ferrolearn-preprocess

Data preprocessing transformers for the ferrolearn machine learning framework. Validated against scikit-learn 1.8.0 head-to-head — bit-identical numerical output (relative diff ≤ 1e-16) for StandardScaler, MinMaxScaler, MaxAbsScaler, and RobustScaler. See the workspace BENCHMARKS.md.

Scalers

Transformer	Description
`StandardScaler`	Zero-mean, unit-variance scaling
`MinMaxScaler`	Scale features to a given range (default [0, 1])
`RobustScaler`	Median/IQR-based scaling, robust to outliers
`MaxAbsScaler`	Scale by maximum absolute value to [-1, 1]
`Normalizer`	Normalize each sample (row) to unit norm (L1, L2, max)
`PowerTransformer`	Yeo-Johnson power transform for Gaussian-like distributions
`QuantileTransformer`	Map to uniform or normal distribution via quantile matching
`KBinsDiscretizer`	Discretize continuous features into bins (uniform / quantile / kmeans)

Encoders

Transformer	Description
`OneHotEncoder`	Encode categorical columns as binary indicator columns
`OrdinalEncoder`	Map categories to integers by order of appearance
`LabelEncoder`	Map labels to integer indices
`LabelBinarizer` / `MultiLabelBinarizer`	Binary indicator encoding for label vectors
`TargetEncoder`	Mean-target encoding for high-cardinality categoricals
`BinaryEncoder`	Binary base-2 encoding for high-cardinality categoricals

Imputers

Transformer	Description
`SimpleImputer`	Fill missing (NaN) values: mean, median, most frequent, constant
`KNNImputer`	Fill missing values using k-nearest-neighbor average
`IterativeImputer`	Round-robin regression imputation (BayesianRidge by default)

Feature selection

Transformer	Description
`VarianceThreshold`	Remove features with variance below a threshold
`SelectKBest` / `SelectPercentile`	Univariate feature selection
`SelectFromModel`	Threshold-based selection from any model with `coef_` / `feature_importances_`
`RFE` / `RFECV`	Recursive feature elimination
`SequentialFeatureSelector`	Forward / backward greedy selection

Feature engineering

Transformer	Description
`PolynomialFeatures`	Polynomial and interaction feature expansion
`SplineTransformer`	B-spline basis expansion
`Binarizer`	Threshold features to {0, 1}
`FunctionTransformer`	Apply a user-provided function element-wise
`ColumnTransformer`	Apply different transformers to different column subsets
`RandomProjection`	Gaussian / sparse random projection (Johnson-Lindenstrauss)

Text feature extraction

Transformer	Description
`CountVectorizer`	Bag-of-words frequency counts
`TfidfTransformer`	TF-IDF reweighting

Example

use ferrolearn_preprocess::StandardScaler;
use ferrolearn_core::FitTransform;
use ndarray::array;

let x = array![[1.0_f64, 10.0], [2.0, 20.0], [3.0, 30.0]];
let scaled = StandardScaler::<f64>::new().fit_transform(&x).unwrap();
// Each column now has mean ~= 0 and std ~= 1

All transformers implement PipelineTransformer for use inside a Pipeline.

License

Licensed under either of Apache License, Version 2.0 or MIT License at your option.

ferrolearn-preprocess 0.3.0