPU, lets train on it
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

Expand Down Expand Up @@ -79,73 +76,8 @@ def forward(self, input_seq):
loss_func = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)

"""
Train the model
"""
epochs = trange(1, n_epochs + 1)
for epoch in epochs:
model.train()

# Accumulate loss for all samples in this epoch
running_loss = 0
num_batches = 0
num_samples = len(train_data)

shuffled_indices = torch.randperm(num_samples)

# Gradient descent algorithm for each data sample
batches = trange(0, num_samples - bs, bs, leave=False)
for count in batches:
# Extract minibatches and send to device
indices = shuffled_indices[count: count + bs]
minibatch_data, minibatch_label = make_minibatch(
indices, train_data, train_labels)
minibatch_data, minibatch_label = minibatch_data.to(
device), minibatch_label.to(device)

# Make predictions on the training data
scores = model(minibatch_data)

# Backpropagation
loss = loss_func(scores, minibatch_label)
loss.backward()
optimizer.step()
optimizer.zero_grad()

# Compute metrics
with torch.no_grad():
num_batches += 1
running_loss += loss.item()

epochs.set_description(
f'Epoch {epoch} / {n_epochs} | Loss: {running_loss/num_batches}')

"""
Evaluate the model
"""
with torch.no_grad():
model.eval()
predicted_labels = []

# cycle through test set in batches
batches = trange(0, len(test_data) - bs, bs,
desc='evaluating on test set', leave=False)
for i in batches:
# extract minibatch
indices = torch.arange(i, i + bs)
minibatch_data, _ = make_minibatch(indices, test_data, test_labels)
minibatch_data = minibatch_data.to(device)

# make and score predictions
scores = model(minibatch_data)
predicted_labels.extend(scores.argmax(dim=1).tolist())

# evaluate remaining samples
indices = torch.arange(len(predicted_labels), len(test_labels))
minibatch_data, _ = make_minibatch(indices, test_data, test_labels)
minibatch_data = minibatch_data.to(device)

scores = model(minibatch_data)
predicted_labels.extend(scores.argmax(dim=1).tolist())

print(classification_report(y_true=test_labels, y_pred=predicted_labels))
train_model(model=model, n_epochs=n_epochs, train_data=train_data, train_labels=train_labels,
bs=bs, device=device, loss_func=loss_func, optimizer=optimizer)

evaluate_model(model=model, test_data=test_data, test_labels=test_labels,
bs=bs, device=device)
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