1  {
  2   "cells": [
  3    {
  4     "cell_type": "code",
  5     "execution_count": 1,
  6     "id": "3001cdaf",
  7     "metadata": {},
  8     "outputs": [
  9      {
 10       "name": "stdout",
 11       "output_type": "stream",
 12       "text": [
 13        "Obtaining file:///Users/olgafilippova/work/evidently\n",
 14        "Requirement already satisfied: dataclasses>=0.6 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from evidently==0.1.40.dev0) (0.6)\n",
 15        "Requirement already satisfied: plotly>=4.12.0 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from evidently==0.1.40.dev0) (4.12.0)\n",
 16        "Requirement already satisfied: statsmodels>=0.12.2 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from evidently==0.1.40.dev0) (0.12.2)\n",
 17        "Requirement already satisfied: scikit-learn>=0.23.2 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from evidently==0.1.40.dev0) (0.24.2)\n",
 18        "Requirement already satisfied: pandas>=1.1.5 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from evidently==0.1.40.dev0) (1.1.5)\n",
 19        "Requirement already satisfied: numpy>=1.19.5 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from evidently==0.1.40.dev0) (1.19.5)\n",
 20        "Requirement already satisfied: scipy>=1.5.4 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from evidently==0.1.40.dev0) (1.5.4)\n",
 21        "Requirement already satisfied: requests>=2.19.0 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from evidently==0.1.40.dev0) (2.26.0)\n",
 22        "Requirement already satisfied: PyYAML~=5.1 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from evidently==0.1.40.dev0) (5.4.1)\n",
 23        "Requirement already satisfied: pytz>=2017.2 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from pandas>=1.1.5->evidently==0.1.40.dev0) (2021.3)\n",
 24        "Requirement already satisfied: python-dateutil>=2.7.3 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from pandas>=1.1.5->evidently==0.1.40.dev0) (2.8.2)\n",
 25        "Requirement already satisfied: retrying>=1.3.3 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from plotly>=4.12.0->evidently==0.1.40.dev0) (1.3.3)\n",
 26        "Requirement already satisfied: six in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from plotly>=4.12.0->evidently==0.1.40.dev0) (1.16.0)\n",
 27        "Requirement already satisfied: idna<4,>=2.5 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.19.0->evidently==0.1.40.dev0) (3.2)\n",
 28        "Requirement already satisfied: certifi>=2017.4.17 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.19.0->evidently==0.1.40.dev0) (2021.10.8)\n",
 29        "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.19.0->evidently==0.1.40.dev0) (1.26.7)\n",
 30        "Requirement already satisfied: charset-normalizer~=2.0.0 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.19.0->evidently==0.1.40.dev0) (2.0.4)\n",
 31        "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from scikit-learn>=0.23.2->evidently==0.1.40.dev0) (2.2.0)\n",
 32        "Requirement already satisfied: joblib>=0.11 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from scikit-learn>=0.23.2->evidently==0.1.40.dev0) (1.1.0)\n",
 33        "Requirement already satisfied: patsy>=0.5 in /Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages (from statsmodels>=0.12.2->evidently==0.1.40.dev0) (0.5.2)\n",
 34        "Installing collected packages: evidently\n",
 35        "  Attempting uninstall: evidently\n",
 36        "    Found existing installation: evidently 0.1.40.dev0\n",
 37        "    Uninstalling evidently-0.1.40.dev0:\n",
 38        "      Successfully uninstalled evidently-0.1.40.dev0\n",
 39        "  Running setup.py develop for evidently\n",
 40        "Successfully installed evidently-0.1.40.dev0\n"
 41       ]
 42      }
 43     ],
 44     "source": [
 45      "!pip install -e /Users/olgafilippova/work/evidently"
 46     ]
 47    },
 48    {
 49     "cell_type": "code",
 50     "execution_count": 47,
 51     "id": "018b1851",
 52     "metadata": {},
 53     "outputs": [],
 54     "source": [
 55      "import pandas as pd\n",
 56      "import numpy as np\n",
 57      "from sklearn.datasets import fetch_openml\n",
 58      "from sklearn.ensemble import RandomForestClassifier\n",
 59      "from sklearn.linear_model import LogisticRegression\n",
 60      "from sklearn.model_selection import train_test_split\n",
 61      "from sklearn.neighbors import KNeighborsClassifier\n",
 62      "import seaborn as sns\n",
 63      "\n",
 64      "import pandas as pd\n",
 65      "\n",
 66      "\n",
 67      "from evidently.legacy.dashboard import Dashboard\n",
 68      "from evidently.legacy.pipeline.column_mapping import ColumnMapping\n",
 69      "from evidently.legacy.dashboard.tabs import ProbClassificationPerformanceTab\n",
 70      "\n",
 71      "from evidently.legacy.model_profile import Profile\n",
 72      "from evidently.legacy.model_profile.sections import ProbClassificationPerformanceProfileSection"
 73     ]
 74    },
 75    {
 76     "cell_type": "markdown",
 77     "id": "9923a4d4",
 78     "metadata": {},
 79     "source": [
 80      "# Load data"
 81     ]
 82    },
 83    {
 84     "cell_type": "code",
 85     "execution_count": 81,
 86     "id": "be6d1ca8",
 87     "metadata": {},
 88     "outputs": [],
 89     "source": [
 90      "wine = fetch_openml(name='wine_quality', version=1)"
 91     ]
 92    },
 93    {
 94     "cell_type": "code",
 95     "execution_count": 82,
 96     "id": "f29ccd4f",
 97     "metadata": {},
 98     "outputs": [],
 99     "source": [
100      "wine_df = wine.frame"
101     ]
102    },
103    {
104     "cell_type": "code",
105     "execution_count": 83,
106     "id": "20f16f3c",
107     "metadata": {},
108     "outputs": [],
109     "source": [
110      "red = wine_df[:1599]\n",
111      "white = wine_df[1599:]"
112     ]
113    },
114    {
115     "cell_type": "code",
116     "execution_count": 84,
117     "id": "4018dd44",
118     "metadata": {},
119     "outputs": [],
120     "source": [
121      "wine_column_mapping = ColumnMapping()\n",
122      "wine_column_mapping.numerical_features = wine.feature_names"
123     ]
124    },
125    {
126     "cell_type": "code",
127     "execution_count": 86,
128     "id": "05fd2521",
129     "metadata": {},
130     "outputs": [],
131     "source": "from evidently.legacy.dashboard.tabs import DataDriftTab"
132    },
133    {
134     "cell_type": "code",
135     "execution_count": null,
136     "id": "27ccd83f",
137     "metadata": {},
138     "outputs": [],
139     "source": [
140      "model_performance_dashboard = Dashboard(tabs=[DataDriftTab()])\n",
141      "model_performance_dashboard.calculate(red.sample(1000, random_state=0), \n",
142      "                                      white.sample(1000, random_state=0), \n",
143      "                                      column_mapping=wine_column_mapping)\n",
144      "model_performance_dashboard.show()"
145     ]
146    },
147    {
148     "cell_type": "code",
149     "execution_count": 57,
150     "id": "4024452e",
151     "metadata": {},
152     "outputs": [],
153     "source": [
154      "default_df['y'] = default_df['y'].astype(int)"
155     ]
156    },
157    {
158     "cell_type": "markdown",
159     "id": "8c48655b",
160     "metadata": {},
161     "source": [
162      "# Train model, get predictions"
163     ]
164    },
165    {
166     "cell_type": "code",
167     "execution_count": 162,
168     "id": "3e959879",
169     "metadata": {},
170     "outputs": [],
171     "source": [
172      "wine_df['target'] = (wine_df['quality'] > 5).astype(int)"
173     ]
174    },
175    {
176     "cell_type": "code",
177     "execution_count": 163,
178     "id": "e2f47845",
179     "metadata": {},
180     "outputs": [],
181     "source": [
182      "train_data, test_data = train_test_split(wine_df, random_state=0)"
183     ]
184    },
185    {
186     "cell_type": "code",
187     "execution_count": 164,
188     "id": "235bbf4b",
189     "metadata": {},
190     "outputs": [],
191     "source": [
192      "model = LogisticRegression()"
193     ]
194    },
195    {
196     "cell_type": "code",
197     "execution_count": 165,
198     "id": "fd392019",
199     "metadata": {},
200     "outputs": [
201      {
202       "name": "stderr",
203       "output_type": "stream",
204       "text": [
205        "/Users/olgafilippova/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning:\n",
206        "\n",
207        "lbfgs failed to converge (status=1):\n",
208        "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
209        "\n",
210        "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
211        "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
212        "Please also refer to the documentation for alternative solver options:\n",
213        "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
214        "\n"
215       ]
216      },
217      {
218       "data": {
219        "text/plain": [
220         "LogisticRegression()"
221        ]
222       },
223       "execution_count": 165,
224       "metadata": {},
225       "output_type": "execute_result"
226      }
227     ],
228     "source": [
229      "model.fit(wine_df[wine.feature_names], wine_df.target)"
230     ]
231    },
232    {
233     "cell_type": "code",
234     "execution_count": 169,
235     "id": "6c547fec",
236     "metadata": {},
237     "outputs": [],
238     "source": [
239      "train_probas = pd.DataFrame(model.predict_proba(train_data[wine.feature_names]))\n",
240      "train_probas.columns = ['bad', 'good']\n",
241      "test_probas = pd.DataFrame(model.predict_proba(test_data[wine.feature_names]))\n",
242      "test_probas.columns = ['bad', 'good']"
243     ]
244    },
245    {
246     "cell_type": "code",
247     "execution_count": 170,
248     "id": "f27defe4",
249     "metadata": {},
250     "outputs": [],
251     "source": [
252      "train_data['target'] = train_data['target'].map({1: 'good', 0: 'bad'}).astype(str).values\n",
253      "test_data['target'] = test_data['target'].map({1: 'good', 0: 'bad'}).astype(str).values"
254     ]
255    },
256    {
257     "cell_type": "code",
258     "execution_count": 172,
259     "id": "57cb6df8",
260     "metadata": {},
261     "outputs": [],
262     "source": [
263      "train_data.reset_index(inplace=True, drop=True)\n",
264      "test_data.reset_index(inplace=True, drop=True)\n",
265      "\n",
266      "merged_train_data = pd.concat([train_data, train_probas], axis=1)\n",
267      "merged_test_data = pd.concat([test_data, test_probas], axis=1)"
268     ]
269    },
270    {
271     "cell_type": "markdown",
272     "id": "dd88eca8",
273     "metadata": {},
274     "source": [
275      "# Probabilistic Model Performance Dashboard full "
276     ]
277    },
278    {
279     "cell_type": "code",
280     "execution_count": 158,
281     "id": "91a9270a",
282     "metadata": {},
283     "outputs": [],
284     "source": [
285      "wine_column_mapping = ColumnMapping()\n",
286      "\n",
287      "wine_column_mapping.target = 'target'\n",
288      "wine_column_mapping.prediction = ['good', 'bad']\n",
289      "wine_column_mapping.numerical_features = wine.feature_names"
290     ]
291    },
292    {
293     "cell_type": "code",
294     "execution_count": 184,
295     "id": "3f73178e",
296     "metadata": {},
297     "outputs": [
298      {
299       "data": {
300        "text/html": [
301         "\n",
302         "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/icon?family=Material+Icons\"/>\n",
303         "<style>\n",
304         ".reset-this-parent {\n",
305         "  all: initial;\n",
306         "}\n",
307         ".reset-this-parent h5 {\n",
308         "  all: initial;\n",
309         "  font: initial;\n",
310         "}\n",
311         "</style>\n",
312         "<script>\n",
313         "    var evidently_dashboard_522f08a9c3494994bcc173c8e0de5a16 = {\"name\": \"evidently_dashboard_522f08a9c3494994bcc173c8e0de5a16\", \"widgets\": [{\"type\": \"counter\", \"title\": \"\", \"size\": 2, \"details\": \"\", \"alertsPosition\": null, \"alertStats\": null, \"params\": {\"counters\": [{\"value\": \"\", \"label\": \"Probabilistic Classification Model Performance Report. 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315         "</script>\n",
316         "<script>\n",
317         "function domReady(fn) {\n",
318         "  // If we're early to the party\n",
319         "  document.addEventListener(\"DOMContentLoaded\", fn);\n",
320         "  // If late; I mean on time.\n",
321         "  if (document.readyState === \"interactive\" || document.readyState === \"complete\" ) {\n",
322         "    fn();\n",
323         "  }\n",
324         "}\n",
325         "\n",
326         "domReady(function () {\n",
327         "    requirejs([\"evidently\"], function(ev) {\n",
328         "        drawDashboard(evidently_dashboard_522f08a9c3494994bcc173c8e0de5a16,\n",
329         "        new Map(Object.entries(additional_graphs_evidently_dashboard_522f08a9c3494994bcc173c8e0de5a16)),\n",
330         "        \"root_evidently_dashboard_522f08a9c3494994bcc173c8e0de5a16\");\n",
331         "    },\n",
332         "    function(err) {\n",
333         "        $(\"#root_evidently_dashboard_522f08a9c3494994bcc173c8e0de5a16\").innerHTML = \"Failed to load\";\n",
334         "    })\n",
335         "});\n",
336         "</script>\n",
337         "<div class=\"reset-this-parent\" id=\"root_evidently_dashboard_522f08a9c3494994bcc173c8e0de5a16\">Loading...</div>\n",
338         "\n"
339        ],
340        "text/plain": [
341         "<IPython.core.display.HTML object>"
342        ]
343       },
344       "execution_count": 184,
345       "metadata": {},
346       "output_type": "execute_result"
347      }
348     ],
349     "source": [
350      "wine_model_performance_dashboard = Dashboard(tabs=[ProbClassificationPerformanceTab(verbose_level=1)])\n",
351      "wine_model_performance_dashboard.calculate(merged_train_data.sample(1000, random_state=0), \n",
352      "                                           merged_test_data.sample(1000, random_state=0), \n",
353      "                                              column_mapping = wine_column_mapping)\n",
354      "wine_model_performance_dashboard.show()"
355     ]
356    },
357    {
358     "cell_type": "code",
359     "execution_count": 181,
360     "id": "9535c77d",
361     "metadata": {},
362     "outputs": [],
363     "source": "from evidently.legacy.options import QualityMetricsOptions"
364    },
365    {
366     "cell_type": "code",
367     "execution_count": 185,
368     "id": "25ef8bf5",
369     "metadata": {},
370     "outputs": [],
371     "source": [
372      "m_options = QualityMetricsOptions(cut_quantile={'chlorides': ('right', 0.95), 'MedHouseVal': ('right', 0.5)}, \n",
373      "                                  classification_threshold = 0.8,\n",
374      "                                  conf_interval_n_sigmas=3)"
375     ]
376    },
377    {
378     "cell_type": "code",
379     "execution_count": 186,
380     "id": "e81bc1da",
381     "metadata": {},
382     "outputs": [
383      {
384       "data": {
385        "text/html": [
386         "\n",
387         "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/icon?family=Material+Icons\"/>\n",
388         "<style>\n",
389         ".reset-this-parent {\n",
390         "  all: initial;\n",
391         "}\n",
392         ".reset-this-parent h5 {\n",
393         "  all: initial;\n",
394         "  font: initial;\n",
395         "}\n",
396         "</style>\n",
397         "<script>\n",
398         "    var evidently_dashboard_cf026d3015f94e9a8797581f20ae8f6c = {\"name\": \"evidently_dashboard_cf026d3015f94e9a8797581f20ae8f6c\", \"widgets\": [{\"type\": \"counter\", \"title\": \"\", \"size\": 2, \"details\": \"\", \"alertsPosition\": null, \"alertStats\": null, \"params\": {\"counters\": [{\"value\": \"\", \"label\": \"Probabilistic Classification Model Performance Report. 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400         "</script>\n",
401         "<script>\n",
402         "function domReady(fn) {\n",
403         "  // If we're early to the party\n",
404         "  document.addEventListener(\"DOMContentLoaded\", fn);\n",
405         "  // If late; I mean on time.\n",
406         "  if (document.readyState === \"interactive\" || document.readyState === \"complete\" ) {\n",
407         "    fn();\n",
408         "  }\n",
409         "}\n",
410         "\n",
411         "domReady(function () {\n",
412         "    requirejs([\"evidently\"], function(ev) {\n",
413         "        drawDashboard(evidently_dashboard_cf026d3015f94e9a8797581f20ae8f6c,\n",
414         "        new Map(Object.entries(additional_graphs_evidently_dashboard_cf026d3015f94e9a8797581f20ae8f6c)),\n",
415         "        \"root_evidently_dashboard_cf026d3015f94e9a8797581f20ae8f6c\");\n",
416         "    },\n",
417         "    function(err) {\n",
418         "        $(\"#root_evidently_dashboard_cf026d3015f94e9a8797581f20ae8f6c\").innerHTML = \"Failed to load\";\n",
419         "    })\n",
420         "});\n",
421         "</script>\n",
422         "<div class=\"reset-this-parent\" id=\"root_evidently_dashboard_cf026d3015f94e9a8797581f20ae8f6c\">Loading...</div>\n",
423         "\n"
424        ],
425        "text/plain": [
426         "<IPython.core.display.HTML object>"
427        ]
428       },
429       "execution_count": 186,
430       "metadata": {},
431       "output_type": "execute_result"
432      }
433     ],
434     "source": [
435      "wine_model_performance_dashboard = Dashboard(tabs=[ProbClassificationPerformanceTab(verbose_level=1)],\n",
436      "                                             options=[m_options])\n",
437      "wine_model_performance_dashboard.calculate(merged_train_data.sample(1000, random_state=0), \n",
438      "                                           merged_test_data.sample(1000, random_state=0), \n",
439      "                                              column_mapping = wine_column_mapping)\n",
440      "wine_model_performance_dashboard.show()"
441     ]
442    },
443    {
444     "cell_type": "code",
445     "execution_count": 202,
446     "id": "19c2cec9",
447     "metadata": {},
448     "outputs": [],
449     "source": "from evidently.legacy.dashboard.tabs import DataDriftTab"
450    },
451    {
452     "cell_type": "code",
453     "execution_count": 203,
454     "id": "661a996a",
455     "metadata": {},
456     "outputs": [
457      {
458       "data": {
459        "text/html": [
460         "\n",
461         "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/icon?family=Material+Icons\"/>\n",
462         "<style>\n",
463         ".reset-this-parent {\n",
464         "  all: initial;\n",
465         "}\n",
466         ".reset-this-parent h5 {\n",
467         "  all: initial;\n",
468         "  font: initial;\n",
469         "}\n",
470         "</style>\n",
471         "<script>\n",
472         "    var evidently_dashboard_3966efb347484c23af70237a11fa6d90 = {\"name\": \"evidently_dashboard_3966efb347484c23af70237a11fa6d90\", \"widgets\": [{\"type\": \"big_table\", \"title\": \"Drift is detected for 0.00% of features (0 out of 11). Dataset Drift is NOT detected.\", \"size\": 2, \"details\": \"\", \"alertsPosition\": \"row\", \"alertStats\": {}, \"params\": {\"rowsPerPage\": 10, \"columns\": [{\"title\": \"Feature\", \"field\": \"f1\"}, {\"title\": \"Type\", \"field\": \"f6\"}, {\"title\": \"Reference Distribution\", \"field\": \"f3\", \"type\": \"histogram\", \"options\": {\"xField\": \"x\", \"yField\": \"y\"}}, {\"title\": \"Current Distribution\", \"field\": \"f4\", \"type\": \"histogram\", \"options\": {\"xField\": \"x\", \"yField\": \"y\"}}, {\"title\": \"Data drift\", \"field\": \"f2\"}, {\"title\": \"P-Value for Similarity Test\", \"field\": \"f5\", \"sort\": \"asc\"}], \"data\": [{\"details\": {\"parts\": [{\"title\": \"Data drift\", \"id\": \"fixed.acidity_drift\", \"type\": \"widget\"}, {\"title\": \"Data distribution\", \"id\": \"fixed.acidity_distr\"}], \"insights\": []}, \"f1\": \"fixed.acidity\", \"f6\": \"num\", \"f3\": {\"x\": [4.4, 5.550000000000001, 6.7, 7.85, 9.0, 10.15, 11.3, 12.45, 13.6, 14.75, 15.9], \"y\": [0.030434782608695643, 0.2947826086956523, 0.36347826086956536, 0.09826086956521736, 0.05043478260869563, 0.018260869565217386, 0.008695652173913054, 0.0026086956521739124, 0.0008695652173913041, 0.0017391304347826081]}, \"f4\": {\"x\": [4.2, 5.34, 6.48, 7.62, 8.76, 9.899999999999999, 11.04, 12.18, 13.32, 14.46, 15.6], \"y\": [0.021052631578947375, 0.19649122807017533, 0.43508771929824575, 0.1342105263157895, 0.04736842105263163, 0.021929824561403497, 0.0131578947368421, 0.005263157894736839, 0.0008771929824561398, 0.0017543859649122827]}, \"f2\": \"Not Detected\", \"f5\": 0.240682}, {\"details\": {\"parts\": [{\"title\": \"Data drift\", \"id\": \"volatile.acidity_drift\", \"type\": \"widget\"}, {\"title\": \"Data distribution\", \"id\": \"volatile.acidity_distr\"}], \"insights\": []}, \"f1\": \"volatile.acidity\", \"f6\": \"num\", \"f3\": {\"x\": [0.08, 0.20500000000000002, 0.33, 0.455, 0.58, 0.705, 0.83, 0.955, 1.08, 1.205, 1.33], \"y\": [1.488, 3.408, 1.68, 0.5600000000000003, 0.584, 0.152, 0.072, 0.02399999999999998, 0.016, 0.016]}, \"f4\": {\"x\": [0.08, 0.22999999999999998, 0.38, 0.5299999999999999, 0.6799999999999999, 0.83, 0.9799999999999999, 1.1300000000000001, 1.28, 1.43, 1.58], \"y\": [1.7400000000000004, 3.093333333333333, 1.0600000000000007, 0.5533333333333332, 0.15333333333333332, 0.04000000000000002, 0.01999999999999997, 0.0, 0.0, 0.006666666666666661]}, \"f2\": \"Not Detected\", \"f5\": 0.954219}, {\"details\": {\"parts\": [{\"title\": \"Data drift\", \"id\": \"citric.acid_drift\", \"type\": \"widget\"}, {\"title\": \"Data distribution\", \"id\": \"citric.acid_distr\"}], \"insights\": []}, \"f1\": \"citric.acid\", \"f6\": \"num\", \"f3\": {\"x\": [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0], \"y\": [0.69, 0.59, 3.4899999999999984, 2.8400000000000003, 1.6400000000000003, 0.4399999999999996, 0.21000000000000005, 0.09000000000000001, 0.0, 0.010000000000000002]}, \"f4\": {\"x\": [0.0, 0.123, 0.246, 0.369, 0.492, 0.615, 0.738, 0.861, 0.984, 1.107, 1.23], \"y\": [0.6991869918699187, 1.2682926829268293, 3.5691056910569103, 1.991869918699187, 0.34959349593495936, 0.13821138211382114, 0.08130081300813008, 0.008130081300813009, 0.016260162601626018, 0.008130081300813009]}, \"f2\": \"Not Detected\", \"f5\": 0.794664}, {\"details\": {\"parts\": [{\"title\": \"Data drift\", \"id\": \"residual.sugar_drift\", \"type\": \"widget\"}, {\"title\": \"Data distribution\", \"id\": \"residual.sugar_distr\"}], \"insights\": []}, \"f1\": \"residual.sugar\", \"f6\": \"num\", \"f3\": {\"x\": [0.6, 3.7, 6.8, 9.9, 13.0, 16.1, 19.200000000000003, 22.3, 25.400000000000002, 28.500000000000004, 31.6], \"y\": [0.17483870967741935, 0.04612903225806452, 0.04032258064516128, 0.027096774193548393, 0.024193548387096763, 0.008064516129032254, 0.0012903225806451622, 0.0, 0.00032258064516129016, 0.00032258064516129054]}, \"f4\": {\"x\": [0.7, 3.79, 6.880000000000001, 9.97, 13.06, 16.150000000000002, 19.240000000000002, 22.330000000000002, 25.42, 28.51, 31.6], \"y\": [0.16957928802588998, 0.04530744336569578, 0.040776699029126215, 0.029449838187702266, 0.02588996763754044, 0.009385113268608414, 0.002912621359223301, 0.0, 0.0, 0.0003236245954692557]}, \"f2\": \"Not Detected\", \"f5\": 0.685497}, {\"details\": {\"parts\": [{\"title\": \"Data drift\", \"id\": \"chlorides_drift\", \"type\": \"widget\"}, {\"title\": \"Data distribution\", \"id\": \"chlorides_distr\"}], \"insights\": []}, \"f1\": \"chlorides\", \"f6\": \"num\", \"f3\": {\"x\": [0.012, 0.057499999999999996, 0.103, 0.14850000000000002, 0.194, 0.2395, 0.28500000000000003, 0.3305, 0.376, 0.4215, 0.467], \"y\": [15.626373626373626, 5.362637362637362, 0.615384615384615, 0.1538461538461539, 0.1318681318681319, 0.021978021978021955, 0.04395604395604397, 0.0, 0.0, 0.021978021978021955]}, \"f4\": {\"x\": [0.012, 0.052199999999999996, 0.0924, 0.1326, 0.1728, 0.21300000000000002, 0.2532, 0.2934, 0.3336, 0.3738, 0.414], \"y\": [15.348258706467663, 7.835820895522388, 1.0696517412935322, 0.2238805970149253, 0.17412935323383077, 0.09950248756218916, 0.024875621890547255, 0.024875621890547255, 0.0, 0.07462686567164187]}, \"f2\": \"Not Detected\", \"f5\": 0.263472}, {\"details\": {\"parts\": [{\"title\": \"Data drift\", \"id\": \"free.sulfur.dioxide_drift\", \"type\": \"widget\"}, {\"title\": \"Data distribution\", \"id\": \"free.sulfur.dioxide_distr\"}], \"insights\": []}, \"f1\": \"free.sulfur.dioxide\", \"f6\": \"num\", \"f3\": {\"x\": [3.0, 17.35, 31.7, 46.05, 60.4, 74.75, 89.1, 103.45, 117.8, 132.15, 146.5], \"y\": [0.01749128919860627, 0.021811846689895473, 0.017003484320557493, 0.009477351916376305, 0.00313588850174216, 0.0004878048780487807, 6.968641114982574e-05, 6.968641114982581e-05, 6.968641114982574e-05, 6.968641114982581e-05]}, \"f4\": {\"x\": [1.0, 11.4, 21.8, 32.2, 42.6, 53.0, 63.400000000000006, 73.8, 84.2, 94.60000000000001, 105.0], \"y\": [0.013557692307692307, 0.019134615384615382, 0.023461538461538457, 0.01682692307692308, 0.011826923076923079, 0.007596153846153842, 0.002980769230769233, 0.0005769230769230767, 0.0, 0.00019230769230769247]}, \"f2\": \"Not Detected\", \"f5\": 0.993703}, {\"details\": {\"parts\": [{\"title\": \"Data drift\", \"id\": \"total.sulfur.dioxide_drift\", \"type\": \"widget\"}, {\"title\": \"Data distribution\", \"id\": \"total.sulfur.dioxide_distr\"}], \"insights\": []}, \"f1\": \"total.sulfur.dioxide\", \"f6\": \"num\", \"f3\": {\"x\": [8.0, 37.95, 67.9, 97.85, 127.8, 157.75, 187.7, 217.65, 247.6, 277.55, 307.5], \"y\": [0.00350584307178631, 0.0032053422370617694, 0.0050083472454090176, 0.007312186978297161, 0.005642737896494157, 0.004908180300500837, 0.0026711185308848067, 0.0009348914858096831, 0.00010016694490818024, 0.00010016694490818035]}, \"f4\": {\"x\": [6.0, 34.8, 63.6, 92.4, 121.2, 150.0, 178.8, 207.6, 236.4, 265.2, 294.0], \"y\": [0.0038541666666666668, 0.003333333333333333, 0.004201388888888888, 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33.31852509995553, 1.7769880053309617, 0.0, 0.0, 0.4442470013327404]}, \"f2\": \"Not Detected\", \"f5\": 0.57289}, {\"details\": {\"parts\": [{\"title\": \"Data drift\", \"id\": \"pH_drift\", \"type\": \"widget\"}, {\"title\": \"Data distribution\", \"id\": \"pH_distr\"}], \"insights\": []}, \"f1\": \"pH\", \"f6\": \"num\", \"f3\": {\"x\": [2.79, 2.888, 2.986, 3.084, 3.182, 3.2800000000000002, 3.378, 3.476, 3.574, 3.672, 3.77], \"y\": [0.08163265306122461, 0.5612244897959167, 1.346938775510206, 2.6632653061224527, 2.3469387755101967, 1.500000000000002, 0.9285714285714298, 0.4897959183673476, 0.2040816326530606, 0.08163265306122461]}, \"f4\": {\"x\": [2.79, 2.892, 2.994, 3.096, 3.198, 3.3, 3.402, 3.504, 3.606, 3.708, 3.81], \"y\": [0.1078431372549021, 0.7156862745098017, 1.4215686274509822, 2.333333333333336, 2.176470588235297, 1.8823529411764648, 0.7450980392156872, 0.31372549019607887, 0.0490196078431371, 0.05882352941176478]}, \"f2\": \"Not Detected\", \"f5\": 0.722825}, {\"details\": {\"parts\": [{\"title\": \"Data drift\", \"id\": \"sulphates_drift\", \"type\": \"widget\"}, {\"title\": \"Data distribution\", \"id\": \"sulphates_distr\"}], \"insights\": []}, \"f1\": \"sulphates\", \"f6\": \"num\", \"f3\": {\"x\": [0.22, 0.393, 0.566, 0.7389999999999999, 0.9119999999999999, 1.085, 1.2579999999999998, 1.4309999999999998, 1.6039999999999999, 1.777, 1.95], \"y\": [0.8439306358381502, 3.0173410404624286, 1.4913294797687868, 0.2947976878612716, 0.09826589595375719, 0.01156069364161851, 0.01734104046242774, 0.0, 0.0, 0.005780346820809247]}, \"f4\": {\"x\": [0.25, 0.359, 0.468, 0.5770000000000001, 0.686, 0.795, 0.9040000000000001, 1.0130000000000001, 1.122, 1.231, 1.34], \"y\": [0.6238532110091743, 2.724770642201834, 2.8440366972477054, 1.6238532110091746, 0.8807339449541286, 0.36697247706421987, 0.07339449541284405, 0.018348623853211014, 0.0, 0.018348623853211014]}, \"f2\": \"Not Detected\", \"f5\": 0.240682}, {\"details\": {\"parts\": [{\"title\": \"Data drift\", \"id\": \"alcohol_drift\", \"type\": \"widget\"}, {\"title\": \"Data