API Reference

September 10, 2026 · View on GitHub

The API reference is generated automatically from the Python docstrings. Select a specific module below or from the sidebar navigation.

Stability tiers

PhilanthroPy follows Semantic Versioning. Which promise applies to a symbol depends on its tier.

Tier	Promise	Breaking change requires
Tier 1: Stable	The signature and the meaning of the return value are fixed.	A major release, preceded by one full published minor emitting `DeprecationWarning`.
Tier 2: Beta	Works and is tested, but the shape may still move.	A minor release, called out under Breaking in CHANGELOG.md.
Tier 3: Experimental	No guarantees at all. May change or disappear without a deprecation cycle.	Nothing.

Everything reachable from philanthropy.__all__ is listed below. A symbol not listed here is not public, whatever its name looks like.

Tier 1: Stable

Symbol	Module
`CRMCleaner`, `FiscalYearTransformer`, `RFMTransformer`	`preprocessing`
`WealthScreeningImputer`, `WealthScreeningImputerKNN`, `WealthPercentileTransformer`	`preprocessing`
`ShareOfWalletScorer`, `PlannedGivingSignalTransformer`	`preprocessing`
`DonorPropensityModel`, `MajorGiftClassifier`, `LapsePredictor`	`models`
`PlannedGivingIntentScorer`, `ShareOfWalletRegressor`, `MovesManagementClassifier`	`models`
`PropensityScorer`	`models`
every function in `philanthropy.metrics`	`metrics`
`FiscalYearGroupedSplitter`	`model_selection`
`generate_synthetic_donor_data`, `load_ciob_fundraising`	`datasets`
`save_model`, `load_model`, `make_donor_dataset`	`utils`
`donor_feature_importance`	`inspection`

Tier 2: Beta

Symbol	Module	Why not Tier 1
`EncounterTransformer`, `EncounterRecencyTransformer`	`preprocessing`	Clinical schema conventions are still settling across AMCs.
`GratefulPatientFeaturizer`	`preprocessing`	Default service-line capacity weights are illustrative, not benchmarked.
`DischargeToSolicitationWindowTransformer`	`preprocessing`	Window bounds and the position-score curve may be re-tuned.
`SolicitationWindowTransformer`	`preprocessing`	Supported alias of the above; shares its tier.
`MatchingGiftFeaturizer`	`preprocessing`	The employer-normalisation rules will grow.
`AskAmountRecommender`	`models`	The ask-ladder multipliers are a heuristic.
`constituent_events_to_features`, `read_constituent_events`	`ingest`	Tracks the UniSchema `ConstituentEvent` schema, which is versioned upstream.
`civicrm_contributions_to_features`, `read_civicrm_contributions`	`ingest`	Tracks CiviCRM's contribution export labels and APIv4 field names, which move with the CRM.
`raisers_edge_gifts_to_features`, `read_raisers_edge_gifts`, `DEFAULT_EXCLUDED_GIFT_TYPES`	`ingest`	Tracks Raiser's Edge export labels and the RE NXT gift-type vocabulary; the excluded-type default will grow as real exports arrive.
`plot_affinity_distribution`, `plot_retention_waterfall`	`visualisation`	Chart composition is presentation, not contract.
`fetch_kdd98_donors`	`datasets`	Returns the raw upstream columns untyped; may gain as-of date parsing as the real-data leakage replication in #124 lands.
`make_donor_panel`	`datasets`	The returned dict may gain keys (pledges, appeals, soft credits) as more of the library needs panel-shaped fixtures; existing keys and their columns will not change silently.
`GiftIntervalCalibrator`, `GiftInterval`	`models`	The conformity-score menu is expected to grow (conformalised quantile regression is not shipped), which adds `score` values rather than changing existing ones.

Tier 3: Experimental

Symbol	Module	Why
`FinancialForecastModel`	`models`	The hybrid LSTM-ARIMA surrogate is an approximation of a method the dependency policy rules out; its accuracy characteristics are not established.
`UpliftTLearner`	`experimental`	`fit(X, y, treatment)` breaks the sklearn signature and the estimator is not `check_estimator` compliant.

Score scales

Every domain method returns a number on its own scale. None of them are calibrated probabilities.

