Fix/update pipelines

embeddings/config/flair_config_space.py

@@ -18,7 +18,7 @@
 class FlairTextClassificationConfigSpaceMapping:
     LOAD_MODEL_KEYS_MAPPING: ClassVar[Mapping[str, Set[str]]] = MappingProxyType(
         {
-            "FlairDocumentCNNEmbeddings": {
+            "FlairDocumentRNNEmbeddings": {
                 "hidden_size",
                 "rnn_type",
                 "rnn_layers",
@@ -27,7 +27,7 @@ class FlairTextClassificationConfigSpaceMapping:
                 "word_dropout",
                 "reproject_words",
             },
-            "FlairDocumentRNNEmbeddings": {
+            "FlairDocumentCNNEmbeddings": {
                 "cnn_pool_kernels",
                 "dropout",
                 "word_dropout",

embeddings/pipeline/__init__.py

@@ -9,6 +9,8 @@
 DOWNSAMPLE_SPLITS_TYPE = Tuple[Optional[float], Optional[float], Optional[float]]
 SAMPLE_MISSING_SPLITS_TYPE = Optional[Tuple[Optional[float], Optional[float]]]
 FLAIR_DATASET_TRANSFORMATIONS_TYPE = Union[
-    Transformation[datasets.DatasetDict, Corpus], Transformation[Corpus, Corpus]
+    Transformation[datasets.DatasetDict, datasets.DatasetDict],
+    Transformation[datasets.DatasetDict, Corpus],
+    Transformation[Corpus, Corpus],
 ]
 FLAIR_PERSISTERS_TYPE = Union[FlairConllPersister[Corpus], FlairPicklePersister[Corpus, Corpus]]

embeddings/pipeline/flair_classification.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Any, Dict, Optional, Tuple
 
 import datasets
 from flair.data import Corpus
@@ -15,6 +15,7 @@
 from embeddings.embedding.flair_loader import FlairDocumentPoolEmbeddingLoader
 from embeddings.evaluator.text_classification_evaluator import TextClassificationEvaluator
 from embeddings.model.flair_model import FlairModel
+from embeddings.pipeline import FLAIR_DATASET_TRANSFORMATIONS_TYPE
 from embeddings.pipeline.standard_pipeline import StandardPipeline
 from embeddings.task.flair_task.text_classification import TextClassification
 from embeddings.transformation.flair_transformation.classification_corpus_transformation import (
@@ -23,7 +24,10 @@
 from embeddings.transformation.flair_transformation.split_sample_corpus_transformation import (
     SampleSplitsFlairCorpusTransformation,
 )
-from embeddings.transformation.transformation import Transformation
+from embeddings.transformation.hf_transformation.class_encode_column_transformation import (
+    ClassEncodeColumnTransformation,
+)
+from embeddings.transformation.transformation import DummyTransformation
 from embeddings.utils.json_dict_persister import JsonPersister
 
 
@@ -45,14 +49,19 @@ def __init__(
         sample_missing_splits: Optional[Tuple[Optional[float], Optional[float]]] = None,
         seed: int = 441,
         load_dataset_kwargs: Optional[Dict[str, Any]] = None,
+        encode_classes: bool = False,
     ):
         output_path = Path(output_path)
         dataset = Dataset(dataset_name, **load_dataset_kwargs if load_dataset_kwargs else {})
         data_loader = HuggingFaceDataLoader()
-        transformation: Union[
-            Transformation[datasets.DatasetDict, Corpus], Transformation[Corpus, Corpus]
-        ]
-        transformation = ClassificationCorpusTransformation(input_column_name, target_column_name)
+        transformation: FLAIR_DATASET_TRANSFORMATIONS_TYPE = DummyTransformation()
+        if encode_classes:
+            transformation = transformation.then(
+                ClassEncodeColumnTransformation(column=target_column_name)
+            )
+        transformation = transformation.then(
+            ClassificationCorpusTransformation(input_column_name, target_column_name)
+        )
         if sample_missing_splits:
             transformation = transformation.then(
                 SampleSplitsFlairCorpusTransformation(*sample_missing_splits, seed=seed)

embeddings/pipeline/flair_hps_pipeline.py

@@ -104,6 +104,14 @@ def _pop_sampled_parameters(
         assert isinstance(load_model_kwargs, dict)
         return embedding_name, document_embedding, task_train_kwargs, load_model_kwargs
 
