02_classification.ipynb

   "metadata": {},
   "source": [
    "## Exercise section 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To prepare the next bigger exercise, we quickly introduce how to add so called polynomial features to our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2         3         4\n",
      "0 -1.539782  0.950822  2.370928 -1.464059  0.904063\n",
      "1  0.436266 -1.768324  0.190328 -0.771460  3.126968\n",
      "2 -1.466436  1.391890  2.150435 -2.041118  1.937358\n",
      "3 -1.037642 -0.953587  1.076700  0.989482  0.909329\n",
      "4 -0.691444 -0.219826  0.478094  0.151997  0.048323\n",
      "5  1.436550 -0.046027  2.063676 -0.066121  0.002119\n",
      "6  0.664361 -1.234410  0.441375 -0.820094  1.523768\n",
      "7  0.164649 -1.848453  0.027109 -0.304346  3.416779\n",
      "8 -1.883945 -0.222088  3.549248  0.418402  0.049323\n",
      "9  0.934993 -1.081893  0.874212 -1.011563  1.170493\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "df = pd.read_csv(\"xor.csv\")\n",
    "features = df.iloc[:10, :-1]\n",
    "preproc = PolynomialFeatures(2, include_bias=False)\n",
    "data = preproc.fit_transform(features)\n",
    "print(pd.DataFrame(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case \n",
    "- columns 0 and 1 are $x$ and $y$ from the original data set.\n",
    "- column 2 is $x^2$\n",
    "- column 3 is $x y$\n",
    "- column 4 is $y^2$.\n",
    "\n",
    "A complete description can be found here: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following script now learns classifiers on different data sets and plots decision surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113 out of 200 predicted correctly\n",
      "[[0.01088167 0.0155522 ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 359,
       "width": 372
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "\n",
    "def train_and_plot_decision_surface(clf, preproc, features, labels, marker=\".\", N=400):\n",
    "    \n",
    "    features = np.array(features)\n",
    "    xmin, ymin = features.min(axis=0)\n",
    "    xmax, ymax = features.max(axis=0)\n",
    "    \n",
    "    x = np.linspace(xmin, xmax, N)\n",
    "    y = np.linspace(ymin, ymax, N) \n",
    "    points = np.array(np.meshgrid(x, y)).T.reshape(-1, 2)\n",
    "  \n",
    "    if preproc is not None:\n",
    "        points_for_clf = preproc.fit_transform(points)\n",
    "        features = preproc.fit_transform(features)\n",
    "    else:\n",
    "        points_for_clf = points\n",
    "    \n",
    "    clf.fit(features, labels)\n",
    "    predicted = clf.predict(features)\n",
    "    print(sum(predicted == labels), \"out of\", len(labels), \"predicted correctly\")\n",
    "    classes = np.array(clf.predict(points_for_clf), dtype=bool) \n",
    "    plt.plot(points[classes][:, 0], points[classes][:, 1], \"b\" + marker, markersize=1, alpha=.05);\n",
    "    plt.plot(points[~classes][:, 0], points[~classes][:, 1], \"r\" + marker, markersize=1, alpha=.05);\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"2d_points.csv\")\n",
    "# df = pd.read_csv(\"xor.csv\")\n",
    "\n",
    "features = df.iloc[:, :-1]\n",
    "labels = df.iloc[:, -1]\n",
    "\n",
    "plt.figure(figsize=(6, 6));\n",
    "\n",
    "clf = LinearSVC()\n",
    "\n",
    "# clf = LogisticRegression()\n",
    "# clf = SVC(gamma=.1)\n",
    "# clf = DecisionTreeClassifier(max_depth=6)\n",
    "# clf = KNeighborsClassifier(10)\n",
    "\n",
    "#preproc = PolynomialFeatures(2, include_bias=False)\n",
    "preproc = None\n",
    "\n",
    "train_and_plot_decision_surface(clf, preproc, features, labels)\n",
    "\n",
    "colors = [\"rb\"[i] for i in labels]\n",
    "plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker='.');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- modify the script to use the `xor.csv` data set.\n",
    "\n",
    "- play with the other classifiers which are outcommented in the script.\n",
    "- play with their parameters.\n",
