|
86 | 86 | "### New to ValidMind?\n",
|
87 | 87 | "If you haven't already seen our [Get started with the ValidMind Developer Framework](https://docs.validmind.ai/developer/get-started-developer-framework.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models, find code samples, or read our developer reference.\n",
|
88 | 88 | "\n",
|
89 |
| - "<div class=\"alert alert-block alert-info\" style=\"background-color: #f7e4ee; color: #222425; border: 1px solid #222425;\">For access to all features available in this notebook, create a free ValidMind account.\n", |
90 |
| - "\n", |
91 |
| - "Signing up is FREE — <a href=\"https://app.prod.validmind.ai\"><b>Sign up now</b></a></div>" |
| 89 | + "<div class=\"alert alert-block alert-info\" style=\"background-color: #f7e4ee; color: black; border: 1px solid black;\"><b>For access to all features available in this notebook, create a free ValidMind account.</b>\n", |
| 90 | + "<br></br>\n", |
| 91 | + "Signing up is FREE — <a href=\"https://docs.validmind.ai/guide/configuration/register-with-validmind.html\"><b>Register with ValidMind</b></a></div>\n" |
92 | 92 | ]
|
93 | 93 | },
|
94 | 94 | {
|
|
155 | 155 | "\n",
|
156 | 156 | "Get your code snippet:\n",
|
157 | 157 | "\n",
|
158 |
| - "1. In a browser, log into the [Platform UI](https://app.prod.validmind.ai).\n", |
| 158 | + "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n", |
159 | 159 | "\n",
|
160 | 160 | "2. In the left sidebar, navigate to **Model Inventory** and click **+ Register new model**.\n",
|
161 | 161 | "\n",
|
|
595 | 595 | "source": [
|
596 | 596 | "test = vm.tests.run_test(\n",
|
597 | 597 | " \"validmind.data_validation.TabularDescriptionTables:raw_dataset\",\n",
|
598 |
| - " inputs={\n", |
599 |
| - " \"dataset\": vm_raw_dataset,\n", |
| 598 | + " input_grid={\n", |
| 599 | + " \"dataset\": [vm_raw_dataset],\n", |
600 | 600 | " },\n",
|
601 | 601 | ")\n",
|
602 | 602 | "test.log()"
|
|
610 | 610 | "source": [
|
611 | 611 | "test = vm.tests.run_test(\n",
|
612 | 612 | " \"validmind.data_validation.MissingValuesBarPlot:raw_dataset\",\n",
|
613 |
| - " inputs={\n", |
614 |
| - " \"dataset\": vm_raw_dataset,\n", |
| 613 | + " input_grid={\n", |
| 614 | + " \"dataset\": [vm_raw_dataset]\n", |
615 | 615 | " },\n",
|
616 | 616 | ")\n",
|
617 | 617 | "test.log()"
|
|
646 | 646 | "source": [
|
647 | 647 | "test = vm.tests.run_test(\n",
|
648 | 648 | " \"validmind.data_validation.TabularDescriptionTables:preprocess_dataset\",\n",
|
649 |
| - " inputs={\n", |
650 |
| - " \"dataset\": vm_preprocess_dataset,\n", |
| 649 | + " input_grid={\n", |
| 650 | + " \"dataset\": [vm_preprocess_dataset]\n", |
651 | 651 | " },\n",
|
652 | 652 | ")\n",
|
653 | 653 | "test.log()"
|
|
661 | 661 | "source": [
|
662 | 662 | "test = vm.tests.run_test(\n",
|
663 | 663 | " \"validmind.data_validation.IQROutliersTable\",\n",
|
664 |
| - " inputs={\n", |
665 |
| - " \"dataset\": vm_preprocess_dataset,\n", |
| 664 | + " input_grid={\n", |
| 665 | + " \"dataset\": [vm_preprocess_dataset]\n", |
666 | 666 | " },\n",
|
667 | 667 | ")\n",
|
668 | 668 | "test.log()"
|
|
676 | 676 | "source": [
|
677 | 677 | "test = vm.tests.run_test(\n",
|
678 | 678 | " \"validmind.data_validation.ClassImbalance\",\n",
|
679 |
| - " inputs={\n", |
