Yet another mypy fixes

azure-pipelines.yml

@@ -75,6 +75,6 @@ jobs:
       addToPath: true
   - script: |
       python -m pip install --upgrade pip setuptools
-      pip install mypy sklearn-stub data-science-types
+      pip install mypy data-science-types
       mypy . --ignore-missing-imports
     displayName: 'mypy check'

cuml_bench/kmeans.py

@@ -16,13 +16,13 @@
 
 import argparse
 import warnings
+from typing import Any
 
 import bench
 import numpy as np
 from cuml import KMeans
 from sklearn.metrics.cluster import davies_bouldin_score
 
-
 warnings.filterwarnings('ignore', category=FutureWarning)
 parser = argparse.ArgumentParser(description='cuML K-means benchmark')
 parser.add_argument('-i', '--filei', '--fileI', '--init',
@@ -39,11 +39,12 @@
 # Load and convert generated data
 X_train, X_test, _, _ = bench.load_data(params)
 
+X_init: Any
 if params.filei == 'k-means++':
     X_init = 'k-means++'
 # Load initial centroids from specified path
 elif params.filei is not None:
-    X_init = np.load(params.filei).astype(params.dtype)
+    X_init = {k: v.astype(params.dtype) for k, v in np.load(params.filei).items()}
     if isinstance(X_init, np.ndarray):
         params.n_clusters = X_init.shape[0]
 # or choose random centroids from training data

daal4py_bench/kmeans.py

@@ -15,13 +15,13 @@
 # ===============================================================================
 
 import argparse
+from typing import Any
 
 import bench
 import numpy as np
 from daal4py import kmeans
 from daal4py.sklearn._utils import getFPType
 
-
 parser = argparse.ArgumentParser(description='daal4py K-Means clustering '
                                              'benchmark')
 parser.add_argument('-i', '--filei', '--fileI', '--init',
@@ -36,9 +36,10 @@
 # Load generated data
 X_train, X_test, _, _ = bench.load_data(params, add_dtype=True)
 
+X_init: Any
 # Load initial centroids from specified path
 if params.filei is not None:
-    X_init = np.load(params.filei).astype(params.dtype)
+    X_init = {k: v.astype(params.dtype) for k, v in np.load(params.filei).items()}
     params.n_clusters = X_init.shape[0]
 # or choose random centroids from training data
 else:

sklearn_bench/kmeans.py

@@ -15,6 +15,7 @@
 # ===============================================================================
 
 import argparse
+from typing import Any
 
 import bench
 import numpy as np
@@ -36,11 +37,12 @@
 # Load and convert generated data
 X_train, X_test, _, _ = bench.load_data(params)
 
+X_init: Any
 if params.filei == 'k-means++':
     X_init = 'k-means++'
 # Load initial centroids from specified path
 elif params.filei is not None:
-    X_init = np.load(params.filei).astype(params.dtype)
+    X_init = {k: v.astype(params.dtype) for k, v in np.load(params.filei).items()}
     if isinstance(X_init, np.ndarray):
         params.n_clusters = X_init.shape[0]
 # or choose random centroids from training data