attack.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating the best training parameters...\n",
      "The best training parameters are:  [('C', 0.1), ('kernel.gamma', 100)]\n",
      "Accuracy on test set: 99.00%\n"
     ]
    }
   ],
   "source": [
    "random_state = 999\n",
    "\n",
    "n_features = 2  # Number of features\n",
    "n_samples = 1100  # Number of samples\n",
    "centers = [[-2, 0], [2, -2], [2, 2]]  # Centers of the clusters\n",
    "cluster_std = 0.8  # Standard deviation of the clusters\n",
    "\n",
    "from secml.data.loader import CDLRandomBlobs\n",
    "dataset = CDLRandomBlobs(n_features=n_features,\n",
    "                         centers=centers,\n",
    "                         cluster_std=cluster_std,\n",
    "                         n_samples=n_samples,\n",
    "                         random_state=random_state).load()\n",
    "\n",
    "n_tr = 1000  # Number of training set samples\n",
    "n_ts = 100  # Number of test set samples\n",
    "\n",
    "# Split in training and test\n",
    "from secml.data.splitter import CTrainTestSplit\n",
    "splitter = CTrainTestSplit(\n",
    "    train_size=n_tr, test_size=n_ts, random_state=random_state)\n",
    "tr, ts = splitter.split(dataset)\n",
    "\n",
    "# Normalize the data\n",
    "from secml.ml.features import CNormalizerMinMax\n",
    "nmz = CNormalizerMinMax()\n",
    "tr.X = nmz.fit_transform(tr.X)\n",
    "ts.X = nmz.transform(ts.X)\n",
    "\n",
    "# Metric to use for training and performance evaluation\n",
    "from secml.ml.peval.metrics import CMetricAccuracy\n",
    "metric = CMetricAccuracy()\n",
    "\n",
    "# Creation of the multiclass classifier\n",
    "from secml.ml.classifiers import CClassifierSVM\n",
    "from secml.ml.classifiers.multiclass import CClassifierMulticlassOVA\n",
    "from secml.ml.kernels import CKernelRBF\n",
    "clf = CClassifierMulticlassOVA(CClassifierSVM, kernel=CKernelRBF())\n",
    "\n",
    "# Parameters for the Cross-Validation procedure\n",
    "xval_params = {'C': [1e-2, 0.1, 1], 'kernel.gamma': [10, 100, 1e3]}\n",
    "\n",
    "# Let's create a 3-Fold data splitter\n",
    "from secml.data.splitter import CDataSplitterKFold\n",
    "xval_splitter = CDataSplitterKFold(num_folds=3, random_state=random_state)\n",
    "\n",
    "# Select and set the best training parameters for the classifier\n",
    "print(\"Estimating the best training parameters...\")\n",
    "best_params = clf.estimate_parameters(\n",
    "    dataset=tr,\n",
    "    parameters=xval_params,\n",
    "    splitter=xval_splitter,\n",
    "    metric='accuracy',\n",
    "    perf_evaluator='xval'\n",
    ")\n",
    "\n",
    "print(\"The best training parameters are: \",\n",
    "      [(k, best_params[k]) for k in sorted(best_params)])\n",
    "\n",
    "# We can now fit the classifier\n",
    "clf.fit(tr)\n",
    "\n",
    "# Compute predictions on a test set\n",
    "y_pred = clf.predict(ts.X)\n",
    "\n",
    "# Evaluate the accuracy of the classifier\n",
    "acc = metric.performance_score(y_true=ts.Y, y_pred=y_pred)\n",
    "\n",
    "print(\"Accuracy on test set: {:.2%}\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CDataset{'X': CArray(1000, 2)(dense: [[0.72373  0.260512] [0.458741 0.596048] [0.146331 0.65994 ] ... [0.749585 0.361154] [0.72219  0.494774] [0.73656  0.330303]]), 'Y': CArray(1000,)(dense: [1 0 0 ... 1 2 1]), 'header': None}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.4 64-bit ('base': conda)",
   "language": "python",
   "name": "python37464bitbaseconda2380905dcdd74b2395fb88157feb624e"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}