examples/sample_notebooks/classification_performance_multiclass_iris.ipynb

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   "source": [
    "import pandas as pd\n",
    "\n",
    "from sklearn import datasets, model_selection, linear_model, neighbors\n",
    "\n",
    "from evidently.dashboard import Dashboard\n",
    "from evidently.pipeline.column_mapping import ColumnMapping\n",
    "from evidently.dashboard.tabs import ClassificationPerformanceTab\n",
    "\n",
    "from evidently.model_profile import Profile\n",
    "from evidently.model_profile.sections import ClassificationPerformanceProfileSection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ebe84cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bce90c2",
   "metadata": {},
   "source": [
    "# Iris Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d1407fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d0fb2f4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_frame = pd.DataFrame(iris.data, columns = iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ceda6195",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_frame['target'] = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b756401a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = model_selection.train_test_split(iris_frame, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "676a496c",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'target'\n",
    "prediction = 'prediction'\n",
    "\n",
    "numerical_features = iris.feature_names\n",
    "categorical_features = []\n",
    "\n",
    "features = numerical_features + categorical_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f2f8a8f",
   "metadata": {},
   "source": [
    "# Model Performance Dashboard "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "df9bc0b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = neighbors.KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a003ae99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_data[features], train_data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c0e4a5a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predictions = model.predict(train_data[features])\n",
    "test_predictions = model.predict(test_data[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f39aa368",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['prediction'] = train_predictions\n",
    "test_data['prediction'] = test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1337ab19",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_column_mapping = ColumnMapping()\n",
    "\n",
    "iris_column_mapping.target = target\n",
    "iris_column_mapping.prediction = prediction\n",
    "iris_column_mapping.numerical_features = numerical_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8478b744",
   "metadata": {},
   "source": [
    "## Model Performance Dashboard full (verbose_level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4784b2d0",
   "metadata": {
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\"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.49], \"title\": {\"text\": \"petal width (cm)\"}}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.51, 1.0], \"matches\": \"x\", \"title\": {\"text\": \"petal width (cm)\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"count\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.0, 1.0], \"matches\": \"y\", \"showticklabels\": false}}}};\n",
       "</script>\n",
       "<script>\n",
       "function domReady(fn) {\n",
       "  // If we're early to the party\n",
       "  document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "  // If late; I mean on time.\n",
       "  if (document.readyState === \"interactive\" || document.readyState === \"complete\" ) {\n",
       "    fn();\n",
       "  }\n",
       "}\n",
       "\n",
       "domReady(function () {\n",
       "    requirejs([\"evidently\"], function(ev) {\n",
       "        drawDashboard(evidently_dashboard_1190539ea3f2412eab58a1eb28088946,\n",
       "        new Map(Object.entries(additional_graphs_evidently_dashboard_1190539ea3f2412eab58a1eb28088946)),\n",
       "        \"root_evidently_dashboard_1190539ea3f2412eab58a1eb28088946\");\n",
       "    },\n",
       "    function(err) {\n",
       "        $(\"#root_evidently_dashboard_1190539ea3f2412eab58a1eb28088946\").innerHTML = \"Failed to load\";\n",
       "    })\n",
       "});\n",
       "</script>\n",
       "<div class=\"reset-this-parent\" id=\"root_evidently_dashboard_1190539ea3f2412eab58a1eb28088946\">Loading...</div>\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_model_performance_dashboard = Dashboard(tabs=[ClassificationPerformanceTab(verbose_level=1)])\n",
    "iris_model_performance_dashboard.calculate(train_data, test_data, column_mapping = iris_column_mapping)\n",
    "iris_model_performance_dashboard.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d3d17cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# iris_model_performance_dashboard.save('iris_model_performance.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4a8faca",
   "metadata": {},
   "source": [
    "## Model Performance Dashboard short (verbose_level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "be7a2e85",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/icon?family=Material+Icons\"/>\n",
       "<style>\n",
       ".reset-this-parent {\n",
       "  all: initial;\n",
       "}\n",
       ".reset-this-parent h5 {\n",
       "  all: initial;\n",