+    @staticmethod
+    def _revert_default_hps_task_train_kwargs(
+        task_train_kwargs: Dict[str, ParameterValues]
+    ) -> Dict[str, ParameterValues]:
+        task_train_kwargs["param_selection_mode"] = False
+        task_train_kwargs["save_final_model"] = True
+        return task_train_kwargs
+
 
 @dataclass
 class OptimizedFlairClassificationPipeline(
@@ -157,6 +165,12 @@ def _get_metadata(self, parameters: SampledParameters) -> FlairClassificationPip
             task_train_kwargs,
             load_model_kwargs,
         ) = self._pop_sampled_parameters(parameters=parameters)
+
+        task_train_kwargs = (
+            OptimizedFlairClassificationPipeline._revert_default_hps_task_train_kwargs(
+                task_train_kwargs
+            )
+        )
         metadata: FlairClassificationPipelineMetadata = {
             "embedding_name": embedding_name,
             "dataset_name": str(self.dataset_name_or_path),
@@ -257,6 +271,11 @@ def _get_metadata(
             task_train_kwargs,
             load_model_kwargs,
         ) = self._pop_sampled_parameters(parameters=parameters)
+        task_train_kwargs = (
+            OptimizedFlairPairClassificationPipeline._revert_default_hps_task_train_kwargs(
+                task_train_kwargs
+            )
+        )
         metadata: FlairPairClassificationPipelineMetadata = {
             "embedding_name": embedding_name,
             "dataset_name": str(self.dataset_name_or_path),
@@ -377,13 +396,26 @@ def _pop_sampled_parameters(
         assert isinstance(task_model_kwargs, dict)
         return embedding_name, hidden_size, task_train_kwargs, task_model_kwargs
 
+    @staticmethod
+    def _revert_default_hps_task_train_kwargs(
+        task_train_kwargs: Dict[str, ParameterValues]
+    ) -> Dict[str, ParameterValues]:
+        task_train_kwargs["param_selection_mode"] = False
+        task_train_kwargs["save_final_model"] = True
+        return task_train_kwargs
+
     def _get_metadata(self, parameters: SampledParameters) -> FlairSequenceLabelingPipelineMetadata:
         (
             embedding_name,
             hidden_size,
             task_train_kwargs,
             task_model_kwargs,
         ) = self._pop_sampled_parameters(parameters)
+        task_train_kwargs = (
+            OptimizedFlairSequenceLabelingPipeline._revert_default_hps_task_train_kwargs(
+                task_train_kwargs
+            )
+        )
         metadata: FlairSequenceLabelingPipelineMetadata = {
             "embedding_name": embedding_name,
             "dataset_name": str(self.dataset_name_or_path),

embeddings/pipeline/flair_pair_classification.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Any, Dict, Optional, Tuple
 
 import datasets
 from flair.data import Corpus
@@ -15,6 +15,7 @@
 from embeddings.embedding.flair_loader import FlairDocumentPoolEmbeddingLoader
 from embeddings.evaluator.text_classification_evaluator import TextClassificationEvaluator
 from embeddings.model.flair_model import FlairModel
+from embeddings.pipeline import FLAIR_DATASET_TRANSFORMATIONS_TYPE
 from embeddings.pipeline.standard_pipeline import StandardPipeline
 from embeddings.task.flair_task.text_pair_classification import TextPairClassification
 from embeddings.transformation.flair_transformation.pair_classification_corpus_transformation import (
@@ -23,7 +24,10 @@
 from embeddings.transformation.flair_transformation.split_sample_corpus_transformation import (
     SampleSplitsFlairCorpusTransformation,
 )
-from embeddings.transformation.transformation import Transformation
+from embeddings.transformation.hf_transformation.class_encode_column_transformation import (
+    ClassEncodeColumnTransformation,
+)
+from embeddings.transformation.transformation import DummyTransformation
 from embeddings.utils.json_dict_persister import JsonPersister
 
