diff --git a/PiML_explainable_models.ipynb b/PiML_explainable_models.ipynb
new file mode 100644
index 00000000..b6dbbd6e
--- /dev/null
+++ b/PiML_explainable_models.ipynb
@@ -0,0 +1,35264 @@
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        padding: 24px;\n            color: white;\n        }\n\n        .card .card-title {\n            font-size: 24px;\n            font-weight: 300;\n        }\n\n        @media only screen and (min-width: 800px) {\n            /* For notebook: */\n            .card {width: 99% !important;}\n        }\n\n        @media only screen and (min-width: 1400px) {\n            /* For colab: */\n            .card {width: 99% !important;}\n        }\n\n        .blue-grey.darken-1 {\n            background-color: #546e7a !important;\n        }\n\n        .small-card {\n            position: relative;\n            margin: 0.5rem 0 1rem 0;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 0px 5px 0px 5px;\n            color: white;\n        }\n\n        .inner-card {\n            position: relative;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 20px;\n            color: white;\n            margin: 0px 1px 1px 1px;\n        }\n\n        .card-with-tab {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card-with-tab button {\n            width: auto;\n            padding: 12px 40px;\n        }\n\n        .list {\n            padding: 0px 10px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            margin: 0px 1px 60px 1px;\n        }\n\n        .list-item {\n            padding: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .divider {\n            margin: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .hr {\n            margin: 0;\n            -webkit-flex-shrink: 0;\n            -ms-flex-negative: 0;\n            flex-shrink: 0;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .notification {\n            transition: box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            border-radius: 4px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 12px;\n            line-height: 1.43;\n            letter-spacing: 0.01071em;\n            background-color: rgb(2, 136, 209);\n            display: flex;\n            padding: 6px 16px;\n            color: rgb(255, 255, 255);\n            margin-top: 10px;\n        }\n\n        .success {\n            background-color: #EDF7ED;\n            color: #1E4620;\n        }\n\n        .error {\n            background-color: #FDEDED;\n            color: #5F2120;\n        }\n\n        .info {\n            background-color: #E5F6FD;\n            color: #528298;\n        }\n\n        .warn {\n            background-color: #FFF4E5;\n            color: #663C00;\n        }\n\n        .margin-top-20 {\n            margin-top: 20px;\n        }\n\n        .hide {\n            display: none;\n        }\n\n        .pbtn {\n            display: inline-flex;\n            -webkit-box-align: center;\n            align-items: center;\n            -webkit-box-pack: center;\n            justify-content: center;\n            position: relative;\n            box-sizing: border-box;\n            -webkit-tap-highlight-color: transparent;\n            outline: 0px;\n            border: 0px;\n            margin: 0px;\n            cursor: pointer;\n            user-select: none;\n            vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 550;\n            font-size: 12px;\n            line-height: 1.75;\n            letter-spacing: 0.02857em;\n            text-transform: uppercase;\n            min-width: 64px;\n            padding: 6px 16px;\n            border-radius: 4px;\n            transition: background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            color: rgb(255, 255, 255);\n            background-color: #D71E28;\n            box-shadow: rgb(0 0 0 / 20%) 0px 3px 1px -2px, rgb(0 0 0 / 14%) 0px 2px 2px 0px, rgb(0 0 0 / 12%) 0px 1px 5px 0px;\n        }\n\n        .jp-OutputArea-output {\n            flex-grow: 1;\n            flex-shrink: 1;\n            margin: 0px;\n        }\n\n        .output_subarea {\n            padding: 0px !important;\n        }\n\n        .ptable, .pth, .ptd {\n            border: none;\n        }\n\n        .pth, .ptd {\n            max-width: 150px;\n            white-space: nowrap;\n            overflow: hidden;\n            -o-text-overflow: ellipsis;\n            -ms-text-overflow: ellipsis;\n            text-overflow: ellipsis;\n        }\n\n        .ptable {\n            display: table;\n            border-collapse: collapse;\n            border-spacing: 0;\n        }\n\n        .ptable.striped .ptr {\n            border-bottom: none;\n        }\n\n        .ptable.striped > .ptbody > .ptr:nth-child(odd) {\n            background-color: rgba(242, 242, 242, 0.5);\n        }\n\n        .ptable.striped > .ptbody > .ptr > .ptd {\n            border-radius: 0;\n        }\n\n        .ptable.highlight > .ptbody > .ptr {\n            -webkit-transition: background-color .25s ease;\n            transition: background-color .25s 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      .widget-toggle-button {\n            display: inline-flex;\n            -webkit-box-align: center;\n            align-items: center;\n            -webkit-box-pack: center;\n            justify-content: center;\n            box-sizing: border-box;\n            -webkit-tap-highlight-color: transparent;\n            background-color: transparent !important;\n            outline: 0px;\n            border: 0px;\n            border-bottom: 2px solid #E4E4E4 !important;\n            box-shadow: none !important;\n            margin: 0px !important;\n            border-radius: 0px;\n            cursor: pointer;\n            user-select: none;\n            vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 14px;\n            line-height: 1.25;\n            letter-spacing: 0.02857em;\n            max-width: 360px;\n            min-width: 90px;\n            position: relative;\n            min-height: 48px;\n            flex-shrink: 0;\n            padding: 12px 16px;\n            overflow: hidden;\n            white-space: normal;\n            text-align: center;\n            flex-direction: column;\n            color: rgba(0, 0, 0, 0.6);\n        }\n\n        .narrow-accordion .jupyter-widget-Accordion-child .jupyter-widget-Collapse-header{\n            padding: 5px;\n        }  \n\n        .mod-active {\n            color: rgb(25, 118, 210) !important;\n            border-bottom: 2px solid rgb(25, 118, 210) !important;\n        }\n\n        .narrow-tab button {\n            max-width: 15%;\n        }\n\n        .narrow-tab-fairness button {\n            max-width: 20%;\n        }\n        .narrow-tab-fs button {\n            max-width: 25%;\n        }\n\n        .overflow-fix .jupyter-widgets {\n            overflow: auto;\n        }\n\n        .inner-padding {\n            padding-top: 20px;\n        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+            "value": "\n        <style>\n\n        .left-label {\n            width: 30%;\n        }\n\n        .card-panel {\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            padding: 24px;\n            margin: 0.5rem 0 1rem 0;\n            border-radius: 2px;\n            background-color: #fff;\n        }\n\n        .card {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card .card-title {\n            font-size: 24px;\n            font-weight: 300;\n        }\n\n        @media only screen and (min-width: 800px) {\n            /* For notebook: */\n            .card {width: 99% !important;}\n        }\n\n        @media only screen and (min-width: 1400px) {\n            /* For colab: */\n            .card {width: 99% !important;}\n        }\n\n        .blue-grey.darken-1 {\n            background-color: #546e7a !important;\n        }\n\n        .small-card {\n            position: relative;\n            margin: 0.5rem 0 1rem 0;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 0px 5px 0px 5px;\n            color: white;\n        }\n\n        .inner-card {\n            position: relative;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 20px;\n            color: white;\n            margin: 0px 1px 1px 1px;\n        }\n\n        .card-with-tab {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card-with-tab button {\n            width: auto;\n            padding: 12px 40px;\n        }\n\n        .list {\n            padding: 0px 10px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            margin: 0px 1px 60px 1px;\n        }\n\n        .list-item {\n            padding: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .divider {\n            margin: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .hr {\n            margin: 0;\n            -webkit-flex-shrink: 0;\n            -ms-flex-negative: 0;\n            flex-shrink: 0;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .notification {\n            transition: box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            border-radius: 4px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 12px;\n            line-height: 1.43;\n            letter-spacing: 0.01071em;\n            background-color: rgb(2, 136, 209);\n            display: flex;\n            padding: 6px 16px;\n            color: rgb(255, 255, 255);\n            margin-top: 10px;\n        }\n\n        .success {\n            background-color: #EDF7ED;\n            color: #1E4620;\n        }\n\n        .error {\n            background-color: #FDEDED;\n            color: #5F2120;\n        }\n\n        .info {\n            background-color: #E5F6FD;\n            color: #528298;\n        }\n\n        .warn {\n            background-color: #FFF4E5;\n            color: #663C00;\n        }\n\n        .margin-top-20 {\n            margin-top: 20px;\n        }\n\n        .hide {\n            display: none;\n        }\n\n        .pbtn {\n            display: inline-flex;\n            -webkit-box-align: center;\n            align-items: center;\n            -webkit-box-pack: center;\n            justify-content: center;\n            position: relative;\n            box-sizing: border-box;\n            -webkit-tap-highlight-color: transparent;\n            outline: 0px;\n            border: 0px;\n            margin: 0px;\n            cursor: pointer;\n            user-select: none;\n            vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 550;\n            font-size: 12px;\n            line-height: 1.75;\n            letter-spacing: 0.02857em;\n            text-transform: uppercase;\n            min-width: 64px;\n            padding: 6px 16px;\n            border-radius: 4px;\n            transition: background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            color: rgb(255, 255, 255);\n            background-color: #D71E28;\n            box-shadow: rgb(0 0 0 / 20%) 0px 3px 1px -2px, rgb(0 0 0 / 14%) 0px 2px 2px 0px, rgb(0 0 0 / 12%) 0px 1px 5px 0px;\n        }\n\n        .jp-OutputArea-output {\n            flex-grow: 1;\n            flex-shrink: 1;\n            margin: 0px;\n        }\n\n        .output_subarea {\n            padding: 0px !important;\n        }\n\n        .ptable, .pth, .ptd {\n            border: none;\n        }\n\n        .pth, .ptd {\n            max-width: 150px;\n            white-space: nowrap;\n            overflow: hidden;\n            -o-text-overflow: ellipsis;\n            -ms-text-overflow: ellipsis;\n            text-overflow: ellipsis;\n        }\n\n        .ptable {\n            display: table;\n            border-collapse: collapse;\n            border-spacing: 0;\n        }\n\n        .ptable.striped .ptr {\n            border-bottom: none;\n        }\n\n        .ptable.striped > .ptbody > .ptr:nth-child(odd) {\n            background-color: rgba(242, 242, 242, 0.5);\n        }\n\n        .ptable.striped > .ptbody > .ptr > .ptd {\n            border-radius: 0;\n        }\n\n        .ptable.highlight > .ptbody > .ptr {\n            -webkit-transition: background-color .25s ease;\n            transition: background-color .25s 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transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card-with-tab button {\n            width: auto;\n            padding: 12px 40px;\n        }\n\n        .list {\n            padding: 0px 10px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            margin: 0px 1px 60px 1px;\n        }\n\n        .list-item {\n            padding: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .divider {\n            margin: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .hr {\n            margin: 0;\n            -webkit-flex-shrink: 0;\n            -ms-flex-negative: 0;\n            flex-shrink: 0;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .notification {\n            transition: box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            border-radius: 4px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 12px;\n            line-height: 1.43;\n            letter-spacing: 0.01071em;\n            background-color: rgb(2, 136, 209);\n            display: flex;\n            padding: 6px 16px;\n            color: rgb(255, 255, 255);\n            margin-top: 10px;\n        }\n\n        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vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 550;\n            font-size: 12px;\n            line-height: 1.75;\n            letter-spacing: 0.02857em;\n            text-transform: uppercase;\n            min-width: 64px;\n            padding: 6px 16px;\n            border-radius: 4px;\n            transition: background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            color: rgb(255, 255, 255);\n            background-color: #D71E28;\n            box-shadow: rgb(0 0 0 / 20%) 0px 3px 1px -2px, rgb(0 0 0 / 14%) 0px 2px 2px 0px, rgb(0 0 0 / 12%) 0px 1px 5px 0px;\n        }\n\n        .jp-OutputArea-output {\n            flex-grow: 1;\n            flex-shrink: 1;\n            margin: 0px;\n        }\n\n        .output_subarea {\n            padding: 0px !important;\n        }\n\n        .ptable, .pth, .ptd {\n            border: none;\n        }\n\n        .pth, .ptd {\n            max-width: 150px;\n            white-space: nowrap;\n            overflow: hidden;\n            -o-text-overflow: ellipsis;\n            -ms-text-overflow: ellipsis;\n            text-overflow: ellipsis;\n        }\n\n        .ptable {\n            display: table;\n            border-collapse: collapse;\n            border-spacing: 0;\n        }\n\n        .ptable.striped .ptr {\n            border-bottom: none;\n        }\n\n        .ptable.striped > .ptbody > .ptr:nth-child(odd) {\n            background-color: rgba(242, 242, 242, 0.5);\n        }\n\n        .ptable.striped > .ptbody > .ptr > .ptd {\n            border-radius: 0;\n        }\n\n        .ptable.highlight > .ptbody > .ptr {\n            -webkit-transition: background-color .25s ease;\n            transition: background-color .25s 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rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 0px 5px 0px 5px;\n            color: white;\n        }\n\n        .inner-card {\n            position: relative;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 20px;\n            color: white;\n            margin: 0px 1px 1px 1px;\n        }\n\n        .card-with-tab {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card-with-tab button {\n            width: auto;\n            padding: 12px 40px;\n        }\n\n        .list {\n            padding: 0px 10px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            margin: 0px 1px 60px 1px;\n        }\n\n        .list-item {\n            padding: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .divider {\n            margin: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .hr {\n            margin: 0;\n            -webkit-flex-shrink: 0;\n            -ms-flex-negative: 0;\n            flex-shrink: 0;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .notification {\n            transition: box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            border-radius: 4px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 12px;\n            line-height: 1.43;\n            letter-spacing: 0.01071em;\n            background-color: rgb(2, 136, 209);\n            display: flex;\n            padding: 6px 16px;\n            color: rgb(255, 255, 255);\n            margin-top: 10px;\n        }\n\n        .success {\n            background-color: #EDF7ED;\n            color: #1E4620;\n        }\n\n        .error {\n            background-color: #FDEDED;\n            color: #5F2120;\n        }\n\n        .info {\n            background-color: #E5F6FD;\n            color: #528298;\n        }\n\n        .warn {\n            background-color: #FFF4E5;\n            color: #663C00;\n        }\n\n        .margin-top-20 {\n            margin-top: 20px;\n        }\n\n        .hide {\n            display: none;\n        }\n\n        .pbtn {\n            display: inline-flex;\n            -webkit-box-align: center;\n            align-items: center;\n            -webkit-box-pack: center;\n            justify-content: center;\n            position: relative;\n            box-sizing: border-box;\n            -webkit-tap-highlight-color: transparent;\n            outline: 0px;\n            border: 0px;\n            margin: 0px;\n            cursor: pointer;\n            user-select: none;\n            vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 550;\n            font-size: 12px;\n            line-height: 1.75;\n            letter-spacing: 0.02857em;\n            text-transform: uppercase;\n            min-width: 64px;\n            padding: 6px 16px;\n            border-radius: 4px;\n            transition: background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            color: rgb(255, 255, 255);\n            background-color: #D71E28;\n            box-shadow: rgb(0 0 0 / 20%) 0px 3px 1px -2px, rgb(0 0 0 / 14%) 0px 2px 2px 0px, rgb(0 0 0 / 12%) 0px 1px 5px 0px;\n        }\n\n        .jp-OutputArea-output {\n            flex-grow: 1;\n            flex-shrink: 1;\n            margin: 0px;\n        }\n\n        .output_subarea {\n            padding: 0px !important;\n        }\n\n        .ptable, .pth, .ptd {\n            border: none;\n        }\n\n        .pth, .ptd {\n            max-width: 150px;\n            white-space: nowrap;\n            overflow: hidden;\n            -o-text-overflow: ellipsis;\n            -ms-text-overflow: ellipsis;\n            text-overflow: ellipsis;\n        }\n\n        .ptable {\n            display: table;\n            border-collapse: collapse;\n            border-spacing: 0;\n        }\n\n        .ptable.striped .ptr {\n            border-bottom: none;\n        }\n\n        .ptable.striped > .ptbody > .ptr:nth-child(odd) {\n            background-color: rgba(242, 242, 242, 0.5);\n        }\n\n        .ptable.striped > .ptbody > .ptr > .ptd {\n            border-radius: 0;\n        }\n\n        .ptable.highlight > .ptbody > .ptr {\n            -webkit-transition: background-color .25s ease;\n            transition: background-color .25s 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+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "BoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "BoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "BoxView",
