From ed51f26a7b670087b5554935dd776fae93d1fbe5 Mon Sep 17 00:00:00 2001
From: mattseddon <37993418+mattseddon@users.noreply.github.com>
Date: Fri, 21 Oct 2022 06:40:34 +1100
Subject: [PATCH] Add survival exp show test fixture (#2638)

* add survivial test fixture (https://dagshub.com/kingabzpro/kaggle-titanic-dvc)

* add survival story
---
 .../src/experiments/columns/model.test.ts     |    8 +
 extension/src/experiments/model/index.test.ts |   10 +
 .../test/fixtures/expShow/survival/columns.ts | 1607 +++++++++++++++++
 .../test/fixtures/expShow/survival/output.ts  | 1232 +++++++++++++
 .../test/fixtures/expShow/survival/rows.ts    | 1098 +++++++++++
 .../fixtures/expShow/survival/tableData.ts    |   19 +
 webview/src/stories/Table.stories.tsx         |    6 +
 7 files changed, 3980 insertions(+)
 create mode 100644 extension/src/test/fixtures/expShow/survival/columns.ts
 create mode 100644 extension/src/test/fixtures/expShow/survival/output.ts
 create mode 100644 extension/src/test/fixtures/expShow/survival/rows.ts
 create mode 100644 extension/src/test/fixtures/expShow/survival/tableData.ts

diff --git a/extension/src/experiments/columns/model.test.ts b/extension/src/experiments/columns/model.test.ts
index 46ad35c186..b09fab983c 100644
--- a/extension/src/experiments/columns/model.test.ts
+++ b/extension/src/experiments/columns/model.test.ts
@@ -17,6 +17,8 @@ import deeplyNestedColumnsFixture from '../../test/fixtures/expShow/deeplyNested
 import deeplyNestedOutputFixture from '../../test/fixtures/expShow/deeplyNested/output'
 import dataTypesColumnsFixture from '../../test/fixtures/expShow/dataTypes/columns'
 import dataTypesOutputFixture from '../../test/fixtures/expShow/dataTypes/output'
+import survivalOutputFixture from '../../test/fixtures/expShow/survival/output'
+import survivalColumnsFixture from '../../test/fixtures/expShow/survival/columns'
 import { getConfigValue } from '../../vscode/config'
 
 jest.mock('../../vscode/config')
@@ -38,6 +40,12 @@ describe('ColumnsModel', () => {
     expect(model.getSelected()).toStrictEqual(columnsFixture)
   })
 
+  it('should return the expected columns when given the survival output fixture', async () => {
+    const model = new ColumnsModel('', buildMockMemento())
+    await model.transformAndSet(survivalOutputFixture)
+    expect(model.getSelected()).toStrictEqual(survivalColumnsFixture)
+  })
+
   it('should return the expected columns when given the deeply nested output fixture', async () => {
     const model = new ColumnsModel('', buildMockMemento())
     await model.transformAndSet(deeplyNestedOutputFixture)
diff --git a/extension/src/experiments/model/index.test.ts b/extension/src/experiments/model/index.test.ts
index 9300d64fc0..b4b1c8c881 100644
--- a/extension/src/experiments/model/index.test.ts
+++ b/extension/src/experiments/model/index.test.ts
@@ -15,6 +15,9 @@ import { Experiment, ColumnType } from '../webview/contract'
 import { definedAndNonEmpty } from '../../util/array'
 import dataTypesRowsFixture from '../../test/fixtures/expShow/dataTypes/rows'
 import dataTypesOutputFixture from '../../test/fixtures/expShow/dataTypes/output'
+import survivalOutputFixture from '../../test/fixtures/expShow/survival/output'
+import survivalRowsFixture from '../../test/fixtures/expShow/survival/rows'
+
 import { ExperimentStatus } from '../../cli/dvc/contract'
 
 jest.mock('vscode')
@@ -74,6 +77,13 @@ describe('ExperimentsModel', () => {
     expect(model.getRowData()).toStrictEqual(rowsFixture)
   })
 
+  it('should return the expected rows when given the survival fixture', () => {
+    const model = new ExperimentsModel('', buildMockMemento())
+    model.transformAndSet(survivalOutputFixture)
+
+    expect(model.getRowData()).toStrictEqual(survivalRowsFixture)
+  })
+
   // eslint-disable-next-line sonarjs/cognitive-complexity
   it('should handle a new dep file being introduced in the workspace', () => {
     const newDep = join('data', '.ldb_workspace')
diff --git a/extension/src/test/fixtures/expShow/survival/columns.ts b/extension/src/test/fixtures/expShow/survival/columns.ts
new file mode 100644
index 0000000000..7fa01c7be9
--- /dev/null
+++ b/extension/src/test/fixtures/expShow/survival/columns.ts
@@ -0,0 +1,1607 @@
+import { timestampColumn } from '../../../../experiments/columns/constants'
+import {
+  buildDepPath,
+  buildMetricOrParamPath
+} from '../../../../experiments/columns/paths'
+import { Column, ColumnType } from '../../../../experiments/webview/contract'
+import { join } from '../../../util/path'
+
+const data: Column[] = [
+  timestampColumn,
+  {
+    hasChildren: true,
+    label: join('results', 'metrics.json'),
+    parentPath: ColumnType.METRICS,
+    path: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json')
+    ),
+    type: ColumnType.METRICS
+  },
+  {
+    hasChildren: false,
+    label: 'fit_time',
+    maxStringLength: 18,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json')
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'fit_time'
+    ),
+    pathArray: [
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'fit_time'
+    ],
+    type: ColumnType.METRICS,
+    types: ['number'],
+    maxNumber: 0.6337410688400269,
+    minNumber: 0.6337410688400269
+  },
+  {
+    hasChildren: false,
+    label: 'score_time',
+    maxStringLength: 19,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json')
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'score_time'
+    ),
+    pathArray: [
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'score_time'
+    ],
+    type: ColumnType.METRICS,
+    types: ['number'],
+    maxNumber: 0.07778854370117187,
+    minNumber: 0.07778854370117187
+  },
+  {
+    hasChildren: false,
+    label: 'accuracy',
+    maxStringLength: 18,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json')
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'accuracy'
+    ),
+    pathArray: [
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'accuracy'
+    ],
+    type: ColumnType.METRICS,
+    types: ['number'],
+    maxNumber: 0.8293632958801498,
+    minNumber: 0.8293632958801498
+  },
+  {
+    hasChildren: false,
+    label: 'balanced_accuracy',
+    maxStringLength: 18,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json')
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'balanced_accuracy'
+    ),
+    pathArray: [