680 |
| - " \"dataset\": vm_preprocess_dataset,\n", |
| 679 | + " input_grid={\n", |
| 680 | + " \"dataset\": [vm_preprocess_dataset]\n", |
681 | 681 | " },\n",
|
682 | 682 | ")\n",
|
683 | 683 | "test.log()"
|
|
691 | 691 | "source": [
|
692 | 692 | "test = vm.tests.run_test(\n",
|
693 | 693 | " \"validmind.data_validation.TabularNumericalHistograms\",\n",
|
694 |
| - " inputs={\n", |
695 |
| - " \"dataset\": vm_preprocess_dataset,\n", |
| 694 | + " input_grid={\n", |
| 695 | + " \"dataset\": [vm_preprocess_dataset]\n", |
696 | 696 | " },\n",
|
697 | 697 | ")\n",
|
698 | 698 | "test.log()"
|
|
706 | 706 | "source": [
|
707 | 707 | "test = vm.tests.run_test(\n",
|
708 | 708 | " \"validmind.data_validation.TabularCategoricalBarPlots\",\n",
|
709 |
| - " inputs={\n", |
710 |
| - " \"dataset\": vm_preprocess_dataset,\n", |
| 709 | + " input_grid={\n", |
| 710 | + " \"dataset\": [vm_preprocess_dataset]\n", |
711 | 711 | " },\n",
|
712 | 712 | ")\n",
|
713 | 713 | "test.log()"
|
|
721 | 721 | "source": [
|
722 | 722 | "test = vm.tests.run_test(\n",
|
723 | 723 | " \"validmind.data_validation.TargetRateBarPlots\",\n",
|
724 |
| - " inputs={\n", |
725 |
| - " \"dataset\": vm_preprocess_dataset,\n", |
| 724 | + " input_grid={\n", |
| 725 | + " \"dataset\": [vm_preprocess_dataset]\n", |
726 | 726 | " },\n",
|
727 | 727 | " params={\n",
|
728 | 728 | " \"default_column\": lending_club.target_column,\n",
|
|
740 | 740 | "source": [
|
741 | 741 | "test = vm.tests.run_test(\n",
|
742 | 742 | " \"validmind.data_validation.PearsonCorrelationMatrix\",\n",
|
743 |
| - " inputs={\n", |
744 |
| - " \"dataset\": vm_preprocess_dataset,\n", |
| 743 | + " input_grid={\n", |
| 744 | + " \"dataset\": [vm_preprocess_dataset]\n", |
745 | 745 | " },\n",
|
746 | 746 | ")\n",
|
747 | 747 | "test.log()"
|
|
755 | 755 | "source": [
|
756 | 756 | "test = vm.tests.run_test(\n",
|
757 | 757 | " \"validmind.data_validation.HighPearsonCorrelation\",\n",
|
758 |
| - " inputs={\n", |
759 |
| - " \"dataset\": vm_preprocess_dataset,\n", |
| 758 | + " input_grid={\n", |
| 759 | + " \"dataset\": [vm_preprocess_dataset]\n", |
760 | 760 | " },\n",
|
761 | 761 | ")\n",
|
762 | 762 | "test.log()"
|
|
784 | 784 | "source": [
|
785 | 785 | "test = vm.tests.run_test(\n",
|
786 | 786 | " \"validmind.data_validation.WOEBinTable\",\n",
|
787 |
| - " inputs={\n", |
788 |
| - " \"dataset\": vm_preprocess_dataset,\n", |
| 787 | + " input_grid={\n", |
| 788 | + " \"dataset\": [vm_preprocess_dataset]\n", |
789 | 789 | " },\n",
|
790 | 790 | " params={\n",
|
791 | 791 | " \"breaks_adj\": lending_club.breaks_adj,\n",
|
|
802 | 802 | "source": [
|
803 | 803 | "test = vm.tests.run_test(\n",
|
804 | 804 | " \"validmind.data_validation.WOEBinPlots\",\n",
|
805 |
| - " inputs={\n", |
806 |
| - " \"dataset\": vm_preprocess_dataset,\n", |
| 805 | + " input_grid={\n", |
| 806 | + " \"dataset\": [vm_preprocess_dataset]\n", |
807 | 807 | " },\n",
|
808 | 808 | " params={\n",
|
809 | 809 | " \"breaks_adj\": lending_club.breaks_adj,\n",
|
|
866 | 866 | "outputs": [],
|
867 | 867 | "source": [
|
868 | 868 | "test = vm.tests.run_test(\n",
|
869 |
| - " \"validmind.model_validation.statsmodels.RegressionCoeffsPlot\",\n", |
870 |
| - " inputs={\n", |
871 |
| - " \"models\": [vm_model],\n", |
