ds-utils-transformers

name	ds-utils-transformers
description	Sklearn pipeline transformers for multi-label binarization and sentence embeddings. Use when the user needs MultiLabelBinarizer in a Pipeline, ColumnTransformer, or set_output(transform="pandas"), or when integrating sentence-transformers models into sklearn pipelines for NLP tasks.
license	MIT
metadata	{"author":"Idan Morad","documentation":"https://datascienceutils.readthedocs.io/en/stable/","package":"data-science-utils","repository":"https://github.com/idanmoradarthas/DataScienceUtils"}

Transformers — ds_utils.transformers

Sklearn-compatible wrappers for preprocessing steps that need the feature-names API and stable dtypes.

Installation

pip install data-science-utils
# or
conda install -c idanmorad data-science-utils

# For NLP features (SentenceEmbeddingTransformer):
pip install data-science-utils[nlp]

Import

from ds_utils.transformers.multi_label_binarizer import MultiLabelBinarizerTransformer
from ds_utils.transformers.sentence_embedding import SentenceEmbeddingTransformer

---

MultiLabelBinarizerTransformer

Wraps sklearn.preprocessing.MultiLabelBinarizer so pipelines get get_feature_names_out and dense float64 matrices.

1. Direct Object Usage:

from ds_utils.transformers.multi_label_binarizer import MultiLabelBinarizerTransformer

X = [["sci-fi", "action"], ["romance"], ["action", "comedy"]]
mlb = MultiLabelBinarizerTransformer()
X_t = mlb.fit_transform(X)
feature_names = mlb.get_feature_names_out()

Output (X_t): will be a numpy array containing the binarized data, with feature_names corresponding to columns.

label_action	label_comedy	label_romance	label_sci-fi
1.0	0.0	0.0	1.0
0.0	0.0	1.0	0.0
1.0	1.0	0.0	0.0

2. Pipeline Usage with Pandas Output:

from ds_utils.transformers.multi_label_binarizer import MultiLabelBinarizerTransformer
from sklearn.pipeline import Pipeline

X = [["sci-fi", "action"], ["romance"], ["action", "comedy"]]
pipe = Pipeline([("mlb", MultiLabelBinarizerTransformer())])
pipe.set_output(transform="pandas")
df = pipe.fit_transform(X)

Output (df): will be a pandas DataFrame containing the binarized data, with feature_names corresponding to columns.

label_action	label_comedy	label_romance	label_sci-fi
1.0	0.0	0.0	1.0
0.0	0.0	1.0	0.0
1.0	1.0	0.0	0.0

Parameters (constructor):

• classes — optional fixed ordering of labels (passed through to MultiLabelBinarizer).
• sparse_output — if True, the inner binarizer may use sparse storage; the transformer still returns a dense float64 array.

Returns from transform: numpy.ndarray, shape (n_samples, n_classes), dtype float64.

Feature names: get_feature_names_out(input_features=None) returns {prefix}_{sanitized_label}; default prefix is label when input_features is omitted, otherwise the first validated input feature name is used as the prefix. Labels are sanitized for safe column names (e.g. Delta tables).

Common mistakes:

• Passing a flat list of genre strings, e.g. ['sci-fi', 'thriller', 'comedy'], as the whole X. Scikit-learn then treats each character as a sample. You need one iterable of labels per row: e.g. [['sci-fi', 'thriller', 'comedy']] for a single sample, or [['a', 'b'], ['c']] for two samples. See the sklearn docs for MultiLabelBinarizer.
• Using a DataFrame with more than one column for X: this transformer expects a single multi-label column (or a 1D array-like of rows).

Typical workflow

from ds_utils.transformers.multi_label_binarizer import MultiLabelBinarizerTransformer
from sklearn.compose import ColumnTransformer

# Single column "tags" in a DataFrame
import pandas as pd

df = pd.DataFrame({"tags": [["x", "y"], ["z"]], "num": [1.0, 2.0]})
pre = ColumnTransformer(
    [("mlb", MultiLabelBinarizerTransformer(), ["tags"])],
    remainder="passthrough",
)
X_out = pre.fit_transform(df)

---

SentenceEmbeddingTransformer

Wraps sentence-transformers models for use in sklearn pipelines. Produces dense embedding matrices from text inputs with lazy model loading, None/NaN handling, and get_feature_names_out support.

Requires: pip install data-science-utils[nlp]

1. Direct Usage:

from ds_utils.transformers.sentence_embedding import SentenceEmbeddingTransformer

texts = ["The quick brown fox", "jumps over the lazy dog", "Hello world"]
embed = SentenceEmbeddingTransformer()
embeddings = embed.fit_transform(texts)
feature_names = embed.get_feature_names_out()

Output (embeddings): will be a numpy array of shape (n_samples, embedding_dimension) (e.g. (3, 384) for the default sentence-transformers/all-MiniLM-L6-v2 model), with feature_names corresponding to columns.

dim_0	dim_1	dim_2	...	dim_383
-0.0123	0.0456	0.0789	...	0.0012
0.0345	-0.0678	0.0901	...	-0.0234
0.0567	0.0890	-0.0123	...	0.0456

2. Pipeline Usage with Classifier:

from ds_utils.transformers.sentence_embedding import SentenceEmbeddingTransformer
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier

pipeline = Pipeline([
    ('embeddings', SentenceEmbeddingTransformer(normalize_embeddings=True)),
    ('classifier', RandomForestClassifier()),
])
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)

3. ColumnTransformer Usage (mixing text and numerical features):

from ds_utils.transformers.sentence_embedding import SentenceEmbeddingTransformer
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler
import pandas as pd

df = pd.DataFrame({
    "description": ["a product", "another item"],
    "price": [9.99, 19.99],
})
pre = ColumnTransformer([
    ("text", SentenceEmbeddingTransformer(), ["description"]),
    ("num", StandardScaler(), ["price"]),
])
X_out = pre.fit_transform(df)

Parameters (constructor):

• model_name — name or path of a sentence-transformers model (default: 'sentence-transformers/all-MiniLM-L6-v2').
• batch_size — batch size for encoding (default: 32).
• show_progress_bar — show progress during encoding (default: False).
• normalize_embeddings — L2-normalize embeddings to unit length (default: False).
• device — computation device: 'cpu', 'cuda', etc. None auto-detects (default: None).
• precision — embedding precision: 'float32', 'int8', 'uint8', 'binary', 'ubinary' (default: 'float32').
• truncate_dim — truncate embeddings to this dimension, useful for Matryoshka models (default: None).
• prompt_name — name of a prompt from the model's prompts dictionary (default: None).
• prompt — raw prompt string to prepend to inputs (default: None).

Returns from transform: numpy.ndarray, shape (n_samples, embedding_dimension).

Feature names: get_feature_names_out() returns dim_0, dim_1, …, dim_{n-1}.

Lazy loading: The SentenceTransformer model is loaded only when fit() is first called. Subsequent fit() calls reuse the cached model.

None/NaN handling: None and NaN values in input are automatically replaced with empty strings before encoding.

Common mistakes:

• Calling transform() before fit(): raises NotFittedError. Always call fit() or fit_transform() first.
• Passing a DataFrame with more than one column: this transformer expects a single text column.
• Forgetting to install the nlp extras: raises ImportError with a helpful message.