tlo.lm module
- class Predictor(property_name: str = None, external: bool = False, conditions_are_mutually_exclusive: bool | None = None, conditions_are_exhaustive: bool | None = False)[source]
Bases:
object
- class LinearModelType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]
Bases:
Enum
The type of model specifies how the results from the predictor are combined: ‘additive’ -> adds the effect_sizes from the predictors ‘logisitic’ -> multiples the effect_sizes from the predictors and applies the transform x/(1+x) [Thus, the intercept can be taken to be an Odds and effect_sizes Odds Ratios, and the prediction is a probability.] ‘multiplicative’ -> multiplies the effect_sizes from the predictors
- ADDITIVE = 1
- LOGISTIC = 2
- MULTIPLICATIVE = 3
- CUSTOM = 4
- class LinearModel(lm_type: LinearModelType, intercept: float | int, *predictors: Predictor)[source]
Bases:
object
- property lm_type: LinearModelType
The model type.
- property intercept: float | int
The intercept value for the model.
- static multiplicative(*predictors: Predictor)[source]
Returns a multplicative LinearModel with intercept=1.0
- Parameters:
predictors – One or more Predictor objects defining the model
- static custom(predict_function, **kwargs)[source]
Define a linear model using the supplied function
The function acts as a drop-in replacement to the predict function and must implement the interface:
- (
self: LinearModel, df: Union[pd.DataFrame, pd.Series], rng: Optional[np.random.RandomState] = None, **kwargs
) -> pd.Series
It is the responsibility of the caller of predict to ensure they pass either a dataframe or an individual record as expected by the custom function.
See test_custom() in test_lm.py for a couple of examples.
- predict(df: DataFrame, rng: RandomState | None = None, squeeze_single_row_output=True, **kwargs) Series [source]
Evaluate linear model output for a given set of input data.
- Parameters:
df – The input
DataFrame
containing the input data to evaluate the model with.rng – If set to a NumPy
RandomState
instance, returned output will be booleanSeries
corresponding to Bernoulli random variables sampled according to probabilities specified by model output. Otherwise model output directly returned.squeeze_single_row_output – If
rng
argument is notNone
and this argument is set toTrue
, the output for adf
input with a single-row will be a scalar boolean value rather than a booleanSeries
.**kwargs –
Values for any external variables included in model predictors.