diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 9446af9..9a564c1 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -21,6 +21,8 @@ jobs: folder: Chapter02 - name: chap3 folder: Chapter03 + - name: chap4 + folder: Chapter04 - name: chap-nn folder: ChapterNN - name: chap5 diff --git a/Chapter04/01_Feature_based_methods.ipynb b/Chapter04/01_Feature_based_methods.ipynb index 47cfa80..72d5b63 100644 --- a/Chapter04/01_Feature_based_methods.ipynb +++ b/Chapter04/01_Feature_based_methods.ipynb @@ -56,6 +56,8 @@ "from stellargraph import datasets\n", "from IPython.display import display, HTML\n", "\n", + "datasets.PROTEINS.url = 'https://www.chrsmrrs.com/graphkerneldatasets/PROTEINS.zip'\n", + "\n", "dataset = datasets.PROTEINS()\n", "display(HTML(dataset.description))\n", "graphs, graph_labels = dataset.load()" @@ -211,9 +213,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": "chap4", "language": "python", - "name": "python3" + "name": "chap4" }, "language_info": { "codemirror_mode": { @@ -225,9 +227,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.8.14" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/Chapter04/02_Shallow_embeddings.ipynb b/Chapter04/02_Shallow_embeddings.ipynb index 4d72379..2143123 100644 --- a/Chapter04/02_Shallow_embeddings.ipynb +++ b/Chapter04/02_Shallow_embeddings.ipynb @@ -429,9 +429,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "chap4", "language": "python", - "name": "python3" + "name": "chap4" }, "language_info": { "codemirror_mode": { @@ -443,7 +443,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.8.14" } }, "nbformat": 4, diff --git a/Chapter04/03_Graph_regularization_graph_neural_training.ipynb b/Chapter04/03_Graph_regularization_graph_neural_training.ipynb index 8f0fa92..8570922 100644 --- a/Chapter04/03_Graph_regularization_graph_neural_training.ipynb +++ b/Chapter04/03_Graph_regularization_graph_neural_training.ipynb @@ -1486,9 +1486,9 @@ ], "metadata": { "kernelspec": { - "display_name": "ml-book-4", + "display_name": "chap4", "language": "python", - "name": "ml-book-4" + "name": "chap4" }, "language_info": { "codemirror_mode": { @@ -1500,7 +1500,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.8" } }, "nbformat": 4, diff --git a/Chapter04/04_Graph_Neural_Networks.ipynb b/Chapter04/04_Graph_Neural_Networks.ipynb index 81d3fe7..beda937 100644 --- a/Chapter04/04_Graph_Neural_Networks.ipynb +++ b/Chapter04/04_Graph_Neural_Networks.ipynb @@ -66,6 +66,8 @@ "from stellargraph import datasets\n", "from IPython.display import display, HTML\n", "\n", + "datasets.PROTEINS.url = 'https://www.chrsmrrs.com/graphkerneldatasets/PROTEINS.zip'\n", + "\n", "dataset = datasets.PROTEINS()\n", "display(HTML(dataset.description))\n", "graphs, graph_labels = dataset.load()" @@ -834,7 +836,7 @@ "source": [ "from tensorflow.keras.losses import categorical_crossentropy\n", "from keras.models import Model\n", - "from keras.optimizers import Adam\n", + "from tensorflow.keras.optimizers import Adam\n", "\n", "model = Model(inputs=gnn_inp, outputs=outputs)\n", "model.compile(optimizer=Adam(lr=0.003), loss=categorical_crossentropy, metrics=[\"acc\"],)" @@ -898,6 +900,234 @@ }, "outputs": [], "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the rest of the notebook, we will be performing a similar example as above using other two popular graph-dl frameworks: PyTorch Geometric (PyG) and Deep Graph Library (DGL)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Graph Classification using PyG" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install fsspec==2024.3.1 # needed for PROTEINS download torch geometric\n", + "#!pip install torch_geometric\n", + "\n", + "import torch\n", + "from torch_geometric.datasets