Create Model

Create and start fitting a new Bayesian Marketing Mix Model.

This queues an async model fit and returns immediately with a model_hash. Use get_model_status to poll for progress until status is 'complete'.

Priors are calculated automatically using smart defaults based on cost shares, industry benchmarks, and channel-type detection. You can override individual channels via the priors parameter.

Args: uploaded_file_id: The file ID returned by upload_data. date_column: Name of the date column in the CSV. kpi_column: Name of the KPI/dependent variable column. hierarchy_column: Name of the brand/segment column (must have exactly 1 unique value). channels: List of channel definitions, each with keys: name, activity_column, spend_column. Example: [{"name": "TV", "activity_column": "tv_grps", "spend_column": "tv_spend"}] multiplier_column: Column to convert KPI to revenue. Defaults to kpi_column. control_columns: Non-media control variable column names (e.g. ["price", "distribution"]). total_media_effect: Controls prior strength. Either an industry name for a benchmark ("FMCG"=6%, "Retail"=9%, "TelCo"=30%, "Financial Services"=19%, "E-Commerce"=22%, "Other"=12%) or a custom decimal like "0.15" meaning "I believe all media drives 15% of my KPI". Default "Other". priors: Optional per-channel prior overrides. Each dict should have "channel" matching a channels[].name, plus any fields to override: distribution, mean, sd, lower, upper, transform, adstock_type, effect_period. Only specified fields are overridden; the rest use smart defaults. Adstock-kernel fields: half_life_lower/half_life_upper (carryover half-life bounds in periods — preferred over the legacy decay_lower/decay_upper), theta_mean/theta_sd (peak-lag prior, adstock_type="delayed" only), dual_weight_mean/dual_weight_sd (long-term/slow-component share prior, adstock_type="dual_geometric" only). SATURATION ANCHOR — state it ONCE, in exactly one of three mutually exclusive forms (two in one override -> 400 "state the saturation prior once"): (1) half_marginal_mean/half_marginal_sd — CANONICAL for saturation_type="generalized_log" (rejected on other families): the activity level where MARGINAL returns have halved, finite at every curvature (#632). sat_shape_mean MUST accompany the pair in the same override (#672) — the fold pairs your coefficient with the stated curvature, so omitting it is a 400, never a silent default. (2) half_saturation_mean/half_saturation_sd — the 50%-of-maximum point in activity units, for the single-parameter families (tanh/michaelis_menten/ negative_exponential). Do NOT use it for generalized_log near-log work: it overflows below sat_shape_mean 0.00097657 and is rejected with a 400 — precisely the regime that family exists for. (3) alpha_sd + scalars — legacy internal coordinates, accepted for backward compat. Curvature (generalized_log only): sat_shape_mean/sat_shape_sd — small values are near-logarithmic, 1.0 is michaelis_menten. COEFFICIENT in a human coordinate (generalized_log only, #671): effect_at_avg_mean/effect_at_avg_sd — the effect share at the channel's AVERAGE activity, as FRACTIONS (mean in (0, 0.95], sd > 0; 0.2 means 20%). Folded server-side into mean/sd at the row's operating point with the same arithmetic as the dashboard. Requires sat_shape_mean in the same override; cannot be combined with mean/sd ("state the coefficient prior once") or with half_saturation_*. Stating half_marginal_* + effect_at_avg_* + sat_shape_mean together is the full (x*, E, k) triple — the recommended generalized_log elicitation, since only beta*k is identified and raw beta spans orders of magnitude. VERIFY what was applied via get_model's model_config.priors_resolved: rows carry the FOLDED mean/sd/scalars/alpha_sd, and overridden_fields lists the field names you sent. UNKNOWN KEYS ARE REJECTED with a 400 naming the field (#630); they used to be dropped silently, fitting a hybrid of the override and the smart defaults. Common misses: "beta"/"beta_mean" -> mean, "beta_sd" -> sd, "sat_shape" -> sat_shape_mean. "name" and "parameter" are rejected too — they identify the smart-prior row the override merges onto. trend: Enable dynamic baseline trend component. seasonality: Enable automatic seasonality detection. The prior sigma on the Fourier coefficients is chosen for the link (#534): 0.5 under link="log", 10 under "identity". The coefficients live on the link's scale, so the additive default would admit e^10x seasonal amplitude on a multiplicative model. likelihood: Likelihood function: "normal" (default), "lognormal", "logit", "studentt", "poisson", "negativebinomial", or "quantile". saturation_type: Diminishing-returns curve family applied to media: "tanh" (default), "michaelis_menten", "negative_exponential", or "generalized_log" (two-parameter Box-Cox/power-log family 1 - (1+x/K)^(-shape); tune per channel via the sat_shape_mean/sat_shape_sd prior fields). transform_order: "adstock_first" (default: carryover accumulates, then saturates) or "saturation_first" (each period's spend saturates, then the effect spreads over time through the normalized adstock kernel). link: Model Form. "identity" (default) fits an additive model — components add on the outcome scale. "log" fits a multiplicative model — components add on the log scale and media effects are percentage lifts. Under the removal_lift attribution convention (the API default), contributions then include an Overlap reconciliation column; the other conventions (aumann_shapley — the dashboard default for multiplicative models since #509 — shapley, and proportional_normalized) allocate the interaction across components and close exactly WITHOUT an Overlap column (see get_model_results). channel_groups: Optional adstock groups: [{"name": ..., "channels": [...], "share_saturation": bool}]. Member channels tie their carryover parameters (decay/theta/dual-weight — plus saturation when share_saturation is true) to one shared value, e.g. grouping channels into shared "Long"/"Short" carryover classes. Members are channels[].name values; each group needs >= 2 members; groups must be disjoint; and tied members must have identical adstock_type/effect_period/bound overrides (the API rejects divergent groups at request time). control_reference: Control attribution reference points (#452), multiplicative models (link="log") only: maps control column names (plus optional "default") to "auto" | "absent" | "average" | "lowest" | "highest" — which counterfactual "remove this control" means in the contributions. "absent" measures against the variable at zero (legacy behavior; honest only when zero is observed). "average"/"lowest"/"highest" reference the control at its observed mean/min/max — use for controls that never approach zero (price indices, distribution levels), where a zero counterfactual produces unbounded contributions and a negative Base. "auto" detects per control whether zero is inside the observed data range. Example: {"default": "auto", "relative_price": "average", "promo_flag": "absent"}. Omit entirely to keep every control at "absent" (byte-identical legacy output). Unknown control names/modes are rejected at request time; any value other than "absent" requires link="log". The fit reports the resolution in model_config.control_references (see get_model_results). name: Display name for the created model, honoured verbatim (#575). Falls back to a generated API_MMM{brand}{hash} string when omitted. Either way the model starts unsaved — invisible to list_models unless include_unsaved=true — until save_model files it into a project. operating_margin: Scalar operating margin as a decimal fraction in (0, 1], e.g. 0.18 = 18%. Mutually exclusive with operating_margin_column (the API 400s when both are given). Storing a margin unlocks the financials results section and lets run_optimizer(objective="profit") use it automatically instead of requiring forward_margin on every call. operating_margin_column: Name of a column in the uploaded CSV holding a per-date margin series. The column may be uniformly in fractions (0, 1] OR uniformly in percentages (1, 100] — the API detects the unit and normalizes percentages; mixed units are rejected. Same unlocks as operating_margin; the column must exist in the uploaded file. CAUTION: the API reads the margin keys from the REQUEST ROOT — a margin placed inside a config dict is silently ignored (no error), and the model fits marginless. attribution: Attribution convention for the contribution decomposition, resolved at fit time: "removal_lift" (the API default; one-at-a-time removal — multiplicative models then emit the Overlap column), "aumann_shapley" (the dashboard default for multiplicative models since #509), "shapley", or "proportional_normalized". Any value other than "removal_lift" requires link="log" (the API rejects it on additive models). The non-removal conventions allocate the interaction across components and close exactly WITHOUT an Overlap column. To reconcile with a dashboard-built multiplicative model, use "aumann_shapley". annual_discount_rate: Annual discount rate (decimal >= 0, e.g. 0.08) used by the display-time financial bridge and cohort ledger PV discounting. Display-time only — does not change the fit. sampler: MCMC sampler overrides, e.g. {"n_samples": 2000, "tune": 1500, "chains": 4, "cores": 2, "target_accept": 0.95}. STRICTLY validated: unknown keys inside sampler are rejected with a 400 naming the field; cores must be 1-8. Only the keys you send are overridden. reporting_kernel: Reporting-kernel class override (#450) for the cohort_ledger section's forward allocation. Shape: {"classes": {...}, "channel_classes": {...}} — ONLY those two top-level keys are accepted (anything else, e.g. "mode", 400s with the unknown key named). channel_classes names channels by channels[].name or activity_column, validated at request time. Affects only how the cohort_ledger allocates effects over the horizon — not the fit, and not the contributions / channel_summary decompositions. (The related "complete" / "in_window" choice is a separate cohort_horizon QUERY parameter on the results endpoint, not part of this config.)

control_priors: Optional root-level control slope overrides. Each entry names a selected control_columns column with "control", plus transform (N, DM, STA, DDM, LOG), distribution (normal, inversegamma, truncatednormal, halfnormal), mean/sd/lower/upper as applicable. LOG is log(x/mean(x)); STA divides by sample sd without centering. Priors are in transformed units; changing transform does not convert coefficients. Nonempty overrides require backend capability version 1; unsupported or unavailable checks stop before creating a model.

calibration: Optional lift-test calibration: likelihood observations the fit must respect. Either {"tests": [{"test_id": ..., "version"?, "channel"?, "confirm_kpi"?}]} (recorded tests from list_incrementality_tests, each derived against THIS model's data), or {"units": "revenue" | "response", "observations": [{channel, x, delta_x, delta_y, sigma}]} for rows you derived yourself. If any test can't calibrate this model, nothing is created: the error has code "calibration_refused" and tests gives each test's reason. Preview a test's row with get_incrementality_test(model_hash=...). The derived rows calibrate this fit only; the model does not keep a link to the tests. To keep that lineage (the test then lists the model under used_by and can't be deleted), build through a study recipe or a recipe draft that references the tests.

Returns the model_hash for status polling.