Scikit- LR vs ADMM Lasso

Diabetes_Test.ipynb

@@ -0,0 +1,261 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "import torch.optim as optim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper functions\n",
+    "def predict(X, W):\n",
+    "    YPred = X.dot(W)\n",
+    "    return YPred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_iterations = 5\n",
+    "N = 10000\n",
+    "D = 10\n",
+    "\n",
+    "X = np.random.randn(N, D)\n",
+    "Y = np.random.randn(N, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the diabetes dataset\n",
+    "diabetes = datasets.load_diabetes()\n",
+    "\n",
+    "# Use only one feature\n",
+    "diabetes_X = diabetes.data[:, np.newaxis, 2]\n",
+    "\n",
+    "# Split the data into training/testing sets\n",
+    "diabetes_X_train = diabetes_X[:-20]\n",
+    "diabetes_X_test = diabetes_X[-20:]\n",
+    "\n",
+    "# Split the targets into training/testing sets\n",
+    "diabetes_y_train = diabetes.target[:-20]\n",
+    "diabetes_y_test = diabetes.target[-20:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print(diabetes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diabetes_X_train = X[:-20]\n",
+    "diabetes_X_test = X[-20:]\n",
+    "diabetes_y_train = Y[:-20]\n",
+    "diabetes_y_test = Y[-20:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Coefficients: \\n', array([938.23786125]))\n",
+      "Mean squared error: 2548.07\n",
+      "Variance score: 0.47\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create linear regression object\n",
+    "regr = linear_model.LinearRegression()\n",
+    "\n",
+    "# Train the model using the training sets\n",
+    "regr.fit(diabetes_X_train, diabetes_y_train)\n",
+    "\n",
+    "# Make predictions using the testing set\n",
+    "diabetes_y_pred = regr.predict(diabetes_X_test)\n",
+    "\n",
+    "# The coefficients\n",
+    "print('Coefficients: \\n', regr.coef_)\n",
+    "# The mean squared error\n",
+    "print(\"Mean squared error: %.2f\"\n",
+    "      % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n",
+    "# Explained variance score: 1 is perfect prediction\n",
+    "print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))\n",
+    "\n",
+    "# Plot outputs\n",
+    "\n",
+    "plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\n",
+    "plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n",
+    "\n",
+    "plt.xticks(())\n",
+    "plt.yticks(())\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2623942383.2329097\n",
+      "('Objective Value:', 2663156030.210076)\n",
+      "('Objective Value:', 2718233711.865551)\n",
+      "('Objective Value:', 2755549908.0834365)\n",
+      "('Objective Value:', 2777025493.9161234)\n",
+      "('Objective Value:', 2788632001.1960063)\n",
+      "('Weights: ', array([[936.25300989]]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "admm = optim.ADMM([diabetes_X_train, diabetes_y_train, 0.01], \"Lasso\") #, parallel = True)\n",
+    "\n",
+    "print(admm.getLoss())\n",
+    "for i in range(0, 5):\n",
+    "    print('Objective Value:', admm.step())\n",
+    "\n",
+    "print('Weights: ',admm.getWeights())\n",
+    "W = admm.getWeights()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Comparison with Scikit - LR\n",
+    "admm_predict = predict(diabetes_X_test, W)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean squared error: 20747.88\n",
+      "Variance score: -3.29\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Mean squared error: %.2f\"\n",
+    "      % mean_squared_error(diabetes_y_test, admm_predict))\n",
+    "# Explained variance score: 1 is perfect prediction\n",
+    "print('Variance score: %.2f' % r2_score(diabetes_y_test, admm_predict))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\n",
+    "plt.plot(diabetes_X_test, admm_predict, color='blue', linewidth=3)\n",
+    "\n",
+    "plt.xticks(())\n",
+    "plt.yticks(())\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Test_ADMM.ipynb

@@ -0,0 +1,105 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch.optim as optim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_iterations = 5\n",
+    "N = 1000\n",
+    "D = 20\n",
+    "\n",
+    "A = np.random.randn(N, D)\n",
+    "b = np.random.randn(N, 1)\n",
+    "Y = np.random.rand(N, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13218.725722259838\n",
+      "('O Val:', 503.7030770044544)\n",
+      "('O Val:', 503.70307384103387)\n",
+      "('O Val:', 503.7030786237145)\n",
+      "('O Val:', 503.7030712532673)\n",
+      "('O Val:', 503.7030778652381)\n",
+      "('Weights: ', array([[-0.02279339],\n",
+      "       [-0.03123076],\n",
+      "       [ 0.03235469],\n",
+      "       [-0.00769174],\n",
+      "       [-0.03006917],\n",
+      "       [-0.01935186],\n",
+      "       [ 0.0200083 ],\n",
+      "       [-0.0228672 ],\n",
+      "       [ 0.03491097],\n",
+      "       [-0.0216022 ],\n",
+      "       [ 0.0118515 ],\n",
+      "       [-0.03075605],\n",
+      "       [-0.019314  ],\n",
+      "       [ 0.05811015],\n",
+      "       [ 0.02316836],\n",
+      "       [ 0.02389774],\n",
+      "       [-0.06124286],\n",
+      "       [-0.02646492],\n",
+      "       [-0.01167727],\n",
+      "       [-0.03814794]]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "admm = optim.ADMM([A, b, 0.01], \"Lasso\") #, parallel = True)\n",
+    "\n",
+    "print(admm.getLoss())\n",
+    "for i in range(0, num_iterations):\n",
+    "    print('O Val:', admm.step())\n",
+    "\n",
+    "print('Weights: ',admm.getWeights())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}