Methods

Methods turn a FeatureMatrix into a Result. Every method is sklearn-compatible where it makes sense (fit, predict, fit_transform).

Attribution — Delta variants

All Delta variants share _DeltaBase, work on z-scored features, and produce a nearest-centroid classifier.

Class	Distance	Reference
BurrowsDelta	mean absolute L1	Burrows 2002
ArgamonLinearDelta	L2 (Euclidean)	Argamon 2008
QuadraticDelta	squared L2	—
CosineDelta	1 − cosine similarity	Smith & Aldridge 2011
EderDelta / EderSimpleDelta	weighted Delta variants	Eder 2015

from tamga import MFWExtractor, BurrowsDelta
fm = MFWExtractor(n=200, scale="zscore", lowercase=True).fit_transform(corpus)
y = np.array(corpus.metadata_column("author"))
clf = BurrowsDelta().fit(fm, y)
predictions = clf.predict(fm)       # nearest-centroid labels
probs = clf.predict_proba(fm)       # softmax over negative distances

BurrowsDelta

BurrowsDelta()

Use when: you have 2+ candidate authors with ~2000+ words of known writing each and want to rank which one most likely wrote a questioned document. Don't use when: you have only one candidate (use GeneralImpostors verification instead), or when documents are shorter than ~500 words (signal gets noisy). Expect: a distance score per candidate; lowest distance is the predicted author.

The classic Burrows (2002) method: z-score features, mean-absolute-difference (L1) distance to each candidate's centroid. Good default for literary English.

CosineDelta

CosineDelta()

Use when: you want a modern default for Delta-family attribution — cosine is robust to document-length differences and less sensitive to outlier words than L1. Don't use when: your corpus mixes wildly different genres without care; cosine is less diagnostic when topic dominates style. Expect: a distance score per candidate in [0, 2]; lowest distance wins.

Smith & Aldridge (2011). Standard choice in modern stylometry; often the best single-method baseline before tuning.

EderDelta / EderSimpleDelta

EderDelta(), EderSimpleDelta()

Use when: you want to dampen noisy low-frequency words in the MFW tail — Eder's weighting penalises contributions from less-frequent features. Don't use when: your MFW list is already short (n < 100); there's little tail to down- weight. Expect: same shape as Burrows Delta; different ranking when tail-MFW contributions would otherwise dominate.

Eder (2015). Two variants: EderDelta with explicit per-feature weights, EderSimpleDelta with a simplified scheme.

ArgamonLinearDelta

ArgamonLinearDelta()

Use when: you specifically want Euclidean (L2) distance rather than L1 — appropriate when features are approximately Gaussian after z-scoring. Don't use when: the MFW distribution is skewed enough to produce outliers; L2 penalises outliers quadratically. Prefer CosineDelta or BurrowsDelta. Expect: a distance score per candidate; same ranking shape as other Deltas with different sensitivity to large per-feature deviations.

Argamon (2008). Probabilistic interpretation of Delta under a Gaussian generative model.

QuadraticDelta

QuadraticDelta()

Use when: you want to replicate experiments that use squared-L2 distance — equivalent to Argamon Delta without the square root. Don't use when: you need a calibrated distance for downstream combining; squared distances aren't a proper metric. Expect: a distance score per candidate; monotonic with Argamon Linear Delta so ranking is identical.

Contrast — Zeta

Craig's Zeta (ZetaClassic) and Eder's smoothed variant (ZetaEder) extract the vocabulary most preferred by one author group versus another.

from tamga.methods.zeta import ZetaClassic
result = ZetaClassic(group_by="author", top_k=50, group_a="Hamilton", group_b="Madison").fit_transform(corpus)
df_a, df_b = result.tables   # top-k A-preferred / B-preferred words with proportions

ZetaClassic

ZetaClassic(group_by=..., top_k=..., group_a=..., group_b=...)

Use when: you have two pre-defined author groups (or authors) and want to know which words each group prefers over the other — Craig's classic Zeta. Don't use when: you only want to rank one unknown document against candidates (use a Delta instead), or when your groups are very small (<10 docs each). Expect: two tables of top-k words; each word has a proportion-in-A and proportion-in-B; large differences are the distinctive vocabulary.

ZetaEder

ZetaEder(group_by=..., top_k=..., group_a=..., group_b=...)

Use when: you want Zeta with the Eder (2017) smoothing — handles very rare or very common words more gracefully than the classic version. Don't use when: you're reproducing Burrows/Craig-era results for comparison; use ZetaClassic for historical parity. Expect: same output shape as ZetaClassic; smoother ranking near the tails.

Dimensionality reduction

All reducers accept a FeatureMatrix and return a Result with 2-D / n-D coordinates.

PCAReducer

PCAReducer(n_components=2)

Use when: you want a fast, interpretable 2-D or 3-D projection where axes are orthogonal variance directions. Default choice for "plot my corpus" questions. Don't use when: authorship differences are highly non-linear; PCA's linear axes will miss curved manifolds. Expect: coords (n_docs × n_components) + explained_variance_ratio_ per component.

