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bass
data
to_bass_dataset
model
F
create_bass_model
f
BassModel
default_model_config
default_sampler_config
fit_result
id
output_var
posterior
posterior_predictive
predictions
prior
prior_predictive
version
idata
sampler_config
model_config
model
is_fitted_
BassPriors
m
p
q
likelihood
plotting
plot_adoption_curve
plot_cumulative
plot_decomposition
plot_peak
causal_utils
same_markov_equivalence_class_CPdag
clv
distributions
BetaGeoBetaBinom
BetaGeoNBD
ModifiedBetaGeoNBD
ParetoNBD
ShiftedBetaGeometric
models
basic
CLVModel
beta_geo
BetaGeoModel
beta_geo_beta_binom
BetaGeoBetaBinomModel
gamma_gamma
BaseGammaGammaModel
GammaGammaModel
GammaGammaModelIndividual
modified_beta_geo
ModifiedBetaGeoModel
pareto_nbd
ParetoNBDModel
shifted_beta_geo
ShiftedBetaGeoModel
plotting
plot_customer_exposure
plot_expected_purchases_over_time
plot_expected_purchases_ppc
plot_frequency_recency_matrix
plot_probability_alive_matrix
utils
customer_lifetime_value
rfm_segments
rfm_summary
rfm_train_test_split
to_xarray
customer_choice
bayesian_blp
BayesianBLP
default_model_config
default_sampler_config
fit_result
id
output_var
posterior
posterior_predictive
predictions
prior
prior_predictive
version
idata
sampler_config
model_config
model
is_fitted_
consideration_set_logit
ConsiderationInstruments
Z_tilde
z_instrument_names
ConsiderationSetMixedLogit
default_model_config
default_sampler_config
fit_result
id
output_var
posterior
posterior_predictive
predictions
prior
prior_predictive
version
idata
sampler_config
model_config
model
is_fitted_
maxdiff
prepare_maxdiff_data
MaxDiffArrays
item_idx
mask
best_pos
worst_pos
resp_idx
n_tasks
n_respondents
n_items
k_max
item_to_idx
respondent_to_idx
MaxDiffMixedLogit
default_model_config
default_sampler_config
fit_result
id
output_var
posterior
posterior_predictive
predictions
prior
prior_predictive
version
idata
sampler_config
model_config
model
is_fitted_
mixed_logit
MixedLogit
default_model_config
default_sampler_config
fit_result
id
output_var
posterior
posterior_predictive
predictions
prior
prior_predictive
version
idata
sampler_config
model_config
model
is_fitted_
mnl_logit
MNLogit
default_model_config
default_sampler_config
fit_result
id
output_var
posterior
posterior_predictive
predictions
prior
prior_predictive
version
idata
sampler_config
model_config
model
is_fitted_
mv_its
plot_product
MVITS
default_model_config
default_sampler_config
fit_result
id
output_var
posterior
posterior_predictive
predictions
prior
prior_predictive
version
idata
sampler_config
model_config
model
is_fitted_
nested_logit
NestedLogit
default_model_config
default_sampler_config
fit_result
id
output_var
posterior
posterior_predictive
predictions
prior
prior_predictive
version
idata
sampler_config
model_config
model
is_fitted_
synthetic_data
generate_blp_panel
generate_maxdiff_conjoint_data
generate_maxdiff_data
generate_saturated_data
generate_unsaturated_data
taste_profiles
brand_buyer_nu
buyer_nu_posterior
consumer_taste_grid
demand_concentration_gini
plot_brand_buyer_heatmap
plot_buyer_profile_heatmap
plot_demand_concentration
plot_taste_profile_stacked
taste_type_demand_share
data
fivetran
process_fivetran_ad_reporting
process_fivetran_shopify_unique_orders
FivetranAccessor
idata
mmm_wrapper
MMMIDataWrapper
schema
InferenceDataGroupSchema
MMMIdataSchema
VariableSchema
utils
aggregate_idata_dims
aggregate_idata_time
filter_idata_by_dates
filter_idata_by_dims
get_posterior_predictive
get_prior
get_prior_predictive
idata_from_zarr
idata_to_zarr
subsample_draws
hsgp_kwargs
CovFunc
ExpQuad
Matern52
Matern32
HSGPKwargs
model_config
m
L
eta_lam
ls_mu
ls_sigma
cov_func
metrics
crps
nmae
nrmse
per_observation_crps
