<filename>lifelines/fitters/__init__.py
# -*- coding: utf-8 -*-
from __future__ import print_function
import collections
from functools import wraps
import sys

import numpy as np
import pandas as pd

from lifelines.plotting import plot_estimate
from lifelines.utils import qth_survival_times, _to_array
from lifelines.compat import PY2, PY3


def _must_call_fit_first(func):
    @wraps(func)
    def error_wrapper(*args, **kwargs):
        self = args[0]
        try:
            self._estimate_name
        except AttributeError:
            raise RuntimeError("Must call `fit` first!")
        return func(*args, **kwargs)

    return error_wrapper


class BaseFitter(object):
    def __init__(self, alpha=0.95):
        if not (0 < alpha <= 1.0):
            raise ValueError("alpha parameter must be between 0 and 1.")
        self.alpha = alpha

    def __repr__(self):
        classname = self.__class__.__name__
        try:
            s = """<lifelines.%s: fitted with %d observations, %d censored>""" % (
                classname,
                self.event_observed.shape[0],
                self.event_observed.shape[0] - np.where(self.event_observed)[0].shape[0],
            )
        except AttributeError:
            s = """<lifelines.%s>""" % classname
        return s


class UnivariateFitter(BaseFitter):
    @_must_call_fit_first
    def _update_docstrings(self):
        # Update their docstrings
        if PY2:
            self.__class__.subtract.__func__.__doc__ = self.subtract.__doc__.format(
                self._estimate_name, self.__class__.__name__
            )
            self.__class__.divide.__func__.__doc__ = self.divide.__doc__.format(
                self._estimate_name, self.__class__.__name__
            )
            self.__class__.predict.__func__.__doc__ = self.predict.__doc__.format(self.__class__.__name__)
            self.__class__.plot.__func__.__doc__ = plot_estimate.__doc__.format(
                self.__class__.__name__, self._estimate_name
            )
        elif PY3:
            self.__class__.subtract.__doc__ = self.subtract.__doc__.format(self._estimate_name, self.__class__.__name__)
            self.__class__.divide.__doc__ = self.divide.__doc__.format(self._estimate_name, self.__class__.__name__)
            self.__class__.predict.__doc__ = self.predict.__doc__.format(self.__class__.__name__)
            self.__class__.plot.__doc__ = plot_estimate.__doc__.format(self.__class__.__name__, self._estimate_name)

    @_must_call_fit_first
    def plot(self, *args, **kwargs):
        return plot_estimate(self, *args, **kwargs)

    @_must_call_fit_first
    def subtract(self, other):
        """
        Subtract the {0} of two {1} objects.

        Parameters
        ----------
        other: an {1} fitted instance.
        """
        self_estimate = getattr(self, self._estimate_name)
        other_estimate = getattr(other, other._estimate_name)
        new_index = np.concatenate((other_estimate.index, self_estimate.index))
        new_index = np.unique(new_index)
        return pd.DataFrame(
            self_estimate.reindex(new_index, method="ffill").values
            - other_estimate.reindex(new_index, method="ffill").values,
            index=new_index,
            columns=["diff"],
        )

    @_must_call_fit_first
    def divide(self, other):
        """
        Divide the {0} of two {1} objects.

        Parameters
        ----------
        other: an {1} fitted instance.

        """
        self_estimate = getattr(self, self._estimate_name)
        other_estimate = getattr(other, other._estimate_name)
        new_index = np.concatenate((other_estimate.index, self_estimate.index))
        new_index = np.unique(new_index)
        return pd.DataFrame(
            self_estimate.reindex(new_index, method="ffill").values
            / other_estimate.reindex(new_index, method="ffill").values,
            index=new_index,
            columns=["ratio"],
        )

    @_must_call_fit_first
    def predict(self, times):
        """
        Predict the {0} at certain point in time. Uses a linear interpolation if
        points in time are not in the index.

