separatix 0.1.0a5

Diagnostic profiling of supervised feature spaces before model training.

Separatix

separatix profiles labeled feature spaces before supervised model training and returns transparent, confidence-aware guidance about apparent classification or regression complexity.

The intended use case includes learned embeddings, but the package is not restricted to embeddings. It also works on raw feature matrices when you want a coarse diagnostic of whether the observed supervised geometry looks mostly linear, smoothly nonlinear, local or kernel-like, fragmented or discontinuous, bottlenecked, or too unreliable to trust.

separatix does not claim to pick the optimal classifier or regressor. It is a pretraining diagnostic and auditing tool designed to make its reasoning visible.

Installation

pip install separatix

To install the latest development version directly from GitHub:

pip install "git+https://github.com/NiklasMelton/Separatix.git@develop"

Quick start

from separatix import diagnose

recommendation = diagnose(X, y, random_state=0)
print(recommendation)

For a structured audit:

from separatix import diagnose

report = diagnose(X, y, return_report=True, random_state=0)
print(report.recommendation_text)
print(report.decision_path)
print(report.scores)
print(report.to_json())

What It Accepts

• Dense NumPy arrays
• SciPy sparse matrices
• pandas DataFrames and Series when pandas is installed
• Binary and multiclass classification targets
• Multilabel binary indicator targets with target_mode="multilabel" or auto-detection for unambiguous 2D indicators
• Continuous single- or multi-target regression with explicit target_mode="regression"
• String or numeric labels treated as categorical class identifiers

Regression is opt-in so numeric class identifiers remain categorical by default. General multioutput classification is not supported.

What It Returns

By default, diagnose(...) returns a plain-text recommendation. With return_report=True, it returns a DiagnosticReport that includes:

• the recommendation label
• plain-text recommendation text
• confidence level
• underlying metric groups
• probe-family evidence, including uncertainty-aware family comparisons
• normalized summary scores
• a visible decision path
• warnings and skipped diagnostics
• sampling and densification events
• preprocessing and runtime metadata

The report is JSON-serializable through report.to_dict() and report.to_json(). Non-finite diagnostic values are represented as JSON null; to_json() never emits non-standard NaN or infinity literals. The default terse form removes large row-level arrays before copying them.

For multilabel targets, separatix compares probe families across micro F1, macro F1, and sample Jaccard rather than collapsing the evidence into a single weighted score. Optional iterative multilabel stratification can be installed with:

pip install "separatix[multilabel]"

For regression targets, call diagnose(X, y, target_mode="regression"). Regression evidence is compared across variance-weighted R2 and uniform-average R2, with normalized RMSE and target-neighborhood smoothness as supporting diagnostics. Classification-only boundary and fragmentation diagnostics are marked not applicable and do not reduce regression confidence.

All non-dummy probe families learn feature scaling inside each validation training fold. Sparse probes use non-centering scaling. Geometry and topology continue to describe the supplied, unscaled coordinate space, and the report records both choices under preprocessing.

Optional feed-forward MLP probes can be installed and enabled explicitly:

pip install "separatix[mlp]"

Set mlp_probes=True and use mlp_device, mlp_trigger_skill_threshold, mlp_min_improvement, and mlp_max_parameters to control them. An MLP can override simpler-family guidance only with complete held-out evidence; failed or infeasible group splits never fall back to in-sample override evidence.

Optional persistent-topology diagnostics can be installed with:

pip install "separatix[tda]"

For multilabel targets, persistent topology is supporting evidence only. When enabled, it is computed on capped boundary-candidate subsets and a small capped set of high-support label-positive subsets.

For regression targets, optional topology is computed only on capped high-residual and high-local-discontinuity subsets. topology="graph" uses a sparse-compatible mutual-nearest-neighbor component summary; topology="persistent" adds persistent homology when ripser is installed. topology="auto" skips topology under the fast budget and otherwise attempts both summaries. Regression topology is descriptive supporting evidence: it is included in the report but never changes the recommendation label or confidence.

