{
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   "attachments": {},
   "cell_type": "markdown",
   "id": "354c9535-8ed8-4494-b6b1-80ec0c74ba9a",
   "metadata": {},
   "source": [
    "# Semi-supervised Learning\n",
    "\n",
    "* Preparing and executing partially and delayed labeling experiments\n",
    "\n",
    "---\n",
    "\n",
    "*More information about CapyMOA can be found in* https://www.capymoa.org\n",
    "\n",
    "**notebook last updated on 25/07/2024**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3bf65301-1626-49e7-a803-145bdc0bb135",
   "metadata": {},
   "outputs": [],
   "source": [
    "from capymoa.stream import stream_from_file\n",
    "from capymoa.evaluation.visualization import plot_windowed_results\n",
    "from capymoa.evaluation import prequential_ssl_evaluation\n",
    "from capymoa.datasets import Electricity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a066760-e731-4018-97b4-75f0a2676fa5",
   "metadata": {},
   "source": [
    "## 1. Learning using a SSL classifier\n",
    "\n",
    "* This example uses the OSNN algorithm to learn from a stream with only 1% labeled data\n",
    "* We utilize the `prequential_ssl_evaluation()` function to simulate the absence of labels (`label_probability`) and delays (`delay_length`)\n",
    "* The results yield by `prequential_ssl_evaluation()` include more information in comparison to `prequential_evaluation()`, such as the number of unlabeled instances (`unlabeled`) and the unlabeled ratio (`unlabeled_ratio`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e2a9f32e-7740-4e1e-b48a-66adccf0451c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function prequential_ssl_evaluation in module capymoa.evaluation.evaluation:\n",
      "\n",
      "prequential_ssl_evaluation(stream, learner, max_instances=None, window_size=1000, initial_window_size=0, delay_length=0, label_probability=0.01, random_seed=1, store_predictions=False, store_y=False, optimise=True)\n",
      "    If the learner is not an SSL learner, then it will be trained only on the labeled instances.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(prequential_ssl_evaluation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "514ceb2e-87c2-4b27-ac9d-52435bbda524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<capymoa.evaluation.results.PrequentialResults at 0x103b37460>"
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    {
     "name": "stdout",
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     "text": [
      "59.3\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instances</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>kappa</th>\n",
       "      <th>kappa_t</th>\n",
       "      <th>kappa_m</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>f1_score_0</th>\n",
       "      <th>f1_score_1</th>\n",
       "      <th>precision</th>\n",
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       "      <th>recall_0</th>\n",
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       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
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       "      <td>65.0</td>\n",
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       "      <td>41.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.787879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>100.000000</td>\n",
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       "      <th>2</th>\n",
       "      <td>300.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>-23.136747</td>\n",
       "      <td>-216.666667</td>\n",
       "      <td>-2.702703</td>\n",
       "      <td>38.271605</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.543210</td>\n",
       "      <td>37.349398</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>74.698795</td>\n",
       "      <td>39.240506</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>3</th>\n",
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       "      <td>63.0</td>\n",
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       "      <td>-236.363636</td>\n",
       "      <td>44.776119</td>\n",
       "      <td>62.922136</td>\n",
       "      <td>77.018634</td>\n",
       "      <td>5.128205</td>\n",
       "      <td>81.313131</td>\n",
       "      <td>62.626263</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>51.315789</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>2.631579</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>-4.009077</td>\n",
       "      <td>-358.333333</td>\n",
       "      <td>-5.769231</td>\n",
       "      <td>31.034483</td>\n",
       "      <td>62.068966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.959184</td>\n",
       "      <td>45.918367</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.872340</td>\n",
       "      <td>95.744681</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>47.0</td>\n",
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       "      <td>-278.571429</td>\n",
       "      <td>-51.428571</td>\n",
       "      <td>55.070599</td>\n",
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       "      <td>55.967450</td>\n",
