Fix annotations

Author: ktsitsi

examples/cloud/serverless_training/pytorch/data_ingestion.py

@@ -15,8 +15,12 @@
 # Your S3 bucket
 S3_BUCKET = "your_s3_bucket"
 
-IMAGES_URI = f"tiledb://{TILEDB_WORKSPACE}/{TILEDB_TEAMSPACE}/s3://{S3_BUCKET}/mnist_images"
-LABELS_URI = f"tiledb://{TILEDB_WORKSPACE}/{TILEDB_TEAMSPACE}/s3://{S3_BUCKET}/mnist_labels"
+IMAGES_URI = (
+    f"tiledb://{TILEDB_WORKSPACE}/{TILEDB_TEAMSPACE}/s3://{S3_BUCKET}/mnist_images"
+)
+LABELS_URI = (
+    f"tiledb://{TILEDB_WORKSPACE}/{TILEDB_TEAMSPACE}/s3://{S3_BUCKET}/mnist_labels"
+)
 
 
 # Let's define an ingestion function
@@ -63,7 +67,9 @@ def mnist_ingest(ingestion_func: Any) -> None:
     ingestion_func(data=labels, batch_size=64, uri=LABELS_URI)
 
 
-tiledb.client.configure(username=TILEDB_USER_NAME, password=TILEDB_PASSWD, workspace=TILEDB_WORKSPACE)
+tiledb.client.configure(
+    username=TILEDB_USER_NAME, password=TILEDB_PASSWD, workspace=TILEDB_WORKSPACE
+)
 tiledb.client.login()
 
 tiledb.client.udf.exec(mnist_ingest, ingestion_func=ingest_in_tiledb)

examples/cloud/serverless_training/pytorch/model_load_and_predict.py

@@ -14,9 +14,15 @@
 # Your S3 bucket
 S3_BUCKET = "your_s3_bucket"
 
-IMAGES_URI = f"tiledb://{TILEDB_WORKSPACE}/{TILEDB_TEAMSPACE}/s3://{S3_BUCKET}/mnist_images"
-LABELS_URI = f"tiledb://{TILEDB_WORKSPACE}/{TILEDB_TEAMSPACE}/s3://{S3_BUCKET}/mnist_labels"
-MODEL_URI = f"tiledb://{TILEDB_WORKSPACE}/{TILEDB_TEAMSPACE}/s3://{S3_BUCKET}/mnist_model"
+IMAGES_URI = (
+    f"tiledb://{TILEDB_WORKSPACE}/{TILEDB_TEAMSPACE}/s3://{S3_BUCKET}/mnist_images"
+)
+LABELS_URI = (
+    f"tiledb://{TILEDB_WORKSPACE}/{TILEDB_TEAMSPACE}/s3://{S3_BUCKET}/mnist_labels"
+)
+MODEL_URI = (
+    f"tiledb://{TILEDB_WORKSPACE}/{TILEDB_TEAMSPACE}/s3://{S3_BUCKET}/mnist_model"
+)
 
 IO_BATCH_SIZE = 20000
 
@@ -71,7 +77,9 @@ def forward(self, x: torch.Tensor) -> Any:
     return [np.argmax(pred) for pred in output.numpy()]
 
 
-tiledb.client.configure(username=TILEDB_USER_NAME, password=TILEDB_PASSWD, workspace=TILEDB_WORKSPACE)
+tiledb.client.configure(
+    username=TILEDB_USER_NAME, password=TILEDB_PASSWD, workspace=TILEDB_WORKSPACE
+)
 tiledb.client.login()
 
 predictions = tiledb.client.udf.exec(predict)

examples/cloud/serverless_training/pytorch/model_training.py

@@ -1,6 +1,7 @@
 import os
 
 import tiledb.client
+
 from tiledb.ml.readers.types import ArrayParams
 
 # Your TileDB username and password, exported as environmental variables
@@ -51,10 +52,10 @@ def forward(self, x: torch.Tensor) -> Any:
             logits = self.linear_relu_stack(x)
             return logits
 
-    def do_random_noise(img, mag=0.1):
-        noise = np.random.uniform(-1, 1,img.shape)*mag
+    def do_random_noise(img: np.ndarray, mag: float = 0.1) -> np.ndarray:
+        noise = np.random.uniform(-1, 1, img.shape) * mag
         img = img + noise
-        img = np.clip(img,0,1)
+        img = np.clip(img, 0, 1)
         return img
 
     with tiledb.open(IMAGES_URI) as x, tiledb.open(LABELS_URI) as y:
@@ -118,7 +119,9 @@ def do_random_noise(img, mag=0.1):
         model.save()
 