+            "box_style": "",
+            "children": [],
+            "layout": "IPY_MODEL_9000ff2b04a04a2ba62f135e9a704cea"
+          }
+        },
+        "c053c97f8bae46e8a940ffb9b16ade21": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": "36%",
+            "justify_content": "space-between",
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "5dfdb68425d1477688f957cf0f42ba87": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_403c2bf7f8314e5983a39369b4f48e9d",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "<Figure size 800x600 with 1 Axes>",
+                  "image/png": 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\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "5b8fe0d321f04ca1b0fc27ead219b1e1": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
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+            "_view_name": "LayoutView",
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+            "_view_name": "StyleView",
+            "description_width": ""
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+        "2ac7c22e8fa64ae0868a44f5eab31fef": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
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+            "_model_module": "@jupyter-widgets/output",
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+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_de26c557428f4f69b194a9fc824d9ccf",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "<Figure size 800x600 with 1 Axes>",
+                  "image/png": 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\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "3145e155a4564d319b10585d6337984b": {
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+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
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+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
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+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
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+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "c9351934dda143108e461596e396a64b": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "4a67670e52054bacb8cc3fc0774dc767": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_d52bc4c7a2e6445381e427c0e5df2bf2",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "<Figure size 800x600 with 2 Axes>",
+                  "image/png": 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\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "74ba50b822d74336b381cbdf39145c05": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
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        padding: 24px;\n            color: white;\n        }\n\n        .card .card-title {\n            font-size: 24px;\n            font-weight: 300;\n        }\n\n        @media only screen and (min-width: 800px) {\n            /* For notebook: */\n            .card {width: 99% !important;}\n        }\n\n        @media only screen and (min-width: 1400px) {\n            /* For colab: */\n            .card {width: 99% !important;}\n        }\n\n        .blue-grey.darken-1 {\n            background-color: #546e7a !important;\n        }\n\n        .small-card {\n            position: relative;\n            margin: 0.5rem 0 1rem 0;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 0px 5px 0px 5px;\n            color: white;\n        }\n\n        .inner-card {\n            position: relative;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 20px;\n            color: white;\n            margin: 0px 1px 1px 1px;\n        }\n\n        .card-with-tab {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card-with-tab button {\n            width: auto;\n            padding: 12px 40px;\n        }\n\n        .list {\n            padding: 0px 10px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            margin: 0px 1px 60px 1px;\n        }\n\n        .list-item {\n            padding: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .divider {\n            margin: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .hr {\n            margin: 0;\n            -webkit-flex-shrink: 0;\n            -ms-flex-negative: 0;\n            flex-shrink: 0;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .notification {\n            transition: box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            border-radius: 4px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 12px;\n            line-height: 1.43;\n            letter-spacing: 0.01071em;\n            background-color: rgb(2, 136, 209);\n            display: flex;\n            padding: 6px 16px;\n            color: rgb(255, 255, 255);\n            margin-top: 10px;\n        }\n\n        .success {\n            background-color: #EDF7ED;\n            color: #1E4620;\n        }\n\n        .error {\n            background-color: #FDEDED;\n            color: #5F2120;\n        }\n\n        .info {\n            background-color: #E5F6FD;\n            color: #528298;\n        }\n\n        .warn {\n            background-color: #FFF4E5;\n            color: #663C00;\n        }\n\n        .margin-top-20 {\n            margin-top: 20px;\n        }\n\n        .hide {\n            display: none;\n        }\n\n        .pbtn {\n            display: inline-flex;\n            -webkit-box-align: center;\n            align-items: center;\n            -webkit-box-pack: center;\n            justify-content: center;\n            position: relative;\n            box-sizing: border-box;\n            -webkit-tap-highlight-color: transparent;\n            outline: 0px;\n            border: 0px;\n            margin: 0px;\n            cursor: pointer;\n            user-select: none;\n            vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 550;\n            font-size: 12px;\n            line-height: 1.75;\n            letter-spacing: 0.02857em;\n            text-transform: uppercase;\n            min-width: 64px;\n            padding: 6px 16px;\n            border-radius: 4px;\n            transition: background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            color: rgb(255, 255, 255);\n            background-color: #D71E28;\n            box-shadow: rgb(0 0 0 / 20%) 0px 3px 1px -2px, rgb(0 0 0 / 14%) 0px 2px 2px 0px, rgb(0 0 0 / 12%) 0px 1px 5px 0px;\n        }\n\n        .jp-OutputArea-output {\n            flex-grow: 1;\n            flex-shrink: 1;\n            margin: 0px;\n        }\n\n        .output_subarea {\n            padding: 0px !important;\n        }\n\n        .ptable, .pth, .ptd {\n            border: none;\n        }\n\n        .pth, .ptd {\n            max-width: 150px;\n            white-space: nowrap;\n            overflow: hidden;\n            -o-text-overflow: ellipsis;\n            -ms-text-overflow: ellipsis;\n            text-overflow: ellipsis;\n        }\n\n        .ptable {\n            display: table;\n            border-collapse: collapse;\n            border-spacing: 0;\n        }\n\n        .ptable.striped .ptr {\n            border-bottom: none;\n        }\n\n        .ptable.striped > .ptbody > .ptr:nth-child(odd) {\n            background-color: rgba(242, 242, 242, 0.5);\n        }\n\n        .ptable.striped > .ptbody > .ptr > .ptd {\n            border-radius: 0;\n        }\n\n        .ptable.highlight > .ptbody > .ptr {\n            -webkit-transition: background-color .25s ease;\n            transition: background-color .25s 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      .widget-toggle-button {\n            display: inline-flex;\n            -webkit-box-align: center;\n            align-items: center;\n            -webkit-box-pack: center;\n            justify-content: center;\n            box-sizing: border-box;\n            -webkit-tap-highlight-color: transparent;\n            background-color: transparent !important;\n            outline: 0px;\n            border: 0px;\n            border-bottom: 2px solid #E4E4E4 !important;\n            box-shadow: none !important;\n            margin: 0px !important;\n            border-radius: 0px;\n            cursor: pointer;\n            user-select: none;\n            vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 14px;\n            line-height: 1.25;\n            letter-spacing: 0.02857em;\n            max-width: 360px;\n            min-width: 90px;\n            position: relative;\n            min-height: 48px;\n            flex-shrink: 0;\n            padding: 12px 16px;\n            overflow: hidden;\n            white-space: normal;\n            text-align: center;\n            flex-direction: column;\n            color: rgba(0, 0, 0, 0.6);\n        }\n\n        .narrow-accordion .jupyter-widget-Accordion-child .jupyter-widget-Collapse-header{\n            padding: 5px;\n        }  \n\n        .mod-active {\n            color: rgb(25, 118, 210) !important;\n            border-bottom: 2px solid rgb(25, 118, 210) !important;\n        }\n\n        .narrow-tab button {\n            max-width: 15%;\n        }\n\n        .narrow-tab-fairness button {\n            max-width: 20%;\n        }\n        .narrow-tab-fs button {\n            max-width: 25%;\n        }\n\n        .overflow-fix .jupyter-widgets {\n            overflow: auto;\n        }\n\n        .inner-padding {\n            padding-top: 20px;\n        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+            "value": "\n        <style>\n\n        .left-label {\n            width: 30%;\n        }\n\n        .card-panel {\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            padding: 24px;\n            margin: 0.5rem 0 1rem 0;\n            border-radius: 2px;\n            background-color: #fff;\n        }\n\n        .card {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card .card-title {\n            font-size: 24px;\n            font-weight: 300;\n        }\n\n        @media only screen and (min-width: 800px) {\n            /* For notebook: */\n            .card {width: 99% !important;}\n        }\n\n        @media only screen and (min-width: 1400px) {\n            /* For colab: */\n            .card {width: 99% !important;}\n        }\n\n        .blue-grey.darken-1 {\n            background-color: #546e7a !important;\n        }\n\n        .small-card {\n            position: relative;\n            margin: 0.5rem 0 1rem 0;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 0px 5px 0px 5px;\n            color: white;\n        }\n\n        .inner-card {\n            position: relative;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 20px;\n            color: white;\n            margin: 0px 1px 1px 1px;\n        }\n\n        .card-with-tab {\n            position: relative;\n            margin: 0.5rem 0.1rem 1rem 0.2rem;\n            background-color: #fff;\n            -webkit-transition: -webkit-box-shadow .25s;\n            transition: -webkit-box-shadow .25s;\n            transition: box-shadow .25s;\n            transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card-with-tab button {\n            width: auto;\n            padding: 12px 40px;\n        }\n\n        .list {\n            padding: 0px 10px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            margin: 0px 1px 60px 1px;\n        }\n\n        .list-item {\n            padding: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .divider {\n            margin: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .hr {\n            margin: 0;\n            -webkit-flex-shrink: 0;\n            -ms-flex-negative: 0;\n            flex-shrink: 0;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .notification {\n            transition: box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            border-radius: 4px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 12px;\n            line-height: 1.43;\n            letter-spacing: 0.01071em;\n            background-color: rgb(2, 136, 209);\n            display: flex;\n            padding: 6px 16px;\n            color: rgb(255, 255, 255);\n            margin-top: 10px;\n        }\n\n        .success {\n            background-color: #EDF7ED;\n            color: #1E4620;\n        }\n\n        .error {\n            background-color: #FDEDED;\n            color: #5F2120;\n        }\n\n        .info {\n            background-color: #E5F6FD;\n            color: #528298;\n        }\n\n        .warn {\n            background-color: #FFF4E5;\n            color: #663C00;\n        }\n\n        .margin-top-20 {\n            margin-top: 20px;\n        }\n\n        .hide {\n            display: none;\n        }\n\n        .pbtn {\n            display: inline-flex;\n            -webkit-box-align: center;\n            align-items: center;\n            -webkit-box-pack: center;\n            justify-content: center;\n            position: relative;\n            box-sizing: border-box;\n            -webkit-tap-highlight-color: transparent;\n            outline: 0px;\n            border: 0px;\n            margin: 0px;\n            cursor: pointer;\n            user-select: none;\n            vertical-align: middle;\n            appearance: none;\n            text-decoration: none;\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 550;\n            font-size: 12px;\n            line-height: 1.75;\n            letter-spacing: 0.02857em;\n            text-transform: uppercase;\n            min-width: 64px;\n            padding: 6px 16px;\n            border-radius: 4px;\n            transition: background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms, color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            color: rgb(255, 255, 255);\n            background-color: #D71E28;\n            box-shadow: rgb(0 0 0 / 20%) 0px 3px 1px -2px, rgb(0 0 0 / 14%) 0px 2px 2px 0px, rgb(0 0 0 / 12%) 0px 1px 5px 0px;\n        }\n\n        .jp-OutputArea-output {\n            flex-grow: 1;\n            flex-shrink: 1;\n            margin: 0px;\n        }\n\n        .output_subarea {\n            padding: 0px !important;\n        }\n\n        .ptable, .pth, .ptd {\n            border: none;\n        }\n\n        .pth, .ptd {\n            max-width: 150px;\n            white-space: nowrap;\n            overflow: hidden;\n            -o-text-overflow: ellipsis;\n            -ms-text-overflow: ellipsis;\n            text-overflow: ellipsis;\n        }\n\n        .ptable {\n            display: table;\n            border-collapse: collapse;\n            border-spacing: 0;\n        }\n\n        .ptable.striped .ptr {\n            border-bottom: none;\n        }\n\n        .ptable.striped > .ptbody > .ptr:nth-child(odd) {\n            background-color: rgba(242, 242, 242, 0.5);\n        }\n\n        .ptable.striped > .ptbody > .ptr > .ptd {\n            border-radius: 0;\n        }\n\n        .ptable.highlight > .ptbody > .ptr {\n            -webkit-transition: background-color .25s ease;\n            transition: background-color .25s 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+            "_view_module_version": "1.5.0",
+            "_view_name": "BoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_10b2818707af4e2186f596b052c8792c"
+            ],
+            "layout": "IPY_MODEL_9c337bff664241d2b681ad111e853644"
+          }
+        },
+        "54b1410397824d5783b5b89e0ee06b65": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": "20%"
+          }
+        },
+        "7b1817a5693f495b9e6ad67ce1693783": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_ed7b61c9bb8646b39e9e1f30ab906466",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "<Figure size 800x600 with 1 Axes>",
+                  "image/png": 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\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "15ef7b14e8954dc1a96ef09048fd1808": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
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transition: box-shadow .25s, -webkit-box-shadow .25s;\n            border-radius: 2px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            padding: 24px;\n            color: white;\n        }\n\n        .card-with-tab button {\n            width: auto;\n            padding: 12px 40px;\n        }\n\n        .list {\n            padding: 0px 10px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            margin: 0px 1px 60px 1px;\n        }\n\n        .list-item {\n            padding: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .divider {\n            margin: 10px 0px;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .hr {\n            margin: 0;\n            -webkit-flex-shrink: 0;\n            -ms-flex-negative: 0;\n            flex-shrink: 0;\n            border-width: 0;\n            border-style: solid;\n            border-color: rgba(0, 0, 0, 0.08);\n            border-bottom-width: thin;\n        }\n\n        .notification {\n            transition: box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n            border-radius: 4px;\n            box-shadow: 0 2px 2px 0 rgb(0 0 0 / 14%), 0 3px 1px -2px rgb(0 0 0 / 12%), 0 1px 5px 0 rgb(0 0 0 / 20%);\n            font-family: Roboto, Helvetica, Arial, sans-serif;\n            font-weight: 500;\n            font-size: 12px;\n            line-height: 1.43;\n            letter-spacing: 0.01071em;\n            background-color: rgb(2, 136, 209);\n            display: flex;\n            padding: 6px 16px;\n            color: rgb(255, 255, 255);\n            margin-top: 10px;\n        }\n\n        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+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_6387cf18defc4e3c8b644b6100a9bdfe",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "<Figure size 800x600 with 2 Axes>",
+                  "image/png": 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+              "LIMIT_BAL x PAY_AMT1",
+              "LIMIT_BAL x PAY_AMT3",
+              "PAY_1 x PAY_5",
+              "PAY_1 x PAY_3",
+              "LIMIT_BAL x PAY_1",
+              "PAY_1 x PAY_AMT1",
+              "PAY_3 x PAY_AMT1",
+              "PAY_5 x PAY_AMT1",
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+            "_model_module": "@jupyter-widgets/output",
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+            "_view_module": "@jupyter-widgets/output",
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+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_876a04a6ea1746a5953ed4a1d922d76b",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "<Figure size 800x600 with 2 Axes>",
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AAMAangEBAAAAYA13QAAAAABYQ4AAAAAAsMbp6wFqq6KiIn355ZeqV6+eHA6Hr8cBAAAArosxRufOnVOzZs3k51f2+xkEiI98+eWXioyM9PUYAAAAQLkcP35cLVq0KPPrCBAfqVevnqTvLlxoaKiPpwEAAACuT15eniIjIz3fz5YVAeIjxR+7Cg0NJUAAAABQ7ZT3MQIeQgcAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANY4fT1AbddxZrb8XHV9PQYAAACqiaPz+/t6hArhDggAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsqbYBMnr0aDkcDjkcDgUEBCg6Olpz5sxRQUGBZ5+EhAT5+/trz549kiS3263Y2FhNmDChxPtNmzZNrVu31rlz56563NzcXA0bNkzt2rWTn5+fHnrooUo9LwAAAKAmq7YBIkn9+vVTbm6uDh48qIcfflizZs3Sk08+KUk6duyYduzYocmTJys9PV2S5HK5lJWVpczMTGVnZ3veZ9euXVq4cKEyMzNVr169qx7T7Xbrpptu0owZM3TLLbdU3ckBAAAANVC1DhCXy6WIiAhFRUVp4sSJ6tOnj9auXStJysjI0IABAzRx4kQtX75cFy9elCR17dpV06dP17hx43TmzBldunRJY8aM0ZQpUxQXF3fNY7Zq1Up//OMfNXLkSIWFhVXp+QEAAAA1TbUOkB8KCgrS5cuXZYxRRkaGUlJSFBMTo+joaK1atcqz3/Tp0xUREaGpU6dqxowZcjgcmjt3rg8nBwAAAGoHp68HqAzGGG3evFnZ2dmaMmWKNm3apAsXLighIUGSlJKSorS0NI0YMUKS5HQ6lZWVpa5du6qoqEjbt29XYGBglc7odrvldrs9f87Ly6vS4wEAAAA3omp9B2TdunUKCQlRYGCg7rrrLg0dOlSzZs1Senq6hg4dKqfzu75KTk7W9u3bdfjwYc9rO3TooMTERPXt21fdunWr8lnnzZunsLAwzxIZGVnlxwQAAABuNNU6QHr37q2cnBwdPHhQFy9e1NKlS+V2u7VmzRo988wzcjqdcjqdat68uQoKCjwPoxcr3m5Damqqzp4961mOHz9u5bgAAADAjaRafwQrODhY0dHRXuuWLVumFi1a6JVXXvFav2HDBi1YsEBz5syRv7+/xSm/43K55HK5rB8XAAAAuJFU6wApTVpamgYPHqyOHTt6rY+MjFRqaqreeOMN9e/fv0LHyMnJkSSdP39eJ0+eVE5OjgICAtShQ4cKvS8AAABQ09WoANm7d6/279+vJUuWlNgWFham+Ph4paWlVThAunTp4nXMv/71r4qKitLRo0cr9L4AAABATecwxhhfD1Eb5eXlffcw+kMvys9V19fjAAAAoJo4Or9if5leUcXfx549e1ahoaFlfn21fggdAAAAQPVCgPxAbGysQkJCSl2WLVvm6/EAAACAaq1GPQNSGdavX6/8/PxSt4WHh1ueBgAAAKhZCJAfiIqK8vUIAAAAQI3FR7AAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANY4fT1AbffR7ASFhob6egwAAADACu6AAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACscfp6gNqu48xs+bnq+nqMGu3o/P6+HgEAAAD/hzsgAAAAAKwhQAAAAABYQ4AAAAAAsIYAAQAAAGANAQIAAADAGgIEAAAAgDUECAAAAABrCBAAAAAA1hAgAAAAAKwhQAAAAABYQ4AAAAAAsIYAAQAAAGANAQIAAADAGgIEAAAAgDUECAAAAABrCBAAAAAA1hAgAAAAAKwhQAAAAABYQ4AAAAAAsIYAAQAAAGANAQIAAADAGgIEAAAAgDUECAAAAABrCBAAAAAA1hAgAAAAAKwhQAAAAABYQ4AAAAAAsKbaBsjo0aPlcDjkcDgUEBCg6OhozZkzRwUFBZ59EhIS5O/vrz179kiS3G63YmNjNWHChBLvN23aNLVu3Vrnzp276nHfeecd3XbbbWrUqJGCgoIUExOjhQsXVu7JAQAAADWU09cDVES/fv2UkZEht9ut9evXa9KkSapTp45SU1N17Ngx7dixQ5MnT1Z6erp++tOfyuVyKSsrSz169FBiYqISEhIkSbt27dLChQu1adMm1atX76rHDA4O1uTJk3XzzTcrODhY77zzjh588EEFBweXGjYAAAAA/s1hjDG+HqI8Ro8erTNnzuiVV17xrLvzzjt17tw57dy5U7Nnz9Y//vEPzZw5Uz/72c+Um5uroKAgSdLs2bO1ZMkSffTRRwoMDFSXLl1011136X/+53/KNcv999+v4OBgPf/889f9mry8PIWFhSnyoRfl56pbruPi+hyd39/XIwAAANQYxd/Hnj17VqGhoWV+fbX9CFZpgoKCdPnyZRljlJGRoZSUFMXExCg6OlqrVq3y7Dd9+nRFRERo6tSpmjFjhhwOh+bOnVuuY+7bt087duxQXFxcZZ0GAAAAUGNV649gFTPGaPPmzcrOztaUKVO0adMmXbhwwfMRq5SUFKWlpWnEiBGSJKfTqaysLHXt2lVFRUXavn27AgMDy3TMFi1a6OTJkyooKNCsWbM0fvz4q+7vdrvldrs9f87LyyvjWQIAAADVX7UOkHXr1ikkJET5+fkqKirSsGHDPDEwdOhQOZ3fnV5ycrL+8z//U4cPH1bbtm0lSR06dFBiYqLOnDmjbt26lfnY27Zt0/nz57Vr1y499thjio6OVnJy8hX3nzdvnmbPnl2+EwUAAABqiGr9EazevXsrJydHBw8e1MWLF7V06VK53W6tWbNGzzzzjJxOp5xOp5o3b66CggKlp6d7vb54e3m0bt1anTp10i9/+Uv95je/0axZs666f2pqqs6ePetZjh8/Xq7jAgAAANVZtb4DEhwcrOjoaK91y5YtU4sWLbweTpekDRs2aMGCBZozZ478/f0rdY6ioiKvj1eVxuVyyeVyVepxAQAAgOqmWgdIadLS0jR48GB17NjRa31kZKRSU1P1xhtvqH//8v9UpMWLF6tly5aKiYmRJL399tt66qmnNHXq1ArNDQAAANQGNSpA9u7dq/3792vJkiUltoWFhSk+Pl5paWkVCpCioiKlpqbqyJEjcjqdatu2rZ544gk9+OCDFRkdAAAAqBWq7e8Bqe74PSD28HtAAAAAKg+/BwQAAABAtUGA/EBsbKxCQkJKXZYtW+br8QAAAIBqrUY9A1IZ1q9fr/z8/FK3hYeHW54GAAAAqFkIkB+Iiory9QgAAABAjcVHsAAAAABYQ4AAAAAAsIYAAQAAAGANAQIAAADAGgIEAAAAgDUECAAAAABrCBAAAAAA1hAgAAAAAKwhQAAAAABYQ4AAAAAAsIYAAQAAAGANAQIAAADAGgIEAAAAgDUECAAAAABrCBAAAAAA1hAgAAAAAKwhQAAAAABYQ4AAAAAAsIYAAQAAAGANAQIAAADAGgIEAAAAgDUECAAAAABrnL4eoLb7aHaCQkNDfT0GAAAAYAV3QAAAAABYQ4AAAAAAsIYAAQAAAGANAQIAAADAGgIEAAAAgDUECAAAAABrCBAAAAAA1hAgAAAAAKwhQAAAAABYQ4AAAAAAsIYAAQAAAGANAQIAAADAGgIEAAAAgDUECAAAAABrCBAAAAAA1hAgAAAAAKxx+nqA2q7jzGz5uer6eowa7ej8/r4eAQAAAP+HOyAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWECAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWECAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWECAAAAAArCFAAAAAAFhDgAAAAACwpsIBMnr0aDkcDjkcDgUEBCg6Olpz5sxRQUGBZ5+EhAT5+/trz549kiS3263Y2FhNmDChxPtNmzZNrVu31rlz567r+BcvXlTDhg3VuHFjud3uEttbtWolh8OhFStWlNgWGxsrh8OhzMxMbdmyxXMeV1q2bNmi3NxcDRs2TO3atZOfn58eeuih6/xKAQAAAKiUOyD9+vVTbm6uDh48qIcfflizZs3Sk08+KUk6duyYduzYocmTJys9PV2S5HK5lJWVpczMTGVnZ3veZ9euXVq4cKEyMzNVr1696zr2yy+/rNjYWMXExOiVV14pdZ/IyEhlZGR4rdu1a5dOnDih4OBgSVLPnj2Vm5vrWYYMGeI5r+KlZ8+ecrvduummmzRjxgzdcsstZf1SAQAAALVapQSIy+VSRESEoqKiNHHiRPXp00dr166VJGVkZGjAgAGaOHGili9frosXL0qSunbtqunTp2vcuHE6c+aMLl26pDFjxmjKlCmKi4u77mOnpaUpJSVFKSkpSktLK3Wf4cOHa+vWrTp+/LhnXXp6uoYPHy6n0ylJCggIUEREhGcJCgrynFfxEhAQoFatWumPf/yjRo4cqbCwsPJ+yQAAAIBaqUqeAQkKCtLly5dljFFGRoZSUlIUExOj6OhorVq1yrPf9OnTFRERoalTp2rGjBlyOByaO3fudR/n8OHD2rlzp4YMGaIhQ4Zo27Zt+vzzz0vsFx4eroSEBC1dulSSdOHCBa1cuVJjx46t+MleJ7fbrby8PK8FAAAAqG0qNUCMMdq0aZOys7P1i1/8Qps2bdKFCxeUkJAgSSXuUjidTmVlZemll17SokWLlJWVpcDAwOs+Xnp6uu666y41aNBADRs2VEJCQomPWhUbO3asMjMzZYzRqlWr1LZtW3Xu3LlC51sW8+bNU1hYmGeJjIy0dmwAAADgRlEpAbJu3TqFhIQoMDBQd911l4YOHapZs2YpPT1dQ4cO9XzMKTk5Wdu3b9fhw4c9r+3QoYMSExPVt29fdevW7bqPWVhYqKVLlyolJcWzLiUlRZmZmSoqKiqxf//+/XX+/Hm9/fbbSk9Pt3r3Q5JSU1N19uxZz/L9j4MBAAAAtYWzMt6kd+/eevbZZxUQEKBmzZrJ6XTq9OnTWrNmjfLz8/Xss8969i0sLFR6err+8Ic//HsIp9MTKdcrOztbX3zxhYYOHeq1vrCwUJs3b1bfvn291judTo0YMUIzZ87U7t27tWbNmnKcafm5XC65XC6rxwQAAABuNJVyByQ4OFjR0dFq2bKlJySWLVumFi1aaP/+/crJyfEsCxYsUGZmpgoLCyt0zLS0NCUlJXm9d05OjpKSkq74MPrYsWO1detWDRw4UA0aNKjQ8QEAAACUXaXcASlNWlqaBg8erI4dO3qtj4yMVGpqqt544w3179+/XO998uRJvfbaa1q7dm2J9x85cqQGDRqk06dPq2HDhl7b2rdvr1OnTqlu3brlOm6xnJwcSdL58+d18uRJ5eTkKCAgQB06dKjQ+wIAAAA1XZX8FKy9e/dq//79SkxMLLEtLCxM8fHxV7xLcT2ysrIUHBys+Pj4Etvi4+MVFBSkF154odTXNmrUSEFBQeU+tiR16dJFXbp00d69e/XXv/5VXbp00d13312h9wQAAABqA4cxxvh6iNooLy/vu5+G9dCL8nNV7I4Mru7o/PLdaQMAAEBJxd/Hnj17VqGhoWV+fZXcAQEAAACA0tywARIbG6uQkJBSl2XLlvl6PAAAAADlUGUPoVfU+vXrlZ+fX+q28PBwy9MAAAAAqAw3bIBERUX5egQAAAAAleyG/QgWAAAAgJqHAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANY4fT1AbffR7ASFhob6egwAAADACu6AAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACscfp6gNqu48xs+bnq+nqMauvo/P6+HgEAAABlwB0QAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCm2gbI6NGj5XA45HA4FBAQoOjoaM2ZM0cFBQWefRISEuTv7689e/ZIktxut2JjYzVhwoQS7zdt2jS1bt1a586du+pxt2zZ4jnu95cTJ05U7gkCAAAANZDT1wNURL9+/ZSRkSG3263169dr0qRJqlOnjlJTU3Xs2DHt2LFDkydPVnp6un7605/K5XIpKytLPXr0UGJiohISEiRJu3bt0sKFC7Vp0ybVq1fvuo594MABhYaGev7cpEmTKjlHAAAAoCaptndAJMnlcikiIkJRUVGaOHGi+vTpo7Vr10qSMjIyNGDAAE2cOFHLly/XxYsXJUldu3bV9OnTNW7cOJ05c0aXLl3SmDFjNGXKFMXFxV33sZs0aaKIiAjP4udXrb+UAAAAgBU16rvmoKAgXb58WcYYZWRkKCUlRTExMYqOjtaqVas8+02fPl0RERGaOnWqZsyYIYfDoblz55bpWJ07d1bTpk3Vt29fbd++/Zr7u91u5eXleS0AAABAbVMjAsQYo02bNik7O1u/+MUvtGnTJl24cMHzEauUlBSlpaV59nc6ncrKytJLL72kRYsWKSsrS4GBgdd1rKZNm+rPf/6zXn75Zb388suKjIzUHXfcoffff/+qr5s3b57CwsI8S2RkZPlPGAAAAKimHMYY4+shymP06NF64YUXFBgYqPz8fBUVFWnYsGF65plnNH78eDVu3FiLFi2SJH311Vdq0aKF/vGPf6ht27ae90hJSdGZM2e0bt26Cs0SFxenli1b6vnnn7/iPm63W2632/PnvLw8RUZGKvKhF+Xnqluh49dmR+f39/UIAAAAtUpeXp7CwsJ09uxZr2eir1e1vgPSu3dv5eTk6ODBg7p48aKWLl0qt9utNWvW6JlnnpHT6ZTT6VTz5s1VUFCg9PR0r9cXb6+oW2+9VYcOHbrqPi6XS6GhoV4LAAAAUNtU65+CFRwcrOjoaK91y5YtU4sWLfTKK694rd+wYYMWLFigOXPmyN/fv1LnyMnJUdOmTSv1PQEAAICaqFoHSGnS0tI0ePBgdezY0Wt9ZGSkUlNT9cYbb6h///J/bOfpp59W69atFRsbq0uXLukvf/mL3nzzTW3YsKGiowMAAAA1XrX+CNYP7d27V/v371diYmKJbWFhYYqPj/d6GL08Ll++rIcfflidOnVSXFyc9u/fr02bNik+Pr5C7wsAAADUBtX2IfTqrvjhHR5CrxgeQgcAALCrVj+EDgAAAKB6IUB+IDY2ViEhIaUuy5Yt8/V4AAAAQLVW4x5Cr6j169crPz+/1G3h4eGWpwEAAABqFgLkB6Kionw9AgAAAFBj8REsAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBqnrweo7T6anaDQ0FBfjwEAAABYwR0QAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1Tl8PUNt1nJktP1ddX4/hE0fn9/f1CAAAALCMOyAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWECAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWECAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWECAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYE2FA2T06NG67777St3WqlUrPf30015/djgcWrFiRYl9Y2Nj5XA4lJmZWeL1W7ZskcPhuOqyZcuWq86ZmZnptX9ISIi6du2q1atXl7r/8uXL5e/vr0mTJpXYVjzPmTNnrnpMAAAAAN6s3wGJjIxURkaG17pdu3bpxIkTCg4OLvU1PXv2VG5urmcZMmSI+vXr57WuZ8+e1zx2aGioZ/99+/YpISFBQ4YM0YEDB0rsm5aWpmnTpmn58uW6dOlS+U4WAAAAgBfrATJ8+HBt3bpVx48f96xLT0/X8OHD5XQ6S31NQECAIiIiPEtQUJBcLpfXuoCAgGse2+FwePb/0Y9+pN///vfy8/PTBx984LXfkSNHtGPHDj322GNq167dFe+SAAAAACgb6wESHh6uhIQELV26VJJ04cIFrVy5UmPHjrU6R2FhoWeGn/zkJ17bMjIy1L9/f4WFhSklJUVpaWkVPp7b7VZeXp7XAgAAANQ2PnkIfezYscrMzJQxRqtWrVLbtm3VuXPnKj/u2bNnFRISopCQEAUEBGjixIl67rnn1LZtW88+RUVFyszMVEpKiiQpKSlJ77zzjo4cOVKhY8+bN09hYWGeJTIyskLvBwAAAFRHPgmQ/v376/z583r77beVnp5u7e5HvXr1lJOTo5ycHO3bt09z587Vr371K7322muefTZu3Khvv/1Wd999tySpcePG6tu3r9LT0yt07NTUVJ09e9azfP8jaAAAAEBtUfpDF1V9UKdTI0aM0MyZM7V7926tWbPGynH9/PwUHR3t+fPNN9+sDRs26IknntA999wj6buHz0+fPq2goCDPfkVFRfrggw80e/Zs+fmVr9lcLpdcLlfFTgAAAACo5nz2e0DGjh2rrVu3auDAgWrQoIGvxpC/v78uXrwoSfr666/16quvasWKFZ47JcV3S7755htt2LDBZ3MCAAAANUGl3AE5e/ascnJyvNY1atToqq9p3769Tp06pbp161bGCNfFGKMTJ05Iki5evKiNGzcqOztbjz/+uCTp+eefV6NGjTRkyBA5HA6v1959991KS0tTv379POs+/PBD1atXz/Nnh8OhW265xcKZAAAAANVTpQTIli1b1KVLF69148aNu+brrhUplS0vL09NmzaV9N1HoqKiojRnzhw9+uijkr77ccCDBg0qER+SlJiYqBEjRujUqVOedb169fLax9/fXwUFBVV4BgAAAED15jDGGF8PURvl5eV999OwHnpRfi57d4FuJEfn9/f1CAAAACij4u9jz549q9DQ0DK/3mfPgAAAAACofWpMgMTGxnp+x8cPl2XLlvl6PAAAAADy0Y/hrQrr169Xfn5+qdvCw8MtTwMAAACgNDUmQKKionw9AgAAAIBrqDEfwQIAAABw4yNAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWECAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWECAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANU5fD1DbfTQ7QaGhob4eAwAAALCCOyAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWECAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGucvh6gtus4M1t+rrq+HqPMjs7v7+sRAAAAUA1xBwQAAACANQQIAAAAAGsIEAAAAADWECAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWECAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWECAAAAAArCFAAAAAAFhDgAAAAACwhgABAAAAYA0BAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWVDhARo8eLYfDIYfDoYCAAEVHR2vOnDkqKCjw7JOQkCB/f3/t2bNHkuR2uxUbG6sJEyaUeL9p06apdevWOnfu3HUd/+LFi2rYsKEaN24st9tdYnurVq3kcDi0YsWKEttiY2PlcDiUmZmpLVu2eM7jSsuWLVv0zjvv6LbbblOjRo0UFBSkmJgYLVy48Hq/XAAAAECtVil3QPr166fc3FwdPHhQDz/8sGbNmqUnn3xSknTs2DHt2LFDkydPVnp6uiTJ5XIpKytLmZmZys7O9rzPrl27tHDhQmVmZqpevXrXdeyXX35ZsbGxiomJ0SuvvFLqPpGRkcrIyPBat2vXLp04cULBwcGSpJ49eyo3N9ezDBkyxHNexUvPnj0VHBysyZMn6+2339Ynn3yiGTNmaMaMGXruuefK+mUDAAAAap1KCRCXy6WIiAhFRUVp4sSJ6tOnj9auXStJysjI0IABAzRx4kQtX75cFy9elCR17dpV06dP17hx43TmzBldunRJY8aM0ZQpUxQXF3fdx05LS1NKSopSUlKUlpZW6j7Dhw/X1q1bdfz4cc+69PR0DR8+XE6nU5IUEBCgiIgIzxIUFOQ5r+IlICBAXbp0UXJysmJjY9WqVSulpKQoISFB27ZtK++XDwAAAKg1quQZkKCgIF2+fFnGGGVkZCglJUUxMTGKjo7WqlWrPPtNnz5dERERmjp1qmbMmCGHw6G5c+de93EOHz6snTt3asiQIRoyZIi2bdumzz//vMR+4eHhSkhI0NKlSyVJFy5c0MqVKzV27NgKn+u+ffu0Y8eOMkUTAAAAUFtVaoAYY7Rp0yZlZ2frF7/4hTZt2qQLFy4oISFBkkrcpXA6ncrKytJLL72kRYsWKSsrS4GBgdd9vPT0dN11111q0KCBGjZsqISEhBIftSo2duxYZWZmyhijVatWqW3bturcuXO5z7VFixZyuVzq1q2bJk2apPHjx191f7fbrby8PK8FAAAAqG0qJUDWrVunkJAQBQYG6q677tLQoUM1a9Yspaena+jQoZ6POSUnJ2v79u06fPiw57UdOnRQYmKi+vbtq27dul33MQsLC7V06VKlpKR41qWkpCgzM1NFRUUl9u/fv7/Onz+vt99+W+np6RW++7Ft2za99957+vOf/6ynn35ay5cvv+r+8+bNU1hYmGeJjIys0PEBAACA6shZGW/Su3dvPfvsswoICFCzZs3kdDp1+vRprVmzRvn5+Xr22Wc9+xYWFio9PV1/+MMf/j2E0+mJlOuVnZ2tL774QkOHDvVaX1hYqM2bN6tv375e651Op0aMGKGZM2dq9+7dWrNmTTnO9N9at24tSerUqZO++uorzZo1S8nJyVfcPzU1Vb/97W89f87LyyNCAAAAUOtUyh2Q4OBgRUdHq2XLlp6QWLZsmVq0aKH9+/crJyfHsyxYsECZmZkqLCys0DHT0tKUlJTk9d45OTlKSkq64sPoY8eO1datWzVw4EA1aNCgQsf/vqKiolJ/BPD3uVwuhYaGei0AAABAbVMpd0BKk5aWpsGDB6tjx45e6yMjI5Wamqo33nhD/fv3L9d7nzx5Uq+99prWrl1b4v1HjhypQYMG6fTp02rYsKHXtvbt2+vUqVOqW7duuY4rSYsXL1bLli0VExMjSXr77bf11FNPaerUqeV+TwAAAKC2qJKfgrV3717t379fiYmJJbaFhYUpPj7+incprkdWVpaCg4MVHx9fYlt8fLyCgoL0wgsvlPra4l8gWF5FRUVKTU1V586d1a1bNy1evFhPPPGE5syZU+73BAAAAGoLhzHG+HqI2igvL++7h9EfelF+rvLfkfGVo/PLd/cKAAAA1Vvx97Fnz54t12MFVXIHBAAAAABKc8MGSGxsrEJCQkpdli1b5uvxAAAAAJRDlT2EXlHr169Xfn5+qdvCw8MtTwMAAACgMtywARIVFeXrEQAAAABUshv2I1gAAAAAah4CBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALDG6esBaruPZicoNDTU12MAAAAAVnAHBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWEOAAAAAALCGAAEAAABgDQECAAAAwBoCBAAAAIA1BAgAAAAAawgQAAAAANYQIAAAAACsIUAAAAAAWOP09QC1lTFGkpSXl+fjSQAAAIDrV/z9a/H3s2VFgPjI119/LUmKjIz08SQAAABA2Z07d05hYWFlfh0B4iMNGzaUJB07dqxcFw5VJy8vT5GRkTp+/LhCQ0N9PQ6+h2tz4+La3Li4Njcurs2NietybcYYnTt3Ts2aNSvX6wkQH/Hz++7xm7CwMP7hvkGFhoZybW5QXJsbF9fmxsW1uXFxbW5MXJerq8hfoPMQOgAAAABrCBAAAAAA1hAgPuJyuTRz5ky5XC5fj4If4NrcuLg2Ny6uzY2La3Pj4trcmLguVc9hyvvzswAAAACgjLgDAgAAAMAaAgQAAACANQQIAAAAAGsIEAAAAADWECBVaPHixWrVqpUCAwPVvXt3vfvuu1fd/6WXXlJMTIwCAwPVqVMnrV+/3tKktU9Zrs3HH3+sxMREtWrVSg6HQ08//bS9QWuhslybJUuW6Pbbb1eDBg3UoEED9enT55r/nqH8ynJtVq9erW7duql+/foKDg5W586d9fzzz1uctnYp639viq1YsUIOh0P33Xdf1Q5YS5XlumRmZsrhcHgtgYGBFqetXcr678yZM2c0adIkNW3aVC6XS+3ateP7tAogQKrIypUr9dvf/lYzZ87U+++/r1tuuUUJCQn617/+Ver+O3bsUHJyssaNG6d9+/bpvvvu03333aePPvrI8uQ1X1mvzYULF9SmTRvNnz9fERERlqetXcp6bbZs2aLk5GS99dZb2rlzpyIjI3XnnXfqiy++sDx5zVfWa9OwYUNNnz5dO3fu1AcffKAxY8ZozJgxys7Otjx5zVfWa1Ps6NGjeuSRR3T77bdbmrR2Kc91CQ0NVW5urmf5/PPPLU5ce5T12ly+fFl9+/bV0aNHtWrVKh04cEBLlixR8+bNLU9egxhUiVtvvdVMmjTJ8+fCwkLTrFkzM2/evFL3HzJkiOnfv7/Xuu7du5sHH3ywSuesjcp6bb4vKirKLFy4sAqnq90qcm2MMaagoMDUq1fPLF26tKpGrLUqem2MMaZLly5mxowZVTFerVaea1NQUGB69uxp/vKXv5hRo0aZgQMHWpi0dinrdcnIyDBhYWGWpqvdynptnn32WdOmTRtz+fJlWyPWeNwBqQKXL1/W3r171adPH886Pz8/9enTRzt37iz1NTt37vTaX5ISEhKuuD/KpzzXBnZUxrW5cOGC8vPz1bBhw6oas1aq6LUxxmjz5s06cOCAevXqVZWj1jrlvTZz5sxRkyZNNG7cOBtj1jrlvS7nz59XVFSUIiMjNXDgQH388cc2xq1VynNt1q5dqx49emjSpEkKDw9Xx44dNXfuXBUWFtoau8YhQKrAqVOnVFhYqPDwcK/14eHhOnHiRKmvOXHiRJn2R/mU59rAjsq4No8++qiaNWtWIuZRMeW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+            "width": "36%"