872 |
| - " },\n", |
873 |
| - ")\n", |
874 |
| - "test.log()" |
875 |
| - ] |
876 |
| - }, |
877 |
| - { |
878 |
| - "cell_type": "code", |
879 |
| - "execution_count": null, |
880 |
| - "metadata": {}, |
881 |
| - "outputs": [], |
882 |
| - "source": [ |
883 |
| - "test = vm.tests.run_test(\n", |
884 |
| - " \"validmind.model_validation.statsmodels.RegressionModelsCoeffs\",\n", |
885 |
| - " inputs={\n", |
886 |
| - " \"models\": [vm_model],\n", |
| 869 | + " \"validmind.model_validation.statsmodels.RegressionCoeffs\",\n", |
| 870 | + " input_grid={\n", |
| 871 | + " \"model\": [vm_model]\n", |
887 | 872 | " },\n",
|
888 | 873 | ")\n",
|
889 | 874 | "test.log()"
|
|
917 | 902 | "outputs": [],
|
918 | 903 | "source": [
|
919 | 904 | "test = vm.tests.run_test(\n",
|
920 |
| - " \"validmind.model_validation.sklearn.ClassifierPerformance:train_dataset\",\n", |
921 |
| - " inputs={\n", |
922 |
| - " \"dataset\": vm_train_ds,\n", |
923 |
| - " \"model\": vm_model,\n", |
924 |
| - " },\n", |
925 |
| - ")\n", |
926 |
| - "test.log()" |
927 |
| - ] |
928 |
| - }, |
929 |
| - { |
930 |
| - "cell_type": "code", |
931 |
| - "execution_count": null, |
932 |
| - "metadata": {}, |
933 |
| - "outputs": [], |
934 |
| - "source": [ |
935 |
| - "test = vm.tests.run_test(\n", |
936 |
| - " \"validmind.model_validation.sklearn.ClassifierPerformance:test_dataset\",\n", |
937 |
| - " inputs={\n", |
938 |
| - " \"dataset\": vm_test_ds,\n", |
939 |
| - " \"model\": vm_model,\n", |
| 905 | + " \"validmind.model_validation.sklearn.ClassifierPerformance\",\n", |
| 906 | + " input_grid={\n", |
| 907 | + " \"dataset\": [vm_train_ds, vm_test_ds],\n", |
| 908 | + " \"model\": [vm_model],\n", |
940 | 909 | " },\n",
|
941 | 910 | ")\n",
|
942 | 911 | "test.log()"
|
|
950 | 919 | "source": [
|
951 | 920 | "test = vm.tests.run_test(\n",
|
952 | 921 | " \"validmind.model_validation.statsmodels.GINITable\",\n",
|
953 |
| - " inputs={\n", |
954 |
| - " \"datasets\": [vm_train_ds, vm_test_ds],\n", |
955 |
| - " \"model\": vm_model,\n", |
| 922 | + " input_grid={\n", |
| 923 | + " \"dataset\": [vm_train_ds, vm_test_ds],\n", |
| 924 | + " \"model\": [vm_model],\n", |
956 | 925 | " },\n",
|
957 | 926 | ")\n",
|
958 | 927 | "test.log()"
|
|
966 | 935 | "source": [
|
967 | 936 | "test = vm.tests.run_test(\n",
|
968 | 937 | " \"validmind.model_validation.sklearn.ConfusionMatrix\",\n",
|
969 |
| - " inputs={\n", |
970 |
| - " \"dataset\": vm_test_ds,\n", |
971 |
| - " \"model\": vm_model,\n", |
| 938 | + " input_grid={\n", |
| 939 | + " \"dataset\": [vm_train_ds, vm_test_ds],\n", |
| 940 | + " \"model\": [vm_model],\n", |
972 | 941 | " },\n",
|
973 | 942 | ")\n",
|
974 | 943 | "test.log()"
|
|
982 | 951 | "source": [
|
983 | 952 | "test = vm.tests.run_test(\n",
|
984 | 953 | " \"validmind.model_validation.sklearn.ROCCurve\",\n",
|
985 |
| - " inputs={\n", |
986 |
| - " \"model\": vm_model,\n", |
987 |
| - " \"dataset\": vm_test_ds,\n", |
| 954 | + " input_grid={\n", |
| 955 | + " \"dataset\": [vm_train_ds, vm_test_ds],\n", |
| 956 | + " \"model\": [vm_model],\n", |
988 | 957 | " },\n",
|
989 | 958 | ")\n",
|
990 | 959 | "test.log()"
|
|
998 | 967 | "source": [
|
999 | 968 | "test = vm.tests.run_test(\n",
|
