Structure

Inspect modules, partitions, rules, bins, and model metadata.
View as Markdown

The Structure view describes a trained model.

Use it to review the model artefact after training, inspect how features were organised into modules, and understand the rules, partitions, and bins that make up the model.

When to use

Use caseDescription
Review a trained modelCheck the model summary, features, targets, module count, partition count, and training parameters.
Inspect feature interactionsSee which features appear in each module and whether a module represents a single feature or an interaction.
Audit rules and partitionsInspect the conditions, expressions, and bins used by the model partitions.
Prepare for explanation workUnderstand the model structure before using deployed model views for prediction-level explanations.

Generate the view

Generate the Structure view from a trained model.

POST
/models/:modelId/views
curl -X POST https://api.hi.umnai.com/models/modelId/views \
-H "X-Views-Cache-Control: no-cache, no-store" \
-H "Authorization: Bearer <token>" \
-H "Content-Type: application/json" \
-d '{
"data": [
{
"view_type": "MODEL_STRUCTURE"
}
]
}'
Response
{
"data": [
{
"view_type": "MODEL_STRUCTURE",
"output_version": {
"major_version": 1,
"minor_version": 1,
"patch_version": 1
},
"view_data": {
"summary": {
"columns": [
"model_id",
"model_name",
"model_title",
"model_created",
"model_last_trained",
"model_uvc",
"model_intercept",
"has_personal_individual_data",
"has_reuse_restrictions",
"model_doi",
"model_copyright",
"n_input_features",
"n_transformed_features",
"n_output_targets",
"features",
"targets",
"n_modules",
"n_partitions",
"max_interaction_degree",
"model_interaction_count",
"max_width",
"max_depth",
"n_categorical_features",
"n_model_bins",
"run_params",
"is_sparse",
"module_names",
"n_feature_groups"
],
"index": [
0
],
"data": [
[
"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533",
"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533",
"",
1769708667000,
1769708667000,
"",
-1.0836982274,
false,
false,
"",
"",
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1,
[
"age",
"workclass",
"fnlwgt",
"education",
"educational_45_num",
"marital_45_status",
"occupation",
"relationship",
"race",
"gender",
"capital_45_gain",
"capital_45_loss",
"hours_45_per_45_week",
"native_45_country"
],
[
"income"
],
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"{\"alpha\": 0.0, \"alpha_factor\": 2.0, \"bin_min_distance\": 0.0, \"binning_strategy\": \"DTREE\", \"bins\": 8, \"boosting_strategy\": \"INTERACTION_DEGREE_ASC\", \"cache_size\": 10, \"dynamic_cv\": true, \"early_stopping_patience\": 3, \"early_stopping_tolerance\": 0.0001, \"estimators\": 2, \"estimators_optimize_winsorize_level\": false, \"estimators_winsorize_level\": 0.0, \"exclude_implicit\": false, \"fine_tuning_evals\": 0, \"floatx\": \"float64\", \"init_depth\": 0, \"l1_ratio\": 0.0, \"l1_ratio_delta\": 2.0, \"learning_rate\": 1.0, \"learning_rate_decay\": 1.0, \"learning_rate_patience\": 16, \"learning_rate_tolerance\": 0.0001, \"log_interval\": 10, \"log_model\": true, \"loss_fn\": null, \"max_depth\": 3, \"max_depth_momentum\": 1, \"max_features\": 1.0, \"max_interaction_degree\": 2, \"max_interactions\": 8, \"max_iterations\": 10000, \"max_polynomial_degree\": 2, \"max_rounds\": 1, \"max_samples\": 1.0, \"model_capabilities\": {\"estimators_data\": true, \"has_causal\": false}, \"model_complexity_max_iterations\": 1, \"model_complexity_patience\": 1, \"model_complexity_tolerance\": 0.0001, \"model_constraints\": [], \"partition_min_delta\": 0.0, \"partition_min_delta_factor\": 0.5, \"partition_min_delta_fraction\": 0.4, \"partition_min_samples\": 20, \"progress_callback\": null, \"prune_patience\": 1, \"prune_threshold\": 0.0, \"regularization_patience\": 10, \"sparse_model\": true, \"subsample\": 10, \"training_size\": 0.8, \"validation_size\": 0.2}",
true,
[
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"capital_45_gain",
"capital_45_loss",
"hours_45_per_45_week",
"education",
"gender",
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"native_45_country",
"occupation",
"race",
"relationship",
"workclass",
"occupation x relationship",
"education x marital_45_status",
"marital_45_status x occupation",
"capital_45_gain x relationship",
"educational_45_num x relationship"
],
0
]
],
"foreign_keys": [],
"labels": [],
"types": [
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{
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},
{
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},
{
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{
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{
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},
{
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{
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{
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{
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{
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{
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{
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},
{
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},
{
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{
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]
},
"feature_groups": {
"columns": [],
"index": [],
"data": [],
"foreign_keys": [],
"labels": [],
"types": []
},
"modules": {
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"module_index",
"module_stable_label",
"module_name",
"module_features",
"n_partitions",
"n_parameters",
"n_nonzero_parameters",
"n_rules",
"module_depth",
"n_module_features",
"feature_count",
"interaction_degree",
"max_polynomial_degree",
"interaction_count",
"n_bins",
"bins",
"partition_exemplars"
],
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"{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"marital_45_status\", 7], \"id\": \"ae0ac88d672745979e8eaa57f00522cb\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Widowed\", \"encoding\": \"ONE_HOT_ENCODING\"}",
1769708,
"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533"
],
[
"daf6694d509448c9bedd8b624a08999f",
"native_45_country",
"",
"",
"fa02faf8539042b7a923262b964dfb20",
"",
"",
"None",
"None",
true,
"France",
12,
"native_45_country[France]",
"{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"native_45_country\", 12], \"id\": \"35d1ff541f744f7a86d4d63b795833ff\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"France\", \"encoding\": \"ONE_HOT_ENCODING\"}",
1769708,
"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533"
],
[
"daf6694d509448c9bedd8b624a08999f",
"relationship",
"",
"",
"fa5946fa467c472990e5775e18d6e3fc",
"",
"",
"None",
"None",
true,
"Not-in-family",
2,
"relationship[Not-in-family]",
"{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"relationship\", 2], \"id\": \"08274dbe11094692ae070e3e16be435b\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Not-in-family\", \"encoding\": \"ONE_HOT_ENCODING\"}",
1769708,
"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533"
],
[
"daf6694d509448c9bedd8b624a08999f",
"hours_45_per_45_week",
"",
"",
"fbb9204a1bad43e186cdbf6f978a048a",
"-INFINITY",
"INFINITY",
"False",
"False",
false,
"None",
"",
"hours_45_per_45_week[>-inf, <inf]",
"{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"hours_45_per_45_week\"], \"id\": \"5a9e97880c654e40ab1d112d12bc1e7f\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}",
1769708,
"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533"
],
[
"daf6694d509448c9bedd8b624a08999f",
"workclass",
"",
"",
"fbf46ffeb5ed4ba0b81fc37db547e0c6",
"",
"",
"None",
"None",
true,
"Federal-gov",
2,
"workclass[Federal-gov]",
"{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"workclass\", 2], \"id\": \"d4edab46831449b19d11373b59ea5416\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Federal-gov\", \"encoding\": \"ONE_HOT_ENCODING\"}",
1769708,
"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533"
]
],
"foreign_keys": [],
"labels": [],
"types": [
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
},
{
"data_type": "STRING"
}
]
}
},
"views_version": {
"major_version": 0,
"minor_version": 5,
"patch_version": 0,
"build_version": "dev76"
}
}
]
}