mlflow
autolog
create_log_callback
load_mmm
log_arviz_summary
log_bass_configuration
log_error
log_inference_data
log_likelihood_type
log_metadata
log_mmm
log_mmm_configuration
log_mmm_evaluation_metrics
log_model_derived_info
log_model_graph
log_sample_diagnostics
log_types_of_parameters
log_versions
MMMWrapper
predict_type_hints
mmm
additive_effect
safe_to_datetime
ControlMuEffect
contribution_var_name
model_config
prior
data_vars
prefix
DataVarMuEffect
contribution_var_name
model_config
data_vars
prefix
EventAdditiveEffect
contribution_var_name
end_dates
model_config
start_dates
df_events
prefix
effect
reference_date
date_dim_name
FourierEffect
contribution_var_name
model_config
fourier
date_dim_name
IncrementalitySpec
model_config
additional_carryover_lags
evaluation_mode
LinearTrendEffect
contribution_var_name
model_config
trend
prefix
date_dim_name
linear_trend_first_date
MediaMuEffect
contribution_var_name
effect_dims
model_config
media_transformation
channel_dim
data_vars
prefix
Model
dims
model
xarray_dataset
MuEffect
contribution_var_name
model_config
budget_optimizer
merge_inference_data
merge_models_and_idata
optimizer_xarray_builder
BudgetOptimizationResult
callback_info
constraint_history
spend_var_allocations
budgets
scipy_result
optimized_vars
spend_var_names
BudgetOptimizer
DEFAULT_MINIMIZE_KWARGS
model_config
optimization_variables
num_periods
model
idata
adstock_periods
carry_in_periods
channel_scales
spend_vars
spend_var_scales
optimizable_vars
response_variable
utility_function
budgets_to_optimize
constraints
budget_distribution_over_period
cost_per_unit
compile_kwargs
frozen_deterministics
channel_data_var
channel_contribution_var
date_dim
BuildMergedModel
model
adstock
idata
ConstraintIterationInfo
key
type
value
jac
ConstraintResidual
constraint_type
value
CustomModelWrapper
channel_scales
model_config
base_model
idata
channel_columns
adstock_periods
channel_data_var
OptimizationIterationInfo
x
fun
jac
constraint_info
OptimizerCompatibleModel
adstock
idata
PlanEvaluation
constraint_residuals
utility
utility_gradient
objective
objective_gradient
builders
factories
build
locate
naming
resolve
schema
suggest_typo_fix
BuildSpec
CalibrationStep
DataConfig
MMMYamlConfig
yaml
build_mmm_from_yaml
causal
BuildModelFromDAG
default_model_config
CausalGraphModel
TBFPC
TestResult
bic0
bic1
delta_bic
logBF10
BF10
independent
conditioning_set
forced
components
adstock
AdstockTransformation
BinomialAdstock
DelayedAdstock
GeometricAdstock
NoAdstock
WeibullCDFAdstock
WeibullPDFAdstock
base
Transformation
saturation
HillSaturation
HillSaturationSigmoid
InverseScaledLogisticSaturation
LogSaturation
LogisticSaturation
MichaelisMentenSaturation
NoSaturation
RootSaturation
SaturationTransformation
TanhSaturation
TanhSaturationBaselined
constraints
build_default_sum_constraint
compile_constraints_for_scipy
Constraint
counterfactual
find_named_node
CounterfactualEvaluator
BATCH_DIM
CHANNEL_DATA
MAX_BATCH_ELEMENTS
TIME_INDEX
channel_dtype
windowed_data_vars
CounterfactualScenarios
spend
period_index
rows
DateIndexedInput
date_axis
dims
dtype
name
values
EvaluationWindows
windows
max_window
dates
PeriodWindow
n_actual
start
end
in_window
actual_dates
in_eval
eval_dates
data_conversion
to_mmm_dataset
decomposition
identity_counterfactual_component
log_counterfactual_remove_component
original_scale_prediction_from_mu
safe_proportional_share
dims
evaluation
calculate_metric_distributions
compute_summary_metrics
summarize_metric_distributions
events
days_from_reference
AsymmetricGaussianBasis
combined_dims
default_priors
function_priors
model_config
prefix
priors
variable_mapping
Basis
combined_dims
function_priors
model_config
prefix
priors
variable_mapping
