Fix some type errors newer mypy found (one follow up)

pyttb/gcp/handles.py

@@ -115,15 +115,17 @@ def huber_grad(data: ttb.tensor, model: ttb.tensor, threshold: float) -> np.ndar
     ) * np.logical_not(below_threshold)
 
 
+# FIXME: Num trials should be enforced as integer here and in MATLAB
+# requires updating our regression test values to calculate MATLAB integer version
 def negative_binomial(
-    data: np.ndarray, model: np.ndarray, num_trials: int
+    data: np.ndarray, model: np.ndarray, num_trials: float
 ) -> np.ndarray:
     """Return objective function for negative binomial distributions"""
     return (num_trials + data) * np.log(model + 1) - data * np.log(model + EPS)
 
 
 def negative_binomial_grad(
-    data: np.ndarray, model: np.ndarray, num_trials: int
+    data: np.ndarray, model: np.ndarray, num_trials: float
 ) -> np.ndarray:
     """Return gradient function for negative binomial distributions"""
     return (num_trials + 1) / (1 + model) - data / (model + EPS)

pyttb/gcp/samplers.py

@@ -425,7 +425,7 @@ def semistrat(data: ttb.sptensor, num_nonzeros: int, num_zeros: int) -> sample_t
 
 
 def stratified(
-    data: ttb.sptensor,
+    data: Union[ttb.sptensor, ttb.tensor],
     nz_idx: np.ndarray,
     num_nonzeros: int,
     num_zeros: int,
@@ -450,6 +450,9 @@ def stratified(
     -------
     Subscripts, values, and weights of samples (Nonzeros then zeros).
     """
+    assert isinstance(
+        data, ttb.sptensor
+    ), "For stratified sampling Sparse Tensor must be provided"
     [nonzero_subs, nonzero_vals] = nonzeros(data, num_nonzeros, with_replacement=True)
     nonzero_weights = np.ones((num_nonzeros,))
     if num_nonzeros > 0: