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ML Modeling

mod_left_sidebar

  • Models - Click and drag to training template model slot to begin training.
    • Model library (PyTorch, Tensorflow, XGBoost, SKLearn, Federated Learning)
    • Model framework (Simple, Advanced, Federated)
  • No-Code Models - Click and drag to training template model slot to begin training.
    • Model library (PyTorch, Tensorflow, XGBoost, SKLearn, Federated Learning)
    • Model framework (No-code, Supervised, Unsupervised, Regression, Federated)
  • Virtual Datasets - Click and drag to training template VDS slot to begin training.
    • Federate status
  • Assets - Click and drag to training template asset slot when a text model has already been added to begin training
  • Show/Hide artifacts
  • Refresh data
  • Upload Model

Models

models

  • Refresh - retrieves the latest Model data
  • Upload a Model
  • Download a Model
  • Delete - Delete a Model
  • View Log - view error log if a training has failed

Training Template/Parameters

mod_template

  • VDS Slot - Drag a VDS from the left sidebar to one of these slots to set it for that phase of training.
  • All VDS Slot - Drag a VDS here to set it to both phases of training.
  • Model Slot - Drag a model here to set it for training.
  • Asset Slot - Drag an asset here when a text model has been selected to set it for training.
  • Training Name - Give the training a name to find it easier at a later time.
  • Default - Reset the value to the saved default.
  • Save Defaults - Save all current values as the new default values.
  • Reset all to defaults - Reset all changed values back to their saved defaults.
  • Clear saved defaults - Reset all saved defaults back to factory settings.
  • Training Parameters - Expand/Collapse parameters

Dragging components for Training

Models and Virtual Datasets can be dragged to the training template. Items in VDS slots can be rearranged.

Modeling_DragModelVDS

mod_trainings

  • Trainings - Click to see additional details.
    • Model library (PyTorch, Tensorflow, XGBoost, SKLearn, Federated Learning)
    • Status - If there is an error, click to see additional details.
  • Training Details
    • Start Train - Click to reload the training parameters to begin a restart
    • Delete training
    • Download - Click to download training artifacts as a .zip file
    • View Profile - Click to see the training profile
  • Assets - Click to see additional details.
    • Status - If there is an error, click to see additional details.
  • Asset Details
    • Start Train - Click to reload the asset parameters to begin a restart
  • Show/Hide Trainings and Assets

Modeling Graph

mod_graph

  • Model node
  • VDS node
  • Asset node
  • Training connector - Click to use these artifacts in a new training
  • Number of trainings - The number of trainings using this combination of artifacts

Training Profile

Details

prof_details

Performance Graph

prof_performance

  • Available Plots Selector - Choose from a list of selected graphs
  • Plot explanation - Get a description of the selected graph type
  • Update interval - How often the graph should update in seconds. Default 100.
    • Number of points displayed is limited to 1000 to keep updates consistent.
  • Line Toggle - Disable this value
  • Line Intersect - Click to freeze in place. Click again to unfreeze.

Confusion Matrix

prof_confusion

  • Interactable Square -Click a square to see details about actual and predicted values. Values only displayed in square if greater than 0
  • Show Records - Toggle box values between percentages and record counts
  • Histogram - Displays all predicted values for an actual value. Clicking a bar will update the table beneath it.
  • Table - A tabular view of sample results from the selected prediction.

Explainability Analysis

BOSS provides the ability to visualize “explanations” of a model’s output given specific inputs. Generally, explanations take the form of computed attribute weights, indicating the significance that an attribute gave to a model’s decision. This supports the ability to either debug a model or scrutinize the data fed to the model. This particular feature is supported by integration of the Lime framework. The figures below illustrate the explainability panel on the model profile view for various model types.

Explainability - Tabular/Regression

For analyzing a tabular model, the user enters sample(s) into the input text box as a list of lists of numbers, where each inner “list” is a single sample. Then click the “Explain” button underneath the box. The time required to run explanation analysis is dependent on the complexity of the model. Models with type tabular_classification or regression can explain tabular data predictions

exp_tabular

  • Input Array
    • Enter values to predict on. Must be valid JSON, as shown above
  • % of Training Data
    • Percentage of training data to build the explainer. Must be greater than 0 and less than or equal to 1
  • Number of Top Explanations
    • Positive integer denoting how many class explanations to show
  • Inputs
    • Colored to show how each influences top class prediction
  • Class Probabilities
    • Class predictions and corresponding likelihood
  • Explanation
    • How each input influences a positive or negative prediction

Explainability - Images

Models with type image_classification can explain image predictions

exp_image

  • Sample Image
    • Select local image to explain
  • Positive Only
    • If True, include only regions of the image contributing to the predicted label.
  • Hide Rest
    • If True, make the non-explanation part of the return image gray
  • Explanation
    • Returned colorized image with shaded regions of positive and negative influence. Red sections detract from the predicted class while green contributes positively to the predicted class.
  • Predicted Probabilities
    • Class predictions and corresponding likelihood

Explainability - Text

For text models, simply type the raw string you would like to have explained by your model. Models with type text_classification can explain text predictions.

exp_text

  • Input Text
    • Text the user would like to predict and explain
  • Output Text
    • Output text with class probabilities highlighted in positive (green) or negative (blue) colors
  • Predicted Probabilities
    • Class probabilites predicted
  • Explanation
    • Words that contribute to positive or negative correlation

Virtual Datasets

vds

  • Open transform - opens the transform workflow that created this VDS
  • Copy ID - useful for finding VDS via RES API calls
  • Create an embedding
  • Delete - Delete a VDS
  • Refresh - retrieves the latest VDS data

Assets

assets

  • Delete an embedding
  • Visualize - See embedding data on a PCA/TSNE chart
  • Refresh - retrieves the latest Asset data

PCA/TSNE

Embeddings can be viewed using PCA/TSNE techniques for visualization.

pca

  • Style - When viewing an embedding’s PCA/TSNE, click to see terms instead of points.
  • Region Select - Toggle to select a cluster of points using a bounding box.
  • Multiple Select - Use to add multiple bounding boxes.
  • Search - Search for a term. All matching terms will be highlighted, as well as shown in a list to the right until there is only one matching term.
  • Filter - Narrow the number of occurrences for a term to a range using.
  • Technique Select - Toggle between PCA and TSNE.