seldonian.parse_tree.nodes.MEDCustomBaseNode

class MEDCustomBaseNode(name, lower=-inf, upper=inf, **kwargs)

Bases: BaseNode

__init__(name, lower=-inf, upper=inf, **kwargs)

Custom base node that calculates pair-wise mean error differences between male and female points. This was used in the Seldonian regression algorithm presented by Thomas et al. (2019): https://www.science.org/stoken/author-tokens/ST-119/full see Figure 2.

Overrides several parent class methods

Parameters:

  • name (str) – The name of the node

  • lower (float) – Lower confidence bound

  • upper (float) – Upper confidence bound

Variables:

delta (float) – The share of the confidence put into this node

__repr__()

Overrides Node.__repr__()

Methods

calculate_bounds(**kwargs)

Calculate confidence bounds given a bound_method, such as t-test.

Returns:

A dictionary mapping the bound name to its value, e.g., {“lower”:-1.0, “upper”: 1.0}

calculate_data_forbound(**kwargs)

Overrides same method from parent class, BaseNode

calculate_value(**kwargs)

Calculate the value of the node given model weights, etc. This is the expected value of the base variable, not the bound.

compute_HC_lowerbound(data, datasize, delta, **kwargs)

Calculate high confidence lower bound Used in safety test

Parameters:

  • data (numpy ndarray) – Vector containing base variable evaluated at each observation in dataset

  • datasize (int) – The number of observations in the safety dataset

  • delta (float) – Confidence level, e.g. 0.05

Returns:

lower, the high-confidence lower bound

compute_HC_upper_and_lowerbound(data, datasize, delta_lower, delta_upper, **kwargs)

Calculate high confidence lower and upper bounds Used in safety test. Confidence levels for lower and upper bound do not have to be equivalent.

Depending on the bound_method, this is not always equivalent to calling compute_HC_lowerbound() and compute_HC_upperbound() independently.

Parameters:

  • data (numpy ndarray) – Vector containing base variable evaluated at each observation in dataset

  • datasize (int) – The number of observations in the safety dataset

  • delta_lower – Confidence level for the lower bound, e.g. 0.05

  • delta_upper – Confidence level for the upper bound, e.g. 0.05

Returns:

(lower,upper) the high-confidence lower and upper bounds.

compute_HC_upperbound(data, datasize, delta, **kwargs)

Calculate high confidence upper bound Used in safety test

Parameters:

  • data (numpy ndarray) – Vector containing base variable evaluated at each observation in dataset

  • datasize (int) – The number of observations in the safety dataset

  • delta (float) – Confidence level, e.g. 0.05

Returns:

upper, the high-confidence upper bound

mask_data(dataset, conditional_columns)

Mask features and labels using a joint AND mask where each of the conditional columns is True.

Parameters:

  • dataset (dataset.Dataset object) – The candidate or safety dataset

  • conditional_columns (List(str)) – List of columns for which to create the joint AND mask on the dataset

Returns:

The masked dataframe

Return type:

numpy ndarray

precalculate_data(X, Y, S)

Preconfigure dataset for candidate selection or safety test so that it does not need to be recalculated on each iteration through the parse tree

Parameters:

  • X (pandas dataframe) – features

  • Y (pandas dataframe) – labels

predict_HC_lowerbound(data, datasize, delta, **kwargs)

Calculate high confidence lower bound that we expect to pass the safety test. Used in candidate selection

Parameters:

  • data (numpy ndarray) – Vector containing base variable evaluated at each observation in dataset

  • datasize (int) – The number of observations in the safety dataset

  • delta (float) – Confidence level, e.g. 0.05

Returns:

lower, the predicted high-confidence lower bound

predict_HC_upper_and_lowerbound(data, datasize, delta_lower, delta_upper, **kwargs)

Calculate high confidence lower and upper bounds that we expect to pass the safety test. Used in candidate selection. Confidence levels for lower and upper bound do not have to be equivalent.

Depending on the bound_method, this is not always equivalent to calling predict_HC_lowerbound() and predict_HC_upperbound() independently.

Parameters:

  • data (numpy ndarray) – Vector containing base variable evaluated at each observation in dataset

  • datasize (int) – The number of observations in the safety dataset

  • delta_lower – Confidence level for the lower bound, e.g. 0.05

  • delta_upper – Confidence level for the upper bound, e.g. 0.05

Returns:

(lower,upper) the predicted high-confidence lower and upper bounds.

predict_HC_upperbound(data, datasize, delta, **kwargs)

Calculate high confidence upper bound that we expect to pass the safety test. Used in candidate selection

Parameters:

  • data (numpy ndarray) – Vector containing base variable evaluated at each observation in dataset

  • datasize (int) – The number of observations in the safety dataset

  • delta (float) – Confidence level, e.g. 0.05

Returns:

upper, the predicted high-confidence upper bound

zhat(model, theta, data_dict, **kwargs)

Pair up male and female columns and compute a vector of: (y_i - y_hat_i | M) - (y_j - y_hat_j | F). There may not be the same number of male and female rows so the number of pairs is min(N_male,N_female)

Parameters:

  • model (models.SeldonianModel object) – machine learning model

  • theta (numpy ndarray) – model weights

  • data_dict (dict) – contains inputs to model, such as features and labels