CoxProportionalHazardModel

class hana_ml.algorithms.pal.regression.CoxProportionalHazardModel(tie_method=None, status_col=None, max_iter=None, convergence_criterion=None, significance_level=None, calculate_hazard=None, output_fitted=None, type_kind=None, thread_ratio=0.0)

Cox proportional hazard model (CoxPHM) is a special generalized linear model. It is a well-known realization-of-survival model that demonstrates failure or death at a certain time.

Parameters:
tie_method{'breslow', 'efron'}, optional

The method to deal with tied events.

Defaults to 'efron'.

status_colbool, optional(deprecated)

If a status column is defined for right-censored data:

  • False : No status column. All response times are failure/death.

  • True : There is a status column, of which 0 indicates right-censored data, and 1 indicates failure/death.

Defaults to True.

Deprecated, please use parameter status_col in the fit() method.

max_iterint, optional

Maximum number of iterations for numeric optimization.

convergence_criterionfloat, optional

Convergence criterion of coefficients for numeric optimization.

Defaults to 0.

significance_levelfloat, optional

Significance level for the confidence interval of estimated coefficients.

Defaults to 0.05.

calculate_hazardbool, optional

Controls whether to calculate hazard function as well as survival function.

  • False : Does not calculate hazard function.

  • True: Calculates hazard function.

Defaults to True.

output_fittedbool, optional

Controls whether to output the fitted response:

  • False : Does not output the fitted response.

  • True: Outputs the fitted response.

Defaults to False.

type_kindstr, optional(deprecated)

The prediction type:

  • 'risk': Predicts in risk space

  • 'lp': Predicts in linear predictor space

Default Value is 'risk'

Deprecated, please use parameter pred_type of the predict() method.

thread_ratiofloat, optional(deprecated)

Adjusts the percentage of available threads to use, from 0 to 1. A value of 0 indicates the use of a single thread, while 1 implies the use of all possible current threads. Values outside the range will be ignored and this function heuristically determines the number of threads to use.

Defaults to 0.

Examples

>>> cox = CoxProportionalHazardModel(significance_level= 0.05, calculate_hazard='yes', type_kind='risk')
>>> cox.fit(data=df_train, key='ID', features=['X1', 'X2'], label='TIME')

Perform predict(): >>> cox.predict(data=df_predict, key='ID',features=['X1', 'X2']).collect()

Attributes:
statistics_DataFrame

Regression-related statistics, such as r-square, log-likelihood, aic.

coefficient_DataFrame

Fitted regression coefficients.

covariance_varianceDataFrame

Co-Variance related data.

hazard_DataFrame

Statistics related to Time, Hazard, Survival.

fitted_DataFrame

Predicted dependent variable values for training data. Set to None if the training data has no row IDs.

Methods

fit(data[, key, features, label, status_col])

Fit the model to the training dataset.

predict(data[, key, features, thread_ratio, ...])

Predict dependent variable values based on fitted model.

score(data[, key, features, label])

Returns the coefficient of determination R2 of the prediction.
fit(data, key=None, features=None, label=None, status_col=None)

Fit the model to the training dataset.

Parameters:
dataDataFrame

Training data.

keystr, optional

Name of the ID column.

Mandatory if data is not indexed, or the index of data contains multiple columns.

Defaults to the single index column of data if not provided.

featuresa list of str, optional

Names of the feature columns(inclusive of covariates as well as status column).

If not provided, defaults to all non-key, non-label columns.

labelstr, optional

Name of the dependent variable(indicating the time before a failure/death event occurs or data is right censored.)

Defaults to the last non-ID column. (This is not the PAL default.)

status_colbool, optional

Specifies if a status column is defined for right-censored data.

False : No status column. All response times are failure/death.

True : There is a status column in data, of which 0 indicates right-censored data and 1 indicates failure/death. The column should correspond to:

  • the 1st column in features if features is specified;

  • the 1st non-key, non-label column in data if features is not specified.

Defaults to True.

Returns:
A fitted object of class "CoxProportionalHazardModel".
predict(data, key=None, features=None, thread_ratio=None, pred_type=None, significance_level=None)

Predict dependent variable values based on fitted model.

Parameters:
dataDataFrame

Independent variable values used for prediction.

keystr, optional

Name of the ID column.

Mandatory if data is not indexed, or the index of data contains multiple columns.

Defaults to the single index column of data if not provided.

featuresa list of str, optional

Names of the covariates.

thread_ratiofloat, optional(deprecated)

Adjusts the percentage of available threads to use, from 0 to 1. A value of 0 indicates the use of a single thread, while 1 implies the use of all possible current threads. Values outside the range will be ignored and this function heuristically determines the number of threads to use.

Defaults to 0.

pred_type: str, optional

The prediction type:

  • 'risk': Predicts in risk space

  • 'lp': Predicts in linear predictor space

Default Value is 'risk'

significance_levelfloat, optional

Significance level for the confidence interval and prediction interval.

Defaults to 0.05.

Returns:
DataFrame

Predicted values, structured as follows:

  • ID column, with same name and type as data 's ID column.

  • VALUE, type DOUBLE, representing predicted values.

score(data, key=None, features=None, label=None)

Returns the coefficient of determination R2 of the prediction.

Parameters:
dataDataFrame

Data on which to assess model performance.

keystr, optional

Name of the ID column.

Mandatory if data is not indexed, or the index of data contains multiple columns.

Defaults to the single index column of data if not provided.

featuresa list of str, optional

Names of the feature columns.

labelstr, optional

Name of the dependent variable.

Defaults to the last non-ID column(this is not the PAL default).

Returns:
float

The coefficient of determination R2 of the prediction on the given data.

Inherited Methods from PALBase

Besides those methods mentioned above, the CoxProportionalHazardModel class also inherits methods from PALBase class, please refer to PAL Base for more details.