UMAPReducer

UMAPReducer(n_components=2, n_neighbors=15, min_dist=0.1)

Use when: you want a non-linear projection that preserves local and global structure — typically the best-looking 2-D visualisation of stylometric features. Don't use when: you need reproducibility without pinning a seed — UMAP is stochastic. Always set random_state. Expect: coords (n_docs × n_components). Requires tamga[viz].

TSNEReducer

TSNEReducer(n_components=2, perplexity=30)

Use when: you want a non-linear projection that emphasises local neighbourhood structure — authors cluster tightly. Don't use when: you need inter-cluster distances to be meaningful (t-SNE warps them), or when you plan to use the coordinates as features for a downstream method. Expect: coords (n_docs × n_components). Non-deterministic without a seed.

MDSReducer

MDSReducer(n_components=2, metric=True)

Use when: you want a projection that tries to preserve pairwise Delta distances as literally as possible — good for interpreting dendrogram + scatter together. Don't use when: you have a large corpus (>500 docs); MDS scales poorly. Expect: coords (n_docs × n_components) + stress (lower = better fit).

Clustering

Clusterers accept a FeatureMatrix and produce cluster labels; hierarchical clustering also returns the linkage matrix for dendrograms.

HierarchicalCluster

HierarchicalCluster(linkage="ward")

Use when: you want a dendrogram — the canonical stylometry visualisation — where leaves are documents and branch heights are distances. Don't use when: your corpus is large enough (>2000 docs) that dendrogram inspection is no longer practical. Expect: labels (n_docs,) + linkage_matrix usable with scipy.cluster.hierarchy.dendrogram.

Supported linkages: "ward" (default, variance-minimising), "average", "complete", "single".

KMeansCluster

KMeansCluster(n_clusters=3, seed=42)

Use when: you have a rough expected cluster count and want spherical clusters of comparable size — fastest clustering option. Don't use when: cluster sizes are very unequal, cluster shapes are elongated, or you don't know n_clusters ahead of time (use HDBSCANCluster). Expect: labels (n_docs,) + cluster centroids.

HDBSCANCluster

HDBSCANCluster(min_cluster_size=5)

Use when: you don't know the cluster count ahead of time, expect variable cluster density, or want "noise" points to be labelled as outliers (-1). Don't use when: your corpus is small (<30 docs); HDBSCAN's density estimates get unstable. Expect: labels (n_docs,) with -1 for noise; probabilities (cluster-membership confidence).

Consensus trees

BootstrapConsensus

BootstrapConsensus(mfw_bands=[100, 200, 300], replicates=20)

Use when: you want robustness evidence for a dendrogram — repeatedly resample the MFW feature set and see which clades survive. Don't use when: you need one quick visualisation; bootstrap runs many Delta + clustering cycles and is slow. Expect: a Newick consensus tree with clade-support values (fraction of replicates where that clade appears).

Eder (2017). Integrates out the "how many MFW?" knob by sampling across bands.

Classification + CV

Any sklearn classifier (Logistic Regression, linear / RBF SVM, Random Forest, HistGBM) via build_classifier(name), plus cross_validate_tamga(fm, y, cv_kind=...) with three stylometry-aware CV strategies:

Use when: you have labelled documents (author or group) and want standard ML performance numbers — accuracy, F1, confusion matrices — with stylometry-aware CV that doesn't leak author identity between folds. Don't use when: you have fewer than ~20 documents per class; CV becomes statistically meaningless. Also don't use for single-case verification (use GeneralImpostors). Expect: per-fold predictions, a mean accuracy / macro-F1, and fold-level Result objects for downstream plots.

• stratified — StratifiedKFold, seed controls the shuffle
• loao — Leave-One-Author-Out (LeaveOneGroupOut with author as group)
• leave_one_text_out — LeaveOneOut

Bayesian

BayesianAuthorshipAttributor

BayesianAuthorshipAttributor()

Use when: you want posterior probabilities over N candidate authors with principled Dirichlet smoothing — the Wallace–Mosteller Federalist approach. Don't use when: your features are z-scored (it expects raw counts; use MFWExtractor(scale="none")). Expect: predict_proba returns per-document posterior probability vectors over candidates. No need for tamga[bayesian] — this variant is pure NumPy.

HierarchicalGroupComparison

HierarchicalGroupComparison(group_a=..., group_b=..., feature_name=...)

Use when: you want to test whether two author populations differ systematically in a stylistic feature, with full per-author uncertainty — a PyMC varying-intercept model. Don't use when: you only have one author per group (no pooling signal) or need a fast screening method (MCMC sampling is slow; use a frequentist Zeta first). Expect: an arviz InferenceData with posterior draws for the group-difference parameter. Requires tamga[bayesian].

Forensic methods

Under tamga.forensic:

Use when: you want tamga's one-case verification or calibration layer — see the dedicated Forensic toolkit pages for gloss-per-method detail. Don't use when: you have a closed candidate set and just want attribution — use Delta variants above. Expect: scorers that return calibrated log-LR + evidence metadata, not classifier accuracy.

Method	Task	Reference
GeneralImpostors	one-class verification	Koppel & Winter 2014
Unmasking	long-text verification (accuracy-degradation curve)	Koppel & Schler 2004
CalibratedScorer	Platt / isotonic calibration of any scorer	Platt 1999; Niculescu-Mizil & Caruana 2005

See Forensic toolkit.

Next

• Results & provenance — what every method returns.