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'balanced_accuracy'
+    ],
+    type: ColumnType.METRICS,
+    types: ['number'],
+    maxNumber: 0.8040020654726536,
+    minNumber: 0.8040020654726536
+  },
+  {
+    hasChildren: false,
+    label: 'f1',
+    maxStringLength: 18,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json')
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'f1'
+    ),
+    pathArray: [ColumnType.METRICS, join('results', 'metrics.json'), 'f1'],
+    type: ColumnType.METRICS,
+    types: ['number'],
+    maxNumber: 0.7572265847252886,
+    minNumber: 0.7572265847252886
+  },
+  {
+    hasChildren: false,
+    label: 'gmpr',
+    maxStringLength: 18,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json')
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'gmpr'
+    ),
+    pathArray: [ColumnType.METRICS, join('results', 'metrics.json'), 'gmpr'],
+    type: ColumnType.METRICS,
+    types: ['number'],
+    maxNumber: 0.7615174102573903,
+    minNumber: 0.7615174102573903
+  },
+  {
+    hasChildren: false,
+    label: 'jaccard',
+    maxStringLength: 18,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json')
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'jaccard'
+    ),
+    pathArray: [ColumnType.METRICS, join('results', 'metrics.json'), 'jaccard'],
+    type: ColumnType.METRICS,
+    types: ['number'],
+    maxNumber: 0.6113136909663465,
+    minNumber: 0.6113136909663465
+  },
+  {
+    hasChildren: false,
+    label: 'precision',
+    maxStringLength: 18,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json')
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'precision'
+    ),
+    pathArray: [
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'precision'
+    ],
+    type: ColumnType.METRICS,
+    types: ['number'],
+    maxNumber: 0.8361572183378356,
+    minNumber: 0.8361572183378356
+  },
+  {
+    hasChildren: false,
+    label: 'recall',
+    maxStringLength: 17,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json')
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'recall'
+    ),
+    pathArray: [ColumnType.METRICS, join('results', 'metrics.json'), 'recall'],
+    type: ColumnType.METRICS,
+    types: ['number'],
+    maxNumber: 0.695546218487395,
+    minNumber: 0.695546218487395
+  },
+  {
+    hasChildren: false,
+    label: 'roc_auc',
+    maxStringLength: 18,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json')
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.METRICS,
+      join('results', 'metrics.json'),
+      'roc_auc'
+    ),
+    pathArray: [ColumnType.METRICS, join('results', 'metrics.json'), 'roc_auc'],
+    type: ColumnType.METRICS,
+    types: ['number'],
+    maxNumber: 0.8703211951447246,
+    minNumber: 0.8703211951447246
+  },
+  {
+    hasChildren: true,
+    label: 'params.yaml',
+    parentPath: ColumnType.PARAMS,
+    path: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml'),
+    type: ColumnType.PARAMS
+  },
+  {
+    hasChildren: false,
+    label: 'classifier',
+    maxStringLength: 13,
+    parentPath: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml'),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'classifier'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'classifier'],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'drop_cols',
+    maxStringLength: 17,
+    parentPath: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml'),
+    path: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml', 'drop_cols'),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'drop_cols'],
+    type: ColumnType.PARAMS,
+    types: ['array']
+  },
+  {
+    hasChildren: true,
+    label: 'dtypes',
+    parentPath: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml'),
+    path: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml', 'dtypes'),
+    type: ColumnType.PARAMS
+  },
+  {
+    hasChildren: false,
+    label: 'Age',
+    maxStringLength: 5,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes',
+      'Age'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'dtypes', 'Age'],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'Embarked',
+    maxStringLength: 8,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes',
+      'Embarked'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'dtypes', 'Embarked'],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'Fare',
+    maxStringLength: 5,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes',
+      'Fare'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'dtypes', 'Fare'],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'Parch',
+    maxStringLength: 3,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes',
+      'Parch'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'dtypes', 'Parch'],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'Pclass',
+    maxStringLength: 8,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes',
+      'Pclass'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'dtypes', 'Pclass'],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'Sex',
+    maxStringLength: 8,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes',
+      'Sex'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'dtypes', 'Sex'],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'SibSp',
+    maxStringLength: 3,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes',
+      'SibSp'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'dtypes', 'SibSp'],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'Survived',
+    maxStringLength: 8,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'dtypes',
+      'Survived'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'dtypes', 'Survived'],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: true,
+    label: 'feature_eng',
+    parentPath: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml'),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'feature_eng'
+    ),
+    type: ColumnType.PARAMS
+  },
+  {
+    hasChildren: false,
+    label: 'featurize',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'feature_eng'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'feature_eng',
+      'featurize'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'feature_eng', 'featurize'],
+    type: ColumnType.PARAMS,
+    types: ['boolean']
+  },
+  {
+    hasChildren: true,
+    label: 'imputation',