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       "      <td>54.202037</td>\n",
       "      <td>84.210526</td>\n",
       "      <td>24.193548</td>\n",
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       "      <th>6</th>\n",
       "      <td>700.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2.890696</td>\n",
       "      <td>-138.888889</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>51.548932</td>\n",
       "      <td>68.613139</td>\n",
       "      <td>31.746032</td>\n",
       "      <td>51.754386</td>\n",
       "      <td>61.842105</td>\n",
       "      <td>41.666667</td>\n",
       "      <td>51.345103</td>\n",
       "      <td>77.049180</td>\n",
       "      <td>25.641026</td>\n",
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       "      <td>78.0</td>\n",
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       "      <td>-29.411765</td>\n",
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       "      <td>67.441860</td>\n",
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       "      <td>22.222222</td>\n",
       "      <td>59.376801</td>\n",
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       "      <td>83.896940</td>\n",
       "      <td>85.185185</td>\n",
       "      <td>82.608696</td>\n",
       "      <td>83.896940</td>\n",
       "      <td>85.185185</td>\n",
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      "text/plain": [
       "   instances  accuracy      kappa     kappa_t     kappa_m   f1_score  \\\n",
       "0      100.0      34.0   0.000000 -340.000000 -112.903226        NaN   \n",
       "1      200.0      65.0   0.000000 -150.000000   41.666667        NaN   \n",
       "2      300.0      62.0 -23.136747 -216.666667   -2.702703  38.271605   \n",
       "3      400.0      63.0   3.242678 -236.363636   44.776119  62.922136   \n",
       "4      500.0      45.0  -4.009077 -358.333333   -5.769231  31.034483   \n",
       "5      600.0      47.0   6.952247 -278.571429  -51.428571  55.070599   \n",
       "6      700.0      57.0   2.890696 -138.888889   14.000000  51.548932   \n",
       "7      800.0      78.0  54.337900  -29.411765   48.837209  77.384628   \n",
       "8      900.0      58.0  17.808219 -121.052632   22.222222  59.376801   \n",
       "9     1000.0      84.0  67.793881 -100.000000   68.000000  83.896940   \n",
       "\n",
       "   f1_score_0  f1_score_1  precision  precision_0  precision_1     recall  \\\n",
       "0   50.746269         NaN        NaN    34.000000          NaN  50.000000   \n",
       "1   78.787879         NaN        NaN    65.000000          NaN  50.000000   \n",
       "2         NaN   76.543210  37.349398     0.000000    74.698795  39.240506   \n",
       "3   77.018634    5.128205  81.313131    62.626263   100.000000  51.315789   \n",
       "4   62.068966         NaN  22.959184    45.918367     0.000000  47.872340   \n",
       "5   54.700855   36.144578  55.967450    40.506329    71.428571  54.202037   \n",
       "6   68.613139   31.746032  51.754386    61.842105    41.666667  51.345103   \n",
       "7   81.666667   72.500000  78.078078    77.777778    78.378378  76.703386   \n",
       "8   55.319149   60.377358  59.562842    66.666667    52.459016  59.191919   \n",
       "9   85.185185   82.608696  83.896940    85.185185    82.608696  83.896940   \n",
       "\n",
       "     recall_0   recall_1  \n",
       "0  100.000000   0.000000  \n",
       "1  100.000000   0.000000  \n",
       "2    0.000000  78.481013  \n",
       "3  100.000000   2.631579  \n",
       "4   95.744681   0.000000  \n",
       "5   84.210526  24.193548  \n",
       "6   77.049180  25.641026  \n",
       "7   85.964912  67.441860  \n",
       "8   47.272727  71.111111  \n",
       "9   85.185185  82.608696  "
      ]
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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from capymoa.ssl.classifier import OSNN\n",
    "\n",
    "stream = Electricity()\n",
    "\n",
    "osnn = OSNN(schema=stream.get_schema())\n",
    "\n",
    "results_osnn = prequential_ssl_evaluation(stream=stream, learner=osnn, label_probability=0.01, window_size=100, max_instances=1000)\n",
    "\n",
    "# The results are stored in a dictionary. \n",
    "display(results_osnn)\n",
    "\n",
    "print(results_osnn['cumulative'].accuracy()) # Test-then-train accuracy, i.e. cumulatively, not windowed. \n",
    "display(results_osnn['windowed'].metrics_per_window()) # A dataframe containing the windowed results. \n",
    "\n",
    "# Plotting over time (default: classifications correct (percent) i.e. accuracy)\n",
    "results_osnn.learner = \"OSNN\"\n",
    "plot_windowed_results(results_osnn, metric='accuracy')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ad6a0a10-ab56-49b7-8e68-ae8eec941ebb",
   "metadata": {},
   "source": [
    "## 1.1 Using a supervised model\n",
    "\n",
    "* If a supervised model is used with `prequential_ssl_evaluation()` it will only be trained on the labeled data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89656078-4044-4863-9fb7-76f23e709033",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47.199999999999996\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instances</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>kappa</th>\n",
