1 - config usage.ipynb

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    "import altair\n",
    "import attr\n",
    "import cattr\n",
    "import yaml\n",
    "import enum\n",
    "from IPython.display import display\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn.datasets import load_iris, load_linnerud"
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   "source": [
    "# Extracting the Config - Usage"
   ]
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    "@enum.unique\n",
    "class Dataset(enum.Enum):\n",
    "    LINNERUD = \"linnerud\"\n",
    "    IRIS = \"iris\"\n",
    "\n",
    "\n",
    "@attr.frozen\n",
    "class ConfigSchema:\n",
    "    dataset: Dataset\n",
    "    outlier_n: int\n",
    "    plot_x: str\n",
    "    plot_y: str"
   ]
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       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
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       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
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       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
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       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-453bdd878de1dc1d4f8ba02c7ea0393a\"}, \"mark\": \"point\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"Outlier\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"Chins\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"Jumps\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-453bdd878de1dc1d4f8ba02c7ea0393a\": [{\"Chins\": 5.0, \"Situps\": 162.0, \"Jumps\": 60.0, \"Outlier\": false}, {\"Chins\": 2.0, \"Situps\": 110.0, \"Jumps\": 60.0, \"Outlier\": false}, {\"Chins\": 12.0, \"Situps\": 101.0, \"Jumps\": 101.0, \"Outlier\": false}, {\"Chins\": 12.0, \"Situps\": 105.0, \"Jumps\": 37.0, \"Outlier\": false}, {\"Chins\": 13.0, \"Situps\": 155.0, \"Jumps\": 58.0, \"Outlier\": false}, {\"Chins\": 4.0, \"Situps\": 101.0, \"Jumps\": 42.0, \"Outlier\": false}, {\"Chins\": 8.0, \"Situps\": 101.0, \"Jumps\": 38.0, \"Outlier\": false}, {\"Chins\": 6.0, \"Situps\": 125.0, \"Jumps\": 40.0, \"Outlier\": false}, {\"Chins\": 15.0, \"Situps\": 200.0, \"Jumps\": 40.0, \"Outlier\": false}, {\"Chins\": 17.0, \"Situps\": 251.0, \"Jumps\": 250.0, \"Outlier\": true}, {\"Chins\": 17.0, \"Situps\": 120.0, \"Jumps\": 38.0, \"Outlier\": false}, {\"Chins\": 13.0, \"Situps\": 210.0, \"Jumps\": 115.0, \"Outlier\": false}, {\"Chins\": 14.0, \"Situps\": 215.0, \"Jumps\": 105.0, \"Outlier\": false}, {\"Chins\": 1.0, \"Situps\": 50.0, \"Jumps\": 50.0, \"Outlier\": false}, {\"Chins\": 6.0, \"Situps\": 70.0, \"Jumps\": 31.0, \"Outlier\": false}, {\"Chins\": 12.0, \"Situps\": 210.0, \"Jumps\": 120.0, \"Outlier\": false}, {\"Chins\": 4.0, \"Situps\": 60.0, \"Jumps\": 25.0, \"Outlier\": false}, {\"Chins\": 11.0, \"Situps\": 230.0, \"Jumps\": 80.0, \"Outlier\": false}, {\"Chins\": 15.0, \"Situps\": 225.0, \"Jumps\": 73.0, \"Outlier\": false}, {\"Chins\": 2.0, \"Situps\": 110.0, \"Jumps\": 43.0, \"Outlier\": false}]}}, {\"mode\": \"vega-lite\"});\n",
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   "source": [
    "with open(\"config_b.yaml\") as f:\n",
    "    config = cattr.structure(yaml.safe_load(f), ConfigSchema)\n",
    "\n",
    "if config.dataset == Dataset.LINNERUD:\n",
    "    data = load_linnerud(as_frame=True).data\n",
    "elif config.dataset == Dataset.IRIS:\n",
    "    data = load_iris(as_frame=True).data\n",
    "else:\n",
    "    raise ValueError(f\"Unsupported dataset {config.dataset}\")\n",
    "\n",
    "data[\"Outlier\"] = (\n",
    "    LocalOutlierFactor(config.outlier_n)\n",
    "    .fit_predict(data) == -1\n",
    ")\n",
    "\n",
    "altair.Chart(data).mark_point().encode(\n",
    "    x=config.plot_x,\n",
    "    y=config.plot_y,\n",
    "    color=\"Outlier\"\n",
    ")"
   ]
  }
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