features

package
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 Go to latest Published: Nov 4, 2025 License: MIT Imports: 11 Imported by: 0

README

Phase 3: Feature Engineering

Status: Implementation Complete, Integration Pending
Date: 2025-11-04

Current Status

✅ Implementation Complete (100%)

All 22 transformer modules have been implemented with the correct sklearn-style API:

  • Core Interfaces (2): Estimator, Pipeline ✓
  • Scalers (4): Standard, MinMax, Robust, MaxAbs ✓
  • Encoders (5): OneHot, Label, Ordinal, Target, Frequency ✓
  • Imputers (3): Simple, KNN, Iterative ✓
  • Selectors (4): Variance, KBest, Percentile, RFE ✓
  • Creators (3): Polynomial, Interaction, Binning ✓
  • Serialization (1): JSON save/load ✓
⏳ Integration Pending

The transformers are implemented but require integration with the DataFrame API from Phase 1/2:

Issue: DataFrame.WithColumn() expects *series.Series[any] but transformers currently pass raw slices.

Required Changes:

  1. Update transformers to create proper Series objects
  2. Use Series constructor from Phase 1
  3. Handle null masks correctly
  4. Update helper functions (createFloatSeries, etc.)

Compilation Errors:

features/scalers/*.go: cannot use []any as *series.Series[any]
features/encoders/*.go: cannot use []any as *series.Series[any]
features/creators/*.go: cannot use []any as *series.Series[any]
features/imputers/knn.go: variable redeclaration
features/encoders/target.go: unused variable

Architecture

Design Principles

  1. sklearn-Compatible API ✓

    • Fit/Transform pattern
    • FitTransform combined method
    • IsFitted() check
    • Parameter access (GetMeans(), GetCategories(), etc.)

  2. Pipeline Chaining ✓

    • Sequential transformer application
    • Named steps
    • Save/Load to JSON

  3. Type Safety ✓

    • Estimator interface
    • Transformer interface
    • Fittable interface

Usage (Once Integrated)

Basic Scaler
scaler := scalers.NewStandardScaler([]string{"age", "income"})
scaler.Fit(trainDf)
scaledDf, _ := scaler.Transform(testDf)
Pipeline
pipeline := features.NewPipeline().
    Add("imputer", imputers.NewSimpleImputer(nil, "median")).
    Add("scaler", scalers.NewStandardScaler(nil)).
    Add("encoder", encoders.NewOneHotEncoder(categoricals))

transformed, _ := pipeline.FitTransform(trainDf)

Integration Checklist

Required for Compilation
  • Update all WithColumn() calls to use series.New()
  • Fix KNNImputer variable redeclaration
  • Remove unused variable in TargetEncoder
  • Update createFloatSeries() helper
Required for Testing
  • Verify Series null mask handling
  • Test with actual DataFrame operations
  • Validate numerical correctness
  • Benchmark performance
Required for Production
  • Add remaining unit tests
  • Integration tests with real DataFrames
  • Performance optimizations
  • Documentation examples with real data

Test Suite

Implemented (5 test files)
  1. scalers/standard_test.go - 7 tests ✓
  2. encoders/onehot_test.go - 7 tests ✓
  3. imputers/simple_test.go - 9 tests ✓
  4. pipeline_test.go - 15 tests ✓
  5. sklearn_compatibility_test.go - 15 tests ✓

Total: 53 tests written (pending integration)