The request only needs view_type set to MODEL_STRUCTURE.

Output

The response follows the shared Response structure format.

The Structure view returns multiple named dataframes inside view_data.

DataframeDescription
summaryHigh-level model metadata, including model identifiers, features, targets, module counts, partition counts, interaction limits, bin counts, training parameters, and sparsity information.
feature_groupsFeature group definitions, when feature groups are available for the model.
modulesOne row per module, including module identifiers, module features, interaction degree, partition counts, parameter counts, rule counts, and associated bins.
partitionsOne row per partition or rule, including the module relationship, rule identifier, condition expression, THEN expressions, partition indexes, output count, and associated bin identifiers.
binsBin definitions used by the model, including the input feature, bin identifier, range bounds for continuous bins, category values for categorical bins, and bin descriptions.

Some fields may be empty depending on the model, schema metadata, and training configuration.

Interpret the result

The Structure view is easiest to read from the top down: start with the model summary, inspect the modules, then trace modules into partitions and bins.

Start with the model summary

Use summary to understand the overall size and shape of the model.

The most useful fields are features, targets, n_modules, n_partitions, n_model_bins, max_interaction_degree, and run_params. These tell you what the model was trained to predict, which inputs it uses, and how complex the resulting structure is.

A high number of modules, partitions, or bins usually means the model has learned more structure from the data. That may be useful, but it can also make review and explanation workflows more involved.

Inspect modules

Use modules to see how the model is organised.

A module can represent a single feature or an interaction between features. Check module_features and interaction_degree to understand what each module captures. Then use n_partitions, n_parameters, n_rules, and n_bins to estimate how much structure each module contains.

This is often the best place to start when you want to understand which feature groups or interactions are most structurally important in the model.

Trace modules to partitions

Use module_id to connect rows in modules with rows in partitions.

Partitions describe the rules inside a module. Use condition_expr_string, then_expr_strings, module_partition_index, and global_partition_index to understand when a rule applies and what expression it contributes.

The condition expression describes the part of the input space where the partition applies. The THEN expression describes the contribution made when that partition is active.

Read bin definitions

Use bin_ids from partitions to find the corresponding rows in bins.

