{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "a1ad3158", "metadata": {}, "outputs": [], "source": [ "from easynn.feedforward import FFNN, Layer, Regularization, LeakyReLU, Softmax, CrossEntropyLoss\n", "from easynn.schedulers import GradientDescentScheduler, AdamScheduler, RMSPropScheduler, AdaGradScheduler, MomentumScheduler\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "e622cfff", "metadata": {}, "outputs": [], "source": [ "def get_classification_model(n_hidden_layers: int, n_neurons: int, activation: type=LeakyReLU, reg_strength: float=0.0) -> list[Layer]:\n", " target_dim = 2\n", " feature_dim = 30\n", " layers = []\n", " layers.append(Layer(feature_dim, n_neurons, activation_function=activation()))\n", " for _ in range(n_hidden_layers - 1):\n", " layers.append(Layer(n_neurons, n_neurons, activation_function=activation()))\n", " layers.append(Layer(n_neurons, target_dim, activation_function=Softmax()))\n", " for layer in layers:\n", " layer.regularization = Regularization(reg_strength)\n", " return layers" ] }, { "cell_type": "code", "execution_count": 3, "id": "6a99b185", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 4, "id": "9a12aca0", "metadata": {}, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "mean radius", "rawType": "float64", "type": "float" }, { "name": "mean area", "rawType": "float64", "type": "float" }, { "name": "radius error", "rawType": "float64", "type": "float" }, { "name": "area error", "rawType": "float64", "type": "float" }, { "name": "worst radius", "rawType": "float64", "type": "float" }, { "name": "worst area", "rawType": "float64", "type": "float" }, { "name": "mean 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mean radiusmean arearadius errorarea errorworst radiusworst areamean texturemean perimetermean smoothnessmean compactness...fractal dimension errorworst textureworst perimeterworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
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569 rows × 31 columns

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" ], "text/plain": [ " mean radius mean area radius error area error worst radius \\\n", "0 1.097064 0.984375 2.489734 2.487578 1.886690 \n", "1 1.829821 1.908708 0.499255 0.742402 1.805927 \n", "2 1.579888 1.558884 1.228676 1.181336 1.511870 \n", "3 -0.768909 -0.764464 0.326373 -0.288378 -0.281464 \n", "4 1.750297 1.826229 1.270543 1.190357 1.298575 \n", ".. ... ... ... ... ... \n", "564 2.110995 2.343856 2.782080 2.604187 1.901185 \n", "565 1.704854 1.723842 1.300499 1.291565 1.536720 \n", "566 0.702284 0.577953 0.184892 0.180698 0.561361 \n", "567 1.838341 1.735218 1.157935 1.009503 1.961239 \n", "568 -1.808401 -1.347789 -0.070279 -0.466152 -1.410893 \n", "\n", " worst area mean texture mean perimeter mean smoothness \\\n", "0 2.001237 -0.001663 -0.002107 -0.008919 \n", "1 1.890489 -0.001663 -0.002107 -0.008919 \n", "2 1.456285 -0.001663 -0.002107 -0.008919 \n", "3 -0.550021 -0.001663 -0.002107 -0.008919 \n", "4 1.220724 -0.001663 -0.002107 -0.008919 \n", ".. ... ... ... ... \n", "564 2.015301 -0.004565 0.002888 0.000308 \n", "565 1.494959 -0.000361 -0.000298 0.000283 \n", "566 0.427906 -0.000361 -0.000298 0.000283 \n", "567 1.653171 -0.000361 -0.000298 0.000283 \n", "568 -1.075813 -0.000361 -0.000298 0.000283 \n", "\n", " mean compactness ... fractal dimension error worst texture \\\n", "0 -0.008518 ... -0.003673 -0.003019 \n", "1 -0.008518 ... -0.003673 -0.003019 \n", "2 -0.008518 ... -0.003673 -0.003019 \n", "3 -0.008518 ... -0.003673 -0.003019 \n", "4 -0.008518 ... -0.003673 -0.003019 \n", ".. ... ... ... ... \n", "564 0.001785 ... 