Model Document Generator

You can use the Model Document Generator to create documentation associated with any trained model. It will generate a Microsoft Word™ .docx file that provide information regarding:

• What the model does

• How the model was built (algorithms, features, processing, …)

• How the model was tuned

• What are the model’s performances

It allows you to prove that you followed industry best practices to build your model.

Generate a model document

Note

To use this feature, the graphical export library must be activated on your DSS instance. Please check with your administrator.

Once a model has been trained, you can generate a document from it with the following steps:

• Go to the trained model you wish to document (either a model trained in a Visual Analysis of the Lab or a version of a saved model deployed in the Flow)

• Click the Actions button on the top-right corner

• Select Export model documentation

• On the modal dialog, select the default template or upload your own template, and click Export to generate the document

• After the document is generated, you will be able to download your generated model document using the Download link

Warning

Any placeholders starting with the keyword design will be linked to the current status of your visual analysis. If you change parameters in your model, DSS will consider that placeholder values are outdated and will replace these placeholders with blanks in the generated documentation. If you delete your visual analysis and then generate a document from a saved model, any placeholders starting with the keyword design will not provide any result.

Custom templates

If you want the document generator to use your own template, you need to use Microsoft Word™ to create a .docx file.

You can create your base document as you would create a normal Word document, and add placeholders when you want to retrieve information from your model.

Sample templates

Instead of starting from scratch, you can modify the default templates:

• Download the default template for regression here

• Download the default template for binary classification here

• Download the default template for multiclass classification here

• Download the default template for clustering here

• Download the default template for time series forecasting here

Placeholders

A placeholder is defined by a placeholder name inside two brackets, like {{design.prediction_type.name}}. The generator will read the placeholder and replace it with the value retrieved from the model.

There are multiples types of placeholders, which can produce text, an image, or a table.

You can format the style of a table placeholder by using a block placeholder and placing an empty table inside it.

Conditional placeholders

If you want to build a single template able to handle several kinds of models, you may need to display information only when they are relevant. For example, you may want to describe what is a binary classification, but this description should only appear on your binary classification models. This can be achieved with a conditional placeholder.

A conditional placeholder contains three elements, each of them needs to be on a separate line:

• a condition

• a text to display if the condition is valid

• a closing element

Example:

{{if design.prediction_type.name == Binary classification}}

The model {{design.prediction_type.name}} is a binary classification.

{{endif design.prediction_type.name}}

{{if design.prediction_type.name != Binary classification}}

The model {{design.prediction_type.name}} is a not binary classification.

{{endif design.prediction_type.name}}

The condition itself is composed of three parts:

• a text placeholder

• an operator (== or !=)

• a reference value

Example:

{{if my.placeholder == myReferenceValue }}

During document generation, the placeholder is replaced by its value and compared to the reference value. If the condition is correct, the text is displayed, otherwise nothing will appear on the final document.

List of placeholders

Placeholders related to the dataset:

Name	Compatibility	Description
dataset.prepare_steps.table	All	Preparation steps used on the dataset
dataset.prepare_steps.status	All	Tell if preparation steps are used on the dataset

Placeholders related to the design phase of a model:

Name	Compatibility	Description
design.mltask.name	All	Name of the modeling task
design.visual_analysis.name	All	Name of the visual analysis
design.algorithm.name	All	Name of the algorithm used to compute the model
design.target.name	Prediction	Name of the target variable
design.features_count.value	All	Number of columns in the train set
design.model_type.name	All	Type of model (Prediction or Clustering)
design.prediction_type.name	Prediction	Type of prediction (Regression, Binary classification or Multi-class classification)
design.target_proportion.plot	Classification	Proportions of classes in the guess sample
design.training_and_testing_strategy.table	Prediction	Strategy used to train and test the model
design.training_and_testing_strategy.policy.value	Prediction	Name of the policy used to train and test the model
design.sampling_method.value	Prediction	Sampling method named used to train and test the model
design.k_fold_cross_testing.value	Prediction	Tell if the K-fold strategy was used to train and test the model
design.sampling_and_splitting.image	Prediction	Sampling and splitting strategy used to train and test the model
design.sampling.image	Time series forecasting	Sampling strategy used to train and test the model
design.splitting.image	Time series forecasting	Splitting strategy used to train and test the model
design.train_set.image	Prediction	Explicit train set strategy
design.test_set.image	Prediction	Explicit test set strategy
design.test_metrics.name	Prediction	Metric used to optimize model hyperparameters
design.input_feature.table	All	Summary of input features
design.feature_generation_pairwise_linear.value	Prediction	Display if pairwise linear combinations are used in the feature generation
design.feature_generation_pairwise_polynomial.value	Prediction	Display if pairwise polynomial combinations are used in the feature generation
design.feature_generation_explicit_pairwise.status	Prediction	Display if the model contains explicit pairwise interactions used in the feature generation
design.feature_generation_explicit_pairwise.table	Prediction	Display explicit pairwise interactions used in the feature generation
design.feature_reduction.image	Prediction	Reduction method applied to the model
design.feature_reduction.name	Prediction	Name of the feature reduction applied to the model
design.chosen_algorithm_search_strategy.table	All	List the parameters used to configure the chosen algorithm
design.chosen_algorithm_search_strategy.text	All	Description of the chosen algorithm
design.other_algorithms_search_strategy.table	All	Parameters and description of the other computed algorithms
design.hyperparameter_search_strategy.image	Prediction	Hyperparameter search strategy used to compute the model
design.hyperparameter_search_strategy.table	Prediction	Summary of the hyperparameter search strategy
design.cross_validation_strategy.value	Prediction	Name of cross-validation strategy used on hyperparameters
design.dimensionality_reduction.table	Clustering	Dimensionality reduction used to compute the model
design.dimensionality_reduction.status	Clustering	Tell if a dimensionality reduction was used to compute the model
design.outliers_detection.table	Clustering	Outliers detection strategy used to compute the model
design.outliers_detection.status	Clustering	Tell if an outliers detection strategy was used to compute the model
design.weighting_strategy.method.name	Prediction	Name of the weighting strategy
design.weighting_strategy.variable.name	Prediction	Name of the weighting strategy associated variable
design.weighting_strategy.text	Prediction	Description of a weighting strategy
design.calibration_strategy.name	Classification	Name of the probability calibration method
design.calibration_strategy.text	Classification	Description of a probability calibration strategy
design.backend.name	All	Name of the backend engine
design.partitioned_model.status	All	Tell if the current model is a partitioned model
design.time_variable.name	Time series forecasting	Name of the time variable
design.num_timeseries.value	Time series forecasting	Number of time series to forecast
design.timeseries_identifiers.value	Time series forecasting	List of columns containing time series identifier values
design.has_multiple_timeseries.value	Time series forecasting	Whether the model has multiple time series
design.time_step.value	Time series forecasting	Time step including its unit
design.forecast_horizon.in_units.value	Time series forecasting	Forecast horizon including its unit
design.forecast_horizon.time_steps.value	Time series forecasting	Number of time steps in one forecast horizon
design.timeseries_num_external_features.value	Time series forecasting	Number of external features provided to the model
design.timeseries_external_features.value	Time series forecasting	List of external features columns
design.timeseries_scoring_data_length.value	Time series forecasting	How much past data must be provided for scoring
design.timeseries_algorithm_can_score_new_series.value	Time series forecasting	Whether the current model can score time series unseen during training
design.timeseries_general_settings.table	Time series forecasting	List the general time series specific settings used to configure the model
design.timeseries_forecasting.table	Time series forecasting	List the settings used to configure the forecast horizon
design.forecasting_parameters.image	Time series forecasting	Forecasting parameters shown visually in a diagram

Placeholders related to the result of a model computation:

Name	Compatibility	Description
result.chosen_algorithm.name	All	Name of the algorithm selected for the mode used in the current document generation
result.train_set.sample_rows_count.value	All	Number of rows in the train set
result.test_metrics.value	Prediction	Value of the test metrics
result.target_value.positive_class.value	Binary classification	Name of the target value representing the positive class
result.target_value.negative_class.value	Binary classification	Name of the target value representing the negative class
result.threshold_metric.name	Binary classification	Name of the threshold (cut off) metric
result.classification_threshold.current.value	Binary classification	Current value of the threshold metric
result.classification_threshold.optimal.value	Binary classification	Optimal value of the threshold metric
result.chosen_algorithm_details.image	All but partitioned models	Summary of the prediction computation
result.chosen_algorithm_details.table	All but partitioned models	Summary of the current model selected hyperparameters
result.cluster_summary.image	Clustering	Summary of the clustering computation
result.cluster_description.image	Clustering	Description of each category of the clustering
result.partitioned.summary.image	Prediction	Summary of the partitioned model execution
result.decision_trees.image	Prediction	Random forest decision tree visualisation
result.decision_trees.status	All	Tell if the model is compatible with a decision tree visualisation
result.regression_coefficients.image	Regression and binary classification	Regression coefficient chart
result.regression_coefficients.status	All	Tell if the model has a regression coefficient chart
result.feature_importance.plot	Prediction	Feature importance chart
result.feature_importance.status	All	Does the model had a feature importance chart
result.absolute_importance.plot	Prediction	Absolute importance chart
result.absolute_importance.status	All	Does the model had an absolute importance chart
result.individual_explanations.image	Prediction	Individual explanation chart
result.individual_explanations.text	Prediction	Description of the individual explanation chart
result.individual_explanations.status	All	Tell if the model contains an individual explanation chart
result.hyperparameter_search.plot	Prediction	Hyperparameter search results as a chart
result.hyperparameter_search.table	Prediction	Displays data associated to the hyperparameter search as an table
result.hyperparameter_search.status	All	Tell if the model had a hyperparameter search results chart
result.confusion_matrix.image	Classification	Confusion matrix results as a table
result.confusion_matrix_table.text	Binary classification	Description of the confusion matrix
result.confusion_matrix_metrics.plot	Classification	Metrics associated to the confusion matrix
result.confusion_matrix_metrics.text	Classification	Description of the different metrics
result.cost_matrix.image	Binary classification	Cost matrix as a table
result.cost_matrix.image	Binary classification	Cost matrix as a table
result.cost_matrix.text	Binary classification	Description of the cost matrix
result.decision_chart.plot	Binary classification	Decision chart
result.decision_chart.text	Binary classification	Description of the decision chart
result.lift_curve.plot	Binary classification	Lift curve charts
result.lift_curve.text	Binary classification	Description of the lift curve charts
result.calibration.plot	Classification	Calibration curve chart
result.calibration.text	Classification	Description of the calibration curve chart
result.roc_curve.plot	Classification	ROC curve chart
result.roc_curve.text	Classification	Description of the ROC curve chart
result.pr_curve.plot	Classification	PR curve chart
result.pr_curve.text	Classification	Description of the PR curve chart
result.density_chart.plot	Classification	Density chart
result.density_chart.text	Classification	Description of the density chart
result.hierarchy.plot	Clustering	Hierarchy of the interactive clustering model
result.anomalies.plot	Clustering	Anomalies detected displayed as a heatmap
result.cluster_heat_map.plot	Clustering	Features heatmap
result.scatter.plot	Regression and clustering	Scatter plot chart
result.error_distribution.table	Regression	Error distribution as a table
result.error_distribution.plot	Regression	Error distribution as a chart
result.error_distribution.text	Regression	Description of the error distribution
result.detailed_metrics.table	All	Detailed summary of the model computation
result.ml_assertions.table	Prediction	Assertions definitions and metrics
result.ml_overrides.definition.table	Prediction	Model overrides definitions
result.ml_overrides.metrics.table	Prediction	Model overrides metrics
result.timings.table	All	Time of the different execution steps
result.diagnostics.table	All	All Diagnostics for the current model
result.diagnostics.table.dataset_sanity_checks	All	Diagnostics of type dataset sanity checks for the current model
result.diagnostics.table.modeling_parameters	All	Diagnostics of type modeling parameters for the current model
result.diagnostics.table.runtime	All	Diagnostics of type runtime that check the model training speed
