# Accessing raw data

Raw data and wrapped class objects for the `TransformedOutcome` method are stored as class attributes. The wrapped class objects are described in the [Usage: modeling](https://github.com/df-foundation/pylift/tree/1b025a2bc1ee3237642bb19d83d54f854133e247/docs/markdown_archive/usage/README.md) section.

Everything else, from processed data to the transformation functions can be accessed as listed below:

```
up.randomized_search_params # Parameters that are used in `up.randomized_search()`
up.grid_search_params       # Parameters that are used in `up.grid_search()`


up.transform                # Outcome transform function.
up.untransform              # Reverse of outcome transform function.

# Data (`y` in any of these can be replaced with `tc` for treatment or `x`).
up.transformed_y_train_pred  # The predicted uplift.
up.transformed_y_train  # The transformed outcome.
up.y_train
up.y_test
up.y                    # All the `y` data.
up.df
up.df_train
up.df_test

# Once a model has been created...
up.model
up.model_final
up.Q_cgains # 'aqini' or 'qini' can be used in place of 'cgains'
up.q1_cgains
up.q2_cgains
```

## Evaluation curve information

The raw data for all evaluation curves can be accessed within any `UpliftEval` object (`upev` below):

```
upev.PLOTTYPE_x  # percentile
upev.PLOTTYPE_y
```

where the phrase `PLOTTYPE` can be replaced with any of the following: `qini`, `aqini`, `cgains`, `cuplift`, `balance`, `uplift`. Because `up.test_results_` and `up.train_results_` are `UpliftEval` class objects, they can also be similarly accessed as shown above.

The theoretical maximum curves can also be extracted:

```
# Overfitting theoretical maximal qini curve.
upev.qini_max_x  # percentile
upev.qini_max_y

# "Practical" max curve.
upev.qini_pmax_x
upev.qini_pmax_y

# No sleeping dogs curve.
upev.qini_nosdmax_x
upev.qini_nosdmax_y
```

`up.train_results_` can be used to plot the qini performance on the training data, as follows: `up.train_results_.plot_qini()`.