+    parentPath: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml'),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'imputation'
+    ),
+    type: ColumnType.PARAMS
+  },
+  {
+    hasChildren: false,
+    label: 'Age',
+    maxStringLength: 7,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'imputation'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'imputation',
+      'Age'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'imputation', 'Age'],
+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 29.6991,
+    minNumber: 29.6991
+  },
+  {
+    hasChildren: false,
+    label: 'Fare',
+    maxStringLength: 7,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'imputation'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'imputation',
+      'Fare'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'imputation', 'Fare'],
+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 32.2042,
+    minNumber: 32.2042
+  },
+  {
+    hasChildren: false,
+    label: 'method',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'imputation'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'imputation',
+      'method'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'imputation', 'method'],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: true,
+    label: 'model_params',
+    parentPath: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml'),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params'
+    ),
+    type: ColumnType.PARAMS
+  },
+  {
+    hasChildren: false,
+    label: 'logistic_regression',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'logistic_regression'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'logistic_regression'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['null']
+  },
+  {
+    hasChildren: false,
+    label: 'naive_bayes',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'naive_bayes'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'naive_bayes'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['null']
+  },
+  {
+    hasChildren: false,
+    label: 'neural_network',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'neural_network'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'neural_network'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['null']
+  },
+  {
+    hasChildren: true,
+    label: 'random_forest',
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest'
+    ),
+    type: ColumnType.PARAMS
+  },
+  {
+    hasChildren: false,
+    label: 'criterion',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest',
+      'criterion'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest',
+      'criterion'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'max_depth',
+    maxStringLength: 2,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest',
+      'max_depth'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest',
+      'max_depth'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 15,
+    minNumber: 15
+  },
+  {
+    hasChildren: false,
+    label: 'max_features',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest',
+      'max_features'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest',
+      'max_features'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'min_samples_leaf',
+    maxStringLength: 1,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest',
+      'min_samples_leaf'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest',
+      'min_samples_leaf'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 6,
+    minNumber: 6
+  },
+  {
+    hasChildren: false,
+    label: 'min_samples_split',
+    maxStringLength: 1,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest',
+      'min_samples_split'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest',
+      'min_samples_split'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 9,
+    minNumber: 9
+  },
+  {
+    hasChildren: false,
+    label: 'n_estimators',
+    maxStringLength: 3,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest',
+      'n_estimators'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'random_forest',
+      'n_estimators'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 460,
+    minNumber: 460
+  },
+  {
+    hasChildren: false,
+    label: 'support_vector_machine',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'support_vector_machine'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'support_vector_machine'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['null']
+  },
+  {
+    hasChildren: false,
+    label: 'xgboost',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'model_params',
+      'xgboost'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'model_params', 'xgboost'],
+    type: ColumnType.PARAMS,
+    types: ['null']
+  },
+  {
+    hasChildren: false,
+    label: 'normalize',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml'),
+    path: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml', 'normalize'),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'normalize'],
+    type: ColumnType.PARAMS,
+    types: ['null']
+  },
+  {
+    hasChildren: true,
+    label: 'param_tuning',
+    parentPath: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml'),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning'
+    ),
+    type: ColumnType.PARAMS
+  },
+  {
+    hasChildren: false,
+    label: 'logistic_regression',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'logistic_regression'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'logistic_regression'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['null']
+  },
+  {
+    hasChildren: false,
+    label: 'naive_bayes',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'naive_bayes'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'naive_bayes'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['null']
+  },
+  {
+    hasChildren: false,
+    label: 'neural_network',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'neural_network'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'neural_network'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['null']
+  },
+  {
+    hasChildren: false,
+    label: 'num_eval',
+    maxStringLength: 3,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'num_eval'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'param_tuning', 'num_eval'],
+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 100,
+    minNumber: 100
+  },
+  {
+    hasChildren: true,
+    label: 'random_forest',
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest'
+    ),
+    type: ColumnType.PARAMS
+  },
+  {
+    hasChildren: false,
+    label: 'criterion',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest',
+      'criterion'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest',
+      'criterion'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'max_depth',
+    maxStringLength: 2,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest',
+      'max_depth'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest',
+      'max_depth'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 15,
+    minNumber: 15
+  },
+  {
+    hasChildren: false,
+    label: 'max_features',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest',
+      'max_features'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest',
+      'max_features'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'min_samples_leaf',
+    maxStringLength: 1,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest',
+      'min_samples_leaf'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest',
+      'min_samples_leaf'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 6,
+    minNumber: 6
+  },
+  {
+    hasChildren: false,
+    label: 'min_samples_split',
+    maxStringLength: 1,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest',
+      'min_samples_split'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest',
+      'min_samples_split'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 9,
+    minNumber: 9
+  },
+  {
+    hasChildren: false,
+    label: 'n_estimators',
+    maxStringLength: 3,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest',
+      'n_estimators'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'random_forest',
+      'n_estimators'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 460,
+    minNumber: 460
+  },
+  {
+    hasChildren: false,
+    label: 'scoring',
+    maxStringLength: 8,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'scoring'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'param_tuning', 'scoring'],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'support_vector_machine',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'support_vector_machine'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'param_tuning',
+      'support_vector_machine'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['null']
+  },
+  {
+    hasChildren: true,
+    label: 'predict',
+    parentPath: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml'),
+    path: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml', 'predict'),
+    type: ColumnType.PARAMS
+  },
+  {
+    hasChildren: false,
+    label: 'js_estimator',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'predict'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'predict',
+      'js_estimator'
+    ),
+    pathArray: [ColumnType.PARAMS, 'params.yaml', 'predict', 'js_estimator'],
+    type: ColumnType.PARAMS,
+    types: ['boolean']
+  },
+  {
+    hasChildren: false,
+    label: 'random_seed',
+    maxStringLength: 5,
+    parentPath: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml'),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'random_seed'
+    ),
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+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 12345,
+    minNumber: 12345
+  },
+  {
+    hasChildren: true,
+    label: 'train_test_split',
+    parentPath: buildMetricOrParamPath(ColumnType.PARAMS, 'params.yaml'),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'train_test_split'
+    ),
+    type: ColumnType.PARAMS
+  },
+  {
+    hasChildren: false,
+    label: 'n_split',
+    maxStringLength: 2,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'train_test_split'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'train_test_split',
+      'n_split'
+    ),
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+      ColumnType.PARAMS,
+      'params.yaml',
+      'train_test_split',
+      'n_split'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['number'],
+    maxNumber: 10,
+    minNumber: 10
+  },
+  {
+    hasChildren: false,
+    label: 'shuffle',
+    maxStringLength: 4,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'train_test_split'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'train_test_split',
+      'shuffle'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'train_test_split',
+      'shuffle'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['boolean']
+  },
+  {
+    hasChildren: false,
+    label: 'target_class',
+    maxStringLength: 8,
+    parentPath: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'train_test_split'
+    ),
+    path: buildMetricOrParamPath(
+      ColumnType.PARAMS,
+      'params.yaml',
+      'train_test_split',
+      'target_class'
+    ),
+    pathArray: [
+      ColumnType.PARAMS,
+      'params.yaml',
+      'train_test_split',
+      'target_class'
+    ],
+    type: ColumnType.PARAMS,
+    types: ['string']
+  },
+  {
+    hasChildren: true,
+    label: 'src',
+    parentPath: ColumnType.DEPS,
+    path: buildDepPath('src'),
+    type: ColumnType.DEPS
+  },
+  {
+    hasChildren: true,
+    label: 'data',
+    parentPath: buildDepPath('src'),
+    path: buildDepPath('src', 'data'),
+    type: ColumnType.DEPS
+  },
+  {
+    hasChildren: false,
+    label: 'make_dataset.py',
+    maxStringLength: 7,
+    parentPath: buildDepPath('src', 'data'),
+    path: buildDepPath('src', 'data', 'make_dataset.py'),
+    pathArray: [ColumnType.DEPS, join('src', 'data', 'make_dataset.py')],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: true,
+    label: 'data',
+    parentPath: ColumnType.DEPS,
+    path: buildDepPath('data'),
+    type: ColumnType.DEPS
+  },
+  {
+    hasChildren: true,
+    label: 'raw',
+    parentPath: buildDepPath('data'),
+    path: buildDepPath('data', 'raw'),
+    type: ColumnType.DEPS
+  },
+  {
+    hasChildren: false,
+    label: 'test.csv',
+    maxStringLength: 7,
+    parentPath: buildDepPath('data', 'raw'),
+    path: buildDepPath('data', 'raw', 'test.csv'),
+    pathArray: [ColumnType.DEPS, join('data', 'raw', 'test.csv')],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'train.csv',
+    maxStringLength: 7,
+    parentPath: buildDepPath('data', 'raw'),
+    path: buildDepPath('data', 'raw', 'train.csv'),
+    pathArray: [ColumnType.DEPS, join('data', 'raw', 'train.csv')],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'encode_labels.py',
+    maxStringLength: 7,
+    parentPath: buildDepPath('src', 'data'),
+    path: buildDepPath('src', 'data', 'encode_labels.py'),
+    pathArray: [ColumnType.DEPS, join('src', 'data', 'encode_labels.py')],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: true,
+    label: 'interim',
+    parentPath: buildDepPath('data'),
+    path: buildDepPath('data', 'interim'),
+    type: ColumnType.DEPS
+  },
+  {
+    hasChildren: false,
+    label: 'test_categorized.csv',
+    maxStringLength: 7,
+    parentPath: buildDepPath('data', 'interim'),
+    path: buildDepPath('data', 'interim', 'test_categorized.csv'),
+    pathArray: [
+      ColumnType.DEPS,
+      join('data', 'interim', 'test_categorized.csv')
+    ],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'train_categorized.csv',
+    maxStringLength: 7,
+    parentPath: buildDepPath('data', 'interim'),
+    path: buildDepPath('data', 'interim', 'train_categorized.csv'),
+    pathArray: [
+      ColumnType.DEPS,
+      join('data', 'interim', 'train_categorized.csv')
+    ],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'replace_nan.py',
+    maxStringLength: 7,
+    parentPath: buildDepPath('src', 'data'),
+    path: buildDepPath('src', 'data', 'replace_nan.py'),
+    pathArray: [ColumnType.DEPS, join('src', 'data', 'replace_nan.py')],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'test_nan_imputed.csv',
+    maxStringLength: 7,
+    parentPath: buildDepPath('data', 'interim'),
+    path: buildDepPath('data', 'interim', 'test_nan_imputed.csv'),
+    pathArray: [
+      ColumnType.DEPS,
+      join('data', 'interim', 'test_nan_imputed.csv')
+    ],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'train_nan_imputed.csv',
+    maxStringLength: 7,
+    parentPath: buildDepPath('data', 'interim'),
+    path: buildDepPath('data', 'interim', 'train_nan_imputed.csv'),
+    pathArray: [
+      ColumnType.DEPS,
+      join('data', 'interim', 'train_nan_imputed.csv')
+    ],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: true,
+    label: 'features',
+    parentPath: buildDepPath('src'),
+    path: buildDepPath('src', 'features'),
+    type: ColumnType.DEPS
+  },
+  {
+    hasChildren: false,
+    label: 'build_features.py',
+    maxStringLength: 7,
+    parentPath: buildDepPath('src', 'features'),
+    path: buildDepPath('src', 'features', 'build_features.py'),
+    pathArray: [ColumnType.DEPS, join('src', 'features', 'build_features.py')],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'test_featurized.csv',
+    maxStringLength: 7,
+    parentPath: buildDepPath('data', 'interim'),
+    path: buildDepPath('data', 'interim', 'test_featurized.csv'),
+    pathArray: [
+      ColumnType.DEPS,
+      join('data', 'interim', 'test_featurized.csv')
+    ],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'train_featurized.csv',
+    maxStringLength: 7,
+    parentPath: buildDepPath('data', 'interim'),
+    path: buildDepPath('data', 'interim', 'train_featurized.csv'),
+    pathArray: [
+      ColumnType.DEPS,
+      join('data', 'interim', 'train_featurized.csv')
+    ],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'normalize.py',
+    maxStringLength: 7,
+    parentPath: buildDepPath('src', 'features'),
+    path: buildDepPath('src', 'features', 'normalize.py'),
+    pathArray: [ColumnType.DEPS, join('src', 'features', 'normalize.py')],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: true,
+    label: 'processed',
+    parentPath: buildDepPath('data'),
+    path: buildDepPath('data', 'processed'),
+    type: ColumnType.DEPS
+  },
+  {
+    hasChildren: false,
+    label: 'train_processed.csv',
+    maxStringLength: 7,
+    parentPath: buildDepPath('data', 'processed'),
+    path: buildDepPath('data', 'processed', 'train_processed.csv'),
+    pathArray: [
+      ColumnType.DEPS,
+      join('data', 'processed', 'train_processed.csv')
+    ],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'split_train_dev.py',
+    maxStringLength: 7,
+    parentPath: buildDepPath('src', 'data'),
+    path: buildDepPath('src', 'data', 'split_train_dev.py'),
+    pathArray: [ColumnType.DEPS, join('src', 'data', 'split_train_dev.py')],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'split_train_dev.csv',
+    maxStringLength: 7,
+    parentPath: buildDepPath('data', 'processed'),
+    path: buildDepPath('data', 'processed', 'split_train_dev.csv'),
+    pathArray: [
+      ColumnType.DEPS,
+      join('data', 'processed', 'split_train_dev.csv')
+    ],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: true,
+    label: 'models',
+    parentPath: buildDepPath('src'),
+    path: buildDepPath('src', 'models'),
+    type: ColumnType.DEPS
+  },
+  {
+    hasChildren: false,
+    label: 'train_model.py',
+    maxStringLength: 7,
+    parentPath: buildDepPath('src', 'models'),
+    path: buildDepPath('src', 'models', 'train_model.py'),
+    pathArray: [ColumnType.DEPS, join('src', 'models', 'train_model.py')],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'test_processed.csv',
+    maxStringLength: 7,
+    parentPath: buildDepPath('data', 'processed'),
+    path: buildDepPath('data', 'processed', 'test_processed.csv'),
+    pathArray: [
+      ColumnType.DEPS,
+      join('data', 'processed', 'test_processed.csv')
+    ],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: true,
+    label: 'models',
+    parentPath: ColumnType.DEPS,
+    path: buildDepPath('models'),
+    type: ColumnType.DEPS
+  },
+  {
+    hasChildren: false,
+    label: 'estimator.pkl',
+    maxStringLength: 7,
+    parentPath: buildDepPath('models'),
+    path: buildDepPath('models', 'estimator.pkl'),
+    pathArray: [ColumnType.DEPS, join('models', 'estimator.pkl')],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'metrics.py',
+    maxStringLength: 7,
+    parentPath: buildDepPath('src', 'models'),
+    path: buildDepPath('src', 'models', 'metrics.py'),
+    pathArray: [ColumnType.DEPS, join('src', 'models', 'metrics.py')],
+    type: ColumnType.DEPS,
+    types: ['string']
+  },
+  {
+    hasChildren: false,
+    label: 'predict.py',
+    maxStringLength: 7,
+    parentPath: buildDepPath('src', 'models'),
+    path: buildDepPath('src', 'models', 'predict.py'),