result.diagnostics.table.training_overfit	All	Diagnostics of type training overfit for the current model
result.diagnostics.table.leakage_detection	All	Diagnostics of type leakage detection for the current model
result.diagnostics.table.model_check	All	Diagnostics of type model check for the current model
result.diagnostics.table.reproduciblity	All	Diagnostics of type training reproducibility for the current model
result.diagnostics.table.ml_assertions	All	Diagnostics of type ML assertions for the current model
result.diagnostics.table.abnormal_predictions_detection	All	Diagnostics of type abnormal predictions detection for the current model
result.diagnostics.table.timeseries_resampling_checks	Time series forecasting	Diagnostics of type resampling checks for the current model
result.full_log.link	All	URL to download the model’s logs
result.training_date.name	All	Date of the training
result.leaderboard.image	All	Image of the default leaderboard of the computed models
result.leaderboard.accuracy.image	Classification	Image of the leaderboard of the computed models for the metric accuracy
result.leaderboard.precision.image	Classification	Image of the leaderboard of the computed models for the metric precision
result.leaderboard.recall.image	Classification	Image of the leaderboard of the computed models for the metric recall
result.leaderboard.f1.image	Classification	Image of the leaderboard of the computed models for the metric F1 score
result.leaderboard.cost_matrix.image	Classification	Image of the leaderboard of the computed models for the metric cost matrix gain
result.leaderboard.log_loss.image	Classification	Image of the leaderboard of the computed models for the metric log loss
result.leaderboard.roc_auc.image	Classification	Image of the leaderboard of the computed models for the metric ROC AUC
result.leaderboard.average_precision.image	Classification	Image of the leaderboard of the computed models for the metric Average Precision score
result.leaderboard.calibration_loss.image	Binary classification	Image of the leaderboard of the computed models for the metric calibration loss
result.leaderboard.lift.image	Binary classification	Image of the leaderboard of the computed models for the metric lift
result.leaderboard.evs.image	Regression	Image of the leaderboard of the computed models for the metric EVS
result.leaderboard.mape.image	Regression and time series forecasting	Image of the leaderboard of the computed models for the metric MAPE
result.leaderboard.mae.image	Regression and time series forecasting	Image of the leaderboard of the computed models for the metric MAE
result.leaderboard.mse.image	Regression and time series forecasting	Image of the leaderboard of the computed models for the metric MSE
result.leaderboard.rmse.image	Regression and time series forecasting	Image of the leaderboard of the computed models for the metric RMSE
result.leaderboard.rmsle.image	Regression	Image of the leaderboard of the computed models for the metric RMSLE
result.leaderboard.r2.image	Regression	Image of the leaderboard of the computed models for the metric R2 score
result.leaderboard.correlation.image	Regression	Image of the leaderboard of the computed models for the metric correlation
result.leaderboard.mase.image	Time series forecasting	Image of the leaderboard of the computed models for the metric MASE
result.leaderboard.smape.image	Time series forecasting	Image of the leaderboard of the computed models for the metric SMAPE
result.leaderboard.mean_absolute_quantile_loss.image	Time series forecasting	Image of the leaderboard of the computed models for the metric Mean absolute quantile loss
result.leaderboard.mean_weighted_quantile_loss.image	Time series forecasting	Image of the leaderboard of the computed models for the metric Mean weighted quantile loss
result.leaderboard.msis.image	Time series forecasting	Image of the leaderboard of the computed models for the metric MSIS
result.leaderboard.nd.image	Time series forecasting	Image of the leaderboard of the computed models for the metric ND
result.leaderboard.silhouette.image	Clustering	Image of the leaderboard of the computed models for the metric silhouette
result.leaderboard.inertia.image	Clustering	Image of the leaderboard of the computed models for the metric inertia
result.leaderboard.clusters.image	Clustering	Image of the leaderboard of the computed models for the metric clusters number
result.timeseries_resampling.table	Time series forecasting	Parameters defining how the time series is resampled
result.per_timeseries_metrics.table	Time series forecasting	For a multi time series model, the metrics for each time series
result.autoarima_orders_single.table	Time series forecasting	For a single time series model trained with the AutoARIMA algorithm, the ARIMA orders
result.autoarima_orders_multi.table	Time series forecasting	For a multi time series model trained with the AutoARIMA algorithm, the ARIMA orders for each time series
result.timeseries_single_forecast.plot	Time series forecasting	For a single time series model, the forecast chart
result.timeseries_multi_forecast.plot	Time series forecasting	For a multi time series model, the first five forecast charts

