diff --git "a/vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k_ep_60_full_suite.html" "b/vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k_ep_60_full_suite.html"
--- "a/vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k_ep_60_full_suite.html"
+++ "b/vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k_ep_60_full_suite.html"
@@ -295,8 +295,8 @@
 <body>
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         <a href=https://gitlab.com/mosquitoscan>MosquitoScan Project</a>
-        <a href="https://mosquitoscan.gitlab.io/experiments-reports/vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k-exp_5_old_bg-subs_1_v_5-run_3/">Current Report Home</a>
-        <a href="https://gitlab.com/mosquitoscan/experiments-reports/vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k-exp_5_old_bg-subs_1_v_5-run_3/">Current Report Repository</a>
+        <a href="https://mosquitoscan.gitlab.io/experiments-reports/vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k-exp_5_old_bg-subs_1_v_5-run_5/">Current Report Home</a>
+        <a href="https://gitlab.com/mosquitoscan/experiments-reports/vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k-exp_5_old_bg-subs_1_v_5-run_5/">Current Report Repository</a>
         <a href="https://gitlab.com/mosquitoscan/experiments-reports/">All Reports Repository</a>
     </div>
     
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Read more about\n    <a href=https://docs.deepchecks.com/stable/general/usage/customizations/auto_examples/plot_create_a_custom_suite.html?utm_source=display_output&utm_medium=referral&utm_campaign=suite_link target=\"_blank\">custom suites</a>.\n</p>\n\n"}}, "7fb913929aa44d31a01f73eb5bd6b917": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8dcc96839ad242d8995456430f0c863e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8594042299f1423aa7fedbbf683cb2d6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5a19ae02af33491080145c46112e646a", "IPY_MODEL_3a298996a189422e84a96e99ed788ef5"], "layout": "IPY_MODEL_c16ec5bd49ef4061bda55c18a5be12cf"}}, "5a19ae02af33491080145c46112e646a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8aee4a9cc24f45ecbda7ddb945ed7ee5", "style": "IPY_MODEL_88cb13f1b7a14d40afa18706ccfc4d25", "value": "<span id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"></span>"}}, "8aee4a9cc24f45ecbda7ddb945ed7ee5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "88cb13f1b7a14d40afa18706ccfc4d25": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3a298996a189422e84a96e99ed788ef5": {"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": "Didn't Pass"}, "children": ["IPY_MODEL_551ea1cd57d44704b69f054c7ae91c26"], "layout": "IPY_MODEL_e173967e404644b2bc3c2a5feab545ac", "selected_index": null}}, "551ea1cd57d44704b69f054c7ae91c26": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_333e93d2db584a34ba1e6d9e6f7e9dcf", "IPY_MODEL_5149d36368214f9eb126c287fceb6d9e", "IPY_MODEL_4725084884984b26b263896b0fd6fbad", "IPY_MODEL_43262bea399e45e6aea4d03546b6b6ac", "IPY_MODEL_f9a2909ceea34958b4403e99f7ece3c5", "IPY_MODEL_43262bea399e45e6aea4d03546b6b6ac", "IPY_MODEL_ef4ed9bb01c147059cfa4fe4defc7039", "IPY_MODEL_43262bea399e45e6aea4d03546b6b6ac", "IPY_MODEL_28eadf165bf04393ba948c6441834278"], "layout": "IPY_MODEL_7c9e7cafdbe346c9bc04df08972324c3"}}, "333e93d2db584a34ba1e6d9e6f7e9dcf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1891c4e7179d4009b604cc8d700cdf21", "style": "IPY_MODEL_2eebeb1e23f64ee0962df1ae9777f7ea", "value": "<style type=\"text/css\">\n#T_59a0a table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_59a0a thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_59a0a tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_59a0a th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_59a0a td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_59a0a\">\n  <thead>\n    <tr>\n      <th id=\"T_59a0a_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_59a0a_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_59a0a_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_59a0a_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_59a0a_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_59a0a_row0_col1\" class=\"data row0 col1\" ><a href=\"#ClassPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Class Performance</a></td>\n      <td id=\"T_59a0a_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_59a0a_row0_col3\" class=\"data row0 col3\" >5 scores failed. Found max degradation of 18.88% for metric Recall and class aedes_vexans.</td>\n    </tr>\n    <tr>\n      <td id=\"T_59a0a_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_59a0a_row1_col1\" class=\"data row1 col1\" ><a href=\"#ImagePropertyDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Image Property Drift</a></td>\n      <td id=\"T_59a0a_row1_col2\" class=\"data row1 col2\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_59a0a_row1_col3\" class=\"data row1 col3\" >Failed for 4 out of 7 properties.\nFound 4 numeric properties with Kolmogorov-Smirnov above threshold: {'RMS Contrast': '0.43', 'Mean Red Relative Intensity': '0.51', 'Mean Green Relative Intensity': '0.53', 'Mean Blue Relative Intensity': '0.51'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_59a0a_row2_col0\" class=\"data row2 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_59a0a_row2_col1\" class=\"data row2 col1\" ><a href=\"#PropertyLabelCorrelationChange_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Property Label Correlation Change</a></td>\n      <td id=\"T_59a0a_row2_col2\" class=\"data row2 col2\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_59a0a_row2_col3\" class=\"data row2 col3\" >Properties and classes with PPS difference above threshold: {'Mean Green Relative Intensity': {'anopheles_arabiensis': '0.52'}}</td>\n    </tr>\n    <tr>\n      <td id=\"T_59a0a_row3_col0\" class=\"data row3 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_59a0a_row3_col1\" class=\"data row3 col1\" ><a href=\"#WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Weak Segments Performance</a></td>\n      <td id=\"T_59a0a_row3_col2\" class=\"data row3 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_59a0a_row3_col3\" class=\"data row3 col3\" >Found a segment with cross entropy of -1.122 in comparison to an average score of -0.245 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1891c4e7179d4009b604cc8d700cdf21": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2eebeb1e23f64ee0962df1ae9777f7ea": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5149d36368214f9eb126c287fceb6d9e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a0cb6de93af540b7afb2468354c1f417", "style": "IPY_MODEL_3dea1ef49e8e43e4b9bc2b1b72be1413", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "a0cb6de93af540b7afb2468354c1f417": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3dea1ef49e8e43e4b9bc2b1b72be1413": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4725084884984b26b263896b0fd6fbad": {"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_386ae2c6831847348a65e93cf51ca443", "IPY_MODEL_d82e42821cfb4164b19bf3b2fdfe0f1e", "IPY_MODEL_cece6f1df8ad47d9aa7a76a7d6cf4ea1", "IPY_MODEL_697f1d152a374b93a50a1eb040d49295"], "layout": "IPY_MODEL_f09bf3b43f22417b8c933aa53db964bc"}}, "386ae2c6831847348a65e93cf51ca443": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ce02bd6435944d50b8183ccec5d703c1", "style": "IPY_MODEL_c80e4f596f64410cbe89500f9554a585", "value": "<h4 id=\"ClassPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Class Performance</b></h4>"}}, "ce02bd6435944d50b8183ccec5d703c1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c80e4f596f64410cbe89500f9554a585": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d82e42821cfb4164b19bf3b2fdfe0f1e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9b2b97470d3d4bedaecebf9cd6b22169", "style": "IPY_MODEL_f78b064e61af4a1eb999f88c487e17d4", "value": "<p>Summarize given metrics on a dataset and model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_class_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "9b2b97470d3d4bedaecebf9cd6b22169": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f78b064e61af4a1eb999f88c487e17d4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cece6f1df8ad47d9aa7a76a7d6cf4ea1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e8812614a0bd4cfb8ed28bc2faf81546", "style": "IPY_MODEL_f51a23151df44e28a71a7da2f08de08a", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_1edb2 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1edb2 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1edb2 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1edb2 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1edb2 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_1edb2\">\n  <thead>\n    <tr>\n      <th id=\"T_1edb2_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_1edb2_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_1edb2_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_1edb2_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_1edb2_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_1edb2_row0_col2\" class=\"data row0 col2\" >5 scores failed. Found max degradation of 18.88% for metric Recall and class aedes_vexans.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e8812614a0bd4cfb8ed28bc2faf81546": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f51a23151df44e28a71a7da2f08de08a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "697f1d152a374b93a50a1eb040d49295": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_09bc65f3ed49431993387dcc81709742", "IPY_MODEL_a97650625035433d981dc7875c656237", "IPY_MODEL_a46e3d229aa84a6fbb9ee0f920459912", "IPY_MODEL_a16e53c957fb4421a0162d17a8956361"], "layout": "IPY_MODEL_8621025a4d8a4f5f939a03bf7fa8f913"}}, "09bc65f3ed49431993387dcc81709742": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_93cce18303274e28a71da3578480a898", "style": "IPY_MODEL_ddd8686a15ea45fba38e01f9456ecd1e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "93cce18303274e28a71da3578480a898": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ddd8686a15ea45fba38e01f9456ecd1e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a97650625035433d981dc7875c656237": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Precision<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#003b47", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": true, "x": ["aedes_aegypti", "aedes_albopictus", "aedes_dorsalis", "aedes_koreicus", "aedes_vexans", "anopheles_arabiensis", "anopheles_freeborni", "culex_inatomii", "culex_pipiens", "culex_quinquefasciatus", "anopheles_sinensis", "culex_tritaeniorhynchus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y", "type": "histogram", "uid": "2fccc1cd-0bd7-4a30-95e3-ead30642ae04"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Recall<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#003b47", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": ["aedes_aegypti", "aedes_albopictus", "aedes_dorsalis", "aedes_koreicus", "anopheles_arabiensis", "anopheles_freeborni", "anopheles_sinensis", "culex_inatomii", "culex_pipiens", "culex_quinquefasciatus", 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{"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f09bf3b43f22417b8c933aa53db964bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "43262bea399e45e6aea4d03546b6b6ac": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d282fb820ed9474b9fb4352cc3b17cdf", "style": "IPY_MODEL_cecf5a608e8446bb9bc1dfc3a2b4fd72", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "d282fb820ed9474b9fb4352cc3b17cdf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cecf5a608e8446bb9bc1dfc3a2b4fd72": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f9a2909ceea34958b4403e99f7ece3c5": {"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_951f5d12f93b4f399a64944291edce1c", "IPY_MODEL_ad84d7c2498c458d805294793493cd96", "IPY_MODEL_3bbadfac485340d58974c376ac92dee6", "IPY_MODEL_b9476fa124fc44249d88c5c71a945df7"], "layout": "IPY_MODEL_814bcd78379b4ef3bbc66ee03da04295"}}, "951f5d12f93b4f399a64944291edce1c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0bd91402394e499fbc4d65622627acb9", "style": "IPY_MODEL_ae9fbd8bc7a645dc8d0a6ccb5330f77e", "value": "<h4 id=\"ImagePropertyDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Image Property Drift</b></h4>"}}, "0bd91402394e499fbc4d65622627acb9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ae9fbd8bc7a645dc8d0a6ccb5330f77e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ad84d7c2498c458d805294793493cd96": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e932db2c396b4e6a817c147f7e54ab0c", "style": "IPY_MODEL_e95e025f226440748cdedcd0f5b3d228", "value": "<p>    Calculate drift between train dataset and test dataset per image property, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_image_property_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "e932db2c396b4e6a817c147f7e54ab0c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e95e025f226440748cdedcd0f5b3d228": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3bbadfac485340d58974c376ac92dee6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c3ec3a8cc9194ce69a7c169cde08d2ec", "style": "IPY_MODEL_7853e3f7af184df5b3b5125f9df3705a", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_68290 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_68290 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_68290 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_68290 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_68290 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_68290\">\n  <thead>\n    <tr>\n      <th id=\"T_68290_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_68290_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_68290_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_68290_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_68290_row0_col1\" class=\"data row0 col1\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_68290_row0_col2\" class=\"data row0 col2\" >Failed for 4 out of 7 properties.\nFound 4 numeric properties with Kolmogorov-Smirnov above threshold: {'RMS Contrast': '0.43', 'Mean Red Relative Intensity': '0.51', 'Mean Green Relative Intensity': '0.53', 'Mean Blue Relative Intensity': '0.51'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c3ec3a8cc9194ce69a7c169cde08d2ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", 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"b118fe78c68b4853ac98287ab57dad58": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_44c4c74edb984228b88c655b8a85107e", "style": "IPY_MODEL_ffd55a022fd14df1b40a1185f6c57944", "value": "<h5><b>Additional Outputs</b></h5>"}}, "44c4c74edb984228b88c655b8a85107e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ffd55a022fd14df1b40a1185f6c57944": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cec46c8616394a09a4b565f8881e18fd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_18b6235655674806af520927fe4ddf20", "style": "IPY_MODEL_0dc2f83bb511429dac7122522689c25d", "value": "<div><span> The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following image properties: ['Area', 'Aspect Ratio', 'Brightness', 'Mean Blue Relative Intensity', 'Mean Green Relative Intensity', 'Mean Red Relative Intensity', 'RMS Contrast'].</span></div>"}}, "18b6235655674806af520927fe4ddf20": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0dc2f83bb511429dac7122522689c25d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "99dedcf1c8ab4bf7bd16d17a31c4a24e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_90792052b84d4df5a3eccdcbff27b3d3", "style": "IPY_MODEL_4c3357c43cfc48ad8d2eb52f782832a1", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, 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"state": {}}, "da140d1b4ca04523beaccabff15e4a3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "814bcd78379b4ef3bbc66ee03da04295": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ef4ed9bb01c147059cfa4fe4defc7039": {"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_e2351ab4bbe640fab2685a8e007260a2", "IPY_MODEL_02352bc345ea431b9b8f074395cda44d", "IPY_MODEL_081781bae3ea4f0dbd69a6e721439e01", "IPY_MODEL_4124d2bb36d2450ab2effd1537e0af64"], "layout": "IPY_MODEL_3ce8354ab9b64699a61ba27800d06b8f"}}, "e2351ab4bbe640fab2685a8e007260a2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2ef3c936e11d4248bb8fc6262a154ef3", "style": "IPY_MODEL_cecd8b99865e4e309ee2262873d30d84", "value": "<h4 id=\"PropertyLabelCorrelationChange_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Property Label Correlation Change</b></h4>"}}, "2ef3c936e11d4248bb8fc6262a154ef3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cecd8b99865e4e309ee2262873d30d84": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "02352bc345ea431b9b8f074395cda44d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9cfeb3c4b1454f9fa6fa7548a60dbb72", "style": "IPY_MODEL_70d8def51e814655b753172762103c5a", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_property_label_correlation_change.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "9cfeb3c4b1454f9fa6fa7548a60dbb72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "70d8def51e814655b753172762103c5a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "081781bae3ea4f0dbd69a6e721439e01": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dd8c6d4af2f14e40855317a88a2734f4", "style": "IPY_MODEL_1c328fb52b9d437898962f1998250ee5", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_b9d0a table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b9d0a thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b9d0a tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b9d0a th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b9d0a td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b9d0a\">\n  <thead>\n    <tr>\n      <th id=\"T_b9d0a_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_b9d0a_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_b9d0a_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_b9d0a_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_b9d0a_row0_col1\" class=\"data row0 col1\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_b9d0a_row0_col2\" class=\"data row0 col2\" >Properties and classes with PPS difference above threshold: 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[0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, 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"linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Mean Blue Relative Intensity: Predictive Power Score (PPS) Per Class"}, "xaxis": {"range": [-3, 5], "title": {"text": "Class"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_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": "qA3pnw3pvz8AAAAAAAAAAAAAAAAAAAAA"}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "JEyTJWq3wT8AAAAAAAAAAAAAAAAAAAAA"}]}, "a05f1f4d10de4b81a54407b06425b570": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_30f9ac81b8064888a3b164c297b2f560", "style": "IPY_MODEL_56cf76fe0a004178a4a8ef9a6d43f0fb", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "30f9ac81b8064888a3b164c297b2f560": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "56cf76fe0a004178a4a8ef9a6d43f0fb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "184735e0cb0b49b3adc615eb181e35ef": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b550e022f41c4ad0bf491245096cb8b3", "style": "IPY_MODEL_1acaf23fbe984161aea075a7b29d0d1b", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "b550e022f41c4ad0bf491245096cb8b3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1acaf23fbe984161aea075a7b29d0d1b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "545c9d029d494f149212fe310c2b289a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6fd505ef84c941099eed1a740222cd16", "style": "IPY_MODEL_1c8521ecb1994a3d9c0f302e520a6feb", "value": "<div>1. <b>Train dataset PPS values are high</b>:</div>"}}, "6fd505ef84c941099eed1a740222cd16": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1c8521ecb1994a3d9c0f302e520a6feb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3b9be127661644249b4ddc8e412228c3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9eaed4b50a924eb28c7ede45290767ce", "style": "IPY_MODEL_95b6ede247b44c509c31cb40d55522ba", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict   the label successfully, using simple classic ML algorithms</div>"}}, "9eaed4b50a924eb28c7ede45290767ce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "95b6ede247b44c509c31cb40d55522ba": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "69808002ac5b40e88743e1a281c2d54f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7172d514f98647ab8377ce08a6686ec9", "style": "IPY_MODEL_f918ff91ba2a48fb9cb115b3d868d7dc", "value": "<div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div>"}}, "7172d514f98647ab8377ce08a6686ec9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f918ff91ba2a48fb9cb115b3d868d7dc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7eb00e9fa59f4ee196d571cc0b36e997": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8507ae6c46ec4c1eaf74ba840b70a27b", "style": "IPY_MODEL_2f234571c29e404aa54e99558c2781da", "value": "<div>   An even more powerful indication of dataset bias, as an image property that was powerful in train</div>"}}, "8507ae6c46ec4c1eaf74ba840b70a27b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2f234571c29e404aa54e99558c2781da": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a0666a24237f4ca18e1722b36b6dd1ed": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_626523193b2b42dc80cd657707ffa07a", "style": "IPY_MODEL_f278b203b69940cf9032a3b1c9401705", "value": "<div>   but not in test can be explained by bias in train that is not relevant to a new dataset.</div>"}}, "626523193b2b42dc80cd657707ffa07a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f278b203b69940cf9032a3b1c9401705": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e7cf408940274b038e98498a18a329f8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7baebbde044c41cd9d85463dca7c5a7b", "style": "IPY_MODEL_5d674f9b34ae4d14b0081b5031ea5333", "value": "<div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div>"}}, "7baebbde044c41cd9d85463dca7c5a7b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d674f9b34ae4d14b0081b5031ea5333": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ed59b68d3f67447a9d855b156286f152": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_712a213952314cf4a4bd71818968d7dd", "style": "IPY_MODEL_6de0d98447c247f3bbfe00f626cffe9d", "value": "<div>   An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.</div>"}}, "712a213952314cf4a4bd71818968d7dd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6de0d98447c247f3bbfe00f626cffe9d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b8002f2925924dcbb2e742276f646332": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8223dad073ef470cac14f4b47fb533bb", "style": "IPY_MODEL_c8a38f87fdb54d29a534e137d74e7b3d", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "8223dad073ef470cac14f4b47fb533bb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c8a38f87fdb54d29a534e137d74e7b3d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d680ed7b197640c8bb2bcbec2c296747": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e146fa15f0d3464288edaaf82a2b0c7a", "style": "IPY_MODEL_075f53f879384a5b9c468f6b80ca1a33", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "e146fa15f0d3464288edaaf82a2b0c7a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "075f53f879384a5b9c468f6b80ca1a33": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "22d9c4c2312f4362802c20ce6c335d87": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3ce8354ab9b64699a61ba27800d06b8f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "28eadf165bf04393ba948c6441834278": {"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_546dec9abce243a681dea7bd208387eb", "IPY_MODEL_3e725cf3e342444ab802fe1e2c7f44df", "IPY_MODEL_265e2f77bd2340ea9acf42b9914f9f43", "IPY_MODEL_bb3de24aa6a5473baff723f8192f405c"], "layout": "IPY_MODEL_8f1dfc60898e4e448eb89c75d1552e56"}}, "546dec9abce243a681dea7bd208387eb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8a233504acd9430c8092b477f4bd52c0", "style": "IPY_MODEL_1f059e023e3140e183129e1b16d88aa0", "value": "<h4 id=\"WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Weak Segments Performance</b></h4>"}}, "8a233504acd9430c8092b477f4bd52c0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1f059e023e3140e183129e1b16d88aa0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3e725cf3e342444ab802fe1e2c7f44df": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b0c6a190a75c44c8b4828f1fff0f6a9d", "style": "IPY_MODEL_93845e5d0ee84f588963f93885c9d31d", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "b0c6a190a75c44c8b4828f1fff0f6a9d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "93845e5d0ee84f588963f93885c9d31d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "265e2f77bd2340ea9acf42b9914f9f43": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_edc068240c344a2c8624ae356542238a", "style": "IPY_MODEL_00e851b15dfe4a708c379a6122d7f7c3", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_52a61 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_52a61 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_52a61 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_52a61 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_52a61 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_52a61\">\n  <thead>\n    <tr>\n      <th id=\"T_52a61_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_52a61_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_52a61_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_52a61_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_52a61_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_52a61_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -1.122 in comparison to an average score of -0.245 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "edc068240c344a2c8624ae356542238a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "00e851b15dfe4a708c379a6122d7f7c3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bb3de24aa6a5473baff723f8192f405c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_8737afc3e8894948ba0ab56e4386e6eb", "IPY_MODEL_09126811577e40739ec0e308a72a3f58", "IPY_MODEL_350c6fb3735544c69699db14f21809cb", "IPY_MODEL_81c158e8f8fa497eba89c9382f811ebe"], "layout": "IPY_MODEL_e54c72c6103d4daaa40f74a80e8b0306"}}, "8737afc3e8894948ba0ab56e4386e6eb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ce68bec35aad493aa11a90c6ac56d183", "style": "IPY_MODEL_4c50814aadba4fc2b4fefbecbe6c98e4", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ce68bec35aad493aa11a90c6ac56d183": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4c50814aadba4fc2b4fefbecbe6c98e4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "09126811577e40739ec0e308a72a3f58": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b999132ad57e462c82c808bdfc2647fa", "style": "IPY_MODEL_68304eabb8164d49962b26e44655bd02", "value": "<div>Showcasing intersections of properties with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "b999132ad57e462c82c808bdfc2647fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "68304eabb8164d49962b26e44655bd02": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "350c6fb3735544c69699db14f21809cb": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_42fdfd1e608f4bc5b5bb1986799b7481", "IPY_MODEL_b06f3364b6e44b07a772b58737e5a15c"], "layout": "IPY_MODEL_2424ab0648d94af1971e9137e6eab6ca"}}, "42fdfd1e608f4bc5b5bb1986799b7481": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fed8af455f5146ba9b23a0a5f911b02a", "style": "IPY_MODEL_5fb93ab036134b999ff2374af46acc37", "value": "<style>.jupyter-widgets.widget-tab > 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[0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Cross Entropy (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "Mean Red Relative Intensity"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "8e7b5be08705470f818d24816eb5ce57": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_94c8f3255fc94b1fa1dae5d6a4fe9b1c", "style": "IPY_MODEL_13f55121ed9346c7b4a2622313950fda", "value": "<div>Check ran on 710 data samples. Cross Entropy on the full data set is -0.24.</div>"}}, "94c8f3255fc94b1fa1dae5d6a4fe9b1c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "13f55121ed9346c7b4a2622313950fda": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b6b3abca8d5e4aa0b60ecfcf9e076305": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ac407b053fed42dea82ce7ddf3611ab6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2424ab0648d94af1971e9137e6eab6ca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "81c158e8f8fa497eba89c9382f811ebe": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_92ec626aaaf84bac85c454861cbc7a1c", "style": "IPY_MODEL_3109b6abfd6948f6bcbd6889ba086453", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "92ec626aaaf84bac85c454861cbc7a1c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3109b6abfd6948f6bcbd6889ba086453": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e54c72c6103d4daaa40f74a80e8b0306": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8f1dfc60898e4e448eb89c75d1552e56": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7c9e7cafdbe346c9bc04df08972324c3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e173967e404644b2bc3c2a5feab545ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c16ec5bd49ef4061bda55c18a5be12cf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c26e2f78cc6f4e11ade08842d169ebb3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1de454aebdc74f74a4b28e8bde0857e1", "IPY_MODEL_6fc12eb51a35462e8d89a5d419b658f8"], "layout": "IPY_MODEL_dcffff18fb624d4fb3fb39b166f57c58"}}, "1de454aebdc74f74a4b28e8bde0857e1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4d483ba0305b4f3fa6e4fef10f82c4e2", "style": "IPY_MODEL_6bc1d3353d43464187d3a97fe98530f5", "value": "<span id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"></span>"}}, "4d483ba0305b4f3fa6e4fef10f82c4e2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6bc1d3353d43464187d3a97fe98530f5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6fc12eb51a35462e8d89a5d419b658f8": {"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_1d60c578e6a74a4bb5f30cb9ef690513"], "layout": "IPY_MODEL_3c2388af39c84178bcfb6acd2c1e62c6", "selected_index": null}}, "1d60c578e6a74a4bb5f30cb9ef690513": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4f3b8e77841f4f4b9e2c2251d024ba4d", "IPY_MODEL_64c0711f6b5d4bfa857401f4e9ce0586", "IPY_MODEL_660bd17415e84dc0a3d766241f87d602", "IPY_MODEL_444a2526a0d54c7cbd72cdd25ae5f0c4", "IPY_MODEL_22813aa427ec45cf8665415bbbba9aac", "IPY_MODEL_444a2526a0d54c7cbd72cdd25ae5f0c4", "IPY_MODEL_fda1006bd4cf4cc2ae32677ff132f108", "IPY_MODEL_444a2526a0d54c7cbd72cdd25ae5f0c4", "IPY_MODEL_a82cd34a93334a39abe04c9fba75454a"], "layout": "IPY_MODEL_cff5aafd87a442e29d50d8eeabe04649"}}, "4f3b8e77841f4f4b9e2c2251d024ba4d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8f04c659544342059be36eae19bce6d9", "style": "IPY_MODEL_75a1835b97ac4da5811a5a3e16b0f363", "value": "<style type=\"text/css\">\n#T_26209 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_26209 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_26209 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_26209 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_26209 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_26209\">\n  <thead>\n    <tr>\n      <th id=\"T_26209_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_26209_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_26209_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_26209_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_26209_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_26209_row0_col1\" class=\"data row0 col1\" ><a href=\"#PredictionDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">Prediction Drift</a></td>\n      <td id=\"T_26209_row0_col2\" class=\"data row0 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_26209_row0_col3\" class=\"data row0 col3\" >Passed for 1 prediction properties out of 1 prediction properties.\nFound prediction property \"Samples Per Class\" has the highest categorical drift score: 0.13</td>\n    </tr>\n    <tr>\n      <td id=\"T_26209_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_26209_row1_col1\" class=\"data row1 col1\" ><a href=\"#SimpleModelComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">Simple Model Comparison</a></td>\n      <td id=\"T_26209_row1_col2\" class=\"data row1 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_26209_row1_col3\" class=\"data row1 col3\" >Found minimal gain of 84.67% for metric F1 and class aedes_vexans</td>\n    </tr>\n    <tr>\n      <td id=\"T_26209_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_26209_row2_col1\" class=\"data row2 col1\" ><a href=\"#NewLabels_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">New Labels</a></td>\n      <td id=\"T_26209_row2_col2\" class=\"data row2 col2\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_26209_row2_col3\" class=\"data row2 col3\" >No new labels were found in test set.</td>\n    </tr>\n    <tr>\n      <td id=\"T_26209_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_26209_row3_col1\" class=\"data row3 col1\" ><a href=\"#LabelDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">Label Drift</a></td>\n      <td id=\"T_26209_row3_col2\" class=\"data row3 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_26209_row3_col3\" class=\"data row3 col3\" >Passed for 1 label properties out of 1 label properties.\nFound label property \"Samples Per Class\" has the highest categorical drift score: 0.14</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "8f04c659544342059be36eae19bce6d9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "75a1835b97ac4da5811a5a3e16b0f363": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "64c0711f6b5d4bfa857401f4e9ce0586": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_413cc306c6be45879a2fda92f5852637", "style": "IPY_MODEL_ecbedb0739d244c9a1124aa588e09dba", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "413cc306c6be45879a2fda92f5852637": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ecbedb0739d244c9a1124aa588e09dba": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "660bd17415e84dc0a3d766241f87d602": {"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_4f3c8b5129b142b5a36db52be704f214", "IPY_MODEL_6ef1cbfb2dc04a159f3ebb27405e5e34", "IPY_MODEL_f304725e925a4a8ca0a3f6de57ba7297", "IPY_MODEL_5cc4e3f656a64b859f25759ce36d2763"], "layout": "IPY_MODEL_66ca9f54f01a472fb1dcbff999cb1991"}}, "4f3c8b5129b142b5a36db52be704f214": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_768775d9e3584075bc985a124255f904", "style": "IPY_MODEL_85d9ea9eccdf4dae9387e7c6184e1b21", "value": "<h4 id=\"PredictionDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>Prediction Drift</b></h4>"}}, "768775d9e3584075bc985a124255f904": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "85d9ea9eccdf4dae9387e7c6184e1b21": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6ef1cbfb2dc04a159f3ebb27405e5e34": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_74173d2bab8e4931a2381436feacd6ad", "style": "IPY_MODEL_a1475c3c3e5e4c50ac5dbd70bc0ebe24", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "74173d2bab8e4931a2381436feacd6ad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a1475c3c3e5e4c50ac5dbd70bc0ebe24": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f304725e925a4a8ca0a3f6de57ba7297": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2b73ab29d74c4139a8f5201b59a71ac4", "style": "IPY_MODEL_3a44e29c2283483da8f563995cb5ed53", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a0428 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a0428 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a0428 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a0428 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a0428 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a0428\">\n  <thead>\n    <tr>\n      <th id=\"T_a0428_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_a0428_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_a0428_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a0428_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_a0428_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_a0428_row0_col2\" class=\"data row0 col2\" >Passed for 1 prediction properties out of 1 prediction properties.\nFound prediction property \"Samples Per Class\" has the highest categorical drift score: 0.13</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "2b73ab29d74c4139a8f5201b59a71ac4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3a44e29c2283483da8f563995cb5ed53": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5cc4e3f656a64b859f25759ce36d2763": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0bfb9d8573084e718c79879c46208b54", "IPY_MODEL_4feb12ca6bef43bcac2cf67eff01cfb0", "IPY_MODEL_c74522493f8c49aeaefc5e7fee13146f", "IPY_MODEL_be2a1558ca6f4145a7e6380fedc58947", "IPY_MODEL_6f1469cbbe534886b114f940ebd4ce4d", "IPY_MODEL_304975b766d24d849c98a2432977fd31"], "layout": "IPY_MODEL_1024b212d1714713b663c30d21b215d4"}}, "0bfb9d8573084e718c79879c46208b54": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_899587fd23e14b068a55d5c99595c546", "style": "IPY_MODEL_f6c16a6c76fe4fe0ac2f7a1ef688382d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "899587fd23e14b068a55d5c99595c546": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f6c16a6c76fe4fe0ac2f7a1ef688382d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4feb12ca6bef43bcac2cf67eff01cfb0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_90f4c014f24347ed9c717952d68b36f6", "style": "IPY_MODEL_9c4a1d2cff2c4fc2bad3d76c47e97f17", "value": "<div><span>The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following prediction properties: ['Samples Per Class']. </span></div>"}}, "90f4c014f24347ed9c717952d68b36f6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9c4a1d2cff2c4fc2bad3d76c47e97f17": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c74522493f8c49aeaefc5e7fee13146f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1a1f4fc79f594df680a240cf89afc5dd", "style": "IPY_MODEL_5d539f17d7b640099f0b8724917cd096", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "1a1f4fc79f594df680a240cf89afc5dd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d539f17d7b640099f0b8724917cd096": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "be2a1558ca6f4145a7e6380fedc58947": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#6DB36D"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "771f44ec-7dcf-49b5-864b-853e4ea2b449"}, {"base": 0.1, "marker": {"color": "#F9F871"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.03125983941965885], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "cae411d5-71b6-4788-9a66-ec6e36a04d43"}, {"marker": {"color": "#003b47"}, "name": "Train Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "aedes_dorsalis", "aedes_koreicus", "anopheles_arabiensis", "anopheles_freeborni", "anopheles_sinensis", "culex_inatomii", "culex_quinquefasciatus", "culex_tritaeniorhynchus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [10]}, "yaxis": "y2", "type": "bar", "uid": "db39d627-a037-4dd0-b19a-167adb2c28a6"}, {"marker": {"color": "#818800"}, "name": "Test Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "aedes_dorsalis", "aedes_koreicus", "anopheles_arabiensis", "anopheles_freeborni", "anopheles_sinensis", "culex_inatomii", "culex_quinquefasciatus", "culex_tritaeniorhynchus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [10]}, "yaxis": "y2", "type": "bar", "uid": "3ccd210f-c168-42da-8167-bf5c703764f6"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"font": {"size": 16}, "showarrow": false, "text": "Drift Score (Cramer's V)", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"font": {"size": 16}, "showarrow": false, "text": "Distribution Plot", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 0.7200000000000001, "yanchor": "bottom", "yref": "paper"}], "bargroupgap": 0, "height": 400, "legend": {"title": {"text": "Legend"}, "y": 0.6, "yanchor": "top"}, "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": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"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": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[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"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Samples Per Class", "x": 0.5, "xanchor": "center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [-3, 12], "type": "category"}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "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", 2, "y", "buffer"], "data": "s73rgG7Msz9Lkf6MpX2+P3LnJ19FL7M/rmTc+clXsT8QJoKsh0OyP455+mg1CbE/ddb4wDt0uD8ni8jkdkmzP2OePlpNQrQ/VesWMKcluD8="}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "igtQe+f1uT84JnnIjv7FP0rTKQIfZ6s/l3oyfrvUsz9ItMLmQKKlP8ckD9nRv6I/C5sDiVbYvD8omC+q3FqmP0rTKQIfZ6s/2/AmLkDtvT8="}]}, "6f1469cbbe534886b114f940ebd4ce4d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_36cd4650bc1e4008b93596f6ba068f3d", "style": "IPY_MODEL_8e192a25f5384e6fa59eb876d9a219fa", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "36cd4650bc1e4008b93596f6ba068f3d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8e192a25f5384e6fa59eb876d9a219fa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "304975b766d24d849c98a2432977fd31": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_07e42974496d4c8d97a0cd2d51f7687c", "style": "IPY_MODEL_5e747e0b602c4f6da2ac4ac674bbe87d", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "07e42974496d4c8d97a0cd2d51f7687c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e747e0b602c4f6da2ac4ac674bbe87d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1024b212d1714713b663c30d21b215d4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "66ca9f54f01a472fb1dcbff999cb1991": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "444a2526a0d54c7cbd72cdd25ae5f0c4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_753454c138134d9483fc3c058ce75199", "style": "IPY_MODEL_5aa746c0e02145cb9c3664ce6da02154", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "753454c138134d9483fc3c058ce75199": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5aa746c0e02145cb9c3664ce6da02154": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "22813aa427ec45cf8665415bbbba9aac": {"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_56206ecf8d984be68d9c92bfa3c65098", "IPY_MODEL_033c475ee944474489363691b32d3354", "IPY_MODEL_4f2e7921417d4568955d93f29907cfae", "IPY_MODEL_b6d211a5d87245b7af5e2bd1dad6efc3"], "layout": "IPY_MODEL_c48f1df873cd45209fc456aa5e34b31c"}}, "56206ecf8d984be68d9c92bfa3c65098": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d3bc7cbb6b6946608c973e730356330b", "style": "IPY_MODEL_d52030a35b93489c83be2ae3f2d1f30b", "value": "<h4 id=\"SimpleModelComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>Simple Model Comparison</b></h4>"}}, "d3bc7cbb6b6946608c973e730356330b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d52030a35b93489c83be2ae3f2d1f30b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "033c475ee944474489363691b32d3354": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7b1574ac23c94d3f89cefb6f028ebffe", "style": "IPY_MODEL_2b62b5ff569e4632a6b3acd28dd07d6d", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "7b1574ac23c94d3f89cefb6f028ebffe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2b62b5ff569e4632a6b3acd28dd07d6d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4f2e7921417d4568955d93f29907cfae": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5f67df843e874f08bf80c4fc3e816112", "style": "IPY_MODEL_bdc925e89ab24e5e9a1e6f9b149d998c", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_42603 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_42603 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_42603 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_42603 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_42603 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_42603\">\n  <thead>\n    <tr>\n      <th id=\"T_42603_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_42603_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_42603_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_42603_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_42603_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_42603_row0_col2\" class=\"data row0 col2\" >Found minimal gain of 84.67% for metric F1 and class aedes_vexans</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "5f67df843e874f08bf80c4fc3e816112": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bdc925e89ab24e5e9a1e6f9b149d998c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b6d211a5d87245b7af5e2bd1dad6efc3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e779676b23b94ff89a7429d512ce90e4", "IPY_MODEL_e2458de9ac3e47da8d618076a47079cf", "IPY_MODEL_51b86ab33184462c92012d14028412f4", "IPY_MODEL_248e1c6390154891b8ea77e774b26c6a"], "layout": "IPY_MODEL_e864866d272e4bbebf62f92ee8b5e5a0"}}, "e779676b23b94ff89a7429d512ce90e4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bae46ecd8d7c47fba2dd2d70973a86a3", "style": "IPY_MODEL_cddcdf78c5384bfca4c33c6bc5423f61", "value": "<h5><b>Additional Outputs</b></h5>"}}, "bae46ecd8d7c47fba2dd2d70973a86a3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cddcdf78c5384bfca4c33c6bc5423f61": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e2458de9ac3e47da8d618076a47079cf": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Simple Model<br>Metric=F1<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Simple Model", "marker": {"color": "#637a0a", "pattern": {"shape": ""}}, "name": "Simple Model", "offsetgroup": "Simple Model", "orientation": "v", "showlegend": true, "x": ["aedes_albopictus", "aedes_aegypti", "aedes_dorsalis", "aedes_koreicus", "aedes_vexans", "anopheles_arabiensis", "anopheles_freeborni", "anopheles_sinensis", "culex_inatomii", "culex_pipiens", "culex_quinquefasciatus", "culex_tritaeniorhynchus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y", "type": "histogram", "uid": "983b96d1-ffda-41f9-b325-962857784810"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Given Model<br>Metric=F1<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Given Model", "marker": {"color": "#e5cb75", "pattern": {"shape": ""}}, "name": "Given Model", "offsetgroup": "Given Model", "orientation": "v", "showlegend": true, "x": ["anopheles_freeborni", "culex_inatomii", "aedes_koreicus", "aedes_dorsalis", "culex_pipiens", "culex_tritaeniorhynchus", "anopheles_arabiensis", "culex_quinquefasciatus", "aedes_albopictus", "anopheles_sinensis", "aedes_aegypti", "aedes_vexans"], "xaxis": "x", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y", "type": "histogram", "uid": "2cabca5e-34e5-4746-a632-eda2bdfdc6c5"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "F1", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Model"}, "tracegroupgap": 0}, "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": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"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": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[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"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Simple Model (Strategy: most_frequent) vs. Given Model"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "type": "category"}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true}}, "_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": "uSEGpGMp0z8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "AAAAAAAA8D8AAAAAAADwP2L7Hb0ytu8/kl8s+cWS7z+qqqqqqqruP6KT9kEyoO4/WAQNJ3Vf7j9S5LNuMEXuP+jgUZeCDu4/4ORv8jf56z8M6bzFkM7rP+45mb5KGOs/"}]}, "51b86ab33184462c92012d14028412f4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c0ee639173d641c2bbd2ca2ade0b1d1d", "style": "IPY_MODEL_1f095cb18e8748da8f55ab153f5def86", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "c0ee639173d641c2bbd2ca2ade0b1d1d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1f095cb18e8748da8f55ab153f5def86": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "248e1c6390154891b8ea77e774b26c6a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d926bef3ecab47a5af86ea7325763f82", "style": "IPY_MODEL_7832bfb07b9a4b70a0e3621f4d6d6fa7", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "d926bef3ecab47a5af86ea7325763f82": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7832bfb07b9a4b70a0e3621f4d6d6fa7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e864866d272e4bbebf62f92ee8b5e5a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c48f1df873cd45209fc456aa5e34b31c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fda1006bd4cf4cc2ae32677ff132f108": {"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_6d2b689f05a4423abb438aa68058d7d6", "IPY_MODEL_aa5202f6d36f4cf7aa5ad8d1c4644600", "IPY_MODEL_316954f6aeb441f485d915b1eef0898c", "IPY_MODEL_bee27195373b4f82acd5afb88926ae32"], "layout": "IPY_MODEL_c6c0028a28764f13bb33426bbd224cda"}}, "6d2b689f05a4423abb438aa68058d7d6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a6b2ae793dfc48e3887a544d14a6895f", "style": "IPY_MODEL_741a9e905d174de8880088cea4d20452", "value": "<h4 id=\"NewLabels_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>New Labels</b></h4>"}}, "a6b2ae793dfc48e3887a544d14a6895f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "741a9e905d174de8880088cea4d20452": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "aa5202f6d36f4cf7aa5ad8d1c4644600": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cb440b5c61b84bd8b6ca7b599af7b47e", "style": "IPY_MODEL_e4a6d611b94e46fb86b702239e932f14", "value": "<p>Detects labels that appear only in the test set. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_new_labels.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "cb440b5c61b84bd8b6ca7b599af7b47e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e4a6d611b94e46fb86b702239e932f14": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "316954f6aeb441f485d915b1eef0898c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6071ab90c3ac44a599916f8086067611", "style": "IPY_MODEL_4dd47187765948f0b69db0bd1851f156", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_7ddad table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7ddad thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7ddad tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7ddad th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7ddad td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_7ddad\">\n  <thead>\n    <tr>\n      <th id=\"T_7ddad_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_7ddad_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_7ddad_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_7ddad_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_7ddad_row0_col1\" class=\"data row0 col1\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_7ddad_row0_col2\" class=\"data row0 col2\" >No new labels were found in test set.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "6071ab90c3ac44a599916f8086067611": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4dd47187765948f0b69db0bd1851f156": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bee27195373b4f82acd5afb88926ae32": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2be7582647d54f98b849dec81efb5923", "IPY_MODEL_e4850858808741f0b985bcc2b40c55bd", "IPY_MODEL_0a94c2c54884468082bbfde55ef3789b"], "layout": "IPY_MODEL_78387af980ea4bedaab69b468335f707"}}, "2be7582647d54f98b849dec81efb5923": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7d56dc8b4e564941ac1ed748eb3aa580", "style": "IPY_MODEL_0649876321a248dba499e17fb6e0adfb", "value": "<h5><b>Additional Outputs</b></h5>"}}, "7d56dc8b4e564941ac1ed748eb3aa580": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0649876321a248dba499e17fb6e0adfb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e4850858808741f0b985bcc2b40c55bd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_47a480c6e9d84ba4a4a91f43b5d7829d", "style": "IPY_MODEL_a6e5510f46fd430c98e8f4a55bc54789", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "47a480c6e9d84ba4a4a91f43b5d7829d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a6e5510f46fd430c98e8f4a55bc54789": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0a94c2c54884468082bbfde55ef3789b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_253357d941dd4cf1927933720066c3ff", "style": "IPY_MODEL_a15a9e0b136a4b1ea6410d4deb3ad1af", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "253357d941dd4cf1927933720066c3ff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a15a9e0b136a4b1ea6410d4deb3ad1af": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "78387af980ea4bedaab69b468335f707": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c6c0028a28764f13bb33426bbd224cda": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a82cd34a93334a39abe04c9fba75454a": {"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_e515f14e4b8f412cb6d3b93d080dae80", "IPY_MODEL_a9ba531eefc14687a34152bd88877420", "IPY_MODEL_2edcde4c37834506bcc43a8d9a905ad0", "IPY_MODEL_a7acbd21742a4d69acf02eca896ab458"], "layout": "IPY_MODEL_9d647de9c74f4deca7e4b17c037a77e2"}}, "e515f14e4b8f412cb6d3b93d080dae80": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7e18909359764581b37812d31f70171b", "style": "IPY_MODEL_9973dd81f65b48d8ab111a63bc27f60e", "value": "<h4 id=\"LabelDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>Label Drift</b></h4>"}}, "7e18909359764581b37812d31f70171b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9973dd81f65b48d8ab111a63bc27f60e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a9ba531eefc14687a34152bd88877420": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e712a116ee74481c9f8a0299d5c41a6a", "style": "IPY_MODEL_185657204f964798889a1dfb1b842d4b", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "e712a116ee74481c9f8a0299d5c41a6a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "185657204f964798889a1dfb1b842d4b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2edcde4c37834506bcc43a8d9a905ad0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_746ec98dc1c04eea9aff62ce66f06d78", "style": "IPY_MODEL_c58f7331bdab4ad2b8b954771035d007", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_0e0aa table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0e0aa thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0e0aa tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0e0aa th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0e0aa td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_0e0aa\">\n  <thead>\n    <tr>\n      <th id=\"T_0e0aa_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_0e0aa_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_0e0aa_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_0e0aa_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_0e0aa_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_0e0aa_row0_col2\" class=\"data row0 col2\" >Passed for 1 label properties out of 1 label properties.\nFound label property \"Samples Per Class\" has the highest categorical drift score: 0.14</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "746ec98dc1c04eea9aff62ce66f06d78": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c58f7331bdab4ad2b8b954771035d007": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a7acbd21742a4d69acf02eca896ab458": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2850626af4164ef295b08fd62b931346", "IPY_MODEL_aa2f8205b23643d99e496ed5dab522dd", "IPY_MODEL_d3552a64981d4e96b90c2b7da0f50e69", "IPY_MODEL_6dce8a33ca4643baa0649d546b29bb4a", "IPY_MODEL_917a33f223b0447c85dbaa1bb1b95cad", "IPY_MODEL_9a1b0831a8174a4e94de34a2252a71df"], "layout": "IPY_MODEL_9eb0583119284943a0551836fab7cdc0"}}, "2850626af4164ef295b08fd62b931346": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2a3ad3de56d647668792cac6a8247b36", "style": "IPY_MODEL_b89cffa630af4b429c7dad8ab031e862", "value": "<h5><b>Additional Outputs</b></h5>"}}, "2a3ad3de56d647668792cac6a8247b36": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b89cffa630af4b429c7dad8ab031e862": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "aa2f8205b23643d99e496ed5dab522dd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4a154e84b95f4524b65a17162aa6d1a9", "style": "IPY_MODEL_395abc73a1ba4542ace91e89483f48c2", "value": "<div><span> The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following label properties: ['Samples Per Class'].</span></div>"}}, "4a154e84b95f4524b65a17162aa6d1a9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "395abc73a1ba4542ace91e89483f48c2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d3552a64981d4e96b90c2b7da0f50e69": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_46091a4c6c6948f5a4a5d5271886e4d3", "style": "IPY_MODEL_be7b8063bf044a1f969ed98a8fdd2479", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "46091a4c6c6948f5a4a5d5271886e4d3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "be7b8063bf044a1f969ed98a8fdd2479": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6dce8a33ca4643baa0649d546b29bb4a": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#6DB36D"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "dd436dbe-75e7-4624-bc2e-b145cbbe76a3"}, {"base": 0.1, "marker": {"color": "#F9F871"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.03891896238644463], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "e7a24f1f-ca59-4f0c-8fdc-da4a96cb63ec"}, {"marker": {"color": "#003b47"}, "name": "Train Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "aedes_dorsalis", "aedes_vexans", "anopheles_arabiensis", "anopheles_freeborni", "culex_inatomii", "culex_pipiens", "culex_quinquefasciatus", "culex_tritaeniorhynchus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [10]}, "yaxis": "y2", "type": "bar", "uid": "b2cbcd29-2d05-4d43-9a2f-ccc132cc129e"}, {"marker": {"color": "#818800"}, "name": "Test Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "aedes_dorsalis", "aedes_vexans", "anopheles_arabiensis", "anopheles_freeborni", "culex_inatomii", "culex_pipiens", "culex_quinquefasciatus", "culex_tritaeniorhynchus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [10]}, "yaxis": "y2", "type": "bar", "uid": "0bda3ec9-cc64-45c1-a76e-874a393a6efa"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"font": {"size": 16}, "showarrow": false, "text": "Drift Score (Cramer's V)", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"font": {"size": 16}, "showarrow": false, "text": "Distribution Plot", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 0.7200000000000001, "yanchor": "bottom", "yref": "paper"}], "bargroupgap": 0, "height": 400, "legend": {"title": {"text": "Legend"}, "y": 0.6, "yanchor": "top"}, "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, 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"#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": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"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": "surface"}], "table": [{"cells": {"fill": {"color": 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[0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Samples Per Class", "x": 0.5, "xanchor": "center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [-3, 12], "type": "category"}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "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", 2, "y", "buffer"], "data": "s73rgG7Msz9Lkf6MpX2+P3LnJ19FL7M/JyGKv8gZtj8QJoKsh0OyP455+mg1CbE/J4vI5HZJsz9armTc+cm3P2OePlpNQrQ/n0d2qnULuD8="}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "en0GXTVSuj8g0QqbA4nGP2rvvD6Drqo/igtQe+f1uT9o0FUjpemkP8ckD9nRv6I/KJgvqtxapj9q77w+g666P4oLUHvn9ak/C5sDiVbYvD8="}]}, "917a33f223b0447c85dbaa1bb1b95cad": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f02cc17a9c424bd983767a8f50f8860b", "style": "IPY_MODEL_7acd2f7be93a43418e9febb0b14c63f3", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "f02cc17a9c424bd983767a8f50f8860b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7acd2f7be93a43418e9febb0b14c63f3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9a1b0831a8174a4e94de34a2252a71df": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4fee53b08a9042f9819d7fd0338706f4", "style": "IPY_MODEL_df06b3ab96054716918f7ebb01fc11dd", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "4fee53b08a9042f9819d7fd0338706f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "df06b3ab96054716918f7ebb01fc11dd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9eb0583119284943a0551836fab7cdc0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9d647de9c74f4deca7e4b17c037a77e2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cff5aafd87a442e29d50d8eeabe04649": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c2388af39c84178bcfb6acd2c1e62c6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dcffff18fb624d4fb3fb39b166f57c58": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fa4ad3268a204d2cabfa961bd1367701": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_782db4ca0ddf4b66ba1428413123eb0c", "IPY_MODEL_51153f9345974d42a497f4db4698fbfc"], "layout": "IPY_MODEL_49f9cb92b54e4c54a397a77ffe01647f"}}, "782db4ca0ddf4b66ba1428413123eb0c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f67dfcd4fb4a4be8a80039adda03873f", "style": "IPY_MODEL_88d2677feb234916a3d818b02566b137", "value": "<span id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"></span>"}}, "f67dfcd4fb4a4be8a80039adda03873f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "88d2677feb234916a3d818b02566b137": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "51153f9345974d42a497f4db4698fbfc": {"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": "Other"}, "children": ["IPY_MODEL_7de2ba1939e14ed18b50539c8057f25d"], "layout": "IPY_MODEL_23a5e2e834fc477cab6841e39e70c1e6", "selected_index": null}}, "7de2ba1939e14ed18b50539c8057f25d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e4b1b5a36678459ea5264606e840f9cb", "IPY_MODEL_8464fb7baea849a0b55f1ed3f1232cf6", "IPY_MODEL_e8f3da63e89747098aae552bf0b7273f", "IPY_MODEL_9e51b1b3cfda40b0973acce717d1f6bb", "IPY_MODEL_7881360401e241559ec5b76a1ccba61d", "IPY_MODEL_9e51b1b3cfda40b0973acce717d1f6bb", "IPY_MODEL_6b09717b48604f1cabb9b1d2cf214924", "IPY_MODEL_9e51b1b3cfda40b0973acce717d1f6bb", "IPY_MODEL_bc58e24f41824cb89bd9cfef63e578b9", "IPY_MODEL_9e51b1b3cfda40b0973acce717d1f6bb", "IPY_MODEL_4b465f6edc61437a98c2a686b4e695fd", "IPY_MODEL_9e51b1b3cfda40b0973acce717d1f6bb", "IPY_MODEL_4ff6f17c0617410986815dbef86f420d", "IPY_MODEL_9e51b1b3cfda40b0973acce717d1f6bb", "IPY_MODEL_3a7fea151d354e99839154d568d672c8", "IPY_MODEL_9e51b1b3cfda40b0973acce717d1f6bb", "IPY_MODEL_b17f45df5ba34720ac3d1478fc2a7ee9"], "layout": "IPY_MODEL_5e6811f94f784c8ba42572f1f35adc16"}}, "e4b1b5a36678459ea5264606e840f9cb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1de47eec9539453f971cec66c430bd5c", "style": "IPY_MODEL_22f1da62b19e41c095e19570bb7ef66d", "value": "<style type=\"text/css\">\n#T_fc06e table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fc06e thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fc06e tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fc06e th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fc06e td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_fc06e\">\n  <thead>\n    <tr>\n      <th id=\"T_fc06e_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_fc06e_level0_col1\" class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_fc06e_row0_col0\" class=\"data row0 col0\" ><a href=\"#ImageDatasetDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Image Dataset Drift</a></td>\n      <td id=\"T_fc06e_row0_col1\" class=\"data row0 col1\" >Calculate drift between the entire train and test datasets (based on image properties) using a trained model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_image_dataset_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_fc06e_row1_col0\" class=\"data row1 col0\" ><a href=\"#ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Image Property Outliers</a></td>\n      <td id=\"T_fc06e_row1_col1\" class=\"data row1 col1\" >Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_fc06e_row2_col0\" class=\"data row2 col0\" ><a href=\"#ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Image Property Outliers</a></td>\n      <td id=\"T_fc06e_row2_col1\" class=\"data row2 col1\" >Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_fc06e_row3_col0\" class=\"data row3 col0\" ><a href=\"#PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Property Label Correlation</a></td>\n      <td id=\"T_fc06e_row3_col1\" class=\"data row3 col1\" >    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_fc06e_row4_col0\" class=\"data row4 col0\" ><a href=\"#PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Property Label Correlation</a></td>\n      <td id=\"T_fc06e_row4_col1\" class=\"data row4 col1\" >    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_fc06e_row5_col0\" class=\"data row5 col0\" ><a href=\"#ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Confusion Matrix</a></td>\n      <td id=\"T_fc06e_row5_col1\" class=\"data row5 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_fc06e_row6_col0\" class=\"data row6 col0\" ><a href=\"#ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Confusion Matrix</a></td>\n      <td id=\"T_fc06e_row6_col1\" class=\"data row6 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_fc06e_row7_col0\" class=\"data row7 col0\" ><a href=\"#HeatmapComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Heatmap Comparison</a></td>\n      <td id=\"T_fc06e_row7_col1\" class=\"data row7 col1\" >Check if the average image brightness (or bbox location if applicable) is similar between train and test set. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_heatmap_comparison.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"}}, "1de47eec9539453f971cec66c430bd5c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "22f1da62b19e41c095e19570bb7ef66d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8464fb7baea849a0b55f1ed3f1232cf6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1c6c6d4235804e14bc061840850eeaed", "style": "IPY_MODEL_e529fb5f4cff4ab29ad71d31ed149fc9", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "1c6c6d4235804e14bc061840850eeaed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e529fb5f4cff4ab29ad71d31ed149fc9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e8f3da63e89747098aae552bf0b7273f": {"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_02037a43ce36408b95be31266b3d4aa8", "IPY_MODEL_4369cf24ad6342f393724b76911a8a2c", "IPY_MODEL_464e3d3aea89482aa6e18f4529051b38"], "layout": "IPY_MODEL_4ce3dd6f49b344189790c26bd4685784"}}, "02037a43ce36408b95be31266b3d4aa8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_64332660725043228d3340d65cc6bc1b", "style": "IPY_MODEL_8d76dc83c7ee4161b830a739582fbe33", "value": "<h4 id=\"ImageDatasetDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Image Dataset Drift</b></h4>"}}, "64332660725043228d3340d65cc6bc1b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8d76dc83c7ee4161b830a739582fbe33": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4369cf24ad6342f393724b76911a8a2c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1a1926e1ffd647dd97c79f8663ef9da1", "style": "IPY_MODEL_9732aaa314234a648e54f287f1130aca", "value": "<p>Calculate drift between the entire train and test datasets (based on image properties) using a trained model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_image_dataset_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "1a1926e1ffd647dd97c79f8663ef9da1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9732aaa314234a648e54f287f1130aca": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "464e3d3aea89482aa6e18f4529051b38": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d4cd97c581c24c259606047eed61db56", "IPY_MODEL_c4ac2e0db9f64703a256537f46c36435", "IPY_MODEL_c65974bb83ff4362bcfc9d9a0170dbf1", "IPY_MODEL_88ea5d00f264400b8f47f60b2a0e54ee", "IPY_MODEL_a2f06ea552d843499504c4ba8c377f43", "IPY_MODEL_1068debf5d22422c86fd3b3ad47d28b7", "IPY_MODEL_868e38b019094ab4a7c6b216a6792ea8", "IPY_MODEL_ef5e113d8c9342e99292498cc04bef42", "IPY_MODEL_614eda197d734509a45cb78dcea9b343", "IPY_MODEL_e3d28bc35e1244878805ee4c36c1b73b"], "layout": "IPY_MODEL_be9b307d21614f90ae3eb91b2ae792e9"}}, "d4cd97c581c24c259606047eed61db56": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_80a10929e02b4569a5cbb06749969fc7", "style": "IPY_MODEL_b56a61addd44405aae327ee6c3949807", "value": "<h5><b>Additional Outputs</b></h5>"}}, "80a10929e02b4569a5cbb06749969fc7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b56a61addd44405aae327ee6c3949807": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c4ac2e0db9f64703a256537f46c36435": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bcaf58993e0946d89987dff84f710a42", "style": "IPY_MODEL_fc09d77dfdd44bcf8bdaddbf7180088e", "value": "<div>\n        <span>\n        The shown features are the image properties (brightness, aspect ratio, etc.) that are most important for the\n        domain classifier - the domain_classifier trained to distinguish between the train and test datasets.<br>\n        </span>\n        </div>"}}, "bcaf58993e0946d89987dff84f710a42": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc09d77dfdd44bcf8bdaddbf7180088e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c65974bb83ff4362bcfc9d9a0170dbf1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a0d7b661271444abbfc425a4729c9141", "style": "IPY_MODEL_181ca0d074d04382be5efa1c36419ac5", "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>"}}, "a0d7b661271444abbfc425a4729c9141": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "181ca0d074d04382be5efa1c36419ac5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "88ea5d00f264400b8f47f60b2a0e54ee": {"model_name": 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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": "Drift Score - Multivariable"}, "xaxis": {"dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.6750181842227071], "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}}, "a2f06ea552d843499504c4ba8c377f43": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6123d59c319940c581d4794800309773", "style": "IPY_MODEL_365aa71b8fd547499af2fc196ff88625", "value": "<div><h3>Main features contributing to drift</h3></div>"}}, "6123d59c319940c581d4794800309773": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "365aa71b8fd547499af2fc196ff88625": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1068debf5d22422c86fd3b3ad47d28b7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1523ef5620034c808f22c9d50bf92399", "style": "IPY_MODEL_01f6d06584fe4b6ea2253a4dbbbab485", "value": "<div>Showing only the top 3 columns, you can change it using n_top_columns param</div>"}}, "1523ef5620034c808f22c9d50bf92399": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "01f6d06584fe4b6ea2253a4dbbbab485": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "868e38b019094ab4a7c6b216a6792ea8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_40dbfa8afb1a408aad9efbd04553d0e7", "style": "IPY_MODEL_f84b73d82d3d449e8451d7ed9ff142e8", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "40dbfa8afb1a408aad9efbd04553d0e7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f84b73d82d3d449e8451d7ed9ff142e8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ef5e113d8c9342e99292498cc04bef42": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"fill": "tozeroy", "line": {"color": "#003b47", "shape": "linear"}, "name": "Train Dataset", "x": [0.2606904606956065, 0.26258187729936383, 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"614eda197d734509a45cb78dcea9b343": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1d6138c8b77542c89cbd761b49a2eb93", "style": "IPY_MODEL_57fd721f685445fb8a316d060b7efd4c", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "1d6138c8b77542c89cbd761b49a2eb93": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "57fd721f685445fb8a316d060b7efd4c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e3d28bc35e1244878805ee4c36c1b73b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_56a8da2c26f14c309acb392d59485979", "style": "IPY_MODEL_9be06cbe369f401db2dd5d7933c4d98a", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "56a8da2c26f14c309acb392d59485979": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9be06cbe369f401db2dd5d7933c4d98a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "be9b307d21614f90ae3eb91b2ae792e9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4ce3dd6f49b344189790c26bd4685784": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9e51b1b3cfda40b0973acce717d1f6bb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_96af9e7e7656499399969a0bf14d62d6", "style": "IPY_MODEL_07da2228e82e46a8b5b577ba38d293b1", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "96af9e7e7656499399969a0bf14d62d6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "07da2228e82e46a8b5b577ba38d293b1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7881360401e241559ec5b76a1ccba61d": {"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_da13f6c24fd14f8f90f42899487d7b65", "IPY_MODEL_a1f7283c59c849bc97da03cdee0cb5ff", "IPY_MODEL_c59bbc337f014be6bb06deb7719a02bf"], "layout": "IPY_MODEL_8eaebbcaa2934c2190dabc8bfa95828d"}}, "da13f6c24fd14f8f90f42899487d7b65": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1c0ee661d287400890a1be6fead60818", "style": "IPY_MODEL_55ce80fd81b64c5887d97b64fc189041", "value": "<h4 id=\"ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Image Property Outliers</b></h4>"}}, "1c0ee661d287400890a1be6fead60818": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "55ce80fd81b64c5887d97b64fc189041": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a1f7283c59c849bc97da03cdee0cb5ff": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ab16dcb8866847ff9b81f0d2c22b48e6", "style": "IPY_MODEL_b84ab7cd526144bd8bc3ae91e7856e6d", "value": "<p>Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "ab16dcb8866847ff9b81f0d2c22b48e6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b84ab7cd526144bd8bc3ae91e7856e6d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c59bbc337f014be6bb06deb7719a02bf": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_f4209a6b1e714256bfcde59c3b90bd64", "IPY_MODEL_08fe6aed7ac842a1be9ff6ff9fb2a1e3", "IPY_MODEL_2216c254e1f048e68814394b2f1166c1", "IPY_MODEL_eae4d93cc79d49c2ad2a835ce9b3917b", "IPY_MODEL_6f724bfd14cd4cc5842c685f2e517924"], "layout": "IPY_MODEL_96636be0be5048fdb162eae5220bf260"}}, "f4209a6b1e714256bfcde59c3b90bd64": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e2efadcd7dd747d0bd15aca8defc6210", "style": "IPY_MODEL_1339a8a5d3d14c4da0ecc97f32b3180a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "e2efadcd7dd747d0bd15aca8defc6210": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1339a8a5d3d14c4da0ecc97f32b3180a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "08fe6aed7ac842a1be9ff6ff9fb2a1e3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9ce8eae293184a7bbc50d6c254da4920", "style": "IPY_MODEL_293d518dda734265b6cb34ddee385e93", "value": "<div>\n<style>\n    .MTSSWW-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .MTSSWW-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .MTSSWW-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .MTSSWW-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"RMS Contrast\"</b></h5>\n<div>\nTotal number of outliers: 15\n</div>\n<div>\nNon-outliers range: 11.6 to 53.35\n</div>\n<div class=\"MTSSWW-container\">\n    <div class=\"MTSSWW-row\">\n        <h5 class=\"MTSSWW-item\">RMS Contrast</h5>\n        <div class=\"MTSSWW-item\">61.04</div><div class=\"MTSSWW-item\">62.88</div><div class=\"MTSSWW-item\">64.8</div>\n    </div>\n    <div class=\"MTSSWW-row\">\n        <h5 class=\"MTSSWW-item\">Image</h5>\n        <div class=\"MTSSWW-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"MTSSWW-item\"><img 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src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .SHMHJL-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .SHMHJL-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .SHMHJL-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .SHMHJL-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Red Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 65\n</div>\n<div>\nNon-outliers range: 0.32 to 0.35\n</div>\n<div class=\"SHMHJL-container\">\n    <div class=\"SHMHJL-row\">\n        <h5 class=\"SHMHJL-item\">Mean Red Relative Intensity</h5>\n        <div class=\"SHMHJL-item\">0.32</div><div class=\"SHMHJL-item\">0.47</div><div class=\"SHMHJL-item\">0.47</div><div class=\"SHMHJL-item\">0.48</div>\n    </div>\n    <div class=\"SHMHJL-row\">\n        <h5 class=\"SHMHJL-item\">Image</h5>\n        <div class=\"SHMHJL-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"SHMHJL-item\"><img 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cEMh0TuBAI4UGD3DMIPfcEBF1QuaNzQY5GUsg5aT/RCXa89kQ8xwFIOHDUQ49kpaVlPcOVlJ5BKIkXBWWUaq8jeSMZhZpgy0rLJjFNqHFzAY6gTKm3CyyUqHTTiMXSbunyssgKPcHUgeRaUoJGPlZZSNW6nB1pIRpwQWHDhCyyjE2zzY4JTPbuYCPw5+FllNAwxEiRyiRt9vt4PdZZBPTky3EoCQcxFoWWQFCbNPOD5RdHpwMTZZZKYGGrYbJWWQRaTzhMTCyygze5Kyyyi//9k=\"/></div>\n    </div>\n</div>\n\n<style>\n    .YXHHWI-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .YXHHWI-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .YXHHWI-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .YXHHWI-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Green Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 9\n</div>\n<div>\nNon-outliers range: 0.32 to 0.35\n</div>\n<div class=\"YXHHWI-container\">\n    <div class=\"YXHHWI-row\">\n        <h5 class=\"YXHHWI-item\">Mean Green Relative Intensity</h5>\n        <div class=\"YXHHWI-item\">0.29</div><div class=\"YXHHWI-item\">0.29</div><div class=\"YXHHWI-item\">0.29</div>\n    </div>\n    <div class=\"YXHHWI-row\">\n        <h5 class=\"YXHHWI-item\">Image</h5>\n        <div class=\"YXHHWI-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"YXHHWI-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"YXHHWI-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .JYHMOV-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .JYHMOV-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .JYHMOV-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .JYHMOV-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Blue Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 70\n</div>\n<div>\nNon-outliers range: 0.31 to 0.34\n</div>\n<div class=\"JYHMOV-container\">\n    <div class=\"JYHMOV-row\">\n        <h5 class=\"JYHMOV-item\">Mean Blue Relative Intensity</h5>\n        <div class=\"JYHMOV-item\">0.23</div><div class=\"JYHMOV-item\">0.24</div><div class=\"JYHMOV-item\">0.24</div><div class=\"JYHMOV-item\">0.34</div><div class=\"JYHMOV-item\">0.34</div><div class=\"JYHMOV-item\">0.35</div>\n    </div>\n    <div class=\"JYHMOV-row\">\n        <h5 class=\"JYHMOV-item\">Image</h5>\n        <div class=\"JYHMOV-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"JYHMOV-item\"><img 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JtZWNHHdO4OwsSi1r3KA0CRGqdri6mAEEQFOyFLdAkAJ2RqgaEtU0AmkhRTG6Ek0DCd0k0CQhCKaCldHCUJCRQoM7rD7R7pXQhaZSPvdFroQgYGoujnXVCEEgNfCkEIQOyAhCAQEIQOyLIQihNCFArpoQopoQhVAgIQgEroQlUihCFlX/9k=\"/></div>\n    </div>\n</div>\n</div>"}}, "9ce8eae293184a7bbc50d6c254da4920": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "293d518dda734265b6cb34ddee385e93": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2216c254e1f048e68814394b2f1166c1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8c43fa81d5964470b67d28bae2b08e95", "style": "IPY_MODEL_b1a524173a8b4a7981fb4a7a97d97f85", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "8c43fa81d5964470b67d28bae2b08e95": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b1a524173a8b4a7981fb4a7a97d97f85": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eae4d93cc79d49c2ad2a835ce9b3917b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a5cacb53851a47de964c1e966a3cbedb", "style": "IPY_MODEL_252114e0ff0d473585a8253bce9b23e3", "value": "<style type=\"text/css\">\n#T_0896a table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0896a thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0896a tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0896a th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0896a td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_0896a\">\n  <thead>\n    <tr>\n      <th id=\"T_0896a_level0_col0\" class=\"col_heading level0 col0\" >More Info</th>\n      <th id=\"T_0896a_level0_col1\" class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_0896a_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_0896a_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area, Brightness</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a5cacb53851a47de964c1e966a3cbedb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "252114e0ff0d473585a8253bce9b23e3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6f724bfd14cd4cc5842c685f2e517924": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4411e310eba94152a6942b1c5cc7f3d1", "style": "IPY_MODEL_3debd340ed784cd5a11749d33b4825df", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "4411e310eba94152a6942b1c5cc7f3d1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3debd340ed784cd5a11749d33b4825df": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "96636be0be5048fdb162eae5220bf260": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8eaebbcaa2934c2190dabc8bfa95828d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6b09717b48604f1cabb9b1d2cf214924": {"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_f8fc6d7d67ca4eaaaa116844b9f6c4e3", "IPY_MODEL_b6791f84cc1d4947888ad658b59d571c", "IPY_MODEL_c74e6475fb024cebbbfc079ae58ee5be"], "layout": "IPY_MODEL_f1bd8b5954d44316add58f1eab5bf0dc"}}, "f8fc6d7d67ca4eaaaa116844b9f6c4e3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_61caf64e4efe47b29908e7bb9b0cf88e", "style": "IPY_MODEL_b96f0d68c5a04201b8e185c515541665", "value": "<h4 id=\"ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Image Property Outliers</b></h4>"}}, "61caf64e4efe47b29908e7bb9b0cf88e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b96f0d68c5a04201b8e185c515541665": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b6791f84cc1d4947888ad658b59d571c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_51be47d3b2ce472e9bd11a16683c55c9", "style": "IPY_MODEL_b53a35cd9b0349e1b5a0ef95d523c9af", "value": "<p>Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "51be47d3b2ce472e9bd11a16683c55c9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b53a35cd9b0349e1b5a0ef95d523c9af": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c74e6475fb024cebbbfc079ae58ee5be": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_608eb54fb98e41619fd17e034d3f05ac", "IPY_MODEL_7847a791a30040fcb942cf10146097e8", "IPY_MODEL_1299f2ed36f4479fb743f2b6b515bcba", "IPY_MODEL_9db09452ed2e412389fe6b7eb4419ed5", "IPY_MODEL_f1ca7b8359c14a4e8c5fc8adb8d77766", "IPY_MODEL_9fb459eac2af4aaeac6082c5e05d3b1a"], "layout": "IPY_MODEL_50b8ff628dd1467fbf3ecfeb1e3df30e"}}, "608eb54fb98e41619fd17e034d3f05ac": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_49df7ce3d39548b881a92de774f36276", "style": "IPY_MODEL_cf5c8e11542146b2b84344c1f5932e45", "value": "<h5><b>Additional Outputs</b></h5>"}}, "49df7ce3d39548b881a92de774f36276": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cf5c8e11542146b2b84344c1f5932e45": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7847a791a30040fcb942cf10146097e8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_594d1bc857864569b2fd6e9535a941c7", "style": "IPY_MODEL_95f6a05a0eab4523a910bcc22f26de5c", "value": "<div>\n<style>\n    .BPIQKW-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .BPIQKW-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .BPIQKW-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .BPIQKW-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Red Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 18\n</div>\n<div>\nNon-outliers range: 0.27 to 0.41\n</div>\n<div class=\"BPIQKW-container\">\n    <div class=\"BPIQKW-row\">\n        <h5 class=\"BPIQKW-item\">Mean Red Relative Intensity</h5>\n        <div class=\"BPIQKW-item\">0.44</div><div class=\"BPIQKW-item\">0.44</div><div class=\"BPIQKW-item\">0.44</div>\n    </div>\n    <div class=\"BPIQKW-row\">\n        <h5 class=\"BPIQKW-item\">Image</h5>\n        <div class=\"BPIQKW-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"BPIQKW-item\"><img 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src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n</div>"}}, "594d1bc857864569b2fd6e9535a941c7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "95f6a05a0eab4523a910bcc22f26de5c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1299f2ed36f4479fb743f2b6b515bcba": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bd074b105b4f47278b843982e119c2d8", "style": "IPY_MODEL_e389d4c19cf54218891f1b8d978eb0e6", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "bd074b105b4f47278b843982e119c2d8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e389d4c19cf54218891f1b8d978eb0e6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9db09452ed2e412389fe6b7eb4419ed5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7db696c87ce6444884c00145ccbaf24c", "style": "IPY_MODEL_90499474664e4c5887a16b7690a98188", "value": "<style type=\"text/css\">\n#T_24bb2 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_24bb2 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_24bb2 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_24bb2 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_24bb2 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_24bb2\">\n  <thead>\n    <tr>\n      <th id=\"T_24bb2_level0_col0\" class=\"col_heading level0 col0\" >More Info</th>\n      <th id=\"T_24bb2_level0_col1\" class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_24bb2_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_24bb2_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area, Brightness, RMS Contrast, Mean Green Relative Intensity, Mean Blue Relative Intensity</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "7db696c87ce6444884c00145ccbaf24c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "90499474664e4c5887a16b7690a98188": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f1ca7b8359c14a4e8c5fc8adb8d77766": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_43c8252b11514c3badbaaf5a1e841c28", "style": "IPY_MODEL_6c4b2709024a4be2b8a6c8bf26daa790", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "43c8252b11514c3badbaaf5a1e841c28": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6c4b2709024a4be2b8a6c8bf26daa790": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9fb459eac2af4aaeac6082c5e05d3b1a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9e97de12694a400cae81c5eedcaca35f", "style": "IPY_MODEL_8cf2e36dfbe94d008efa5a94d78cd3e2", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "9e97de12694a400cae81c5eedcaca35f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8cf2e36dfbe94d008efa5a94d78cd3e2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "50b8ff628dd1467fbf3ecfeb1e3df30e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f1bd8b5954d44316add58f1eab5bf0dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc58e24f41824cb89bd9cfef63e578b9": {"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_0e6ddcaff52545c6a50da2a776d0876d", "IPY_MODEL_333879d22ce940adba5427eaae689cb8", "IPY_MODEL_664ddf43a25a431dbbadc502fad2e97a"], "layout": "IPY_MODEL_2923055f53784b0fbe1b81fb83f1e81b"}}, "0e6ddcaff52545c6a50da2a776d0876d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c63d60b2c6104a8480b228b9db0958fa", "style": "IPY_MODEL_7d59996d396845f2bb01a3094e19b65d", "value": "<h4 id=\"PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Property Label Correlation</b></h4>"}}, "c63d60b2c6104a8480b228b9db0958fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7d59996d396845f2bb01a3094e19b65d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "333879d22ce940adba5427eaae689cb8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3a2d0864afca4bf090d6110ef868f1e5", "style": "IPY_MODEL_92c5fc16e1034abc8e2043902a1539d4", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "3a2d0864afca4bf090d6110ef868f1e5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "92c5fc16e1034abc8e2043902a1539d4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "664ddf43a25a431dbbadc502fad2e97a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_653bee92155a4e889395a7bc744fe474", "IPY_MODEL_65e3345cb4c24053905d1c1963328d3b", "IPY_MODEL_de3c2e4ebdb74298a55f0f09a4435041", "IPY_MODEL_4be3e0c89cc44d4287a10464f6f11dcc", "IPY_MODEL_8e66446814e8402b91e05d699bf0e8a8", "IPY_MODEL_816a3b405f3047849cf1cc5b0fc91066", "IPY_MODEL_67c5cff3643e4f3481ea58414a0dad69"], "layout": "IPY_MODEL_d35c0ee15cb247e2ac6022b86396a382"}}, "653bee92155a4e889395a7bc744fe474": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e5134ae781424d06b4138029d384bc3d", "style": "IPY_MODEL_8bc9d2b2f18146ca8fc1696409174f58", "value": "<h5><b>Additional Outputs</b></h5>"}}, "e5134ae781424d06b4138029d384bc3d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8bc9d2b2f18146ca8fc1696409174f58": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "65e3345cb4c24053905d1c1963328d3b": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#818800"}, "name": "Test", "text": ["<b>0.24</b>", "<b>0.15</b>", "<b>0.15</b>"], "textposition": "outside", "x": ["Mean Green Relative Intensity", "Mean Blue Relative Intensity", "Mean Red Relative Intensity"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "5b1d5914-b08b-4da7-9f65-9e97157f1735"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "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": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"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": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[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"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 5], "title": {"text": "Column"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_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": "QZJdGtE5zj/6tenVw6rDP8R7EfbSdMM/"}]}, "de3c2e4ebdb74298a55f0f09a4435041": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5abac62ffd1646208123219fc5366b8b", "style": "IPY_MODEL_9ff03d987a0345f997728539b01e5a00", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/data_integrity/plot_property_label_correlation.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "5abac62ffd1646208123219fc5366b8b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9ff03d987a0345f997728539b01e5a00": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4be3e0c89cc44d4287a10464f6f11dcc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2fa32b4caf20406f945b839c324f5bc3", "style": "IPY_MODEL_6fead7484ca546b2a703e1eb9254a0f7", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "2fa32b4caf20406f945b839c324f5bc3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6fead7484ca546b2a703e1eb9254a0f7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8e66446814e8402b91e05d699bf0e8a8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4e3656fbd654408c82e4caa487d26a2f", "style": "IPY_MODEL_f202c21c13bc457591733fc600fdbaa6", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "4e3656fbd654408c82e4caa487d26a2f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f202c21c13bc457591733fc600fdbaa6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "816a3b405f3047849cf1cc5b0fc91066": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0a1263ddbbc54980a9ab668fac2ff9cb", "style": "IPY_MODEL_a3aa9c67a5b14c199adbd450a93b0022", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict the label successfully, using simple classic ML algorithms</div>"}}, "0a1263ddbbc54980a9ab668fac2ff9cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a3aa9c67a5b14c199adbd450a93b0022": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "67c5cff3643e4f3481ea58414a0dad69": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_48e64670154a442c8f9b5812d9174a7e", "style": "IPY_MODEL_bf2b6e150bd44d64a7967ea26e203dff", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "48e64670154a442c8f9b5812d9174a7e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bf2b6e150bd44d64a7967ea26e203dff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d35c0ee15cb247e2ac6022b86396a382": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2923055f53784b0fbe1b81fb83f1e81b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4b465f6edc61437a98c2a686b4e695fd": {"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_4e899121e81f4a18aa30a6857489d8f4", "IPY_MODEL_1523434818494561989f02108c10d9ac", "IPY_MODEL_a344c5efed7142aebce4d0eaa8e95cfa"], "layout": "IPY_MODEL_59f3d570b2f244fca149680b30f145ee"}}, "4e899121e81f4a18aa30a6857489d8f4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6bf2cedb6c154d85bb050b0921987e51", "style": "IPY_MODEL_0b1c07f8a1ba4f8483c1c01146bf63de", "value": "<h4 id=\"PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Property Label Correlation</b></h4>"}}, "6bf2cedb6c154d85bb050b0921987e51": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0b1c07f8a1ba4f8483c1c01146bf63de": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1523434818494561989f02108c10d9ac": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_084121edde4e4f6bb2e93bcb846f8469", "style": "IPY_MODEL_977a340be8fe45e89f09b7c5815bb1b4", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "084121edde4e4f6bb2e93bcb846f8469": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "977a340be8fe45e89f09b7c5815bb1b4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a344c5efed7142aebce4d0eaa8e95cfa": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c672120454e4447f955ff634810ed132", "IPY_MODEL_81caacc1198b470daefc6366f8391edd", "IPY_MODEL_90f2bc86a9544008bcf61eebc0c61e64", "IPY_MODEL_b8a9515f99b44a0fbae6f59ac9bd18da", "IPY_MODEL_ce2d54cf63674395813fcb12a5224a6b", "IPY_MODEL_eab6e7c81145487c9a1787c71c28eb74", "IPY_MODEL_2378e66a1bfe43d89b9914e03320c69b", "IPY_MODEL_84b76519c7d2411789379b2818f3ea7b"], "layout": "IPY_MODEL_70144fbdf4be4597af5d71c5fd562c10"}}, "c672120454e4447f955ff634810ed132": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_61337868a8b94a9b88b1fceb1ac05c89", "style": "IPY_MODEL_a7d747866b934ab5beab5f9b73d365b0", "value": "<h5><b>Additional Outputs</b></h5>"}}, "61337868a8b94a9b88b1fceb1ac05c89": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a7d747866b934ab5beab5f9b73d365b0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "81caacc1198b470daefc6366f8391edd": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#003b47"}, "name": "Train", "text": ["<b>0.25</b>", "<b>0.17</b>", "<b>0.17</b>"], "textposition": "outside", "x": ["Mean Green Relative Intensity", "Mean Red Relative Intensity", "Mean Blue Relative Intensity"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "ff5a6967-073e-4ffd-b7b1-00e5e784ed46"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "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": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"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": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[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"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 5], "title": {"text": "Column"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_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": "jHu9pGgv0D8eOo1LzunFP7FhXZgIz8U/"}]}, "90f2bc86a9544008bcf61eebc0c61e64": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d1dda27b43704a9ab24653cae316a964", "style": "IPY_MODEL_5137d792d2654c6a9c73048311871d1c", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/data_integrity/plot_property_label_correlation.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "d1dda27b43704a9ab24653cae316a964": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5137d792d2654c6a9c73048311871d1c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b8a9515f99b44a0fbae6f59ac9bd18da": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a970380a270c4f669464cfbe7f8dfc40", "style": "IPY_MODEL_5754abec5a834ea2b68f1cc8e4908947", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "a970380a270c4f669464cfbe7f8dfc40": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5754abec5a834ea2b68f1cc8e4908947": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ce2d54cf63674395813fcb12a5224a6b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_499b5c4674c24a53bf255dce25c688fe", "style": "IPY_MODEL_8a5b77f976534ef0a2ab0d18c762f317", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "499b5c4674c24a53bf255dce25c688fe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8a5b77f976534ef0a2ab0d18c762f317": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eab6e7c81145487c9a1787c71c28eb74": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0d0b767a8590420c9f8bbeab94ca1331", "style": "IPY_MODEL_54d62da465cc4cd08242ba18ee6c691a", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict the label successfully, using simple classic ML algorithms</div>"}}, "0d0b767a8590420c9f8bbeab94ca1331": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "54d62da465cc4cd08242ba18ee6c691a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2378e66a1bfe43d89b9914e03320c69b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_482178082a27418e9499ddadc9b7faf0", "style": "IPY_MODEL_172bb91aef724b3fad1155a027e66586", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "482178082a27418e9499ddadc9b7faf0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "172bb91aef724b3fad1155a027e66586": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "84b76519c7d2411789379b2818f3ea7b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fb0d843d5ac44b8f865ecb38994154ad", "style": "IPY_MODEL_dc1ebe226f3549228e8c3f47ea1e0b91", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "fb0d843d5ac44b8f865ecb38994154ad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc1ebe226f3549228e8c3f47ea1e0b91": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "70144fbdf4be4597af5d71c5fd562c10": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "59f3d570b2f244fca149680b30f145ee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4ff6f17c0617410986815dbef86f420d": {"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_aecef7d7e593454fa314c87cc291df4f", "IPY_MODEL_1bffee5885594fbcb4bfb22b14ad603d", "IPY_MODEL_2a2a1f3a0c48474280b3a717ab6de809"], "layout": "IPY_MODEL_5bbcd0c1021f495bb0e8dfcfffa4e8fb"}}, "aecef7d7e593454fa314c87cc291df4f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_60790de3253c443f8d1aca022d9fbf06", "style": "IPY_MODEL_ca2b8a50250b46aab3abb559f4e7af5c", "value": "<h4 id=\"ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Confusion Matrix</b></h4>"}}, "60790de3253c443f8d1aca022d9fbf06": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ca2b8a50250b46aab3abb559f4e7af5c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1bffee5885594fbcb4bfb22b14ad603d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1821dee3c9334b6e96cb3c44162e4e6b", "style": "IPY_MODEL_8bfd0d08aee1417684e89667feebc679", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "1821dee3c9334b6e96cb3c44162e4e6b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8bfd0d08aee1417684e89667feebc679": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2a2a1f3a0c48474280b3a717ab6de809": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a7aea690c4634bb38c7b02d144b663b6", "IPY_MODEL_af1fd9074ee64383ae4ff02fe5338c12", "IPY_MODEL_c831d0241f224e9dbe13fcb89b0f1ed0", "IPY_MODEL_ba5993f9ff0d437d8c6f966d7d8b3fd9", "IPY_MODEL_37ead36be7a2411686d118404e1ed77d"], "layout": "IPY_MODEL_d942dc1798e64ece847de66157faf4eb"}}, "a7aea690c4634bb38c7b02d144b663b6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7eb056038ad14f2a9570eb88b34e880d", "style": "IPY_MODEL_079f0a4595f94eddbdc5c3ecb86959b9", "value": "<h5><b>Additional Outputs</b></h5>"}}, "7eb056038ad14f2a9570eb88b34e880d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "079f0a4595f94eddbdc5c3ecb86959b9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "af1fd9074ee64383ae4ff02fe5338c12": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1f58bf250cda46db81ad9e19ac210447", "style": "IPY_MODEL_e3147ad87e0249e38487bd835120c70a", "value": "<div>Showing 10 of 12 classes:</div>"}}, "1f58bf250cda46db81ad9e19ac210447": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e3147ad87e0249e38487bd835120c70a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c831d0241f224e9dbe13fcb89b0f1ed0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7165eed9d6dc48a9a7f442335050e311", "style": "IPY_MODEL_7579e21d0be6438687afa5b56edcf8cd", "value": "<div>The overall accuracy of your model is: 93.53%.<br>Best accuracy achieved on samples with <b>aedes_dorsalis</b> label (100.0%).<br>Worst accuracy achieved on samples with <b>aedes_vexans</b> label (80.56%).</div>"}}, "7165eed9d6dc48a9a7f442335050e311": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", 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"gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "37ead36be7a2411686d118404e1ed77d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b999a1624f3d4c55be2cba7139409c46", "style": "IPY_MODEL_08ab82e885a54a70b8343383c51b2010", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "b999a1624f3d4c55be2cba7139409c46": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "08ab82e885a54a70b8343383c51b2010": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d942dc1798e64ece847de66157faf4eb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5bbcd0c1021f495bb0e8dfcfffa4e8fb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3a7fea151d354e99839154d568d672c8": {"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_246f8b8a249e4866a6ccfbfecb22f576", "IPY_MODEL_29c98816de8d42678487032311566c5c", "IPY_MODEL_ee5dd93898e14ba3b02312d3a0b91838"], "layout": "IPY_MODEL_11f4712c74a04ce7b3af06a11aa1c06b"}}, "246f8b8a249e4866a6ccfbfecb22f576": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4f65af5dfaf44d78baed4d82b977edf9", "style": "IPY_MODEL_030cca56335b426eaa3f3550d31d1602", "value": "<h4 id=\"ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Confusion Matrix</b></h4>"}}, "4f65af5dfaf44d78baed4d82b977edf9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "030cca56335b426eaa3f3550d31d1602": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "29c98816de8d42678487032311566c5c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d918774943e74700986047dc473e07bc", "style": "IPY_MODEL_ea60558f4cf9494792d96dfb1cacc41c", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "d918774943e74700986047dc473e07bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ea60558f4cf9494792d96dfb1cacc41c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ee5dd93898e14ba3b02312d3a0b91838": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a411f08633f742bfa7b4dc37968e3eed", "IPY_MODEL_792080868ce849b98b93ee888497b27d", "IPY_MODEL_19574c0a4c31499bbfe48f78bd1c0aff", "IPY_MODEL_15307e91f70746daab6cc23dff0b9281", "IPY_MODEL_cd78785710ab468b87a7be6384962dc2", "IPY_MODEL_301acc1c665b4213ae2f6d4f0394f361"], "layout": "IPY_MODEL_6de22f5e30c744b9882f7484a7612902"}}, "a411f08633f742bfa7b4dc37968e3eed": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_35f5bd9197774a1ba3c4b5c7b7a42769", "style": "IPY_MODEL_73ee2d2fe2974bf2936b6f9563356087", "value": "<h5><b>Additional Outputs</b></h5>"}}, "35f5bd9197774a1ba3c4b5c7b7a42769": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "73ee2d2fe2974bf2936b6f9563356087": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "792080868ce849b98b93ee888497b27d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7ff46bec2687427c922c9bb9390a0188", "style": "IPY_MODEL_59f71a920dd0446b8e6d388c68260017", "value": "<div>Showing 10 of 12 classes:</div>"}}, "7ff46bec2687427c922c9bb9390a0188": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "59f71a920dd0446b8e6d388c68260017": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "19574c0a4c31499bbfe48f78bd1c0aff": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_12deed6c3e664a558c60226eaf5f3e53", "style": "IPY_MODEL_60cfeb4dd06d4cc4937354a884b0b864", "value": "<div>The overall accuracy of your model is: 99.93%.<br>Best accuracy achieved on samples with <b>aedes_aegypti</b> label (100.0%).<br>Worst accuracy achieved on samples with <b>aedes_vexans</b> label (99.31%).</div>"}}, "12deed6c3e664a558c60226eaf5f3e53": {"model_name": "LayoutModel", 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"gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "cd78785710ab468b87a7be6384962dc2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_87e216b39ace4bcb88713e4cbf7015e8", "style": "IPY_MODEL_3bdfa36ef18e460fb9bf250cdb8d3321", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "87e216b39ace4bcb88713e4cbf7015e8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3bdfa36ef18e460fb9bf250cdb8d3321": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "301acc1c665b4213ae2f6d4f0394f361": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f9404c6eb21b4e49a38fc0148e521447", "style": "IPY_MODEL_fa15a18fc8284ebea799dd185c5c041e", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "f9404c6eb21b4e49a38fc0148e521447": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fa15a18fc8284ebea799dd185c5c041e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6de22f5e30c744b9882f7484a7612902": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "11f4712c74a04ce7b3af06a11aa1c06b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b17f45df5ba34720ac3d1478fc2a7ee9": {"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_982ad541da8343df9b71f204713ece51", "IPY_MODEL_45bdc9b6a4e04e1c92ad47a56db71962", "IPY_MODEL_66c6a70e20b54d189d9d3536b0b24807"], "layout": "IPY_MODEL_6d6657bb5d7c43d49c60189ceaeb4699"}}, "982ad541da8343df9b71f204713ece51": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ac1bfa84d3e446d485f1f78f20aa214e", "style": "IPY_MODEL_b3f17b4a72d34feca9da81cbcec9d810", "value": "<h4 id=\"HeatmapComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Heatmap Comparison</b></h4>"}}, "ac1bfa84d3e446d485f1f78f20aa214e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b3f17b4a72d34feca9da81cbcec9d810": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "45bdc9b6a4e04e1c92ad47a56db71962": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_62d82430e0a0453fb33fa5309f01f910", "style": "IPY_MODEL_f318fc86898e45b9a811a3fd0e16d4d5", "value": "<p>Check if the average image brightness (or bbox location if applicable) is similar between train and test set. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_heatmap_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "62d82430e0a0453fb33fa5309f01f910": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f318fc86898e45b9a811a3fd0e16d4d5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "66c6a70e20b54d189d9d3536b0b24807": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_dddcf5efaa4d43ab92c371d6fe78c414", "IPY_MODEL_3bdf4f29ec8f4147b45c8b30e55dd13a", "IPY_MODEL_232845e4061d4e9ebd0ffb1794692ea9", "IPY_MODEL_804cc14e76054ff7bbaf8d78f7e71938"], "layout": 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href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "2e76bd9c41ba482da8ee7796406e020a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c5523ebef944029859739de852109b4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cf47c334e30b4719b4044b22c9880dc4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6d6657bb5d7c43d49c60189ceaeb4699": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e6811f94f784c8ba42572f1f35adc16": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "23a5e2e834fc477cab6841e39e70c1e6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "49f9cb92b54e4c54a397a77ffe01647f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e285d765515e49c482d98d7f9d5a1732": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_060e7f4ac74c42a8b58ef58f09002ce7", "IPY_MODEL_d969ba968a464debbc1bacdfad86ef8d"], "layout": "IPY_MODEL_e2a74230cea64d289f072a6bae292734"}}, "060e7f4ac74c42a8b58ef58f09002ce7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d1d0cab5e45b4c0b8d2130fb14c9e0ed", "style": "IPY_MODEL_c72d73b1573547d9905619ce2e9ce810"}}, "d1d0cab5e45b4c0b8d2130fb14c9e0ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c72d73b1573547d9905619ce2e9ce810": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d969ba968a464debbc1bacdfad86ef8d": {"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": "Didn't Run"}, "children": ["IPY_MODEL_30c8c1aab6d5463699a6e05c2e5d40ce"], "layout": "IPY_MODEL_9f17f8e3667d4c53a8d7f7653fc92627", "selected_index": null}}, "30c8c1aab6d5463699a6e05c2e5d40ce": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5c29e05c15cc44d9a287fe8978ea4f19", "style": "IPY_MODEL_184b696dea874ab9b813f8f518c4c120", "value": "<style type=\"text/css\">\n#T_cb5ed table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cb5ed thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cb5ed tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cb5ed th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cb5ed td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_cb5ed\">\n  <thead>\n    <tr>\n      <th id=\"T_cb5ed_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_cb5ed_level0_col1\" class=\"col_heading level0 col1\" >Reason</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_cb5ed_row0_col0\" class=\"data row0 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_cb5ed_row0_col1\" class=\"data row0 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_cb5ed_row1_col0\" class=\"data row1 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_cb5ed_row1_col1\" class=\"data row1 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_cb5ed_row2_col0\" class=\"data row2 col0\" >Label Property Outliers</td>\n      <td id=\"T_cb5ed_row2_col1\" class=\"data row2 col1\" >DeepchecksProcessError: task type classification does not have default label properties for label outliers.</td>\n    </tr>\n    <tr>\n      <td id=\"T_cb5ed_row3_col0\" class=\"data row3 col0\" >Label Property Outliers</td>\n      <td id=\"T_cb5ed_row3_col1\" class=\"data row3 col1\" >DeepchecksProcessError: task type classification does not have default label properties for label outliers.</td>\n    </tr>\n    <tr>\n      <td id=\"T_cb5ed_row4_col0\" class=\"data row4 col0\" >Mean Average Recall Report</td>\n      <td id=\"T_cb5ed_row4_col1\" class=\"data row4 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_cb5ed_row5_col0\" class=\"data row5 col0\" >Mean Average Recall Report</td>\n      <td id=\"T_cb5ed_row5_col1\" class=\"data row5 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_cb5ed_row6_col0\" class=\"data row6 col0\" >Weak Segments PerformanceTrain</td>\n      <td id=\"T_cb5ed_row6_col1\" class=\"data row6 col1\" >DeepchecksProcessError: WeakSegmentsPerformance was unable to train an error model to find weak segments. Try increasing n_samples or supply additional properties.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "5c29e05c15cc44d9a287fe8978ea4f19": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "184b696dea874ab9b813f8f518c4c120": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9f17f8e3667d4c53a8d7f7653fc92627": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e2a74230cea64d289f072a6bae292734": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bb2a632df1794b468f46bcbcac28a5c9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c7ac10a326e443689459d365261632fc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}}}
+{"version_major": 2, "version_minor": 0, "state": {"877f40505ea141c88feccebd4b126a83": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "Full Suite"}, "children": ["IPY_MODEL_c3718ea0174b48458741aab671b457ef"], "layout": "IPY_MODEL_298fc3f680204df0adb1787c0cb697e4"}}, "c3718ea0174b48458741aab671b457ef": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0c0ec6c2b26f4baa956e64669928cb5a", "IPY_MODEL_240ce11a394c43468ccff8b984b5af69", "IPY_MODEL_88dc6b7ca8be4fe492e69930a26834c0", "IPY_MODEL_e6f3e91a87bb49c382ace2ecb0688e21", "IPY_MODEL_6301c9ab4db247348438aef7ac2db6d4"], "layout": "IPY_MODEL_e28a3672252b4314ab26ee21f07f895e"}}, "0c0ec6c2b26f4baa956e64669928cb5a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_51f2daaa43304a0db4b496aefe267b03", "style": "IPY_MODEL_14813b315ae24e25b9c0777d0c4c7713", "value": "\n<h1 id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT\">Full Suite</h1>\n<p>\n    The suite is composed of various checks such as: Image Dataset Drift, Image Property Outliers, Weak Segments Performance, etc...<br>\n    Each check may contain conditions (which will result in pass <span style=\"color: green;display:inline-block\">\u2713</span> /\n    fail <span style=\"color: red;display:inline-block\">\u2716</span> / warning <span style=\"color: orange;font-weight:bold;display:inline-block\">!</span> / error <span style=\"color: firebrick;font-weight:bold;display:inline-block\">\u2048</span>) as well as\n    other outputs such as plots or tables.<br>\n    Suites, checks and conditions can all be modified. Read more about\n    <a href=https://docs.deepchecks.com/stable/general/usage/customizations/auto_examples/plot_create_a_custom_suite.html?utm_source=display_output&utm_medium=referral&utm_campaign=suite_link target=\"_blank\">custom suites</a>.\n</p>\n\n"}}, "51f2daaa43304a0db4b496aefe267b03": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "14813b315ae24e25b9c0777d0c4c7713": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "240ce11a394c43468ccff8b984b5af69": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_475232c5a206420fba7f2f8289901b64", "IPY_MODEL_0eadcac3f4ae4cb4b7558ad985f9f54c"], "layout": "IPY_MODEL_827ea46247064373b536d35d9f4f867d"}}, "475232c5a206420fba7f2f8289901b64": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fef3f3db19d64789a9297678d00e08ea", "style": "IPY_MODEL_de7c46562c154d42a02471c289be90b3", "value": "<span id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"></span>"}}, "fef3f3db19d64789a9297678d00e08ea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "de7c46562c154d42a02471c289be90b3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0eadcac3f4ae4cb4b7558ad985f9f54c": {"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": "Didn't Pass"}, "children": ["IPY_MODEL_ef20374797584d6a9a2a08806f2ee0e1"], "layout": "IPY_MODEL_42b13d5a8e5c4b7a80443ba0d6a1e558", "selected_index": null}}, "ef20374797584d6a9a2a08806f2ee0e1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a885c5e76c4743f1968d7d49759dab8f", "IPY_MODEL_ec10680286804c55ab4675897087ced3", "IPY_MODEL_06d1b313ee8d45e2a248c45fdc37cdd5", "IPY_MODEL_b184469c1c7e4c8086ef0b0b358c1c3e", "IPY_MODEL_9c0c2ed08dbe41b1b3ee54a56f1fd602", "IPY_MODEL_b184469c1c7e4c8086ef0b0b358c1c3e", "IPY_MODEL_2a726c39d7774b1887fe0868d8aaf77c", "IPY_MODEL_b184469c1c7e4c8086ef0b0b358c1c3e", "IPY_MODEL_3788064a542a479ca11bd923013662b5", "IPY_MODEL_b184469c1c7e4c8086ef0b0b358c1c3e", "IPY_MODEL_af1379344c314ab99342eab2a88e530e", "IPY_MODEL_b184469c1c7e4c8086ef0b0b358c1c3e", "IPY_MODEL_b314ee81aa544ae18b4213cdbbd3866f", "IPY_MODEL_b184469c1c7e4c8086ef0b0b358c1c3e", "IPY_MODEL_75c24f2eb347438aa0615a43f0616779"], "layout": "IPY_MODEL_0c871d0c95d842b085f4a295cf1fb7e2"}}, "a885c5e76c4743f1968d7d49759dab8f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2d0bd222dabc49ffa2239f326a100bec", "style": "IPY_MODEL_10448d9e2eca4eabb31ff0d7559fde4f", "value": "<style type=\"text/css\">\n#T_a515a table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a515a thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a515a tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a515a th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a515a td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a515a\">\n  <thead>\n    <tr>\n      <th id=\"T_a515a_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_a515a_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_a515a_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_a515a_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a515a_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_a515a_row0_col1\" class=\"data row0 col1\" ><a href=\"#ClassPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Class Performance</a></td>\n      <td id=\"T_a515a_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_a515a_row0_col3\" class=\"data row0 col3\" >12 scores failed. Found max degradation of 57% for metric Recall and class aedes_vexans.</td>\n    </tr>\n    <tr>\n      <td id=\"T_a515a_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_a515a_row1_col1\" class=\"data row1 col1\" ><a href=\"#ImagePropertyDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Image Property Drift</a></td>\n      <td id=\"T_a515a_row1_col2\" class=\"data row1 col2\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_a515a_row1_col3\" class=\"data row1 col3\" >Failed for 4 out of 7 properties.\nFound 4 numeric properties with Kolmogorov-Smirnov above threshold: {'RMS Contrast': '0.43', 'Mean Red Relative Intensity': '0.52', 'Mean Green Relative Intensity': '0.53', 'Mean Blue Relative Intensity': '0.52'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_a515a_row2_col0\" class=\"data row2 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_a515a_row2_col1\" class=\"data row2 col1\" ><a href=\"#PropertyLabelCorrelationChange_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Property Label Correlation Change</a></td>\n      <td id=\"T_a515a_row2_col2\" class=\"data row2 col2\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_a515a_row2_col3\" class=\"data row2 col3\" >Properties and classes with PPS difference above threshold: {'Mean Green Relative Intensity': {'anopheles_arabiensis': '0.45'}}</td>\n    </tr>\n    <tr>\n      <td id=\"T_a515a_row3_col0\" class=\"data row3 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_a515a_row3_col1\" class=\"data row3 col1\" ><a href=\"#PredictionDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Prediction Drift</a></td>\n      <td id=\"T_a515a_row3_col2\" class=\"data row3 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_a515a_row3_col3\" class=\"data row3 col3\" >Failed for 1 out of 1 prediction properties.\nFound 1 categorical prediction properties with Cramer's V above threshold: {'Samples Per Class': '0.25'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_a515a_row4_col0\" class=\"data row4 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_a515a_row4_col1\" class=\"data row4 col1\" ><a href=\"#LabelDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Label Drift</a></td>\n      <td id=\"T_a515a_row4_col2\" class=\"data row4 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_a515a_row4_col3\" class=\"data row4 col3\" >Failed for 1 out of 1 label properties.\nFound 1 categorical label properties with Cramer's V above threshold: {'Samples Per Class': '0.16'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_a515a_row5_col0\" class=\"data row5 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_a515a_row5_col1\" class=\"data row5 col1\" ><a href=\"#WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Weak Segments Performance</a></td>\n      <td id=\"T_a515a_row5_col2\" class=\"data row5 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_a515a_row5_col3\" class=\"data row5 col3\" >Found a segment with cross entropy of -6.761 in comparison to an average score of -1.678 in sampled data.</td>\n    </tr>\n    <tr>\n      <td id=\"T_a515a_row6_col0\" class=\"data row6 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_a515a_row6_col1\" class=\"data row6 col1\" ><a href=\"#WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Weak Segments Performance</a></td>\n      <td id=\"T_a515a_row6_col2\" class=\"data row6 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_a515a_row6_col3\" class=\"data row6 col3\" >Found a segment with cross entropy of -3.91 in comparison to an average score of -0.538 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "2d0bd222dabc49ffa2239f326a100bec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "10448d9e2eca4eabb31ff0d7559fde4f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ec10680286804c55ab4675897087ced3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f8d655c8580942c7ae380a47dbd06bfc", "style": "IPY_MODEL_20a4f412c2c2489a92172a1c43f57c4d", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "f8d655c8580942c7ae380a47dbd06bfc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "20a4f412c2c2489a92172a1c43f57c4d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "06d1b313ee8d45e2a248c45fdc37cdd5": {"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_1c51823731b1470a892d84f09ac1b804", "IPY_MODEL_a1f2efc58ecf4504b90935e2c6de1048", "IPY_MODEL_2ff3014254e640098ddabed99ec0b5cf", "IPY_MODEL_9b013b2d3f7c4294aa7b75306c3391f7"], "layout": "IPY_MODEL_6ab4278d641944ceb1a22735a6ded7ad"}}, "1c51823731b1470a892d84f09ac1b804": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_92a180f30f4e404f8304029682cf40d8", "style": "IPY_MODEL_07cddbe39ac14e3188e9143237d1cde0", "value": "<h4 id=\"ClassPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Class Performance</b></h4>"}}, "92a180f30f4e404f8304029682cf40d8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "07cddbe39ac14e3188e9143237d1cde0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a1f2efc58ecf4504b90935e2c6de1048": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_31016994e8e042b29dc4b6de6dda108a", "style": "IPY_MODEL_dbb08eb279a046f6bad28ad8ae08191d", "value": "<p>Summarize given metrics on a dataset and model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_class_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "31016994e8e042b29dc4b6de6dda108a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dbb08eb279a046f6bad28ad8ae08191d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2ff3014254e640098ddabed99ec0b5cf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_42737c1e8fda4a54958f246116b960f1", "style": "IPY_MODEL_3d6aa62bffa449d78e4b0fe5d5ec9a9a", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_3ebd7 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3ebd7 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3ebd7 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3ebd7 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3ebd7 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3ebd7\">\n  <thead>\n    <tr>\n      <th id=\"T_3ebd7_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_3ebd7_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_3ebd7_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_3ebd7_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_3ebd7_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_3ebd7_row0_col2\" class=\"data row0 col2\" >12 scores failed. Found max degradation of 57% for metric Recall and class aedes_vexans.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "42737c1e8fda4a54958f246116b960f1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3d6aa62bffa449d78e4b0fe5d5ec9a9a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9b013b2d3f7c4294aa7b75306c3391f7": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_eea33fd5f3294533bd5730ac95cdcaf4", "IPY_MODEL_08694c2e3e2340eb8741085eb49f7775", "IPY_MODEL_5521b39f1fcd4bd2b4cd767cbb4e0dcc", "IPY_MODEL_40b0f3e129c447189c677dd131f99ed0"], "layout": "IPY_MODEL_e5c2d4d8e5be4e6caf32039bd8d76693"}}, "eea33fd5f3294533bd5730ac95cdcaf4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b51fb9f0a2f54d38ae17593cab243555", "style": "IPY_MODEL_1f7db3620ee440468b351960107056a9", "value": "<h5><b>Additional Outputs</b></h5>"}}, "b51fb9f0a2f54d38ae17593cab243555": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1f7db3620ee440468b351960107056a9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "08694c2e3e2340eb8741085eb49f7775": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Precision<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#003b47", "pattern": {"shape": ""}}, "name": "Train", 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"title": {"text": "Value"}}, "xaxis2": {"anchor": "y2", "domain": [0.525, 1.0], "matches": "x", "title": {"text": "Class"}, "type": "category"}, "yaxis2": {"anchor": "x2", "domain": [0.0, 1.0], "showticklabels": true, "title": {"text": "Value"}}}, "_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": "AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwP2uJ9WBb0+8/B4vQONW/7z9P7MRO7MTuPx6Px+PxeOw/HMdxHMdx7D8vaWjLSxrqP2jVLBRJR+M/"}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/a9+w9g1r7z/4kYinXz3vP7B1heZ2Ku8/dFNSHWID7z8GS7avscrsPwjFJPv2XOo/inMdozjX6T9yvy6pfBjnP5HN3vNileY/"}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/T+zETuzE7j+uHZhrB+bqP6c3velNb+o/SZIkSZIk6T8dIbZpxb3jP76E9hLaS+A/"}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "WDm0yHa+7z/btm3btm3vP42w3NMIy+0/7MRO7MRO7D9mZmZmZmbqP3Icx3Ecx+k/FjYHEq2w6T/Lli1btmzpP7bWWmutteY/whT5rBtM4T/QusEU+azbP47jOI7jONY/"}]}, "5521b39f1fcd4bd2b4cd767cbb4e0dcc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e37c41ab0b1046aa8c497bf591c39739", "style": "IPY_MODEL_e5bd3e33a9dd4fec980c03f8541d4b21", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "e37c41ab0b1046aa8c497bf591c39739": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e5bd3e33a9dd4fec980c03f8541d4b21": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "40b0f3e129c447189c677dd131f99ed0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4829b765a8b948709752fb4829eeffb7", "style": "IPY_MODEL_c92e3f01bab44f538400bb36e3a53620", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "4829b765a8b948709752fb4829eeffb7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c92e3f01bab44f538400bb36e3a53620": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e5c2d4d8e5be4e6caf32039bd8d76693": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6ab4278d641944ceb1a22735a6ded7ad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b184469c1c7e4c8086ef0b0b358c1c3e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7442d69a90dc4d5c9c5cfcfae173a729", "style": "IPY_MODEL_19b81eef0faa4ffea7334023b5d09347", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "7442d69a90dc4d5c9c5cfcfae173a729": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "19b81eef0faa4ffea7334023b5d09347": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9c0c2ed08dbe41b1b3ee54a56f1fd602": {"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_2918979b3c314842afccc0637a805510", "IPY_MODEL_f95fe3d81aef4169996daf59da953a27", "IPY_MODEL_e79f0a2be5654445870ff3ed0a47d838", "IPY_MODEL_dc5244cd6e1644a5a363997dce3beb5a"], "layout": "IPY_MODEL_1016e7878d7b4fb09de9de9a6cdcfdc6"}}, "2918979b3c314842afccc0637a805510": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7b81a88658a146baa1f6052f0d20c387", "style": "IPY_MODEL_9e5d79dd6cb84c4f80db8007b422d418", "value": "<h4 id=\"ImagePropertyDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Image Property Drift</b></h4>"}}, "7b81a88658a146baa1f6052f0d20c387": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9e5d79dd6cb84c4f80db8007b422d418": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f95fe3d81aef4169996daf59da953a27": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ceba344ac3944e0cb7d588e68edf5ac2", "style": "IPY_MODEL_2ea1052efa5c4425810195029c579d8c", "value": "<p>    Calculate drift between train dataset and test dataset per image property, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_image_property_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "ceba344ac3944e0cb7d588e68edf5ac2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2ea1052efa5c4425810195029c579d8c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e79f0a2be5654445870ff3ed0a47d838": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a3eb571db22a4bd6ab176490e5491355", "style": "IPY_MODEL_ed8c121c4641411bae24f693dada9f8a", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a2d40 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2d40 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2d40 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2d40 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2d40 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a2d40\">\n  <thead>\n    <tr>\n      <th id=\"T_a2d40_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_a2d40_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_a2d40_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a2d40_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_a2d40_row0_col1\" class=\"data row0 col1\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_a2d40_row0_col2\" class=\"data row0 col2\" >Failed for 4 out of 7 properties.\nFound 4 numeric properties with Kolmogorov-Smirnov above threshold: {'RMS Contrast': '0.43', 'Mean Red Relative Intensity': '0.52', 'Mean Green Relative Intensity': '0.53', 'Mean Blue Relative Intensity': '0.52'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a3eb571db22a4bd6ab176490e5491355": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ed8c121c4641411bae24f693dada9f8a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dc5244cd6e1644a5a363997dce3beb5a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_adbe71d8f3914d84acf3bd7cc556b9ce", "IPY_MODEL_5e738ed8f1e1493c87ec4a5e884ed31b", "IPY_MODEL_748fd0f75986481fb552ceb4170a94af", "IPY_MODEL_a7629fdaa4b246dcab7b45ae7346fcf8", "IPY_MODEL_935f0e1529a7487385b2a15c2c6dc097", "IPY_MODEL_f433575098d149a2be108213096a7415", "IPY_MODEL_f9bec25f90324a378969bb691d18da46", "IPY_MODEL_bf3e1d84b5574b68a11636b13429a958", "IPY_MODEL_26a700b031194e468ff3806cef81314c", "IPY_MODEL_692c58f0485f455598402336fe273924", "IPY_MODEL_3051b8bf2a1a457eac5a08c4e5d56e1c", "IPY_MODEL_27c4aa6a6a4f4dc19996a2eb2fc3b92c"], "layout": "IPY_MODEL_631dc92154a54abda642173bdc4bdf4b"}}, "adbe71d8f3914d84acf3bd7cc556b9ce": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_85ee721cb356490e9aabb12ea30136fb", "style": "IPY_MODEL_ac91153f2aa140b9aa907fd9f3d54a23", "value": "<h5><b>Additional Outputs</b></h5>"}}, "85ee721cb356490e9aabb12ea30136fb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ac91153f2aa140b9aa907fd9f3d54a23": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5e738ed8f1e1493c87ec4a5e884ed31b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_322e118d06a84c2fba9b4b036e9bbf8e", "style": "IPY_MODEL_5e2d9ba6e4174a3c802827aa5886695d", "value": "<div><span> The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following image properties: ['Area', 'Aspect Ratio', 'Brightness', 'Mean Blue Relative Intensity', 'Mean Green Relative Intensity', 'Mean Red Relative Intensity', 'RMS Contrast'].</span></div>"}}, "322e118d06a84c2fba9b4b036e9bbf8e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e2d9ba6e4174a3c802827aa5886695d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "748fd0f75986481fb552ceb4170a94af": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_be550c461256402380ba026625d53cec", "style": "IPY_MODEL_748af423d9a944ca9f9c73aab67985e0", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, 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[0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Aspect Ratio", "x": 0.5, "xanchor": "center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [1.0, 1.0], "tickmode": "array", "ticks": "outside", "tickvals": {"dtype": "float64", "shape": [1]}}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "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", 1, "x", "buffer"], "data": "AAAAAAAA8D8="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "AAAAAAAA8D8="}, {"encoding": "base64", "path": ["_data", 2, "x", "buffer"], "data": "AAAAAAAA8D8="}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "AAAAAAAA8D8="}, {"encoding": "base64", "path": ["_layout", "xaxis2", "tickvals", "buffer"], "data": "AAAAAAAA8D8="}]}, "3051b8bf2a1a457eac5a08c4e5d56e1c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e30030008f96423381495162d67aba6e", "style": "IPY_MODEL_80074777bc9c4844bc4efc0124b6adaf", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "e30030008f96423381495162d67aba6e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "80074777bc9c4844bc4efc0124b6adaf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "27c4aa6a6a4f4dc19996a2eb2fc3b92c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ad2be8c5b48846bd9354332699576341", "style": "IPY_MODEL_837f92f45e944b56893b0ec3072d7b0d", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "ad2be8c5b48846bd9354332699576341": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "837f92f45e944b56893b0ec3072d7b0d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "631dc92154a54abda642173bdc4bdf4b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1016e7878d7b4fb09de9de9a6cdcfdc6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2a726c39d7774b1887fe0868d8aaf77c": {"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_ad8cd05f41e54db3b5ec7bf4e65f2ef0", "IPY_MODEL_a16499667b9c49f2a2dd583fd1aef08a", "IPY_MODEL_6b449d75923f4eb38344f727f6bb9a33", "IPY_MODEL_20b735fe02734312a3667e04f3ebe4af"], "layout": "IPY_MODEL_77929448347740789a4ec6352bb26840"}}, "ad8cd05f41e54db3b5ec7bf4e65f2ef0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_854ba24d1171436b8ff2ec0876ac7b5e", "style": "IPY_MODEL_7ba0d6e0e4e4425f8d3b7a90bdebc015", "value": "<h4 id=\"PropertyLabelCorrelationChange_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Property Label Correlation Change</b></h4>"}}, "854ba24d1171436b8ff2ec0876ac7b5e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7ba0d6e0e4e4425f8d3b7a90bdebc015": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a16499667b9c49f2a2dd583fd1aef08a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c59a6030e5be481abee9c340c790c8b6", "style": "IPY_MODEL_6b2f6aeea95b4e3da7f2abe3ecfb8ffc", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_property_label_correlation_change.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c59a6030e5be481abee9c340c790c8b6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6b2f6aeea95b4e3da7f2abe3ecfb8ffc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6b449d75923f4eb38344f727f6bb9a33": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_81eaccfb3f98420c8a5d1981a297bb0f", "style": "IPY_MODEL_cadd1ad0743c4282a69c35f82ce9185f", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_ee144 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ee144 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ee144 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ee144 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ee144 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_ee144\">\n  <thead>\n    <tr>\n      <th id=\"T_ee144_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_ee144_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_ee144_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_ee144_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_ee144_row0_col1\" class=\"data row0 col1\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_ee144_row0_col2\" class=\"data row0 col2\" >Properties and classes with PPS difference above threshold: {'Mean Green Relative Intensity': {'anopheles_arabiensis': '0.45'}}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "81eaccfb3f98420c8a5d1981a297bb0f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cadd1ad0743c4282a69c35f82ce9185f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "20b735fe02734312a3667e04f3ebe4af": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3a3b93b97992436a815d69789402c6bd", "IPY_MODEL_9c89bf49e71e463f91dbf23345f6dd6b", "IPY_MODEL_5bfd76cc8dbb447ba28c4f62398a0bf9", "IPY_MODEL_d60a5ac570514093b6570d7c9099d8a5", "IPY_MODEL_f33519a140184bf9b9be82b434bd0fb2", "IPY_MODEL_5474257bf18946128ed22e8bfc678a04", "IPY_MODEL_114ff051407a4e6e85c2b35571ed12fb", "IPY_MODEL_04339da5c0f14de1a889bffb3fa1d7a9", "IPY_MODEL_7f0eae4c0ac84f9b98b304819979d3cb", "IPY_MODEL_b4021207d21a49d6b9c6041b56e9a140", "IPY_MODEL_8faa6e8a7a7743d1a42c68a93b54e7c7", "IPY_MODEL_4766f7ff86304b608565058dc33b6e95", "IPY_MODEL_40d7533e0e944b4d9e05434948027b06", "IPY_MODEL_3e001a3f0eb545cdb9fd0845d390a740"], "layout": "IPY_MODEL_ed53c6169ce64f93bb11be7845f1f2ce"}}, "3a3b93b97992436a815d69789402c6bd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1a0b31bfd24f42ec935751113262b034", "style": "IPY_MODEL_b3d8ace5962243768a89f65541a48ac9", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1a0b31bfd24f42ec935751113262b034": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b3d8ace5962243768a89f65541a48ac9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, 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(Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "1ae74aad09b3458cb98640e8d691a98f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1bd114d5fe0f42d0931f63bb15c85fa5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f33519a140184bf9b9be82b434bd0fb2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cf591d8b55954d148a1dc66bdccee2c5", "style": "IPY_MODEL_c390b54dfda44978b977beb011784ef0", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "cf591d8b55954d148a1dc66bdccee2c5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c390b54dfda44978b977beb011784ef0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5474257bf18946128ed22e8bfc678a04": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cab0f221cf2440ff8ef7501d66441103", "style": "IPY_MODEL_bf1165b3d7c24672916375892571d913", "value": "<div>1. <b>Train dataset PPS values are high</b>:</div>"}}, "cab0f221cf2440ff8ef7501d66441103": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bf1165b3d7c24672916375892571d913": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "114ff051407a4e6e85c2b35571ed12fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_376c5d2045f74d02b4ef364b69ea9fef", "style": "IPY_MODEL_2e5550b9bcf8401ab52535cf94c37d08", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict   the label successfully, using simple classic ML algorithms</div>"}}, "376c5d2045f74d02b4ef364b69ea9fef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2e5550b9bcf8401ab52535cf94c37d08": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "04339da5c0f14de1a889bffb3fa1d7a9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_44b59725e44646fea8e026b2bfbe5245", "style": "IPY_MODEL_2539e9d4865145d785bbd9cbda38ed70", "value": "<div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div>"}}, "44b59725e44646fea8e026b2bfbe5245": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2539e9d4865145d785bbd9cbda38ed70": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f0eae4c0ac84f9b98b304819979d3cb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0a71f66d197446b6b5c2f082ed33aec2", "style": "IPY_MODEL_92db10f976304f4aae53505e5fb1a70a", "value": "<div>   An even more powerful indication of dataset bias, as an image property that was powerful in train</div>"}}, "0a71f66d197446b6b5c2f082ed33aec2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "92db10f976304f4aae53505e5fb1a70a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b4021207d21a49d6b9c6041b56e9a140": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8d9e0791cb13446a9b9699eab43e0949", "style": "IPY_MODEL_c4f3df2a9dd54af489957c51f8fe28a8", "value": "<div>   but not in test can be explained by bias in train that is not relevant to a new dataset.</div>"}}, "8d9e0791cb13446a9b9699eab43e0949": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c4f3df2a9dd54af489957c51f8fe28a8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8faa6e8a7a7743d1a42c68a93b54e7c7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_654d87ec16e94b2ab4e9053b28327487", "style": "IPY_MODEL_9a471223b8ad495e955b27d608faae53", "value": "<div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div>"}}, "654d87ec16e94b2ab4e9053b28327487": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a471223b8ad495e955b27d608faae53": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4766f7ff86304b608565058dc33b6e95": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2e106d7fc56b42fbb21bcb4c37186176", "style": "IPY_MODEL_6cf070a7fde040bfb48c6c9163c37c63", "value": "<div>   An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.</div>"}}, "2e106d7fc56b42fbb21bcb4c37186176": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6cf070a7fde040bfb48c6c9163c37c63": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "40d7533e0e944b4d9e05434948027b06": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fdc7d81f77604b2991d61f7994b12269", "style": "IPY_MODEL_b5d0146117664308b61d249a46dbb85c", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "fdc7d81f77604b2991d61f7994b12269": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b5d0146117664308b61d249a46dbb85c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3e001a3f0eb545cdb9fd0845d390a740": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_95a4b6762095468086296a0a44ab775c", "style": "IPY_MODEL_1db81c2172f7460db0d28d62f844136c", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "95a4b6762095468086296a0a44ab775c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1db81c2172f7460db0d28d62f844136c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ed53c6169ce64f93bb11be7845f1f2ce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "77929448347740789a4ec6352bb26840": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3788064a542a479ca11bd923013662b5": {"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_2ff9465fe0ef479abe29936e20ce6c6a", "IPY_MODEL_57bf560cce914c9c9f14210e98edc102", "IPY_MODEL_36a3b470a6354b8bb7e52b2b907b2b2b", "IPY_MODEL_f883a4609b164b8190872a1b6209abd6"], "layout": "IPY_MODEL_f704be82d02d4548a69864a8db139d8c"}}, "2ff9465fe0ef479abe29936e20ce6c6a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5fd1de8eeff74d24bc3714d6194ff4f1", "style": "IPY_MODEL_d79a7d6140874ca0a85a2f4f77f446a3", "value": "<h4 id=\"PredictionDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Prediction Drift</b></h4>"}}, "5fd1de8eeff74d24bc3714d6194ff4f1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d79a7d6140874ca0a85a2f4f77f446a3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "57bf560cce914c9c9f14210e98edc102": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9b8b82cd29534bfabb861dad6b142bc2", "style": "IPY_MODEL_684d23b2c8864eec9a4032b66f0d3383", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "9b8b82cd29534bfabb861dad6b142bc2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "684d23b2c8864eec9a4032b66f0d3383": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "36a3b470a6354b8bb7e52b2b907b2b2b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_51fe01f400ff483d8fe068232ca8132e", "style": "IPY_MODEL_0efbdf72a4b245379cb42ca655474f2d", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_2129c table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2129c thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2129c tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2129c th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2129c td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2129c\">\n  <thead>\n    <tr>\n      <th id=\"T_2129c_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_2129c_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_2129c_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_2129c_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_2129c_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_2129c_row0_col2\" class=\"data row0 col2\" >Failed for 1 out of 1 prediction properties.