0.000690 -0.002535 \n", "565 -0.000251 ... -0.000012 -0.000781 \n", "566 -0.000251 ... -0.000012 -0.000781 \n", "567 -0.000251 ... -0.000012 -0.000781 \n", "568 -0.000251 ... -0.000012 -0.000781 \n", "\n", " worst perimeter worst smoothness worst compactness worst concavity \\\n", "0 -0.004072 -0.008980 -0.009043 -0.007940 \n", "1 -0.004072 -0.008980 -0.009043 -0.007940 \n", "2 -0.004072 -0.008980 -0.009043 -0.007940 \n", "3 -0.004072 -0.008980 -0.009043 -0.007940 \n", "4 -0.004072 -0.008980 -0.009043 -0.007940 \n", ".. ... ... ... ... \n", "564 0.003346 0.000592 0.002893 0.003207 \n", "565 -0.000446 0.000236 -0.000611 -0.000296 \n", "566 -0.000446 0.000236 -0.000611 -0.000296 \n", "567 -0.000446 0.000236 -0.000611 -0.000296 \n", "568 -0.000446 0.000236 -0.000611 -0.000296 \n", "\n", " worst concave points worst symmetry worst fractal dimension target \n", "0 -0.007927 -0.009600 -0.008111 0 \n", "1 -0.007927 -0.009600 -0.008111 0 \n", "2 -0.007927 -0.009600 -0.008111 0 \n", "3 -0.007927 -0.009600 -0.008111 0 \n", "4 -0.007927 -0.009600 -0.008111 0 \n", ".. ... ... ... ... \n", "564 0.003698 0.005682 0.001362 0 \n", "565 -0.000066 -0.000029 -0.000017 0 \n", "566 -0.000066 -0.000029 -0.000017 0 \n", "567 -0.000066 -0.000029 -0.000017 0 \n", "568 -0.000066 -0.000029 -0.000017 1 \n", "\n", "[569 rows x 31 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"breast_cancer_regression_results.csv\")\n", "df" ] }, { "cell_type": "code", "execution_count": 5, "id": "ad5a486f", "metadata": {}, "outputs": [], "source": [ "X = df.iloc[:, :-1].to_numpy()\n", "y = df.iloc[:, -1].to_numpy().reshape(-1, 1)\n", "encoder = OneHotEncoder(sparse_output=False)\n", "y_encoded = encoder.fit_transform(y)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2)" ] }, { "cell_type": "code", "execution_count": 6, "id": "3b85a4a9", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score, roc_auc_score\n", "\n", "def test_classification_model(n_hidden_layers: int, n_neurons: int, reg_strength: float, n_iterations: int=50) -> tuple[float, float, FFNN]:\n", " assert n_iterations > 0, \"n_iterations must be greater than 0\"\n", " accs = []\n", " aucs = []\n", " for _ in range(n_iterations): # Run n_iterations trials\n", " X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2)\n", " layers = get_classification_model(n_hidden_layers=n_hidden_layers, n_neurons=n_neurons, reg_strength=reg_strength)\n", " model = FFNN(layers, AdamScheduler(epochs=500, learning_rate=0.05), loss_fn=CrossEntropyLoss())\n", " model.fit(X_train, y_train)\n", " y_pred = model.predict(X_test)\n", " acc = accuracy_score(y_test.argmax(axis=1), y_pred.argmax(axis=1))\n", " auc = roc_auc_score(y_test, y_pred)\n", " accs.append(acc)\n", " aucs.append(auc)\n", " return np.mean(accs), np.mean(aucs), model\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "2b38e789", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Scanning hyperparameters: 100%|██████████| 100/100 [03:19<00:00, 2.00s/it]\n" ] } ], "source": [ "import itertools\n", "\n", "results = []\n", "best_model = None\n", "best_score = -np.inf\n", "\n", "# Define hyperparameter ranges\n", "hidden_layers_list = [1, 2, 3, 4]\n", "neurons_list = [8, 16, 32, 64, 128]\n", "reg_strength_list = [0.0, 1e-8, 1e-7, 1e-6, 1e-5]\n", "\n", "from tqdm import tqdm\n", "\n", "for n_hidden_layers, n_neurons, reg_strength in tqdm(\n", " itertools.product(hidden_layers_list, neurons_list, reg_strength_list),\n", " total=len(hidden_layers_list) * len(neurons_list) * len(reg_strength_list),\n", " desc=\"Scanning hyperparameters\",\n", "):\n", " ...