dataforge-ml 1.0.1

A automated feature engineering and designing pipeline library

DataForgeML

Automated data profiling and splitting pipeline for ML datasets.

DataForgeML inspects your dataset, detects each column's semantic type (numeric, categorical, boolean, text, datetime, or identifier), computes per-column statistics and missingness, and produces a structured result ready for downstream feature engineering — no manual schema wrangling required.

Installation

pip install dataforge-ml

Quick Start

from dataforge_ml import DataLoader, PipelineConfig, StructuralProfiler

df = DataLoader().load("titanic.csv")

config = PipelineConfig()
result = StructuralProfiler(config).profile(df)

print(result.columns["Age"].semantic_type)  # SemanticType.Numeric
print(result.dataset.row_count)             # total rows

DataLoader auto-detects encoding and delimiter. Supported formats: CSV, TSV, Parquet, JSON, NDJSON, JSONL, XLSX, XLS, Arrow, Feather.

Column Type Overrides

Override the auto-detected type for any column before profiling:

config = PipelineConfig()
config.set_column_type("PassengerId", "identifier")           # skip stats entirely
config.set_columns_type(["Survived", "Pclass"], "categorical")

result = StructuralProfiler(config).profile(df)

To drop a column from all processing entirely, use exclude_columns:

config = PipelineConfig(exclude_columns=["PassengerId", "Name"])

Splitting

from dataforge_ml import DataLoader, DataSplitter

df = DataLoader().load("titanic.csv")
splitter = DataSplitter(df, target="Survived", random_seed=42)

# Random train/test split (stratified by default when target is set)
split = splitter.random_split(test_size=0.2)
print(split.train.shape, split.test.shape)

# Chronological split (no temporal leakage)
split = splitter.time_split(time_column="date", test_size=0.2)

# K-fold cross-validation
for fold in splitter.kfold(k=5):
    print(f"Fold {fold.fold_index}: train={fold.train_size}, val={fold.val_size}")

License

MIT