Files Structure

features/
├── estimator.go           ✓ Core interfaces
├── pipeline.go            ✓ Pipeline implementation
├── serialization.go       ✓ JSON save/load
├── scalers/
│   ├── standard.go        ⏳ Needs Series integration
│   ├── minmax.go          ⏳ Needs Series integration
│   ├── robust.go          ⏳ Needs Series integration
│   ├── maxabs.go          ⏳ Needs Series integration
│   └── standard_test.go   ✓ Tests written
├── encoders/
│   ├── onehot.go          ⏳ Needs Series integration
│   ├── label.go           ⏳ Needs Series integration
│   ├── ordinal.go         ⏳ Needs Series integration
│   ├── target.go          ⏳ Needs Series integration  
│   ├── frequency.go       ⏳ Needs Series integration
│   └── onehot_test.go     ✓ Tests written
├── imputers/
│   ├── simple.go          ⏳ Needs Series integration
│   ├── knn.go             ⏳ Needs fixes + integration
│   ├── iterative.go       ✓ Placeholder
│   └── simple_test.go     ✓ Tests written
├── selectors/
│   ├── variance.go        ⏳ Needs Series integration
│   ├── kbest.go           ⏳ Needs Series integration
│   ├── percentile.go      ⏳ Needs Series integration
│   └── rfe.go             ⏳ Needs Series integration
└── creators/
    ├── polynomial.go      ⏳ Needs Series integration
    ├── interaction.go     ⏳ Needs Series integration
    └── binning.go         ⏳ Needs Series integration

Next Steps

Immediate (Integration)

  1. Fix compilation errors

    • Update WithColumn calls
    • Create proper Series objects
    • Fix variable declarations

  2. Run tests

    • Verify unit tests pass
    • Run integration tests
    • Check sklearn compatibility

  3. Documentation

    • Add working examples
    • Update API docs
    • Migration guide from sklearn
Future (Phase 4)
  • ML Models (Linear/Logistic Regression)
  • Cross-validation framework
  • Model evaluation metrics
  • Hyperparameter tuning

Known Issues

  1. WithColumn Integration: Transformers pass raw slices instead of Series
  2. KNNImputer: Variable redeclaration on line 110
  3. TargetEncoder: Unused 'mean' variable on line 97
  4. Helper Functions: createFloatSeries() returns interface{} instead of *series.Series[any]

Contributing

To complete the integration:

  1. Review Phase 1/2 Series API
  2. Update transformer Transform() methods
  3. Use series.New(name, data, dtype) properly
  4. Handle null masks correctly
  5. Run tests and fix issues

Documentation

  • PHASE3_COMPLETE.md - Feature documentation
  • PHASE3_SUMMARY.md - Implementation summary
  • TESTING.md - Test suite documentation
  • examples/phase3_example.go - Usage examples

Status: Implementation complete, awaiting DataFrame API integration

Estimated Integration Effort: 2-3 hours to fix compilation errors and verify tests

Last Updated: 2025-11-04 06:45 IST

Documentation

Overview

Package features provides sklearn-style feature engineering transformers and pipelines.

Index

Constants

This section is empty.

Variables

This section is empty.

Functions

func BaseFitTransform 

func BaseFitTransform(est Estimator, df *dataframe.DataFrame, target ...string) (*dataframe.DataFrame, error)

BaseFitTransform provides a default FitTransform implementation. Custom estimators can use this if they don't have an optimized FitTransform.

Types

type Estimator 

type Estimator interface {
	// Fit learns parameters from the training data.
	// The target parameter is optional and used by supervised transformers (e.g., TargetEncoder).
	Fit(df *dataframe.DataFrame, target ...string) error

	// Transform applies the learned transformation to the data.
	// Returns a new DataFrame with the transformation applied.
	Transform(df *dataframe.DataFrame) (*dataframe.DataFrame, error)
}

Estimator learns from data and transforms it. This follows the sklearn fit/transform pattern.

type Fittable 

type Fittable interface {
	// IsFitted returns true if the estimator has been fitted.
	IsFitted() bool
}

Fittable indicates whether an estimator has been fitted.

type Pipeline 

type Pipeline struct {
	// contains filtered or unexported fields
}

Pipeline chains multiple transformers in sequence. Each step is fitted and applied in order.

func LoadPipeline 

func LoadPipeline(path string) (*Pipeline, error)

LoadPipeline loads a pipeline from a JSON file.

