Release/glm 2.0.0 #106
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Release/glm 2.0.0 #106
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…r-all Feature/select base relativity for all
Passing model training failures to the front end --------- Co-authored-by: admin <admin> Co-authored-by: Matthew Galloway <matthew.galloway@dataiku.com>
clean up of relativities
…small-separation-between-second-to Add separator for tabs
…or-model-configuration-and-variable Add title in header
…table Feature/260609 model management table
…iku-container-issue Feature/dss14 sc 261019 fix webaiku container issue
fix new analysis
…l-configuration-tab Change first tab ui
…tab-from-figma-spec Fix left panel
…-chart-panel Fix left panel
…on-create-graph-at-the-end-of-the-page Add sticky button for onewayvariabletab
…on-create-graph-at-the-end-of-the-page ajust scroll in the scrollbox
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The major addition in this version is a visual webapp that allows to train a GLM, through the visual ML, and build interactive visualizations and metrics to assess their fit.
Front end is developed in Vue.js with business solutions components built on Quasar. Code for the frontend can be found in resource/frontend/
The backend entry point is python-lib/backend/fetch-api.py, that can be linked to two services. A MockDataService for local development and a DataikuDataService when run on a Dataiku instance. Code for the backend is contained in directories backend, chart_formatters, dku_visual_ml, glm_handler, model_cache, processors.