change return type of classmethods-constructors to Self

autointent/modules/scoring/dnnc/head_training.py

@@ -21,6 +21,7 @@
 import torch
 from sentence_transformers import CrossEncoder
 from sklearn.linear_model import LogisticRegressionCV
+from typing_extensions import Self
 
 from autointent.custom_types import LabelType
 
@@ -133,7 +134,7 @@ def set_classifier(self, clf: LogisticRegressionCV) -> None:
         self._clf = clf
 
     @classmethod
-    def load(cls, path: str) -> "CrossEncoderWithLogreg":
+    def load(cls, path: str) -> Self:
         dump_dir = Path(path)
 
         # load sklearn model
@@ -144,7 +145,7 @@ def load(cls, path: str) -> "CrossEncoderWithLogreg":
         crossencoder_dir = str(dump_dir / "crossencoder")
         model = CrossEncoder(crossencoder_dir)  # TODO control device
 
-        res = CrossEncoderWithLogreg(model)
+        res = cls(model)
         res.set_classifier(clf)
 
         return res

autointent/nodes/inference/inference_node.py

@@ -1,6 +1,7 @@
 import gc
 
 import torch
+from typing_extensions import Self
 
 from autointent.configs.node import InferenceNodeConfig
 from autointent.modules.base import Module
@@ -13,7 +14,7 @@ def __init__(self, module: Module, node_type: str) -> None:
         self.node_type = node_type
 
     @classmethod
-    def from_config(cls, config: InferenceNodeConfig) -> "InferenceNode":
+    def from_config(cls, config: InferenceNodeConfig) -> Self:
         node_info = NODES_INFO[config.node_type]
         module = node_info.modules_available[config.module_type](**config.module_config)
         if config.load_path is not None:

autointent/pipeline/inference/inference_pipeline.py

@@ -1,5 +1,7 @@
 from typing import Any
 
+from typing_extensions import Self
+
 from autointent.configs.node import InferenceNodeConfig
 from autointent.context import Context
 from autointent.custom_types import LabelType, NodeType
@@ -11,13 +13,13 @@ def __init__(self, nodes: list[InferenceNode]) -> None:
         self.nodes = {node.node_type: node for node in nodes}
 
     @classmethod
-    def from_dict_config(cls, nodes_configs: list[dict[str, Any]]) -> "InferencePipeline":
+    def from_dict_config(cls, nodes_configs: list[dict[str, Any]]) -> Self:
         nodes_configs_ = [InferenceNodeConfig(**cfg) for cfg in nodes_configs]
         nodes = [InferenceNode.from_config(cfg) for cfg in nodes_configs_]
         return cls(nodes)
 
     @classmethod
-    def from_config(cls, nodes_configs: list[InferenceNodeConfig]) -> "InferencePipeline":
+    def from_config(cls, nodes_configs: list[InferenceNodeConfig]) -> Self:
         nodes = [InferenceNode.from_config(cfg) for cfg in nodes_configs]
         return cls(nodes)
 
@@ -29,12 +31,12 @@ def fit(self, utterances: list[str], labels: list[LabelType]) -> None:
         pass
 
     @classmethod
-    def from_context(cls, context: Context) -> "InferencePipeline":
+    def from_context(cls, context: Context) -> Self:
         if not context.has_saved_modules():
             config = context.optimization_info.get_inference_nodes_config()
             return cls.from_config(config)
         nodes = [
             InferenceNode(module, node_type)
             for node_type, module in context.optimization_info.get_best_modules().items()
         ]
-        return InferencePipeline(nodes)
+        return cls(nodes)

autointent/pipeline/optimization/pipeline_optimizer.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 from hydra.utils import instantiate
+from typing_extensions import Self
 
 from autointent import Context
 from autointent.configs.optimization_cli import EmbedderConfig, LoggingConfig, VectorIndexConfig
@@ -37,7 +38,7 @@ def set_config(self, config: LoggingConfig | VectorIndexConfig | EmbedderConfig)
             raise TypeError(msg)
 
     @classmethod
-    def from_dict_config(cls, config: dict[str, Any]) -> "PipelineOptimizer":
+    def from_dict_config(cls, config: dict[str, Any]) -> Self:
         return instantiate(PipelineOptimizerConfig, **config)  # type: ignore[no-any-return]
 
     def optimize(self, context: Context) -> None: