fix stuff

cog-imperial · Aug 20, 2024 · 39c6608 · 39c6608
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diff --git a/docs/notebooks/neuralnet/auto-thermal-reformer-relu.ipynb b/docs/notebooks/neuralnet/auto-thermal-reformer-relu.ipynb
@@ -82,16 +82,16 @@
     "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"  # suppress CUDA warnings from tensorflow\n",
     "\n",
     "# import the necessary packages\n",
-    "from omlt import OmltBlock, OffsetScaling\n",
-    "from omlt.io.keras import load_keras_sequential\n",
-    "from omlt.neuralnet import ReluBigMFormulation\n",
-    "import pyomo.environ as pyo\n",
     "import pandas as pd\n",
-    "import tensorflow.keras as keras\n",
-    "from tensorflow.keras.models import Sequential\n",
+    "import pyomo.environ as pyo\n",
+    "from tensorflow import keras\n",
     "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.models import Sequential\n",
     "from tensorflow.keras.optimizers import Adam\n",
-    "from tensorflow.keras.callbacks import ModelCheckpoint"
+    "\n",
+    "from omlt import OffsetScaling, OmltBlock\n",
+    "from omlt.io.keras import load_keras_sequential\n",
+    "from omlt.neuralnet import ReluBigMFormulation"
    ]
   },
   {

diff --git a/docs/notebooks/neuralnet/auto-thermal-reformer.ipynb b/docs/notebooks/neuralnet/auto-thermal-reformer.ipynb
@@ -71,16 +71,16 @@
     "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"  # suppress CUDA warnings from tensorflow\n",
     "\n",
     "# import the necessary packages\n",
-    "from omlt import OmltBlock, OffsetScaling\n",
-    "from omlt.io.keras import load_keras_sequential\n",
-    "from omlt.neuralnet import FullSpaceSmoothNNFormulation\n",
-    "import pyomo.environ as pyo\n",
     "import pandas as pd\n",
-    "import tensorflow.keras as keras\n",
-    "from tensorflow.keras.models import Sequential\n",
+    "import pyomo.environ as pyo\n",
+    "from tensorflow import keras\n",
     "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.models import Sequential\n",
     "from tensorflow.keras.optimizers import Adam\n",
-    "from tensorflow.keras.callbacks import ModelCheckpoint"
+    "\n",
+    "from omlt import OffsetScaling, OmltBlock\n",
+    "from omlt.io.keras import load_keras_sequential\n",
+    "from omlt.neuralnet import FullSpaceSmoothNNFormulation"
    ]
   },
   {

diff --git a/docs/notebooks/neuralnet/build_network.ipynb b/docs/notebooks/neuralnet/build_network.ipynb
@@ -37,11 +37,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pyomo.environ as pyo\n",
     "import numpy as np\n",
+    "import pyomo.environ as pyo\n",
     "\n",
     "from omlt.neuralnet import NetworkDefinition\n",
-    "from omlt.neuralnet.layer import InputLayer, DenseLayer, IndexMapper"
+    "from omlt.neuralnet.layer import DenseLayer, IndexMapper, InputLayer"
    ]
   },
   {

diff --git a/docs/notebooks/neuralnet/graph_neural_network_formulation.ipynb b/docs/notebooks/neuralnet/graph_neural_network_formulation.ipynb
@@ -54,13 +54,13 @@
    ],
    "source": [
     "import numpy as np\n",
+    "import pyomo.environ as pyo\n",
     "import torch\n",
     "from torch.nn import Linear, ReLU, Sigmoid\n",
-    "from torch_geometric.nn import Sequential, GCNConv\n",
-    "from torch_geometric.nn import global_mean_pool\n",
-    "from omlt.io.torch_geometric import gnn_with_fixed_graph\n",
-    "import pyomo.environ as pyo\n",
+    "from torch_geometric.nn import GCNConv, Sequential, global_mean_pool\n",
+    "\n",
     "from omlt import OmltBlock\n",
+    "from omlt.io.torch_geometric import gnn_with_fixed_graph\n",
     "\n",
     "\n",
     "def GCN_Sequential(activation, pooling):\n",
@@ -478,14 +478,13 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
+    "import pyomo.environ as pyo\n",
     "import torch\n",
-    "from torch.nn import Linear, ReLU\n",
-    "from torch_geometric.nn import Sequential, SAGEConv\n",
-    "from torch_geometric.nn import global_add_pool\n",
-    "from omlt.io.torch_geometric import gnn_with_non_fixed_graph\n",
+    "from torch.nn import ReLU\n",
+    "from torch_geometric.nn import SAGEConv, global_add_pool\n",
     "\n",
-    "import pyomo.environ as pyo\n",
     "from omlt import OmltBlock\n",
+    "from omlt.io.torch_geometric import gnn_with_non_fixed_graph\n",
     "\n",
     "\n",
     "def SAGE_Sequential(activation, pooling):\n",