default_priors
function
EventEffect
model_config
basis
effect_size
dims
GaussianBasis
combined_dims
default_priors
function_priors
model_config
prefix
priors
variable_mapping
HalfGaussianBasis
combined_dims
default_priors
function_priors
model_config
prefix
priors
variable_mapping
fourier
generate_fourier_modes
FourierBase
model_config
nodes
n_order
days_in_period
prefix
prior
variable_name
MonthlyFourier
model_config
nodes
days_in_period
n_order
prefix
prior
variable_name
WeeklyFourier
model_config
nodes
days_in_period
n_order
prefix
prior
variable_name
YearlyFourier
model_config
nodes
days_in_period
n_order
prefix
prior
variable_name
hsgp
approx_hsgp_hyperparams
create_complexity_penalizing_prior
create_constrained_inverse_gamma_prior
create_eta_prior
create_m_and_L_recommendations
HSGP
model_config
ls
eta
L
centered
drop_first
cov_func
m
X
X_mid
dims
transform
demeaned_basis
HSGPBase
model_config
m
X
X_mid
dims
transform
demeaned_basis
HSGPPeriodic
model_config
ls
scale
cov_func
period
m
X
X_mid
dims
transform
demeaned_basis
PeriodicCovFunc
Periodic
SoftPlusHSGP
model_config
ls
eta
L
centered
drop_first
cov_func
m
X
X_mid
dims
transform
demeaned_basis
incrementality
IdentityLinkReducer
IncrementalReducer
Incrementality
LogLinkReducer
lift_test
add_cost_per_target_observations
add_cost_per_target_potentials
add_lift_measurements_to_likelihood_from_saturation
add_saturation_observations
assert_is_subset
assert_monotonic
create_time_varying_saturation
exact_row_indices
scale_channel_lift_measurements
scale_lift_measurements
scale_target_for_lift_measurements
linear_regression
FancyLinearRegression
linear_trend
LinearTrend
default_priors
model_config
priors
dims
n_changepoints
include_intercept
prefix
link
get_link_spec
IdentityLinkSpec
link
LinkFunction
IDENTITY
LOG
LinkSpec
link
LogLinkSpec
link
media_transformation
MediaConfig
name
columns
media_transformation
MediaConfigList
media_values
MediaTransformation
dims
adstock
saturation
adstock_first
mmm
BudgetOptimizerWrapper
plot
summary
adstock
idata
MMM
data
default_model_config
default_sampler_config
fit_result
frozen_deterministics
id
incrementality
output_var
plot
plot_interactive
plot_suite
posterior
posterior_predictive
predictions
prior
prior_predictive
sensitivity
summary
version
y
idata
sampler_config
model_config
model
is_fitted_
multidimensional
optimization_variables
align_to_model_coords
LeverVariable
size
name
dims
coords
flat_dim
MediaVariable
shape
size
name
dims
coords
flat_dim
OptimizationVariable
size
name
dims
coords
flat_dim
OptimizationVariables
plot
MMMPlotSuite
plot_interactive
MMMPlotlyFactory
custom_dims
plotting
budget
BudgetPlots
cv
MMMCVPlotSuite
decomposition
DecompositionPlots
diagnostics
DiagnosticsPlots
sensitivity
SensitivityPlots
suite
MMMPlotSuiteFacade
transformations
TransformationPlots
preprocessing
preprocessing_method_X
preprocessing_method_y
MaxAbsScaleChannels
channel_columns
MaxAbsScaleTarget
target_transformer
StandardizeControls
control_columns
scaling
deserialize_variable_scaling
panel_channel_fixed_scaling_remaining_dims
validate_fixed_scaling_keys
DataDerivedScaling
model_config
method
dims
FixedScaling
method
model_config
value
dims
Scaling
model_config
target
channel
VariableScaling
model_config
dims
sensitivity_analysis
SensitivityAnalysis
spend_reach
linear_predictor
resolve_channel_dependent_effects
ChannelDependentEffect
contribution_var
label
declared_carryover_lags
declared_evaluation_mode
SpendProbe
COMPLETENESS_TOLERANCE
REACH_TOLERANCE
SpendReach
effective_l_max
requires_full_axis
measured
TemporalReach
additional_carryover_lags
requires_full_axis
summary
budget
prepare_allocation_roas
prepare_contribution_over_time
BudgetSummaryFactory
cv
