add --profile=black to isort args (#1702)

* add --profile=black to isort args

* Reformat impacted notebooks

Author: LukeWood

docs/ipynb/customized.ipynb

@@ -8,7 +8,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install autokeras\n"
+    "!pip install autokeras"
    ]
   },
   {
@@ -23,7 +23,7 @@
     "import tensorflow as tf\n",
     "from tensorflow.keras.datasets import mnist\n",
     "\n",
-    "import autokeras as ak\n"
+    "import autokeras as ak"
    ]
   },
   {
@@ -67,7 +67,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "input_node = ak.ImageInput()\n",
     "output_node = ak.Normalization()(input_node)\n",
     "output_node1 = ak.ConvBlock()(output_node)\n",
@@ -77,7 +76,7 @@
     "\n",
     "auto_model = ak.AutoModel(\n",
     "    inputs=input_node, outputs=output_node, overwrite=True, max_trials=1\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -108,7 +107,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
     "print(x_train.shape)  # (60000, 28, 28)\n",
     "print(y_train.shape)  # (60000,)\n",
@@ -119,7 +117,7 @@
     "# Predict with the best model.\n",
     "predicted_y = auto_model.predict(x_test)\n",
     "# Evaluate the best model with testing data.\n",
-    "print(auto_model.evaluate(x_test, y_test))\n"
+    "print(auto_model.evaluate(x_test, y_test))"
    ]
   },
   {
@@ -176,7 +174,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "class SingleDenseLayerBlock(ak.Block):\n",
     "    def build(self, hp, inputs=None):\n",
     "        # Get the input_node from inputs.\n",
@@ -185,8 +182,7 @@
     "            hp.Int(\"num_units\", min_value=32, max_value=512, step=32)\n",
     "        )\n",
     "        output_node = layer(input_node)\n",
-    "        return output_node\n",
-    "\n"
+    "        return output_node"
    ]
   },
   {
@@ -220,7 +216,7 @@
     "y_test = np.random.rand(num_instances, 1).astype(np.float32)\n",
     "# Train the model\n",
     "auto_model.fit(x_train, y_train, epochs=1)\n",
-    "print(auto_model.evaluate(x_test, y_test))\n"
+    "print(auto_model.evaluate(x_test, y_test))"
    ]
   },
   {

docs/ipynb/export.ipynb

@@ -8,7 +8,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install autokeras\n"
+    "!pip install autokeras"
    ]
   },
   {
@@ -23,7 +23,7 @@
     "from tensorflow.keras.datasets import mnist\n",
     "from tensorflow.keras.models import load_model\n",
     "\n",
-    "import autokeras as ak\n"
+    "import autokeras as ak"
    ]
   },
   {
@@ -47,7 +47,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "print(tf.__version__)\n",
     "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
     "\n",
@@ -71,7 +70,7 @@
     "loaded_model = load_model(\"model_autokeras\", custom_objects=ak.CUSTOM_OBJECTS)\n",
     "\n",
     "predicted_y = loaded_model.predict(tf.expand_dims(x_test, -1))\n",
-    "print(predicted_y)\n"
+    "print(predicted_y)"
    ]
   }
  ],

docs/ipynb/image_classification.ipynb

@@ -8,7 +8,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install autokeras\n"
+    "!pip install autokeras"
    ]
   },
   {
@@ -23,7 +23,7 @@
     "import tensorflow as tf\n",
     "from tensorflow.keras.datasets import mnist\n",
     "\n",
-    "import autokeras as ak\n"
+    "import autokeras as ak"
    ]
   },
   {
@@ -47,7 +47,7 @@
     "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
     "print(x_train.shape)  # (60000, 28, 28)\n",
     "print(y_train.shape)  # (60000,)\n",
-    "print(y_train[:3])  # array([7, 2, 1], dtype=uint8)\n"
+    "print(y_train[:3])  # array([7, 2, 1], dtype=uint8)"
    ]
   },
   {
@@ -83,7 +83,7 @@
     "\n",
     "\n",
     "# Evaluate the best model with testing data.\n",
-    "print(clf.evaluate(x_test, y_test))\n"
+    "print(clf.evaluate(x_test, y_test))"
    ]
   },
   {
@@ -112,7 +112,7 @@
     "    # Split the training data and use the last 15% as validation data.\n",
     "    validation_split=0.15,\n",
     "    epochs=10,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -144,7 +144,7 @@
     "    # Use your own validation set.\n",
     "    validation_data=(x_val, y_val),\n",
     "    epochs=10,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -184,7 +184,7 @@
     "clf = ak.AutoModel(\n",
     "    inputs=input_node, outputs=output_node, overwrite=True, max_trials=1\n",
     ")\n",
-    "clf.fit(x_train, y_train, epochs=10)\n"
+    "clf.fit(x_train, y_train, epochs=10)"
    ]
   },
   {
@@ -218,7 +218,7 @@
     "clf = ak.AutoModel(\n",
     "    inputs=input_node, outputs=output_node, overwrite=True, max_trials=1\n",
     ")\n",
-    "clf.fit(x_train, y_train, epochs=10)\n"
+    "clf.fit(x_train, y_train, epochs=10)"
    ]
   },
   {
@@ -268,7 +268,7 @@
     "print(y_train[:3])\n",
     "# array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
     "#        [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
-    "#        [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]])\n"
+    "#        [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]])"
    ]
   },
   {
@@ -297,7 +297,7 @@
     "# Predict with the best model.\n",
     "predicted_y = clf.predict(test_set)\n",
     "# Evaluate the best model with testing data.\n",
-    "print(clf.evaluate(test_set))\n"
+    "print(clf.evaluate(test_set))"
    ]
   },
   {