+    pathArray: [ColumnType.DEPS, join('src', 'models', 'predict.py')],
+    type: ColumnType.DEPS,
+    types: ['string']
+  }
+]
+
+export default data
diff --git a/extension/src/test/fixtures/expShow/survival/output.ts b/extension/src/test/fixtures/expShow/survival/output.ts
new file mode 100644
index 0000000000..7b4b4287ff
--- /dev/null
+++ b/extension/src/test/fixtures/expShow/survival/output.ts
@@ -0,0 +1,1232 @@
+import {
+  ExperimentsOutput,
+  ExperimentStatus
+} from '../../../../cli/dvc/contract'
+import { join } from '../../../util/path'
+
+// https://dagshub.com/kingabzpro/kaggle-titanic-dvc
+const data: ExperimentsOutput = {
+  workspace: {
+    baseline: {
+      data: {
+        timestamp: null,
+        params: {
+          'params.yaml': {
+            data: {
+              classifier: 'random_forest',
+              drop_cols: ['Name', 'Cabin', 'Ticket'],
+              dtypes: {
+                Age: 'float',
+                Embarked: 'category',
+                Fare: 'float',
+                Parch: 'int',
+                Pclass: 'category',
+                Sex: 'category',
+                SibSp: 'int',
+                Survived: 'category'
+              },
+              feature_eng: { featurize: true },
+              imputation: { Age: 29.6991, Fare: 32.2042, method: 'mean' },
+              model_params: {
+                logistic_regression: null,
+                naive_bayes: null,
+                neural_network: null,
+                random_forest: {
+                  criterion: 'gini',
+                  max_depth: 15,
+                  max_features: 'auto',
+                  min_samples_leaf: 6,
+                  min_samples_split: 9,
+                  n_estimators: 460
+                },
+                support_vector_machine: null,
+                xgboost: null
+              },
+              normalize: null,
+              param_tuning: {
+                logistic_regression: null,
+                naive_bayes: null,
+                neural_network: null,
+                num_eval: 100,
+                random_forest: {
+                  criterion: 'gini',
+                  max_depth: 15,
+                  max_features: 'auto',
+                  min_samples_leaf: 6,
+                  min_samples_split: 9,
+                  n_estimators: 460
+                },
+                scoring: 'accuracy',
+                support_vector_machine: null
+              },
+              predict: { js_estimator: true },
+              random_seed: 12345,
+              train_test_split: {
+                n_split: 10,
+                shuffle: true,
+                target_class: 'Survived'
+              }
+            }
+          }
+        },
+        deps: {
+          [join('src', 'data', 'make_dataset.py')]: {
+            hash: '4f66b01ce7fbc7219ddd5479027b4fce',
+            size: 1966,
+            nfiles: null
+          },
+          [join('data', 'raw', 'test.csv')]: {
+            hash: '029c9cd22461f6dbe8d9ab01def965c6',
+            size: 28629,
+            nfiles: null
+          },
+          [join('data', 'raw', 'train.csv')]: {
+            hash: '61fdd54abdbf6a85b778e937122e1194',
+            size: 61194,
+            nfiles: null
+          },
+          [join('src', 'data', 'encode_labels.py')]: {
+            hash: '71c20e2dd8094132c4ca78915c4af31c',
+            size: 3311,
+            nfiles: null
+          },
+          [join('data', 'interim', 'test_categorized.csv')]: {
+            hash: 'f0fcdcd7bb08c23d382a665ac1436034',
+            size: 10788,
+            nfiles: null
+          },
+          [join('data', 'interim', 'train_categorized.csv')]: {
+            hash: '5d06666c95fed743140b44190fb67c77',
+            size: 23884,
+            nfiles: null
+          },
+          [join('src', 'data', 'replace_nan.py')]: {
+            hash: 'a292443ade2893463ea8e78a2b2cb7fe',
+            size: 3527,
+            nfiles: null
+          },
+          [join('data', 'interim', 'test_nan_imputed.csv')]: {
+            hash: 'cbc38434c407b0761da80a422ba97cff',
+            size: 11136,
+            nfiles: null
+          },
+          [join('data', 'interim', 'train_nan_imputed.csv')]: {
+            hash: '9edd0421f46d2f0786ea6d82fdcf4e12',
+            size: 24592,
+            nfiles: null
+          },
+          [join('src', 'features', 'build_features.py')]: {
+            hash: '15a0db18893a1dea5b5c425bbc04e2fb',
+            size: 4716,
+            nfiles: null
+          },
+          [join('data', 'interim', 'test_featurized.csv')]: {
+            hash: '6879b369c8d9f93c8ddeff61baea9ada',
+            size: 59474,
+            nfiles: null
+          },
+          [join('data', 'interim', 'train_featurized.csv')]: {
+            hash: '980d370c7991c5b991bf8c47d13beb02',
+            size: 127169,
+            nfiles: null
+          },
+          [join('src', 'features', 'normalize.py')]: {
+            hash: '06e7d4f840c84d24a8892496db8f3e19',
+            size: 1610,
+            nfiles: null
+          },
+          [join('data', 'processed', 'train_processed.csv')]: {
+            hash: '55fc818f9babfe04c7bd9a605e0f6240',
+            size: 126326,
+            nfiles: null
+          },
+          [join('src', 'data', 'split_train_dev.py')]: {
+            hash: '3ccd2f141aa14c9a0ea84e4fa6f461ca',
+            size: 2652,
+            nfiles: null
+          },
+          [join('data', 'processed', 'split_train_dev.csv')]: {
+            hash: 'd4d2c3159380a986fc2f04a8bcffda08',
+            size: 56115,
+            nfiles: null
+          },
+          [join('src', 'models', 'train_model.py')]: {
+            hash: '3edee7d3e727ce6d3587557a3924eca3',
+            size: 4110,
+            nfiles: null
+          },
+          [join('data', 'processed', 'test_processed.csv')]: {
+            hash: '0cb34fc53024fa12b32a098a32870612',
+            size: 59004,
+            nfiles: null
+          },
+          [join('models', 'estimator.pkl')]: {
+            hash: 'a97b560743390021fc662ba0496e6237',
+            size: 31660351,
+            nfiles: null
+          },
+          [join('src', 'models', 'metrics.py')]: {
+            hash: '71807a31d25c031180a695981df6fe9c',
+            size: 1742,
+            nfiles: null
+          },
+          [join('src', 'models', 'predict.py')]: {
+            hash: 'ffcea00661810b8f82c86a6c39309253',
+            size: 2954,
+            nfiles: null
+          }
+        },
+        outs: {
+          [join('data', 'raw', 'test.csv')]: {
+            hash: '029c9cd22461f6dbe8d9ab01def965c6',
+            size: 28629,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'raw', 'train.csv')]: {
+            hash: '61fdd54abdbf6a85b778e937122e1194',
+            size: 61194,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('reports', 'figures', 'data_dictionary.tex')]: {
+            hash: '10c5361db59b330722bd70b83ce0fcee',
+            size: 1521,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('reports', 'figures', 'table_one.tex')]: {
+            hash: '4581508bdb37e12d9b9b5ff03244390d',
+            size: 844,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'interim', 'test_categorized.csv')]: {
+            hash: 'f0fcdcd7bb08c23d382a665ac1436034',
+            size: 10788,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'interim', 'train_categorized.csv')]: {
+            hash: '5d06666c95fed743140b44190fb67c77',
+            size: 23884,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'interim', 'test_nan_imputed.csv')]: {
+            hash: 'cbc38434c407b0761da80a422ba97cff',
+            size: 11136,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'interim', 'train_nan_imputed.csv')]: {
+            hash: '9edd0421f46d2f0786ea6d82fdcf4e12',
+            size: 24592,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'interim', 'test_featurized.csv')]: {
+            hash: '6879b369c8d9f93c8ddeff61baea9ada',
+            size: 59474,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'interim', 'train_featurized.csv')]: {
+            hash: '980d370c7991c5b991bf8c47d13beb02',
+            size: 127169,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'processed', 'test_processed.csv')]: {
+            hash: '0cb34fc53024fa12b32a098a32870612',
+            size: 59004,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'processed', 'train_processed.csv')]: {
+            hash: '55fc818f9babfe04c7bd9a605e0f6240',
+            size: 126326,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'processed', 'split_train_dev.csv')]: {
+            hash: 'd4d2c3159380a986fc2f04a8bcffda08',
+            size: 56115,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('models', 'estimator.pkl')]: {
+            hash: 'a97b560743390021fc662ba0496e6237',
+            size: 31660351,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('results', 'test_predict_binary.csv')]: {
+            hash: '76577b506c3bc22a50d1aa61f3b940d0',
+            size: 2839,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('results', 'test_predict_proba.csv')]: {
+            hash: '84ac915213a1b4f510486a0d049f39df',
+            size: 10087,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          }
+        },
+        status: ExperimentStatus.SUCCESS,
+        executor: null,
+        metrics: {
+          [join('results', 'metrics.json')]: {
+            data: {
+              fit_time: 0.6337410688400269,
+              score_time: 0.07778854370117187,
+              accuracy: 0.8293632958801498,
+              balanced_accuracy: 0.8040020654726536,
+              f1: 0.7572265847252886,
+              gmpr: 0.7615174102573903,
+              jaccard: 0.6113136909663465,
+              precision: 0.8361572183378356,
+              recall: 0.695546218487395,
+              roc_auc: 0.8703211951447246
+            }
+          }
+        }
+      }
+    }
+  },
+  '3d5adcb974bb2c85917a5d61a489b933adaa2b7f': {
+    baseline: {
+      data: {
+        timestamp: '2021-07-16T19:54:42',
+        params: {
+          'params.yaml': {
+            data: {
+              classifier: 'random_forest',
+              drop_cols: ['Name', 'Cabin', 'Ticket'],
+              dtypes: {
+                Age: 'float',
+                Embarked: 'category',
+                Fare: 'float',
+                Parch: 'int',
+                Pclass: 'category',
+                Sex: 'category',
+                SibSp: 'int',
+                Survived: 'category'
+              },
+              feature_eng: { featurize: true },
+              imputation: { Age: 29.6991, Fare: 32.2042, method: 'mean' },
+              model_params: {
+                logistic_regression: null,
+                naive_bayes: null,
+                neural_network: null,
+                random_forest: {
+                  criterion: 'gini',
+                  max_depth: 15,
+                  max_features: 'auto',
+                  min_samples_leaf: 6,
+                  min_samples_split: 9,
+                  n_estimators: 460
+                },
+                support_vector_machine: null,
+                xgboost: null
+              },
+              normalize: null,
+              param_tuning: {
+                logistic_regression: null,
+                naive_bayes: null,
+                neural_network: null,
+                num_eval: 100,
+                random_forest: {
+                  criterion: 'gini',
+                  max_depth: 15,
+                  max_features: 'auto',
+                  min_samples_leaf: 6,
+                  min_samples_split: 9,
+                  n_estimators: 460
+                },
+                scoring: 'accuracy',
+                support_vector_machine: null
+              },
+              predict: { js_estimator: true },
+              random_seed: 12345,
+              train_test_split: {
+                n_split: 10,
+                shuffle: true,
+                target_class: 'Survived'
+              }
+            }
+          }
+        },
+        deps: {
+          [join('src', 'data', 'make_dataset.py')]: {
+            hash: '4f66b01ce7fbc7219ddd5479027b4fce',
+            size: 1966,
+            nfiles: null
+          },
+          [join('data', 'raw', 'test.csv')]: {
+            hash: '029c9cd22461f6dbe8d9ab01def965c6',
+            size: 28629,
+            nfiles: null
+          },
+          [join('data', 'raw', 'train.csv')]: {
+            hash: '61fdd54abdbf6a85b778e937122e1194',
+            size: 61194,
+            nfiles: null
+          },
+          [join('src', 'data', 'encode_labels.py')]: {
+            hash: '71c20e2dd8094132c4ca78915c4af31c',
+            size: 3311,
