feat: implement flair sklearn vectorizer wrappers

embeddings/embedding/sklearn_embedding.py

@@ -1,6 +1,7 @@
 from typing import Any, Dict, Optional
 
 import pandas as pd
+import scipy
 from sklearn.base import BaseEstimator as AnySklearnVectorizer
 
 from embeddings.embedding.embedding import Embedding
@@ -9,21 +10,16 @@
 
 class SklearnEmbedding(Embedding[ArrayLike, pd.DataFrame]):
     def __init__(
-        self,
-        vectorizer: AnySklearnVectorizer,
-        vectorizer_has_sparse_output: bool = True,
-        vectorizer_kwargs: Optional[Dict[str, Any]] = None,
+        self, vectorizer: AnySklearnVectorizer, vectorizer_kwargs: Optional[Dict[str, Any]] = None
     ):
         super().__init__()
-        self.vectorizer_kwargs = vectorizer_kwargs if vectorizer_kwargs else {}
-        self.vectorizer_has_sparse_output = vectorizer_has_sparse_output
-        self.vectorizer = vectorizer(**self.vectorizer_kwargs)
+        self.vectorizer = vectorizer(**vectorizer_kwargs if vectorizer_kwargs else {})
 
     def fit(self, data: ArrayLike) -> None:
         self.vectorizer.fit(data)
 
     def embed(self, data: ArrayLike) -> pd.DataFrame:
         embedded = self.vectorizer.transform(data)
-        if self.vectorizer_has_sparse_output:
+        if scipy.sparse.issparse(embedded):
             embedded = embedded.A
-        return pd.DataFrame(embedded, columns=self.vectorizer.get_feature_names_out())
+        return pd.DataFrame(embedded)

embeddings/embedding/vectorizer/flair.py

@@ -0,0 +1,46 @@
+import abc
+from typing import Any, Dict, Generic, List, Optional, TypeVar
+
+import numpy as np
+from flair.data import Sentence
+from numpy import typing as nptyping
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.feature_extraction.text import _VectorizerMixin
+
+from embeddings.embedding.flair_embedding import FlairEmbedding
+from embeddings.utils.array_like import ArrayLike
+
+Output = TypeVar("Output")
+
+
+# ignoring the mypy error due to no types (Any) in TransformerMixin and BaseEstimator classes
+class FlairVectorizer(TransformerMixin, _VectorizerMixin, BaseEstimator, Generic[Output]):  # type: ignore
+    def __init__(self, flair_embedding: FlairEmbedding) -> None:
+        self.embedder = flair_embedding
+
+    def fit(self, x: ArrayLike, y: Optional[ArrayLike] = None) -> None:
+        pass
+
+    @abc.abstractmethod
+    def transform(self, x: ArrayLike) -> Output:
+        pass
+
+    def fit_transform(self, x: ArrayLike, y: Optional[ArrayLike] = None, **kwargs: Any) -> Output:
+        return self.transform(x)
+
+
+class FlairDocumentVectorizer(FlairVectorizer[nptyping.NDArray[np.float_]]):
+    def transform(self, x: ArrayLike) -> nptyping.NDArray[np.float_]:
+        sentences = [Sentence(example) for example in x]
+        embeddings = [sentence.embedding.numpy() for sentence in self.embedder.embed(sentences)]
+        return np.vstack(embeddings)
+
+
+class FlairWordVectorizer(FlairVectorizer[List[List[Dict[int, float]]]]):
+    def transform(self, x: ArrayLike) -> List[List[Dict[int, float]]]:
+        sentences = [Sentence(example) for example in x]
+        embeddings = [sentence for sentence in self.embedder.embed(sentences)]
+        return [
+            [{i: value for i, value in enumerate(word.embedding.numpy())} for word in sent]
+            for sent in embeddings
+        ]

pyproject.toml

@@ -119,7 +119,8 @@ module = [
     "spacy",
     "appdirs",
     "dataset.arrow_dataset",
-    "seqeval.*"
+    "seqeval.*",
+    "scipy"
 ]
 ignore_missing_imports = true