Recommendation Categories

• linear_likely_sufficient
• smooth_nonlinear_recommended
• kernel_or_local_recommended
• high_capacity_or_partitioning_recommended
• feedforward_mlp_recommended
• feature_or_label_bottleneck_likely
• insufficient_data_or_unreliable_geometry
• inconclusive
• linear_response_likely_sufficient
• smooth_nonlinear_response_recommended
• kernel_or_local_regression_recommended
• higher_capacity_or_partitioning_regression_recommended
• feedforward_mlp_regression_recommended
• feature_or_target_bottleneck_likely
• insufficient_data_or_unreliable_regression_geometry
• inconclusive_regression_diagnostic

These categories are intentionally coarse. They describe the apparent geometry and difficulty of the labeled feature space, not a guaranteed best model choice.

The synthetic recommendation ladder below shows how separatix responds as the designed dataset geometry moves from simple linear structure toward smoother nonlinearity, local or kernel-like structure, fragmented boundaries, and finally weak-signal or random-label bottlenecks. The x-axis is the intended dataset complexity, while the y-axis is the coarse recommendation level reported by separatix.

Decision Pipeline

The recommendation is produced by a fixed, inspectable pipeline:

1. Validate inputs and encode labels.
2. Audit class counts, imbalance, sparsity, and basic dataset conditions.
3. Compute geometry, neighborhood, boundary, fragmentation, and optional topology diagnostics, using a distinct multilabel path for binary indicator targets.
4. Run simple probe models and compare them to a dummy baseline.
5. Build probe-family evidence with uncertainty estimates for linear, smooth_nonlinear, and local_kernel.
6. Apply a 95% signal-vs-dummy gate before making any model-family recommendation for single-label targets, or a two-of-three primary-metric signal gate for multilabel targets.
7. Use conservative escalation: keep the simpler family unless a more complex family has a clear uncertainty-adjusted advantage.
8. Treat fragmentation and optional topology as supporting structural evidence, not as shortcuts around weak probe evidence.
9. Render both a plain-language summary and a structured report, including raw_best_family and recommended_family when a report is requested.

The full rationale and decision rules are documented in docs/decision_pipeline.md.

Sparse Inputs And Memory Behavior

Sparse matrices are accepted directly. Diagnostics that need dense data use a shared densification policy rather than a separate dense-only code path. When a step would require densification, separatix can fail, skip, or warn and subsample before densifying, depending on configuration. These events are recorded in the report.

max_samples and max_dense_mb are hard limits. Group-aware sampling never splits a group or exceeds the row cap. If no support-preserving sample fits, the affected supervised diagnostic is skipped and reliability is marked insufficient instead of silently dropping classes or labels. The dense-memory budget applies to sparse multilabel targets as well as feature matrices.

When groups are supplied, sampling keeps groups whole and predictive evidence must come from group-disjoint held-out splits. Each evaluated class or label side needs support in both training and test partitions. A single group, an oversized group, or a class confined to too few groups therefore causes the affected supervised evidence to be skipped instead of evaluated on its training rows. Geometry and topology remain descriptive in those cases.

Numeric one-dimensional targets—including non-integral values—remain categorical unless target_mode="regression" is explicit. High-cardinality numeric classification targets produce a warning to make accidental routing visible.

Examples

• examples/basic_breast_cancer.py
• examples/linear_hyperplane_visual.py
• examples/curvilinear_boundary_visual.py
• examples/high_dimensional_linear_hyperplane.py
• examples/high_dimensional_curvilinear_hyperplane.py
• examples/moons_vs_linear.py
• examples/circles_kernel_signal.py
• examples/recommendation_complexity_ladder.py
• examples/multiclass_wine.py
• examples/openml_multilabel_yeast.py
• examples/sparse_text_like_embeddings.py

Related Work

This package is not an implementation of a published dataset-complexity procedure, but the project is adjacent to and inspired by prior work on classification complexity and data geometry. In particular, would like to acknowledge:

• Ho and Basu, "Complexity Measures of Supervised Classification Problems" (PDF)
• Lorena, Garcia, Lehmann, Souto, and Ho, "How Complex Is Your Classification Problem? A Survey on Measuring Classification Complexity" (DOI, PDF)

We do not follow those procedures directly, but they are relevant background for why geometry-aware pretraining diagnostics are useful.