MMMCVSummaryFactory
factory
MMMSummaryFactory
helpers
compute_channel_shares
compute_residuals
compute_summary_stats_with_hdi
compute_waterfall_components
convert_output
dataframe_to_json_records
get_channel_x_data
get_prior_for_plot
prepare_sensitivity_data
StatsHelper
sensitivity
sensitivity_analysis
sensitivity_marginal
sensitivity_uplift
time_slice_cross_validation
TimeSliceCrossValidationResult
mmm
X_train
y_train
X_test
y_test
idata
TimeSliceCrossValidator
plot
plot_suite
summary
transformers
batched_convolution
binomial_adstock
delayed_adstock
geometric_adstock
hill_function
hill_saturation_sigmoid
inverse_scaled_logistic_saturation
logistic_saturation
michaelis_menten
root_saturation
tanh_saturation
tanh_saturation_baselined
weibull_adstock
ConvMode
After
Before
Overlap
TanhSaturationBaselinedParameters
gain
r
x0
TanhSaturationParameters
b
c
WeibullType
PDF
CDF
tvp
create_hsgp_from_config
create_time_varying_gp_multiplier
infer_time_index
is_hsgp_kwargs_format
time_varying_prior
types
MMMBuilder
utility
adjusted_value_at_risk_score
average_response
conditional_value_at_risk
diversification_ratio
mean_tightness_score
portfolio_entropy
raroc
sharpe_ratio
tail_distance
value_at_risk
utils
add_noise_to_channel_allocation
apply_sklearn_transformer_across_dim
build_contributions
cdf
create_index
create_new_spend_data
create_zero_dataset
density
transform_1d_array
validating
validation_method_X
validation_method_y
ValidateChannelColumns
channel_columns
ValidateControlColumns
control_columns
ValidateDateColumn
date_column
ValidateTargetColumn
xarray_utils
model_builder
create_idata_accessor
create_sample_kwargs
requires_model
ModelBuilder
default_model_config
default_sampler_config
fit_result
id
posterior
posterior_predictive
predictions
prior
prior_predictive
version
idata
sampler_config
model_config
model
is_fitted_
ModelFitter
model
idata
sampler_config
is_fitted_
ModelIO
id
version
idata
sampler_config
model_config
RegressionModelBuilder
default_model_config
default_sampler_config
fit_result
id
output_var
posterior
posterior_predictive
predictions
prior
prior_predictive
version
idata
sampler_config
model_config
model
is_fitted_
model_config
parse_model_config
model_graph
deterministics_to_flat
paths
create_data_url
URLPath
url
pie
model
PIEModel
default_model_config
default_sampler_config
fit_result
id
output_var
posterior
posterior_predictive
predictions
prior
prior_predictive
version
idata
sampler_config
model_config
model
is_fitted_
plot
create_legend_handles
drop_scalar_coords
generate_colors
get_plot_coords
get_total_coord_size
plot_curve
plot_hdi
plot_samples
random_samples
selections
set_subplot_kwargs_defaults
pytensor_utils
extract_response_distribution
merge_models
validate_unique_value_vars
ModelSamplerEstimator
default_mcmc_kwargs
default_nuts_kwargs
SharedPosterior
dims
variables
r2d2
R2D2
built
centered
error_sigma
splits
r2
total_sigma
dims
R2D2Sigma
dims
decomposition
R2D2Split
dims
decomposition
component_name
serialization
DeferredFactory
model_config
factory
kwargs
DeserializationContext
idata
Serializable
SerializableBaseModel
model_config
TypeRegistry
serialization_migration
main
migrate_idata
special_priors
deserialize_alternative_prior
is_alternative_prior
LaplacePrior
LogNormalPrior
MaskedPrior
SpecialPrior
terms
build_param
collect_coords
collect_terms
get_coords
register_data
set_data
Dot
name
var_name
prior
Intercept
name
prior
ModelTerm
Parameter
name
prior
Product
left
right
Sum
terms
Transform
inner
func
utils
from_netcdf
API
mmm
additive_effect
Model
dims
dims
#
property
Model.
dims
:
tuple
[
str
,
...
]
[source]
#
The additional dimensions of the MMM target.
On this page
Model.dims
Edit on GitHub
Consulting and training
