Wizard

The Wizard is your personal assistant for advanced steps composition when you need to do some transformation or processing on your data.

The Wizard really just helps you compose steps within the recipe, but does so in a more intuitive and opinionated way, when you are not entirely sure of what to write.

The wizard menu, showcasing the main worflows that you can perform

The menu guides you through all the steps necessary to accomplish what you have in mind.

Here you can see how easy it is to make a model that predicts price based on some coordinates and other potentially important factors:

Dragging and dropping important variables into the slots

And this is all the code it generated for us. Among other things, it mainly extracts some time components from date-based columns, creates tags, uses the train_classification and test_classification steps to train a model with the default configuration and creates a bunch of columns that hold this prediction data.

Some of the intermediate columns are hidden, since they are important for the model but not revelant for human interpretation. Nevertheless, these can be brought back if needed.

extract_date_component(ds.last_scraped, {
    "component": "hour"
}) => (ds.hour)

extract_date_component(ds.last_scraped, {
    "component": "month_name"
}) => (ds.month_name)

extract_date_component(ds.last_scraped, {
    "component": "weekday_name"
}) => (ds.weekday_name)

configure_tagged_columns(ds[["neighbourhood_cleansed", "host_verifications", "host_total_listings_count", "longitude", "latitude", "price"]],
                         {
                             "Target": [
                                 "price"
                             ],
                             "Factors": [
                                 "latitude",
                                 "longitude",
                                 "host_total_listings_count",
                                 "host_verifications",
                                 "neighbourhood_cleansed"
                             ]
                         })

train_classification(ds[["price", "neighbourhood_cleansed", "host_verifications", "host_total_listings_count", "longitude", "latitude"]],
                     {
                         "target": "price",
                         "model": "CatboostClassifier",
                         "params": {
                             "depth": 6,
                             "nan_mode": "Min",
                             "iterations": 1000,
                             "one_hot_max_size": 10,
                             "max_ctr_complexity": 2,
                             "boosting_type": "Plain"
                         },
                         "validate": {
                             "n_splits": 5,
                             "metrics": [
                                 "accuracy"
                             ]
                         }
                     }) => (ds.gx_prediction,
                            "ds-model-yFqI")

test_classification(ds[["price", "neighbourhood_cleansed", "host_verifications", "host_total_listings_count", "longitude", "latitude"]],
                    "ds-model-yFqI",
                    {
                        "refit": true,
                        "split": {
                            "test_size": 0.2
                        },
                        "target": "price"
                    }) => (ds.gx_prediction,
                           ds.prob,
                           ds.Error,
                           ds.split)

configure_column_metadata(ds.Error,
                          {
                              "label": "Error",
                              "description": "Whether the predicted class was correct or wrong"
                          })

configure_column_metadata(ds.gx_prediction, {
    "label": "Prediction",
    "description": "Prediction made for the target varible"
})

configure_tagged_columns(ds[["split", "prob", "Error", "gx_prediction"]],
                         {
                             "Output Variables": [
                                 "gx_prediction",
                                 "Error",
                                 "prob",
                                 "split"
                             ]
                         })

configure_columns_order(ds.price,
                        ds.gx_prediction,
                        ds.Error,
                        ds.prob,
                        ds.split,
                        ds.latitude,
                        ds.longitude,
                        ds.host_total_listings_count,
                        ds.host_verifications,
                        ds.neighbourhood_cleansed)

configure_column_visibility(ds.Error, {
    "graph": "pinned"
})

configure_column_visibility(ds.prob, {
    "graph": "pinned"
})

configure_column_visibility(ds.split, {
    "graph": "pinned"
})

Graphext concepts

Data science concepts