       "  font: initial;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "    var evidently_dashboard_51ff5042392a42b698a42cc70880781a = {\"name\": \"evidently_dashboard_51ff5042392a42b698a42cc70880781a\", \"widgets\": [{\"type\": \"counter\", \"title\": \"\", \"size\": 2, \"details\": \"\", \"alertsPosition\": null, \"alertStats\": null, \"params\": {\"counters\": [{\"value\": \"\", \"label\": \"Classification Model Performance Report. Target: 'target'\"}]}, \"insights\": [], \"additionalGraphs\": [], \"alerts\": [], \"tabs\": []}, {\"type\": \"counter\", \"title\": \"Reference: Model Quality With Macro-average Metrics\", \"size\": 2, \"details\": \"\", \"alertsPosition\": null, \"alertStats\": null, \"params\": {\"counters\": [{\"value\": \"1.0\", \"label\": \"Accuracy\"}, {\"value\": \"1.0\", \"label\": \"Precision\"}, {\"value\": \"1.0\", \"label\": \"Recall\"}, {\"value\": \"1.0\", \"label\": \"F1\"}]}, \"insights\": [], \"additionalGraphs\": [], \"alerts\": [], \"tabs\": []}, {\"type\": \"counter\", \"title\": \"Current: Model Quality With Macro-average Metrics\", \"size\": 2, \"details\": \"\", \"alertsPosition\": null, \"alertStats\": null, \"params\": {\"counters\": [{\"value\": \"0.974\", \"label\": \"Accuracy\"}, {\"value\": \"0.967\", \"label\": \"Precision\"}, {\"value\": \"0.979\", \"label\": \"Recall\"}, {\"value\": \"0.972\", \"label\": \"F1\"}]}, \"insights\": [], \"additionalGraphs\": [], \"alerts\": [], \"tabs\": []}, {\"type\": \"big_graph\", \"title\": \"Reference: Class Representation\", \"size\": 1, \"details\": \"\", \"alertsPosition\": null, \"alertStats\": null, \"params\": {\"data\": [{\"marker\": {\"color\": \"#ed0400\"}, \"name\": \"Support\", \"type\": \"bar\", \"x\": [\"0\", \"1\", \"2\"], \"y\": [37.0, 34.0, 41.0]}], \"layout\": {\"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, 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\"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"dtick\": 1, \"gridcolor\": \"rgb(0, 0, 0)\", \"side\": \"top\", \"ticks\": \"\", \"title\": {\"text\": \"Class\"}}, \"yaxis\": {\"dtick\": 1, \"ticks\": \"\", \"ticksuffix\": \"  \", \"title\": {\"text\": \"Metric\"}}}}, \"insights\": [], \"additionalGraphs\": [], \"alerts\": [], \"tabs\": []}]};\n",
       "    var additional_graphs_evidently_dashboard_51ff5042392a42b698a42cc70880781a = {};\n",
       "</script>\n",
       "<script>\n",
       "function domReady(fn) {\n",
       "  // If we're early to the party\n",
       "  document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "  // If late; I mean on time.\n",
       "  if (document.readyState === \"interactive\" || document.readyState === \"complete\" ) {\n",
       "    fn();\n",
       "  }\n",
       "}\n",
       "\n",
       "domReady(function () {\n",
       "    requirejs([\"evidently\"], function(ev) {\n",
       "        drawDashboard(evidently_dashboard_51ff5042392a42b698a42cc70880781a,\n",
       "        new Map(Object.entries(additional_graphs_evidently_dashboard_51ff5042392a42b698a42cc70880781a)),\n",
       "        \"root_evidently_dashboard_51ff5042392a42b698a42cc70880781a\");\n",
       "    },\n",
       "    function(err) {\n",
       "        $(\"#root_evidently_dashboard_51ff5042392a42b698a42cc70880781a\").innerHTML = \"Failed to load\";\n",
       "    })\n",
       "});\n",
       "</script>\n",
       "<div class=\"reset-this-parent\" id=\"root_evidently_dashboard_51ff5042392a42b698a42cc70880781a\">Loading...</div>\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_model_performance_dashboard = Dashboard(tabs=[ClassificationPerformanceTab(verbose_level=0)])\n",
    "iris_model_performance_dashboard.calculate(train_data, test_data, column_mapping = iris_column_mapping)\n",
    "iris_model_performance_dashboard.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6ceef5e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# iris_model_performance_dashboard.save('iris_model_performance.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8e4586",
   "metadata": {},
   "source": [
    "## Model Performance Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a10634c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_classification_performance_profile = Profile(sections=[ClassificationPerformanceProfileSection()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8ec25a1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_classification_performance_profile.calculate(train_data, test_data, column_mapping = iris_column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a05afe1a",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"classification_performance\": {\"name\": \"classification_performance\", \"datetime\": \"2022-01-03 01:55:53.285913\", \"data\": {\"utility_columns\": {\"date\": null, \"id\": null, \"target\": \"target\", \"prediction\": \"prediction\"}, \"cat_feature_names\": [], \"num_feature_names\": [\"sepal length (cm)\", \"sepal width (cm)\", \"petal length (cm)\", \"petal width (cm)\"], \"target_names\": null, \"metrics\": {\"reference\": {\"accuracy\": 1.0, \"precision\": 1.0, \"recall\": 1.0, \"f1\": 1.0, \"metrics_matrix\": {\"0\": {\"precision\": 1.0, \"recall\": 1.0, \"f1-score\": 1.0, \"support\": 37}, \"1\": {\"precision\": 1.0, \"recall\": 1.0, \"f1-score\": 1.0, \"support\": 34}, \"2\": {\"precision\": 1.0, \"recall\": 1.0, \"f1-score\": 1.0, \"support\": 41}, \"accuracy\": 1.0, \"macro avg\": {\"precision\": 1.0, \"recall\": 1.0, \"f1-score\": 1.0, \"support\": 112}, \"weighted avg\": {\"precision\": 1.0, \"recall\": 1.0, \"f1-score\": 1.0, \"support\": 112}}, \"confusion_matrix\": {\"labels\": [0, 1, 2], \"values\": [[37, 0, 0], [0, 34, 0], [0, 0, 41]]}}, \"current\": {\"accuracy\": 0.9736842105263158, \"precision\": 0.9666666666666667, \"recall\": 0.9791666666666666, \"f1\": 0.9717034521788342, \"metrics_matrix\": {\"0\": {\"precision\": 1.0, \"recall\": 1.0, \"f1-score\": 1.0, \"support\": 13}, \"1\": {\"precision\": 1.0, \"recall\": 0.9375, \"f1-score\": 0.967741935483871, \"support\": 16}, \"2\": {\"precision\": 0.9, \"recall\": 1.0, \"f1-score\": 0.9473684210526316, \"support\": 9}, \"accuracy\": 0.9736842105263158, \"macro avg\": {\"precision\": 0.9666666666666667, \"recall\": 0.9791666666666666, \"f1-score\": 0.9717034521788342, \"support\": 38}, \"weighted avg\": {\"precision\": 0.9763157894736842, \"recall\": 0.9736842105263158, \"f1-score\": 0.9739522830846216, \"support\": 38}}, \"confusion_matrix\": {\"labels\": [0, 1, 2], \"values\": [[13, 0, 0], [0, 15, 1], [0, 0, 9]]}}}}}, \"timestamp\": \"2022-01-03 01:55:53.777178\"}'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_classification_performance_profile.json() "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
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