1000 | 969 | " \"validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram\",\n",
|
1001 |
| - " inputs={\n", |
1002 |
| - " \"model\": vm_model,\n", |
1003 |
| - " \"datasets\": [vm_train_ds, vm_test_ds],\n", |
| 970 | + " input_grid={\n", |
| 971 | + " \"dataset\": [vm_train_ds, vm_test_ds],\n", |
| 972 | + " \"model\": [vm_model],\n", |
1004 | 973 | " },\n",
|
1005 | 974 | ")\n",
|
1006 | 975 | "test.log()"
|
|
1014 | 983 | "source": [
|
1015 | 984 | "test = vm.tests.run_test(\n",
|
1016 | 985 | " \"validmind.model_validation.statsmodels.CumulativePredictionProbabilities\",\n",
|
1017 |
| - " inputs={\n", |
1018 |
| - " \"model\": vm_model,\n", |
1019 |
| - " \"datasets\": [vm_train_ds, vm_test_ds],\n", |
| 986 | + " input_grid={\n", |
| 987 | + " \"model\": [vm_model],\n", |
| 988 | + " \"dataset\": [vm_train_ds, vm_test_ds],\n", |
1020 | 989 | " },\n",
|
1021 | 990 | ")\n",
|
1022 | 991 | "test.log()"
|
|
1030 | 999 | "source": [
|
1031 | 1000 | "test = vm.tests.run_test(\n",
|
1032 | 1001 | " \"validmind.model_validation.statsmodels.ScorecardHistogram\",\n",
|
1033 |
| - " inputs={\n", |
1034 |
| - " \"model\": vm_model,\n", |
1035 |
| - " \"datasets\": [vm_train_ds, vm_test_ds],\n", |
| 1002 | + " input_grid={\n", |
| 1003 | + " \"dataset\": [vm_train_ds, vm_test_ds],\n", |
1036 | 1004 | " },\n",
|
1037 | 1005 | " params={\n",
|
1038 | 1006 | " \"score_column\": \"glm_scores\",\n",
|
|
1066 | 1034 | "source": [
|
1067 | 1035 | "test = vm.tests.run_test(\n",
|
1068 | 1036 | " \"validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance\",\n",
|
1069 |
| - " inputs={\n", |
1070 |
| - " \"model\": vm_model,\n", |
1071 |
| - " \"dataset\": vm_test_ds,\n", |
| 1037 | + " input_grid={\n", |
| 1038 | + " \"model\": [vm_model],\n", |
| 1039 | + " \"dataset\": [vm_train_ds, vm_test_ds],\n", |
1072 | 1040 | " },\n",
|
1073 | 1041 | ")\n",
|
1074 | 1042 | "test.log()"
|
|
1082 | 1050 | "source": [
|
1083 | 1051 | "test = vm.tests.run_test(\n",
|
1084 | 1052 | " \"validmind.model_validation.FeaturesAUC\",\n",
|
1085 |
| - " inputs={\n", |
1086 |
| - " \"model\": vm_model,\n", |
1087 |
| - " \"dataset\": vm_test_ds,\n", |
| 1053 | + " input_grid={\n", |
| 1054 | + " \"model\": [vm_model],\n", |
| 1055 | + " \"dataset\": [vm_train_ds, vm_test_ds],\n", |
1088 | 1056 | " },\n",
|
1089 | 1057 | ")\n",
|
1090 | 1058 | "test.log()"
|
|
1104 | 1072 | "\n",
|
1105 | 1073 | "### Work with your model documentation\n",
|
1106 | 1074 | "\n",
|
1107 |
| - "1. In the [Platform UI](https://app.prod.validmind.ai), go to the **Documentation** page for the model you registered earlier.\n", |
| 1075 | + "1. In the ValidMind Platform UI, go to the **Documentation** page for the model you registered earlier. ([Need more help?](https://docs.validmind.ai/guide/model-documentation/working-with-model-documentation.html))\n", |
1108 | 1076 | "\n",
|
1109 | 1077 | "2. Expand the following sections and take a look around:\n",
|
1110 | 1078 | "\n",
|
|
1142 | 1110 | "name": "python",
|
1143 | 1111 | "nbconvert_exporter": "python",
|
1144 | 1112 | "pygments_lexer": "ipython3",
|
1145 |
| - "version": "3.10.13" |
| 1113 | + "version": "3.10.14" |
1146 | 1114 | }
|
1147 | 1115 | },
|
1148 | 1116 | "nbformat": 4,
|
|
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