distribution\", \"id\": \"alcohol_distr\"}], \"insights\": []}, \"f1\": \"alcohol\", \"f6\": \"num\", \"f3\": {\"x\": [8.4, 9.05, 9.700000000000001, 10.350000000000001, 11.0, 11.65, 12.3, 12.95, 13.600000000000001, 14.25, 14.9], \"y\": [0.12153846153846147, 0.3876923076923075, 0.24769230769230757, 0.2615384615384621, 0.2323076923076922, 0.1307692307692307, 0.12307692307692335, 0.030769230769230667, 0.001538461538461542, 0.0015384615384615376]}, \"f4\": {\"x\": [8.0, 8.62, 9.24, 9.86, 10.48, 11.1, 11.719999999999999, 12.34, 12.959999999999999, 13.579999999999998, 14.2], \"y\": [0.008064516129032268, 0.2338709677419351, 0.35645161290322624, 0.26774193548387054, 0.25645161290322616, 0.20645161290322608, 0.12258064516129012, 0.11935483870967757, 0.03225806451612907, 0.009677419354838694]}, \"f2\": \"Not Detected\", \"f5\": 0.969001}]}, \"insights\": [], \"additionalGraphs\": [{\"id\": \"fixed.acidity_distr\", 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\"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 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\"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, 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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\": {\"title\": {\"text\": \"Index\"}}, \"yaxis\": {\"title\": {\"text\": \"alcohol\"}}}}}};\n",
474         "</script>\n",
475         "<script>\n",
476         "function domReady(fn) {\n",
477         "  // If we're early to the party\n",
478         "  document.addEventListener(\"DOMContentLoaded\", fn);\n",
479         "  // If late; I mean on time.\n",
480         "  if (document.readyState === \"interactive\" || document.readyState === \"complete\" ) {\n",
481         "    fn();\n",
482         "  }\n",
483         "}\n",
484         "\n",
485         "domReady(function () {\n",
486         "    requirejs([\"evidently\"], function(ev) {\n",
487         "        drawDashboard(evidently_dashboard_3966efb347484c23af70237a11fa6d90,\n",
488         "        new Map(Object.entries(additional_graphs_evidently_dashboard_3966efb347484c23af70237a11fa6d90)),\n",
489         "        \"root_evidently_dashboard_3966efb347484c23af70237a11fa6d90\");\n",
490         "    },\n",
491         "    function(err) {\n",
492         "        $(\"#root_evidently_dashboard_3966efb347484c23af70237a11fa6d90\").innerHTML = \"Failed to load\";\n",
493         "    })\n",
494         "});\n",
495         "</script>\n",
496         "<div class=\"reset-this-parent\" id=\"root_evidently_dashboard_3966efb347484c23af70237a11fa6d90\">Loading...</div>\n",
497         "\n"
498        ],
499        "text/plain": [
500         "<IPython.core.display.HTML object>"
501        ]
502       },
503       "execution_count": 203,
504       "metadata": {},
505       "output_type": "execute_result"
506      }
507     ],
508     "source": [
509      "model_performance_dashboard = Dashboard(tabs=[DataDriftTab()], options=[m_options])\n",
510      "model_performance_dashboard.calculate(merged_train_data.sample(1000, random_state=0), \n",
511      "                                      merged_test_data.sample(1000, random_state=0),\n",
512      "                                      column_mapping=wine_column_mapping)\n",
513      "model_performance_dashboard.show()"
514     ]
515    },
516    {
517     "cell_type": "code",
518     "execution_count": null,
519     "id": "63c3be1f",
520     "metadata": {},
521     "outputs": [],
522     "source": []
523    }
524   ],
525   "metadata": {
526    "kernelspec": {
527     "display_name": "Python 3 (ipykernel)",
528     "language": "python",
529     "name": "python3"
530    },
531    "language_info": {
532     "codemirror_mode": {
533      "name": "ipython",
534      "version": 3
535     },
536     "file_extension": ".py",
537     "mimetype": "text/x-python",
538     "name": "python",
539     "nbconvert_exporter": "python",
540     "pygments_lexer": "ipython3",
541     "version": "3.9.7"
542    }
543   },
544   "nbformat": 4,
545   "nbformat_minor": 5
546  }