# Basic usage

As part of our persona-driven development BlueCast offers two ways of
configuration:

* a non-programmatic way which uses widgets for most important options
* a programmatic way with ull control and flexibility

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* [Basic usage](#basic-usage)
  * [Non-programmatic way for starters](#non-programmatic-way-for-starters)
  * [Programmatic way](#programmatic-way)

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## Non-programmatic way for starters

The non-programmatic way should be helpful for new BlueCast users
or ml practitioners with little ml experience. The interface can be
used like this:

```python
from bluecast.blueprints.welcome import WelcomeToBlueCast

welcome = WelcomeToBlueCast()
welcome.automl_configurator()

# here users can chose from the given options and click 'submit' to create the instance
# after submit the automl instance can be retrieved and used like:
automl = welcome.automl_instance
automl.fit(df_train, target_col="target")
y_hat = automl.predict(df_val)
```

![Welcome UI example](welcome_ui.png)

## Programmatic way

The programmatic way is suitable for experienced users.

The module blueprints contains the main functionality of the library. The main
entry point is the `Blueprint` class. It already includes needed preprocessing
(including some convenience functionality like feature type detection)
and model hyperparameter tuning.

```sh
from bluecast.blueprints.cast import BlueCast

automl = BlueCast(
        class_problem="binary",
    )

automl.fit(df_train, target_col="target")
y_probs, y_classes = automl.predict(df_val)

# from version 0.95 also predict_proba is directly available (also for BlueCastCV)
y_probs = automl.predict_proba(df_val)
```

BlueCast has simple utilities to save and load your pipeline:

```sh
from bluecast.general_utils.general_utils import save_to_production, load_for_production

# save pipeline including tracker
save_to_production(automl, "/kaggle/working/", "bluecast_cv_pipeline")

# in production or for further experiments this can be loaded again
automl = load_for_production("/kaggle/working/", "bluecast_cv_pipeline")
```

Since version 0.80 BlueCast offers regression as well:

```sh
from bluecast.blueprints.cast_regression import BlueCastRegression

automl = BlueCast(
        class_problem="regression",
    )

automl.fit(df_train, target_col="target")
y_hat = automl.predict(df_val)
```

During classification taks the target labels might arrive as strings. BlueCast
will return these as numeric representations by default at the monment.
However the `predict` method accepts a parameter `return_original_labels` to
return the original labels.

```python
from bluecast.blueprints.cast_regression import BlueCastRegression

automl = BlueCast(
        class_problem="binary",
    )

automl.fit(df_train, target_col="target")
y_probs, y_classes = automl.predict(df_val, return_original_labels=True)
```

The default behaviour might change in a future release.