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+        },
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+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
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+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
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+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
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+            "max_height": null,
+            "max_width": null,
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+            "object_fit": null,
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+            "overflow_x": null,
+            "overflow_y": null,
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+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "2dd67ab70fad40e7b3b0309c64653c70": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "393ef65c477a4ded9dc534d7b51d27ab": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_12bf2869d59944e29f1f88f703cae35b",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "<Figure size 800x600 with 1 Axes>",
+                  "image/png": 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+                  "text/plain": "<Figure size 800x600 with 2 Axes>",
+                  "image/png": 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+          }
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+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_f421c2ecb15b4f1eaaedd5369469e7a6",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "<Figure size 800x600 with 2 Axes>",
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+              "PAY_3 x PAY_5",
+              "PAY_5 x PAY_AMT3",
+              "PAY_1 x PAY_AMT3",
+              "PAY_1 x PAY_5",
+              "PAY_3 x PAY_AMT3",
+              "PAY_5 x PAY_AMT1",
+              "PAY_3 x PAY_AMT1",
+              "PAY_1 x PAY_AMT1"
+            ],
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "DropdownView",
+            "description": "Interaction effect:",
+            "description_tooltip": null,
+            "disabled": false,
+            "index": 0,
+            "layout": "IPY_MODEL_78bee11c8ccf4bfca164c3c4c58784e1",
+            "style": "IPY_MODEL_650d66257e494711bedc3a0a8f5321b2"
+          }
+        },
+        "83ef54d2dbca43d5a1d14203d4b90d62": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_efdf307907d843c996a3a02c0dcb0055",
+            "placeholder": "​",
+            "style": "IPY_MODEL_3f6123a8692f444e9410e3de65def414",
+            "value": "<div style='margin-top: 10px'></div>"
+          }
+        },
+        "d16365cb5146483ab714e86c60fdf6d4": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_85754448e6e54590a9d96340d1fb9080",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "<Figure size 800x600 with 2 Axes>",
+                  "image/png": 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\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "2b2e5519c81448c59cf9fd430d4a1747": {
+          "model_module": "@jupyter-widgets/base",
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+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_c3e90ac63713441db00dad0ecfc88dc9",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "<Figure size 800x600 with 1 Axes>",
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+          }
+        },
+        "ed0b4c9c6a904110bc2e21a46e55392d": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "f2b7fd2b637a41068b319af9125dab05": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/VinLucero/OmniXAI/blob/main/PiML_explainable_models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Installs\n",
+        "If necessary:\n",
+        "- Run once\n",
+        "- Then restart Kernel\n",
+        "- Then run next cell and continue"
+      ],
+      "metadata": {
+        "id": "GPunOgeT_Mxm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "AGI7nxnUkgti",
+        "outputId": "2221339b-0be9-4797-9054-eee1e1f7f75f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting piml\n",
+            "  Downloading PiML-0.6.0.post2-cp310-none-manylinux_2_17_x86_64.whl (12.2 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.2/12.2 MB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: ipykernel in /usr/local/lib/python3.10/dist-packages (from piml) (5.5.6)\n",
+            "Requirement already satisfied: ipywidgets>=7.7.0 in /usr/local/lib/python3.10/dist-packages (from piml) (7.7.1)\n",
+            "Requirement already satisfied: joblib>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from piml) (1.4.2)\n",
+            "Requirement already satisfied: ipython>=7.12.0 in /usr/local/lib/python3.10/dist-packages (from piml) (7.34.0)\n",
+            "Collecting numpy<1.24.0,>=1.21.4 (from piml)\n",
+            "  Downloading numpy-1.23.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.1/17.1 MB\u001b[0m \u001b[31m29.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting scipy<=1.10.1,>=1.5.3 (from piml)\n",
+            "  Downloading scipy-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (34.4 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.4/34.4 MB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting pandas<2.0.0,>=1.3.5 (from piml)\n",
+            "  Downloading pandas-1.5.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.1 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.1/12.1 MB\u001b[0m \u001b[31m24.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: matplotlib<3.8.0,>=3.1.2 in /usr/local/lib/python3.10/dist-packages (from piml) (3.7.1)\n",
+            "Requirement already satisfied: seaborn>=0.11.2 in /usr/local/lib/python3.10/dist-packages (from piml) (0.13.1)\n",
+            "Requirement already satisfied: xlrd>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from piml) (2.0.1)\n",
+            "Requirement already satisfied: scikit-learn<1.4.0,>=0.24.2 in /usr/local/lib/python3.10/dist-packages (from piml) (1.2.2)\n",
+            "Requirement already satisfied: xgboost>=1.4.2 in /usr/local/lib/python3.10/dist-packages (from piml) (2.0.3)\n",
+            "Requirement already satisfied: statsmodels>=0.12.2 in /usr/local/lib/python3.10/dist-packages (from piml) (0.14.2)\n",
+            "Collecting lime>=0.2.0.1 (from piml)\n",
+            "  Downloading lime-0.2.0.1.tar.gz (275 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m275.7/275.7 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting shap>=0.39.0 (from piml)\n",
+            "  Downloading shap-0.46.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (540 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m540.1/540.1 kB\u001b[0m \u001b[31m17.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from piml) (2.3.0+cu121)\n",
+            "Collecting pygam==0.8.0 (from piml)\n",
+            "  Downloading pygam-0.8.0-py2.py3-none-any.whl (1.8 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: natsort>=8.2.0 in /usr/local/lib/python3.10/dist-packages (from piml) (8.4.0)\n",
+            "Requirement already satisfied: psutil>=5.9.0 in /usr/local/lib/python3.10/dist-packages (from piml) (5.9.5)\n",
+            "Collecting dill>=0.3.6 (from piml)\n",
+            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: packaging>=20.5 in /usr/local/lib/python3.10/dist-packages (from piml) (24.1)\n",
+            "Requirement already satisfied: networkx>=2.6.3 in /usr/local/lib/python3.10/dist-packages (from piml) (3.3)\n",
+            "Collecting numba<0.57.0 (from piml)\n",
+            "  Downloading numba-0.56.4-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (3.5 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.5/3.5 MB\u001b[0m \u001b[31m24.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: jupyter-client<=7.4.9 in /usr/local/lib/python3.10/dist-packages (from piml) (6.1.12)\n",
+            "Collecting optbinning>=0.17.3 (from piml)\n",
+            "  Downloading optbinning-0.19.0-py3-none-any.whl (213 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.5/213.5 kB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting momentchi2 (from piml)\n",
+            "  Downloading momentchi2-0.1.8-py3-none-any.whl (11 kB)\n",
+            "Requirement already satisfied: future in /usr/local/lib/python3.10/dist-packages (from pygam==0.8.0->piml) (0.18.3)\n",
+            "Requirement already satisfied: progressbar2 in /usr/local/lib/python3.10/dist-packages (from pygam==0.8.0->piml) (4.2.0)\n",
+            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython>=7.12.0->piml) (67.7.2)\n",
+            "Collecting jedi>=0.16 (from ipython>=7.12.0->piml)\n",
+            "  Downloading jedi-0.19.1-py2.py3-none-any.whl (1.6 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m48.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython>=7.12.0->piml) (4.4.2)\n",
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+            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipython>=7.12.0->piml) (5.7.1)\n",
+            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython>=7.12.0->piml) (3.0.47)\n",
+            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython>=7.12.0->piml) (2.16.1)\n",
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+            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.7.0->piml) (0.2.0)\n",
+            "Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.7.0->piml) (3.6.6)\n",
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+            "Requirement already satisfied: jupyter-core>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from jupyter-client<=7.4.9->piml) (5.7.2)\n",
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+            "Collecting llvmlite<0.40,>=0.39.0dev0 (from numba<0.57.0->piml)\n",
+            "  Downloading llvmlite-0.39.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (34.6 MB)\n",
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+            "\u001b[?25hCollecting ortools>=9.4 (from optbinning>=0.17.3->piml)\n",
+            "  Downloading ortools-9.10.4067-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (26.7 MB)\n",
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+            "\u001b[?25hCollecting ropwr>=1.0.0 (from optbinning>=0.17.3->piml)\n",
+            "  Downloading ropwr-1.0.0-py3-none-any.whl (17 kB)\n",
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+            "  Downloading slicer-0.0.8-py3-none-any.whl (15 kB)\n",
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+            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.11.0->piml)\n",
+            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
+            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.11.0->piml)\n",
+            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
+            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.11.0->piml)\n",
+            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
+            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.11.0->piml)\n",
+            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
+            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.11.0->piml)\n",
+            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
+            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.11.0->piml)\n",
+            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
+            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.11.0->piml)\n",
+            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
+            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.11.0->piml)\n",
+            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
+            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.11.0->piml)\n",
+            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
+            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.11.0->piml)\n",
+            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
+            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.11.0->piml)\n",
+            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
+            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->piml) (2.3.0)\n",
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+            "  Downloading nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
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+            "Collecting absl-py>=2.0.0 (from ortools>=9.4->optbinning>=0.17.3->piml)\n",
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+            "\u001b[?25hINFO: pip is looking at multiple versions of ortools to determine which version is compatible with other requirements. This could take a while.\n",
+            "Collecting ortools>=9.4 (from optbinning>=0.17.3->piml)\n",
+            "  Downloading ortools-9.9.3963-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (24.8 MB)\n",
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+            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.7.0->piml) (1.16.0)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.7.0->piml) (2.5)\n",
+            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.7.0->piml) (0.5.1)\n",
+            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.7.0->piml) (2.22)\n",
+            "Requirement already satisfied: anyio<4,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.7.0->piml) (3.7.1)\n",
+            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.10/dist-packages (from jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.7.0->piml) (1.8.0)\n",
+            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.7.0->piml) (3.7)\n",
+            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.7.0->piml) (1.3.1)\n",
+            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.7.0->piml) (1.2.1)\n",
+            "Building wheels for collected packages: lime\n",
+            "  Building wheel for lime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for lime: filename=lime-0.2.0.1-py3-none-any.whl size=283835 sha256=ad1d07a4f44f35377f6f6a5e3e764d1cad7ca51963fc1d6675239ef32da6ec88\n",
+            "  Stored in directory: /root/.cache/pip/wheels/fd/a2/af/9ac0a1a85a27f314a06b39e1f492bee1547d52549a4606ed89\n",
+            "Successfully built lime\n",
+            "Installing collected packages: slicer, protobuf, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, llvmlite, jedi, dill, scipy, pandas, ortools, nvidia-cusparse-cu12, nvidia-cudnn-cu12, numba, pygam, nvidia-cusolver-cu12, momentchi2, shap, lime, ropwr, optbinning, piml\n",
+            "  Attempting uninstall: protobuf\n",
+            "    Found existing installation: protobuf 3.20.3\n",
+            "    Uninstalling protobuf-3.20.3:\n",
+            "      Successfully uninstalled protobuf-3.20.3\n",
+            "  Attempting uninstall: numpy\n",
+            "    Found existing installation: numpy 1.25.2\n",
+            "    Uninstalling numpy-1.25.2:\n",
+            "      Successfully uninstalled numpy-1.25.2\n",
+            "  Attempting uninstall: llvmlite\n",
+            "    Found existing installation: llvmlite 0.41.1\n",
+            "    Uninstalling llvmlite-0.41.1:\n",
+            "      Successfully uninstalled llvmlite-0.41.1\n",
+            "  Attempting uninstall: scipy\n",
+            "    Found existing installation: scipy 1.11.4\n",
+            "    Uninstalling scipy-1.11.4:\n",
+            "      Successfully uninstalled scipy-1.11.4\n",
+            "  Attempting uninstall: pandas\n",
+            "    Found existing installation: pandas 2.0.3\n",
+            "    Uninstalling pandas-2.0.3:\n",
+            "      Successfully uninstalled pandas-2.0.3\n",
+            "  Attempting uninstall: numba\n",
+            "    Found existing installation: numba 0.58.1\n",
+            "    Uninstalling numba-0.58.1:\n",
+            "      Successfully uninstalled numba-0.58.1\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "chex 0.1.86 requires numpy>=1.24.1, but you have numpy 1.23.5 which is incompatible.\n",
+            "cudf-cu12 24.4.1 requires numba>=0.57, but you have numba 0.56.4 which is incompatible.\n",
+            "cudf-cu12 24.4.1 requires pandas<2.2.2dev0,>=2.0, but you have pandas 1.5.3 which is incompatible.\n",
+            "cudf-cu12 24.4.1 requires protobuf<5,>=3.20, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-ai-generativelanguage 0.6.4 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-api-core 2.16.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-cloud-aiplatform 1.57.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-cloud-bigquery-connection 1.12.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-cloud-bigquery-storage 2.25.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-cloud-bigtable 2.24.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-cloud-datastore 2.15.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-cloud-firestore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-cloud-functions 1.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-cloud-iam 2.15.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-cloud-language 2.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-cloud-resource-manager 1.12.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-cloud-translate 3.11.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
+            "google-colab 1.0.0 requires pandas==2.0.3, but you have pandas 1.5.3 which is incompatible.\n",
+            "pandas-stubs 2.0.3.230814 requires numpy>=1.25.0; python_version >= \"3.9\", but you have numpy 1.23.5 which is incompatible.\n",
+            "rmm-cu12 24.4.0 requires numba>=0.57, but you have numba 0.56.4 which is incompatible.\n",
+            "tensorflow 2.15.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.2 which is incompatible.\n",
+            "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 5.27.2 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0mSuccessfully installed dill-0.3.8 jedi-0.19.1 lime-0.2.0.1 llvmlite-0.39.1 momentchi2-0.1.8 numba-0.56.4 numpy-1.23.5 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 optbinning-0.19.0 ortools-9.7.2996 pandas-1.5.3 piml-0.6.0.post2 protobuf-5.27.2 pygam-0.8.0 ropwr-1.0.0 scipy-1.10.1 shap-0.46.0 slicer-0.0.8\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.colab-display-data+json": {
+              "pip_warning": {
+                "packages": [
+                  "numpy"
+                ]
+              },
+              "id": "3a797ce313fa45889fe77258245ee444"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "!pip install piml # so many dependencies ..."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Imports"
+      ],
+      "metadata": {
+        "id": "2yTCbVO9nQNY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from piml import Experiment # Experiment is main class of PiML\n",
+        "from matplotlib import pyplot as plt\n",
+        "%matplotlib inline\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# CVXPY exceptions are expected"
+      ],
+      "metadata": {
+        "id": "-x_XiHNTnQxt",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cec806f8-0540-4248-808e-69ded990b476"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(CVXPY) Jul 09 02:44:34 PM: Encountered unexpected exception importing solver GLOP:\n",
+            "RuntimeError('Unrecognized new version of ortools (9.7.2996). Expected < 9.5.0.Please open a feature request on cvxpy to enable support for this version.')\n",
+            "(CVXPY) Jul 09 02:44:34 PM: Encountered unexpected exception importing solver PDLP:\n",
+            "RuntimeError('Unrecognized new version of ortools (9.7.2996). Expected < 9.5.0.Please open a feature request on cvxpy to enable support for this version.')\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Initialize PiML"
+      ],
+      "metadata": {
+        "id": "3o1wUFue_UWJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "exp = Experiment()"
+      ],
+      "metadata": {
+        "id": "_QMjILWnk9WX"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Load Taiwan Credit data"
+      ],
+      "metadata": {
+        "id": "isQ6kPXE_YAu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "exp.data_loader(data='TaiwanCredit')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447,
+          "referenced_widgets": [
+            "1cea91a4238c4f55b9a521ab73bf0aab",
+            "98e56fb322934a53aa22bc80111b1610",
+            "5183f565a7dc49dda07a6ce2d35bf45a"
+          ]
+        },
+        "id": "AKigzGcglVqp",
+        "outputId": "1a6694a0-df85-4b7c-d0ad-85cbaa88d981"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1cea91a4238c4f55b9a521ab73bf0aab"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE   AGE  PAY_1  PAY_2  PAY_3  PAY_4  \\\n",
+              "0        20000.0  2.0        2.0       1.0  24.0    2.0    2.0   -1.0   -1.0   \n",
+              "1       120000.0  2.0        2.0       2.0  26.0   -1.0    2.0    0.0    0.0   \n",
+              "2        90000.0  2.0        2.0       2.0  34.0    0.0    0.0    0.0    0.0   \n",
+              "3        50000.0  2.0        2.0       1.0  37.0    0.0    0.0    0.0    0.0   \n",
+              "4        50000.0  1.0        2.0       1.0  57.0   -1.0    0.0   -1.0    0.0   \n",
+              "...          ...  ...        ...       ...   ...    ...    ...    ...    ...   \n",
+              "29995   220000.0  1.0        3.0       1.0  39.0    0.0    0.0    0.0    0.0   \n",
+              "29996   150000.0  1.0        3.0       2.0  43.0   -1.0   -1.0   -1.0   -1.0   \n",
+              "29997    30000.0  1.0        2.0       2.0  37.0    4.0    3.0    2.0   -1.0   \n",
+              "29998    80000.0  1.0        3.0       1.0  41.0    1.0   -1.0    0.0    0.0   \n",
+              "29999    50000.0  1.0        2.0       1.0  46.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "       PAY_5  PAY_6  BILL_AMT1  BILL_AMT2  BILL_AMT3  BILL_AMT4  BILL_AMT5  \\\n",
+              "0        0.0    0.0   3.592621   3.491782   2.838849   0.000000   0.000000   \n",
+              "1        0.0    2.0   3.428621   3.237041   3.428621   3.514946   3.538574   \n",
+              "2        0.0    0.0   4.465977   4.146996   4.132260   4.156307   4.174612   \n",
+              "3        0.0    0.0   4.672015   4.683353   4.692776   4.452016   4.461799   \n",
+              "4        0.0    0.0   3.935406   3.753660   4.554319   4.320997   4.282101   \n",
+              "...      ...    ...        ...        ...        ...        ...        ...   \n",
+              "29995    0.0    0.0   5.276345   5.285143   5.318827   4.944507   4.494683   \n",
+              "29996    0.0    0.0   3.226342   3.262214   3.544440   3.953276   3.715251   \n",
+              "29997    0.0    0.0   3.552181   3.525951   3.440752   4.319710   4.313509   \n",
+              "29998    0.0   -1.0  -3.216430   4.894205   4.882553   4.722428   4.073938   \n",
+              "29999    0.0    0.0   4.680607   4.689362   4.696924   4.562721   4.510933   \n",
+              "\n",
+              "       BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
+              "0       0.000000  0.000000  2.838849  0.000000  0.000000  0.000000  0.000000   \n",
+              "1       3.513484  0.000000  3.000434  3.000434  3.000434  0.000000  3.301247   \n",
+              "2       4.191731  3.181558  3.176381  3.000434  3.000434  3.000434  3.699057   \n",
+              "3       4.470528  3.301247  3.305351  3.079543  3.041787  3.029384  3.000434   \n",
+              "4       4.281760  3.301247  4.564453  4.000043  3.954291  2.838849  2.832509   \n",
+              "...          ...       ...       ...       ...       ...       ...       ...   \n",
+              "29995   4.203604  3.929470  4.301052  3.699317  3.484015  3.699057  3.000434   \n",
+              "29996   0.000000  3.264345  3.547405  3.954194  2.113943  0.000000  0.000000   \n",
+              "29997   4.286861  0.000000  0.000000  4.342442  3.623353  3.301247  3.491502   \n",
+              "29998   4.689708  4.933998  3.532754  3.071514  3.284882  4.723989  3.256477   \n",
+              "29999   4.185089  3.317854  3.255514  3.155640  3.000434  3.000434  3.000434   \n",
+              "\n",
+              "       FlagDefault  \n",
+              "0              1.0  \n",
+              "1              1.0  \n",
+              "2              0.0  \n",
+              "3              0.0  \n",
+              "4              0.0  \n",
+              "...            ...  \n",
+              "29995          0.0  \n",
+              "29996          0.0  \n",
+              "29997          1.0  \n",
+              "29998          1.0  \n",
+              "29999          1.0  \n",
+              "\n",
+              "[30000 rows x 24 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6ed15de5-a2a2-4b45-ac06-6559fbe9a59a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LIMIT_BAL</th>\n",
+              "      <th>SEX</th>\n",
+              "      <th>EDUCATION</th>\n",
+              "      <th>MARRIAGE</th>\n",
+              "      <th>AGE</th>\n",
+              "      <th>PAY_1</th>\n",
+              "      <th>PAY_2</th>\n",
+              "      <th>PAY_3</th>\n",
+              "      <th>PAY_4</th>\n",
+              "      <th>PAY_5</th>\n",
+              "      <th>PAY_6</th>\n",
+              "      <th>BILL_AMT1</th>\n",
+              "      <th>BILL_AMT2</th>\n",
+              "      <th>BILL_AMT3</th>\n",
+              "      <th>BILL_AMT4</th>\n",
+              "      <th>BILL_AMT5</th>\n",
+              "      <th>BILL_AMT6</th>\n",
+              "      <th>PAY_AMT1</th>\n",
+              "      <th>PAY_AMT2</th>\n",
+              "      <th>PAY_AMT3</th>\n",
+              "      <th>PAY_AMT4</th>\n",
+              "      <th>PAY_AMT5</th>\n",
+              "      <th>PAY_AMT6</th>\n",
+              "      <th>FlagDefault</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>20000.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.592621</td>\n",
+              "      <td>3.491782</td>\n",
+              "      <td>2.838849</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.838849</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>120000.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.428621</td>\n",
+              "      <td>3.237041</td>\n",
+              "      <td>3.428621</td>\n",
+              "      <td>3.514946</td>\n",
+              "      <td>3.538574</td>\n",
+              "      <td>3.513484</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>3.301247</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>90000.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>4.465977</td>\n",
+              "      <td>4.146996</td>\n",
+              "      <td>4.132260</td>\n",
+              "      <td>4.156307</td>\n",
+              "      <td>4.174612</td>\n",
+              "      <td>4.191731</td>\n",
+              "      <td>3.181558</td>\n",
+              "      <td>3.176381</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>3.699057</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>50000.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>4.672015</td>\n",
+              "      <td>4.683353</td>\n",
+              "      <td>4.692776</td>\n",
+              "      <td>4.452016</td>\n",
+              "      <td>4.461799</td>\n",
+              "      <td>4.470528</td>\n",
+              "      <td>3.301247</td>\n",
+              "      <td>3.305351</td>\n",
+              "      <td>3.079543</td>\n",
+              "      <td>3.041787</td>\n",
+              "      <td>3.029384</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>50000.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>57.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.935406</td>\n",
+              "      <td>3.753660</td>\n",
+              "      <td>4.554319</td>\n",
+              "      <td>4.320997</td>\n",
+              "      <td>4.282101</td>\n",
+              "      <td>4.281760</td>\n",
+              "      <td>3.301247</td>\n",
+              "      <td>4.564453</td>\n",
+              "      <td>4.000043</td>\n",
+              "      <td>3.954291</td>\n",
+              "      <td>2.838849</td>\n",
+              "      <td>2.832509</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29995</th>\n",
+              "      <td>220000.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>39.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>5.276345</td>\n",
+              "      <td>5.285143</td>\n",
+              "      <td>5.318827</td>\n",
+              "      <td>4.944507</td>\n",
+              "      <td>4.494683</td>\n",
+              "      <td>4.203604</td>\n",
+              "      <td>3.929470</td>\n",
+              "      <td>4.301052</td>\n",
+              "      <td>3.699317</td>\n",
+              "      <td>3.484015</td>\n",
+              "      <td>3.699057</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29996</th>\n",
+              "      <td>150000.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>43.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.226342</td>\n",
+              "      <td>3.262214</td>\n",
+              "      <td>3.544440</td>\n",
+              "      <td>3.953276</td>\n",
+              "      <td>3.715251</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>3.264345</td>\n",
+              "      <td>3.547405</td>\n",
+              "      <td>3.954194</td>\n",
+              "      <td>2.113943</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29997</th>\n",
+              "      <td>30000.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.552181</td>\n",
+              "      <td>3.525951</td>\n",
+              "      <td>3.440752</td>\n",
+              "      <td>4.319710</td>\n",
+              "      <td>4.313509</td>\n",
+              "      <td>4.286861</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>4.342442</td>\n",
+              "      <td>3.623353</td>\n",
+              "      <td>3.301247</td>\n",
+              "      <td>3.491502</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29998</th>\n",
+              "      <td>80000.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>41.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>-3.216430</td>\n",
+              "      <td>4.894205</td>\n",
+              "      <td>4.882553</td>\n",
+              "      <td>4.722428</td>\n",
+              "      <td>4.073938</td>\n",
+              "      <td>4.689708</td>\n",
+              "      <td>4.933998</td>\n",
+              "      <td>3.532754</td>\n",
+              "      <td>3.071514</td>\n",
+              "      <td>3.284882</td>\n",
+              "      <td>4.723989</td>\n",
+              "      <td>3.256477</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29999</th>\n",
+              "      <td>50000.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>46.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>4.680607</td>\n",
+              "      <td>4.689362</td>\n",
+              "      <td>4.696924</td>\n",
+              "      <td>4.562721</td>\n",
+              "      <td>4.510933</td>\n",
+              "      <td>4.185089</td>\n",
+              "      <td>3.317854</td>\n",
+              "      <td>3.255514</td>\n",
+              "      <td>3.155640</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>30000 rows × 24 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ed15de5-a2a2-4b45-ac06-6559fbe9a59a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6ed15de5-a2a2-4b45-ac06-6559fbe9a59a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6ed15de5-a2a2-4b45-ac06-6559fbe9a59a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-4fa9d07a-2a2b-4736-853e-17a18535ee71\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4fa9d07a-2a2b-4736-853e-17a18535ee71')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-4fa9d07a-2a2b-4736-853e-17a18535ee71 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Descriptive stats and basic data preparation\n",
+        "* Remove `AGE`, `SEX`, `EDUCATION` and `MARRIAGE`"
+      ],
+      "metadata": {
+        "id": "MYdx6d1V_hWj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "exp.data_summary(feature_exclude=['AGE', 'SEX', 'EDUCATION', 'MARRIAGE'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 497,
+          "referenced_widgets": [
+            "dd7dc08a9b6b4c5ba834a6c56e28ea08",
+            "5175e1312fc447d4bc412838a07ab38c",
+            "bd64033ba84e4c9c81d4b58e0d6a2570"
+          ]
+        },
+        "id": "vy54PpdFfmlR",
+        "outputId": "b2b43e15-dde6-47c5-8b0e-f38a83ae62a6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "dd7dc08a9b6b4c5ba834a6c56e28ea08"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<style type=\"text/css\">\n",
+              "#T_128c4_row1_col0, #T_128c4_row1_col1, #T_128c4_row1_col2, #T_128c4_row1_col3, #T_128c4_row1_col4, #T_128c4_row1_col5, #T_128c4_row1_col6, #T_128c4_row1_col7, #T_128c4_row1_col8 {\n",
+              "  background-color: grey;\n",
+              "}\n",
+              "</style>\n",
+              "<table id=\"T_128c4\" style='display:inline'>\n",
+              "  <caption>Numerical Attributes</caption>\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th class=\"blank level0\" >&nbsp;</th>\n",
+              "      <th id=\"T_128c4_level0_col0\" class=\"col_heading level0 col0\" >name</th>\n",
+              "      <th id=\"T_128c4_level0_col1\" class=\"col_heading level0 col1\" >n_missing</th>\n",
+              "      <th id=\"T_128c4_level0_col2\" class=\"col_heading level0 col2\" >mean</th>\n",