    "- activate the feature engineering step and experiment with classifiers and their parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/uweschmitt/Projects/machinelearning-introduction-workshop/venv3.6/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: get_ipython_dir has moved to the IPython.paths module since IPython 4.0.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
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       "        background: #fff9d8 !important;\n",
       "        padding: 5pt;\n",
       "       \n",
       "       -webkit-print-color-adjust: exact; \n",
       "        \n",
       "    }\n",
       " \n",
       "    \n",
       " \n",
       "    h1, h2, h3, h4 {\n",
       "        font-family: Kameron, arial;\n",
       "    }\n",
       "    \n",
       "    div#maintoolbar {display: none !important;}\n",
       "    </style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#REMOVEBEGIN\n",
    "# THE LINES BELOW ARE JUST FOR STYLING THE CONTENT ABOVE !\n",
    "\n",
    "from IPython import utils\n",
    "from IPython.core.display import HTML\n",
    "import os\n",
    "def css_styling():\n",
    "    \"\"\"Load default custom.css file from ipython profile\"\"\"\n",
    "    base = utils.path.get_ipython_dir()\n",
    "    styles = \"\"\"<style>\n",
    "    \n",
    "    @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
    "    \n",
    "    @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
    "    @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
    "    \n",
    "    @import url('http://fonts.googleapis.com/css?family=Lato');\n",
    "    @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
    "    \n",
    "    @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
    "\n",
    "    \n",
    "    body {\n",
    "        font-family: 'Lora', Consolas, sans-serif;\n",
    "       \n",
    "        -webkit-print-color-adjust: exact important !;\n",
    "        \n",
    "      \n",
    "       \n",
    "    }\n",
    "    .rendered_html code\n",
    "    {\n",
    "        color: black;\n",
    "        background: #eaf0ff;\n",
    "        background: #f5f5f5; \n",
    "        padding: 1pt;\n",
    "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
    "    }\n",
    "    \n",
    "    p {\n",
    "      line-height: 140%;\n",
    "    }\n",
    "    \n",
    "    strong code {\n",
    "        background: red;\n",
    "    }\n",
    "    \n",
    "    em  {\n",
    "        color: green;\n",
    "    }\n",
    "    \n",
    "    .rendered_html strong code\n",
    "    {\n",
    "        background: #f5f5f5;\n",
    "    }\n",
    "    \n",
    "    .CodeMirror pre {\n",
    "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
    "    }\n",
    "    \n",
    "    .cm-s-ipython span.cm-keyword {\n",
    "        font-weight: normal;\n",
    "     }\n",
    "     \n",
    "     strong {\n",
    "         background: #f5f5f5;\n",
    "         margin-top: 4pt;\n",
    "         margin-bottom: 4pt;\n",
    "         padding: 2pt;\n",
    "         border: 0.5px solid #a0a0a0;\n",
    "         font-weight: bold;\n",
    "         color: darkred;\n",
    "     }\n",
    "     \n",
    "    \n",
    "    div #notebook {\n",
    "        # font-size: 10pt; \n",
    "        line-height: 145%;\n",
    "        }\n",
    "        \n",
    "    li {\n",
    "        line-height: 145%;\n",
    "    }\n",
    "\n",
    "    div.output_area pre {\n",
    "        background: #fff9d8 !important;\n",
    "        padding: 5pt;\n",
    "       \n",
    "       -webkit-print-color-adjust: exact; \n",
    "        \n",
    "    }\n",
    " \n",
    "    \n",
    " \n",
    "    h1, h2, h3, h4 {\n",
    "        font-family: Kameron, arial;\n",
    "    }\n",
    "    \n",
    "    div#maintoolbar {display: none !important;}\n",
    "    </style>\"\"\"\n",
    "    return HTML(styles)\n",
    "css_styling()\n",
    "#REMOVEEND"
   ]
  },
  {
   "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": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}