       "      <th>kappa_t</th>\n",
       "      <th>kappa_m</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>f1_score_0</th>\n",
       "      <th>f1_score_1</th>\n",
       "      <th>precision</th>\n",
       "      <th>precision_0</th>\n",
       "      <th>precision_1</th>\n",
       "      <th>recall</th>\n",
       "      <th>recall_0</th>\n",
       "      <th>recall_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-340.000000</td>\n",
       "      <td>-112.903226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.746269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-150.000000</td>\n",
       "      <td>41.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.787879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-558.333333</td>\n",
       "      <td>-113.513514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.710744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-245.454545</td>\n",
       "      <td>43.283582</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.543210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.338887</td>\n",
       "      <td>-316.666667</td>\n",
       "      <td>3.846154</td>\n",
       "      <td>61.726883</td>\n",
       "      <td>65.277778</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>74.226804</td>\n",
       "      <td>48.453608</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>52.830189</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>5.660377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>600.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-7.489598</td>\n",
       "      <td>-342.857143</td>\n",
       "      <td>-77.142857</td>\n",
       "      <td>44.007051</td>\n",
       "      <td>48.333333</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>42.682927</td>\n",
       "      <td>35.365854</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.415959</td>\n",
       "      <td>76.315789</td>\n",
       "      <td>14.516129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>700.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-39.992433</td>\n",
       "      <td>-311.111111</td>\n",
       "      <td>-48.000000</td>\n",
       "      <td>27.554157</td>\n",
       "      <td>24.489796</td>\n",
       "      <td>27.450980</td>\n",
       "      <td>27.327327</td>\n",
       "      <td>32.432432</td>\n",
       "      <td>22.222222</td>\n",
       "      <td>27.784784</td>\n",
       "      <td>19.672131</td>\n",
       "      <td>35.897436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>800.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>13.043478</td>\n",
       "      <td>-123.529412</td>\n",
       "      <td>11.627907</td>\n",
       "      <td>65.753425</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>20.833333</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>55.813953</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>11.627907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>10.480349</td>\n",
       "      <td>-115.789474</td>\n",
       "      <td>24.074074</td>\n",
       "      <td>60.034800</td>\n",
       "      <td>72.108844</td>\n",
       "      <td>22.641509</td>\n",
       "      <td>66.304348</td>\n",
       "      <td>57.608696</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>54.848485</td>\n",
       "      <td>96.363636</td>\n",
       "      <td>13.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2.343750</td>\n",
       "      <td>-462.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>61.508853</td>\n",
       "      <td>70.588235</td>\n",
       "      <td>4.255319</td>\n",
       "      <td>77.272727</td>\n",
       "      <td>54.545455</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>51.086957</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>2.173913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instances  accuracy      kappa     kappa_t     kappa_m   f1_score  \\\n",
       "0      100.0      34.0   0.000000 -340.000000 -112.903226        NaN   \n",
       "1      200.0      65.0   0.000000 -150.000000   41.666667        NaN   \n",
       "2      300.0      21.0   0.000000 -558.333333 -113.513514        NaN   \n",
       "3      400.0      62.0   0.000000 -245.454545   43.283582        NaN   \n",
       "4      500.0      50.0   5.338887 -316.666667    3.846154  61.726883   \n",
       "5      600.0      38.0  -7.489598 -342.857143  -77.142857  44.007051   \n",
       "6      700.0      26.0 -39.992433 -311.111111  -48.000000  27.554157   \n",
       "7      800.0      62.0  13.043478 -123.529412   11.627907  65.753425   \n",
       "8      900.0      59.0  10.480349 -115.789474   24.074074  60.034800   \n",
       "9     1000.0      55.0   2.343750 -462.500000   10.000000  61.508853   \n",
       "\n",
       "   f1_score_0  f1_score_1  precision  precision_0  precision_1     recall  \\\n",
       "0   50.746269         NaN        NaN    34.000000          NaN  50.000000   \n",
