def setup_method(self):
"""Called before every method."""
self.X = [[4, 2, 3, 0], [2, 4, 3, 3], [2, 3, 0, 0]]
self.y = [[0, 1], [1, 1], [1, 0]]
self.max_filter_size = 2
self.batch_size = 1
self.dataset = data.CNNTextDataset(X=self.X, y=self.y, max_filter_size=self.max_filter_size)
def evaluate(
df: pd.DataFrame,
artifacts: Dict,
device: torch.device = torch.device("cpu"),
) -> Tuple:
"""Evaluate performance on data.
Args:
df (pd.DataFrame): Dataframe (used for slicing).
artifacts (Dict): Artifacts needed for inference.
device (torch.device): Device to run model on. Defaults to CPU.
Returns:
Ground truth and predicted labels, performance.
"""
# Artifacts
params = artifacts["params"]
model = artifacts["model"]
tokenizer = artifacts["tokenizer"]
label_encoder = artifacts["label_encoder"]
model = model.to(device)
classes = label_encoder.classes
# Create dataloader
X = np.array(tokenizer.texts_to_sequences(df.text.to_numpy()),
dtype="object")
y = label_encoder.encode(df.tags)
dataset = data.CNNTextDataset(X=X,
y=y,
max_filter_size=int(params.max_filter_size))
dataloader = dataset.create_dataloader(batch_size=int(params.batch_size))
# Determine predictions using threshold
trainer = train.Trainer(model=model, device=device)
y_true, y_prob = trainer.predict_step(dataloader=dataloader)
y_pred = np.array(
[np.where(prob >= float(params.threshold), 1, 0) for prob in y_prob])
# Evaluate performance
performance = {}
performance = get_metrics(df=df,
y_true=y_true,
y_pred=y_pred,
classes=classes)
performance["behavioral"] = get_behavioral_report(artifacts=artifacts)
return y_true, y_pred, performance
def predict(
texts: List, artifacts: Dict,
device: torch.device = torch.device("cpu")) -> Dict:
"""Predict tags for an input text using the
best model from the `best` experiment.
Usage:
```python
texts = ["Transfer learning with BERT."]
artifacts = load_artifacts(run_id="264ac530b78c42608e5dea1086bc2c73")
predict(texts=texts, artifacts=artifacts)
```
<pre>
[
{
"input_text": "Transfer learning with BERT.",
"preprocessed_text": "transfer learning bert",
"predicted_tags": [
"attention",
"language-modeling",
"natural-language-processing",
"transfer-learning",
"transformers"
]
}
]
</pre>
Note:
The input parameter `texts` can hold multiple input texts and so the resulting prediction dictionary will have `len(texts)` items.
Args:
texts (List): List of input texts to predict tags for.
artifacts (Dict): Artifacts needed for inference.
device (torch.device): Device to run model on. Defaults to CPU.
Returns:
Predicted tags for each of the input texts.
"""
# Retrieve artifacts
params = artifacts["params"]
label_encoder = artifacts["label_encoder"]
tokenizer = artifacts["tokenizer"]
model = artifacts["model"]
# Prepare data
preprocessed_texts = [
data.preprocess(
text,
lower=bool(strtobool(str(
params.lower))), # params.lower could be str/bool
stem=bool(strtobool(str(params.stem))),
) for text in texts
]
X = np.array(tokenizer.texts_to_sequences(preprocessed_texts),
dtype="object")
y_filler = np.zeros((len(X), len(label_encoder)))
dataset = data.CNNTextDataset(X=X,
y=y_filler,
max_filter_size=int(params.max_filter_size))
dataloader = dataset.create_dataloader(batch_size=int(params.batch_size))
# Get predictions
trainer = train.Trainer(model=model, device=device)
_, y_prob = trainer.predict_step(dataloader)
y_pred = [
np.where(prob >= float(params.threshold), 1, 0) for prob in y_prob
]
tags = label_encoder.decode(y_pred)
predictions = [{
"input_text": texts[i],
"preprocessed_text": preprocessed_texts[i],
"predicted_tags": tags[i],
} for i in range(len(tags))]
return predictions
print(f"{len(cv_transformers_df)} projects")
print(cv_transformers_df[["text", "tags"]].head())
short_text_df = slice_dataframe(test_df, short_text)
print(f"{len(short_text_df)} projects")
print(short_text_df[["text", "tags"]].head())
# 8. Tokenize inputs
tokenizer = data.Tokenizer(char_level=args.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=args.max_filter_size)
val_dataset = data.CNNTextDataset(X=X_val,
y=y_val,
max_filter_size=args.max_filter_size)
test_dataset = data.CNNTextDataset(X=X_test,
y=y_test,
max_filter_size=args.max_filter_size)
train_dataloader = train_dataset.create_dataloader(
batch_size=args.batch_size)
val_dataloader = val_dataset.create_dataloader(batch_size=args.batch_size)
test_dataloader = test_dataset.create_dataloader(
batch_size=args.batch_size)
# Load artifacts
runs = utils.get_sorted_runs(experiment_name="best",
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 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
def predict(texts: List, run_id: str) -> Dict:
"""Predict tags for an input text using the
best model from the `best` experiment.
Usage:
```python
texts = ["Transfer learning with BERT."]
predict(texts=texts, run_id="264ac530b78c42608e5dea1086bc2c73")
```
<pre>
[
{
"input_text": "Transfer learning with BERT.",
"preprocessed_text": "transfer learning bert",
"predicted_tags": [
"attention",
"language-modeling",
"natural-language-processing",
"transfer-learning",
"transformers"
]
}
]
</pre>
Note:
The input argument `texts` can hold multiple input texts and so the resulting prediction dictionary will have `len(texts)` items.
Args:
texts (List): List of input text to predict tags for.
run_id (str): ID of the run to load model artifacts from.
Returns:
Predicted tags for input texts.
"""
# Load artifacts from run
client = mlflow.tracking.MlflowClient()
run = mlflow.get_run(run_id=run_id)
device = torch.device("cpu")
with tempfile.TemporaryDirectory() as fp:
client.download_artifacts(run_id=run_id, path="", dst_path=fp)
args = Namespace(**utils.load_dict(
filepath=Path(config.CONFIG_DIR, "args.json")))
label_encoder = data.LabelEncoder.load(
fp=Path(fp, "label_encoder.json"))
tokenizer = data.Tokenizer.load(fp=Path(fp, "tokenizer.json"))
model_state = torch.load(Path(fp, "model.pt"), map_location=device)
# performance = utils.load_dict(filepath=Path(fp, "performance.json"))
# Load model
args = Namespace(**run.data.params)
model = train.initialize_model(args=args,
vocab_size=len(tokenizer),
num_classes=len(label_encoder))
model.load_state_dict(model_state)
# Prepare data
preprocessed_texts = [data.preprocess(text) for text in texts]
X = np.array(tokenizer.texts_to_sequences(preprocessed_texts))
y_filler = label_encoder.encode(
[np.array([label_encoder.classes[0]] * len(X))])
dataset = data.CNNTextDataset(X=X,
y=y_filler,
max_filter_size=int(args.max_filter_size))
dataloader = dataset.create_dataloader(batch_size=int(args.batch_size))
# Get predictions
trainer = train.Trainer(model=model, device=device)
_, y_prob = trainer.predict_step(dataloader)
y_pred = np.array(
[np.where(prob >= float(args.threshold), 1, 0) for prob in y_prob])
tags = label_encoder.decode(y_pred)
predictions = [{
"input_text": texts[i],
"preprocessed_text": preprocessed_texts[i],
"predicted_tags": tags[i],
} for i in range(len(tags))]
return predictions