\n", "\n", " acc, auc, model = test_classification_model(n_hidden_layers, n_neurons, reg_strength, 2)\n", " results.append({\n", " \"n_hidden_layers\": n_hidden_layers,\n", " \"n_neurons\": n_neurons,\n", " \"reg_strength\": reg_strength,\n", " \"accuracy\": acc,\n", " \"auc\": auc,\n", " })\n", "\n", " # You can use AUC as the main criterion, or combine them\n", " score = auc + 0.1 * acc\n", " if score > best_score:\n", " best_score = score\n", " best_model = model\n", "\n", "results_df = pd.DataFrame(results)" ] }, { "cell_type": "code", "execution_count": 8, "id": "872999e7", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_94429/2740648130.py:44: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", " plt.tight_layout(rect=[0, 0, 0.9, 0.97])\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import plotting\n", "\n", "params = [\"n_hidden_layers\", \"n_neurons\", \"reg_strength\"]\n", "\n", "fig, axes = plt.subplots(3, 3, figsize=(12, 12))\n", "cmap = sns.color_palette(\"rocket\", as_cmap=True)\n", "\n", "for i, yparam in enumerate(params):\n", " for j, xparam in enumerate(params):\n", " ax = axes[i, j]\n", " if i == j:\n", " # Diagonal: 1D line plot of AUC vs parameter\n", " sns.lineplot(\n", " data=results_df,\n", " x=xparam,\n", " y=\"auc\",\n", " marker=\"o\",\n", " ax=ax,\n", " color=\"C0\"\n", " )\n", " ax.set_xlabel(xparam)\n", " ax.set_ylabel(\"AUC\")\n", " if i == 2:\n", " ax.set_xscale('log')\n", " else:\n", " # Off-diagonal: 2D heatmap colored by AUC\n", " hm = results_df.pivot_table(index=yparam, columns=xparam, values=\"auc\")\n", " sc = ax.imshow(hm, origin='lower', aspect='auto', cmap=cmap)\n", " ax.set_xticks(np.arange(len(hm.columns)))\n", " ax.set_yticks(np.arange(len(hm.index)))\n", " ax.set_xticklabels(hm.columns)\n", " ax.set_yticklabels(hm.index)\n", " ax.set_xlabel(xparam)\n", " ax.set_ylabel(yparam)\n", "\n", "# Shared colorbar for AUC\n", "cbar_ax = fig.add_axes([0.92, 0.3, 0.02, 0.4])\n", "fig.colorbar(sc, cax=cbar_ax, label=\"AUC\")\n", "\n", "fig.suptitle(\"Pairwise Hyperparameter Relationships (AUC)\", fontsize=18)\n", "plt.tight_layout(rect=[0, 0, 0.9, 0.97])\n", "plt.savefig(\"classification_hyperparameter_scan.pdf\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "27d26bcb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Model - Accuracy: 0.9270, AUC: 0.9781\n" ] } ], "source": [ "n_hidden_layers = 1\n", "n_neurons = 64\n", "reg_strength = 1e-6\n", "\n", "acc, auc, best_model = test_classification_model(n_hidden_layers, n_neurons, reg_strength)\n", "print(f\"Best Model - Accuracy: {acc:.4f}, AUC: {auc:.4f}\")\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "0c40bfbd", "metadata": {}, "outputs": [], "source": [ "y_pred = best_model.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 17, "id": "0e4a6f37", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import roc_curve, confusion_matrix\n", "\n", "fpr, tpr, thresholds = roc_curve(y_test.argmax(axis=1), y_pred[:, 1])\n", "cm = confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))" ] }, { "cell_type": "code", "execution_count": null, "id": "c94858f9", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.heatmap(cm, annot=True, fmt='d')\n", "plt.xlabel(\"Predicted Label\")\n", "plt.ylabel(\"True Label\")\n", "plt.tight_layout()\n", "plt.savefig(\"classification_confusion_matrix.pdf\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "66af4e3e", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lineplot(x=fpr, y=tpr)\n", "plt.plot([0, 1], [0, 1], linestyle='--', color='gray')\n", "plt.xlabel(\"False Positive Rate\")\n", "plt.ylabel(\"True Positive Rate\")\n", "plt.tight_layout()\n", "plt.savefig(\"classification_roc_curve.pdf\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "952baf6b", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "project2", "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": "3.13.7" } }, "nbformat": 4, "nbformat_minor": 5 }