[#41] add initial data_class

sgnlp/models/asgcn/data_class.py

@@ -0,0 +1,53 @@
+from dataclasses import dataclass, field
+
+
+@dataclass
+class SenticGCNTrainArgs:
+    initializer: str = field(
+        default="xavier_uniform", metadata={"help": "Type of initalizer to use."}
+    )
+    optimizer: str = field(
+        default="adam", metadata={"help": "Type of optimizer to use."}
+    )
+    learning_rate: float = field(
+        default=0.001, metadata={"help": "Default learning rate for training."}
+    )
+    l2reg: float = field(default=0.00001, metadata={"help": "Default l2reg value."})
+    epochs: int = field(default=100, metadata={"help": "Number of epochs to train."})
+    batch_size: int = field(default=32, metadata={"help": "Training batch size."})
+    log_step: int = field(default=5, metadata={"help": "Default log step."})
+    embed_dim: int = field(
+        default=300, metadata={"help": "Number of neurons for embed layer."}
+    )
+    hidden_dim: int = field(
+        default=300, metadata={"help": "Number of neurons for hidden layer."}
+    )
+    polarities_dim: int = field(
+        default=3, metadata={"help": "Default dimension for polarities."}
+    )
+    save: bool = field(
+        default=False, metadata={"help": "Flag to indicate if results should be saved."}
+    )
+    seed: int = field(
+        default=776, metadata={"help": "Default random seed for training."}
+    )
+    device: str = field(
+        default="cuda", metadata={"help": "Type of compute device to use for training."}
+    )
+
+    def __post_init__(self):
+        assert self.initializer in [
+            "xavier_uniform",
+            "xavier_uniform",
+            "orthogonal",
+        ], "Invalid initializer type!"
+        assert self.optimizer in [
+            "adadelta",
+            "adagrad",
+            "adam",
+            "adamax",
+            "asgd",
+            "rmsprop",
+            "sgd",
+        ], "Invalid optimizer"
+        assert self.device in ["cuda", "cpu"], "Invalid device type."