Fix cython-lint issues. (#5536)

This PR fixes some style issues from `cython-lint` that appear to be blocking CI for PR #5534. It seems that these issues only appear when using `cython>=3`. It is not clear to me why these issues are just now showing up (rather than appearing immediately once Cython 3 was released).

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Simon Adorf (https://github.com/csadorf)

URL: #5536

python/cuml/cluster/hdbscan/prediction.pyx

@@ -469,5 +469,5 @@ def approximate_predict(clusterer, points_to_predict, convert_dtype=True):
                           <float*> prediction_probs_ptr)
 
     clusterer.handle.sync()
-    return prediction_labels.to_output(output_type="numpy"),\
+    return prediction_labels.to_output(output_type="numpy"), \
         prediction_probs.to_output(output_type="numpy", output_dtype="float32")

python/cuml/ensemble/randomforest_common.pyx

@@ -156,9 +156,9 @@ class BaseRandomForestModel(Base):
         self.treelite_serialized_model = None
 
     def _get_max_feat_val(self) -> float:
-        if type(self.max_features) == int:
+        if isinstance(self.max_features, int):
             return self.max_features/self.n_cols
-        elif type(self.max_features) == float:
+        elif isinstance(self.max_features, float):
             return self.max_features
         elif self.max_features == 'sqrt':
             return 1/np.sqrt(self.n_cols)
@@ -302,10 +302,10 @@ class BaseRandomForestModel(Base):
                           "to fit the estimator")
 
         max_feature_val = self._get_max_feat_val()
-        if type(self.min_samples_leaf) == float:
+        if isinstance(self.min_samples_leaf, float):
             self.min_samples_leaf = \
                 math.ceil(self.min_samples_leaf * self.n_rows)
-        if type(self.min_samples_split) == float:
+        if isinstance(self.min_samples_split, float):
             self.min_samples_split = \
                 max(2, math.ceil(self.min_samples_split * self.n_rows))
         return X_m, y_m, max_feature_val

python/cuml/tsa/holtwinters.pyx

@@ -194,7 +194,7 @@ class ExponentialSmoothing(Base):
                          output_type=output_type)
 
         # Total number of Time Series for forecasting
-        if type(ts_num) != int:
+        if not isinstance(ts_num, int):
             raise TypeError("Type of ts_num must be int. Given: " +
                             type(ts_num))
         if ts_num <= 0:
@@ -203,7 +203,7 @@ class ExponentialSmoothing(Base):
         self.ts_num = ts_num
 
         # Season length in the time series
-        if type(seasonal_periods) != int:
+        if not isinstance(seasonal_periods, int):
             raise TypeError("Type of seasonal_periods must be int."
                             " Given: " + type(seasonal_periods))
         if seasonal_periods < 2:
@@ -223,7 +223,7 @@ class ExponentialSmoothing(Base):
                              "\"additive\" or \"multiplicative\".")
 
         # number of seasons to be used for seasonal seed values
-        if type(start_periods) != int:
+        if not isinstance(start_periods, int):
             raise TypeError("Type of start_periods must be int. Given: " +
                             type(start_periods))
         if start_periods < 2:
@@ -390,7 +390,7 @@ class ExponentialSmoothing(Base):
         cdef uintptr_t forecast_ptr, level_ptr, trend_ptr, season_ptr
         cdef handle_t* handle_ = <handle_t*><size_t>self.handle.getHandle()
 
-        if type(h) != int or (type(index) != int and index is not None):
+        if not isinstance(h, int) or (not isinstance(index, int) and index is not None):
             raise TypeError("Input arguments must be of type int."
                             "Index has type: " + str(type(index))
                             + "\nh has type: " + str(type(h)))