.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "user-guide/tabular/auto_quickstarts/plot_quick_train_test_validation.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_user-guide_tabular_auto_quickstarts_plot_quick_train_test_validation.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_user-guide_tabular_auto_quickstarts_plot_quick_train_test_validation.py:


.. _quick_train_test_validation:

Quickstart - Train-Test Validation Suite
****************************************

The deepchecks train-test validation suite is relevant any time you wish to 
validate two data subsets. For example:

- Comparing distributions across different train-test splits (e.g. before 
  training a model or when splitting data for cross-validation)
- Comparing a new data batch to previous data batches

Here we'll use a loans' dataset
(:mod:`deepchecks.tabular.datasets.classification.lending_club`),
to demonstrate how you can run the suite with only a few simple lines of code, 
and see which kind of insights it can find.

.. code-block:: bash

    # Before we start, if you don't have deepchecks installed yet, run:
    import sys
    !{sys.executable} -m pip install deepchecks -U --quiet

    # or install using pip from your python environment

.. GENERATED FROM PYTHON SOURCE LINES 30-35

Load Data and Prepare Data
====================================================

Load Data
-----------

.. GENERATED FROM PYTHON SOURCE LINES 35-45

.. code-block:: default



    from deepchecks.tabular.datasets.classification import lending_club
    import pandas as pd

    data = lending_club.load_data(data_format='Dataframe', as_train_test=False)
    data.head(2)








.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>issue_d</th>
          <th>sub_grade</th>
          <th>term</th>
          <th>home_ownership</th>
          <th>fico_range_low</th>
          <th>total_acc</th>
          <th>pub_rec</th>
          <th>revol_util</th>
          <th>annual_inc</th>
          <th>int_rate</th>
          <th>dti</th>
          <th>purpose</th>
          <th>mort_acc</th>
          <th>loan_amnt</th>
          <th>application_type</th>
          <th>installment</th>
          <th>verification_status</th>
          <th>pub_rec_bankruptcies</th>
          <th>addr_state</th>
          <th>initial_list_status</th>
          <th>fico_range_high</th>
          <th>revol_bal</th>
          <th>id</th>
          <th>open_acc</th>
          <th>emp_length</th>
          <th>loan_status</th>
          <th>time_to_earliest_cr_line</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>2017-06-01</td>
          <td>D1</td>
          <td>36 months</td>
          <td>MORTGAGE</td>
          <td>665.0</td>
          <td>29.0</td>
          <td>0.0</td>
          <td>85.0</td>
          <td>112600.0</td>
          <td>17.09</td>
          <td>22.71</td>
          <td>debt_consolidation</td>
          <td>4.0</td>
          <td>24000.0</td>
          <td>Individual</td>
          <td>856.75</td>
          <td>Verified</td>
          <td>0.0</td>
          <td>CO</td>
          <td>w</td>
          <td>669.0</td>
          <td>25779.0</td>
          <td>110680237</td>
          <td>13.0</td>
          <td>2.0</td>
          <td>0</td>
          <td>794188.8</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2017-06-01</td>
          <td>C2</td>
          <td>36 months</td>
          <td>RENT</td>
          <td>670.0</td>
          <td>14.0</td>
          <td>0.0</td>
          <td>34.8</td>
          <td>35000.0</td>
          <td>13.59</td>
          <td>9.95</td>
          <td>debt_consolidation</td>
          <td>1.0</td>
          <td>4200.0</td>
          <td>Individual</td>
          <td>142.72</td>
          <td>Source Verified</td>
          <td>NaN</td>
          <td>FL</td>
          <td>f</td>
          <td>674.0</td>
          <td>3798.0</td>
          <td>109936186</td>
          <td>7.0</td>
          <td>2.0</td>
          <td>1</td>
          <td>470793.6</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 46-48

Split Data to Train and Test
-----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 48-57

.. code-block:: default


    # convert date column to datetime, `issue_d`` is date column
    data['issue_d'] = pd.to_datetime(data['issue_d'])

    # Use data from June and July for train and August for test:
    train_df = data[data['issue_d'].dt.month.isin([6, 7])]
    test_df = data[data['issue_d'].dt.month.isin([8])]









.. GENERATED FROM PYTHON SOURCE LINES 58-67

Run Deepchecks for Train Test Validation
===========================================

Define a Dataset Object
-------------------------

Create a deepchecks Dataset, including the relevant metadata (label, date, index, etc.).
Check out :class:`deepchecks.tabular.Dataset` to see all of the columns and types 
that can be declared.

.. GENERATED FROM PYTHON SOURCE LINES 70-72

Define Lending Club Metadata
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 72-80

.. code-block:: default


    categorical_features = ['addr_state', 'application_type', 'home_ownership', \
      'initial_list_status', 'purpose', 'term', 'verification_status', 'sub_grade']
    index_name = 'id'
    label = 'loan_status' # 0 is DEFAULT, 1 is OK
    datetime_name = 'issue_d'









.. GENERATED FROM PYTHON SOURCE LINES 81-83

Create Dataset
^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 83-96

.. code-block:: default


    from deepchecks.tabular import Dataset

    # Categorical features can be heuristically inferred, however we
    # recommend to state them explicitly to avoid misclassification.

    # Metadata attributes are optional. Some checks will run only if specific attributes are declared.

    train_ds = Dataset(train_df, label=label,cat_features=categorical_features, \
                       index_name=index_name, datetime_name=datetime_name)
    test_ds = Dataset(test_df, label=label,cat_features=categorical_features, \
                       index_name=index_name, datetime_name=datetime_name)








.. GENERATED FROM PYTHON SOURCE LINES 97-102

.. code-block:: default


    # for convenience lets save it in a dictionary so we can reuse them for future Dataset initializations
    columns_metadata = {'cat_features' : categorical_features, 'index_name': index_name,
                        'label':label, 'datetime_name':datetime_name}








.. GENERATED FROM PYTHON SOURCE LINES 103-112

Run the Deepchecks Suite
--------------------------

Validate your data with the :class:`deepchecks.tabular.suites.train_test_validation` suite.
It runs on two datasets, so you can use it to compare any two batches of data (e.g. train data, test data, a new batch of data
that recently arrived)

Check out the :doc:`"when should you use deepchecks guide" </getting-started/when_should_you_use>`
for some more info about the existing suites and when to use them.

.. GENERATED FROM PYTHON SOURCE LINES 112-121

.. code-block:: default


    from deepchecks.tabular.suites import train_test_validation

    validation_suite = train_test_validation()
    suite_result = validation_suite.run(train_ds, test_ds)
    # Note: the result can be saved as html using suite_result.save_as_html()
    # or exported to json using suite_result.to_json()
    suite_result





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Train Test Validation Suite:
    |            | 0/12 [Time: 00:00]
    Train Test Validation Suite:
    |#######     | 7/12 [Time: 00:00, Check=Index Train Test Leakage]
                                                                                              

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        ~ Public License (version 3 or later).
        ~ You should have received a copy of the GNU Affero General Public License
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text-align: left;\n  white-space: pre-wrap;\n}\n#T_291f9_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_291f9_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_291f9_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_291f9_row0_col1\" class=\"data row0 col1\" ><a href=\"#DateTrain-TestLeakage(overlap)_1RIPZIM6CFNFHOLQC3O18A5UR-section-7KO64\">Date Train-Test Leakage (overlap)</a></td>\n      <td id=\"T_291f9_row0_col2\" class=\"data row0 col2\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_291f9_row0_col3\" class=\"data row0 col3\" >Found 78.21% leaked dates</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "7adda8a29c904e9484f880b1d362829f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "25c17acaaeb04ae9a979a2472d6ecbfd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "43590a1df12b4aeb8516729c4cc89841": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dd865cfb36eb40f4bf54541fdcde9ff0", "style": "IPY_MODEL_1b4a1902a57344b3a31a877b8fd8e5d8", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "dd865cfb36eb40f4bf54541fdcde9ff0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b4a1902a57344b3a31a877b8fd8e5d8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "399e7d6232854c86aa700cf56e66bcc5": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_e1377ecb5d17436c8c34144c907076ba", "IPY_MODEL_0d091197510d42cba70aa3e674d9ed8c", "IPY_MODEL_04631bd103124b78a3d1713d62506767", "IPY_MODEL_1ed7636fe5c3472caaf93555f57700d4"], "layout": "IPY_MODEL_789c3b51e08c4629aceb5756cb1695df"}}, "e1377ecb5d17436c8c34144c907076ba": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_40ea2ded9bf34632ba3ce6b4fe3af92c", "style": "IPY_MODEL_e08c425ebf9d4a51b5f251ebd67b13bc", "value": "<h4 id=\"DateTrain-TestLeakage(overlap)_1RIPZIM6CFNFHOLQC3O18A5UR-section-7KO64\"><b>Date Train-Test Leakage (overlap)</b></h4>"}}, "40ea2ded9bf34632ba3ce6b4fe3af92c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e08c425ebf9d4a51b5f251ebd67b13bc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0d091197510d42cba70aa3e674d9ed8c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_82a3c4cfb1514352814431890120706f", "style": "IPY_MODEL_16a2b1b72f654a88b7a526b3d24a8f3a", "value": "<p>Check test data that is dated earlier than the latest date in train. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_date_train_test_leakage_overlap.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "82a3c4cfb1514352814431890120706f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "16a2b1b72f654a88b7a526b3d24a8f3a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "04631bd103124b78a3d1713d62506767": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_29373e96c3044ab9b46c69004fd89c20", "style": "IPY_MODEL_ff279d3e00894daf8eb11bcdf4176be7", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a8973_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a8973_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a8973_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a8973_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a8973_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a8973_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a8973_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_a8973_row0_col1\" class=\"data row0 col1\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_a8973_row0_col2\" class=\"data row0 col2\" >Found 78.21% leaked dates</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "29373e96c3044ab9b46c69004fd89c20": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ff279d3e00894daf8eb11bcdf4176be7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1ed7636fe5c3472caaf93555f57700d4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d2b6dc97ae594fa8b5bb80879d1b146f", "IPY_MODEL_cb9ba6afb7f24d1285800d764235f6ee", "IPY_MODEL_d17091fc17524f599b275ac5ad18277d"], "layout": "IPY_MODEL_f7cc58a4f9b841639a8afcda8e32cb01"}}, "d2b6dc97ae594fa8b5bb80879d1b146f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7fe3bab2720f4244aeebe8e3e3679ec4", "style": "IPY_MODEL_ab782d8ca96e45f696b737f0ab656152", "value": "<h5><b>Additional Outputs</b></h5>"}}, "7fe3bab2720f4244aeebe8e3e3679ec4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ab782d8ca96e45f696b737f0ab656152": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cb9ba6afb7f24d1285800d764235f6ee": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a7e1035127e846e384eb05513f57c1de", "style": "IPY_MODEL_9ffe422d0a194e28a8fb6f4e756a5e9d", "value": "<div>78.21% of test data samples are in the date range 2017-08-01 - 2018-07-01, which occurs before last training data date (2018-07-01)</div>"}}, "a7e1035127e846e384eb05513f57c1de": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9ffe422d0a194e28a8fb6f4e756a5e9d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d17091fc17524f599b275ac5ad18277d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_379e5a17e77c4959853a5f1829152715", "style": "IPY_MODEL_bd82862e52cc430da4b6d257a4bb8316", "value": "<br><a href=\"#summary_1RIPZIM6CFNFHOLQC3O18A5UR-section-7KO64\" style=\"font-size: 14px;\">Go to top</a>"}}, "379e5a17e77c4959853a5f1829152715": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bd82862e52cc430da4b6d257a4bb8316": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f7cc58a4f9b841639a8afcda8e32cb01": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "789c3b51e08c4629aceb5756cb1695df": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ae55b0aa20c5416488c7fb70168e6baa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b78c1b4dff7e41008cb85e28da74ee77": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5f84c599887e4491b0ba3485d7cc8f08": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5c05cf6e96d643019992bbca97e3044a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3e211c7ec6674e7c99fb4d5667162c1d", "IPY_MODEL_e5b72d6740524b66b49b93cf53fc578c"], "layout": "IPY_MODEL_201cf1c0d60043afb5e6d3aabee97975"}}, "3e211c7ec6674e7c99fb4d5667162c1d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_72699fb5b8f54ade80a18adcd054b7ad", "style": "IPY_MODEL_8fadfd5a88234417a88c094981da491b", "value": "<span id=\"summary_1RIPZIM6CFNFHOLQC3O18A5UR-section-T026R\"></span>"}}, "72699fb5b8f54ade80a18adcd054b7ad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8fadfd5a88234417a88c094981da491b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e5b72d6740524b66b49b93cf53fc578c": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Passed"}, "children": ["IPY_MODEL_e34733706ee04e228ee5f850a00df87a"], "layout": "IPY_MODEL_8856624796fe4867b03557380659aa7d", "selected_index": null}}, "e34733706ee04e228ee5f850a00df87a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c710cb1a7e9d48b68767b32f3c8c1730", "IPY_MODEL_ca9c5e9e41c14e919db6f616e18dc7fd", "IPY_MODEL_c07937ce2bbc410cb7e2ecee454987e7", "IPY_MODEL_239ce9c306204dc888d421bb30e13a26", "IPY_MODEL_99986e69b51a4a68b646c34274e38dec", "IPY_MODEL_239ce9c306204dc888d421bb30e13a26", "IPY_MODEL_fffba25349a543fc8e155ec9606ed524"], "layout": "IPY_MODEL_25dfa04f2afb41648dd6267f7d29064a"}}, "c710cb1a7e9d48b68767b32f3c8c1730": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_647362daaa5e4efb94b4a7a72b311c7d", "style": "IPY_MODEL_162c11180e2946f0894599b559f13388", "value": "<style type=\"text/css\">\n#T_acacc_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_acacc_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_acacc_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_acacc_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_acacc_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_acacc_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_acacc_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acacc_row0_col1\" class=\"data row0 col1\" ><a href=\"#DatasetsSizeComparison_1RIPZIM6CFNFHOLQC3O18A5UR-section-T026R\">Datasets Size Comparison</a></td>\n      <td id=\"T_acacc_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_acacc_row0_col3\" class=\"data row0 col3\" >Test-Train size ratio is 0.49</td>\n    </tr>\n    <tr>\n      <td id=\"T_acacc_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acacc_row1_col1\" class=\"data row1 col1\" ><a href=\"#TrainTestFeatureDrift_1RIPZIM6CFNFHOLQC3O18A5UR-section-T026R\">Train Test Feature Drift</a></td>\n      <td id=\"T_acacc_row1_col2\" class=\"data row1 col2\" >categorical drift score < 0.2 and numerical drift score < 0.1</td>\n      <td id=\"T_acacc_row1_col3\" class=\"data row1 col3\" >Passed for 24 columns out of 24 columns.\nFound column \"application_type\" has the highest categorical drift score: 0.04\nFound column \"open_acc\" has the highest numerical drift score: 0.02</td>\n    </tr>\n    <tr>\n      <td id=\"T_acacc_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acacc_row2_col1\" class=\"data row2 col1\" ><a href=\"#TrainTestLabelDrift_1RIPZIM6CFNFHOLQC3O18A5UR-section-T026R\">Train Test Label Drift</a></td>\n      <td id=\"T_acacc_row2_col2\" class=\"data row2 col2\" >categorical drift score < 0.2 and numerical drift score < 0.1 for label drift</td>\n      <td id=\"T_acacc_row2_col3\" class=\"data row2 col3\" >Label's drift score Cramer's V is 6.61E-3</td>\n    </tr>\n    <tr>\n      <td id=\"T_acacc_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acacc_row3_col1\" class=\"data row3 col1\" >New Label Train Test</td>\n      <td id=\"T_acacc_row3_col2\" class=\"data row3 col2\" >Number of new label values is less or equal to 0</td>\n      <td id=\"T_acacc_row3_col3\" class=\"data row3 col3\" >No new labels found</td>\n    </tr>\n    <tr>\n      <td id=\"T_acacc_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acacc_row4_col1\" class=\"data row4 col1\" >Category Mismatch Train Test</td>\n      <td id=\"T_acacc_row4_col2\" class=\"data row4 col2\" >Ratio of samples with a new category is less or equal to 0%</td>\n      <td id=\"T_acacc_row4_col3\" class=\"data row4 col3\" >Passed for 2 relevant columns. Top columns with new categories ratio:\n{'home_ownership': '0%', 'application_type': '0%', 'verification_status': '0%', 'term': '0%', 'initial_list_status': '0%'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_acacc_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acacc_row5_col1\" class=\"data row5 col1\" >String Mismatch Comparison</td>\n      <td id=\"T_acacc_row5_col2\" class=\"data row5 col2\" >No new variants allowed in test data</td>\n      <td id=\"T_acacc_row5_col3\" class=\"data row5 col3\" >Passed for 8 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_acacc_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acacc_row6_col1\" class=\"data row6 col1\" >Date Train-Test Leakage (duplicates)</td>\n      <td id=\"T_acacc_row6_col2\" class=\"data row6 col2\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_acacc_row6_col3\" class=\"data row6 col3\" >No leaked dates found</td>\n    </tr>\n    <tr>\n      <td id=\"T_acacc_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acacc_row7_col1\" class=\"data row7 col1\" >Index Train-Test Leakage</td>\n      <td id=\"T_acacc_row7_col2\" class=\"data row7 col2\" >Ratio of leaking indices is less or equal to 0%</td>\n      <td id=\"T_acacc_row7_col3\" class=\"data row7 col3\" >No index leakage found</td>\n    </tr>\n    <tr>\n      <td id=\"T_acacc_row8_col0\" class=\"data row8 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acacc_row8_col1\" class=\"data row8 col1\" >Train Test Samples Mix</td>\n      <td id=\"T_acacc_row8_col2\" class=\"data row8 col2\" >Percentage of test data samples that appear in train data is less or equal to 10%</td>\n      <td id=\"T_acacc_row8_col3\" class=\"data row8 col3\" >No samples mix found</td>\n    </tr>\n    <tr>\n      <td id=\"T_acacc_row9_col0\" class=\"data row9 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acacc_row9_col1\" class=\"data row9 col1\" >Feature Label Correlation Change</td>\n      <td id=\"T_acacc_row9_col2\" class=\"data row9 col2\" >Train-Test features' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_acacc_row9_col3\" class=\"data row9 col3\" >Passed for 24 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_acacc_row10_col0\" class=\"data row10 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acacc_row10_col1\" class=\"data row10 col1\" >Feature Label Correlation Change</td>\n      <td id=\"T_acacc_row10_col2\" class=\"data row10 col2\" >Train features' Predictive Power Score is less than 0.7</td>\n      <td id=\"T_acacc_row10_col3\" class=\"data row10 col3\" >Passed for 24 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_acacc_row11_col0\" class=\"data row11 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acacc_row11_col1\" class=\"data row11 col1\" >Whole Dataset Drift</td>\n      <td id=\"T_acacc_row11_col2\" class=\"data row11 col2\" >Drift value is less than 0.25</td>\n      <td id=\"T_acacc_row11_col3\" class=\"data row11 col3\" >Found drift value of: 0.04, corresponding to a domain classifier AUC of: 0.52</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "647362daaa5e4efb94b4a7a72b311c7d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "162c11180e2946f0894599b559f13388": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ca9c5e9e41c14e919db6f616e18dc7fd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cdc8c8fa45d2479792f4a6f948bc4a6a", "style": "IPY_MODEL_29c165b0afca47b5acab4f10b3b988db", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "cdc8c8fa45d2479792f4a6f948bc4a6a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "29c165b0afca47b5acab4f10b3b988db": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c07937ce2bbc410cb7e2ecee454987e7": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_034bbc8d0c084ecf95a1977f75b3432d", "IPY_MODEL_ea4a41f7a12f448980cb3867dad2ba01", "IPY_MODEL_f2412ae3af9f464a8c961b70b7ec1fde", "IPY_MODEL_149261a8d22f4875877d8c687d3e2d95"], "layout": "IPY_MODEL_bfd6ad299f7d4915a6d39d738a2bc1bb"}}, "034bbc8d0c084ecf95a1977f75b3432d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fa57c951ca554cfe8826c4f19a829f11", "style": "IPY_MODEL_e6e1ac9ab4944f94af68b1a1f4bab805", "value": "<h4 id=\"DatasetsSizeComparison_1RIPZIM6CFNFHOLQC3O18A5UR-section-T026R\"><b>Datasets Size Comparison</b></h4>"}}, "fa57c951ca554cfe8826c4f19a829f11": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e6e1ac9ab4944f94af68b1a1f4bab805": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ea4a41f7a12f448980cb3867dad2ba01": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_262f558ef1d44e67845aef465b49f836", "style": "IPY_MODEL_d5eb4c19242c4e6e82580520df9e0137", "value": "<p>Verify test dataset size comparing it to the train dataset size. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_datasets_size_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "262f558ef1d44e67845aef465b49f836": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d5eb4c19242c4e6e82580520df9e0137": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f2412ae3af9f464a8c961b70b7ec1fde": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_39465962367c4420835a596506a8b9da", "style": "IPY_MODEL_1060805dfeed482ea6c6be50c80f63ad", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_dbde4_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dbde4_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dbde4_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dbde4_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dbde4_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_dbde4_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_dbde4_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_dbde4_row0_col1\" class=\"data row0 col1\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_dbde4_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is 0.49</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "39465962367c4420835a596506a8b9da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1060805dfeed482ea6c6be50c80f63ad": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "149261a8d22f4875877d8c687d3e2d95": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ab5e4327ec994c9d8c03c83322f4bf22", "IPY_MODEL_9f23a3246c2b4497ba1962c8492c2534", "IPY_MODEL_bad8e0887cfc482b8a035e673de9eb28"], "layout": "IPY_MODEL_34af15077ace4a3481889d719ab9cd4b"}}, "ab5e4327ec994c9d8c03c83322f4bf22": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3e94770c3ddd4425b82cf31cdccbe157", "style": "IPY_MODEL_dc4eb0462a294db4809a4add527b66aa", "value": "<h5><b>Additional Outputs</b></h5>"}}, "3e94770c3ddd4425b82cf31cdccbe157": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc4eb0462a294db4809a4add527b66aa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9f23a3246c2b4497ba1962c8492c2534": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6d24ce16a44e45ff8ebfeb6c64441898", "style": "IPY_MODEL_387f6bbe5d744467860a2a8d2b3da689", "value": "<style type=\"text/css\">\n#T_05932_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_05932_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_05932_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_05932_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_05932_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_05932_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >Train</th>\n      <th class=\"col_heading level0 col1\" >Test</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_05932_level0_row0\" class=\"row_heading level0 row0\" >Size</th>\n      <td id=\"T_05932_row0_col0\" class=\"data row0 col0\" >14556</td>\n      <td id=\"T_05932_row0_col1\" class=\"data row0 col1\" >7112</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "6d24ce16a44e45ff8ebfeb6c64441898": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "387f6bbe5d744467860a2a8d2b3da689": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bad8e0887cfc482b8a035e673de9eb28": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e62cf7851acd4a03ac729502c3e531e5", "style": "IPY_MODEL_2edd78ee423442858833097041054a9c", "value": "<br><a href=\"#summary_1RIPZIM6CFNFHOLQC3O18A5UR-section-T026R\" style=\"font-size: 14px;\">Go to top</a>"}}, "e62cf7851acd4a03ac729502c3e531e5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2edd78ee423442858833097041054a9c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "34af15077ace4a3481889d719ab9cd4b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bfd6ad299f7d4915a6d39d738a2bc1bb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "239ce9c306204dc888d421bb30e13a26": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7a49de85a7674917a7d1bb2e3d5c798a", "style": "IPY_MODEL_70b8a17c56c34428b0a49ba04bc661ba", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "7a49de85a7674917a7d1bb2e3d5c798a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "70b8a17c56c34428b0a49ba04bc661ba": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "99986e69b51a4a68b646c34274e38dec": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_5400e3f4d11d4043ba1830f36ff18026", "IPY_MODEL_ab561888f96e47e2a3200faa5af518a9", "IPY_MODEL_79176742ac034ef896d0488e097170f8", "IPY_MODEL_fde3897b19174a1baa0ac36228bf4c57"], "layout": "IPY_MODEL_dcd7458927d34df99dcab149201ebdef"}}, "5400e3f4d11d4043ba1830f36ff18026": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2a4a9c9232d34ea6b0bd615db337592d", "style": "IPY_MODEL_280e28b3058d4543a36c58e31d059bd3", "value": "<h4 id=\"TrainTestFeatureDrift_1RIPZIM6CFNFHOLQC3O18A5UR-section-T026R\"><b>Train Test Feature Drift</b></h4>"}}, "2a4a9c9232d34ea6b0bd615db337592d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "280e28b3058d4543a36c58e31d059bd3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ab561888f96e47e2a3200faa5af518a9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a5c8502256944154929ae21689e6298b", "style": "IPY_MODEL_3fd2080f795240b1b7344155c76e1097", "value": "<p>    Calculate drift between train dataset and test dataset per feature, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_train_test_feature_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "a5c8502256944154929ae21689e6298b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3fd2080f795240b1b7344155c76e1097": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "79176742ac034ef896d0488e097170f8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bfdec0174dad40d5952a612be87ad9d4", "style": "IPY_MODEL_927e5be017874326965192acbe27819c", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_d9ea6_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d9ea6_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d9ea6_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d9ea6_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d9ea6_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d9ea6_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_d9ea6_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d9ea6_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.1</td>\n      <td id=\"T_d9ea6_row0_col2\" class=\"data row0 col2\" >Passed for 24 columns out of 24 columns.\nFound column \"application_type\" has the highest categorical drift score: 0.04\nFound column \"open_acc\" has the highest numerical drift score: 0.02</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "bfdec0174dad40d5952a612be87ad9d4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "927e5be017874326965192acbe27819c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fde3897b19174a1baa0ac36228bf4c57": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_af7143e967da423daae8611df9593afb", "IPY_MODEL_cd0410f2489549dc8f07579bfceebd86", "IPY_MODEL_cce4aa8f7f564f8fbbe2f04a2e436a27", "IPY_MODEL_3ccd2248d3fb437f9f40d0e88a569a92", "IPY_MODEL_43655f230d3f4562ae39bc37906b2745", "IPY_MODEL_f301ff897a4946e59c53c63e6bbd4ff6", "IPY_MODEL_47028bd837a943bcb2468fc646ef5eb8", "IPY_MODEL_66e7cea1651042e39d4dbfe37a27f310"], "layout": "IPY_MODEL_896340fbad054079a4ee3c91f5f7a130"}}, "af7143e967da423daae8611df9593afb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dd4d1ce634ae4caabf5e259479fdbbb5", "style": "IPY_MODEL_5674358fba2e463cbd48a4cc7ce8ab8b", "value": "<h5><b>Additional Outputs</b></h5>"}}, "dd4d1ce634ae4caabf5e259479fdbbb5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5674358fba2e463cbd48a4cc7ce8ab8b": {"model_name": "DescriptionStyleModel", 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"model_module_version": "1.2.0", "state": {}}, "fffba25349a543fc8e155ec9606ed524": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_9fd40a225b65437d9a197bb543ec6d05", "IPY_MODEL_685b19a046a145fda49113bf1a7b1e72", "IPY_MODEL_f0a54cef2fc446d986f78255063b88c7", "IPY_MODEL_633c3ab8e5144064aa8d8688605c18cf"], "layout": "IPY_MODEL_1b7dead6ea2246a9bbe200507ac21a21"}}, "9fd40a225b65437d9a197bb543ec6d05": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8bef0a2b6fe94a4a8464150fc3db13d1", "style": "IPY_MODEL_179972e4f78e47fd898bdaffb83f2160", "value": "<h4 id=\"TrainTestLabelDrift_1RIPZIM6CFNFHOLQC3O18A5UR-section-T026R\"><b>Train Test Label Drift</b></h4>"}}, "8bef0a2b6fe94a4a8464150fc3db13d1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "179972e4f78e47fd898bdaffb83f2160": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "685b19a046a145fda49113bf1a7b1e72": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_34fa617d64ba477b9b1c04d1a15073ab", "style": "IPY_MODEL_38a8dafd95964ea3ac7839890a1d129c", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_train_test_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "34fa617d64ba477b9b1c04d1a15073ab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "38a8dafd95964ea3ac7839890a1d129c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f0a54cef2fc446d986f78255063b88c7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eebf5eadb94745e59253ac3f43df2c92", "style": "IPY_MODEL_ad88ad87bfa5481bbc42452772b739e3", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_eea7b_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_eea7b_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_eea7b_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_eea7b_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_eea7b_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_eea7b_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_eea7b_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_eea7b_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.1 for label drift</td>\n      <td id=\"T_eea7b_row0_col2\" class=\"data row0 col2\" >Label's drift score Cramer's V is 6.61E-3</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "eebf5eadb94745e59253ac3f43df2c92": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ad88ad87bfa5481bbc42452772b739e3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "633c3ab8e5144064aa8d8688605c18cf": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a387791ba1d5404b9612b3512b02fd59", "IPY_MODEL_44ae0c165dbf464798a1a623ca4fbe43", "IPY_MODEL_9a40167c36a841c780b72294ea078a9f", "IPY_MODEL_55c3adca27dc4f93a89c187b7aa0d0ac"], "layout": "IPY_MODEL_8edab44e7d79457c8c7930b1dcfee0c0"}}, "a387791ba1d5404b9612b3512b02fd59": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9edcb95774844e9daac8b1c3595dca98", "style": "IPY_MODEL_9ed7baed17234803a87a60a0b53d7493", "value": "<h5><b>Additional Outputs</b></h5>"}}, "9edcb95774844e9daac8b1c3595dca98": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9ed7baed17234803a87a60a0b53d7493": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "44ae0c165dbf464798a1a623ca4fbe43": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fa9857fde2314ab9add6d72e748ed7ed", "style": "IPY_MODEL_c6bb0eab432d48ceb9c982bbc184a814", "value": "<div><span>\n                The Drift score is a measure for the difference between two distributions, in this check - the test\n                and train distributions.<br> The check shows the drift score and distributions for the label.\n            </span></div>"}}, "fa9857fde2314ab9add6d72e748ed7ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c6bb0eab432d48ceb9c982bbc184a814": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9a40167c36a841c780b72294ea078a9f": {"model_name": "FigureModel", 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.. GENERATED FROM PYTHON SOURCE LINES 122-130

As you can see in the suite's results: the Date Train-Test Leakage check failed,
indicating that we may have a problem in the way we've split our data!
We've mixed up data from two years, causing a leakage of future data
in the training dataset.
Let's fix this.

Fix Data
^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 130-135

.. code-block:: default


    dt_col = data[datetime_name]
    train_df = data[dt_col.dt.year.isin([2017]) & dt_col.dt.month.isin([6,7,8])]
    test_df = data[dt_col.dt.year.isin([2018]) & dt_col.dt.month.isin([6,7,8])]








.. GENERATED FROM PYTHON SOURCE LINES 136-143

.. code-block:: default


    from deepchecks.tabular import Dataset

    # Create the new Datasets
    train_ds = Dataset(train_df, **columns_metadata)
    test_ds = Dataset(test_df, **columns_metadata)








.. GENERATED FROM PYTHON SOURCE LINES 144-147

Re-run Validation Suite
^^^^^^^^^^^^^^^^^^^^^^^^^


.. GENERATED FROM PYTHON SOURCE LINES 148-152

.. code-block:: default


    suite_result = validation_suite.run(train_ds, test_ds)
    suite_result.show()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Train Test Validation Suite:
    |            | 0/12 [Time: 00:00]
    Train Test Validation Suite:
    |#######     | 7/12 [Time: 00:00, Check=Index Train Test Leakage]
                                                                                              