docs/ipynb/image_regression.ipynb

@@ -8,7 +8,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install autokeras\n"
+    "!pip install autokeras"
    ]
   },
   {
@@ -22,7 +22,7 @@
     "import tensorflow as tf\n",
     "from tensorflow.keras.datasets import mnist\n",
     "\n",
-    "import autokeras as ak\n"
+    "import autokeras as ak"
    ]
   },
   {
@@ -49,13 +49,12 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
     "x_train = x_train[:100]\n",
     "y_train = y_train[:100]\n",
     "print(x_train.shape)  # (60000, 28, 28)\n",
     "print(y_train.shape)  # (60000,)\n",
-    "print(y_train[:3])  # array([7, 2, 1], dtype=uint8)\n"
+    "print(y_train[:3])  # array([7, 2, 1], dtype=uint8)"
    ]
   },
   {
@@ -90,7 +89,7 @@
     "\n",
     "\n",
     "# Evaluate the best model with testing data.\n",
-    "print(reg.evaluate(x_test, y_test))\n"
+    "print(reg.evaluate(x_test, y_test))"
    ]
   },
   {
@@ -119,7 +118,7 @@
     "    # Split the training data and use the last 15% as validation data.\n",
     "    validation_split=0.15,\n",
     "    epochs=2,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -151,7 +150,7 @@
     "    # Use your own validation set.\n",
     "    validation_data=(x_val, y_val),\n",
     "    epochs=2,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -191,7 +190,7 @@
     "reg = ak.AutoModel(\n",
     "    inputs=input_node, outputs=output_node, overwrite=True, max_trials=1\n",
     ")\n",
-    "reg.fit(x_train, y_train, epochs=2)\n"
+    "reg.fit(x_train, y_train, epochs=2)"
    ]
   },
   {
@@ -225,7 +224,7 @@
     "reg = ak.AutoModel(\n",
     "    inputs=input_node, outputs=output_node, overwrite=True, max_trials=1\n",
     ")\n",
-    "reg.fit(x_train, y_train, epochs=2)\n"
+    "reg.fit(x_train, y_train, epochs=2)"
    ]
   },
   {
@@ -278,7 +277,7 @@
     "# Predict with the best model.\n",
     "predicted_y = reg.predict(test_set)\n",
     "# Evaluate the best model with testing data.\n",
-    "print(reg.evaluate(test_set))\n"
+    "print(reg.evaluate(test_set))"
    ]
   },
   {

docs/ipynb/load.ipynb

@@ -8,7 +8,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install autokeras\n"
+    "!pip install autokeras"
    ]
   },
   {
@@ -25,7 +25,7 @@
     "import numpy as np\n",
     "import tensorflow as tf\n",
     "\n",
-    "import autokeras as ak\n"
+    "import autokeras as ak"
    ]
   },
   {
@@ -61,7 +61,7 @@
     "local_dir_path = os.path.dirname(local_file_path)\n",
     "# After check mannually, we know the extracted data is in 'flower_photos'.\n",
     "data_dir = os.path.join(local_dir_path, \"flower_photos\")\n",
-    "print(data_dir)\n"
+    "print(data_dir)"
    ]
   },
   {
@@ -115,7 +115,7 @@
     "    seed=123,\n",
     "    image_size=(img_height, img_width),\n",
     "    batch_size=batch_size,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -137,7 +137,7 @@
    "source": [
     "clf = ak.ImageClassifier(overwrite=True, max_trials=1)\n",
     "clf.fit(train_data, epochs=1)\n",
-    "print(clf.evaluate(test_data))\n"
+    "print(clf.evaluate(test_data))"
    ]
   },
   {
@@ -171,8 +171,7 @@
     "data_dir = os.path.join(local_dir_path, \"aclImdb\")\n",
     "# Remove the unused data folder.\n",
     "\n",
-    "shutil.rmtree(os.path.join(data_dir, \"train/unsup\"))\n",
-    "\n"
+    "shutil.rmtree(os.path.join(data_dir, \"train/unsup\"))"
    ]
   },
   {
@@ -204,8 +203,7 @@
     "\n",
     "clf = ak.TextClassifier(overwrite=True, max_trials=1)\n",
     "clf.fit(train_data, epochs=2)\n",
-    "print(clf.evaluate(test_data))\n",
-    "\n"
+    "print(clf.evaluate(test_data))"
    ]
   },
   {
@@ -226,7 +224,6 @@
    },
    "outputs": [],
    "source": [
-    "\n",
     "N_BATCHES = 30\n",
     "BATCH_SIZE = 100\n",
     "N_FEATURES = 10\n",
@@ -255,7 +252,7 @@
     "\n",
     "clf = ak.StructuredDataClassifier(overwrite=True, max_trials=1, seed=5)\n",
     "clf.fit(x=dataset, validation_data=dataset, batch_size=BATCH_SIZE)\n",
-    "print(clf.evaluate(dataset))\n"
+    "print(clf.evaluate(dataset))"
    ]
   },
   {