import numpy as np
import pandas as pd
from tagifai import config, data, main, utils
from tagifai.config import logger
# Set experiment and start run
args_fp = Path(config.CONFIG_DIR, "args.json")
args = Namespace(**utils.load_dict(filepath=args_fp))
# 1. Set seed
utils.set_seed(seed=args.seed)
# 2. Set device
device = utils.set_device(cuda=args.cuda)
# 3. Load data
projects_fp = Path(config.DATA_DIR, "projects.json")
tags_fp = Path(config.DATA_DIR, "tags.json")
projects = utils.load_dict(filepath=projects_fp)
tags_dict = utils.list_to_dict(utils.load_dict(filepath=tags_fp),
key="tag")
df = pd.DataFrame(projects)
if args.shuffle:
df = df.sample(frac=1).reset_index(drop=True)
df = df[:args.num_samples] # None = all samples
# 4. Clean data
df, tags_above_frequency = data.clean(
df=df,
def run(params: Namespace, trial: optuna.trial._trial.Trial = None) -> Dict:
"""Operations for training.
Args:
params (Namespace): Input parameters for operations.
trial (optuna.trial._trial.Trail, optional): Optuna optimization trial. Defaults to None.
Returns:
Artifacts to save and load for later.
"""
# 1. Set seed
utils.set_seed(seed=params.seed)
# 2. Set device
device = utils.set_device(cuda=params.cuda)
# 3. Load data
projects_fp = Path(config.DATA_DIR, "projects.json")
tags_fp = Path(config.DATA_DIR, "tags.json")
projects = utils.load_dict(filepath=projects_fp)
tags_dict = utils.list_to_dict(utils.load_dict(filepath=tags_fp),
key="tag")
df = pd.DataFrame(projects)
if params.shuffle:
df = df.sample(frac=1).reset_index(drop=True)
df = df[:params.subset] # None = all samples
# 4. Prepare data (feature engineering, filter, clean)
df, tags_above_freq, tags_below_freq = data.prepare(
df=df,
include=list(tags_dict.keys()),
exclude=config.EXCLUDED_TAGS,
min_tag_freq=params.min_tag_freq,
)
params.num_samples = len(df)
# 5. Preprocess data
df.text = df.text.apply(data.preprocess,
lower=params.lower,
stem=params.stem)
# 6. Encode labels
labels = df.tags
label_encoder = data.MultiLabelLabelEncoder()
label_encoder.fit(labels)
y = label_encoder.encode(labels)
# Class weights
all_tags = list(itertools.chain.from_iterable(labels.values))
counts = np.bincount(
[label_encoder.class_to_index[class_] for class_ in all_tags])
class_weights = {i: 1.0 / count for i, count in enumerate(counts)}
# 7. Split data
utils.set_seed(seed=params.seed) # needed for skmultilearn
X = df.text.to_numpy()
X_train, X_, y_train, y_ = data.iterative_train_test_split(
X=X, y=y, train_size=params.train_size)
X_val, X_test, y_val, y_test = data.iterative_train_test_split(
X=X_, y=y_, train_size=0.5)
test_df = pd.DataFrame({
"text": X_test,
"tags": label_encoder.decode(y_test)
})
# 8. Tokenize inputs
tokenizer = data.Tokenizer(char_level=params.char_level)
tokenizer.fit_on_texts(texts=X_train)
X_train = np.array(tokenizer.texts_to_sequences(X_train), dtype=object)
X_val = np.array(tokenizer.texts_to_sequences(X_val), dtype=object)
X_test = np.array(tokenizer.texts_to_sequences(X_test), dtype=object)
# 9. Create dataloaders
train_dataset = data.CNNTextDataset(X=X_train,
y=y_train,
max_filter_size=params.max_filter_size)
val_dataset = data.CNNTextDataset(X=X_val,
y=y_val,
max_filter_size=params.max_filter_size)
train_dataloader = train_dataset.create_dataloader(
batch_size=params.batch_size)
val_dataloader = val_dataset.create_dataloader(
batch_size=params.batch_size)
# 10. Initialize model
model = models.initialize_model(
params=params,
vocab_size=len(tokenizer),
num_classes=len(label_encoder),
device=device,
)
# 11. Train model
logger.info(
f"Parameters: {json.dumps(params.__dict__, indent=2, cls=NumpyEncoder)}"
)
params, model, loss = train.train(
params=params,
train_dataloader=train_dataloader,
val_dataloader=val_dataloader,
model=model,
device=device,
class_weights=class_weights,
trial=trial,
)
# 12. Evaluate model
artifacts = {
"params": params,
"label_encoder": label_encoder,
"tokenizer": tokenizer,
"model": model,
"loss": loss,
}
device = torch.device("cpu")
y_true, y_pred, performance = eval.evaluate(df=test_df,
artifacts=artifacts)
artifacts["performance"] = performance
return artifacts
def run(args: Namespace, trial: optuna.trial._trial.Trial = None) -> Dict:
"""Operations for training.
