Update default hyper-parameters

awe/training/params.py

@@ -39,6 +39,16 @@ class AttentionNormalization(str, enum.Enum):
     vector = 'vector'
     softmax = 'softmax'
 
+def _freeze(value):
+    """
+    Creates field with a mutable default value.
+
+    Python doesn't like this being done directly. But `Params` are never
+    mutated anyway, so this workaround is easiest.
+    """
+
+    return dataclasses.field(default_factory=lambda: value)
+
 @dataclasses.dataclass
 class Params:
     """
@@ -57,14 +67,14 @@ class Params:
     Only considered when using the SWDE dataset for now.
     """
 
-    label_keys: list[str] = ('name', 'price', 'shortDescription', 'images')
+    label_keys: list[str] = ()
     """
     Set of keys to select from the dataset.
 
     Only considered when using the Apify dataset for now.
     """
 
-    train_website_indices: list[int] = (0, 3, 4, 5, 7)
+    train_website_indices: list[int] = (0, 1, 2, 3, 4)
     """
     Indices of websites to put in the training set.
 
@@ -74,23 +84,23 @@ class Params:
     exclude_websites: list[str] = ()
     """Website names to exclude from loading."""
 
-    train_subset: Optional[int] = 2000
+    train_subset: Optional[int] = 100
     """Number of pages per website to use for training."""
 
-    val_subset: Optional[int] = 50
+    val_subset: Optional[int] = 5
     """
     Number of pages per website to use for validation (evaluation after each
     training epoch).
     """
 
-    test_subset: Optional[int] = None
+    test_subset: Optional[int] = 250
     """
     Number of pages per website to use for testing (evaluation of each
     cross-validation run).
     """
 
     # Trainer
-    epochs: int = 5
+    epochs: int = 3
     """
     Number of epochs (passes over all training samples) to train the model for.
     """
@@ -103,13 +113,13 @@ class Params:
     restore_num: Optional[int] = None
     """Existing version number to restore."""
 
-    batch_size: int = 16
+    batch_size: int = 32
     """Number of samples to have in a mini-batch during training/evaluation."""
 
-    save_every_n_epochs: Optional[int] = 1
+    save_every_n_epochs: Optional[int] = None
     """How often a checkpoint should be saved."""
 
-    save_better_val_loss_checkpoint: bool = True
+    save_better_val_loss_checkpoint: bool = False
     """
     Save checkpoint after each epoch when better validation loss is achieved.
     """
@@ -124,12 +134,12 @@ class Params:
     the storage space is not wasted by saving all checkpoints.
     """
 
-    log_every_n_steps: int = 10
+    log_every_n_steps: int = 100
     """
     How often to evaluate and write metrics to TensorBoard during training.
     """
 
-    eval_every_n_steps: Optional[int] = 50
+    eval_every_n_steps: Optional[int] = None
     """
     How often to execute full evaluation pass on the validation subset during
     training.
@@ -150,13 +160,13 @@ class Params:
     """
 
     # Sampling
-    load_visuals: bool = False
+    load_visuals: bool = True
     """
     When loading HTML for pages, also load JSON visuals, parse visual attributes
     and attach them to DOM nodes in memory.
     """
 
-    classify_only_text_nodes: bool = False
+    classify_only_text_nodes: bool = True
     """Sample only text fragments."""
 
     classify_only_variable_nodes: bool = False
@@ -179,10 +189,10 @@ class Params:
     validate_data: bool = True
     """Validate sampled page DOMs."""
 
-    ignore_invalid_pages: bool = False
+    ignore_invalid_pages: bool = True
     """Of sampled and validated pages, ignore those that are invalid."""
 
-    none_cutoff: Optional[int] = None
+    none_cutoff: Optional[int] = 30_000
     """
     From 0 to 100,000. The higher, the more non-target nodes will be sampled.
     """
@@ -195,16 +205,16 @@ class Params:
     """Number of friends in the friend cycle."""
 
     # Visual neighbors
-    visual_neighbors: bool = False
+    visual_neighbors: bool = True
     """Use visual neighbors as a feature when classifying nodes."""
 
-    n_neighbors: int = 4
+    n_neighbors: int = 10
     """Number of visual neighbors (the closes ones)."""
 
     neighbor_distance: VisualNeighborDistance = VisualNeighborDistance.rect
     """How to determine the closes visual neighbors."""
 
-    neighbor_normalize: Optional[AttentionNormalization] = AttentionNormalization.softmax
+    neighbor_normalize: Optional[AttentionNormalization] = AttentionNormalization.vector
     """
     How to normalize neighbor distances before feeding them to the attention
     module.
@@ -217,16 +227,16 @@ class Params:
     """
 
     # Ancestor chain
-    ancestor_chain: bool = False
+    ancestor_chain: bool = True
     """Use DOM ancestors as a feature when classifying nodes."""
 
-    n_ancestors: Optional[int] = 5
+    n_ancestors: Optional[int] = None
     """`None` to use all ancestors."""
 
     ancestor_lstm_out_dim: int = 10
     """Output dimension of the LSTM aggregating ancestor features."""
 
-    ancestor_lstm_args: Optional[dict[str]] = None
+    ancestor_lstm_args: Optional[dict[str]] = _freeze({'bidirectional': True})
     """
     Additional keyword arguments to the LSTM layer aggregating ancestor
     features.
@@ -243,7 +253,7 @@ class Params:
     """
 
     # Word vectors
-    tokenizer_family: TokenizerFamily = TokenizerFamily.custom
+    tokenizer_family: TokenizerFamily = TokenizerFamily.bert
     """Which tokenizer to use."""
 
     tokenizer_id: str = ''
@@ -266,7 +276,7 @@ class Params:
     """
 
     # HTML attributes
-    tokenize_node_attrs: list[str] = ()
+    tokenize_node_attrs: list[str] = ('itemprop',)
     """
     DOM attributes to tokenize and use as a feature when classifying nodes and
     also as a feature of each ancestor in the ancestor chain.
@@ -278,7 +288,7 @@ class Params:
     """Use the DOM attribute feature only for nodes of the ancestor chain."""
 
     # LSTM
-    word_vector_function: Optional[str] = 'sum'
+    word_vector_function: Optional[str] = 'lstm'
     """
     How to aggregate word vectors.
 
@@ -288,7 +298,7 @@ class Params:
     lstm_dim: int = 100
     """Output dimension of the LSTM aggregating word vectors."""
 
-    lstm_args: Optional[dict[str]] = None
+    lstm_args: Optional[dict[str]] = _freeze({'bidirectional': True})
     """
     Additional keyword arguments to the LSTM layer aggregating word vectors.
     """
@@ -313,7 +323,7 @@ class Params:
     """
 
     # HTML DOM features
-    tag_name_embedding: bool = False
+    tag_name_embedding: bool = True
     """
     Whether to use HTML tag name as a feature when classifying nodes.
 
@@ -323,15 +333,17 @@ class Params:
     tag_name_embedding_dim: int = 30
     """Dimension of the output vector of HTML tag name embedding."""
 
-    position: bool = False
+    position: bool = True
     """
     Whether to use visual position as a feature when classifying nodes.
 
     See `awe.features.dom.Position`.
     """
 
     # Visual features
-    enabled_visuals: Optional[list[str]] = None
+    enabled_visuals: Optional[list[str]] = (
+        "font_size", "font_style", "font_weight", "font_color"
+    )
     """
     Filter visual attributes to only those in this list.
 
@@ -358,10 +370,10 @@ class Params:
     layer_norm: bool = False
     """Use layer normalization in the classification head."""
 
-    head_dims: list[int] = (128, 64)
+    head_dims: list[int] = (100, 10)
     """Dimensions of feed-forward layers in the classification head."""
 
-    head_dropout: float = 0.5
+    head_dropout: float = 0.3
     """Dropout probability in the classification head."""
 
     gradient_clipping: Optional[float] = None