 
@@ -45,15 +49,18 @@ def __init__(
         sample_missing_splits: Optional[Tuple[Optional[float], Optional[float]]] = None,
         seed: int = 441,
         load_dataset_kwargs: Optional[Dict[str, Any]] = None,
+        encode_classes: bool = False,
     ):
         output_path = Path(output_path)
         dataset = Dataset(dataset_name, **load_dataset_kwargs if load_dataset_kwargs else {})
         data_loader = HuggingFaceDataLoader()
-        transformation: Union[
-            Transformation[datasets.DatasetDict, Corpus], Transformation[Corpus, Corpus]
-        ]
-        transformation = PairClassificationCorpusTransformation(
-            input_columns_names_pair, target_column_name
+        transformation: FLAIR_DATASET_TRANSFORMATIONS_TYPE = DummyTransformation()
+        if encode_classes:
+            transformation = transformation.then(
+                ClassEncodeColumnTransformation(column=target_column_name)
+            )
+        transformation = transformation.then(
+            PairClassificationCorpusTransformation(input_columns_names_pair, target_column_name)
         )
         if sample_missing_splits:
             transformation = transformation.then(

embeddings/pipeline/flair_preprocessing_pipeline.py

@@ -39,6 +39,9 @@
 from embeddings.transformation.flair_transformation.split_sample_corpus_transformation import (
     SampleSplitsFlairCorpusTransformation,
 )
+from embeddings.transformation.hf_transformation.class_encode_column_transformation import (
+    ClassEncodeColumnTransformation,
+)
 from embeddings.transformation.transformation import DummyTransformation
 from embeddings.utils.flair_corpus_persister import FlairConllPersister, FlairPicklePersister
 
@@ -62,6 +65,7 @@ class FlairPreprocessingPipeline(
     ignore_test_subset: bool = False
     seed: int = 441
     load_dataset_kwargs: Optional[Dict[str, Any]] = None
+    encode_labels: bool = False
 
     def __post_init__(self) -> None:
         self.persister = self._get_persister()
@@ -71,7 +75,7 @@ def __post_init__(self) -> None:
         super(FlairPreprocessingPipeline, self).__init__(dataset, data_loader, transformation)
 
     @abc.abstractmethod
-    def _get_base_dataset_transformation(self) -> FLAIR_DATASET_TRANSFORMATIONS_TYPE:
+    def _get_to_flair_dataset_transformation(self) -> FLAIR_DATASET_TRANSFORMATIONS_TYPE:
         pass
 
     @abc.abstractmethod
@@ -89,17 +93,30 @@ def _get_dataloader(self, dataset: Dataset) -> FLAIR_DATALOADERS:
 
     def _get_dataset_transformation(
         self, data_loader: FLAIR_DATALOADERS
-    ) -> FLAIR_DATASET_TRANSFORMATIONS_TYPE:
+    ) -> Optional[FLAIR_DATASET_TRANSFORMATIONS_TYPE]:
         if isinstance(data_loader, (ConllFlairCorpusDataLoader, PickleFlairCorpusDataLoader)):
-            return DummyTransformation()
+            return None
 
-        return self._get_base_dataset_transformation()
+        return self._get_to_flair_dataset_transformation()
 
     def _get_transformations(
         self, data_loader: FLAIR_DATALOADERS
     ) -> FLAIR_DATASET_TRANSFORMATIONS_TYPE:
 
-        transformation = self._get_dataset_transformation(data_loader)
+        transformation: FLAIR_DATASET_TRANSFORMATIONS_TYPE = DummyTransformation()
+        if self.encode_labels:
+            if isinstance(data_loader, (ConllFlairCorpusDataLoader, PickleFlairCorpusDataLoader)):
+                raise ValueError(
+                    "ClassEncodeColumnTransformation transformation is unavailable for Flair DataLoaders. "
+                    "Set parameter `encode_labels` value to True"
+                )
+            transformation = transformation.then(
+                ClassEncodeColumnTransformation(column=self.target_column_name)
+            )
+
+        to_flair_dataset_transformation = self._get_dataset_transformation(data_loader)
+        if to_flair_dataset_transformation:
+            transformation = transformation.then(to_flair_dataset_transformation)
 
         if self.sample_missing_splits:
             transformation = transformation.then(
@@ -126,7 +143,7 @@ class FlairTextClassificationPreprocessingPipeline(FlairPreprocessingPipeline):
     def _get_persister(self) -> FLAIR_PERSISTERS_TYPE:
         return FlairPicklePersister(self.persist_path)
 