+              "      <th id=\"T_128c4_level0_col3\" class=\"col_heading level0 col3\" >std</th>\n",
+              "      <th id=\"T_128c4_level0_col4\" class=\"col_heading level0 col4\" >min</th>\n",
+              "      <th id=\"T_128c4_level0_col5\" class=\"col_heading level0 col5\" >q1</th>\n",
+              "      <th id=\"T_128c4_level0_col6\" class=\"col_heading level0 col6\" >median</th>\n",
+              "      <th id=\"T_128c4_level0_col7\" class=\"col_heading level0 col7\" >q3</th>\n",
+              "      <th id=\"T_128c4_level0_col8\" class=\"col_heading level0 col8\" >max</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+              "      <td id=\"T_128c4_row0_col0\" class=\"data row0 col0\" >LIMIT_BAL</td>\n",
+              "      <td id=\"T_128c4_row0_col1\" class=\"data row0 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row0_col2\" class=\"data row0 col2\" >167484.3227</td>\n",
+              "      <td id=\"T_128c4_row0_col3\" class=\"data row0 col3\" >129747.6616</td>\n",
+              "      <td id=\"T_128c4_row0_col4\" class=\"data row0 col4\" >10000.0000</td>\n",
+              "      <td id=\"T_128c4_row0_col5\" class=\"data row0 col5\" >50000.0000</td>\n",
+              "      <td id=\"T_128c4_row0_col6\" class=\"data row0 col6\" >140000.0000</td>\n",
+              "      <td id=\"T_128c4_row0_col7\" class=\"data row0 col7\" >240000.0000</td>\n",
+              "      <td id=\"T_128c4_row0_col8\" class=\"data row0 col8\" >1000000.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+              "      <td id=\"T_128c4_row1_col0\" class=\"data row1 col0\" >AGE</td>\n",
+              "      <td id=\"T_128c4_row1_col1\" class=\"data row1 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row1_col2\" class=\"data row1 col2\" >35.4855</td>\n",
+              "      <td id=\"T_128c4_row1_col3\" class=\"data row1 col3\" >9.2179</td>\n",
+              "      <td id=\"T_128c4_row1_col4\" class=\"data row1 col4\" >21.0000</td>\n",
+              "      <td id=\"T_128c4_row1_col5\" class=\"data row1 col5\" >28.0000</td>\n",
+              "      <td id=\"T_128c4_row1_col6\" class=\"data row1 col6\" >34.0000</td>\n",
+              "      <td id=\"T_128c4_row1_col7\" class=\"data row1 col7\" >41.0000</td>\n",
+              "      <td id=\"T_128c4_row1_col8\" class=\"data row1 col8\" >79.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+              "      <td id=\"T_128c4_row2_col0\" class=\"data row2 col0\" >PAY_1</td>\n",
+              "      <td id=\"T_128c4_row2_col1\" class=\"data row2 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row2_col2\" class=\"data row2 col2\" >0.1672</td>\n",
+              "      <td id=\"T_128c4_row2_col3\" class=\"data row2 col3\" >0.9313</td>\n",
+              "      <td id=\"T_128c4_row2_col4\" class=\"data row2 col4\" >-1.0000</td>\n",
+              "      <td id=\"T_128c4_row2_col5\" class=\"data row2 col5\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row2_col6\" class=\"data row2 col6\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row2_col7\" class=\"data row2 col7\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row2_col8\" class=\"data row2 col8\" >8.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+              "      <td id=\"T_128c4_row3_col0\" class=\"data row3 col0\" >PAY_2</td>\n",
+              "      <td id=\"T_128c4_row3_col1\" class=\"data row3 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row3_col2\" class=\"data row3 col2\" >0.1184</td>\n",
+              "      <td id=\"T_128c4_row3_col3\" class=\"data row3 col3\" >0.9658</td>\n",
+              "      <td id=\"T_128c4_row3_col4\" class=\"data row3 col4\" >-1.0000</td>\n",
+              "      <td id=\"T_128c4_row3_col5\" class=\"data row3 col5\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row3_col6\" class=\"data row3 col6\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row3_col7\" class=\"data row3 col7\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row3_col8\" class=\"data row3 col8\" >8.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+              "      <td id=\"T_128c4_row4_col0\" class=\"data row4 col0\" >PAY_3</td>\n",
+              "      <td id=\"T_128c4_row4_col1\" class=\"data row4 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row4_col2\" class=\"data row4 col2\" >0.1061</td>\n",
+              "      <td id=\"T_128c4_row4_col3\" class=\"data row4 col3\" >0.9509</td>\n",
+              "      <td id=\"T_128c4_row4_col4\" class=\"data row4 col4\" >-1.0000</td>\n",
+              "      <td id=\"T_128c4_row4_col5\" class=\"data row4 col5\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row4_col6\" class=\"data row4 col6\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row4_col7\" class=\"data row4 col7\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row4_col8\" class=\"data row4 col8\" >8.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+              "      <td id=\"T_128c4_row5_col0\" class=\"data row5 col0\" >PAY_4</td>\n",
+              "      <td id=\"T_128c4_row5_col1\" class=\"data row5 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row5_col2\" class=\"data row5 col2\" >0.0692</td>\n",
+              "      <td id=\"T_128c4_row5_col3\" class=\"data row5 col3\" >0.9116</td>\n",
+              "      <td id=\"T_128c4_row5_col4\" class=\"data row5 col4\" >-1.0000</td>\n",
+              "      <td id=\"T_128c4_row5_col5\" class=\"data row5 col5\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row5_col6\" class=\"data row5 col6\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row5_col7\" class=\"data row5 col7\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row5_col8\" class=\"data row5 col8\" >8.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+              "      <td id=\"T_128c4_row6_col0\" class=\"data row6 col0\" >PAY_5</td>\n",
+              "      <td id=\"T_128c4_row6_col1\" class=\"data row6 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row6_col2\" class=\"data row6 col2\" >0.0369</td>\n",
+              "      <td id=\"T_128c4_row6_col3\" class=\"data row6 col3\" >0.8646</td>\n",
+              "      <td id=\"T_128c4_row6_col4\" class=\"data row6 col4\" >-1.0000</td>\n",
+              "      <td id=\"T_128c4_row6_col5\" class=\"data row6 col5\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row6_col6\" class=\"data row6 col6\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row6_col7\" class=\"data row6 col7\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row6_col8\" class=\"data row6 col8\" >8.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+              "      <td id=\"T_128c4_row7_col0\" class=\"data row7 col0\" >PAY_6</td>\n",
+              "      <td id=\"T_128c4_row7_col1\" class=\"data row7 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row7_col2\" class=\"data row7 col2\" >0.0352</td>\n",
+              "      <td id=\"T_128c4_row7_col3\" class=\"data row7 col3\" >0.8679</td>\n",
+              "      <td id=\"T_128c4_row7_col4\" class=\"data row7 col4\" >-1.0000</td>\n",
+              "      <td id=\"T_128c4_row7_col5\" class=\"data row7 col5\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row7_col6\" class=\"data row7 col6\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row7_col7\" class=\"data row7 col7\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row7_col8\" class=\"data row7 col8\" >8.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+              "      <td id=\"T_128c4_row8_col0\" class=\"data row8 col0\" >BILL_AMT1</td>\n",
+              "      <td id=\"T_128c4_row8_col1\" class=\"data row8 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row8_col2\" class=\"data row8 col2\" >3.8860</td>\n",
+              "      <td id=\"T_128c4_row8_col3\" class=\"data row8 col3\" >1.5470</td>\n",
+              "      <td id=\"T_128c4_row8_col4\" class=\"data row8 col4\" >-5.2190</td>\n",
+              "      <td id=\"T_128c4_row8_col5\" class=\"data row8 col5\" >3.5514</td>\n",
+              "      <td id=\"T_128c4_row8_col6\" class=\"data row8 col6\" >4.3499</td>\n",
+              "      <td id=\"T_128c4_row8_col7\" class=\"data row8 col7\" >4.8267</td>\n",
+              "      <td id=\"T_128c4_row8_col8\" class=\"data row8 col8\" >5.9843</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+              "      <td id=\"T_128c4_row9_col0\" class=\"data row9 col0\" >BILL_AMT2</td>\n",
+              "      <td id=\"T_128c4_row9_col1\" class=\"data row9 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row9_col2\" class=\"data row9 col2\" >3.7954</td>\n",
+              "      <td id=\"T_128c4_row9_col3\" class=\"data row9 col3\" >1.6545</td>\n",
+              "      <td id=\"T_128c4_row9_col4\" class=\"data row9 col4\" >-4.8437</td>\n",
+              "      <td id=\"T_128c4_row9_col5\" class=\"data row9 col5\" >3.4751</td>\n",
+              "      <td id=\"T_128c4_row9_col6\" class=\"data row9 col6\" >4.3264</td>\n",
+              "      <td id=\"T_128c4_row9_col7\" class=\"data row9 col7\" >4.8062</td>\n",
+              "      <td id=\"T_128c4_row9_col8\" class=\"data row9 col8\" >5.9930</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+              "      <td id=\"T_128c4_row10_col0\" class=\"data row10 col0\" >BILL_AMT3</td>\n",
+              "      <td id=\"T_128c4_row10_col1\" class=\"data row10 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row10_col2\" class=\"data row10 col2\" >3.7394</td>\n",
+              "      <td id=\"T_128c4_row10_col3\" class=\"data row10 col3\" >1.6968</td>\n",
+              "      <td id=\"T_128c4_row10_col4\" class=\"data row10 col4\" >-5.1966</td>\n",
+              "      <td id=\"T_128c4_row10_col5\" class=\"data row10 col5\" >3.4261</td>\n",
+              "      <td id=\"T_128c4_row10_col6\" class=\"data row10 col6\" >4.3030</td>\n",
+              "      <td id=\"T_128c4_row10_col7\" class=\"data row10 col7\" >4.7793</td>\n",
+              "      <td id=\"T_128c4_row10_col8\" class=\"data row10 col8\" >6.2212</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+              "      <td id=\"T_128c4_row11_col0\" class=\"data row11 col0\" >BILL_AMT4</td>\n",
+              "      <td id=\"T_128c4_row11_col1\" class=\"data row11 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row11_col2\" class=\"data row11 col2\" >3.6701</td>\n",
+              "      <td id=\"T_128c4_row11_col3\" class=\"data row11 col3\" >1.7339</td>\n",
+              "      <td id=\"T_128c4_row11_col4\" class=\"data row11 col4\" >-5.2305</td>\n",
+              "      <td id=\"T_128c4_row11_col5\" class=\"data row11 col5\" >3.3669</td>\n",
+              "      <td id=\"T_128c4_row11_col6\" class=\"data row11 col6\" >4.2800</td>\n",
+              "      <td id=\"T_128c4_row11_col7\" class=\"data row11 col7\" >4.7365</td>\n",
+              "      <td id=\"T_128c4_row11_col8\" class=\"data row11 col8\" >5.9502</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+              "      <td id=\"T_128c4_row12_col0\" class=\"data row12 col0\" >BILL_AMT5</td>\n",
+              "      <td id=\"T_128c4_row12_col1\" class=\"data row12 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row12_col2\" class=\"data row12 col2\" >3.5993</td>\n",
+              "      <td id=\"T_128c4_row12_col3\" class=\"data row12 col3\" >1.7625</td>\n",
+              "      <td id=\"T_128c4_row12_col4\" class=\"data row12 col4\" >-4.9103</td>\n",
+              "      <td id=\"T_128c4_row12_col5\" class=\"data row12 col5\" >3.2465</td>\n",
+              "      <td id=\"T_128c4_row12_col6\" class=\"data row12 col6\" >4.2578</td>\n",
+              "      <td id=\"T_128c4_row12_col7\" class=\"data row12 col7\" >4.7006</td>\n",
+              "      <td id=\"T_128c4_row12_col8\" class=\"data row12 col8\" >5.9672</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+              "      <td id=\"T_128c4_row13_col0\" class=\"data row13 col0\" >BILL_AMT6</td>\n",
+              "      <td id=\"T_128c4_row13_col1\" class=\"data row13 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row13_col2\" class=\"data row13 col2\" >3.5075</td>\n",
+              "      <td id=\"T_128c4_row13_col3\" class=\"data row13 col3\" >1.8331</td>\n",
+              "      <td id=\"T_128c4_row13_col4\" class=\"data row13 col4\" >-5.5310</td>\n",
+              "      <td id=\"T_128c4_row13_col5\" class=\"data row13 col5\" >3.0993</td>\n",
+              "      <td id=\"T_128c4_row13_col6\" class=\"data row13 col6\" >4.2323</td>\n",
+              "      <td id=\"T_128c4_row13_col7\" class=\"data row13 col7\" >4.6920</td>\n",
+              "      <td id=\"T_128c4_row13_col8\" class=\"data row13 col8\" >5.9830</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+              "      <td id=\"T_128c4_row14_col0\" class=\"data row14 col0\" >PAY_AMT1</td>\n",
+              "      <td id=\"T_128c4_row14_col1\" class=\"data row14 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row14_col2\" class=\"data row14 col2\" >2.8794</td>\n",
+              "      <td id=\"T_128c4_row14_col3\" class=\"data row14 col3\" >1.4116</td>\n",
+              "      <td id=\"T_128c4_row14_col4\" class=\"data row14 col4\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row14_col5\" class=\"data row14 col5\" >3.0004</td>\n",
+              "      <td id=\"T_128c4_row14_col6\" class=\"data row14 col6\" >3.3224</td>\n",
+              "      <td id=\"T_128c4_row14_col7\" class=\"data row14 col7\" >3.6996</td>\n",
+              "      <td id=\"T_128c4_row14_col8\" class=\"data row14 col8\" >5.9413</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+              "      <td id=\"T_128c4_row15_col0\" class=\"data row15 col0\" >PAY_AMT2</td>\n",
+              "      <td id=\"T_128c4_row15_col1\" class=\"data row15 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row15_col2\" class=\"data row15 col2\" >2.8501</td>\n",
+              "      <td id=\"T_128c4_row15_col3\" class=\"data row15 col3\" >1.4239</td>\n",
+              "      <td id=\"T_128c4_row15_col4\" class=\"data row15 col4\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row15_col5\" class=\"data row15 col5\" >2.9212</td>\n",
+              "      <td id=\"T_128c4_row15_col6\" class=\"data row15 col6\" >3.3032</td>\n",
+              "      <td id=\"T_128c4_row15_col7\" class=\"data row15 col7\" >3.6991</td>\n",
+              "      <td id=\"T_128c4_row15_col8\" class=\"data row15 col8\" >6.2264</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
+              "      <td id=\"T_128c4_row16_col0\" class=\"data row16 col0\" >PAY_AMT3</td>\n",
+              "      <td id=\"T_128c4_row16_col1\" class=\"data row16 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row16_col2\" class=\"data row16 col2\" >2.7288</td>\n",
+              "      <td id=\"T_128c4_row16_col3\" class=\"data row16 col3\" >1.4548</td>\n",
+              "      <td id=\"T_128c4_row16_col4\" class=\"data row16 col4\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row16_col5\" class=\"data row16 col5\" >2.5922</td>\n",
+              "      <td id=\"T_128c4_row16_col6\" class=\"data row16 col6\" >3.2555</td>\n",
+              "      <td id=\"T_128c4_row16_col7\" class=\"data row16 col7\" >3.6538</td>\n",
+              "      <td id=\"T_128c4_row16_col8\" class=\"data row16 col8\" >5.9523</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
+              "      <td id=\"T_128c4_row17_col0\" class=\"data row17 col0\" >PAY_AMT4</td>\n",
+              "      <td id=\"T_128c4_row17_col1\" class=\"data row17 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row17_col2\" class=\"data row17 col2\" >2.6395</td>\n",
+              "      <td id=\"T_128c4_row17_col3\" class=\"data row17 col3\" >1.4751</td>\n",
+              "      <td id=\"T_128c4_row17_col4\" class=\"data row17 col4\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row17_col5\" class=\"data row17 col5\" >2.4728</td>\n",
+              "      <td id=\"T_128c4_row17_col6\" class=\"data row17 col6\" >3.1764</td>\n",
+              "      <td id=\"T_128c4_row17_col7\" class=\"data row17 col7\" >3.6036</td>\n",
+              "      <td id=\"T_128c4_row17_col8\" class=\"data row17 col8\" >5.7931</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
+              "      <td id=\"T_128c4_row18_col0\" class=\"data row18 col0\" >PAY_AMT5</td>\n",
+              "      <td id=\"T_128c4_row18_col1\" class=\"data row18 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row18_col2\" class=\"data row18 col2\" >2.6197</td>\n",
+              "      <td id=\"T_128c4_row18_col3\" class=\"data row18 col3\" >1.4957</td>\n",
+              "      <td id=\"T_128c4_row18_col4\" class=\"data row18 col4\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row18_col5\" class=\"data row18 col5\" >2.4040</td>\n",
+              "      <td id=\"T_128c4_row18_col6\" class=\"data row18 col6\" >3.1764</td>\n",
+              "      <td id=\"T_128c4_row18_col7\" class=\"data row18 col7\" >3.6056</td>\n",