import TUDataset\n", + "from torch_geometric.data import DataLoader\n", + "from torch_geometric.nn import GCNConv, global_mean_pool\n", + "from torch.nn import Linear\n", + "import torch.nn.functional as F\n", + "\n", + "# Load the PROTEINS dataset\n", + "dataset = TUDataset(root='data/PROTEINS', name='PROTEINS')\n", + "\n", + "# Set random seed for reproducibility\n", + "torch.manual_seed(42)\n", + "\n", + "# Shuffle and split the dataset into training and test sets\n", + "dataset = dataset.shuffle()\n", + "split_idx = int(0.8 * len(dataset)) # 80/20 train/test split\n", + "train_dataset = dataset[:split_idx]\n", + "test_dataset = dataset[split_idx:]\n", + "\n", + "# Print dataset statistics\n", + "print(f'Training graphs: {len(train_dataset)}, Test graphs: {len(test_dataset)}')\n", + "\n", + "# Create DataLoader for batching\n", + "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n", + "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n", + "\n", + "# Define the GCN model\n", + "class GCN(torch.nn.Module):\n", + " def __init__(self, input_dim, hidden_dim, output_dim):\n", + " super(GCN, self).__init__()\n", + " self.conv1 = GCNConv(input_dim, hidden_dim)\n", + " self.conv2 = GCNConv(hidden_dim, hidden_dim)\n", + " self.conv3 = GCNConv(hidden_dim, hidden_dim)\n", + " self.lin = Linear(hidden_dim, output_dim)\n", + " \n", + " def forward(self, x, edge_index, batch):\n", + " # Graph convolution layers with ReLU activations\n", + " x = F.relu(self.conv1(x, edge_index))\n", + " x = F.relu(self.conv2(x, edge_index))\n", + " x = self.conv3(x, edge_index)\n", + " \n", + " # Global pooling to obtain graph-level representation\n", + " x = global_mean_pool(x, batch)\n", + " \n", + " # Apply dropout and final linear layer\n", + " x = F.dropout(x, p=0.5, training=self.training)\n", + " x = self.lin(x)\n", + " return x\n", + "\n", + "# Instantiate the model\n", + "print(dataset.num_node_features)\n", + "model = GCN(input_dim=dataset.num_node_features, hidden_dim=64, output_dim=dataset.num_classes)\n", + "print(model)\n", + "\n", + "# Define optimizer and loss function\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n", + "criterion = torch.nn.CrossEntropyLoss()\n", + "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.5) # Learning rate decay\n", + "\n", + "# Training function\n", + "def train():\n", + " model.train()\n", + " total_loss = 0\n", + " for data in train_loader:\n", + " optimizer.zero_grad()\n", + " out = model(data.x, data.edge_index, data.batch)\n", + " loss = criterion(out, data.y)\n", + " loss.backward()\n", + " optimizer.step()\n", + " total_loss += loss.item()\n", + " return total_loss / len(train_loader)\n", + "\n", + "# Evaluation function\n", + "def evaluate(loader):\n", + " model.eval()\n", + " correct = 0\n", + " for data in loader:\n", + " with torch.no_grad():\n", + " out = model(data.x, data.edge_index, data.batch)\n", + " pred = out.argmax(dim=1)\n", + " correct += int((pred == data.y).sum())\n", + " return correct / len(loader.dataset)\n", + "\n", + "# Training loop\n", + "num_epochs = 200\n", + "for epoch in range(1, num_epochs + 1):\n", + " loss = train()\n", + " train_acc = evaluate(train_loader)\n", + " test_acc = evaluate(test_loader)\n", + " scheduler.step() # Adjust learning rate\n", + "\n", + " print(f'Epoch {epoch:03d}, Loss: {loss:.4f}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Graph Classification using DGL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install torch==2.1.1 # needed for dgl\n", + "#!pip install dgl -f https://data.dgl.ai/wheels/torch-2.1/repo.html\n", + "\n", + "import dgl\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from torch.nn import Linear\n", + "from dgl.data import