1. Set seed
2. Set device
3. Load data
4. Clean data
5. Preprocess data
6. Encode labels
7. Split data
8. Tokenize inputs
9. Create dataloaders
10. Initialize model
11. Train model
12. Evaluate model
Args:
args (Namespace): Input arguments for operations.
trial (optuna.trial._trial.Trail, optional): Optuna optimization trial. Defaults to None.
Returns:
Artifacts to save and load for later.
"""
# 1. Set seed
utils.set_seed(seed=args.seed)
# 2. Set device
device = utils.set_device(cuda=args.cuda)
# 3. Load data
df, projects_dict, tags_dict = data.load(
shuffle=args.shuffle, num_samples=args.num_samples
)
# 4. Clean data
df, tags_dict, tags_above_frequency = data.clean(
df=df, tags_dict=tags_dict, min_tag_freq=args.min_tag_freq
)
# 5. Preprocess data
df.text = df.text.apply(data.preprocess, lower=args.lower, stem=args.stem)
# 6. Encode labels
y, class_weights, label_encoder = data.encode_labels(labels=df.tags)
# 7. Split data
utils.set_seed(seed=args.seed) # needed for skmultilearn
X_train, X_val, X_test, y_train, y_val, y_test = data.split(
X=df.text.to_numpy(), y=y, train_size=args.train_size
)
# 8. Tokenize inputs
X_train, tokenizer = data.tokenize_text(
X=X_train, char_level=args.char_level
)
X_val, _ = data.tokenize_text(
X=X_val, char_level=args.char_level, tokenizer=tokenizer
)
X_test, _ = data.tokenize_text(
X=X_test, char_level=args.char_level, tokenizer=tokenizer
)
# 9. Create dataloaders
train_dataloader = data.get_dataloader(
data=[X_train, y_train],
max_filter_size=args.max_filter_size,
batch_size=args.batch_size,
)
val_dataloader = data.get_dataloader(
data=[X_val, y_val],
max_filter_size=args.max_filter_size,
batch_size=args.batch_size,
)
test_dataloader = data.get_dataloader(
data=[X_test, y_test],
max_filter_size=args.max_filter_size,
batch_size=args.batch_size,
)
# 10. Initialize model
model = models.initialize_model(
args=args,
vocab_size=len(tokenizer),
num_classes=len(label_encoder),
device=device,
)
# 11. Train model
logger.info(
f"Arguments: {json.dumps(args.__dict__, indent=2, cls=NumpyEncoder)}"
)
args, model, loss = train(
args=args,
train_dataloader=train_dataloader,
val_dataloader=val_dataloader,
model=model,
device=device,
class_weights=class_weights,
trial=trial,
)
# 12. Evaluate model
device = torch.device("cpu")
performance = evaluate(
dataloader=test_dataloader,
model=model.to(device),
device=device,
threshold=args.threshold,
classes=label_encoder.classes,
)
return {
"args": args,
"label_encoder": label_encoder,
"tokenizer": tokenizer,
"model": model,
"loss": loss,
"performance": performance,
}
def train(params: Namespace, trial: optuna.trial._trial.Trial = None) -> Dict:
"""Operations for training.
Args:
params (Namespace): Input parameters for operations.
trial (optuna.trial._trial.Trail, optional): Optuna optimization trial. Defaults to None.