       "1   78.787879         NaN        NaN    65.000000          NaN  50.000000   \n",
       "2   34.710744         NaN        NaN    21.000000          NaN  50.000000   \n",
       "3   76.543210         NaN        NaN    62.000000          NaN  50.000000   \n",
       "4   65.277778   10.714286  74.226804    48.453608   100.000000  52.830189   \n",
       "5   48.333333   22.500000  42.682927    35.365854    50.000000  45.415959   \n",
       "6   24.489796   27.450980  27.327327    32.432432    22.222222  27.784784   \n",
       "7   75.000000   20.833333  80.000000    60.000000   100.000000  55.813953   \n",
       "8   72.108844   22.641509  66.304348    57.608696    75.000000  54.848485   \n",
       "9   70.588235    4.255319  77.272727    54.545455   100.000000  51.086957   \n",
       "\n",
       "     recall_0   recall_1  \n",
       "0  100.000000   0.000000  \n",
       "1  100.000000   0.000000  \n",
       "2  100.000000   0.000000  \n",
       "3  100.000000   0.000000  \n",
       "4  100.000000   5.660377  \n",
       "5   76.315789  14.516129  \n",
       "6   19.672131  35.897436  \n",
       "7  100.000000  11.627907  \n",
       "8   96.363636  13.333333  \n",
       "9  100.000000   2.173913  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from capymoa.classifier import StreamingRandomPatches\n",
    "\n",
    "srp10 = StreamingRandomPatches(schema=stream.get_schema(), ensemble_size=10)\n",
    "\n",
    "results_srp10 = prequential_ssl_evaluation(stream=stream, learner=srp10, label_probability=0.01, window_size=100, max_instances=1000)\n",
    "\n",
    "print(results_srp10['cumulative'].accuracy())\n",
    "display(results_srp10['windowed'].metrics_per_window())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db85c89b-6020-47b6-97ee-14266fcdaa0c",
   "metadata": {},
   "source": [
    "## 1.2 Comparing a SSL and supervised classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4003f5b7-6193-4f93-ac5c-4e2db840a6f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting all the results together\n",
    "# Adding an experiment_id to the results dictionary allows controlling the legend of each learner. \n",
    "results_osnn.learner = 'OSNN'\n",
    "results_srp10.learner = 'SRP10'\n",
    "\n",
    "plot_windowed_results(results_osnn, results_srp10, metric='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a97afba-790e-4d55-9d57-c7f2b0bd115c",
   "metadata": {},
   "source": [
    "## 2. Delay example\n",
    "* Comparing the effect of delay on a stream\n",
    "* It is particularly interesting to see the effect after a drift takes place. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f34a321e-d73e-412e-a5da-815dfa78ef89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy immediate: 86.031\n",
      "Accuracy delayed by 1000 instances: 82.865\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from capymoa.stream.generator import SEA\n",
    "from capymoa.stream.drift import *\n",
    "from capymoa.classifier import HoeffdingTree\n",
    "from capymoa.evaluation import prequential_evaluation\n",
    "\n",
    "## Creating a stream with drift\n",
    "sea2drifts = DriftStream(stream=[SEA(function=1), \n",
    "                                 AbruptDrift(position=25000), \n",
    "                                 SEA(function=2), \n",
    "                                 AbruptDrift(position=50000), \n",
    "                                 SEA(function=3)])\n",
    "\n",
    "\n",
    "ht_immediate = HoeffdingTree(schema=sea2drifts.get_schema())\n",
    "ht_delayed = HoeffdingTree(schema=sea2drifts.get_schema())\n",
    "\n",
    "results_ht_immediate = prequential_ssl_evaluation(stream=sea2drifts, \n",
    "                                                     learner=ht_immediate, \n",
    "                                                     label_probability=0.1, \n",
    "                                                     window_size=1000, \n",
    "                                                     max_instances=100000)\n",
    "\n",
    "results_ht_delayed_1000 = prequential_ssl_evaluation(stream=sea2drifts, \n",
    "                                                       learner=ht_delayed, \n",
    "                                                       label_probability=0.01, \n",
    "                                                       delay_length=1000, # adding the delay\n",
    "                                                       window_size=1000, \n",
    "                                                       max_instances=100000)\n",
    "\n",
    "results_ht_immediate.learner = 'HT_immediate'\n",
    "results_ht_delayed_1000.learner = 'HT_delayed_1000'\n",
    "\n",
    "print(f\"Accuracy immediate: {results_ht_immediate['cumulative'].accuracy()}\")\n",
    "print(f\"Accuracy delayed by 1000 instances: {results_ht_delayed_1000['cumulative'].accuracy()}\")\n",
    "\n",
    "plot_windowed_results(results_ht_immediate, results_ht_delayed_1000, metric='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6de2a5d-ac19-4b05-ace4-bba2ac300379",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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