GINDataset\n", + "from dgl.dataloading import GraphDataLoader\n", + "from dgl.nn.pytorch import GraphConv\n", + "from dgl.data.utils import split_dataset\n", + "\n", + "dataset = dgl.data.GINDataset('PROTEINS', self_loop=True)\n", + "\n", + "# Set random seed for reproducibility\n", + "torch.manual_seed(42)\n", + "\n", + "# 2. Split dataset into training and test sets\n", + "train_dataset, val_dataset, test_dataset = split_dataset(dataset, frac_list=[0.8, 0.1, 0.1], shuffle=False, random_state=42)\n", + "\n", + "# Print dataset statistics\n", + "print(f'Training graphs: {len(train_dataset)}, Test graphs: {len(test_dataset)}')\n", + "\n", + "# 3. Create DGL DataLoader for batching\n", + "train_loader = GraphDataLoader(train_dataset, batch_size=64, shuffle=True)\n", + "test_loader = GraphDataLoader(test_dataset, batch_size=64, shuffle=False)\n", + "\n", + "# 4. Define the GCN model using DGL's GraphConv layers\n", + "class GCN(torch.nn.Module):\n", + " def __init__(self, input_dim, hidden_dim, output_dim):\n", + " super(GCN, self).__init__()\n", + " self.conv1 = GraphConv(input_dim, hidden_dim)\n", + " self.conv2 = GraphConv(hidden_dim, hidden_dim)\n", + " self.conv3 = GraphConv(hidden_dim, hidden_dim)\n", + " self.fc = Linear(hidden_dim, output_dim)\n", + "\n", + " def forward(self, g, features):\n", + " # Apply GraphConv layers with ReLU activations\n", + " h = F.relu(self.conv1(g, features))\n", + " h = F.relu(self.conv2(g, h))\n", + " h = self.conv3(g, h)\n", + " \n", + " # Global mean pooling to obtain graph-level representation\n", + " with g.local_scope():\n", + " g.ndata['h'] = h\n", + " hg = dgl.mean_nodes(g, 'h')\n", + " \n", + " # Apply dropout and final linear layer for classification\n", + " hg = F.dropout(hg, p=0.5, training=self.training)\n", + " return self.fc(hg)\n", + "\n", + "# 5. Initialize the model, optimizer, and loss function\n", + "input_dim = dataset.dim_nfeats\n", + "output_dim = dataset.num_classes\n", + "hidden_dim = 64\n", + "\n", + "print(\"Input dim:\", input_dim)\n", + "print(\"Output dim:\", output_dim)\n", + "\n", + "model = GCN(input_dim, hidden_dim, output_dim)\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n", + "criterion = torch.nn.CrossEntropyLoss()\n", + "\n", + "# 6. Training function\n", + "def train():\n", + " model.train()\n", + " total_loss = 0\n", + " for batched_graph, labels in train_loader:\n", + " optimizer.zero_grad()\n", + " features = batched_graph.ndata['attr']\n", + " out = model(batched_graph, features)\n", + " loss = criterion(out, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + " total_loss += loss.item()\n", + " return total_loss / len(train_loader)\n", + "\n", + "# 7. Evaluation function\n", + "def evaluate(loader):\n", + " model.eval()\n", + " correct = 0\n", + " for batched_graph, labels in loader:\n", + " features = batched_graph.ndata['attr']\n", + " with torch.no_grad():\n", + " out = model(batched_graph, features)\n", + " pred = out.argmax(dim=1)\n", + " correct += (pred == labels).sum().item()\n", + " return correct / len(loader.dataset)\n", + "\n", + "# 8. Training loop\n", + "num_epochs = 200\n", + "for epoch in range(1, num_epochs + 1):\n", + " loss = train()\n", + " train_acc = evaluate(train_loader)\n", + " test_acc = evaluate(test_loader)\n", + " print(f'Epoch {epoch:03d}, Loss: {loss:.4f}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')" + ] } ], "metadata": { @@ -907,9 +1137,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": "chap4", "language": "python", - "name": "python3" + "name": "chap4" }, "language_info": { "codemirror_mode": { @@ -921,9 +1151,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.0" + "version": "3.8.14" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/Chapter04/poetry.lock