Bins describe the feature ranges or categories used by rules. Continuous bins use low, high, low_inclusive, and high_inclusive. Categorical bins use category_value and category_index.

Reading bins together with partitions helps explain how raw feature values map into the rules learned by the model.

Use the structure before explaining predictions

The Structure view describes the model artefact. It does not explain a specific prediction.

After you understand the model structure, use deployed model views to inspect how that structure behaves for inference requests, feature attribution, module attribution, diagnostics, and evaluation.

Field reference

The Structure view returns dataframes, so individual columns are not documented as standalone API schema properties. This section describes the main columns returned in each dataframe.

summary

The summary dataframe contains one row with high-level information about the model.

FieldDescription
model_idUnique ID of the model.
model_nameModel name.
model_titleOptional descriptive title for the model.
model_createdTimestamp when the model was created.
model_last_trainedTimestamp of the latest training run for the model.
model_uvcUnique Verification Code for the model, when available.
model_interceptIntercept value used by the model.
has_personal_individual_dataIndicates whether the model metadata records personal or individual data.
has_reuse_restrictionsIndicates whether the model metadata records reuse restrictions.
model_doiDOI or persistent identifier for the model, when available.
model_copyrightCopyright information for the model, when available.
n_input_featuresNumber of input features used by the model.
n_transformed_featuresNumber of transformed features used internally by the model.
n_output_targetsNumber of output targets predicted by the model.
featuresList of input feature names.
targetsList of output target names.
n_modulesNumber of modules in the model.
n_partitionsTotal number of partitions across the model.
max_interaction_degreeHighest interaction degree allowed or observed in the model.
model_interaction_countTotal number of feature interactions in the model.
max_widthMaximum module width.
max_depthMaximum module depth.
n_categorical_featuresNumber of categorical input features.
n_model_binsTotal number of bins used by the model.
run_paramsTraining parameters used to create the model.
is_sparseWhether the model is sparse.
module_namesList of module names in the model.
n_feature_groupsNumber of feature groups associated with the model.

feature_groups

The feature_groups dataframe lists feature groups when feature groups are available for the model.

FieldDescription
featureName of the feature group.
valuesFeatures included in the feature group.

Feature group data may be empty when the model does not include feature groups.

modules

The modules dataframe contains one row per module.

FieldDescription
module_idUnique ID of the module.
module_indexIndex of the module within the model.
module_stable_labelStable label for the module, when available.
module_nameModule name.
module_featuresInput features used by the module.
n_partitionsNumber of partitions in the module.
n_parametersNumber of parameters in the module.
n_nonzero_parametersNumber of non-zero parameters in the module.
n_rulesNumber of rules in the module.
module_depthMaximum depth of the module.
n_module_featuresNumber of features used by the module.
feature_countCount of features used by the module, when available.
interaction_degreeDegree of feature interaction represented by the module.
max_polynomial_degreeHighest polynomial degree used in the module expressions.
interaction_countNumber of interactions in the module.
n_binsNumber of bins associated with the module.
binsList of bin IDs associated with the module.
partition_exemplarsExemplar partition IDs, when available.

partitions

The partitions dataframe contains one row per partition or rule.

FieldDescription
dataset_idID of the dataset used to train the model.
module_idID of the module that contains the partition.
module_indexIndex of the module that contains the partition.
rule_idUnique ID of the rule.
groupFeature group or symbolic grouping used by the partition, when available.
transformed_featuresFeatures or transformed features used in the rule’s THEN expression.
coefficientsCoefficients for the transformed features in the THEN expression.
interceptIntercept of the THEN expression.
module_partition_indexIndex of the partition within its module.
global_partition_indexIndex of the partition across the whole model.
condition_expr_stringCondition expression for the rule as a string.
then_expr_stringsTHEN expressions for the rule as strings. There may be one expression per output target.
condition_expr_objectStructured representation of the condition expression.
then_expr_objectsStructured representation of the THEN expressions.
bin_idsIDs of the bins used by the rule.
n_outputsNumber of outputs produced by the rule.
condition_expr_friendlyHuman-friendly condition expression, when available.
condition_expr_jsonJSON representation of the condition expression, when available.
created_timeTimestamp when the partition was created.
model_idID of the model that contains the partition.

bins

The bins dataframe contains one row per bin used by the model.

FieldDescription
dataset_idID of the dataset used to train the model.
input_featureInput feature associated with the bin.
titleHuman-readable bin title, when available.
short_titleShort bin title, when available.
bin_idUnique ID of the bin.
lowLower bound for a continuous bin.
highUpper bound for a continuous bin.
low_inclusiveWhether the lower bound is included in the bin interval.
high_inclusiveWhether the upper bound is included in the bin interval.
is_categoricalWhether the bin is for a categorical feature.
category_valueCategory value used by the bin, for categorical bins.
category_indexIndex of the category value, for categorical bins.
bin_descriptionHuman-readable description of the bin.
symbolSymbolic representation of the bin, when available.
created_timeTimestamp when the bin was created.
model_idID of the model that contains the bin.