        Parameters
        ----------
        times: a scalar or an array of times to predict the value of {0} at.

        Returns
        -------
        predictions: a scalar if time is a scalar, a numpy array if time in an array.
        """
        if callable(self._estimation_method):
            return pd.DataFrame(self._estimation_method(_to_array(times)), index=_to_array(times)).loc[times].squeeze()
        estimate = getattr(self, self._estimation_method)
        # non-linear interpolations can push the survival curves above 1 and below 0.
        return estimate.reindex(estimate.index.union(_to_array(times))).interpolate("index").loc[times].squeeze()

    @property
    @_must_call_fit_first
    def conditional_time_to_event_(self):
        return self._conditional_time_to_event_()

    @_must_call_fit_first
    def _conditional_time_to_event_(self):
        """
        Return a DataFrame, with index equal to survival_function_, that estimates the median
        duration remaining until the death event, given survival up until time t. For example, if an
        individual exists until age 1, their expected life remaining *given they lived to time 1*
        might be 9 years.

        Returns
        -------
        conditional_time_to_: DataFrame 
            with index equal to survival_function_

        """
        age = self.survival_function_.index.values[:, None]
        columns = ["%s - Conditional time remaining to event" % self._label]
        return (
            pd.DataFrame(
                qth_survival_times(self.survival_function_[self._label] * 0.5, self.survival_function_)
                .sort_index(ascending=False)
                .values,
                index=self.survival_function_.index,
                columns=columns,
            )
            - age
        )

<filename>lifelines/fitters/__init__.py
# -*- coding: utf-8 -*-
from __future__ import print_function
import collections
from functools import wraps
import sys

import numpy as np
import pandas as pd

from lifelines.plotting import plot_estimate
from lifelines.utils import qth_survival_times, _to_array
from lifelines.compat import PY2, PY3


def _must_call_fit_first(func):
    @wraps(func)
    def error_wrapper(*args, **kwargs):
        self = args[0]
        try:
            self._estimate_name
        except AttributeError:
            raise RuntimeError("Must call `fit` first!")
        return func(*args, **kwargs)

    return error_wrapper


class BaseFitter(object):
    def __init__(self, alpha=0.95):
        if not (0 < alpha <= 1.0):
            raise ValueError("alpha parameter must be between 0 and 1.")
        self.alpha = alpha

    def __repr__(self):
        classname = self.__class__.__name__
        try:
            s = """<lifelines.%s: fitted with %d observations, %d censored>""" % (
                classname,
                self.event_observed.shape[0],
                self.event_observed.shape[0] - np.where(self.event_observed)[0].shape[0],
            )
        except AttributeError:
            s = """<lifelines.%s>""" % classname
        return s


class UnivariateFitter(BaseFitter):
    @_must_call_fit_first
    def _update_docstrings(self):
        # Update their docstrings
        if PY2:
            self.__class__.subtract.__func__.__doc__ = self.subtract.__doc__.format(
                self._estimate_name, self.__class__.__name__
            )
            self.__class__.divide.__func__.__doc__ = self.divide.__doc__.format(
                self._estimate_name, self.__class__.__name__
            )
            self.__class__.predict.__func__.__doc__ = self.predict.__doc__.format(self.__class__.__name__)
            self.__class__.plot.__func__.__doc__ = plot_estimate.__doc__.format(
                self.__class__.__name__, self._estimate_name
            )
        elif PY3:
            self.__class__.subtract.__doc__ = self.subtract.__doc__.format(self._estimate_name, self.__class__.__name__)
            self.__class__.divide.__doc__ = self.divide.__doc__.format(self._estimate_name, self.__class__.__name__)
            self.__class__.predict.__doc__ = self.predict.__doc__.format(self.__class__.__name__)
            self.__class__.plot.__doc__ = plot_estimate.__doc__.format(self.__class__.__name__, self._estimate_name)

    @_must_call_fit_first
    def plot(self, *args, **kwargs):
        return plot_estimate(self, *args, **kwargs)

    @_must_call_fit_first
    def subtract(self, other):
        """
        Subtract the {0} of two {1} objects.

        Parameters
        ----------
        other: an {1} fitted instance.
        """
        self_estimate = getattr(self, self._estimate_name)
        other_estimate = getattr(other, other._estimate_name)
        new_index = np.concatenate((other_estimate.index, self_estimate.index))
        new_index = np.unique(new_index)
        return pd.DataFrame(
            self_estimate.reindex(new_index, method="ffill").values
            - other_estimate.reindex(new_index, method="ffill").values,
            index=new_index,
            columns=["diff"],
        )

    @_must_call_fit_first
    def divide(self, other):
        """
        Divide the {0} of two {1} objects.

        Parameters
        ----------
        other: an {1} fitted instance.

        """
        self_estimate = getattr(self, self._estimate_name)
        other_estimate = getattr(other, other._estimate_name)
        new_index = np.concatenate((other_estimate.index, self_estimate.index))
        new_index = np.unique(new_index)
        return pd.DataFrame(
            self_estimate.reindex(new_index, method="ffill").values
            / other_estimate.reindex(new_index, method="ffill").values,
            index=new_index,
            columns=["ratio"],
        )

    @_must_call_fit_first
    def predict(self, times):
        """
        Predict the {0} at certain point in time. Uses a linear interpolation if
        points in time are not in the index.

        Parameters
        ----------
        times: a scalar or an array of times to predict the value of {0} at.

        Returns
        -------
        predictions: a scalar if time is a scalar, a numpy array if time in an array.
        """
        if callable(self._estimation_method):
            return pd.DataFrame(self._estimation_method(_to_array(times)), index=_to_array(times)).loc[times].squeeze()
        estimate = getattr(self, self._estimation_method)
        # non-linear interpolations can push the survival curves above 1 and below 0.
        return estimate.reindex(estimate.index.union(_to_array(times))).interpolate("index").loc[times].squeeze()

    @property
    @_must_call_fit_first
    def conditional_time_to_event_(self):
        return self._conditional_time_to_event_()

    @_must_call_fit_first
    def _conditional_time_to_event_(self):
        """
        Return a DataFrame, with index equal to survival_function_, that estimates the median
        duration remaining until the death event, given survival up until time t. For example, if an
        individual exists until age 1, their expected life remaining *given they lived to time 1*
        might be 9 years.

        Returns
        -------
        conditional_time_to_: DataFrame 
            with index equal to survival_function_

        """
        age = self.survival_function_.index.values[:, None]
        columns = ["%s - Conditional time remaining to event" % self._label]
        return (
            pd.DataFrame(
                qth_survival_times(self.survival_function_[self._label] * 0.5, self.survival_function_)
                .sort_index(ascending=False)
                .values,
                index=self.survival_function_.index,
                columns=columns,
            )
            - age
        )

# -*- coding: utf-8 -*- <lifelines.%s: fitted with %d observations, %d censored> <lifelines.%s> # Update their docstrings Subtract the {0} of two {1} objects.          Parameters         ----------         other: an {1} fitted instance. Divide the {0} of two {1} objects.          Parameters         ----------         other: an {1} fitted instance. Predict the {0} at certain point in time. Uses a linear interpolation if         points in time are not in the index.          Parameters         ----------         times: a scalar or an array of times to predict the value of {0} at.          Returns         -------         predictions: a scalar if time is a scalar, a numpy array if time in an array. # non-linear interpolations can push the survival curves above 1 and below 0. Return a DataFrame, with index equal to survival_function_, that estimates the median         duration remaining until the death event, given survival up until time t. For example, if an         individual exists until age 1, their expected life remaining *given they lived to time 1*         might be 9 years.          Returns         -------         conditional_time_to_: DataFrame              with index equal to survival_function_