Placeholders related to the DSS configuration:

Name	Compatibility	Description
config.author.name	All	Name of the user that launched the generation
config.author.email	All	E-mail of the user that launched the generation
config.environment.name	All	Name of the DSS environment
config.dss.version	All	Current version of DSS
config.is_saved_model.value	All	Yes when documenting a model exported into the Flow, No otherwise
config.generation_date.name	All	Generation date of the output document
config.project.link	All	URL to access the project
config.output_file.name	All	Name of the generated file

List of conditional placeholders

Placeholders that can be used as conditional placeholders:

• dataset.prepare_steps.status

• design.mltask.name

• design.visual_analysis.name

• design.algorithm.name

• design.target.name

• design.features_count.value

• design.model_type.name

• design.prediction_type.name

• design.training_and_validation_strategy.policy.value

• design.sampling_method.value

• design.k_fold_cross_testing.value

• design.feature_generation_pairwise_linear.value

• design.feature_generation_pairwise_polynomial.value

• design.feature_generation_explicit_pairwise.status

• design.feature_reduction.name

• design.chosen_algorithm_search_strategy.text

• design.cross_validation_strategy.value

• design.dimensionality_reduction.status

• design.outliers_detection.status

• design.weighting_strategy.method.name

• design.weighting_strategy.variable.name

• design.calibration_strategy.name

• design.backend.name

• design.partitioned_model.status

• design.has_multiple_timeseries.value

• design.timeseries_algorithm_can_score_new_series.value

• result.chosen_algorithm.name

• result.train_set.sample_rows_count.value

• result.target_value.positive_class.value

• result.target_value.negative_class.value

• result.threshold_metric.name

• result.classification_threshold.current.value

• result.classification_threshold.optimal.value

• result.decision_trees.status

• result.regression_coefficients.status

• result.feature_importance.status

• result.individual_explanations.status

• result.hyperparameter_search.status

• result.training_date.name

• config.author.name

• config.author.email

• config.environment.name

• config.dss.version

• config.is_saved_model.value

• config.generation_date.name

• config.output_file.name

Deprecated placeholders

Name	Compatibility	Description	Replaced by
result.detailed_metrics.image	All	Detailed summary of the model computation	result.detailed_metrics.table
design.other_algorithms_search_strategy.image	All	Image of the parameters and description of the other computed algorithms	design.other_algorithms_search_strategy.table
design.chosen_algorithm_search_strategy.image	All	Image of the parameters used to configure the chosen algorithm	design.chosen_algorithm_search_strategy.table

Limitations

• You need to upgrade the table of contents manually after a document generation

• The Model Document Generator is currently not compatible with ensemble models

• The Model Document Generator is currently not compatible with Keras nor computer vision models

• The Model Document Generator is currently not compatible with models imported from MLflow

• The Model Document Generator is not compatible with plugin algorithms

• When generating a document from a visual analysis, the model document generator will not display outdated design information. To fix this issue, you can either run a new analysis or revert the design to match the analysis

• When generating a document from a saved model version, some information may be extracted from the original Visual Analysis Design settings. As a result, any placeholders starting with design will be empty when the Visual Analysis was modified or erased

• Each part of a conditional placeholder must necessarily be in a new line

• Table and conditional placeholders are not supported on headers and footers