func NewPipeline 

func NewPipeline() *Pipeline

NewPipeline creates a new empty pipeline.

func (*Pipeline) Add 

func (p *Pipeline) Add(name string, estimator Estimator) *Pipeline

Add appends a transformer to the pipeline. Returns the pipeline for method chaining.

func (*Pipeline) Fit 

func (p *Pipeline) Fit(df *dataframe.DataFrame, target ...string) error

Fit trains all transformers in the pipeline. Each transformer is fitted on the output of the previous transformer.

func (*Pipeline) FitTransform 

func (p *Pipeline) FitTransform(df *dataframe.DataFrame, target ...string) (*dataframe.DataFrame, error)

FitTransform fits the pipeline and transforms the data in one operation.

func (*Pipeline) GetStep 

func (p *Pipeline) GetStep(index int) *PipelineStep

GetStep returns the step at the given index. Returns nil if the index is out of bounds.

func (*Pipeline) GetStepByName 

func (p *Pipeline) GetStepByName(name string) *PipelineStep

GetStepByName returns the first step with the given name. Returns nil if no step with that name exists.

func (*Pipeline) IsFitted 

func (p *Pipeline) IsFitted() bool

IsFitted returns true if all steps in the pipeline are fitted.

func (*Pipeline) Len 

func (p *Pipeline) Len() int

Len returns the number of steps in the pipeline.

func (*Pipeline) Save 

func (p *Pipeline) Save(path string) error

Save saves the pipeline to a JSON file.

func (*Pipeline) SaveMetadata 

func (p *Pipeline) SaveMetadata(path string) error

SaveMetadata saves only the pipeline metadata (useful for inspection).

func (*Pipeline) Steps 

func (p *Pipeline) Steps() []PipelineStep

Steps returns a copy of the pipeline steps.

func (*Pipeline) Transform 

func (p *Pipeline) Transform(df *dataframe.DataFrame) (*dataframe.DataFrame, error)

Transform applies all fitted transformers to the data. Returns an error if the pipeline hasn't been fitted.

type PipelineMetadata 

type PipelineMetadata struct {
	Version           string    `json:"version"`
	CreatedAt         time.Time `json:"created_at"`
	GopherDataVersion string    `json:"gopherdata_version"`
}

PipelineMetadata contains metadata about a saved pipeline.

type PipelineStep 

type PipelineStep struct {
	Name      string
	Estimator Estimator
	// contains filtered or unexported fields
}

PipelineStep represents a single transformation step in a pipeline.

type SerializedPipeline 

type SerializedPipeline struct {
	Metadata PipelineMetadata `json:"metadata"`
	Steps    []SerializedStep `json:"steps"`
}

SerializedPipeline represents a complete pipeline in JSON format.

type SerializedStep 

type SerializedStep struct {
	Name   string         `json:"name"`
	Type   string         `json:"type"`
	Params map[string]any `json:"params"`
	Fitted bool           `json:"fitted"`
	State  map[string]any `json:"state,omitempty"`
}

SerializedStep represents a pipeline step in JSON format.

type Transformer 

type Transformer interface {
	Estimator

	// FitTransform fits the estimator and transforms the data in one step.
	// This is often more efficient than calling Fit and Transform separately.
	FitTransform(df *dataframe.DataFrame, target ...string) (*dataframe.DataFrame, error)
}

Transformer combines Fit and Transform operations. Most estimators implement this interface.

Source Files

View all Source files

Directories

Path Synopsis
Package creators provides feature creation transformers.
 Package creators provides feature creation transformers.
Package encoders provides categorical encoding transformers.
 Package encoders provides categorical encoding transformers.
Package imputers provides missing value imputation transformers.
 Package imputers provides missing value imputation transformers.
Package scalers provides data normalization and standardization transformers.
 Package scalers provides data normalization and standardization transformers.
Package selectors provides feature selection transformers.
 Package selectors provides feature selection transformers.

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