notebooks/iris_classification.ipynb

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    "# Classifying the Iris Dataset with Einets\n",
    "\n",
    "This tutorial demonstrates how to classify the Iris dataset using Einets, which are a type of probabilistic model well-suited for tasks like classification. We'll go through setting up the environment, preparing the data, building the model, training it, and finally visualizing the decision boundary.\n",
    "\n",
    "## Environment Setup\n",
    "\n",
    "First, we need to import the necessary libraries. Make sure to install these using `pip` or `conda` before starting.\n"
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      "File \u001b[0;32m~/.conda/envs/simple-einet/lib/python3.11/site-packages/simple_einet/einet.py:10\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m nn\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msimple_einet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdistributions\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mabstract_leaf\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AbstractLeaf\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msimple_einet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01meinsum\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     12\u001b[0m     EinsumLayer,\n\u001b[1;32m     13\u001b[0m )\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msimple_einet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmixing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MixingLayer\n",
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   "source": [
    "import torch\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from simple_einet.einet import Einet, EinetConfig\n",
    "from simple_einet.layers.distributions.normal import Normal\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
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   "source": [
    "## Data Preparation\n",
    "\n",
    "The Iris dataset can be loaded directly from scikit-learn, and we will use PyTorch for handling the data.\n"
   ]
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    "# Load the Iris dataset\n",
    "iris = datasets.load_iris()\n",
    "# Reduce the dataset to 2 dimensions for visualization purposes\n",
    "iris.data = iris.data[:, [0, 1]]\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.33, random_state=42)\n",
    "\n",
    "# Convert the data to PyTorch tensors\n",
    "X_train = torch.tensor(X_train).float()\n",
    "y_train = torch.tensor(y_train).long()\n",
    "X_test = torch.tensor(X_test).float()\n",
    "y_test = torch.tensor(y_test).long()\n"
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   "source": [
    "## Model Configuration\n",
    "\n",
    "Here, we set up the Einet model configuration using the predefined structure and parameters.\n"
   ]
  },
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     "text": [
      "Number of parameters: 222\n"
     ]
    }
   ],
   "source": [
    "# Define model configuration using default values\n",
    "config = EinetConfig(\n",
    "    num_features=2,\n",
    "    depth=1,\n",
    "    num_sums=5,\n",
    "    num_leaves=5,\n",
    "    num_repetitions=5,\n",
    "    num_classes=3,\n",
    "    leaf_type=Normal,\n",
    "    leaf_kwargs={},\n",
    "    dropout=0.0,\n",
    ")\n",
    "\n",
    "# Initialize the model\n",
    "model = Einet(config)\n"
   ]
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   "source": [
    "## Training the Model\n",
    "\n",
    "The training process involves defining an optimizer, loss function, and iterating over the training data for a number of epochs.\n"
   ]
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     "text": [
      "Epoch: 1, Loss: 1.06, Accuracy Train: 69.00 %, Accuracy Test: 68.00 %\n",
      "Epoch: 2, Loss: 0.98, Accuracy Train: 75.00 %, Accuracy Test: 76.00 %\n",
      "Epoch: 3, Loss: 0.91, Accuracy Train: 78.00 %, Accuracy Test: 74.00 %\n",
      "Epoch: 4, Loss: 0.84, Accuracy Train: 81.00 %, Accuracy Test: 74.00 %\n",
      "Epoch: 5, Loss: 0.77, Accuracy Train: 75.00 %, Accuracy Test: 70.00 %\n",
      "Epoch: 6, Loss: 0.71, Accuracy Train: 73.00 %, Accuracy Test: 72.00 %\n",
      "Epoch: 7, Loss: 0.65, Accuracy Train: 77.00 %, Accuracy Test: 70.00 %\n",
      "Epoch: 8, Loss: 0.61, Accuracy Train: 77.00 %, Accuracy Test: 74.00 %\n",
      "Epoch: 9, Loss: 0.57, Accuracy Train: 73.00 %, Accuracy Test: 74.00 %\n",
      "Epoch: 10, Loss: 0.55, Accuracy Train: 80.00 %, Accuracy Test: 72.00 %\n",
      "Epoch: 11, Loss: 0.52, Accuracy Train: 75.00 %, Accuracy Test: 72.00 %\n",
      "Epoch: 12, Loss: 0.50, Accuracy Train: 73.00 %, Accuracy Test: 76.00 %\n",
      "Epoch: 13, Loss: 0.49, Accuracy Train: 81.00 %, Accuracy Test: 72.00 %\n",
      "Epoch: 14, Loss: 0.48, Accuracy Train: 75.00 %, Accuracy Test: 72.00 %\n",
      "Epoch: 15, Loss: 0.46, Accuracy Train: 80.00 %, Accuracy Test: 74.00 %\n",
      "Epoch: 16, Loss: 0.45, Accuracy Train: 82.00 %, Accuracy Test: 74.00 %\n",
      "Epoch: 17, Loss: 0.45, Accuracy Train: 76.00 %, Accuracy Test: 76.00 %\n",
      "Epoch: 18, Loss: 0.46, Accuracy Train: 81.00 %, Accuracy Test: 74.00 %\n",
      "Epoch: 19, Loss: 0.45, Accuracy Train: 78.00 %, Accuracy Test: 82.00 %\n",
      "Epoch: 20, Loss: 0.43, Accuracy Train: 80.00 %, Accuracy Test: 80.00 %\n"
     ]
    }
   ],
   "source": [
    "# Set up the optimizer and loss function\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.1)\n",
    "cross_entropy = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "# Define a function to compute accuracy\n",
    "def accuracy(model, X, y):\n",
    "    with torch.no_grad():\n",
    "        outputs = model(X)\n",
    "        predictions = outputs.argmax(-1)\n",
    "        correct = (predictions == y).sum()\n",
    "        total = y.shape[0]\n",
    "        return correct / total * 100\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(50):\n",
    "    optimizer.zero_grad()\n",
    "    log_likelihoods = model(X_train)\n",
    "    loss = cross_entropy(log_likelihoods, y_train)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    acc_train = accuracy(model, X_train, y_train)\n",
    "    acc_test = accuracy(model, X_test, y_test)\n",
    "    print(f\"Epoch: {epoch + 1}, Loss: {loss.item():.2f}, Accuracy Train: {acc_train:.2f} %, Accuracy Test: {acc_test:.2f} %\")\n"
   ]
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   "source": [
    "## Visualizing the Decision Boundary\n",
    "\n",
    "Finally, let's visualize the decision boundary of our trained model along with the test data points.\n"
   ]
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set seaborn style for plotting\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create a meshgrid for the contour plot\n",
    "x_0_min, x_0_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1\n",
    "x_1_min, x_1_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1\n",
    "xx_0, xx_1 = np.meshgrid(np.arange(x_0_min, x_0_max, 0.1), np.arange(x_1_min, x_1_max, 0.1))\n",
    "\n",
    "# Evaluate the model on the grid to get the decision boundary\n",
    "mesh_input = torch.tensor(np.c_[xx_0.ravel(), xx_1.ravel()]).float()\n",
    "with torch.no_grad():\n",
    "    mesh_output = model(mesh_input)\n",
    "    mesh_classes = mesh_output.argmax(-1).view(xx_0.shape)\n",
    "\n",
    "# Plot the decision boundary\n",
    "plt.figure(figsize=(10, 6))\n",
    "cmap = sns.color_palette(\"deep\", 3)\n",
    "cmap = ListedColormap(cmap.as_hex())\n",
    "plt.contourf(xx_0, xx_1, mesh_classes.numpy(), alpha=0.25, cmap=cmap)\n",
    "\n",
    "# Overlay the test data points\n",
    "sns.scatterplot(x=X_test[:, 0].numpy(), y=X_test[:, 1].numpy(), hue=y_test.numpy(), palette=cmap, edgecolor=\"w\", s=100)\n",
    "\n",
    "# Add plot title and labels\n",
    "plt.title(\"Decision Boundary with Test Data\")\n",
    "plt.xlabel(iris.feature_names[0].capitalize())\n",
    "plt.ylabel(iris.feature_names[1].capitalize())\n",
    "plt.legend(loc=\"upper left\", title=\"Class\")\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}