diff --git a/docs/notebooks/neuralnet/import_network.ipynb b/docs/notebooks/neuralnet/import_network.ipynb
@@ -292,7 +292,7 @@
     }
    ],
    "source": [
-    "from omlt.io import write_onnx_model_with_bounds, load_onnx_neural_network_with_bounds"
+    "from omlt.io import load_onnx_neural_network_with_bounds, write_onnx_model_with_bounds"
    ]
   },
   {
@@ -352,9 +352,8 @@
     "import os\n",
     "\n",
     "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"  # or any {'0', '1', '2'}\n",
-    "import keras\n",
-    "from keras.models import Sequential\n",
     "from keras.layers import Dense\n",
+    "from keras.models import Sequential\n",
     "\n",
     "model = Sequential()\n",
     "model.add(Dense(12, input_dim=8, activation=\"relu\"))\n",
@@ -731,8 +730,8 @@
     "# Add output_names for compatibility:\n",
     "model.output_names = [output.name for output in model.outputs]\n",
     "\n",
-    "from tensorflow import TensorSpec\n",
     "import tf2onnx\n",
+    "from tensorflow import TensorSpec\n",
     "\n",
     "spec = [TensorSpec(input.shape, input.dtype, input.name) for input in model.inputs]\n",
     "onnx_model, _ = tf2onnx.convert.from_keras(model, input_signature=spec)\n",
@@ -817,8 +816,8 @@
    ],
    "source": [
     "import torch\n",
-    "import torch.nn as nn\n",
     "import torch.nn.functional as F\n",
+    "from torch import nn\n",
     "from torch.utils.data import DataLoader, TensorDataset\n",
     "\n",
     "\n",

diff --git a/docs/notebooks/neuralnet/index_handling.ipynb b/docs/notebooks/neuralnet/index_handling.ipynb
@@ -29,8 +29,9 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
+    "\n",
     "from omlt.neuralnet import NetworkDefinition\n",
-    "from omlt.neuralnet.layer import IndexMapper, InputLayer, DenseLayer, PoolingLayer2D"
+    "from omlt.neuralnet.layer import DenseLayer, IndexMapper, InputLayer, PoolingLayer2D"
    ]
   },
   {

diff --git a/docs/notebooks/neuralnet/mnist_example_convolutional.ipynb b/docs/notebooks/neuralnet/mnist_example_convolutional.ipynb
@@ -49,27 +49,27 @@
    "source": [
     "# Import requisite packages\n",
     "# data manipulation\n",
-    "import numpy as np\n",
     "import tempfile\n",
     "\n",
-    "# pytorch for training neural network\n",
-    "import torch, torch.onnx\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "import torch.optim as optim\n",
-    "from torchvision import datasets, transforms\n",
-    "from torch.optim.lr_scheduler import StepLR\n",
+    "import numpy as np\n",
     "\n",
     "# pyomo for optimization\n",
     "import pyomo.environ as pyo\n",
     "\n",
+    "# pytorch for training neural network\n",
+    "import torch\n",
+    "import torch.onnx\n",
+    "from torch import nn, optim\n",
+    "from torch.optim.lr_scheduler import StepLR\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
     "# omlt for interfacing our neural network with pyomo\n",
     "from omlt import OmltBlock\n",
-    "from omlt.neuralnet import FullSpaceNNFormulation\n",
     "from omlt.io.onnx import (\n",
-    "    write_onnx_model_with_bounds,\n",
     "    load_onnx_neural_network_with_bounds,\n",
-    ")"
+    "    write_onnx_model_with_bounds,\n",
+    ")\n",
+    "from omlt.neuralnet import FullSpaceNNFormulation"
    ]
   },
   {
@@ -165,13 +165,7 @@
     "        optimizer.step()\n",
     "        if batch_idx % 200 == 0:\n",
     "            print(\n",
-    "                \"Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\".format(\n",
-    "                    epoch,\n",
-    "                    batch_idx * len(data),\n",
-    "                    len(train_loader.dataset),\n",
-    "                    100.0 * batch_idx / len(train_loader),\n",
-    "                    loss.item(),\n",
-    "                )\n",
+    "                f\"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} ({100.0 * batch_idx / len(train_loader):.0f}%)]\\tLoss: {loss.item():.6f}\"\n",
     "            )\n",
     "\n",
     "\n",
@@ -189,12 +183,7 @@
     "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
     "    test_loss /= len(test_loader.dataset)\n",
     "    print(\n",
-    "        \"\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n\".format(\n",
-    "            test_loss,\n",
-    "            correct,\n",
-    "            len(test_loader.dataset),\n",
-    "            100.0 * correct / len(test_loader.dataset),\n",
-    "        )\n",
+    "        f\"\\nTest set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} ({100.0 * correct / len(test_loader.dataset):.0f}%)\\n\"\n",
     "    )"
    ]
   },