.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <!--
        ~ ----------------------------------------------------------------------------
        ~ Copyright (C) 2021-2022 Deepchecks (https://www.deepchecks.com)
        ~
        ~ This file is part of Deepchecks.
        ~ Deepchecks is distributed under the terms of the GNU Affero General
        ~ Public License (version 3 or later).
        ~ You should have received a copy of the GNU Affero General Public License
        ~ along with Deepchecks.  If not, see <http://www.gnu.org/licenses/>.
        ~ ----------------------------------------------------------------------------
        ~
    -->
    <!DOCTYPE html>
    <html lang="en">
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style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_d078e_row0_col1\" class=\"data row0 col1\" >Ratio of samples with a new category is less or equal to 0%</td>\n      <td id=\"T_d078e_row0_col2\" class=\"data row0 col2\" >Found 1 out of 8 columns with ratio of new category samples above threshold:\n{'addr_state': '0.45%'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "6ac148d031fc413e80bf337638201f5d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5793788a851148bcbedb0d4aa891b6d7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "99a5a270fd7249f2a98832c2e4acf8c4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_afbc36171e8b4129a238b1998c14dc81", "IPY_MODEL_2422f5e1252341b9a43a702583259bc4", "IPY_MODEL_f46887c492cd4e8782a46f2a66dbcde5"], "layout": "IPY_MODEL_5c0610a0cf5f45268589329b94601ce9"}}, "afbc36171e8b4129a238b1998c14dc81": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_832ba2c066844dd78275d9cc93bcca12", "style": "IPY_MODEL_0e67c9239db14afda3f0ac76c15b6231", "value": "<h5><b>Additional Outputs</b></h5>"}}, "832ba2c066844dd78275d9cc93bcca12": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0e67c9239db14afda3f0ac76c15b6231": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2422f5e1252341b9a43a702583259bc4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9fe12d2cc0534aaaa64b5be86f35e35a", "style": "IPY_MODEL_9d3382fcc1024a9991554a673c5955dc", "value": "<style type=\"text/css\">\n#T_17397_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_17397_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_17397_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_17397_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_17397_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_17397_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >Number of new categories</th>\n      <th class=\"col_heading level0 col1\" >Percent of new categories in sample</th>\n      <th class=\"col_heading level0 col2\" >New categories examples</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >Column</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_17397_level0_row0\" class=\"row_heading level0 row0\" >addr_state</th>\n      <td id=\"T_17397_row0_col0\" class=\"data row0 col0\" >1</td>\n      <td id=\"T_17397_row0_col1\" class=\"data row0 col1\" >0.45%</td>\n      <td id=\"T_17397_row0_col2\" class=\"data row0 col2\" >['WV']</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "9fe12d2cc0534aaaa64b5be86f35e35a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9d3382fcc1024a9991554a673c5955dc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f46887c492cd4e8782a46f2a66dbcde5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_141dc1f3e55642158bc8e420a5cf4932", "style": "IPY_MODEL_c9d10261576c4961950c6295f6ffb3f8", "value": "<br><a href=\"#summary_JRNY20479T6VXDP5GJLQM5K8P-section-4Q0B8\" style=\"font-size: 14px;\">Go to top</a>"}}, "141dc1f3e55642158bc8e420a5cf4932": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c9d10261576c4961950c6295f6ffb3f8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5c0610a0cf5f45268589329b94601ce9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a6dc25ae48c744818a68e94936759bf5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0b4d1fbea7504557ab15323af4150515": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6be7792700b0418b829dafaeafa0e882", "style": "IPY_MODEL_03e5565b985d492d9f11f46867437ba6", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "6be7792700b0418b829dafaeafa0e882": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "03e5565b985d492d9f11f46867437ba6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "87dc5d9cc42041e7a1f8022bd538576f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_e5c04642b1f84cb88f54de8143f7178d", "IPY_MODEL_b2292274c50f47fbb15e0e5817abb2ea", "IPY_MODEL_2be27185936e478080108563eda92de6", "IPY_MODEL_4450d62136fd4f92be9036a7ee9bcb68"], "layout": "IPY_MODEL_6b7b028ccad2462a90da3ca12cd85779"}}, "e5c04642b1f84cb88f54de8143f7178d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fb400845fe5347d9a19de575add5649f", "style": "IPY_MODEL_bed3dcc71dbb483caa3d2c86718def82", "value": "<h4 id=\"WholeDatasetDrift_JRNY20479T6VXDP5GJLQM5K8P-section-4Q0B8\"><b>Whole Dataset Drift</b></h4>"}}, "fb400845fe5347d9a19de575add5649f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bed3dcc71dbb483caa3d2c86718def82": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b2292274c50f47fbb15e0e5817abb2ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5a75a604016b45d49ab2dd47a24147f9", "style": "IPY_MODEL_b732ab1fe82f46ceb3e548064e3a7984", "value": "<p>    Calculate drift between the entire train and test datasets using a model trained to distinguish between them. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_whole_dataset_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "5a75a604016b45d49ab2dd47a24147f9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b732ab1fe82f46ceb3e548064e3a7984": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2be27185936e478080108563eda92de6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_89feb45c8d9e4afbb46fdf07522b2fe5", "style": "IPY_MODEL_b693168e89814453addfc03b12c55343", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_f3d52_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3d52_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3d52_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3d52_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3d52_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f3d52_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_f3d52_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_f3d52_row0_col1\" class=\"data row0 col1\" >Drift value is less than 0.25</td>\n      <td id=\"T_f3d52_row0_col2\" class=\"data row0 col2\" >Found drift value of: 0.47, corresponding to a domain classifier AUC of: 0.73</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "89feb45c8d9e4afbb46fdf07522b2fe5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b693168e89814453addfc03b12c55343": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4450d62136fd4f92be9036a7ee9bcb68": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_995e1178b6e04af5bce36e736028140c", "IPY_MODEL_b47aae03fc1544f8a6e9de3ec58c8e53", "IPY_MODEL_6e4cea59db8d480eab5973803bbc58a2", "IPY_MODEL_25eecd7493f0452b8aff414312e53162", "IPY_MODEL_19d3c751b4ce4944aa4ee7dcf52b1dff", "IPY_MODEL_85d3dc1aae1e4f59823bfb3cb9258794", "IPY_MODEL_d52b6b6339144771a6c18e3fd255ce3b", "IPY_MODEL_4e581fef6d2e459dbee1c76e59fba340", "IPY_MODEL_d9813c6f6afb42afb5a90ae7e61a7275"], "layout": "IPY_MODEL_dff892da615844d0b9f3770c662444c6"}}, "995e1178b6e04af5bce36e736028140c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7fb4cff0d1744f63acdf75e7a714c802", "style": "IPY_MODEL_e0dff1e935c0401aa8f8e4687a321a27", "value": "<h5><b>Additional Outputs</b></h5>"}}, "7fb4cff0d1744f63acdf75e7a714c802": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e0dff1e935c0401aa8f8e4687a321a27": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b47aae03fc1544f8a6e9de3ec58c8e53": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8c7cce642e074bcaabc8244e4b81f9fb", "style": "IPY_MODEL_9a0f21a520ea4367bebdec2f05db3eb2", "value": "<div>\n        <span>\n        The shown features are the features that are most important for the domain classifier - the\n        domain_classifier trained to distinguish between the train and test datasets.<br>\n        </span>\n        </div>"}}, "8c7cce642e074bcaabc8244e4b81f9fb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a0f21a520ea4367bebdec2f05db3eb2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6e4cea59db8d480eab5973803bbc58a2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a593bd732246416e82a98dc7c3bd5e87", "style": "IPY_MODEL_1bd2e0ef562e4231b332bc3453136107", "value": "<div>\n    <span>\n    The percents of explained dataset difference are the importance values for the feature calculated\n    using `permutation_importance`.\n    </span><br><br>\n    </div>"}}, "a593bd732246416e82a98dc7c3bd5e87": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1bd2e0ef562e4231b332bc3453136107": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "25eecd7493f0452b8aff414312e53162": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "y": ["Drift Score"], "type": "bar", "uid": "b3812727-7086-4a60-bbe0-7ffddbbc2c44"}, {"base": 0.1, "marker": {"color": "#F2C80F"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "y": ["Drift Score"], "type": "bar", "uid": "34adf7a2-e4b3-4f96-b8fb-57c6b908d85a"}, {"base": 0.2, "marker": {"color": "#FE9666"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.09999999999999998], "y": ["Drift Score"], "type": "bar", "uid": "e3b438f7-e0d1-4c79-a816-d8c4dacf70ef"}, {"base": 0.3, "marker": {"color": "#FD625E"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.16568521648083184], "y": ["Drift Score"], "type": "bar", "uid": "c4364ee0-df49-4a1b-93e1-ba82f0176a33"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 200, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "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": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "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": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "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": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "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"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "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": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": 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href=\"#summary_JRNY20479T6VXDP5GJLQM5K8P-section-4Q0B8\" style=\"font-size: 14px;\">Go to top</a>"}}, "623248ea03e1401e8afc9d36570da7c0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "864ac8507a4f41c08409595a2b981c95": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dff892da615844d0b9f3770c662444c6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6b7b028ccad2462a90da3ca12cd85779": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0e5dd341a9214df0a92de21b328f6b25": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e5b140e1515f4bd7a840311484f2d629": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ad67b926f57c458cb557a33094c6f52a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ab1e3083971f4c81a1e5f368274b2793": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e4cdb9813fa14ccdb3df555f5a929fc0", "IPY_MODEL_520055aef23647309e9c3b24f984cfd2"], "layout": "IPY_MODEL_032ae20be9dc4a988c6d7d4a3d28ba92"}}, "e4cdb9813fa14ccdb3df555f5a929fc0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_44fbcd81ac43425baee54ac5296fe302", "style": "IPY_MODEL_554b12f658cb4894830e4cacd9fad238", "value": "<span id=\"summary_JRNY20479T6VXDP5GJLQM5K8P-section-IC8BB\"></span>"}}, "44fbcd81ac43425baee54ac5296fe302": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "554b12f658cb4894830e4cacd9fad238": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "520055aef23647309e9c3b24f984cfd2": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Passed"}, "children": ["IPY_MODEL_e9972b0e038a4d038f772c03bc016bab"], "layout": "IPY_MODEL_5a2d29459a9145faa1aa7ba0eeff600f", "selected_index": null}}, "e9972b0e038a4d038f772c03bc016bab": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b4953aeb08344342b8caca250e282b6b", "IPY_MODEL_37dfe4daff6c4de0a9d8f9fc8f258508", "IPY_MODEL_b7e20eb922854f88abb9653cb433e6fc", "IPY_MODEL_363d03cb90464e8b91b86ac33a68d597", "IPY_MODEL_bddb4696bea144f5848a4428ef34dd4e", "IPY_MODEL_363d03cb90464e8b91b86ac33a68d597", "IPY_MODEL_55bf8a8eee43455e81175c57d770661c"], "layout": "IPY_MODEL_f6643ab2212145a39b8cc4c5df4a7b6e"}}, "b4953aeb08344342b8caca250e282b6b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_aff2531f32a04927a6331adbf1489253", "style": "IPY_MODEL_fe8b9dc2d4c045e5bcbc08457399bcc7", "value": "<style type=\"text/css\">\n#T_f6b3e_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f6b3e_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f6b3e_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f6b3e_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f6b3e_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f6b3e_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_f6b3e_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f6b3e_row0_col1\" class=\"data row0 col1\" ><a href=\"#DatasetsSizeComparison_JRNY20479T6VXDP5GJLQM5K8P-section-IC8BB\">Datasets Size Comparison</a></td>\n      <td id=\"T_f6b3e_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_f6b3e_row0_col3\" class=\"data row0 col3\" >Test-Train size ratio is 0.33</td>\n    </tr>\n    <tr>\n      <td id=\"T_f6b3e_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f6b3e_row1_col1\" class=\"data row1 col1\" ><a href=\"#TrainTestFeatureDrift_JRNY20479T6VXDP5GJLQM5K8P-section-IC8BB\">Train Test Feature Drift</a></td>\n      <td id=\"T_f6b3e_row1_col2\" class=\"data row1 col2\" >categorical drift score < 0.2 and numerical drift score < 0.1</td>\n      <td id=\"T_f6b3e_row1_col3\" class=\"data row1 col3\" >Passed for 24 columns out of 24 columns.\nFound column \"sub_grade\" has the highest categorical drift score: 0.15\nFound column \"revol_util\" has the highest numerical drift score: 0.07</td>\n    </tr>\n    <tr>\n      <td id=\"T_f6b3e_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f6b3e_row2_col1\" class=\"data row2 col1\" ><a href=\"#TrainTestLabelDrift_JRNY20479T6VXDP5GJLQM5K8P-section-IC8BB\">Train Test Label Drift</a></td>\n      <td id=\"T_f6b3e_row2_col2\" class=\"data row2 col2\" >categorical drift score < 0.2 and numerical drift score < 0.1 for label drift</td>\n      <td id=\"T_f6b3e_row2_col3\" class=\"data row2 col3\" >Label's drift score Cramer's V is 0.02</td>\n    </tr>\n    <tr>\n      <td id=\"T_f6b3e_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f6b3e_row3_col1\" class=\"data row3 col1\" >New Label Train Test</td>\n      <td id=\"T_f6b3e_row3_col2\" class=\"data row3 col2\" >Number of new label values is less or equal to 0</td>\n      <td id=\"T_f6b3e_row3_col3\" class=\"data row3 col3\" >No new labels found</td>\n    </tr>\n    <tr>\n      <td id=\"T_f6b3e_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f6b3e_row4_col1\" class=\"data row4 col1\" >String Mismatch Comparison</td>\n      <td id=\"T_f6b3e_row4_col2\" class=\"data row4 col2\" >No new variants allowed in test data</td>\n      <td id=\"T_f6b3e_row4_col3\" class=\"data row4 col3\" >Passed for 8 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_f6b3e_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f6b3e_row5_col1\" class=\"data row5 col1\" >Date Train-Test Leakage (duplicates)</td>\n      <td id=\"T_f6b3e_row5_col2\" class=\"data row5 col2\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_f6b3e_row5_col3\" class=\"data row5 col3\" >No leaked dates found</td>\n    </tr>\n    <tr>\n      <td id=\"T_f6b3e_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f6b3e_row6_col1\" class=\"data row6 col1\" >Date Train-Test Leakage (overlap)</td>\n      <td id=\"T_f6b3e_row6_col2\" class=\"data row6 col2\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_f6b3e_row6_col3\" class=\"data row6 col3\" >No leaked dates found</td>\n    </tr>\n    <tr>\n      <td id=\"T_f6b3e_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f6b3e_row7_col1\" class=\"data row7 col1\" >Index Train-Test Leakage</td>\n      <td id=\"T_f6b3e_row7_col2\" class=\"data row7 col2\" >Ratio of leaking indices is less or equal to 0%</td>\n      <td id=\"T_f6b3e_row7_col3\" class=\"data row7 col3\" >No index leakage found</td>\n    </tr>\n    <tr>\n      <td id=\"T_f6b3e_row8_col0\" class=\"data row8 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f6b3e_row8_col1\" class=\"data row8 col1\" >Train Test Samples Mix</td>\n      <td id=\"T_f6b3e_row8_col2\" class=\"data row8 col2\" >Percentage of test data samples that appear in train data is less or equal to 10%</td>\n      <td id=\"T_f6b3e_row8_col3\" class=\"data row8 col3\" >No samples mix found</td>\n    </tr>\n    <tr>\n      <td id=\"T_f6b3e_row9_col0\" class=\"data row9 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f6b3e_row9_col1\" class=\"data row9 col1\" >Feature Label Correlation Change</td>\n      <td id=\"T_f6b3e_row9_col2\" class=\"data row9 col2\" >Train-Test features' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_f6b3e_row9_col3\" class=\"data row9 col3\" >Passed for 24 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_f6b3e_row10_col0\" class=\"data row10 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f6b3e_row10_col1\" class=\"data row10 col1\" >Feature Label Correlation Change</td>\n      <td id=\"T_f6b3e_row10_col2\" class=\"data row10 col2\" >Train features' Predictive Power Score is less than 0.7</td>\n      <td id=\"T_f6b3e_row10_col3\" class=\"data row10 col3\" >Passed for 24 relevant columns</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "aff2531f32a04927a6331adbf1489253": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fe8b9dc2d4c045e5bcbc08457399bcc7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "37dfe4daff6c4de0a9d8f9fc8f258508": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ec44bb15c7f84da5b4a11240f7ed1c55", "style": "IPY_MODEL_671d012be7694bfcb7a917a1b42986a6", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "ec44bb15c7f84da5b4a11240f7ed1c55": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "671d012be7694bfcb7a917a1b42986a6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b7e20eb922854f88abb9653cb433e6fc": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_7817c5b9ee22446bbc1f02fa51b06743", "IPY_MODEL_9fbe159cb7d343e195b0b0ee0ddf749f", "IPY_MODEL_08c694ea1cce4fd5ba12e5db7a3adb02", "IPY_MODEL_a1c4ff0be03f4bedbb9c508a71adfb37"], "layout": "IPY_MODEL_f27adde3626148f094c2e51c7f969afa"}}, "7817c5b9ee22446bbc1f02fa51b06743": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_394f42376fb145438c117da992e8c953", "style": "IPY_MODEL_932ad953a7fb4a1283139331e4fba303", "value": "<h4 id=\"DatasetsSizeComparison_JRNY20479T6VXDP5GJLQM5K8P-section-IC8BB\"><b>Datasets Size Comparison</b></h4>"}}, "394f42376fb145438c117da992e8c953": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "932ad953a7fb4a1283139331e4fba303": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9fbe159cb7d343e195b0b0ee0ddf749f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e55704e065b7472fa4f64c30f444533f", "style": "IPY_MODEL_bcf41f6b28ad498897f50d67e38ce8f2", "value": "<p>Verify test dataset size comparing it to the train dataset size. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_datasets_size_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "e55704e065b7472fa4f64c30f444533f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bcf41f6b28ad498897f50d67e38ce8f2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "08c694ea1cce4fd5ba12e5db7a3adb02": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_943dc03925e74590ba5aea8b2e11aa25", "style": "IPY_MODEL_88c7486b03374904ac720d2de33a6316", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_8fba6_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8fba6_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8fba6_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8fba6_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8fba6_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_8fba6_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_8fba6_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_8fba6_row0_col1\" class=\"data row0 col1\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_8fba6_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is 0.33</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "943dc03925e74590ba5aea8b2e11aa25": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "88c7486b03374904ac720d2de33a6316": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a1c4ff0be03f4bedbb9c508a71adfb37": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b1bc2d4d1a2d40af821fff963c01096b", "IPY_MODEL_6be5e76caba34ac5a935672447089cb4", "IPY_MODEL_a4e5f78a3c0846abb3fd4a861d8fcb35"], "layout": "IPY_MODEL_245204b042ed400aa3a5ff92596d819d"}}, "b1bc2d4d1a2d40af821fff963c01096b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_71ca648339f54c1884cbad953d60b269", "style": "IPY_MODEL_3d6a9904069940d1acaa3bd0054a405a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "71ca648339f54c1884cbad953d60b269": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3d6a9904069940d1acaa3bd0054a405a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6be5e76caba34ac5a935672447089cb4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_65a61d074f85444ea806b2f4a2974372", "style": "IPY_MODEL_80056583b5344a839a11286797e4deae", "value": "<style type=\"text/css\">\n#T_a2d65_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2d65_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2d65_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2d65_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2d65_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a2d65_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >Train</th>\n      <th class=\"col_heading level0 col1\" >Test</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a2d65_level0_row0\" class=\"row_heading level0 row0\" >Size</th>\n      <td id=\"T_a2d65_row0_col0\" class=\"data row0 col0\" >16281</td>\n      <td id=\"T_a2d65_row0_col1\" class=\"data row0 col1\" >5387</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "65a61d074f85444ea806b2f4a2974372": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "80056583b5344a839a11286797e4deae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a4e5f78a3c0846abb3fd4a861d8fcb35": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_51738745c9c648b8a74bd7adcf7e82d6", "style": "IPY_MODEL_3d4d787675074fe9bc2289f6233cc84d", "value": "<br><a href=\"#summary_JRNY20479T6VXDP5GJLQM5K8P-section-IC8BB\" style=\"font-size: 14px;\">Go to top</a>"}}, "51738745c9c648b8a74bd7adcf7e82d6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3d4d787675074fe9bc2289f6233cc84d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, 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{}}, "bddb4696bea144f5848a4428ef34dd4e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_19330b1e493742e3a61347f40ff17301", "IPY_MODEL_3865552ca4f744c3bb146e512e0b75c9", "IPY_MODEL_4f391e73d7ef48858ad546d3ee0521eb", "IPY_MODEL_16137a84089e4af59f7177bec7cd4008"], "layout": "IPY_MODEL_2bc69a9eabdf4e00a46e3c4fb6f9e5fe"}}, "19330b1e493742e3a61347f40ff17301": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0209638471fc4544851af66d49626fb3", "style": "IPY_MODEL_de363a6f09d14252ab61c0cabf72c421", "value": "<h4 id=\"TrainTestFeatureDrift_JRNY20479T6VXDP5GJLQM5K8P-section-IC8BB\"><b>Train Test Feature Drift</b></h4>"}}, "0209638471fc4544851af66d49626fb3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "de363a6f09d14252ab61c0cabf72c421": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3865552ca4f744c3bb146e512e0b75c9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cc89deb4c2d5443f98aaeb4a8bdb626d", "style": "IPY_MODEL_709bf22008b748ef86390241126e1076", "value": "<p>    Calculate drift between train dataset and test dataset per feature, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_train_test_feature_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "cc89deb4c2d5443f98aaeb4a8bdb626d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "709bf22008b748ef86390241126e1076": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4f391e73d7ef48858ad546d3ee0521eb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c3698fbb14444f8681bca4ad03483880", "style": "IPY_MODEL_8f2030f651b9473892901ab55eabad26", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_c8c1c_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c8c1c_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c8c1c_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c8c1c_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c8c1c_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c8c1c_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c8c1c_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c8c1c_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.1</td>\n      <td id=\"T_c8c1c_row0_col2\" class=\"data row0 col2\" >Passed for 24 columns out of 24 columns.\nFound column \"sub_grade\" has the highest categorical drift score: 0.15\nFound column \"revol_util\" has the highest numerical drift score: 0.07</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c3698fbb14444f8681bca4ad03483880": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8f2030f651b9473892901ab55eabad26": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "16137a84089e4af59f7177bec7cd4008": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6bc5b3a090534639b07e702559747710", "IPY_MODEL_b867fd9186bc4ad59de0b64ac852f08d", "IPY_MODEL_3d7ee9b1c70946f0b85973c80d1857de", "IPY_MODEL_0ee9810517d144cba3bed193333df4ba", "IPY_MODEL_0521848cfcb74ca7a7c5a2608e145200", "IPY_MODEL_7525ad4b4b6c4cc598396b4abe39baa6", "IPY_MODEL_ac718f9721f04a238ce3170d372bcf58", "IPY_MODEL_ae123db4a4be4903bb4bce8060724e80"], "layout": "IPY_MODEL_dd031026c49f459689521382272fcdc6"}}, "6bc5b3a090534639b07e702559747710": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_03d54bf1dc7440dc8d8f5f74f8bdbb38", "style": "IPY_MODEL_6552a3a15abf46b4acbe6ce606f0f002", "value": "<h5><b>Additional Outputs</b></h5>"}}, "03d54bf1dc7440dc8d8f5f74f8bdbb38": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6552a3a15abf46b4acbe6ce606f0f002": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b867fd9186bc4ad59de0b64ac852f08d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d89f99a7142c4df1bc49759df3fc2c6c", "style": "IPY_MODEL_9d7d299d3da44f2d9731cc91451640c2", "value": "<div><span>\n                The Drift score is a measure for the difference between two distributions, in this check - the test\n                and train distributions.<br> The check shows the drift score and distributions for the features, sorted\n                by drift score and showing only the top 5 features, according to drift score.\n                <br>If available, the plot titles also show the feature importance (FI) rank.\n            </span></div>"}}, "d89f99a7142c4df1bc49759df3fc2c6c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9d7d299d3da44f2d9731cc91451640c2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3d7ee9b1c70946f0b85973c80d1857de": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "087c7abc-c9a2-4230-98c9-c53c4ee97405"}, {"base": 0.1, "marker": {"color": "#F2C80F"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": 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"ae123db4a4be4903bb4bce8060724e80": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_11bd59c241a04d4b851d2b4ea0ffffa8", "style": "IPY_MODEL_0a3b17842ce343c9aff3bfa8d44de15f", "value": "<br><a href=\"#summary_JRNY20479T6VXDP5GJLQM5K8P-section-IC8BB\" style=\"font-size: 14px;\">Go to top</a>"}}, "11bd59c241a04d4b851d2b4ea0ffffa8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0a3b17842ce343c9aff3bfa8d44de15f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dd031026c49f459689521382272fcdc6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2bc69a9eabdf4e00a46e3c4fb6f9e5fe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "55bf8a8eee43455e81175c57d770661c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_41871b366b5b4f28b5f1e163d7504671", "IPY_MODEL_8370b209e0f040f8b3637986c0d21162", "IPY_MODEL_d867c43c912f4a6c82d86bbc3c41f5fa", "IPY_MODEL_c22929e06efe48d59f13585d408529ac"], "layout": "IPY_MODEL_0081f739f87c4d0185295087636c4cb0"}}, "41871b366b5b4f28b5f1e163d7504671": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_41ed53f09f914ee48c02b8e90d3c1a06", "style": "IPY_MODEL_e5f066ce9bfc439fb89eb9b5558eb1a6", "value": "<h4 id=\"TrainTestLabelDrift_JRNY20479T6VXDP5GJLQM5K8P-section-IC8BB\"><b>Train Test Label Drift</b></h4>"}}, "41ed53f09f914ee48c02b8e90d3c1a06": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e5f066ce9bfc439fb89eb9b5558eb1a6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8370b209e0f040f8b3637986c0d21162": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_46697d2284b948588ec7f096705d28fa", "style": "IPY_MODEL_7d125fa6184f47d7a75d4fb263eeab7b", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_train_test_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "46697d2284b948588ec7f096705d28fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7d125fa6184f47d7a75d4fb263eeab7b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d867c43c912f4a6c82d86bbc3c41f5fa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b9326bd65b214a0fbbf5a560cf7d50cb", "style": "IPY_MODEL_1b189340fdcb4b5ea77ff69b8ab55f17", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_18f45_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_18f45_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_18f45_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_18f45_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_18f45_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_18f45_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_18f45_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_18f45_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.1 for label drift</td>\n      <td id=\"T_18f45_row0_col2\" class=\"data row0 col2\" >Label's drift score Cramer's V is 0.02</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b9326bd65b214a0fbbf5a560cf7d50cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b189340fdcb4b5ea77ff69b8ab55f17": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c22929e06efe48d59f13585d408529ac": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_38427dc8ace641afa0d9e857f2a9ca13", "IPY_MODEL_5ca9291856cf4d38b26ef1ef865d54e2", "IPY_MODEL_b07f77331c6d474e9c25d61e469c08ff", "IPY_MODEL_aac67a4bd20c42658853b7f363336897"], "layout": "IPY_MODEL_5e6864fc66914d9cb9a877beb0364eb3"}}, "38427dc8ace641afa0d9e857f2a9ca13": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ff79a34f2e2a4e6a8abcad1ea98ca1e1", "style": "IPY_MODEL_fbc3db8d9c1c4710ac719db686f082da", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ff79a34f2e2a4e6a8abcad1ea98ca1e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fbc3db8d9c1c4710ac719db686f082da": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5ca9291856cf4d38b26ef1ef865d54e2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6536abbb39a04ec6ab8da64038c9d3e6", "style": "IPY_MODEL_1dfb3bbb377e4c1aad9e6732aeb749bc", "value": "<div><span>\n                The Drift score is a measure for the difference between two distributions, in this check - the test\n                and train distributions.<br> The check shows the drift score and distributions for the label.\n            </span></div>"}}, "6536abbb39a04ec6ab8da64038c9d3e6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1dfb3bbb377e4c1aad9e6732aeb749bc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b07f77331c6d474e9c25d61e469c08ff": {"model_name": "FigureModel", 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.. GENERATED FROM PYTHON SOURCE LINES 153-172

Ok, the date leakage doesn't happen anymore!

However, in the current split after the fix, we can see that we have a multivariate drift, 
detected by the :doc:`</checks_gallery/tabular/train_test_validation/plot_whole_dataset_drift>` check.
The drift is caused mainly by a combination of features representing the loan's interest rate (``int_rate``) 
and its grade (``sub_grade``). In order to proceed, we should think about the two options we have: 
To split the data in a different manner, or to stay with the current split.

For working with different data splits: We can consider examining other sampling techniques 
(e.g. using only data from the same year), ideally achieving one in which the training data's
univariate and multivariate distribution is similar to the data 
on which the model will run (test / production data). 
Of course, we can use deepchecks to validate the new splits.

If the current split is representative and we are planning on training a model with it, 
it is worth understanding this drift (do we expect this kind of drift in the model's 
production environment? can we do something about it?).

For more details about drift, see the :doc:`</user-guide/general/drift_guide>`.

.. GENERATED FROM PYTHON SOURCE LINES 177-181

Run a Single Check
-------------------

We can run a single check on a dataset, and see the results.

.. GENERATED FROM PYTHON SOURCE LINES 181-190

.. code-block:: default


    # If we want to run only that check (possible with or without condition)
    from deepchecks.tabular.checks import WholeDatasetDrift

    check_with_condition = WholeDatasetDrift().add_condition_overall_drift_value_less_than(0.4)
    # or just the check without the condition:
    # check = WholeDatasetDrift()
    dataset_drift_result = check_with_condition.run(train_ds, test_ds)








.. GENERATED FROM PYTHON SOURCE LINES 191-192

We can also inspect and use the result's value:

.. GENERATED FROM PYTHON SOURCE LINES 192-195

.. code-block:: default


    dataset_drift_result.value





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    {'domain_classifier_auc': 0.7328426082404159, 'domain_classifier_drift_score': 0.46568521648083183, 'domain_classifier_feature_importance': {'int_rate': 0.5092728878422134, 'sub_grade': 0.38754783632616996, 'dti': 0.031056814836620684, 'revol_bal': 0.030762437444804377, 'initial_list_status': 0.02458051221666191, 'emp_length': 0.012952605239917654, 'pub_rec': 0.0038269060936120774, 'time_to_earliest_cr_line': 0.0, 'verification_status': 0.0, 'term': 0.0, 'purpose': 0.0, 'home_ownership': 0.0, 'application_type': 0.0, 'addr_state': 0.0, 'annual_inc': 0.0, 'revol_util': 0.0, 'open_acc': 0.0, 'total_acc': 0.0, 'fico_range_high': 0.0, 'pub_rec_bankruptcies': 0.0, 'installment': 0.0, 'loan_amnt': 0.0, 'mort_acc': 0.0, 'fico_range_low': -0.0}}



.. GENERATED FROM PYTHON SOURCE LINES 196-197

and see if the conditions have passed

.. GENERATED FROM PYTHON SOURCE LINES 197-199

.. code-block:: default

    dataset_drift_result.passed_conditions()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    False



.. GENERATED FROM PYTHON SOURCE LINES 200-204

Create a Custom Suite
----------------------

To create our own suite, we can simply write all of the checks, and add optional conditions.

.. GENERATED FROM PYTHON SOURCE LINES 204-217

.. code-block:: default


    from deepchecks.tabular import Suite
    from deepchecks.tabular.checks import TrainTestFeatureDrift, WholeDatasetDrift, \
     TrainTestPredictionDrift, TrainTestLabelDrift

    drift_suite = Suite('drift suite',
    TrainTestFeatureDrift().add_condition_drift_score_less_than(
      max_allowed_categorical_score=0.2, max_allowed_numeric_score=0.1),
    WholeDatasetDrift().add_condition_overall_drift_value_less_than(0.4),
    TrainTestLabelDrift(),
    TrainTestPredictionDrift()
    )








.. GENERATED FROM PYTHON SOURCE LINES 218-219

we can run our new suite using:

.. GENERATED FROM PYTHON SOURCE LINES 220-223

.. code-block:: default


    result = drift_suite.run(train_ds, test_ds)
    result.show()




.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    drift suite:
    |     | 0/4 [Time: 00:00]
    drift suite:
    |#2   | 1/4 [Time: 00:01, Check=Train Test Feature Drift]
    drift suite:
    |##5  | 2/4 [Time: 00:02, Check=Whole Dataset Drift]     
                                                                 