\nFound 1 categorical prediction properties with Cramer's V above threshold: {'Samples Per Class': '0.25'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "51fe01f400ff483d8fe068232ca8132e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0efbdf72a4b245379cb42ca655474f2d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f883a4609b164b8190872a1b6209abd6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ae8a507a32d2454293edf91b8acbcf1b", "IPY_MODEL_b8d59b7d44a64877a1944ec2f2452766", "IPY_MODEL_f333fc2b6c734635bf49ba14ea35c95e", "IPY_MODEL_cee69d384cf241eb8ec8302c766d991d", "IPY_MODEL_cf1c8d8b6fec4513a0766f9eedcf076d", "IPY_MODEL_58ccedb3ae8b461c92a695cabd8494cc"], "layout": "IPY_MODEL_1940b17535f64a83b6ac634f67de25a9"}}, "ae8a507a32d2454293edf91b8acbcf1b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_58bd233c193344e9ad41f92e76cd9478", "style": "IPY_MODEL_c9782c064ed744d585294ce380758511", "value": "<h5><b>Additional Outputs</b></h5>"}}, "58bd233c193344e9ad41f92e76cd9478": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c9782c064ed744d585294ce380758511": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b8d59b7d44a64877a1944ec2f2452766": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eaaacac80ba14f5eaa4ff8b110583d04", "style": "IPY_MODEL_3c90fedcc3ca4a009b4ee99fd24d242f", "value": "<div><span>The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following prediction properties: ['Samples Per Class']. </span></div>"}}, "eaaacac80ba14f5eaa4ff8b110583d04": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c90fedcc3ca4a009b4ee99fd24d242f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f333fc2b6c734635bf49ba14ea35c95e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a574f4c6199e4e3cb9c04a8723487e25", "style": "IPY_MODEL_1201d09a746b404b806a45349b9b98f2", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "a574f4c6199e4e3cb9c04a8723487e25": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1201d09a746b404b806a45349b9b98f2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cee69d384cf241eb8ec8302c766d991d": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#6DB36D"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "2ce47e88-371d-41d7-a8e2-51b64c41ddf1"}, {"base": 0.1, "marker": {"color": "#F9F871"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "94039cb0-3063-44d9-957a-b853c0d7e490"}, {"base": 0.2, "marker": {"color": "#FF8749"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": 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"radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Samples Per Class", "x": 0.5, "xanchor": "center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [-3, 12], "type": "category"}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "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", 3, "y", "buffer"], "data": "C2M0ADWfsj/Y1VnjA+/AP6j5lLi+9Kw/7pKmhjo2vT+cWKVIf8ayP0h2qnULmLM/3Zinj1aTsD+MZ8KuzhqvP7MnKqYc/LA/NWpzxMAGtT8="}, {"encoding": "base64", "path": ["_data", 4, "y", "buffer"], "data": "ubUs1v3guD9j3Q+OSR7SPwubA4lW2Jw/pwh8nG14wz/HJA/Z0b+iP6cIfJxteKM/JnnIjv6VoD+MKreWxbqfPwl8nG14E6c/aNBVI6XppD8="}]}, "cf1c8d8b6fec4513a0766f9eedcf076d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c748592503dd4248a7f17a11f355e541", "style": "IPY_MODEL_69bf6147425845acb7eaa4dee376bac2", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "c748592503dd4248a7f17a11f355e541": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "69bf6147425845acb7eaa4dee376bac2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "58ccedb3ae8b461c92a695cabd8494cc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_04b30628c1344605b2ba58db6727faf8", "style": "IPY_MODEL_0c1fb80f90994d28a6290e700fd55393", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "04b30628c1344605b2ba58db6727faf8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0c1fb80f90994d28a6290e700fd55393": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1940b17535f64a83b6ac634f67de25a9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f704be82d02d4548a69864a8db139d8c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "af1379344c314ab99342eab2a88e530e": {"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_265415d787e14f909979e4f0be7557d8", "IPY_MODEL_18d158509de2401b926f6de6e3be87e8", "IPY_MODEL_e7f2805d2bb54324ba3f0e628a5a2dca", "IPY_MODEL_32a811140ca34525ac55fd24a5f57fe6"], "layout": "IPY_MODEL_86635327a7234005858ccc99e1b2292b"}}, "265415d787e14f909979e4f0be7557d8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_67ecc62a8b4f48d4897c03ca73bedc2d", "style": "IPY_MODEL_9f56d962e33f4141b3b4bd8a7aeaf54b", "value": "<h4 id=\"LabelDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Label Drift</b></h4>"}}, "67ecc62a8b4f48d4897c03ca73bedc2d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9f56d962e33f4141b3b4bd8a7aeaf54b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "18d158509de2401b926f6de6e3be87e8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0844ef1adb874787aa3d1041ec5fe260", "style": "IPY_MODEL_0c5eef0660fd464b963cb67ca72f04c6", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "0844ef1adb874787aa3d1041ec5fe260": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0c5eef0660fd464b963cb67ca72f04c6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e7f2805d2bb54324ba3f0e628a5a2dca": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c19fb6984df6442ca2eb7c8382f0e5ce", "style": "IPY_MODEL_d762043f8f6e432d9b286db46fa383df", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_aea27 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_aea27 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_aea27 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_aea27 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_aea27 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_aea27\">\n  <thead>\n    <tr>\n      <th id=\"T_aea27_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_aea27_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_aea27_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_aea27_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_aea27_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_aea27_row0_col2\" class=\"data row0 col2\" >Failed for 1 out of 1 label properties.\nFound 1 categorical label properties with Cramer's V above threshold: {'Samples Per Class': '0.16'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c19fb6984df6442ca2eb7c8382f0e5ce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d762043f8f6e432d9b286db46fa383df": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "32a811140ca34525ac55fd24a5f57fe6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_7be54ca282de4ef4836ea1b03f6f6c63", "IPY_MODEL_9e4fad5d341a42b9b9f0d43820acfd3b", "IPY_MODEL_70c35309e6b14a71a92f0db6bfcc8872", "IPY_MODEL_539abe6141db4d50a68655492c463e30", "IPY_MODEL_3cccc578be374320b581a16b4d1ed2cb", "IPY_MODEL_144c6ee27f7a448c85ec52db257832b2"], "layout": "IPY_MODEL_b97eeabb93ba43e9acfd71e9e644ef45"}}, "7be54ca282de4ef4836ea1b03f6f6c63": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_340e31cd8d95450da37ac92c223eb503", "style": "IPY_MODEL_9cc3fe3aeda5489bb3b3b3ef26c54b48", "value": "<h5><b>Additional Outputs</b></h5>"}}, "340e31cd8d95450da37ac92c223eb503": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9cc3fe3aeda5489bb3b3b3ef26c54b48": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9e4fad5d341a42b9b9f0d43820acfd3b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_95e06f53c8844d4487f8fbf90ffe44f2", "style": "IPY_MODEL_6d2a25e93c4f46cf80e4193c08141a30", "value": "<div><span> The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following label properties: ['Samples Per Class'].</span></div>"}}, "95e06f53c8844d4487f8fbf90ffe44f2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6d2a25e93c4f46cf80e4193c08141a30": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "70c35309e6b14a71a92f0db6bfcc8872": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_22fe83a3dc8741299c45b7f30992a5a0", "style": "IPY_MODEL_aae9f9d81857409b825ff79ef0a04064", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "22fe83a3dc8741299c45b7f30992a5a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aae9f9d81857409b825ff79ef0a04064": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "539abe6141db4d50a68655492c463e30": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#6DB36D"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "51c40b51-29d4-4e91-9cf8-b44df2d2ba9b"}, {"base": 0.1, "marker": {"color": "#F9F871"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.05771148345926694], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "b46cd4b4-f571-4d0f-8f56-b0156ef9972c"}, {"marker": {"color": "#003b47"}, "name": "Train Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "aedes_dorsalis", 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"xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"font": {"size": 16}, "showarrow": false, "text": "Distribution Plot", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 0.7200000000000001, "yanchor": "bottom", "yref": "paper"}], "bargroupgap": 0, "height": 400, "legend": {"title": {"text": "Legend"}, "y": 0.6, "yanchor": "top"}, "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": 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[0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Samples Per Class", "x": 0.5, "xanchor": "center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [-3, 12], "type": "category"}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "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", 2, "y", "buffer"], "data": "aGGMBqDmsz9wqWzvOqC7P6k3UCjJg7Q/PobQ97cftz+3gDktwXCzP/6DiSDr4bA/UfxFzrDgsj8UFVMOfoi3P5vuZiPRlrU/pArEVsivtz8="}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "en0GXTVSuj8g0QqbA4nGP2rvvD6Drqo/igtQe+f1uT9o0FUjpemkP8ckD9nRv6I/KJgvqtxapj9q77w+g666P4oLUHvn9ak/C5sDiVbYvD8="}]}, "3cccc578be374320b581a16b4d1ed2cb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c5f4abfc55de4f1398c83136d92fc803", "style": "IPY_MODEL_c708b2ff551f4349a722801dfa32c66f", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "c5f4abfc55de4f1398c83136d92fc803": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c708b2ff551f4349a722801dfa32c66f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "144c6ee27f7a448c85ec52db257832b2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d7563b3b5b0346548973e24b9701d441", "style": "IPY_MODEL_f2f91c38ce844122a612cecaaaa77a8a", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "d7563b3b5b0346548973e24b9701d441": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f2f91c38ce844122a612cecaaaa77a8a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b97eeabb93ba43e9acfd71e9e644ef45": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "86635327a7234005858ccc99e1b2292b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b314ee81aa544ae18b4213cdbbd3866f": {"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_412f296e4f2444709459eea5d02edfc1", "IPY_MODEL_ee21449985d54122866cc1986218a7f9", "IPY_MODEL_083f6dbfe9a943b09af5d46ee735a27a", "IPY_MODEL_a5903cc8718a4b19b0f4e464a5ad0c63"], "layout": "IPY_MODEL_3d300270bcf54996bf922ca9f746430b"}}, "412f296e4f2444709459eea5d02edfc1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0caff4b9c0e54da68fb02d8aa976065c", "style": "IPY_MODEL_f57347090bf0492f84f5eccbf191c6e4", "value": "<h4 id=\"WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Weak Segments Performance</b></h4>"}}, "0caff4b9c0e54da68fb02d8aa976065c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f57347090bf0492f84f5eccbf191c6e4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ee21449985d54122866cc1986218a7f9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f84fd785ad83414f9a0ae2d6bfcee552", "style": "IPY_MODEL_2186c6ce9acb4a359e0cf21994ff04a6", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "f84fd785ad83414f9a0ae2d6bfcee552": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2186c6ce9acb4a359e0cf21994ff04a6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "083f6dbfe9a943b09af5d46ee735a27a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_55bb3786c0da45d3ab50673a14119d7c", "style": "IPY_MODEL_c73fb6396c484e16a4a868a6e1f2ed53", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_e596b table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e596b thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e596b tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e596b th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e596b td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_e596b\">\n  <thead>\n    <tr>\n      <th id=\"T_e596b_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_e596b_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_e596b_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_e596b_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_e596b_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_e596b_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -6.761 in comparison to an average score of -1.678 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "55bb3786c0da45d3ab50673a14119d7c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c73fb6396c484e16a4a868a6e1f2ed53": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a5903cc8718a4b19b0f4e464a5ad0c63": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3a8dbdddc3a3471187829754435e62b4", "IPY_MODEL_c1a2be1f30c04fe3b1c4160b1cbd7e96", "IPY_MODEL_cf1249f3f97b4a23a6e597544929959c", "IPY_MODEL_0c7f0c5dc0b24ff8bc84842d803976fb"], "layout": "IPY_MODEL_811de6b9ca6f441187e50dc414a8a74d"}}, "3a8dbdddc3a3471187829754435e62b4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d66285aeef7a4a26adff95d47b5ed69a", "style": "IPY_MODEL_14458505554f4300974d2bb4fed5b32c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d66285aeef7a4a26adff95d47b5ed69a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "14458505554f4300974d2bb4fed5b32c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c1a2be1f30c04fe3b1c4160b1cbd7e96": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_608cf880d049487b97b2c254e7865125", "style": "IPY_MODEL_be102656991b4080bdcb15b094508900", "value": "<div>Showcasing intersections of properties with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. 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"domain": [0.0, 1.0], "showgrid": false, "title": {"text": "Mean Red Relative Intensity"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "e1afd35ddc4a4115b037e4f3239815c3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0c1f60d7471b4c94b4ca1314329be908", "style": "IPY_MODEL_515c54e5637b49278d28319d288a5c10", "value": "<div>Check ran on 710 data samples. Cross Entropy on the full data set is -1.68.</div>"}}, "0c1f60d7471b4c94b4ca1314329be908": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "515c54e5637b49278d28319d288a5c10": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8345c8f415e44a97b6ddbc9706ce5e72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b996d6cd3dcc4ce7974edde5c9bbd03d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a9c15a2746e74bd794e735cf607e2725": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0c7f0c5dc0b24ff8bc84842d803976fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6b99f59a856b4abb8487586d1edaeb0b", "style": "IPY_MODEL_6dc84cf0b7d64ddc8e62f1c8f942b8ae", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "6b99f59a856b4abb8487586d1edaeb0b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6dc84cf0b7d64ddc8e62f1c8f942b8ae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "811de6b9ca6f441187e50dc414a8a74d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3d300270bcf54996bf922ca9f746430b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "75c24f2eb347438aa0615a43f0616779": {"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_83f935dcb0494e369762341cd4030918", "IPY_MODEL_0b7f83b591bc465799790c954ac8f9ce", "IPY_MODEL_7a141b32d8134392bdfc47c81c6248f1", "IPY_MODEL_7c13492a6c434369aa66da1f03f42dd6"], "layout": "IPY_MODEL_8da9819f36db47c5871f1d271c353aab"}}, "83f935dcb0494e369762341cd4030918": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_31b79e8c7cef44c9b256ff702b029a42", "style": "IPY_MODEL_7848cc179b374dfc87da812a0d713c27", "value": "<h4 id=\"WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Weak Segments Performance</b></h4>"}}, "31b79e8c7cef44c9b256ff702b029a42": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7848cc179b374dfc87da812a0d713c27": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0b7f83b591bc465799790c954ac8f9ce": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_815893a27e294ca6a0f365108c1853a8", "style": "IPY_MODEL_dfc3a8e0c7e14f4d98a422beff31c180", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "815893a27e294ca6a0f365108c1853a8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dfc3a8e0c7e14f4d98a422beff31c180": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7a141b32d8134392bdfc47c81c6248f1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d6235c02873449d195c23011531322b4", "style": "IPY_MODEL_1dbe45ea65334bbdb763ecd079e0ff01", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_67084 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_67084 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_67084 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_67084 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_67084 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_67084\">\n  <thead>\n    <tr>\n      <th id=\"T_67084_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_67084_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_67084_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_67084_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_67084_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_67084_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -3.91 in comparison to an average score of -0.538 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "d6235c02873449d195c23011531322b4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1dbe45ea65334bbdb763ecd079e0ff01": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7c13492a6c434369aa66da1f03f42dd6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_9ab400d0aa8c4d3a80c94a3ddd1a7c03", "IPY_MODEL_056ab782f94a4d82b24b5c27bb8691d6", "IPY_MODEL_563b92200361469b956e5a6a222ea3d5", "IPY_MODEL_bfd59373c0a84a0194b8ec91c1196a6a", "IPY_MODEL_7ffe72c3bddf4c739d1c1ce355e7a446"], "layout": "IPY_MODEL_59f9a8743be4459db55afa1d5415340d"}}, "9ab400d0aa8c4d3a80c94a3ddd1a7c03": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_008080fd831449bdb52c1ee9a1ae331a", "style": "IPY_MODEL_e04a3b69c8f04bf1b4129d863aaecf7e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "008080fd831449bdb52c1ee9a1ae331a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e04a3b69c8f04bf1b4129d863aaecf7e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "056ab782f94a4d82b24b5c27bb8691d6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b1161f75462c41dca2a1c87fab531e2c", "style": "IPY_MODEL_cea883ac54a7449e82bbc8574f96446f", "value": "<div>Showcasing intersections of properties with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "b1161f75462c41dca2a1c87fab531e2c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cea883ac54a7449e82bbc8574f96446f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "563b92200361469b956e5a6a222ea3d5": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_fa64d1495a9f4dbaa5f200fa85a68d0b", "IPY_MODEL_dbe39c98f4a64ceb91c2eae5862bc4c4"], "layout": "IPY_MODEL_05615e1986c248cf94c0ea95c0fe241e"}}, "fa64d1495a9f4dbaa5f200fa85a68d0b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7c9b2cd0a9ab4e7196670fe378630605", "style": "IPY_MODEL_81d2fe0448e744eba1a64f687a6bfa00", "value": "<style>.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {min-width: fit-content;}</style>"}}, "7c9b2cd0a9ab4e7196670fe378630605": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "81d2fe0448e744eba1a64f687a6bfa00": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dbe39c98f4a64ceb91c2eae5862bc4c4": {"model_name": 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"linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Cross Entropy (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "Brightness"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "53ff895d9ec4466bb31b740a02d6101b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9bee6cf803544d6f9d6e2b40c8a1fbc9", "style": "IPY_MODEL_43e477842bb0401fa3ae810b4bc24205", "value": "<div>Check ran on 5004 data samples. Cross Entropy on the full data set is -0.54.</div>"}}, "9bee6cf803544d6f9d6e2b40c8a1fbc9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "43e477842bb0401fa3ae810b4bc24205": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9f57075a03164a81b4c04f5d90bfa344": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8c8c3511dc9841449b77495e5c7343ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "05615e1986c248cf94c0ea95c0fe241e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bfd59373c0a84a0194b8ec91c1196a6a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1305ccd900944bea922f5349502d3942", "style": "IPY_MODEL_f1bcccf55b5f42249674b2c8ec8dd176", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "1305ccd900944bea922f5349502d3942": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f1bcccf55b5f42249674b2c8ec8dd176": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7ffe72c3bddf4c739d1c1ce355e7a446": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0464e3b7738a4989b22108838466c5f8", "style": "IPY_MODEL_d6f96f816d924869a6b4e494c426a438", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "0464e3b7738a4989b22108838466c5f8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d6f96f816d924869a6b4e494c426a438": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59f9a8743be4459db55afa1d5415340d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8da9819f36db47c5871f1d271c353aab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0c871d0c95d842b085f4a295cf1fb7e2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "42b13d5a8e5c4b7a80443ba0d6a1e558": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "827ea46247064373b536d35d9f4f867d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "88dc6b7ca8be4fe492e69930a26834c0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_740a1c321c694093b3c5d3bbb1e7b171", "IPY_MODEL_a9ed916af8c047c193602a8701755e76"], "layout": "IPY_MODEL_557f4b1dc1954d3394b4d5e7579a488a"}}, "740a1c321c694093b3c5d3bbb1e7b171": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_742fe1b4666d4c35af40fdcaded82e48", "style": "IPY_MODEL_48fb84c290ae4f9f8fb4ca7250e1076d", "value": "<span id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"></span>"}}, "742fe1b4666d4c35af40fdcaded82e48": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "48fb84c290ae4f9f8fb4ca7250e1076d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a9ed916af8c047c193602a8701755e76": {"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_602521ceac1b4c5fab0832f20497db93"], "layout": "IPY_MODEL_3cae7ddacd61461eb612cfd5a590a1d8", "selected_index": null}}, "602521ceac1b4c5fab0832f20497db93": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_01cb4a415a114971aa9c2eff7fc3289f", "IPY_MODEL_4136514d87c94e5d85b352685b3bb670", "IPY_MODEL_af5ce6d2ebc94db4a57732de62e274c2", "IPY_MODEL_959cd340d7604c02877e33abb8ecdad9", "IPY_MODEL_3e70c57bbf7f464590bd4e922d74ee5f"], "layout": "IPY_MODEL_65183e9cf9d146db950b495525b980aa"}}, "01cb4a415a114971aa9c2eff7fc3289f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3d6c3f77aa314cc19277d911770b814b", "style": "IPY_MODEL_d8243f426cbf4d89a7eef51d053954fb", "value": "<style type=\"text/css\">\n#T_f3576 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3576 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3576 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3576 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3576 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f3576\">\n  <thead>\n    <tr>\n      <th id=\"T_f3576_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_f3576_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_f3576_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_f3576_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_f3576_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f3576_row0_col1\" class=\"data row0 col1\" ><a href=\"#SimpleModelComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">Simple Model Comparison</a></td>\n      <td id=\"T_f3576_row0_col2\" class=\"data row0 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_f3576_row0_col3\" class=\"data row0 col3\" >Found minimal gain of 51.02% for metric F1 and class aedes_vexans</td>\n    </tr>\n    <tr>\n      <td id=\"T_f3576_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f3576_row1_col1\" class=\"data row1 col1\" ><a href=\"#NewLabels_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">New Labels</a></td>\n      <td id=\"T_f3576_row1_col2\" class=\"data row1 col2\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_f3576_row1_col3\" class=\"data row1 col3\" >No new labels were found in test set.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3d6c3f77aa314cc19277d911770b814b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d8243f426cbf4d89a7eef51d053954fb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4136514d87c94e5d85b352685b3bb670": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dc870233fe114175910c60cc6f4eabb1", "style": "IPY_MODEL_6ee3e9d7506d462ab2a815c733110153", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "dc870233fe114175910c60cc6f4eabb1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6ee3e9d7506d462ab2a815c733110153": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "af5ce6d2ebc94db4a57732de62e274c2": {"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_382051520f6946f6a114578d3635e96c", "IPY_MODEL_75b45f4d4a0342969ad9a4712d641185", "IPY_MODEL_c70cda59b9594318b653877255e7651f", "IPY_MODEL_f533ca34e1ec4db79c8045c0dd0bfc9f"], "layout": "IPY_MODEL_35824d717e754fa5a3c86325823ecf41"}}, "382051520f6946f6a114578d3635e96c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ff3c05f12d38424784fe5a249fefbbdf", "style": "IPY_MODEL_0f1f3cc31c6540738ca5ced1dbee3a03", "value": "<h4 id=\"SimpleModelComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>Simple Model Comparison</b></h4>"}}, "ff3c05f12d38424784fe5a249fefbbdf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0f1f3cc31c6540738ca5ced1dbee3a03": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "75b45f4d4a0342969ad9a4712d641185": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8483758774b645ad9f7fd0304aa4718d", "style": "IPY_MODEL_8b9d915e3fff4715b8d5451738bdd5bb", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "8483758774b645ad9f7fd0304aa4718d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8b9d915e3fff4715b8d5451738bdd5bb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c70cda59b9594318b653877255e7651f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0c49329071b74935968775e189accde0", "style": "IPY_MODEL_1218117d08e1403f8deee08f10baf11b", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_3cacd table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3cacd thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3cacd tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3cacd th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3cacd td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3cacd\">\n  <thead>\n    <tr>\n      <th id=\"T_3cacd_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_3cacd_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_3cacd_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_3cacd_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3cacd_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_3cacd_row0_col2\" class=\"data row0 col2\" >Found minimal gain of 51.02% for metric F1 and class aedes_vexans</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "0c49329071b74935968775e189accde0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1218117d08e1403f8deee08f10baf11b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f533ca34e1ec4db79c8045c0dd0bfc9f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c7c6b8732a8d4d5dab7b849baa8c18d7", "IPY_MODEL_2df4ef042a8c470287928a52d7a6e2d2", "IPY_MODEL_8912527ee3384fb0a2b3d955817a6217", "IPY_MODEL_56ff602e52ff4cca95565afb3c92f3c8"], "layout": "IPY_MODEL_5a8d72cf2a8a4429a9cba2b23a6d44da"}}, "c7c6b8732a8d4d5dab7b849baa8c18d7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8c892d3c7b654560b5a1826d2972a1b9", "style": "IPY_MODEL_ede8827f1d3e46938d39e1ceb94b08bf", "value": "<h5><b>Additional Outputs</b></h5>"}}, "8c892d3c7b654560b5a1826d2972a1b9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ede8827f1d3e46938d39e1ceb94b08bf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2df4ef042a8c470287928a52d7a6e2d2": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Simple Model<br>Metric=F1<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Simple Model", "marker": {"color": "#637a0a", "pattern": {"shape": ""}}, "name": "Simple Model", "offsetgroup": "Simple Model", "orientation": "v", "showlegend": true, "x": ["aedes_albopictus", "aedes_aegypti", "aedes_dorsalis", "aedes_koreicus", "aedes_vexans", "anopheles_arabiensis", "anopheles_freeborni", "anopheles_sinensis", "culex_inatomii", "culex_pipiens", "culex_quinquefasciatus", "culex_tritaeniorhynchus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y", "type": "histogram", "uid": "54540612-338f-4338-bd30-369877a11041"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Given Model<br>Metric=F1<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Given Model", "marker": {"color": "#e5cb75", "pattern": {"shape": ""}}, "name": "Given Model", "offsetgroup": "Given Model", "orientation": "v", "showlegend": true, "x": ["anopheles_arabiensis", "anopheles_freeborni", "culex_quinquefasciatus", "culex_inatomii", "aedes_aegypti", "anopheles_sinensis", "culex_tritaeniorhynchus", "aedes_albopictus", "aedes_dorsalis", "aedes_koreicus", "culex_pipiens", "aedes_vexans"], "xaxis": "x", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y", "type": "histogram", "uid": "ee88c151-cb4f-4321-b89e-b2ea7203d5fb"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "F1", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Model"}, "tracegroupgap": 0}, "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": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"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": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[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"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Simple Model (Strategy: most_frequent) vs. Given Model"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "type": "category"}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true}}, "_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": "uSEGpGMp0z8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "t23btm3b7j+Cl1PwcgruP93IjdzIjew/CsZb2eeQ6j+CRCtsDiTqP+ugDuqgDuo/wPkYnI/B6T8YW4Jv81foP01nMZ3FdOY/eCVqV6J25T/BeCv7HFLjPxW8nIKXU+A/"}]}, "8912527ee3384fb0a2b3d955817a6217": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1f9ec3353bb1459ca767520eabcd128a", "style": "IPY_MODEL_5335ae23955d46a5a546bce87cb58421", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "1f9ec3353bb1459ca767520eabcd128a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5335ae23955d46a5a546bce87cb58421": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "56ff602e52ff4cca95565afb3c92f3c8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c5f37029f9374f719d791eee488faf93", "style": "IPY_MODEL_82440e31de894519aea95624f1e94735", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "c5f37029f9374f719d791eee488faf93": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "82440e31de894519aea95624f1e94735": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5a8d72cf2a8a4429a9cba2b23a6d44da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "35824d717e754fa5a3c86325823ecf41": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "959cd340d7604c02877e33abb8ecdad9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_61c2940bbc5548758b3ebca879f79e0f", "style": "IPY_MODEL_ea6c3396cee84e46b7732cbffac69150", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "61c2940bbc5548758b3ebca879f79e0f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ea6c3396cee84e46b7732cbffac69150": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3e70c57bbf7f464590bd4e922d74ee5f": {"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_d08a0832d323436c945f9d89a501ce59", "IPY_MODEL_13e54f5f874547078f344fe01b8d44bf", "IPY_MODEL_468fb5b123e444a0864b21429a33e918", "IPY_MODEL_528515984e2d4a94a36e30f6622d467b"], "layout": "IPY_MODEL_8ae04d62bdf64ed389a8a24b730f64a7"}}, "d08a0832d323436c945f9d89a501ce59": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4722817586314aac8c2725717d1e74bb", "style": "IPY_MODEL_117224cac3a94eacacd4eb7a64334e7a", "value": "<h4 id=\"NewLabels_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>New Labels</b></h4>"}}, "4722817586314aac8c2725717d1e74bb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "117224cac3a94eacacd4eb7a64334e7a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "13e54f5f874547078f344fe01b8d44bf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bcaed91d59ae4d1ebabbdfb1f84f0b7c", "style": "IPY_MODEL_2c54d0fddddc4ae3975bbf9d93295536", "value": "<p>Detects labels that appear only in the test set. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_new_labels.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "bcaed91d59ae4d1ebabbdfb1f84f0b7c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2c54d0fddddc4ae3975bbf9d93295536": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "468fb5b123e444a0864b21429a33e918": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_20dd72acb3064fa0bd59c1c206247716", "style": "IPY_MODEL_114aea9197674130b3dfea722889a358", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_f9e07 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f9e07 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f9e07 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f9e07 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f9e07 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f9e07\">\n  <thead>\n    <tr>\n      <th id=\"T_f9e07_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_f9e07_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_f9e07_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_f9e07_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f9e07_row0_col1\" class=\"data row0 col1\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_f9e07_row0_col2\" class=\"data row0 col2\" >No new labels were found in test set.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "20dd72acb3064fa0bd59c1c206247716": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "114aea9197674130b3dfea722889a358": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "528515984e2d4a94a36e30f6622d467b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_22d5dc2a917f4fd3b49b322ec0a24a23", "IPY_MODEL_218c254f72ee4d309e3b01733fd3d765", "IPY_MODEL_7f4209c44e6f4e458e38a222bc06cbd6"], "layout": "IPY_MODEL_734f3822e2694496a2bf908cfa105065"}}, "22d5dc2a917f4fd3b49b322ec0a24a23": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7232354e4b2243d1a9ecc82dc270d4ac", "style": "IPY_MODEL_95c485ee9bf8464d8b1c87abd7679215", "value": "<h5><b>Additional Outputs</b></h5>"}}, "7232354e4b2243d1a9ecc82dc270d4ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "95c485ee9bf8464d8b1c87abd7679215": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "218c254f72ee4d309e3b01733fd3d765": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b1a017a672a04c98b10b59e20a378e2e", "style": "IPY_MODEL_ad74f36b955c45e9ad611ac50fcf13bc", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "b1a017a672a04c98b10b59e20a378e2e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ad74f36b955c45e9ad611ac50fcf13bc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f4209c44e6f4e458e38a222bc06cbd6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0c4a8a66c71345e68db07ee18635d886", "style": "IPY_MODEL_570d81030c264a52905bca4fd4370d4f", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "0c4a8a66c71345e68db07ee18635d886": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "570d81030c264a52905bca4fd4370d4f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "734f3822e2694496a2bf908cfa105065": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8ae04d62bdf64ed389a8a24b730f64a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "65183e9cf9d146db950b495525b980aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3cae7ddacd61461eb612cfd5a590a1d8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "557f4b1dc1954d3394b4d5e7579a488a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e6f3e91a87bb49c382ace2ecb0688e21": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_98164664e5694e039a80f8dbd3586fa8", "IPY_MODEL_be3d4d4ac9814abe94e076320782326e"], "layout": "IPY_MODEL_ed268859f3254267beca9c61c649495d"}}, "98164664e5694e039a80f8dbd3586fa8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7062465aabcc46a4865464fc5c617022", "style": "IPY_MODEL_9c90c8cf176a47049b0364c672a5c129", "value": "<span id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"></span>"}}, "7062465aabcc46a4865464fc5c617022": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9c90c8cf176a47049b0364c672a5c129": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "be3d4d4ac9814abe94e076320782326e": {"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": "Other"}, "children": ["IPY_MODEL_68d6a43dbff34b878f5ef623b1db8fe8"], "layout": "IPY_MODEL_8f3c08e8076e496998ec8c96619999dd", "selected_index": null}}, "68d6a43dbff34b878f5ef623b1db8fe8": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_acaa4acbbfef49d1b7ad6083a74c4ad4", "IPY_MODEL_86cd909cc06c445fbe50cdb933aadc01", "IPY_MODEL_f52507d9f4e34c32a41f6eb4683df937", "IPY_MODEL_e156554544974620ac4fcba9e74c5f2d", "IPY_MODEL_7a1820a996534ac08c4e7b73722be373", "IPY_MODEL_e156554544974620ac4fcba9e74c5f2d", "IPY_MODEL_a4323aa084314ac89d45fca358151daf", "IPY_MODEL_e156554544974620ac4fcba9e74c5f2d", "IPY_MODEL_4d64276637724eee8450f316b025a534", "IPY_MODEL_e156554544974620ac4fcba9e74c5f2d", "IPY_MODEL_c591d5e0e4c1422da8329108668f7b2f", "IPY_MODEL_e156554544974620ac4fcba9e74c5f2d", "IPY_MODEL_10793e2162b0458ba00f539b2d9c2b35", "IPY_MODEL_e156554544974620ac4fcba9e74c5f2d", "IPY_MODEL_6e23f317ae3f45e69b5b0075b0ded51f", "IPY_MODEL_e156554544974620ac4fcba9e74c5f2d", "IPY_MODEL_b7c584873c9d41c4a8fe6c42da334ca6"], "layout": "IPY_MODEL_eac05ced52f7425a95ac286fea775345"}}, "acaa4acbbfef49d1b7ad6083a74c4ad4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_047730dd04a548d98a190d2aecd09d91", "style": "IPY_MODEL_71a4878a0e814210b4e4050fa03dab23", "value": "<style type=\"text/css\">\n#T_baecf table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_baecf thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_baecf tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_baecf th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_baecf td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_baecf\">\n  <thead>\n    <tr>\n      <th id=\"T_baecf_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_baecf_level0_col1\" class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_baecf_row0_col0\" class=\"data row0 col0\" ><a href=\"#ImageDatasetDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Image Dataset Drift</a></td>\n      <td id=\"T_baecf_row0_col1\" class=\"data row0 col1\" >Calculate drift between the entire train and test datasets (based on image properties) using a trained model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_image_dataset_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_baecf_row1_col0\" class=\"data row1 col0\" ><a href=\"#ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Image Property Outliers</a></td>\n      <td id=\"T_baecf_row1_col1\" class=\"data row1 col1\" >Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_baecf_row2_col0\" class=\"data row2 col0\" ><a href=\"#ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Image Property Outliers</a></td>\n      <td id=\"T_baecf_row2_col1\" class=\"data row2 col1\" >Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_baecf_row3_col0\" class=\"data row3 col0\" ><a href=\"#PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Property Label Correlation</a></td>\n      <td id=\"T_baecf_row3_col1\" class=\"data row3 col1\" >    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_baecf_row4_col0\" class=\"data row4 col0\" ><a href=\"#PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Property Label Correlation</a></td>\n      <td id=\"T_baecf_row4_col1\" class=\"data row4 col1\" >    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_baecf_row5_col0\" class=\"data row5 col0\" ><a href=\"#ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Confusion Matrix</a></td>\n      <td id=\"T_baecf_row5_col1\" class=\"data row5 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_baecf_row6_col0\" class=\"data row6 col0\" ><a href=\"#ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Confusion Matrix</a></td>\n      <td id=\"T_baecf_row6_col1\" class=\"data row6 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_baecf_row7_col0\" class=\"data row7 col0\" ><a href=\"#HeatmapComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Heatmap Comparison</a></td>\n      <td id=\"T_baecf_row7_col1\" class=\"data row7 col1\" >Check if the average image brightness (or bbox location if applicable) is similar between train and test set. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_heatmap_comparison.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"}}, "047730dd04a548d98a190d2aecd09d91": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "71a4878a0e814210b4e4050fa03dab23": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "86cd909cc06c445fbe50cdb933aadc01": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b3b0d170bda54d5b99c74fc5f6eeeeff", "style": "IPY_MODEL_b4de2db8de71465192a410394f555797", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "b3b0d170bda54d5b99c74fc5f6eeeeff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b4de2db8de71465192a410394f555797": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f52507d9f4e34c32a41f6eb4683df937": {"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_185feac6fd2d4ccb9b954eea7b453e85", "IPY_MODEL_070d3f5ca81d4a5f96ef7b864151d4be", "IPY_MODEL_7066fad0f61b41df8c26ec069834d0df"], "layout": "IPY_MODEL_61927f0dfa00448094cde484a8fd869f"}}, "185feac6fd2d4ccb9b954eea7b453e85": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_359c41f3d16c4f338e9b0c779c2079de", "style": "IPY_MODEL_ad678a7adbda41e0a4c85b00aed41dfb", "value": "<h4 id=\"ImageDatasetDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Image Dataset Drift</b></h4>"}}, "359c41f3d16c4f338e9b0c779c2079de": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ad678a7adbda41e0a4c85b00aed41dfb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "070d3f5ca81d4a5f96ef7b864151d4be": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_018d129a7bec420f99409c3be8f3ab76", "style": "IPY_MODEL_26ced53de6c244d1b573333249c023e9", "value": "<p>Calculate drift between the entire train and test datasets (based on image properties) using a trained model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_image_dataset_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "018d129a7bec420f99409c3be8f3ab76": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "26ced53de6c244d1b573333249c023e9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7066fad0f61b41df8c26ec069834d0df": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a50158997faf49268668ad4961058cb2", "IPY_MODEL_c5662f63ca2348fd9cd0cde4c9360ac2", "IPY_MODEL_7eda1f3f76164094ac0cc016a0e5750b", "IPY_MODEL_bb8d922ba9f344df8ae8251342b65259", "IPY_MODEL_28b438107bb44034bfd5104b2898b97c", "IPY_MODEL_136e6c62c58240f486f8e2697673878b", "IPY_MODEL_9e36f3e87788422b81dcd6ca9fa8e4a4", "IPY_MODEL_a20815746d414cf1a68bd87f51861ba5", "IPY_MODEL_2cda4faac18a4341940ad2fdea454068", "IPY_MODEL_9a9bf990de2c426e8b6b4ba40a9b393b"], "layout": "IPY_MODEL_ade21657961343db9241cebf259e9701"}}, "a50158997faf49268668ad4961058cb2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ad71cf33574c461eadb82c9da8949883", "style": "IPY_MODEL_b512511a2b2d48eea051f90c7391927f", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ad71cf33574c461eadb82c9da8949883": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b512511a2b2d48eea051f90c7391927f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c5662f63ca2348fd9cd0cde4c9360ac2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9efb91a479ea415190f1ade855cc743e", "style": "IPY_MODEL_2f914342d10e4089b01e75698cae1d1d", "value": "<div>\n        <span>\n        The shown features are the image properties (brightness, aspect ratio, etc.) that are most important for the\n        domain classifier - the domain_classifier trained to distinguish between the train and test datasets.<br>\n        </span>\n        </div>"}}, "9efb91a479ea415190f1ade855cc743e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2f914342d10e4089b01e75698cae1d1d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7eda1f3f76164094ac0cc016a0e5750b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e4ae8dbcb9d0422fa138aa1e963b747d", "style": "IPY_MODEL_3a016cccf29543f992ca76af2ea900d5", "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>"}}, "e4ae8dbcb9d0422fa138aa1e963b747d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3a016cccf29543f992ca76af2ea900d5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bb8d922ba9f344df8ae8251342b65259": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#6DB36D"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "y": ["Drift Score"], "type": "bar", "uid": "3197842a-7914-472e-b8b9-fffbaf74fdd7"}, {"base": 0.1, "marker": {"color": "#F9F871"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "y": ["Drift Score"], "type": "bar", "uid": "7e2aa3c7-1dfc-485f-9bfb-b28bf9156db6"}, {"base": 0.2, "marker": {"color": "#FF8749"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.09999999999999998], "y": ["Drift Score"], "type": "bar", "uid": "56919c85-4fc7-4a41-a944-6012788437a8"}, {"base": 0.3, "marker": {"color": "#BB5B3E"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.2947893936388282], "y": ["Drift Score"], "type": "bar", "uid": "f9a3fa23-bb36-468d-b9dd-c5739d05a160"}], "_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": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"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": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, 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"linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Drift Score - Multivariable"}, "xaxis": {"dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.6947893936388282], "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}}, "28b438107bb44034bfd5104b2898b97c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eb3445c0333844929cf003508e1b8054", "style": "IPY_MODEL_01e2c3b5cb5e4f3caeea7b0ab7c554fd", "value": "<div><h3>Main features contributing to drift</h3></div>"}}, "eb3445c0333844929cf003508e1b8054": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "01e2c3b5cb5e4f3caeea7b0ab7c554fd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "136e6c62c58240f486f8e2697673878b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e2cc814c8fd84e9db4183d4359a67345", "style": "IPY_MODEL_4e7fea0ff3ee4950b46fd7cc787d6ca3", "value": "<div>Showing only the top 3 columns, you can change it using n_top_columns param</div>"}}, "e2cc814c8fd84e9db4183d4359a67345": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4e7fea0ff3ee4950b46fd7cc787d6ca3": {"model_name": "DescriptionStyleModel", 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"2cda4faac18a4341940ad2fdea454068": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_910622afc7c4488e9493c4875076cd72", "style": "IPY_MODEL_39b742f4412641249b307a96cc0ae677", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "910622afc7c4488e9493c4875076cd72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "39b742f4412641249b307a96cc0ae677": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9a9bf990de2c426e8b6b4ba40a9b393b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f8b204e2f7684d0db586cabb17338e33", "style": "IPY_MODEL_8bcc1dade8394cf985774295f06a5168", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "f8b204e2f7684d0db586cabb17338e33": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8bcc1dade8394cf985774295f06a5168": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ade21657961343db9241cebf259e9701": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "61927f0dfa00448094cde484a8fd869f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e156554544974620ac4fcba9e74c5f2d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f783cd844813480b90034f1f53654b7d", "style": "IPY_MODEL_4a0d0e7267d140ca88c60bc9f5dad285", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "f783cd844813480b90034f1f53654b7d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4a0d0e7267d140ca88c60bc9f5dad285": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7a1820a996534ac08c4e7b73722be373": {"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_16f5e40e75904ca1a7423b3034d1608c", "IPY_MODEL_e2089df6c8ad4b3f870f92ecfd07e494", "IPY_MODEL_05c0f2dfec37488483fa3ee58003adc0"], "layout": "IPY_MODEL_e3151ae86dbd4ad4b777332a531033c2"}}, "16f5e40e75904ca1a7423b3034d1608c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_92501affd43c4e079aab542980cdead5", "style": "IPY_MODEL_d4820d67c9a34181b0b4a17b1c1e6446", "value": "<h4 id=\"ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Image Property Outliers</b></h4>"}}, "92501affd43c4e079aab542980cdead5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d4820d67c9a34181b0b4a17b1c1e6446": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e2089df6c8ad4b3f870f92ecfd07e494": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_069070fc7b0344269602a0fc760c7e6b", "style": "IPY_MODEL_793de93b712b4d1bb15301ea8e00a42a", "value": "<p>Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "069070fc7b0344269602a0fc760c7e6b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "793de93b712b4d1bb15301ea8e00a42a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "05c0f2dfec37488483fa3ee58003adc0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b418bbde26d9405ca07a7b0b7f0ea54b", "IPY_MODEL_4ff4cd4b0e754b8190f4249ceef9947f", "IPY_MODEL_794cf18756e84ad69c0427e65d7ee0b7", "IPY_MODEL_acdd0362747546d4888ee72bacea7b10", "IPY_MODEL_2c19c81f0fa54fb1b4ef59af6ec9a396"], "layout": "IPY_MODEL_153872a02c184579a4c6aa56510a302a"}}, "b418bbde26d9405ca07a7b0b7f0ea54b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_55e7e2b6972c4b928bd951bc56acb273", "style": "IPY_MODEL_a57ca9681b7d41fbbbd6d72280c51933", "value": "<h5><b>Additional Outputs</b></h5>"}}, "55e7e2b6972c4b928bd951bc56acb273": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a57ca9681b7d41fbbbd6d72280c51933": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4ff4cd4b0e754b8190f4249ceef9947f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cb19a77237b143d0bd6a24cc84e0ae45", "style": "IPY_MODEL_e648435d2a5f43efa82ed52b1fd5a565", "value": "<div>\n<style>\n    .EWBZUF-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .EWBZUF-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .EWBZUF-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .EWBZUF-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"RMS Contrast\"</b></h5>\n<div>\nTotal number of outliers: 15\n</div>\n<div>\nNon-outliers range: 11.86 to 53.17\n</div>\n<div class=\"EWBZUF-container\">\n    <div class=\"EWBZUF-row\">\n        <h5 class=\"EWBZUF-item\">RMS Contrast</h5>\n        <div class=\"EWBZUF-item\">62.7</div><div class=\"EWBZUF-item\">62.72</div><div class=\"EWBZUF-item\">64.92</div>\n    </div>\n    <div class=\"EWBZUF-row\">\n        <h5 class=\"EWBZUF-item\">Image</h5>\n        <div class=\"EWBZUF-item\"><img src=\"data:image/png;base64,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class=\"EWBZUF-item\"><img 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src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .NOMYHY-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .NOMYHY-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .NOMYHY-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .NOMYHY-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Red Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 65\n</div>\n<div>\nNon-outliers range: 0.32 to 0.35\n</div>\n<div class=\"NOMYHY-container\">\n    <div class=\"NOMYHY-row\">\n        <h5 class=\"NOMYHY-item\">Mean Red Relative Intensity</h5>\n        <div class=\"NOMYHY-item\">0.32</div><div class=\"NOMYHY-item\">0.47</div><div class=\"NOMYHY-item\">0.47</div><div class=\"NOMYHY-item\">0.48</div>\n    </div>\n    <div class=\"NOMYHY-row\">\n        <h5 class=\"NOMYHY-item\">Image</h5>\n        <div class=\"NOMYHY-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"NOMYHY-item\"><img 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cEMh0TuBAI4UGD3DMIPfcEBF1QuaNzQY5GUsg5aT/RCXa89kQ8xwFIOHDUQ49kpaVlPcOVlJ5BKIkXBWWUaq8jeSMZhZpgy0rLJjFNqHFzAY6gTKm3CyyUqHTTiMXSbunyssgKPcHUgeRaUoJGPlZZSNW6nB1pIRpwQWHDhCyyjE2zzY4JTPbuYCPw5+FllNAwxEiRyiRt9vt4PdZZBPTky3EoCQcxFoWWQFCbNPOD5RdHpwMTZZZKYGGrYbJWWQRaTzhMTCyygze5Kyyyi//9k=\"/></div>\n    </div>\n</div>\n\n<style>\n    .GCGCGW-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .GCGCGW-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .GCGCGW-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .GCGCGW-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Green Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 9\n</div>\n<div>\nNon-outliers range: 0.32 to 0.35\n</div>\n<div class=\"GCGCGW-container\">\n    <div class=\"GCGCGW-row\">\n        <h5 class=\"GCGCGW-item\">Mean Green Relative Intensity</h5>\n        <div class=\"GCGCGW-item\">0.29</div><div class=\"GCGCGW-item\">0.29</div><div class=\"GCGCGW-item\">0.29</div>\n    </div>\n    <div class=\"GCGCGW-row\">\n        <h5 class=\"GCGCGW-item\">Image</h5>\n        <div class=\"GCGCGW-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"GCGCGW-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"GCGCGW-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .CHYYGK-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .CHYYGK-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .CHYYGK-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .CHYYGK-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Blue Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 66\n</div>\n<div>\nNon-outliers range: 0.31 to 0.34\n</div>\n<div class=\"CHYYGK-container\">\n    <div class=\"CHYYGK-row\">\n        <h5 class=\"CHYYGK-item\">Mean Blue Relative Intensity</h5>\n        <div class=\"CHYYGK-item\">0.23</div><div class=\"CHYYGK-item\">0.24</div><div class=\"CHYYGK-item\">0.24</div><div class=\"CHYYGK-item\">0.34</div><div class=\"CHYYGK-item\">0.34</div><div class=\"CHYYGK-item\">0.35</div>\n    </div>\n    <div class=\"CHYYGK-row\">\n        <h5 class=\"CHYYGK-item\">Image</h5>\n        <div class=\"CHYYGK-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"CHYYGK-item\"><img 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JtZWNHHdO4OwsSi1r3KA0CRGqdri6mAEEQFOyFLdAkAJ2RqgaEtU0AmkhRTG6Ek0DCd0k0CQhCKaCldHCUJCRQoM7rD7R7pXQhaZSPvdFroQgYGoujnXVCEEgNfCkEIQOyAhCAQEIQOyLIQihNCFArpoQopoQhVAgIQgEroQlUihCFlX/9k=\"/></div>\n    </div>\n</div>\n</div>"}}, "cb19a77237b143d0bd6a24cc84e0ae45": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e648435d2a5f43efa82ed52b1fd5a565": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "794cf18756e84ad69c0427e65d7ee0b7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0c94311d4f2344619fbf1d278b44ee27", "style": "IPY_MODEL_c14131d8d05044cbbc61394e4d741423", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "0c94311d4f2344619fbf1d278b44ee27": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c14131d8d05044cbbc61394e4d741423": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "acdd0362747546d4888ee72bacea7b10": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b6d791bf7e4d48f185b625d406bcf055", "style": "IPY_MODEL_479f492f918c4900bd3d63489cc95dad", "value": "<style type=\"text/css\">\n#T_b4e7f table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b4e7f thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b4e7f tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b4e7f th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b4e7f td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b4e7f\">\n  <thead>\n    <tr>\n      <th id=\"T_b4e7f_level0_col0\" class=\"col_heading level0 col0\" >More Info</th>\n      <th id=\"T_b4e7f_level0_col1\" class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_b4e7f_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_b4e7f_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area, Brightness</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b6d791bf7e4d48f185b625d406bcf055": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "479f492f918c4900bd3d63489cc95dad": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2c19c81f0fa54fb1b4ef59af6ec9a396": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ce35dbe9bd8c4ca3885a2b340deb7a02", "style": "IPY_MODEL_38719ccea315424794f8e313543a8933", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "ce35dbe9bd8c4ca3885a2b340deb7a02": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "38719ccea315424794f8e313543a8933": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "153872a02c184579a4c6aa56510a302a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e3151ae86dbd4ad4b777332a531033c2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a4323aa084314ac89d45fca358151daf": {"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_d397b509b4d34916b8a51c9149de72a5", "IPY_MODEL_ea575dd3d8b14f36b3dbfdb1e6f68927", "IPY_MODEL_1480ebff6e224756b21262f116397d5c"], "layout": "IPY_MODEL_ff13dfde43e64d909e5ab643e0da056a"}}, "d397b509b4d34916b8a51c9149de72a5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5dd37fe47a7d4bdc98b62eb82b642bf0", "style": "IPY_MODEL_4d2a508d8a6b4223b4146ddfdb02180f", "value": "<h4 id=\"ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Image Property Outliers</b></h4>"}}, "5dd37fe47a7d4bdc98b62eb82b642bf0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4d2a508d8a6b4223b4146ddfdb02180f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ea575dd3d8b14f36b3dbfdb1e6f68927": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7f8339a1025e4f67a34880859ab69de8", "style": "IPY_MODEL_a0e8428b48bf420ebf40e1a7e6c04750", "value": "<p>Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "7f8339a1025e4f67a34880859ab69de8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a0e8428b48bf420ebf40e1a7e6c04750": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1480ebff6e224756b21262f116397d5c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_988e2f0530eb4fdc99f47ebe6a32b8ea", "IPY_MODEL_9258a6ff8c204487a056c3263a6b6848", "IPY_MODEL_dbafed4e6c604771996a6be8c812375e", "IPY_MODEL_8893ec9e6e044b29b04e8d63342092b9", "IPY_MODEL_7b4f1a47510a4590bf2ab77ce214ac25", "IPY_MODEL_f8ee4682031c4894a86c6331a803f4c7"], "layout": "IPY_MODEL_3c764ccc44854c37a379c027f918b82f"}}, "988e2f0530eb4fdc99f47ebe6a32b8ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c7423b7ddcb94ac0a23e4079ce5fd783", "style": "IPY_MODEL_bc6572051fc147b0af4552b0ec2d701b", "value": "<h5><b>Additional Outputs</b></h5>"}}, "c7423b7ddcb94ac0a23e4079ce5fd783": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc6572051fc147b0af4552b0ec2d701b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9258a6ff8c204487a056c3263a6b6848": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8d9716df2bdf4891a4cfb8bee7725975", "style": "IPY_MODEL_5e1ba02df43a4f8da9acc26237614545", "value": "<div>\n<style>\n    .EPCXZW-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .EPCXZW-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .EPCXZW-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .EPCXZW-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Red Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 15\n</div>\n<div>\nNon-outliers range: 0.27 to 0.41\n</div>\n<div class=\"EPCXZW-container\">\n    <div class=\"EPCXZW-row\">\n        <h5 class=\"EPCXZW-item\">Mean Red Relative Intensity</h5>\n        <div class=\"EPCXZW-item\">0.43</div><div class=\"EPCXZW-item\">0.44</div><div class=\"EPCXZW-item\">0.44</div>\n    </div>\n    <div class=\"EPCXZW-row\">\n        <h5 class=\"EPCXZW-item\">Image</h5>\n        <div class=\"EPCXZW-item\"><img src=\"data:image/png;base64,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\"/></div><div 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src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n</div>"}}, "8d9716df2bdf4891a4cfb8bee7725975": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e1ba02df43a4f8da9acc26237614545": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dbafed4e6c604771996a6be8c812375e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f3aad6683e154e3db1fea9dbd405cd45", "style": "IPY_MODEL_4ce346e1d22b4df6b084ce097515771b", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "f3aad6683e154e3db1fea9dbd405cd45": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4ce346e1d22b4df6b084ce097515771b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8893ec9e6e044b29b04e8d63342092b9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_83550fa22e834a53a5662c8b41c6a764", "style": "IPY_MODEL_fc8f125924e048f2a8c6c6ded035c8a4", "value": "<style type=\"text/css\">\n#T_c78ec table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c78ec thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c78ec tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c78ec th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c78ec td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c78ec\">\n  <thead>\n    <tr>\n      <th id=\"T_c78ec_level0_col0\" class=\"col_heading level0 col0\" >More Info</th>\n      <th id=\"T_c78ec_level0_col1\" class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c78ec_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_c78ec_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area, Brightness, RMS Contrast, Mean Green Relative Intensity, Mean Blue Relative Intensity</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "83550fa22e834a53a5662c8b41c6a764": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc8f125924e048f2a8c6c6ded035c8a4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7b4f1a47510a4590bf2ab77ce214ac25": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_298ca1c368f54bbcad42b8c4d94dff72", "style": "IPY_MODEL_cad5b18e483c46779b5309bfee9e06a0", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "298ca1c368f54bbcad42b8c4d94dff72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cad5b18e483c46779b5309bfee9e06a0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f8ee4682031c4894a86c6331a803f4c7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6f79d663620a4b96a302ca7eb800a7b1", "style": "IPY_MODEL_5d520b194c7d43e290f170f6c7d22926", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "6f79d663620a4b96a302ca7eb800a7b1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d520b194c7d43e290f170f6c7d22926": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3c764ccc44854c37a379c027f918b82f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ff13dfde43e64d909e5ab643e0da056a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4d64276637724eee8450f316b025a534": {"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_13a23c767f5a4603a3da95e2402c17dc", "IPY_MODEL_e4e5135ed515442c864aa0dd07646971", "IPY_MODEL_450c420bb7dd44bc8e010cc2aeb5f5a4"], "layout": "IPY_MODEL_8ff31f2388574b919265aa4cbb6e9e5f"}}, "13a23c767f5a4603a3da95e2402c17dc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f15d3fab17044ebd854757dd83491466", "style": "IPY_MODEL_a2a2a9a6110445fab2e29a86a431b5cf", "value": "<h4 id=\"PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Property Label Correlation</b></h4>"}}, "f15d3fab17044ebd854757dd83491466": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a2a2a9a6110445fab2e29a86a431b5cf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e4e5135ed515442c864aa0dd07646971": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c79521f98a064e8cb46562d88150e5a7", "style": "IPY_MODEL_9fc5c42716644f86a327edc67d13869e", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c79521f98a064e8cb46562d88150e5a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9fc5c42716644f86a327edc67d13869e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "450c420bb7dd44bc8e010cc2aeb5f5a4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_826706cab1eb4898982d4418488a2d6c", "IPY_MODEL_f626029ec8934dd495d61225acf607fe", "IPY_MODEL_d9a0176174434bcbae1ecd15e546e1e3", "IPY_MODEL_accdb1cf4e5b4f3499fc89a883f976e5", "IPY_MODEL_42ab287a58fd494483c6681c95cd2bbd", "IPY_MODEL_f09bfdd6e364427481cc1e434a13aeae", "IPY_MODEL_2cb526a1a3a048b58cada156d2dc61cb"], "layout": "IPY_MODEL_cbc7137df70e40999ed83d9a08a6d0be"}}, "826706cab1eb4898982d4418488a2d6c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_93273ccfaf2a470398b90d073f174dd4", "style": "IPY_MODEL_5afda70adb7342559367d5029b9772c7", "value": "<h5><b>Additional Outputs</b></h5>"}}, "93273ccfaf2a470398b90d073f174dd4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5afda70adb7342559367d5029b9772c7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f626029ec8934dd495d61225acf607fe": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#818800"}, "name": "Test", "text": ["<b>0.26</b>", "<b>0.17</b>", "<b>0.17</b>"], "textposition": "outside", "x": ["Mean Green Relative Intensity", "Mean Blue Relative Intensity", "Mean Red Relative Intensity"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "90bb8ce0-60a2-45fb-adcf-78bdbe9ab07c"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "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": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"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": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, 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"bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 5], "title": {"text": "Column"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_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": "YV6um41i0D+5Kpd49PLFPyVq99jQgMU/"}]}, "d9a0176174434bcbae1ecd15e546e1e3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8d8b9319a8764f80b50ac49a9fc26169", "style": "IPY_MODEL_8e38e6f1d0e24e4789d00904995b53de", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/data_integrity/plot_property_label_correlation.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "8d8b9319a8764f80b50ac49a9fc26169": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8e38e6f1d0e24e4789d00904995b53de": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "accdb1cf4e5b4f3499fc89a883f976e5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b49a73cd92a2479ba54d32d7a295fa6f", "style": "IPY_MODEL_366ccf90cc4943dab17afe06e0f48b66", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "b49a73cd92a2479ba54d32d7a295fa6f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "366ccf90cc4943dab17afe06e0f48b66": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "42ab287a58fd494483c6681c95cd2bbd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3d2521a167f3477388c45526d0292f64", "style": "IPY_MODEL_1ba8bd1f4b53463b945de5826264d07f", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "3d2521a167f3477388c45526d0292f64": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1ba8bd1f4b53463b945de5826264d07f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f09bfdd6e364427481cc1e434a13aeae": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c94a458849cb4c35afc0b345ea24a21a", "style": "IPY_MODEL_fd7b14d978c547b493282f4412c24048", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict the label successfully, using simple classic ML algorithms</div>"}}, "c94a458849cb4c35afc0b345ea24a21a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fd7b14d978c547b493282f4412c24048": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2cb526a1a3a048b58cada156d2dc61cb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_58439c9b7b744db6a7a41a969b3db70c", "style": "IPY_MODEL_2d50e1dec1644495b9b89aff2ef0c995", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "58439c9b7b744db6a7a41a969b3db70c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2d50e1dec1644495b9b89aff2ef0c995": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cbc7137df70e40999ed83d9a08a6d0be": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8ff31f2388574b919265aa4cbb6e9e5f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c591d5e0e4c1422da8329108668f7b2f": {"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_b030cb196615477986c98f90d077a16a", "IPY_MODEL_82a5e448f36a48f889dfe1810671faa4", "IPY_MODEL_c4fd7206e23142d185bce3309a3f7026"], "layout": "IPY_MODEL_d9bcdf377219450ea13b1a8c956da8d6"}}, "b030cb196615477986c98f90d077a16a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3f743986a1d34f8699c2a7bf32906054", "style": "IPY_MODEL_066b44799bd94df3b43a161317d9c3c7", "value": "<h4 id=\"PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Property Label Correlation</b></h4>"}}, "3f743986a1d34f8699c2a7bf32906054": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "066b44799bd94df3b43a161317d9c3c7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "82a5e448f36a48f889dfe1810671faa4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c70bd0d377c54e9e8ef05c03e4b1cec2", "style": "IPY_MODEL_d3e356814ee8418984d59c2d5073a5ec", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c70bd0d377c54e9e8ef05c03e4b1cec2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d3e356814ee8418984d59c2d5073a5ec": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c4fd7206e23142d185bce3309a3f7026": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6fe9848e17dc448eb60d379e39249d07", "IPY_MODEL_d418e6ed95ff498093abeca1d07aa598", "IPY_MODEL_8f521e42040345da94e962c6d7f75e8c", "IPY_MODEL_cdc82c80ff0d41579f7ac1a27decc9a1", "IPY_MODEL_7695c929b76a4c7fafa63289343899c8", "IPY_MODEL_04d4943f1dd046258ec4988951a4036c", "IPY_MODEL_ebb0c318adcf43be986d8d548e31c36c", "IPY_MODEL_d6bf46072e364a47aa4416e50edc0565"], "layout": "IPY_MODEL_4a2a00331fe140b5b9b1d9fd387eb76c"}}, "6fe9848e17dc448eb60d379e39249d07": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9284a6977b444e58973f6d4ed8f0df7d", "style": "IPY_MODEL_b2d75d86b3f94a8a996c3ad0201ad46b", "value": "<h5><b>Additional Outputs</b></h5>"}}, "9284a6977b444e58973f6d4ed8f0df7d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b2d75d86b3f94a8a996c3ad0201ad46b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d418e6ed95ff498093abeca1d07aa598": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#003b47"}, "name": "Train", "text": ["<b>0.26</b>", "<b>0.18</b>", "<b>0.17</b>"], "textposition": "outside", "x": ["Mean Green Relative Intensity", "Mean Red Relative Intensity", "Mean Blue Relative Intensity"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "01dae70e-6a79-42ad-bf9f-f2d4f72edf65"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "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, 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"#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": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"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": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[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"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 5], "title": {"text": "Column"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_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": "IPMp9KDK0D92JuqAcqTGPz0nQFZPOMY/"}]}, "8f521e42040345da94e962c6d7f75e8c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a67ddbd3037c4cafade18bd0ea36e65b", "style": "IPY_MODEL_3b99b95f5a034584bb1273a551b08c12", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/data_integrity/plot_property_label_correlation.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "a67ddbd3037c4cafade18bd0ea36e65b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3b99b95f5a034584bb1273a551b08c12": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cdc82c80ff0d41579f7ac1a27decc9a1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8a548c17aca24249a75eb0a5c2334206", "style": "IPY_MODEL_7239fdb01b5241aa9e8019ffa333aace", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "8a548c17aca24249a75eb0a5c2334206": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7239fdb01b5241aa9e8019ffa333aace": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7695c929b76a4c7fafa63289343899c8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c6fe398dc7334baa9bc4a345e5f644de", "style": "IPY_MODEL_a37588fe27c94977874de62804d73a15", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "c6fe398dc7334baa9bc4a345e5f644de": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a37588fe27c94977874de62804d73a15": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "04d4943f1dd046258ec4988951a4036c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e0c08deeb1144cc4b72fd07666b67dd4", "style": "IPY_MODEL_30f9eccf3d454d12a3eeecb6ff86ce27", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict the label successfully, using simple classic ML algorithms</div>"}}, "e0c08deeb1144cc4b72fd07666b67dd4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "30f9eccf3d454d12a3eeecb6ff86ce27": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ebb0c318adcf43be986d8d548e31c36c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_96251af874454ff286d73a8a83c39015", "style": "IPY_MODEL_bd97746934cc4088b0410612aac1ed79", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "96251af874454ff286d73a8a83c39015": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bd97746934cc4088b0410612aac1ed79": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d6bf46072e364a47aa4416e50edc0565": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_582ee13784fe40d2bf24f8bc5aa94908", "style": "IPY_MODEL_07801d2fccd040acabac4a58e60ddb85", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "582ee13784fe40d2bf24f8bc5aa94908": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "07801d2fccd040acabac4a58e60ddb85": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4a2a00331fe140b5b9b1d9fd387eb76c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d9bcdf377219450ea13b1a8c956da8d6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "10793e2162b0458ba00f539b2d9c2b35": {"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_ff38c5c3da1944d6a4d7b14f9907c3e9", "IPY_MODEL_4dd147b054fb44238c2ea0064639058a", "IPY_MODEL_3d5ce40e88464d29b754eaeaeffb566a"], "layout": "IPY_MODEL_df446e5b8b854913853d8abe0f71e87d"}}, "ff38c5c3da1944d6a4d7b14f9907c3e9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_039a7a3e59cd4a16b055178bd811cb95", "style": "IPY_MODEL_51f1b643a9674bcbb4b5f3f9ecd2a7da", "value": "<h4 id=\"ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Confusion Matrix</b></h4>"}}, "039a7a3e59cd4a16b055178bd811cb95": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "51f1b643a9674bcbb4b5f3f9ecd2a7da": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4dd147b054fb44238c2ea0064639058a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c0c3f86498da439e8c90ff72dd58230e", "style": "IPY_MODEL_c95ea2be680849b5aaa6a7bbabfef674", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c0c3f86498da439e8c90ff72dd58230e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c95ea2be680849b5aaa6a7bbabfef674": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3d5ce40e88464d29b754eaeaeffb566a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_bfe8f3e32b0447b4adb14fae87f19593", "IPY_MODEL_a9e9f8213184429aab9b06f42888d98d", "IPY_MODEL_a14a6f20e7ab4a15a1d9463df050502f", "IPY_MODEL_e8918249310a4f89a090866a69426920", "IPY_MODEL_4a38b292100641c6aea94d4184cafb6a"], "layout": "IPY_MODEL_17bf7baf274a4bb8b7efe91d152dcc4e"}}, "bfe8f3e32b0447b4adb14fae87f19593": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cb5eadf8e99949148f0838393999391f", "style": "IPY_MODEL_dd4726ee5826408082e33d4fa5e1a056", "value": "<h5><b>Additional Outputs</b></h5>"}}, "cb5eadf8e99949148f0838393999391f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dd4726ee5826408082e33d4fa5e1a056": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a9e9f8213184429aab9b06f42888d98d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bbbccf663d134a18843507568df43b3d", "style": "IPY_MODEL_cbb14c2c9b494216a8c8db1dfd991fb4", "value": "<div>Showing 10 of 12 classes:</div>"}}, "bbbccf663d134a18843507568df43b3d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cbb14c2c9b494216a8c8db1dfd991fb4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a14a6f20e7ab4a15a1d9463df050502f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d2eb179bd0fa40b2a6037cb5e0b7baca", "style": "IPY_MODEL_c9a3b34e1b2c4b98a1cf7cbcd7370550", "value": "<div>The overall accuracy of your model is: 72.32%.<br>Best accuracy achieved on samples with <b>aedes_albopictus</b> label (99.2%).<br>Worst accuracy achieved on samples with <b>aedes_vexans</b> label (34.72%).</div>"}}, "d2eb179bd0fa40b2a6037cb5e0b7baca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", 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{"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "4a38b292100641c6aea94d4184cafb6a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_35bf800acb2b4a3ba6d3af24be4f87f3", "style": "IPY_MODEL_b53b11aaaa044ff1b2398f5280552bbc", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "35bf800acb2b4a3ba6d3af24be4f87f3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b53b11aaaa044ff1b2398f5280552bbc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "17bf7baf274a4bb8b7efe91d152dcc4e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "df446e5b8b854913853d8abe0f71e87d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6e23f317ae3f45e69b5b0075b0ded51f": {"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_7022c0094acd4b94857fdb26da08b9fd", "IPY_MODEL_7b35fdb305b041d1b5181c5421feb70a", "IPY_MODEL_fac1a73f240e4e14b30d0a58ac9946d1"], "layout": "IPY_MODEL_54a6a095064140848138d0e48babf57d"}}, "7022c0094acd4b94857fdb26da08b9fd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c9791691620847df90a08dc8ea111d17", "style": "IPY_MODEL_5fd3f9f4b7574e3087de6ff22e95a319", "value": "<h4 id=\"ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Confusion Matrix</b></h4>"}}, "c9791691620847df90a08dc8ea111d17": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5fd3f9f4b7574e3087de6ff22e95a319": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7b35fdb305b041d1b5181c5421feb70a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6a13c26531f54b138fe66b78b0b99c0d", "style": "IPY_MODEL_b3863155410446199fa550df28a9c4f1", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "6a13c26531f54b138fe66b78b0b99c0d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b3863155410446199fa550df28a9c4f1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fac1a73f240e4e14b30d0a58ac9946d1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_02187bda6f134386886d4a9a3e05fd74", "IPY_MODEL_7774e398d8de41a08a3fc706dbf4ae06", "IPY_MODEL_2c8f21528be3485c90272bd1d09b6614", "IPY_MODEL_afb2ddee9ce342048af1ebcf8166cb5e", "IPY_MODEL_4a4aa6bb656e4088ba51fcb09054faa1", "IPY_MODEL_b2cb953ff10f4b90bd1be993cc41b50b"], "layout": "IPY_MODEL_cb755330565e46bcae3a9ec4582ce209"}}, "02187bda6f134386886d4a9a3e05fd74": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ce847d25620e493aa75623390b7c644a", "style": "IPY_MODEL_54a96a1ac2a54c9d9bf2862affd9550a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ce847d25620e493aa75623390b7c644a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "54a96a1ac2a54c9d9bf2862affd9550a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7774e398d8de41a08a3fc706dbf4ae06": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f9e60ff9c87c42e68c25226b29fe0961", "style": "IPY_MODEL_b60c335c016547808bf0fa4e4ccdab0f", "value": "<div>Showing 10 of 12 classes:</div>"}}, "f9e60ff9c87c42e68c25226b29fe0961": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b60c335c016547808bf0fa4e4ccdab0f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2c8f21528be3485c90272bd1d09b6614": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5a8317788a8944c38243a11e4e81782a", "style": "IPY_MODEL_a0c3e0da006e422d80fa2a33f83646b9", "value": "<div>The overall accuracy of your model is: 88.78%.<br>Best accuracy achieved on samples with <b>aedes_albopictus</b> label (100.0%).<br>Worst accuracy achieved on samples with <b>aedes_dorsalis</b> label (70.57%).</div>"}}, "5a8317788a8944c38243a11e4e81782a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a0c3e0da006e422d80fa2a33f83646b9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "afb2ddee9ce342048af1ebcf8166cb5e": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[8.15, 89.97, 96.95], [0.91, 10.03, 10.8], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [12.58, 100.0, 81.57], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [6.59, 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"white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "4a4aa6bb656e4088ba51fcb09054faa1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fe8176d0179f45ada8b4fc3e6d65eff6", "style": "IPY_MODEL_9a2263a261564c24ae42cbe092baf5f2", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "fe8176d0179f45ada8b4fc3e6d65eff6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a2263a261564c24ae42cbe092baf5f2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b2cb953ff10f4b90bd1be993cc41b50b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4eaa60fc41e24d6a8e6fcf824a271da0", "style": "IPY_MODEL_5283f415093d40c8a9aa2003c9cbfbdb", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "4eaa60fc41e24d6a8e6fcf824a271da0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5283f415093d40c8a9aa2003c9cbfbdb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cb755330565e46bcae3a9ec4582ce209": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "54a6a095064140848138d0e48babf57d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b7c584873c9d41c4a8fe6c42da334ca6": {"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_b85e2bd317c34f4a90a158fc63b2162f", "IPY_MODEL_2ec64f94c4734a78b25ef33dfe974159", "IPY_MODEL_12e7e10a2dd04c8faea5cbdd538f9faf"], "layout": "IPY_MODEL_7d76a260737d45b1a4c202f6a16b3ef7"}}, "b85e2bd317c34f4a90a158fc63b2162f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ce5ddfc705cc42b590d6bd6bdff19fa7", "style": "IPY_MODEL_d0a432f25fb94daf8664d1b3f9b0e1ae", "value": "<h4 id=\"HeatmapComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Heatmap Comparison</b></h4>"}}, "ce5ddfc705cc42b590d6bd6bdff19fa7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d0a432f25fb94daf8664d1b3f9b0e1ae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2ec64f94c4734a78b25ef33dfe974159": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c446353ee2964d5d8e6fa86ea12d8f9b", "style": "IPY_MODEL_074bc2a81c8a4b75b6e10512a6e309e7", "value": "<p>Check if the average image brightness (or bbox location if applicable) is similar between train and test set. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_heatmap_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c446353ee2964d5d8e6fa86ea12d8f9b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "074bc2a81c8a4b75b6e10512a6e309e7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "12e7e10a2dd04c8faea5cbdd538f9faf": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_39cbec84e8a74a29ae70bf268b9709fc", "IPY_MODEL_376637f7d49d416a8d2c482d43ec5be5", "IPY_MODEL_e7b05d3acd2a4208878582f86eed1f2f", "IPY_MODEL_0458348185eb4c4298aa1af7f3648640"], "layout": "IPY_MODEL_20e1d28b05fa497bab8d31e55c0f6d82"}}, "39cbec84e8a74a29ae70bf268b9709fc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9651ae7949174d79a6f1efa95dd04005", "style": "IPY_MODEL_bcf69c5044e145d895b3cb02a95e723c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "9651ae7949174d79a6f1efa95dd04005": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bcf69c5044e145d895b3cb02a95e723c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "376637f7d49d416a8d2c482d43ec5be5": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"coloraxis": "coloraxis", "hoverinfo": "skip", "xaxis": "x", "yaxis": "y", "z": [[73, 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"be4a56cfd5bf4a7ebb210c8692b84781": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c0ba94e55a7247869650ea9da6765485": {"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": "Didn't Run"}, "children": ["IPY_MODEL_cfb4e27963ff4d2d9853825e0d824bba"], "layout": "IPY_MODEL_94a67e3b7409407c801b607ff103a4f3", "selected_index": null}}, "cfb4e27963ff4d2d9853825e0d824bba": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4c63158689084632b96cc8356fd59dd5", "style": "IPY_MODEL_dbac38bc512947c79c80c05012c5ca89", "value": "<style type=\"text/css\">\n#T_9394e table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9394e thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9394e tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9394e th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9394e td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_9394e\">\n  <thead>\n    <tr>\n      <th id=\"T_9394e_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_9394e_level0_col1\" class=\"col_heading level0 col1\" >Reason</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_9394e_row0_col0\" class=\"data row0 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_9394e_row0_col1\" class=\"data row0 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_9394e_row1_col0\" class=\"data row1 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_9394e_row1_col1\" class=\"data row1 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_9394e_row2_col0\" class=\"data row2 col0\" >Label Property Outliers</td>\n      <td id=\"T_9394e_row2_col1\" class=\"data row2 col1\" >DeepchecksProcessError: task type classification does not have default label properties for label outliers.</td>\n    </tr>\n    <tr>\n      <td id=\"T_9394e_row3_col0\" class=\"data row3 col0\" >Label Property Outliers</td>\n      <td id=\"T_9394e_row3_col1\" class=\"data row3 col1\" >DeepchecksProcessError: task type classification does not have default label properties for label outliers.</td>\n    </tr>\n    <tr>\n      <td id=\"T_9394e_row4_col0\" class=\"data row4 col0\" >Mean Average Recall Report</td>\n      <td id=\"T_9394e_row4_col1\" class=\"data row4 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_9394e_row5_col0\" class=\"data row5 col0\" >Mean Average Recall Report</td>\n      <td id=\"T_9394e_row5_col1\" class=\"data row5 col1\" >Check is 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