diff --git a/docs/notebooks/neuralnet/mnist_example_dense.ipynb b/docs/notebooks/neuralnet/mnist_example_dense.ipynb
@@ -48,27 +48,27 @@
    "source": [
     "# Import requisite packages\n",
     "# data manipulation\n",
-    "import numpy as np\n",
     "import tempfile\n",
     "\n",
-    "# pytorch for training neural network\n",
-    "import torch, torch.onnx\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "import torch.optim as optim\n",
-    "from torchvision import datasets, transforms\n",
-    "from torch.optim.lr_scheduler import StepLR\n",
+    "import numpy as np\n",
     "\n",
     "# pyomo for optimization\n",
     "import pyomo.environ as pyo\n",
     "\n",
+    "# pytorch for training neural network\n",
+    "import torch\n",
+    "import torch.onnx\n",
+    "from torch import nn, optim\n",
+    "from torch.optim.lr_scheduler import StepLR\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
     "# omlt for interfacing our neural network with pyomo\n",
     "from omlt import OmltBlock\n",
-    "from omlt.neuralnet import FullSpaceNNFormulation\n",
     "from omlt.io.onnx import (\n",
-    "    write_onnx_model_with_bounds,\n",
     "    load_onnx_neural_network_with_bounds,\n",
-    ")"
+    "    write_onnx_model_with_bounds,\n",
+    ")\n",
+    "from omlt.neuralnet import FullSpaceNNFormulation"
    ]
   },
   {
@@ -161,13 +161,7 @@
     "        optimizer.step()\n",
     "        if batch_idx % 200 == 0:\n",
     "            print(\n",
-    "                \"Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\".format(\n",
-    "                    epoch,\n",
-    "                    batch_idx * len(data),\n",
-    "                    len(train_loader.dataset),\n",
-    "                    100.0 * batch_idx / len(train_loader),\n",
-    "                    loss.item(),\n",
-    "                )\n",
+    "                f\"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} ({100.0 * batch_idx / len(train_loader):.0f}%)]\\tLoss: {loss.item():.6f}\"\n",
     "            )\n",
     "\n",
     "\n",
@@ -185,12 +179,7 @@
     "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
     "    test_loss /= len(test_loader.dataset)\n",
     "    print(\n",
-    "        \"\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n\".format(\n",
-    "            test_loss,\n",
-    "            correct,\n",
-    "            len(test_loader.dataset),\n",
-    "            100.0 * correct / len(test_loader.dataset),\n",
-    "        )\n",
+    "        f\"\\nTest set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} ({100.0 * correct / len(test_loader.dataset):.0f}%)\\n\"\n",
     "    )"
    ]
   },

diff --git a/docs/notebooks/neuralnet/neural_network_formulations.ipynb b/docs/notebooks/neuralnet/neural_network_formulations.ipynb
@@ -65,36 +65,35 @@
    "source": [
     "# Start by importing the following libraries\n",
     "# data manipulation and plotting\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
     "\n",
     "matplotlib.rc(\"font\", size=24)\n",
     "plt.rc(\"axes\", titlesize=24)\n",
     "\n",
     "# tensorflow objects\n",
-    "from tensorflow.keras.models import Sequential, Model\n",
+    "# pyomo for optimization\n",
+    "import pyomo.environ as pyo\n",
     "from tensorflow.keras.layers import Dense, Input\n",
+    "from tensorflow.keras.models import Sequential\n",
     "from tensorflow.keras.optimizers import Adam\n",
     "\n",
-    "# pyomo for optimization\n",
-    "import pyomo.environ as pyo\n",
+    "import omlt\n",
     "\n",
     "# omlt for interfacing our neural network with pyomo\n",
     "from omlt import OmltBlock\n",
+    "from omlt.io.keras import keras_reader\n",
     "from omlt.neuralnet import (\n",
-    "    NetworkDefinition,\n",
     "    FullSpaceNNFormulation,\n",
     "    FullSpaceSmoothNNFormulation,\n",
     "    ReducedSpaceSmoothNNFormulation,\n",
     "    ReluBigMFormulation,\n",
     "    ReluComplementarityFormulation,\n",
     "    ReluPartitionFormulation,\n",
     ")\n",
-    "from omlt.neuralnet.activations import ComplementarityReLUActivation\n",
-    "from omlt.io.keras import keras_reader\n",
-    "import omlt"
+    "from omlt.neuralnet.activations import ComplementarityReLUActivation"
    ]
   },
   {

diff --git a/docs/notebooks/trees/bo_with_trees.ipynb b/docs/notebooks/trees/bo_with_trees.ipynb
@@ -47,9 +47,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import random\n",
     "\n",
+    "import numpy as np\n",
+    "\n",
     "\n",
     "def f(x, y):\n",
     "    return (1 - x) ** 2 + 100 * (y - x**2) ** 2\n",
@@ -92,9 +93,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import lightgbm as lgb\n",
     "import warnings\n",
     "\n",
+    "import lightgbm as lgb\n",
+    "\n",
     "\n",
     "def train_tree(data):\n",
     "    with warnings.catch_warnings():\n",
@@ -184,6 +186,7 @@
    "outputs": [],
    "source": [
     "import pyomo.environ as pe\n",
+    "\n",
     "from omlt.block import OmltBlock\n",
     "from omlt.gbt import GBTBigMFormulation, GradientBoostedTreeModel\n",
     "\n",
@@ -213,7 +216,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "\n",
     "\n",
     "def add_unc_metric(opt_model, data):\n",