+              "      <td id=\"T_128c4_row18_col8\" class=\"data row18 col8\" >5.6299</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_128c4_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
+              "      <td id=\"T_128c4_row19_col0\" class=\"data row19 col0\" >PAY_AMT6</td>\n",
+              "      <td id=\"T_128c4_row19_col1\" class=\"data row19 col1\" >0</td>\n",
+              "      <td id=\"T_128c4_row19_col2\" class=\"data row19 col2\" >2.5766</td>\n",
+              "      <td id=\"T_128c4_row19_col3\" class=\"data row19 col3\" >1.5322</td>\n",
+              "      <td id=\"T_128c4_row19_col4\" class=\"data row19 col4\" >0.0000</td>\n",
+              "      <td id=\"T_128c4_row19_col5\" class=\"data row19 col5\" >2.0746</td>\n",
+              "      <td id=\"T_128c4_row19_col6\" class=\"data row19 col6\" >3.1764</td>\n",
+              "      <td id=\"T_128c4_row19_col7\" class=\"data row19 col7\" >3.6022</td>\n",
+              "      <td id=\"T_128c4_row19_col8\" class=\"data row19 col8\" >5.7232</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<style type=\"text/css\">\n",
+              "#T_8bff4_row0_col0, #T_8bff4_row0_col1, #T_8bff4_row0_col2, #T_8bff4_row0_col3, #T_8bff4_row0_col4, #T_8bff4_row0_col5, #T_8bff4_row0_col6, #T_8bff4_row1_col0, #T_8bff4_row1_col1, #T_8bff4_row1_col2, #T_8bff4_row1_col3, #T_8bff4_row1_col4, #T_8bff4_row1_col5, #T_8bff4_row1_col6, #T_8bff4_row2_col0, #T_8bff4_row2_col1, #T_8bff4_row2_col2, #T_8bff4_row2_col3, #T_8bff4_row2_col4, #T_8bff4_row2_col5, #T_8bff4_row2_col6 {\n",
+              "  background-color: grey;\n",
+              "}\n",
+              "</style>\n",
+              "<table id=\"T_8bff4\" style='display:inline'>\n",
+              "  <caption>Categorical Attributes</caption>\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th class=\"blank level0\" >&nbsp;</th>\n",
+              "      <th id=\"T_8bff4_level0_col0\" class=\"col_heading level0 col0\" >name</th>\n",
+              "      <th id=\"T_8bff4_level0_col1\" class=\"col_heading level0 col1\" >n_missing</th>\n",
+              "      <th id=\"T_8bff4_level0_col2\" class=\"col_heading level0 col2\" >n_unique</th>\n",
+              "      <th id=\"T_8bff4_level0_col3\" class=\"col_heading level0 col3\" >top1</th>\n",
+              "      <th id=\"T_8bff4_level0_col4\" class=\"col_heading level0 col4\" >top2</th>\n",
+              "      <th id=\"T_8bff4_level0_col5\" class=\"col_heading level0 col5\" >top3</th>\n",
+              "      <th id=\"T_8bff4_level0_col6\" class=\"col_heading level0 col6\" >n_others</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th id=\"T_8bff4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+              "      <td id=\"T_8bff4_row0_col0\" class=\"data row0 col0\" >SEX</td>\n",
+              "      <td id=\"T_8bff4_row0_col1\" class=\"data row0 col1\" >0</td>\n",
+              "      <td id=\"T_8bff4_row0_col2\" class=\"data row0 col2\" >2</td>\n",
+              "      <td id=\"T_8bff4_row0_col3\" class=\"data row0 col3\" >2.0 : 18112</td>\n",
+              "      <td id=\"T_8bff4_row0_col4\" class=\"data row0 col4\" >1.0 : 11888</td>\n",
+              "      <td id=\"T_8bff4_row0_col5\" class=\"data row0 col5\" >0</td>\n",
+              "      <td id=\"T_8bff4_row0_col6\" class=\"data row0 col6\" >0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_8bff4_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+              "      <td id=\"T_8bff4_row1_col0\" class=\"data row1 col0\" >EDUCATION</td>\n",
+              "      <td id=\"T_8bff4_row1_col1\" class=\"data row1 col1\" >0</td>\n",
+              "      <td id=\"T_8bff4_row1_col2\" class=\"data row1 col2\" >4</td>\n",
+              "      <td id=\"T_8bff4_row1_col3\" class=\"data row1 col3\" >2.0 : 14030</td>\n",
+              "      <td id=\"T_8bff4_row1_col4\" class=\"data row1 col4\" >1.0 : 10585</td>\n",
+              "      <td id=\"T_8bff4_row1_col5\" class=\"data row1 col5\" >3.0 : 4917</td>\n",
+              "      <td id=\"T_8bff4_row1_col6\" class=\"data row1 col6\" >468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_8bff4_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+              "      <td id=\"T_8bff4_row2_col0\" class=\"data row2 col0\" >MARRIAGE</td>\n",
+              "      <td id=\"T_8bff4_row2_col1\" class=\"data row2 col1\" >0</td>\n",
+              "      <td id=\"T_8bff4_row2_col2\" class=\"data row2 col2\" >3</td>\n",
+              "      <td id=\"T_8bff4_row2_col3\" class=\"data row2 col3\" >2.0 : 15964</td>\n",
+              "      <td id=\"T_8bff4_row2_col4\" class=\"data row2 col4\" >1.0 : 13659</td>\n",
+              "      <td id=\"T_8bff4_row2_col5\" class=\"data row2 col5\" >0.0 : 377</td>\n",
+              "      <td id=\"T_8bff4_row2_col6\" class=\"data row2 col6\" >0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_8bff4_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+              "      <td id=\"T_8bff4_row3_col0\" class=\"data row3 col0\" >FlagDefault</td>\n",
+              "      <td id=\"T_8bff4_row3_col1\" class=\"data row3 col1\" >0</td>\n",
+              "      <td id=\"T_8bff4_row3_col2\" class=\"data row3 col2\" >2</td>\n",
+              "      <td id=\"T_8bff4_row3_col3\" class=\"data row3 col3\" >0.0 : 23364</td>\n",
+              "      <td id=\"T_8bff4_row3_col4\" class=\"data row3 col4\" >1.0 : 6636</td>\n",
+              "      <td id=\"T_8bff4_row3_col5\" class=\"data row3 col5\" >0</td>\n",
+              "      <td id=\"T_8bff4_row3_col6\" class=\"data row3 col6\" >0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Data Shape:(30000, 24)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Assign default modeling roles and train/validation split"
+      ],
+      "metadata": {
+        "id": "4NfhgmodA0CC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "exp.data_prepare()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 492,
+          "referenced_widgets": [
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+            "47e025fc098e4e88a568800921c4afb0",
+            "5373143af0db498eb39cafcb0d451256",
+            "98b567083a164f01a465cec2a14174c3",
+            "0d9d6d42d5e24e92b82cb5edf2d34b05",
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+            "8ac81dfa1d8c428e9721e08cf2a1a101",
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+            "5abc031d1f3448f38140c1db74b51147",
+            "ffb430387ce14886abd8ade9678cd680",
+            "5ae78aff01804bc49efbaec485baefc1",
+            "8985ef74c4354d23869c3b5300c97ab9",
+            "2abc166ed84c49ef8cdef02ce3ea47cb",
+            "afd08b1481714e5d9505d6478dda1d47",
+            "a22b6aa6aa944d6e98dbd34137b7c6e4"
+          ]
+        },
+        "id": "R0Iaq_hDlZKa",
+        "outputId": "0fffe041-a39f-4530-c4ce-699a95242d66"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3bb29238de994c6aa2a9752897183483"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "VBox(children=(HBox(children=(VBox(children=(HTML(value='<p>Target Variable:</p>'), HTML(value='<p>Split Metho…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7f2be01970f04b64bf513f8d6f5c050c"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Select features based on randomized conditional independence test (RCIT)\n",
+        "* Basic causal discovery\n",
+        "* RCIT uses properties of Fourier features to calculate independence tests without expensive eigen decompositions or matrix inversions\n",
+        "* RCIT is paired with forward and backward selection passes\n",
+        "* Goal is to find a minimal set of features with maximum predictive power (Markov boundary set)\n",
+        "* Can be slow ...\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "HncgoOyLA9Bp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "exp.feature_select(method='rcit')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 621,
+          "referenced_widgets": [
+            "d12528a7b2c84aedba7a280e1005e3c5",
+            "f466ed09ce214e7d87aff307bf41d595",
+            "69712a03fdb845b2a2a0a7f9b2f226db"
+          ]
+        },
+        "id": "jJyazQD_lhJa",
+        "outputId": "b2d5a182-2695-4403-ed8d-50281c7aabc8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d12528a7b2c84aedba7a280e1005e3c5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### View selected features"
+      ],
+      "metadata": {
+        "id": "-z1RnHyvoSqx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "exp.get_feature_names()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 38,
+          "referenced_widgets": [
+            "4528a1e81f614691b576d3be339397c4",
+            "71db473e3d444a9e8924ae5b90b86dd4",
+            "887979f5aef44bdf997cda872c1ec4b9"
+          ]
+        },
+        "id": "dPpDRvr7oNhy",
+        "outputId": "bdaa7653-7521-4fa2-e21e-f7e6f18363ac"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4528a1e81f614691b576d3be339397c4"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['LIMIT_BAL', 'PAY_1', 'PAY_3', 'PAY_5', 'PAY_AMT1', 'PAY_AMT3']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### View Spearman correlation for monotonicity constraints"
+      ],
+      "metadata": {
+        "id": "P84Oz4udoZB4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_x = pd.DataFrame(exp.get_raw_data()[0], columns=exp.get_raw_data()[6]) # X\n",
+        "train_y = pd.DataFrame(exp.get_raw_data()[1], columns=[exp.get_raw_data()[7]]) # y\n",
+        "pd.concat([train_x, train_y], axis=1).corr(method='spearman').iloc[0:6, -1]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142,
+          "referenced_widgets": [
+            "1789c12f5a54434c9d880a713953a301",
+            "967b192576ce47a0b9777c8f57def08e",
+            "da5c36749f73482e84adac183b23c1e1"
+          ]
+        },
+        "id": "B21W0FsKogQe",
+        "outputId": "1565ba40-a4af-4eb8-a5b8-c9f35ae2c1e7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1789c12f5a54434c9d880a713953a301"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LIMIT_BAL   -0.164743\n",
+              "PAY_1        0.301563\n",
+              "PAY_3        0.228218\n",
+              "PAY_5        0.197430\n",
+              "PAY_AMT1    -0.160398\n",
+              "PAY_AMT3    -0.135545\n",
+              "Name: FlagDefault, dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Train and register models:\n",
+        "* GLM\n",
+        "* GAM\n",
+        "* EBM\n",
+        "* Monotonic XGB2\n",
+        "  * Positive constraints: \"PAY_1\", \"PAY_3\", \"PAY_5\"\n",
+        "  * Negative constraints: \"LIMIT_BAL\", \"PAY_AMT1\", \"PAY_AMT3\"\n"
+      ],
+      "metadata": {
+        "id": "0Ees-j4lBbkO"
+      }
+    },
+    {
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+        "exp.model_train()"
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+        },
+        "id": "MQdqW3M8lq87",
+        "outputId": "f41a3f25-f8e8-4cc1-e369-d168ce5211ab"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ab179ddf164540c9bedb37ba02041251"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "google.colab.output.setIframeHeight(0, true, {maxHeight: 5000})"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css\"> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "VBox(children=(Box(children=(Box(children=(HTML(value=\"<h4 style='margin: 10px 0px;'>Choose Model</h4>\"), Box(…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "eb47423fb7234892a374032f906d204f"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Extract GLM predictions"
+      ],
+      "metadata": {
+        "id": "1-R_Hv-tTmUJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "id = pd.DataFrame(np.arange(0, 24000), columns=['ID']) # include ID for better traceability\n",
+        "glm = exp.get_model('GLM') # get GLM model from experiment object\n",
+        "glm_phat = pd.DataFrame(glm.predict_proba(train_x.values)[:, 1].reshape(24000, 1), columns=[str('p_' + exp.get_raw_data()[7])]) # phat\n",
+        "glm_train_phat = pd.concat([id, train_x, train_y, glm_phat], axis=1)\n",
+        "glm_train_phat"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 427,
+          "referenced_widgets": [
+            "396cfd81ded941139ceb07d01af49481",
+            "7c7656c09a3341d4ad4c036178a1d089",
+            "1adfbb49340f4c9fb4051ab0d8e38d88"
+          ]
+        },
+        "id": "HFhBd9XQWoGP",
+        "outputId": "5c81283a-3bce-4869-8e91-8dd1be91f7d2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "396cfd81ded941139ceb07d01af49481"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          ID  LIMIT_BAL  PAY_1  PAY_3  PAY_5  PAY_AMT1  PAY_AMT3  FlagDefault  \\\n",
+              "0          0    20000.0    0.0    2.0    0.0  3.477266  3.000434          0.0   \n",
+              "1          1   260000.0   -1.0   -1.0   -1.0  2.220108  2.220108          0.0   \n",
+              "2          2    20000.0    2.0    0.0    0.0  3.176381  2.954725          1.0   \n",
+              "3          3    30000.0    2.0    2.0    2.0  3.255514  3.352375          1.0   \n",
+              "4          4    10000.0    0.0    0.0    0.0  3.447313  3.176381          1.0   \n",
+              "...      ...        ...    ...    ...    ...       ...       ...          ...   \n",
+              "23995  23995    30000.0    0.0    0.0    0.0  3.322426  3.000434          0.0   \n",
+              "23996  23996   210000.0    0.0    0.0    0.0  3.845160  3.845160          1.0   \n",
+              "23997  23997   130000.0    0.0    0.0    0.0  3.699057  3.398114          0.0   \n",
+              "23998  23998    50000.0    0.0    0.0    0.0  3.398114  2.621176          0.0   \n",
+              "23999  23999   140000.0    0.0    0.0    0.0  3.301247  3.176381          0.0   \n",
+              "\n",
+              "       p_FlagDefault  \n",
+              "0           0.261295  \n",
+              "1           0.071186  \n",
+              "2           0.450111  \n",
+              "3           0.632888  \n",
+              "4           0.191187  \n",
+              "...              ...  \n",
+              "23995       0.191524  \n",
+              "23996       0.137878  \n",
+              "23997       0.159425  \n",
+              "23998       0.190885  \n",
+              "23999       0.166704  \n",
+              "\n",
+              "[24000 rows x 9 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-88a7577c-1e28-4b6f-a77d-882a6b2dde46\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
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+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ID</th>\n",
+              "      <th>LIMIT_BAL</th>\n",
+              "      <th>PAY_1</th>\n",
+              "      <th>PAY_3</th>\n",
+              "      <th>PAY_5</th>\n",
+              "      <th>PAY_AMT1</th>\n",
+              "      <th>PAY_AMT3</th>\n",
+              "      <th>FlagDefault</th>\n",
+              "      <th>p_FlagDefault</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>20000.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.477266</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.261295</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>260000.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>2.220108</td>\n",
+              "      <td>2.220108</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.071186</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>20000.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.176381</td>\n",
+              "      <td>2.954725</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.450111</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>30000.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.255514</td>\n",
+              "      <td>3.352375</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.632888</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.447313</td>\n",
+              "      <td>3.176381</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.191187</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23995</th>\n",
+              "      <td>23995</td>\n",
+              "      <td>30000.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.322426</td>\n",
+              "      <td>3.000434</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.191524</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23996</th>\n",
+              "      <td>23996</td>\n",
+              "      <td>210000.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.845160</td>\n",
+              "      <td>3.845160</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.137878</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23997</th>\n",
+              "      <td>23997</td>\n",
+              "      <td>130000.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.699057</td>\n",
+              "      <td>3.398114</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.159425</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23998</th>\n",
+              "      <td>23998</td>\n",
