Models CoxPhModel

---

class CoxPhModel

Entity CoxPhModel

Attributes

last_read_date	Read-only property - Last time this model’s data was accessed.
name	Set or get the name of the model object.
status	Read-only property - Current model life cycle status.

Methods

__init__(self[, name, _info])	Create a ‘new’ instance of a Multivariate Cox Proportional Hazards model
predict(self, predict_frame[, comparison_frame, feature_columns])	[ALPHA] <Missing Doc>
train(self, frame, time_column, covariate_columns, censor_column[, ...])	[ALPHA] Build Cox proportional hazard model.

__init__(self, name=None)

Create a ‘new’ instance of a Multivariate Cox Proportional Hazards model

Parameters:

name : unicode (default=None)

User supplied name.

Returns:

: Model

Examples

Consider the following model trained and tested on the sample data set in frame ‘frame’.

Consider the following frame containing three columns.

>>> frame.inspect()
[#]  time    bmi   censor
=========================
[0]     6.0  31.4     1.0
[1]    98.0  21.5     1.0
[2]   189.0  27.1     1.0
[3]   374.0  22.7     1.0
[4]  1002.0  35.7     1.0
[5]  1205.0  30.7     1.0
[6]  2065.0  26.5     1.0
[7]  2201.0  28.3     1.0
[8]  2421.0  27.9     1.0

>>> model = ta.CoxPhModel()
[===Job Progress===]
>>> train_output = model.train(frame,time_column='time',covariate_columns=['bmi'],censor_column='censor',convergence_tolerance=0.01,max_steps=10)
[===Job Progress===]
>>> train_output
{u'beta': [-0.03351902788328831], u'mean': [27.977777777777778]}
>>> train_output['beta']
[-0.03351902788328831]
>>> predict_output = model.predict(frame)
[===Job Progress===]
>>> predict_output.inspect()
[#]  time    bmi   censor  hazard_ratio
=========================================
[0]     6.0  31.4     1.0  0.891625068026
[1]    98.0  21.5     1.0    1.2425041437
[2]   189.0  27.1     1.0   1.02985936884
[3]   374.0  22.7     1.0    1.1935188738
[4]  1002.0  35.7     1.0  0.771945457787
[5]  1205.0  30.7     1.0  0.912792914749
[6]  2065.0  26.5     1.0   1.05078097618
[7]  2201.0  28.3     1.0  0.989257541146
[8]  2421.0  27.9     1.0   1.00261043677