Method	Returns	Scale
`DonorPropensityModel.predict_affinity_score`	`(n,)` float	0–100, monotone in `predict_proba`
`MajorGiftClassifier.predict_affinity_score`	`(n,)` float	0–100, from calibrated probabilities
`LapsePredictor.predict_lapse_score`	`(n,)` float	0–100, higher = more likely to lapse
`PlannedGivingIntentScorer.predict_intent_score`	`(n,)` float	0–100
`UpliftTLearner.predict_uplift_score`	`(n,)` float	−1 to 1, negative means the appeal suppresses giving
`ShareOfWalletScorer.transform`	`(n, 2)` float	`capacity_utilisation_ratio` 0–1; `capacity_tier` in {0, 1, 2}
`ShareOfWalletRegressor.capacity_ratio`	`(n,)` float	Unbounded ratio ≥ 0 (capacity ÷ historical giving)
`DischargeToSolicitationWindowTransformer.transform`	`(n, 2)` float	`in_solicitation_window` in {0, 1}; `window_position_score` 0–1
`GratefulPatientFeaturizer.transform`	`(n, 4)` float	Unbounded counts and weighted sums, all ≥ 0
`AskAmountRecommender.ask_ladder`	`(n, 3)` float	Dollars, not a score: conservative / target / stretch
`MovesManagementClassifier.action_priority`	`dict`	Not an array: `stage`, `confidence` (0–1), `portfolio_summary`
`FinancialForecastModel.predict_revenue_forecast`	`(horizon,)` float	Dollars per future period, length is `horizon`, not `len(X)`
`GiftIntervalCalibrator.predict_gift_interval`	`GiftInterval`	Not a score: two `(n,)` dollar bounds, plus the `attained_level` they certify, which is `r / (n + 1)` and not the requested `1 - alpha`

!!! info "This page is built from main, which is ahead of the release" pip install philanthropy currently gives you 0.7.1. The tier tables above describe the API as it stands on main; the deprecations below are live in the version you actually have installed.

Removed in 0.7.0

If you are upgrading from 0.6.0, these are gone, each after one full published minor of DeprecationWarning:

Removed	Use instead
`AskAmountRecommender.predict_ask_array`	`ask_ladder`
`ShareOfWalletRegressor.predict_capacity_ratio`	`capacity_ratio`
`MovesManagementClassifier.predict_action_priority`	`action_priority`
`PlannedGivingIntentScorer.predict_bequest_intent_score`	`predict_intent_score`

The predict_ prefix is reserved for methods that take X alone and return one value per row. The first three returned a (n, 3) dollar matrix, required a second argument, and returned a dict respectively.

Three constructor parameters that had no effect are gone too; passing any of them is now a TypeError: LapsePredictor(lapse_window_years=...), PropensityScorer(estimator=...), FiscalYearGroupedSplitter(fiscal_year_start=...).

donor_acquisition_cost, cost_per_dollar_raised and fundraising_roi are now keyword-only: they do not share an argument order, so a positional call was silently accepted and returned a plausible wrong number. Write them with keywords and the upgrade is a no-op.

Four accidental second import paths moved behind underscores, so 1.0 does not freeze them: metrics.scoring → metrics._scoring, preprocessing.transformers → preprocessing._transformers, models.propensity → models._propensity_baseline, utils.testing → utils._testing. Every public symbol they export is unchanged; import from the subpackage (from philanthropy.metrics import ...), as the documented examples always have, and nothing breaks.

Live in 0.7.0, removed in 0.8.0

Deprecated	Use instead
`WealthScreeningImputerKNN(group_col_idx=...)`	nothing; see below

group_col_idx has no replacement because there is nothing to replace. It was documented for a long time as stratifying KNN imputation per group "improving local accuracy" while being stored and never read, then implemented, and the implementation is what retired it: measured across several synthetic two-group pools and on five Python versions in CI, per-group and global KNN imputation produce bit-identical output (50263.48615163204 both ways). A donor's nearest neighbours by feature distance almost always share their group already, and KNNImputer weights distance by column magnitude, so a 0/1 group flag barely registers.

If you need per-group behaviour, split the frame by group and fit one imputer per part. That is explicit, and it costs nothing that the parameter was buying.

Live in 0.7.1, removed in 0.9.0

Deprecated	Use instead
`ShareOfWalletScorer` output name `sow_score`	`capacity_utilisation_ratio`

ShareOfWalletScorer.transform column 0 is now named capacity_utilisation_ratio in get_feature_names_out(): the formula is capacity ÷ clipped modelled wealth, which is capacity utilisation. It was never share of wallet; the docstring has said so since the class shipped, and the old name claimed a quantity the formula does not compute. The values, the column order, and the capacity_tier encoding are unchanged, so code that reads the column positionally needs nothing. Code that spells the name can call get_legacy_feature_names_out() for one published minor: it returns the old ["sow_score", "capacity_tier"] spelling under a DeprecationWarning and is removed in 0.9.0.