fastsymbolicgp 0.7.0

Scalable symbolic learning with island evolution, DAG execution, shared multiclass models, distillation, and deployment export.

FastSymbolicGP V0.7.0

FastSymbolicGP is a scikit-learn-compatible symbolic machine-learning library for classification, regression, supervised symbolic feature construction, model distillation, and experimental symbolic networks.

V0.7.0 is the Scalable Symbolic Learning release. It expands the V0.6.0 engine with island evolution, DAG execution, a persistent bounded subtree cache, shared-backbone multiclass learning, resource budgets, deployment export, model profiling, native missing-value operators, robustness objectives, and Publication Benchmark V3.

Main V0.7.0 capabilities

• binary classification and symbolic regression;
• multiclass OVR and shared_softmax symbolic learning;
• NSGA-II-style Pareto ranking and compact final-model selection;
• symbolic probability ensembles and ensemble distillation;
• island evolution with migration and heterogeneous island profiles;
• DAG compilation and common-subexpression compression;
• bounded persistent LRU subtree cache;
• NumPy/Numba postfix evaluator fallback when DAG execution is disabled;
• FastSymbolicTransformer V2 with mRMR/diversity/Pareto selection;
• checkpoint, resume, warm start, and checkpoint branching;
• automatic presets and time/evaluation/memory budgets;
• native missing-value primitives and robust-fitness perturbations;
• local feature-occlusion explanations and experimental counterfactual search;
• Python, C, C++, Java, Kotlin, and JavaScript source export;
• inference latency and structural-cost profiling;
• reproducible Publication Benchmark V3 with common saved folds and LaTeX tables;
• a live island-grid terminal dashboard.

Installation

From the release wheel:

python -m pip install fastsymbolicgp-0.7.0-py3-none-any.whl
python -m fastsymbolicgp.cli

For optional Numba acceleration:

python -m pip install "fastsymbolicgp[acceleration]"

The source ZIP also includes install_fastsymbolicgp_v070.bat.

Binary classification with islands and the live dashboard

from fastsymbolicgp import FastSymbolicClassifier

model = FastSymbolicClassifier(
    population_size=32,
    generations=120,
    optimization="nsga2",
    evolution_model="islands",
    n_islands=4,
    island_profiles=("accurate", "compact", "diverse", "robust"),
    migration_interval=10,
    prediction_mode="symbolic_ensemble",
    ensemble_size=5,
    subtree_cache=True,
    dag_execution="auto",
    probability_calibration="auto",
    preset="balanced",
    display="grid",
    dashboard_interval=5,
    random_state=42,
)
model.fit(X_train, y_train)

The report distinguishes the requested postfix evaluator from the actual execution path:

print(model.evaluation_backend_)  # e.g. numba
print(model.execution_engine_)    # e.g. dag_cached_numpy

Shared-backbone multiclass model

model = FastSymbolicClassifier(
    multiclass_strategy="shared_softmax",
    shared_n_components=8,
    multiclass_calibration="temperature",
    population_size=24,
    generations=80,
    optimization="nsga2",
    random_state=42,
)
model.fit(X_train, y_train)

The model evolves a shared bank of symbolic features and trains one joint multinomial softmax head. OVR remains available through multiclass_strategy="ovr".

Symbolic feature construction

from fastsymbolicgp import FastSymbolicTransformer

transformer = FastSymbolicTransformer(
    n_components=12,
    component_selection="mrmr",
    max_correlation=0.90,
    include_original_features=True,
    model_params={"population_size": 30, "generations": 50, "verbose": 0},
    random_state=42,
)
Z_train = transformer.fit_transform(X_train, y_train)

Distillation

distilled = model.distill(
    X_train,
    max_nodes=35,
    population_size=48,
    generations=80,
    verbose=0,
)

The distilled model learns the teacher's probability surface using one compact symbolic expression per required output.

Native missing values and robust fitness

model = FastSymbolicClassifier(
    missing_value_strategy="native",
    function_set=("add", "sub", "mul", "div", "is_missing", "coalesce"),
    robustness_training=True,
    robustness_method="combined",
    robustness_weight=0.05,
)

Resource budgets

model = FastSymbolicClassifier(
    preset="auto",
    time_budget=120,
    evaluation_budget=1_000_000,
    memory_budget_mb=2048,
)

stop_reason_ records whether evolution ended by maximum generations, early stopping, time budget, or evaluation budget.

Deployment export and profiling

model.export_python("deployed_model.py")
model.export_c("deployed_model.c")
model.export_cpp("deployed_model.hpp")
model.export_java("FastSymbolicModel.java")
model.export_kotlin("FastSymbolicModel.kt")
model.export_javascript("fast_symbolic_model.js")

print(model.profile_prediction(X_test[:100], repeats=100))

Python export supports binary, multiclass OVR/shared-softmax, and regression. Portable C/C++/Java/Kotlin/JavaScript export supports binary classification and regression in V0.7.0.

Checkpoint branching

compact_branch = model.branch(
    parsimony_target_nodes=25,
    generations=180,
)
compact_branch.continue_evolution(X_train, y_train, additional_generations=60)

Publication Benchmark V3

fastsymbolicgp-benchmark ^
  --datasets breast_cancer,iris,wine ^
  --algorithms fastsymbolicgp,logistic,random_forest,hist_gradient_boosting,svm ^
  --runs 6 ^
  --folds 5 ^
  --population-size 30 ^
  --generations 120 ^
  --islands 4 ^
  --output-dir benchmark_v070_results

The benchmark stores common fold indices, raw and summary results, failures, expressions, environment data, algorithm ranks, and LaTeX-ready tables.

Stable versus experimental

Stable in V0.7.0:

• classifier, regressor, OVR/shared-softmax multiclass;
• island evolution, DAG/cache, Pareto selection, ensembles;
• transformer V2, distillation, checkpointing;
• Python export, binary/regression portable exports;
• benchmark framework and reporting.

Experimental in V0.7.0:

• FastSymbolicNetworkClassifier;
• greedy counterfactual search;
• adaptive population sizing and dynamic function weighting;
• robustness fitness for deployment-hardening studies.

These experimental components are implemented and tested but should be benchmarked carefully before strong scientific claims.