 
-tiledb.client.configure(username=TILEDB_USER_NAME, password=TILEDB_PASSWD, workspace=TILEDB_WORKSPACE)
+tiledb.client.configure(
+    username=TILEDB_USER_NAME, password=TILEDB_PASSWD, workspace=TILEDB_WORKSPACE
+)
 tiledb.client.login()
 
 tiledb.client.udf.exec(train)

setup.cfg

@@ -41,7 +41,7 @@ test=pytest
 [flake8]
 statistics = true
 exclude = .git
-ignore = E203, E501, W503, B950
+ignore = E203, E231, E501, E713, W503, B950
 select = B,C,E,F,W,T4,B9
 per-file-ignores =
     __init__.py: F401, F403

tests/models/test_cloud_utils.py

@@ -1,11 +1,7 @@
-import pytest
+from tiledb.ml.models._cloud_utils import update_file_properties
 
-from tiledb.ml.models._cloud_utils import (
-    update_file_properties,
-)
 
 class TestCloudUtils:
-
     def test_update_file_properties(self, mocker):
         mock_tiledb_cloud_update_file_properties = mocker.patch(
             "tiledb.client.array.update_file_properties"

tests/models/test_tensorflow_keras_models.py

@@ -7,18 +7,18 @@
 import platform
 import shutil
 
+import keras
 import numpy as np
 import pytest
 import tensorflow as tf
-import keras
 
 import tiledb
 from tiledb.ml import __version__ as tiledb_ml_version
 from tiledb.ml.models import SHORT_PREVIEW_LIMIT
 from tiledb.ml.models.tensorflow_keras import TensorflowKerasTileDBModel
 
 # Detect Keras version for conditional test behavior
-keras_version = tuple(int(x) for x in keras.__version__.split('.')[:2])
+keras_version = tuple(int(x) for x in keras.__version__.split(".")[:2])
 KERAS_3_OR_HIGHER = keras_version[0] >= 3
 
 try:
@@ -35,10 +35,7 @@
     except ImportError:
         try:
             # For Keras 3.x (TensorFlow >=2.16), use the public API
-            from keras.testing import (
-                get_small_functional_mlp,
-                get_small_sequential_mlp,
-            )
+            from keras.testing import get_small_functional_mlp, get_small_sequential_mlp
         except ImportError:
             try:
                 # Fallback for older versions with keras.src
@@ -50,21 +47,26 @@
                 # Final fallback: implement these functions ourselves for Keras 3.x
                 def get_small_sequential_mlp(num_hidden, num_classes, input_dim):
                     """Create a small sequential MLP for testing."""
-                    model = tf.keras.Sequential([
-                        tf.keras.layers.Input(shape=(input_dim,)),
-                        tf.keras.layers.Dense(num_hidden, activation='relu'),
-                        tf.keras.layers.Dense(num_classes, activation='softmax')
-                    ])
+                    model = tf.keras.Sequential(
+                        [
+                            tf.keras.layers.Input(shape=(input_dim,)),
+                            tf.keras.layers.Dense(num_hidden, activation="relu"),
+                            tf.keras.layers.Dense(num_classes, activation="softmax"),
+                        ]
+                    )
                     return model
-                
+
                 def get_small_functional_mlp(num_hidden, num_classes, input_dim):
                     """Create a small functional MLP for testing."""
                     inputs = tf.keras.Input(shape=(input_dim,))
-                    x = tf.keras.layers.Dense(num_hidden, activation='relu')(inputs)
-                    outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(x)
+                    x = tf.keras.layers.Dense(num_hidden, activation="relu")(inputs)
+                    outputs = tf.keras.layers.Dense(num_classes, activation="softmax")(
+                        x
+                    )
                     model = tf.keras.Model(inputs=inputs, outputs=outputs)
                     return model
 
+
 # Suppress all Tensorflow messages
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 batch_get_value = tf.keras.backend.batch_get_value
@@ -372,8 +374,10 @@ def test_save_load_with_sequence_features(self, tmpdir, loss, optimizer, metrics
 
     def test_functional_model_save_load_with_custom_loss_and_metric(self, tmpdir):
         if KERAS_3_OR_HIGHER:
-            pytest.skip("Custom loss/metric with Lambda layers not fully supported in Keras 3.x serialization")
-        
+            pytest.skip(
+                "Custom loss/metric with Lambda layers not fully supported in Keras 3.x serialization"
+            )
+
         inputs = tf.keras.layers.Input(shape=(4,))
         x = tf.keras.layers.Dense(8, activation="relu")(inputs)
         outputs = tf.keras.layers.Dense(3, activation="softmax")(x)
@@ -441,7 +445,7 @@ def test_sequential_model_save_load_without_input_shape(self, tmpdir):
         model.add(tf.keras.layers.Dense(2))
         model.add(tf.keras.layers.RepeatVector(3))
         model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(3)))
-        
+
         # sample_weight_mode was removed in Keras 3.x
         if KERAS_3_OR_HIGHER:
             model.compile(

tiledb/ml/models/_cloud_utils.py

@@ -1,4 +1,3 @@
-import os
 from typing import Mapping, Optional
 
 try:
@@ -37,7 +36,7 @@
 #                 s3_path = profile.default_s3_path
 #             else:
 #                 raise
-    
+
 #     return os.path.join(s3_path, CLOUD_MODELS) if s3_path is not None else None
 
 #     # if namespace is None: