Lifelines - CoxTimeVaryingFitter - 'numpy.float64' object has no attribute 'exp'

Question

I am trying to use the CoxTimeVaryingFitter on my dataset, however there seems to be a type issue in baseline_cumulative_hazard_.

I attempted reducing the individual features to isolate the problem but was not able to fit on the dataset below. Is the issue with my data or the model? Thanks!

Code:

from lifelines import CoxTimeVaryingFitter
import autograd.numpy as np
ctv = CoxTimeVaryingFitter()

comp = 'comp_comp1' #start with comp1
event = 'failure_'+comp.split("_")[1]
cols = ['start', 'stop',
        'machineID', 
        'age', 
         event,
        'volt_24_ma','rotate_24_ma', 'vibration_24_ma', 'pressure_24_ma'
         ]

ctv.fit(df_X_train[cols].dropna(),
    id_col='machineID',
    event_col=event,
    start_col='start',
    stop_col='stop', 
    show_progress=True,
    fit_options={'step_size':0.25})
ctv.print_summary()
ctv.plot()

Data Types

Time-series data

Error: loop of ufunc does not support argument 0 of type numpy.float64 which has no callable exp method

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
AttributeError: 'numpy.float64' object has no attribute 'exp'

The above exception was the direct cause of the following exception:

TypeError                                 Traceback (most recent call last)
<command-1950361299690996> in <module>
     23     ]
     24 
---> 25 ctv.fit(df_X_train[cols].dropna(),
     26     id_col='machineID',
     27     event_col=event,

/databricks/python/lib/python3.8/site-packages/lifelines/fitters/cox_time_varying_fitter.py in fit(self, df, event_col, start_col, stop_col, weights_col, id_col, show_progress, robust, strata, initial_point, formula, fit_options)
    237         self.confidence_intervals_ = self._compute_confidence_intervals()
    238         self.baseline_cumulative_hazard_ = self._compute_cumulative_baseline_hazard(df, events, start, stop, weights)
--> 239         self.baseline_survival_ = self._compute_baseline_survival()
    240         self.event_observed = events
    241         self.start_stop_and_events = pd.DataFrame({"event": events, "start": start, "stop": stop})

/databricks/python/lib/python3.8/site-packages/lifelines/fitters/cox_time_varying_fitter.py in _compute_baseline_survival(self)
    815 
    816     def _compute_baseline_survival(self):
--> 817         survival_df = np.exp(-self.baseline_cumulative_hazard_)
    818         survival_df.columns = ["baseline survival"]
    819         return survival_df

/databricks/python/lib/python3.8/site-packages/pandas/core/generic.py in __array_ufunc__(self, ufunc, method, *inputs, **kwargs)
   1934         self, ufunc: Callable, method: str, *inputs: Any, **kwargs: Any
   1935     ):
-> 1936         return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)
   1937 
   1938     # ideally we would define this to avoid the getattr checks, but

/databricks/python/lib/python3.8/site-packages/pandas/core/arraylike.py in array_ufunc(self, ufunc, method, *inputs, **kwargs)
    364             # take this path if there are no kwargs
    365             mgr = inputs[0]._mgr
--> 366             result = mgr.apply(getattr(ufunc, method))
    367         else:
    368             # otherwise specific ufunc methods (eg np.<ufunc>.accumulate(..))

/databricks/python/lib/python3.8/site-packages/pandas/core/internals/managers.py in apply(self, f, align_keys, ignore_failures, **kwargs)
    423             try:
    424                 if callable(f):
--> 425                     applied = b.apply(f, **kwargs)
    426                 else:
    427                     applied = getattr(b, f)(**kwargs)

/databricks/python/lib/python3.8/site-packages/pandas/core/internals/blocks.py in apply(self, func, **kwargs)
    376         """
    377         with np.errstate(all="ignore"):
--> 378             result = func(self.values, **kwargs)
    379 
    380         return self._split_op_result(result)

TypeError: loop of ufunc does not support argument 0 of type numpy.float64 which has no callable exp method

Answer 1

Looks like it is trying to apply np.exp to a dataframe (or Series or array) with object dtype.

From another question I have a simple pandas Series:

In [120]: a
Out[120]: 
0    1
1    3
2    5
3    7
4    9
dtype: int64

With int dtype, I can apply np.exp and get a float dtypes Series:

In [121]: np.exp(a)
Out[121]: 
0       2.718282
1      20.085537
2     148.413159
3    1096.633158
4    8103.083928
dtype: float64

But if I convert the Series to object dtype, I get your error:

In [122]: np.exp(a.astype(object))
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
AttributeError: 'int' object has no attribute 'exp'

The above exception was the direct cause of the following exception:

TypeError                                 Traceback (most recent call last)
Input In [122], in <cell line: 1>()
----> 1 np.exp(a.astype(object))

File ~\anaconda3\lib\site-packages\pandas\core\generic.py:2101, in NDFrame.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
   2097 @final
   2098 def __array_ufunc__(
   2099     self, ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any
   2100 ):
-> 2101     return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)

File ~\anaconda3\lib\site-packages\pandas\core\arraylike.py:397, in array_ufunc(self, ufunc, method, *inputs, **kwargs)
    394 elif self.ndim == 1:
    395     # ufunc(series, ...)
    396     inputs = tuple(extract_array(x, extract_numpy=True) for x in inputs)
--> 397     result = getattr(ufunc, method)(*inputs, **kwargs)
    398 else:
    399     # ufunc(dataframe)
    400     if method == "__call__" and not kwargs:
    401         # for np.<ufunc>(..) calls
    402         # kwargs cannot necessarily be handled block-by-block, so only
    403         # take this path if there are no kwargs

TypeError: loop of ufunc does not support argument 0 of type int which has no callable exp method

The traceback would be even closer if a was a dataframe instead of a Series.