b/Chapter04/poetry.lock new file mode 100644 index 0000000..0e13afb --- /dev/null +++ b/Chapter04/poetry.lock @@ -0,0 +1,3640 @@ +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by 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https://stackoverflow.com/a/76477590 +stellargraph= "^1.2.1" +protobuf= "^3.20" +torch = "^2.1.0" +chardet = "==5.2.0" +torch_geometric = "^2.5.2" +torchvision = "^0.16.0" +torchmetrics="^1.3.0" +# dgl = https://data.dgl.ai/wheels/torch-2.1/dgl-2.4.0-cp38-cp38-manylinux1_x86_64.whl +dgl = {url = "https://data.dgl.ai/wheels/torch-2.1/dgl-2.4.0-cp38-cp38-manylinux1_x86_64.whl"} + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + diff --git a/Chapter04/requirements.txt b/Chapter04/requirements.txt new file mode 100644 index 0000000..104a080 --- /dev/null +++ b/Chapter04/requirements.txt @@ -0,0 +1,132 @@ +absl-py==2.1.0 ; python_version >= "3.8" and python_version < "3.9" +aiohappyeyeballs==2.4.3 ; python_version >= "3.8" and python_version < "3.9" +aiohttp==3.10.10 ; python_version >= "3.8" and python_version < "3.9" +aiosignal==1.3.1 ; python_version >= "3.8" and python_version < "3.9" +annotated-types==0.7.0 ; python_version >= "3.8" and python_version < "3.9" +appnope==0.1.4 ; python_version >= "3.8" and python_version < "3.9" and (platform_system == "Darwin" or sys_platform == "darwin") +asttokens==2.4.1 ; python_version >= "3.8" and python_version < "3.9" +astunparse==1.6.3 ; python_version >= "3.8" and python_version < "3.9" +async-timeout==4.0.3 ; python_version >= "3.8" and python_version < "3.9" +attrs==24.2.0 ; python_version >= "3.8" and python_version < "3.9" +backcall==0.2.0 ; python_version >= "3.8" and python_version < "3.9" +cachetools==5.5.0 ; python_version >= "3.8" and python_version < "3.9" +certifi==2024.8.30 ; python_version >= "3.8" and python_version < "3.9" +cffi==1.17.1 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy" +chardet==5.2.0 ; python_version >= "3.8" and python_version < "3.9" +charset-normalizer==3.4.0 ; python_version >= "3.8" and python_version < "3.9" +colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.9" and (sys_platform == "win32" or platform_system == "Windows") +comm==0.2.2 ; python_version >= "3.8" and python_version < "3.9" +cycler==0.12.1 ; python_version >= "3.8" and python_version < "3.9" +debugpy==1.8.7 ; python_version >= "3.8" and python_version < "3.9" +decorator==5.1.1 ; python_version >= "3.8" and python_version < "3.9" +dgl @ https://data.dgl.ai/wheels/torch-2.1/dgl-2.4.0-cp38-cp38-manylinux1_x86_64.whl ; python_version >= "3.8" and python_version < "3.9" +executing==2.1.0 ; python_version >= "3.8" and python_version < "3.9" +filelock==3.16.1 ; python_version >= "3.8" and python_version < "3.9" +flatbuffers==2.0.7 ; python_version >= "3.8" and python_version < "3.9" +frozenlist==1.4.1 ; python_version >= "3.8" and python_version < "3.9" +fsspec==2024.9.0 ; python_version >= "3.8" and python_version < "3.9" +gast==0.4.0 ; python_version >= "3.8" and python_version < "3.9" +gensim==4.3.3 ; python_version >= "3.8" and python_version < "3.9" +google-auth-oauthlib==1.0.0 ; python_version >= "3.8" and python_version < "3.9" +google-auth==2.35.0 ; python_version >= "3.8" and python_version < "3.9" +google-pasta==0.2.0 ; python_version >= "3.8" and python_version < "3.9" +grpcio==1.66.2 ; python_version >= "3.8" and python_version < "3.9" +h5py==3.11.0 ; python_version >= "3.8" and python_version < "3.9" +idna==3.10 ; python_version >= "3.8" and python_version < "3.9" +importlib-metadata==8.5.0 ; python_version >= "3.8" and python_version < "3.9" +ipykernel==6.29.5 ; python_version >= "3.8" and python_version < "3.9" +ipython==8.12.3 ; python_version >= "3.8" and python_version < "3.9" +jedi==0.19.1 ; python_version >= "3.8" and python_version < "3.9" +jinja2==3.1.4 ; python_version >= "3.8" and python_version < "3.9" +joblib==1.4.2 ; python_version >= "3.8" and python_version < "3.9" +jupyter-client==8.6.3 ; python_version >= "3.8" and python_version < "3.9" +jupyter-core==5.7.2 ; python_version >= "3.8" and python_version < "3.9" +keras-preprocessing==1.1.2 ; python_version >= "3.8" and python_version < "3.9" +keras==2.7.0 ; python_version >= "3.8" and python_version < "3.9" +kiwisolver==1.4.7 ; python_version >= "3.8" and python_version < "3.9" +libclang==18.1.1 ; python_version >= "3.8" and python_version < "3.9" +lightning-utilities==0.11.7 ; python_version >= "3.8" and python_version < "3.9" +markdown==3.7 ; python_version >= "3.8" and python_version < "3.9" +markupsafe==2.1.5 ; python_version >= "3.8" and python_version < "3.9" +matplotlib-inline==0.1.7 ; python_version >= "3.8" and python_version < "3.9" +matplotlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9" +mpmath==1.3.0 ; python_version >= "3.8" and python_version < "3.9" +multidict==6.1.0 ; python_version >= "3.8" and python_version < "3.9" +nest-asyncio==1.6.0 ; python_version >= "3.8" and python_version < "3.9" +networkx==2.5 ; python_version >= "3.8" and python_version < "3.9" +neural-structured-learning==1.3.1 ; python_version >= "3.8" and python_version < "3.9" +numpy==1.21.6 ; python_version >= "3.8" and python_version < "3.9" +nvidia-cublas-cu12==12.1.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cuda-cupti-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cuda-runtime-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cudnn-cu12==8.9.2.26 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cufft-cu12==11.0.2.54 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-curand-cu12==10.3.2.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cusolver-cu12==11.4.5.107 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cusparse-cu12==12.1.0.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-nccl-cu12==2.18.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-nvjitlink-cu12==12.6.77 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-nvtx-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +oauthlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9" +opt-einsum==3.4.0 ; python_version >= "3.8" and python_version < "3.9" +packaging==24.1 ; python_version >= "3.8" and python_version < "3.9" +pandas==2.0.3 ; python_version >= "3.8" and python_version < "3.9" +parso==0.8.4 ; python_version >= "3.8" and python_version < "3.9" +pexpect==4.9.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32" +pickleshare==0.7.5 ; python_version >= "3.8" and python_version < "3.9" +pillow==10.4.0 ; python_version >= "3.8" and python_version < "3.9" +platformdirs==4.3.6 ; python_version >= "3.8" and python_version < "3.9" +prompt-toolkit==3.0.48 ; python_version >= "3.8" and python_version < "3.9" +propcache==0.2.0 ; python_version >= "3.8" and python_version < "3.9" +protobuf==3.20.3 ; python_version >= "3.8" and python_version < "3.9" +psutil==6.0.0 ; python_version >= "3.8" and python_version < "3.9" +ptyprocess==0.7.