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 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().