Returns:
Artifacts to save and load for later.
"""
# Set up
utils.set_seed(seed=params.seed)
device = utils.set_device(cuda=params.cuda)
# Load features
features_fp = Path(config.DATA_DIR, "features.json")
tags_fp = Path(config.DATA_DIR, "tags.json")
features = utils.load_dict(filepath=features_fp)
tags_dict = utils.list_to_dict(utils.load_dict(filepath=tags_fp),
key="tag")
df = pd.DataFrame(features)
if params.shuffle:
df = df.sample(frac=1).reset_index(drop=True)
df = df[:params.subset] # None = all samples
# Prepare data (filter, clean, etc.)
df, tags_above_freq, tags_below_freq = data.prepare(
df=df,
include=list(tags_dict.keys()),
exclude=config.EXCLUDED_TAGS,
min_tag_freq=params.min_tag_freq,
)
params.num_samples = len(df)
# Preprocess data
df.text = df.text.apply(data.preprocess,
lower=params.lower,
stem=params.stem)
# Encode labels
labels = df.tags
label_encoder = data.MultiLabelLabelEncoder()
label_encoder.fit(labels)
y = label_encoder.encode(labels)
# Class weights
all_tags = list(itertools.chain.from_iterable(labels.values))
counts = np.bincount(
[label_encoder.class_to_index[class_] for class_ in all_tags])
class_weights = {i: 1.0 / count for i, count in enumerate(counts)}
# Split data
utils.set_seed(seed=params.seed) # needed for skmultilearn
X = df.text.to_numpy()
X_train, X_, y_train, y_ = data.iterative_train_test_split(
X=X, y=y, train_size=params.train_size)
X_val, X_test, y_val, y_test = data.iterative_train_test_split(
X=X_, y=y_, train_size=0.5)
test_df = pd.DataFrame({
"text": X_test,
"tags": label_encoder.decode(y_test)
})
# Tokenize inputs
tokenizer = data.Tokenizer(char_level=params.char_level)
tokenizer.fit_on_texts(texts=X_train)
X_train = np.array(tokenizer.texts_to_sequences(X_train), dtype=object)
X_val = np.array(tokenizer.texts_to_sequences(X_val), dtype=object)
X_test = np.array(tokenizer.texts_to_sequences(X_test), dtype=object)
# Create dataloaders
train_dataset = data.CNNTextDataset(X=X_train,
y=y_train,
max_filter_size=params.max_filter_size)
val_dataset = data.CNNTextDataset(X=X_val,
y=y_val,
max_filter_size=params.max_filter_size)
train_dataloader = train_dataset.create_dataloader(
batch_size=params.batch_size)
val_dataloader = val_dataset.create_dataloader(
batch_size=params.batch_size)
# Initialize model
model = models.initialize_model(
params=params,
vocab_size=len(tokenizer),
num_classes=len(label_encoder),
device=device,
)
# Train model
logger.info(
f"Parameters: {json.dumps(params.__dict__, indent=2, cls=NumpyEncoder)}"
)
class_weights_tensor = torch.Tensor(np.array(list(class_weights.values())))
loss_fn = nn.BCEWithLogitsLoss(weight=class_weights_tensor)
optimizer = torch.optim.Adam(model.parameters(), lr=params.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode="min",
factor=0.05,
patience=5)
# Trainer module
trainer = Trainer(
model=model,
device=device,
loss_fn=loss_fn,
optimizer=optimizer,
scheduler=scheduler,
trial=trial,
)
# Train
best_val_loss, best_model = trainer.train(params.num_epochs,
params.patience,
train_dataloader, val_dataloader)
# Find best threshold
_, y_true, y_prob = trainer.eval_step(dataloader=train_dataloader)
params.threshold = find_best_threshold(y_true=y_true, y_prob=y_prob)
# Evaluate model
artifacts = {
"params": params,
"label_encoder": label_encoder,
"tokenizer": tokenizer,
"model": best_model,
"loss": best_val_loss,
}
device = torch.device("cpu")
y_true, y_pred, performance = eval.evaluate(df=test_df,
artifacts=artifacts)
artifacts["performance"] = performance
return artifacts