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32" +pure-eval==0.2.3 ; python_version >= "3.8" and python_version < "3.9" +pyasn1-modules==0.4.1 ; python_version >= "3.8" and python_version < "3.9" +pyasn1==0.6.1 ; python_version >= "3.8" and python_version < "3.9" +pycparser==2.22 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy" +pydantic-core==2.23.4 ; python_version >= "3.8" and python_version < "3.9" +pydantic==2.9.2 ; python_version >= "3.8" and python_version < "3.9" +pygments==2.18.0 ; python_version >= "3.8" and python_version < "3.9" +pyparsing==3.1.4 ; python_version >= "3.8" and python_version < "3.9" +python-dateutil==2.9.0.post0 ; python_version >= "3.8" and python_version < "3.9" +pytz==2024.2 ; python_version >= "3.8" and python_version < "3.9" +pywin32==307 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.8" and python_version < "3.9" +pyyaml==6.0.2 ; python_version >= "3.8" and python_version < "3.9" +pyzmq==26.2.0 ; python_version >= "3.8" and python_version < "3.9" +requests-oauthlib==2.0.0 ; python_version >= "3.8" and python_version < "3.9" +requests==2.32.3 ; python_version >= "3.8" and python_version < "3.9" +rsa==4.9 ; python_version >= "3.8" and python_version < "3.9" +scikit-learn==1.3.2 ; python_version >= "3.8" and python_version < "3.9" +scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.9" +setuptools==75.1.0 ; python_version >= "3.8" and python_version < "3.9" +six==1.16.0 ; python_version >= "3.8" and python_version < "3.9" +smart-open==7.0.5 ; python_version >= "3.8" and python_version < "3.9" +stack-data==0.6.3 ; python_version >= "3.8" and python_version < "3.9" +stellargraph==1.2.1 ; python_version >= "3.8" and python_version < "3.9" +sympy==1.13.3 ; python_version >= "3.8" and python_version < "3.9" +tensorboard-data-server==0.7.2 ; python_version >= "3.8" and python_version < "3.9" +tensorboard==2.14.0 ; python_version >= "3.8" and python_version < "3.9" +tensorflow-estimator==2.7.0 ; python_version >= "3.8" and python_version < "3.9" +tensorflow-io-gcs-filesystem==0.21.0 ; python_version >= "3.8" and python_version < "3.9" +tensorflow==2.7.2 ; python_version >= "3.8" and python_version < "3.9" +termcolor==2.4.0 ; python_version >= "3.8" and python_version < "3.9" +threadpoolctl==3.5.0 ; python_version >= "3.8" and python_version < "3.9" +torch-geometric==2.6.1 ; python_version >= "3.8" and python_version < "3.9" +torch==2.1.2 ; python_version >= "3.8" and python_version < "3.9" +torchmetrics==1.4.3 ; python_version >= "3.8" and python_version < "3.9" +torchvision==0.16.2 ; python_version >= "3.8" and python_version < "3.9" +tornado==6.4.1 ; python_version >= "3.8" and python_version < "3.9" +tqdm==4.66.5 ; python_version >= "3.8" and python_version < "3.9" +traitlets==5.14.3 ; python_version >= "3.8" and python_version < "3.9" +triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +typing-extensions==4.12.2 ; python_version >= "3.8" and python_version < "3.9" +tzdata==2024.2 ; python_version >= "3.8" and python_version < "3.9" +urllib3==2.2.3 ; python_version >= "3.8" and python_version < "3.9" +wcwidth==0.2.13 ; python_version >= "3.8" and python_version < "3.9" +werkzeug==3.0.4 ; python_version >= "3.8" and python_version < "3.9" +wheel==0.44.0 ; python_version >= "3.8" and python_version < "3.9" +wrapt==1.16.0 ; python_version >= "3.8" and python_version < "3.9" +yarl==1.14.0 ; python_version >= "3.8" and python_version < "3.9" +zipp==3.20.2 ; python_version >= "3.8" and python_version < "3.9" diff --git a/docker/Dockerfile b/docker/Dockerfile index d4e495a..71229ae 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -43,6 +43,12 @@ RUN conda create -n chap3 python=3.8 RUN conda run -n chap3 pip install -r Chapter03/requirements.txt RUN conda run -n chap3 python -m ipykernel install --name chap3 --user +FROM base as chap4 +RUN ls -d -1 */ | grep -v -e Chapter04 | xargs rm -rf +RUN conda create -n chap4 python=3.8 +RUN conda run -n chap4 pip install -r Chapter04/requirements.txt +RUN conda run -n chap4 python -m ipykernel install --name chap4 --user + FROM base as chap5 RUN ls -d -1 */ | grep -v -e Chapter05 | xargs rm -rf RUN conda create -n chap5 python=3.8