-    def _get_base_dataset_transformation(self) -> FLAIR_DATASET_TRANSFORMATIONS_TYPE:
+    def _get_to_flair_dataset_transformation(self) -> FLAIR_DATASET_TRANSFORMATIONS_TYPE:
         assert isinstance(self.input_column_name, str)
         return ClassificationCorpusTransformation(
             input_column_name=self.input_column_name,
@@ -138,7 +155,7 @@ def _get_base_dataset_transformation(self) -> FLAIR_DATASET_TRANSFORMATIONS_TYPE
 class FlairTextPairClassificationPreprocessingPipeline(
     FlairTextClassificationPreprocessingPipeline
 ):
-    def _get_base_dataset_transformation(self) -> FLAIR_DATASET_TRANSFORMATIONS_TYPE:
+    def _get_to_flair_dataset_transformation(self) -> FLAIR_DATASET_TRANSFORMATIONS_TYPE:
         assert isinstance(self.input_column_name, (tuple, list))
         return PairClassificationCorpusTransformation(
             input_columns_names_pair=self.input_column_name,
@@ -151,7 +168,7 @@ class FlairSequenceLabelingPreprocessingPipeline(FlairPreprocessingPipeline):
     def _get_persister(self) -> FLAIR_PERSISTERS_TYPE:
         return FlairConllPersister(self.persist_path)
 
-    def _get_base_dataset_transformation(self) -> FLAIR_DATASET_TRANSFORMATIONS_TYPE:
+    def _get_to_flair_dataset_transformation(self) -> FLAIR_DATASET_TRANSFORMATIONS_TYPE:
         assert isinstance(self.input_column_name, str)
         return ColumnCorpusTransformation(
             input_column_name=self.input_column_name,

embeddings/pipeline/flair_sequence_labeling.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Any, Dict, Optional, Tuple
 
 import datasets
 from flair.data import Corpus
@@ -15,6 +15,7 @@
 from embeddings.embedding.flair_loader import FlairWordEmbeddingLoader
 from embeddings.evaluator.sequence_labeling_evaluator import SequenceLabelingEvaluator
 from embeddings.model.flair_model import FlairModel
+from embeddings.pipeline import FLAIR_DATASET_TRANSFORMATIONS_TYPE
 from embeddings.pipeline.standard_pipeline import StandardPipeline
 from embeddings.task.flair_task.sequence_labeling import SequenceLabeling
 from embeddings.transformation.flair_transformation.column_corpus_transformation import (
@@ -23,7 +24,10 @@
 from embeddings.transformation.flair_transformation.split_sample_corpus_transformation import (
     SampleSplitsFlairCorpusTransformation,
 )
-from embeddings.transformation.transformation import Transformation
+from embeddings.transformation.hf_transformation.class_encode_column_transformation import (
+    ClassEncodeColumnTransformation,
+)
+from embeddings.transformation.transformation import DummyTransformation
 from embeddings.utils.json_dict_persister import JsonPersister
 
 
@@ -47,14 +51,21 @@ def __init__(
         sample_missing_splits: Optional[Tuple[Optional[float], Optional[float]]] = None,
         seed: int = 441,
         load_dataset_kwargs: Optional[Dict[str, Any]] = None,
+        encode_classes: bool = True,
     ):
         output_path = Path(output_path)
         dataset = Dataset(dataset_name, **load_dataset_kwargs if load_dataset_kwargs else {})
         data_loader = HuggingFaceDataLoader()
-        transformation: Union[
-            Transformation[datasets.DatasetDict, Corpus], Transformation[Corpus, Corpus]
-        ]
-        transformation = ColumnCorpusTransformation(input_column_name, target_column_name)
+
+        transformation: FLAIR_DATASET_TRANSFORMATIONS_TYPE = DummyTransformation()
+        if encode_classes:
+            transformation = transformation.then(
+                ClassEncodeColumnTransformation(column=target_column_name)
+            )
+        transformation = transformation.then(
+            ColumnCorpusTransformation(input_column_name, target_column_name)
+        )
+
         if sample_missing_splits:
             transformation = transformation.then(
                 SampleSplitsFlairCorpusTransformation(*sample_missing_splits, seed=seed)

embeddings/transformation/hf_transformation/class_encode_column_transformation.py

@@ -0,0 +1,14 @@
+import datasets
+
+from embeddings.transformation.transformation import Transformation
+
+
+class ClassEncodeColumnTransformation(Transformation[datasets.DatasetDict, datasets.DatasetDict]):
+    def __init__(
+        self,
+        column: str,
+    ):
+        self.column = column
+
+    def transform(self, data: datasets.DatasetDict) -> datasets.DatasetDict:
+        return data.class_encode_column(column=self.column)