+              "      <td>50000.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.398114</td>\n",
+              "      <td>2.621176</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.190885</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23999</th>\n",
+              "      <td>23999</td>\n",
+              "      <td>140000.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.301247</td>\n",
+              "      <td>3.176381</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.166704</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>24000 rows × 9 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88a7577c-1e28-4b6f-a77d-882a6b2dde46')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-88a7577c-1e28-4b6f-a77d-882a6b2dde46 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-88a7577c-1e28-4b6f-a77d-882a6b2dde46');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-8b8a2db3-c5d5-4ab6-a729-49b26941c18c\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b8a2db3-c5d5-4ab6-a729-49b26941c18c')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-8b8a2db3-c5d5-4ab6-a729-49b26941c18c button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_7776dd70-bf16-428d-acfc-e217efed9d8a\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('glm_train_phat')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_7776dd70-bf16-428d-acfc-e217efed9d8a button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('glm_train_phat');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "glm_train_phat",
+              "summary": "{\n  \"name\": \"glm_train_phat\",\n  \"rows\": 24000,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6928,\n        \"min\": 0,\n        \"max\": 23999,\n        \"num_unique_values\": 24000,\n        \"samples\": [\n          3111,\n          18679,\n          17472\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LIMIT_BAL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 129482.39625478946,\n        \"min\": 10000.0,\n        \"max\": 800000.0,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          140000.0,\n          20000.0,\n          170000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PAY_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9372552631834461,\n        \"min\": -1.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8.0,\n          -1.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PAY_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9554672123043417,\n        \"min\": -1.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.0,\n          -1.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PAY_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8658931749690234,\n        \"min\": -1.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          5.0,\n          -1.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PAY_AMT1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4088576945826756,\n        \"min\": 0.0,\n        \"max\": 5.9412894,\n        \"num_unique_values\": 6929,\n        \"samples\": [\n          3.7622283,\n          4.3326,\n          2.445604\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PAY_AMT3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4558411826806303,\n        \"min\": 0.0,\n        \"max\": 5.948923,\n        \"num_unique_values\": 6517,\n        \"samples\": [\n          3.2148438,\n          3.8825245,\n          4.005438\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FlagDefault\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.41590603587048397,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"p_FlagDefault\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16417148628096925,\n        \"min\": 0.026153564010771128,\n        \"max\": 0.9976897537223318,\n        \"num_unique_values\": 21040,\n        \"samples\": [\n          0.1520511470779966,\n          0.11738072288931468\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Determine percentiles of GLM predictions for ICE plots and local explanations"
+      ],
+      "metadata": {
+        "id": "W05iyORlRght"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_percentile_dict(yhat_name, valid, id_):\n",
+        "\n",
+        "    \"\"\" Returns the percentiles of a column, yhat_name, as the indices based on\n",
+        "        another column id_.\n",
+        "\n",
+        "    :param yhat_name: Name of column in valid in which to find percentiles.\n",
+        "    :param valid: Pandas validation frame.\n",
+        "    :param id_: explicit id column in validation frame.\n",
+        "\n",
+        "    :return: Dictionary of percentile values and index column values.\n",
+        "\n",
+        "    \"\"\"\n",
+        "\n",
+        "    # create a copy of frame and sort it by yhat\n",
+        "    sort_df = valid.copy(deep=True)\n",
+        "    sort_df.sort_values(yhat_name, inplace=True)\n",
+        "    sort_df.reset_index(inplace=True)\n",
+        "\n",
+        "    # find top and bottom percentiles\n",
+        "    percentiles_dict = {0: sort_df.loc[0, id_], 99: sort_df.loc[sort_df.shape[0] - 1, id_]}\n",
+        "\n",
+        "    # find 10th-90th percentiles\n",
+        "    inc = sort_df.shape[0] // 10\n",
+        "    for i in range(1, 10):\n",
+        "        percentiles_dict[i * 10] = sort_df.loc[i * inc, id_]\n",
+        "\n",
+        "    return percentiles_dict\n",
+        "\n",
+        "glm_percentile_dict = get_percentile_dict('p_FlagDefault', glm_train_phat, 'ID')\n",
+        "sorted(glm_percentile_dict.items()) # sorted list of row numbers for local investigation"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 212,
+          "referenced_widgets": [
+            "736d983e8844457a81494dc42c81d20d",
+            "9e06fe62379f4373af1e5248b1af8056",
+            "1d19f91fdc164668ac7e26b0e20022c2"
+          ]
+        },
+        "id": "AmjkwY0SSV08",
+        "outputId": "551bccb2-f80e-4d37-849b-fae7037d4754"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "736d983e8844457a81494dc42c81d20d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[(0, 11250),\n",
+              " (10, 2385),\n",
+              " (20, 10582),\n",
+              " (30, 3520),\n",
+              " (40, 4739),\n",
+              " (50, 20143),\n",
+              " (60, 22513),\n",
+              " (70, 17433),\n",
+              " (80, 23578),\n",
+              " (90, 5990),\n",
+              " (99, 14502)]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Determine densities for important features"
+      ],
+      "metadata": {
+        "id": "XeqgcUwFj8g4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def hist_mean(x_name, y_name, valid):\n",
+        "\n",
+        "    \"\"\" Plots diagnostic plot of histogram with mean line overlay.\n",
+        "\n",
+        "    :param x_name: Name of variable for which to plot ICE and partial dependence.\n",
+        "    :param y_name: Name of target variable.\n",
+        "    :param valid: Pandas validation frame.\n",
+        "    :param pd_ice_dict: Dict of Pandas DataFrames containing partial dependence\n",
+        "                        and ICE values.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    # initialize figure and axis\n",
+        "    fig, ax = plt.subplots()\n",
+        "    plt.tight_layout()\n",
+        "\n",
+        "    # create histogram\n",
+        "    mean_df = valid[[x_name, y_name]].groupby(by=x_name).mean()\n",
+        "    bins = sorted(valid[x_name].unique())\n",
+        "\n",
+        "    # annotate hist\n",
+        "    ax.set_xlabel(x_name)\n",
+        "    ax.set_ylabel('Frequency')\n",
+        "    ax.set_title('Histogram with Mean ' + y_name + ' Overlay')\n",
+        "    ax.hist(valid[x_name])\n",
+        "\n",
+        "    # create a new twin axis\n",
+        "    # on which to plot a line showing mean value\n",
+        "    # across hist bins\n",
+        "    ax1 = ax.twinx()\n",
+        "    _ = ax1.set_ylim((0, 1))\n",
+        "    _ = ax1.plot(bins, mean_df.reindex(labels=bins)[y_name], color='r')\n",
+        "    _ = ax1.set_ylabel('Mean ' + y_name)\n",
+        "    _ = ax1.legend(['Mean ' + y_name], loc=1)\n",
+        "\n",
+        "hist_mean('PAY_1', 'FlagDefault', glm_train_phat)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 501,
+          "referenced_widgets": [
+            "f1ac9577e781435188293115cdd3a67f",
+            "99a15a3085684463a192c26896ba8741",
+            "41a1b882a94546409827386b5dec93df"
+          ]
+        },
+        "id": "Yv2invkBkC9n",
+        "outputId": "c7eff2a5-0282-4756-898d-e1c202e91682"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f1ac9577e781435188293115cdd3a67f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Explain GLM\n",
+        "- FI: variance of the marginal effect\n",
+        "- Effect: is `beta_j*x_{i,j} - beta_0`, `weight is beta_j`\n",
+        "- Standardized coefficients\n",
+        "- Row `5990` is 90th percentile of GLM phat\n"
+      ],
+      "metadata": {
+        "id": "ezDwGJ4iRcBo"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "exp.model_interpret()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 702,
+          "referenced_widgets": [
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+            "32640d7bab2742bcb87289f0c878f631",
+            "08af7a7560b74b1da9d729c9633ff924",
+            "b8c634d0e40f4ff8888d3e732f12b6e3",
+            "06cb9e5eb39d436dbb49fc494bd015d3",
+            "70f2527ff6144bb7922ebab2ce5d13bb",
+            "07c78967cd04494cad37dfa3c8b0f171",
+            "aa7d48c27caf4d6ca649352c8de74e01",
+            "da64ac46796c4354b8c0a49810db1073",
+            "88691fe10ac746109a98abbf27b88608",
+            "ba2188f056e74d6d832dde3485541cae",
+            "13abed42c0a144a8816d846c0e8acfc3",
+            "c1b93dacc11e410aa951439db0fadc78",
+            "ef1376ca501c482e8596a2203860851d",
+            "f0c7086a6e5e44919c47aaa0c8566a7f",
+            "560c73b4ba584a3e99c525f405e75474",
+            "c340bc270ca946d3afa6832765530919",
+            "2de7487fe0af44afbaefbfbfc86b6db0",
+            "0f1d5112bb3545fb9a61dc4475f8a9a0",
+            "d52bc4c7a2e6445381e427c0e5df2bf2"
+          ]
+        },
+        "id": "p9ylWc6hmQek",
+        "outputId": "70d7ea33-443c-45ac-e24d-bc59b4357551"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "735623b729a1469b8043626d96d900d8"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "VBox(children=(Dropdown(layout=Layout(width='20%'), options=('Select Model', 'GLM', 'XGB2', 'EBM', 'GAM'), sty…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "18a9de3c57b64618a6349e99f4b00a55"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# exp.model_explain() # for Session 3"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 21,
+          "referenced_widgets": [
+            "7f2cc5b57a4f4707a63551029f20de68",
+            "f26b0d46661d49218ea76f615fc2ce1d",
+            "74bcd4104d064c78a8437c627a4722d7"
+          ]
+        },
+        "id": "FqfvfALjd0sY",
+        "outputId": "1280bcf3-3554-438f-f2b9-5a7f1d77dd68"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7f2cc5b57a4f4707a63551029f20de68"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### View row to confirm index"
+      ],
+      "metadata": {
+        "id": "PR3MejMhtxKG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_x.iloc[5990, :]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142,
+          "referenced_widgets": [
+            "6fd2d25bb8d845d8822eb0f5e854aa2b",
+            "ab964db89497408491f31d5531b52ebc",
+            "5bfafa1955164ee28aaf63c9211a54f0"
+          ]
+        },
+        "id": "lKybWnPbq9CE",
+        "outputId": "5d92e9fa-a201-48b9-ebfc-76c5788fba26"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6fd2d25bb8d845d8822eb0f5e854aa2b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LIMIT_BAL    60000.000000\n",
+              "PAY_1            1.000000\n",
+              "PAY_3            2.000000\n",
+              "PAY_5            2.000000\n",
+              "PAY_AMT1         3.477266\n",
+              "PAY_AMT3         3.699057\n",
+              "Name: 5990, dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Explain GAM\n",
+        "- FI: Variance of shape function"
+      ],
+      "metadata": {
+        "id": "iYl19OyPojqB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "exp.model_interpret()"
+      ],
+      "metadata": {
+        "id": "7f-aNlYunU-T",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 702,
+          "referenced_widgets": [
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+          ]
+        },
+        "outputId": "77b4e42b-d0a1-4cde-9b73-775c14a466b9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "50136f54c4dc45ea87f0b23c98cfe817"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "VBox(children=(Dropdown(layout=Layout(width='20%'), options=('Select Model', 'GLM', 'XGB2', 'EBM', 'GAM'), sty…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0f8e203af53442c28175cb5ba0c346b5"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# exp.model_explain() # for Session 3"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 21,
+          "referenced_widgets": [
+            "7b0524a1662d455c9d6e936c27b4bec4",
+            "bbf9a085ed5549a78a09c1fcd20429f7",
+            "9939f912732841cb98718b7ab549184a"
+          ]
+        },
+        "id": "iQk3oSjpd_BI",
+        "outputId": "24daba03-72a3-48ce-c7ef-ca53464199aa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7b0524a1662d455c9d6e936c27b4bec4"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Explain EBM\n",
+        "- FI: sum of the effects of each feature over the main effect and all related pairwise interactions\n",
+        "- Effect: variance of shape function\n",
+        "- Local FI: sum of the effects of each feature over the main effect and all related pairwise interactions for a single observation"
+      ],
+      "metadata": {
+        "id": "VzErIRr9omsB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "exp.model_interpret()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1104,
+          "referenced_widgets": [
+            "61679a710274435abe8c43c6ab06dfae",
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+        "outputId": "1b834ff2-4b30-4f78-8638-b98917488693"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "61679a710274435abe8c43c6ab06dfae"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "VBox(children=(Dropdown(layout=Layout(width='20%'), options=('Select Model', 'GLM', 'XGB2', 'EBM', 'GAM'), sty…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "257fb578ad044b9d85fee14e30438a42"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# exp.model_explain() # for Session 3"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 21,
+          "referenced_widgets": [
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+            "d5dfd34211b94c1da242a051257e283d",
+            "45c41ea3765b4244939d3a50e50a6a03"
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+        "outputId": "3e05941c-0966-4a78-e72f-8158b2c0a8b8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+              "model_id": "d862da574114426181f04cc2896171d1"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Explain Constrained XGB2\n",
+        "- Also a GAM! See: https://proceedings.mlr.press/v108/lengerich20a.html\n",
+        "- FI: sum of the effects of each feature over the main effect and all related pairwise interactions\n",
+        "- Effect: variance of shape function\n",
+        "- Local FI: sum of the effects of each feature over the main effect and all related pairwise interactions for a single observation\n"
+      ],
+      "metadata": {
+        "id": "ZelBgIIWo03A"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "exp.model_interpret()"
+      ],
+      "metadata": {
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+        "outputId": "80f8b0fe-fecf-4809-8174-bb8ba60605db"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0d5dbb4d45554714994b58c41211d865"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "VBox(children=(Dropdown(layout=Layout(width='20%'), options=('Select Model', 'GLM', 'XGB2', 'EBM', 'GAM', 'XGB…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2483de1585bd4b798064111afb90ff6d"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# exp.model_explain() # for Session 3"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 21,
+          "referenced_widgets": [
+            "9417b25168c140a5a4d7694e907761d4",
+            "ed0b4c9c6a904110bc2e21a46e55392d",
+            "f2b7fd2b637a41068b319af9125dab05"
+          ]
+        },
+        "id": "N4Bpb3yQeyyQ",
+        "outputId": "cded9d9c-9ddb-424a-ac0e-97ae614002a7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "HTML(value='\\n        <style>\\n\\n        .left-label {\\n            width: 30%;\\n        }\\n\\n        .card-pa…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9417b25168c140a5a4d7694e907761d4"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file