survivalist

  1from importlib.metadata import PackageNotFoundError, version
  2import platform
  3import sys
  4
  5from sklearn.pipeline import Pipeline, _final_estimator_has
  6from sklearn.utils.metaestimators import available_if
  7
  8from .util import property_available_if
  9
 10
 11def _get_version(name):
 12    try:
 13        pkg_version = version(name)
 14    except ImportError:
 15        pkg_version = None
 16    return pkg_version
 17
 18
 19def show_versions():
 20    sys_info = {
 21        "Platform": platform.platform(),
 22        "Python version": f"{platform.python_implementation()} {platform}",
 23        "Python interpreter": sys.executable,
 24    }
 25
 26    deps = [
 27        "survivalist",
 28        "scikit-learn",
 29        "numpy",
 30        "scipy",
 31        "pandas",
 32        "numexpr",
 33        "ecos",
 34        "osqp",
 35        "joblib",
 36        "matplotlib",
 37        "pytest",
 38        "sphinx",
 39        "Cython",
 40        "pip",
 41        "setuptools",
 42    ]
 43    minwidth = max(
 44        max(map(len, deps)),
 45        max(map(len, sys_info.keys())),
 46    )
 47    fmt = "{0:<%ds}: {1}" % minwidth
 48
 49    print("SYSTEM")
 50    print("------")
 51    for name, version_string in sys_info.items():
 52        print(fmt.format(name, version_string))
 53
 54    print("\nDEPENDENCIES")
 55    print("------------")
 56    for dep in deps:
 57        version_string = _get_version(dep)
 58        print(fmt.format(dep, version_string))
 59
 60
 61@available_if(_final_estimator_has("predict_cumulative_hazard_function"))
 62def predict_cumulative_hazard_function(self, X, **kwargs):
 63    """Predict cumulative hazard function.
 64
 65    The cumulative hazard function for an individual
 66    with feature vector :math:`x` is defined as
 67
 68    .. math::
 69
 70        H(t \\mid x) = \\exp(x^\\top \\beta) H_0(t) ,
 71
 72    where :math:`H_0(t)` is the baseline hazard function,
 73    estimated by Breslow's estimator.
 74
 75    Parameters
 76    ----------
 77    X : array-like, shape = (n_samples, n_features)
 78        Data matrix.
 79
 80    Returns
 81    -------
 82    cum_hazard : ndarray, shape = (n_samples,)
 83        Predicted cumulative hazard functions.
 84    """
 85    Xt = X
 86    for _, _, transform in self._iter(with_final=False):
 87        Xt = transform.transform(Xt)
 88    return self.steps[-1][-1].predict_cumulative_hazard_function(Xt, **kwargs)
 89
 90
 91@available_if(_final_estimator_has("predict_survival_function"))
 92def predict_survival_function(self, X, **kwargs):
 93    """Predict survival function.
 94
 95    The survival function for an individual
 96    with feature vector :math:`x` is defined as
 97
 98    .. math::
 99
100        S(t \\mid x) = S_0(t)^{\\exp(x^\\top \\beta)} ,
101
102    where :math:`S_0(t)` is the baseline survival function,
103    estimated by Breslow's estimator.
104
105    Parameters
106    ----------
107    X : array-like, shape = (n_samples, n_features)
108        Data matrix.
109
110    Returns
111    -------
112    survival : ndarray, shape = (n_samples,)
113        Predicted survival functions.
114    """
115    Xt = X
116    for _, _, transform in self._iter(with_final=False):
117        Xt = transform.transform(Xt)
118    return self.steps[-1][-1].predict_survival_function(Xt, **kwargs)
119
120
121@property_available_if(_final_estimator_has("_predict_risk_score"))
122def _predict_risk_score(self):
123    return self.steps[-1][-1]._predict_risk_score
124
125
126def patch_pipeline():
127    Pipeline.predict_survival_function = predict_survival_function
128    Pipeline.predict_cumulative_hazard_function = predict_cumulative_hazard_function
129    Pipeline._predict_risk_score = _predict_risk_score
130
131
132try:
133    __version__ = version("survivalist")
134except PackageNotFoundError:  # pragma: no cover
135    # package is not installed
136    __version__ = "unknown"
137
138patch_pipeline()