Finalize rport

notebooks/regression-usecase.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "73b01c17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.datasets import load_breast_cancer\n",
+    "from easynn.feedforward import FFNN, Layer, Regularization, LeakyReLU, MSELoss, Linear\n",
+    "from easynn.schedulers import AdamScheduler\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b53c542f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = load_breast_cancer()\n",
+    "\n",
+    "\n",
+    "feature_names = [n for n in data.feature_names if 'radius' in n or 'area' in n]\n",
+    "\n",
+    "X = data.data[:, [data.feature_names.tolist().index(n) for n in feature_names]]\n",
+    "X = StandardScaler().fit_transform(X)\n",
+    "y = data.data[:, [data.feature_names.tolist().index(n) for n in data.feature_names if n not in feature_names]]\n",
+    "y = StandardScaler().fit_transform(y)\n",
+    "feature_dim = X.shape[1]\n",
+    "target_dim = y.shape[1]\n",
+    "\n",
+    "def get_regression_model(n_hidden_layers: int, n_neurons: int, activation: type=LeakyReLU, regularization_strength: float = 1e-3) -> list[Layer]:\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=Linear()))\n",
+    "    for layer in layers:\n",
+    "        layer.regularization = Regularization(regularization_strength, 'l2')\n",
+    "    return layers\n",
+    "\n",
+    "def get_model():\n",
+    "    return FFNN(get_regression_model(n_hidden_layers=2, n_neurons=32, activation=LeakyReLU, regularization_strength=1e-3), AdamScheduler(epochs=10000, learning_rate=1e-2), MSELoss())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "faa68894",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "crossvalidation_groups = 5\n",
+    "group_length = X.shape[0] // crossvalidation_groups\n",
+    "predictions = np.zeros_like(y)\n",
+    "for g in range(crossvalidation_groups + 1): # One final smaller group to catch the rest\n",
+    "    indices = [i for i in range(X.shape[0]) if i < g*group_length or i > (g+1)*group_length]\n",
+    "    prediction_indices = [i for i in range(X.shape[0]) if i not in indices]\n",
+    "    X_train = X[indices]\n",
+    "    X_pred = X[prediction_indices]\n",
+    "    y_train = y[indices]\n",
+    "\n",
+    "    model = get_model()\n",
+    "    model.fit(X_train, y_train)\n",
+    "    predictions[prediction_indices] = model.predict(X_pred)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2adacb9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.66264192e-03, -2.10728169e-03, -8.91893024e-03, ...,\n",
+       "        -7.92692008e-03, -9.60004239e-03, -8.11141572e-03],\n",
+       "       [-1.66264235e-03, -2.10728108e-03, -8.91893066e-03, ...,\n",
+       "        -7.92692057e-03, -9.60004241e-03, -8.11141547e-03],\n",
+       "       [-1.66264225e-03, -2.10728127e-03, -8.91893096e-03, ...,\n",
+       "        -7.92692035e-03, -9.60004219e-03, -8.11141554e-03],\n",
+       "       ...,\n",
+       "       [-3.60719147e-04, -2.98012242e-04,  2.82998606e-04, ...,\n",
+       "        -6.55977181e-05, -2.86525840e-05, -1.67033211e-05],\n",
+       "       [-3.60718156e-04, -2.98012138e-04,  2.82997361e-04, ...,\n",
+       "        -6.55992365e-05, -2.86528173e-05, -1.67018688e-05],\n",
+       "       [-3.60719175e-04, -2.98012516e-04,  2.82998898e-04, ...,\n",
+       "        -6.55989362e-05, -2.86521880e-05, -1.67059487e-05]],\n",
+       "      shape=(569, 24))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "690b4c57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "460e364e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combined_data = np.zeros_like(data.data)\n",
+    "combined_data[:,:6] = X\n",
+    "combined_data[:, 6:] = predictions\n",
+    "\n",
+    "combined_df = pd.DataFrame(combined_data, columns=[*feature_names, *[n for n in data.feature_names if n not in feature_names]])\n",
+    "combined_df['target'] = data.target\n",
+    "combined_df.to_csv(\"breast_cancer_regression_results.csv\", index=False)\n",
+    "\n",
+    "original_df = pd.DataFrame(data.data, columns=data.feature_names)\n",
+    "original_df['target'] = data.target\n",
+    "original_df.to_csv(\"breast_cancer_original_data.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11b2a0ad",
+   "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
+}