Merge pull request #19 from FNALLPC/pre-commit-ci-update-config

[pre-commit.ci] pre-commit autoupdate
FNALLPC · Sep 12, 2023 · 826caaf · 826caaf
2 parents 8496604 + db8a29a
commit 826caaf
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Showing 5 changed files with 21 additions and 21 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -1,5 +1,5 @@
 repos:
   - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: "23.7.0"
+    rev: "23.9.1"
     hooks:
       - id: black-jupyter
diff --git a/machine-learning-hats/notebooks/2.1-dense-keras.ipynb b/machine-learning-hats/notebooks/2.1-dense-keras.ipynb
@@ -198,9 +198,7 @@
     "\n",
     "NDIM = len(VARS)\n",
     "inputs = Input(shape=(NDIM,), name=\"input\")\n",
-    "outputs = Dense(1, name=\"output\", kernel_initializer=\"normal\", activation=\"sigmoid\")(\n",
-    "    inputs\n",
-    ")\n",
+    "outputs = Dense(1, name=\"output\", kernel_initializer=\"normal\", activation=\"sigmoid\")(inputs)\n",
     "\n",
     "# creae the model\n",
     "model = Model(inputs=inputs, outputs=outputs)\n",
@@ -244,9 +242,7 @@
     "\n",
     "from sklearn.model_selection import train_test_split\n",
     "\n",
-    "X_train_val, X_test, Y_train_val, Y_test = train_test_split(\n",
-    "    X, Y, test_size=0.2, random_state=7\n",
-    ")\n",
+    "X_train_val, X_test, Y_train_val, Y_test = train_test_split(X, Y, test_size=0.2, random_state=7)\n",
     "\n",
     "# preprocessing: standard scalar\n",
     "from sklearn.preprocessing import StandardScaler\n",

diff --git a/machine-learning-hats/notebooks/3-conv2d.ipynb b/machine-learning-hats/notebooks/3-conv2d.ipynb
@@ -448,7 +448,7 @@
     "    save_best_only=True,\n",
     "    save_weights_only=False,\n",
     "    mode=\"auto\",\n",
-    "    save_freq=\"epoch\"\n",
+    "    save_freq=\"epoch\",\n",
     ")"
    ]
   },

diff --git a/machine-learning-hats/notebooks/4-gnn-cora.ipynb b/machine-learning-hats/notebooks/4-gnn-cora.ipynb
@@ -184,7 +184,7 @@
    ],
    "source": [
     "# Load Cora dataset\n",
-    "dataset = Planetoid(root='/tmp/Cora', name='Cora')\n",
+    "dataset = Planetoid(root=\"/tmp/Cora\", name=\"Cora\")\n",
     "data = dataset[0]"
    ]
   },
@@ -269,13 +269,13 @@
     }
    ],
    "source": [
-    "print(\"node vectors: \\n\", data.x,          '\\n')\n",
-    "print(\"node classes:  \\n\", data.y,          '\\n')\n",
-    "print(\"edge indeces: \\n\", data.edge_index, '\\n\\n\\n')\n",
+    "print(\"node vectors: \\n\", data.x, \"\\n\")\n",
+    "print(\"node classes:  \\n\", data.y, \"\\n\")\n",
+    "print(\"edge indeces: \\n\", data.edge_index, \"\\n\\n\\n\")\n",
     "\n",
-    "print(\"train_mask: \\n\", data.train_mask, '\\n')\n",
-    "print(\"val_mask: \\n\", data.val_mask, '\\n')\n",
-    "print(\"test_mask: \\n\", data.test_mask, '\\n')"
+    "print(\"train_mask: \\n\", data.train_mask, \"\\n\")\n",
+    "print(\"val_mask: \\n\", data.val_mask, \"\\n\")\n",
+    "print(\"test_mask: \\n\", data.test_mask, \"\\n\")"
    ]
   },
   {
@@ -316,8 +316,8 @@
     "\n",
     "plt.figure(figsize=(12, 12))\n",
     "nx.draw(subset_graph, with_labels=False, node_size=10)\n",
-    "plt.title('Visualization of a Subset of the Cora Graph')\n",
-    "plt.show()\n"
+    "plt.title(\"Visualization of a Subset of the Cora Graph\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -374,7 +374,7 @@
    "outputs": [],
    "source": [
     "# Training and evaluation\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "model = GNN(hidden_channels=16).to(device)\n",
     "data = data.to(device)\n",
     "optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)"
@@ -437,6 +437,7 @@
     "train_loss_history = []\n",
     "test_accuracy_history = []\n",
     "\n",
+    "\n",
     "def train():\n",
     "    model.train()\n",
     "    optimizer.zero_grad()\n",
@@ -446,6 +447,7 @@
     "    optimizer.step()\n",
     "    return loss.item()\n",
     "\n",
+    "\n",
     "def test():\n",
     "    model.eval()\n",
     "    out = model(data.x, data.edge_index)\n",
@@ -454,13 +456,14 @@
     "    acc = int(correct.sum()) / int(data.test_mask.sum())\n",
     "    return acc\n",
     "\n",
+    "\n",
     "for epoch in range(300):\n",
     "    loss = train()\n",
     "    train_loss_history.append(loss)\n",
     "    accuracy = test()\n",
     "    test_accuracy_history.append(accuracy)\n",
     "    if epoch % 10 == 0:\n",
-    "        print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Accuracy: {accuracy:.4f}')\n",
+    "        print(f\"Epoch: {epoch:03d}, Loss: {loss:.4f}, Accuracy: {accuracy:.4f}\")\n",
     "\n",
     "print(\"Test Accuracy:\", test())"
    ]

diff --git a/machine-learning-hats/notebooks/6-gan-mnist.ipynb b/machine-learning-hats/notebooks/6-gan-mnist.ipynb
@@ -98,7 +98,8 @@
     "from tensorflow.keras.layers import Input, Reshape, Dense, Dropout, LeakyReLU\n",
     "from tensorflow.keras.models import Model, Sequential\n",
     "from tensorflow.keras.datasets import mnist\n",
-    "# temporarily importing legacy optimizer because of \n",
+    "\n",
+    "# temporarily importing legacy optimizer because of\n",
     "# https://github.com/keras-team/keras-io/issues/1241#issuecomment-1442383703\n",
     "from tensorflow.keras.optimizers.legacy import Adam\n",
     "from tensorflow.keras import backend as K\n",
@@ -310,7 +311,7 @@
     "        )\n",
     "        plt.text(5, 37, val, fontsize=12)\n",
     "        plt.axis(\"off\")\n",
-    "    \n",
+    "\n",
     "    plt.show()"
    ]
   },