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        ~ Public License (version 3 or later).
        ~ You should have received a copy of the GNU Affero General Public License
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{"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "807d7aea47ec4475a28d21c83d106624": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "343648ba63c148ea9485bcdf3009d6ff": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cdfd2abfa3884edfa900befe5b76937e", "style": "IPY_MODEL_70a95a69ee244aeabb34610b9595ef0b", "value": "<p>    Calculate drift between the entire train and test datasets using a model trained to distinguish between them. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_whole_dataset_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "cdfd2abfa3884edfa900befe5b76937e": {"model_name": "LayoutModel", 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<tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a5443_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_a5443_row0_col1\" class=\"data row0 col1\" >Drift value is less than 0.4</td>\n      <td id=\"T_a5443_row0_col2\" class=\"data row0 col2\" >Found drift value of: 0.47, corresponding to a domain classifier AUC of: 0.73</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "d01e5390bf6e4e26b0e4a807b40c7a3f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a1d56f2dd93149ac8114ae9dc71f1de4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d96eecd5c97e489c84df0e39749bccae": 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0.5656852164808318], "showgrid": false, "title": {"text": "Drift score"}}, "yaxis": {"autorange": true, "color": "black", "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "d7bd3a59e54c43f4b28a6beb69a9787b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a80f31b5f3a04fb5970e39885bf71a21", "style": "IPY_MODEL_556326ab1e7342d98a0f5a9af5aacffe", "value": "<div><h3>Main features contributing to drift</h3></div>"}}, "a80f31b5f3a04fb5970e39885bf71a21": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": 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"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}}}, "title": {"text": "Feature: sub_grade - Explains 38.75% of dataset difference"}, "xaxis": {"range": [-3, 12], "type": "category"}, "yaxis": {"autorange": true, "fixedrange": true, "rangemode": "normal", "title": {"text": "Frequency"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "PV3C0idulj8UlPzXHSSYP5o3Chu1d6s/BFXptkXvrz97ifmXxiexPw1y4Mf2hbc/MBjp5Y5jkz/syPRDfj2WPxSU/NcdJJg/xfuqFkm6cD8="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "nDOG6Ik+pD+2u/YoJremPz1cIQbdn6Q/SaM8jAtHqz9X5tPfDrWqPwVTpx2wUqw/qXe+CNh6pT+cMuUbP3CiP0yeF42VP6I/tL7ZjgYijD8="}]}, "43e1579c2b574b5a8f9a90b19f6462b5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4857989731cb4df8af8bc0a0608d0ab5", "style": "IPY_MODEL_113f73b3109f45138fd4e4ad613d441f", "value": "<br><a href=\"#summary_I4WIRE8QJNV8Z2FLU7U6N7HV3-section-IZKY2\" style=\"font-size: 14px;\">Go to top</a>"}}, "4857989731cb4df8af8bc0a0608d0ab5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "113f73b3109f45138fd4e4ad613d441f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3851f86c8677453fa1eeb19873d26f0d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "303b0103b0c743e683480a6f15cdd161": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d5298c99c75f4ee39f147aa318328217": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "58e1b729ffcc454380dc6df3aa8539bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b780406f32f541a39774014018dbaf5d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8a81e11f158f41528eb792e1d6db061b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3609280c6fb848c0b2430392ac54d2a8", "IPY_MODEL_27312a422fee4eb6b4d5b4c3327dda2b"], "layout": "IPY_MODEL_57aa836a85cb4a61929a8e31a0c7566b"}}, "3609280c6fb848c0b2430392ac54d2a8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8507fd32ed374d19bba2f983f5d18b20", "style": "IPY_MODEL_23636b23b0e544ea89efb8cb01e5c8c1", "value": "<span id=\"summary_I4WIRE8QJNV8Z2FLU7U6N7HV3-section-2UBTX\"></span>"}}, "8507fd32ed374d19bba2f983f5d18b20": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "23636b23b0e544ea89efb8cb01e5c8c1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "27312a422fee4eb6b4d5b4c3327dda2b": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Passed"}, "children": ["IPY_MODEL_a1c0e94f3b3842c8807b4b4dffb13f1d"], "layout": "IPY_MODEL_9c3ca5892c1546f18eaedb6827b82c4d", "selected_index": null}}, "a1c0e94f3b3842c8807b4b4dffb13f1d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d3b49ad49c1340689a873cc87eedeeba", "IPY_MODEL_7ad7be96a8f94883b53d850c9c209846", "IPY_MODEL_a2039ce05d084c03825269190b5c09d4"], "layout": "IPY_MODEL_c0eda0292d1b4726b4ccb4c6f695b2c3"}}, "d3b49ad49c1340689a873cc87eedeeba": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fb03040b7e224982b54f34df56cd7393", "style": "IPY_MODEL_15b15f4c69e44877a2429c37605db15a", "value": "<style type=\"text/css\">\n#T_11746_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_11746_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_11746_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_11746_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_11746_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_11746_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_11746_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_11746_row0_col1\" class=\"data row0 col1\" ><a href=\"#TrainTestFeatureDrift_I4WIRE8QJNV8Z2FLU7U6N7HV3-section-2UBTX\">Train Test Feature Drift</a></td>\n      <td id=\"T_11746_row0_col2\" class=\"data row0 col2\" >categorical drift score < 0.2 and numerical drift score < 0.1</td>\n      <td id=\"T_11746_row0_col3\" class=\"data row0 col3\" >Passed for 24 columns out of 24 columns.\nFound column \"sub_grade\" has the highest categorical drift score: 0.15\nFound column \"revol_util\" has the highest numerical drift score: 0.07</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "fb03040b7e224982b54f34df56cd7393": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "15b15f4c69e44877a2429c37605db15a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7ad7be96a8f94883b53d850c9c209846": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_45124fe9960c45bc837daf7362882353", "style": "IPY_MODEL_243437180d484cb193c7c645e504b8e5", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "45124fe9960c45bc837daf7362882353": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "243437180d484cb193c7c645e504b8e5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a2039ce05d084c03825269190b5c09d4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_3bb85e56986f4eddbc87a4d8f89b78a8", "IPY_MODEL_a674c24554584a8095cc8ce293475b51", "IPY_MODEL_23cf2a58233e44cca3ccece604305a10", "IPY_MODEL_0810bb877a3b4aa693e71a0801d9109c"], "layout": "IPY_MODEL_d02342b9d54347af81e48579a23b4df4"}}, "3bb85e56986f4eddbc87a4d8f89b78a8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1d82196a3c7a487189ebe12dd711f935", "style": "IPY_MODEL_ab675806207742cf8c961767d3a7b543", "value": "<h4 id=\"TrainTestFeatureDrift_I4WIRE8QJNV8Z2FLU7U6N7HV3-section-2UBTX\"><b>Train Test Feature Drift</b></h4>"}}, "1d82196a3c7a487189ebe12dd711f935": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ab675806207742cf8c961767d3a7b543": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a674c24554584a8095cc8ce293475b51": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7afaa7315cd14b678e1a00aeaca429cf", "style": "IPY_MODEL_b74886723b3a4b929402daa8ddde14e5", "value": "<p>    Calculate drift between train dataset and test dataset per feature, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_train_test_feature_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "7afaa7315cd14b678e1a00aeaca429cf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b74886723b3a4b929402daa8ddde14e5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "23cf2a58233e44cca3ccece604305a10": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3ed6e960499241eab4c17cdd3146e94f", "style": "IPY_MODEL_011148193f2c4a3bbcb9782f86af0c5b", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_9d08e_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9d08e_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9d08e_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9d08e_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9d08e_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_9d08e_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_9d08e_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_9d08e_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.1</td>\n      <td id=\"T_9d08e_row0_col2\" class=\"data row0 col2\" >Passed for 24 columns out of 24 columns.\nFound column \"sub_grade\" has the highest categorical drift score: 0.15\nFound column \"revol_util\" has the highest numerical drift score: 0.07</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3ed6e960499241eab4c17cdd3146e94f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "011148193f2c4a3bbcb9782f86af0c5b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0810bb877a3b4aa693e71a0801d9109c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", 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"9cbb82329bbf45d193408b55ee89c4f2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6153d39749ac44cf90130ef15e89ad22", "IPY_MODEL_310df2ddb85c41df80add7a94e0acefc", "IPY_MODEL_e0a02a16fb044d9c8f3aa58e500287fc"], "layout": "IPY_MODEL_2ca7d33e4b3c49749fbced3c4bcab633"}}, "6153d39749ac44cf90130ef15e89ad22": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_158ee2ec0b804e05b051f187518cebc3", "style": "IPY_MODEL_0f8b193e93c74efe899644b40dac1664", "value": "<style type=\"text/css\">\n#T_dd436_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dd436_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dd436_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dd436_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dd436_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_dd436_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Check</th>\n      <th class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_dd436_row0_col0\" class=\"data row0 col0\" ><a href=\"#TrainTestLabelDrift_I4WIRE8QJNV8Z2FLU7U6N7HV3-section-CXAPR\">Train Test Label Drift</a></td>\n      <td id=\"T_dd436_row0_col1\" class=\"data row0 col1\" >    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_train_test_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "158ee2ec0b804e05b051f187518cebc3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", 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"model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_052a4bbed7f243b0a9489f959a926c40", "IPY_MODEL_6423591dc48840b88d1b6ca6879448f7", "IPY_MODEL_46932fe7d0544d1c8bca8d6ffe565691"], "layout": "IPY_MODEL_207a70e9bc924a63a8e9fd164b861000"}}, "052a4bbed7f243b0a9489f959a926c40": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f90e48f50e67405e9264106fb783ba52", "style": "IPY_MODEL_c1bc339081af487ba28a2cd89d165d69", "value": "<h4 id=\"TrainTestLabelDrift_I4WIRE8QJNV8Z2FLU7U6N7HV3-section-CXAPR\"><b>Train Test Label Drift</b></h4>"}}, "f90e48f50e67405e9264106fb783ba52": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c1bc339081af487ba28a2cd89d165d69": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6423591dc48840b88d1b6ca6879448f7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d75b52353cd94e44aa8970ddbd8a1a57", "style": "IPY_MODEL_115082478a46486391a19c92116070fe", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_train_test_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "d75b52353cd94e44aa8970ddbd8a1a57": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "115082478a46486391a19c92116070fe": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "46932fe7d0544d1c8bca8d6ffe565691": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ec4ed57500b34bcdb10445bb2e687d1c", "IPY_MODEL_2021e69776ea4a369aedb79b4aa7fa5a", "IPY_MODEL_bb44714e607c4ea3bcb49198b62d1efb", "IPY_MODEL_98bd95679aee49e5b7e958970d464083"], "layout": "IPY_MODEL_2a7a1fc099254117b04bcfaba018ebed"}}, "ec4ed57500b34bcdb10445bb2e687d1c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_204393aab3ec4941b881c651992503e4", "style": "IPY_MODEL_9116aeb33f5c4ab7a1d957efd768c336", "value": "<h5><b>Additional Outputs</b></h5>"}}, "204393aab3ec4941b881c651992503e4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9116aeb33f5c4ab7a1d957efd768c336": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2021e69776ea4a369aedb79b4aa7fa5a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9a2f6e117bf74551b5d9cc7de92cb22b", "style": "IPY_MODEL_9433a97060cc4bd7be3b16ff1224fceb", "value": "<div><span>\n                The Drift score is a measure for the difference between two distributions, in this check - the test\n                and train distributions.<br> The check shows the drift score and distributions for the label.\n            </span></div>"}}, "9a2f6e117bf74551b5d9cc7de92cb22b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9433a97060cc4bd7be3b16ff1224fceb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": 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.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  17.131 seconds)


.. _sphx_glr_download_user-guide_tabular_auto_quickstarts_plot_quick_train_test_validation.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_quick_train_test_validation.py <plot_quick_train_test_validation.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_quick_train_test_validation.ipynb <plot_quick_train_test_validation.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_