+            nfiles: null
+          },
+          [join('data', 'interim', 'test_categorized.csv')]: {
+            hash: 'f0fcdcd7bb08c23d382a665ac1436034',
+            size: 10788,
+            nfiles: null
+          },
+          [join('data', 'interim', 'train_categorized.csv')]: {
+            hash: '5d06666c95fed743140b44190fb67c77',
+            size: 23884,
+            nfiles: null
+          },
+          [join('src', 'data', 'replace_nan.py')]: {
+            hash: 'a292443ade2893463ea8e78a2b2cb7fe',
+            size: 3527,
+            nfiles: null
+          },
+          [join('data', 'interim', 'test_nan_imputed.csv')]: {
+            hash: 'cbc38434c407b0761da80a422ba97cff',
+            size: 11136,
+            nfiles: null
+          },
+          [join('data', 'interim', 'train_nan_imputed.csv')]: {
+            hash: '9edd0421f46d2f0786ea6d82fdcf4e12',
+            size: 24592,
+            nfiles: null
+          },
+          [join('src', 'features', 'build_features.py')]: {
+            hash: '15a0db18893a1dea5b5c425bbc04e2fb',
+            size: 4716,
+            nfiles: null
+          },
+          [join('data', 'interim', 'test_featurized.csv')]: {
+            hash: '6879b369c8d9f93c8ddeff61baea9ada',
+            size: 59474,
+            nfiles: null
+          },
+          [join('data', 'interim', 'train_featurized.csv')]: {
+            hash: '980d370c7991c5b991bf8c47d13beb02',
+            size: 127169,
+            nfiles: null
+          },
+          [join('src', 'features', 'normalize.py')]: {
+            hash: '06e7d4f840c84d24a8892496db8f3e19',
+            size: 1610,
+            nfiles: null
+          },
+          [join('data', 'processed', 'train_processed.csv')]: {
+            hash: '55fc818f9babfe04c7bd9a605e0f6240',
+            size: 126326,
+            nfiles: null
+          },
+          [join('src', 'data', 'split_train_dev.py')]: {
+            hash: '3ccd2f141aa14c9a0ea84e4fa6f461ca',
+            size: 2652,
+            nfiles: null
+          },
+          [join('data', 'processed', 'split_train_dev.csv')]: {
+            hash: 'd4d2c3159380a986fc2f04a8bcffda08',
+            size: 56115,
+            nfiles: null
+          },
+          [join('src', 'models', 'train_model.py')]: {
+            hash: '3edee7d3e727ce6d3587557a3924eca3',
+            size: 4110,
+            nfiles: null
+          },
+          [join('data', 'processed', 'test_processed.csv')]: {
+            hash: '0cb34fc53024fa12b32a098a32870612',
+            size: 59004,
+            nfiles: null
+          },
+          [join('models', 'estimator.pkl')]: {
+            hash: 'a97b560743390021fc662ba0496e6237',
+            size: 31660351,
+            nfiles: null
+          },
+          [join('src', 'models', 'metrics.py')]: {
+            hash: '71807a31d25c031180a695981df6fe9c',
+            size: 1742,
+            nfiles: null
+          },
+          [join('src', 'models', 'predict.py')]: {
+            hash: 'ffcea00661810b8f82c86a6c39309253',
+            size: 2954,
+            nfiles: null
+          }
+        },
+        outs: {
+          [join('data', 'raw', 'test.csv')]: {
+            hash: '029c9cd22461f6dbe8d9ab01def965c6',
+            size: 28629,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'raw', 'train.csv')]: {
+            hash: '61fdd54abdbf6a85b778e937122e1194',
+            size: 61194,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('reports', 'figures', 'data_dictionary.tex')]: {
+            hash: '10c5361db59b330722bd70b83ce0fcee',
+            size: 1521,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('reports', 'figures', 'table_one.tex')]: {
+            hash: '4581508bdb37e12d9b9b5ff03244390d',
+            size: 844,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'interim', 'test_categorized.csv')]: {
+            hash: 'f0fcdcd7bb08c23d382a665ac1436034',
+            size: 10788,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'interim', 'train_categorized.csv')]: {
+            hash: '5d06666c95fed743140b44190fb67c77',
+            size: 23884,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'interim', 'test_nan_imputed.csv')]: {
+            hash: 'cbc38434c407b0761da80a422ba97cff',
+            size: 11136,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'interim', 'train_nan_imputed.csv')]: {
+            hash: '9edd0421f46d2f0786ea6d82fdcf4e12',
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+          [join('data', 'processed', 'train_processed.csv')]: {
+            hash: '55fc818f9babfe04c7bd9a605e0f6240',
+            size: 126326,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('data', 'processed', 'split_train_dev.csv')]: {
+            hash: 'd4d2c3159380a986fc2f04a8bcffda08',
+            size: 56115,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('models', 'estimator.pkl')]: {
+            hash: 'a97b560743390021fc662ba0496e6237',
+            size: 31660351,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('results', 'test_predict_binary.csv')]: {
+            hash: '76577b506c3bc22a50d1aa61f3b940d0',
+            size: 2839,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          },
+          [join('results', 'test_predict_proba.csv')]: {
+            hash: '84ac915213a1b4f510486a0d049f39df',
+            size: 10087,
+            nfiles: null,
+            use_cache: true,
+            is_data_source: false
+          }
+        },
+        status: ExperimentStatus.SUCCESS,
+        executor: null,
+        metrics: {
+          [join('results', 'metrics.json')]: {
+            data: {
+              fit_time: 0.6337410688400269,
+              score_time: 0.07778854370117187,
+              accuracy: 0.8293632958801498,
+              balanced_accuracy: 0.8040020654726536,
+              f1: 0.7572265847252886,
+              gmpr: 0.7615174102573903,
+              jaccard: 0.6113136909663465,
+              precision: 0.8361572183378356,
+              recall: 0.695546218487395,
+              roc_auc: 0.8703211951447246
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+export default data
diff --git a/extension/src/test/fixtures/expShow/survival/rows.ts b/extension/src/test/fixtures/expShow/survival/rows.ts
new file mode 100644
index 0000000000..7d61d24a07
--- /dev/null
+++ b/extension/src/test/fixtures/expShow/survival/rows.ts
@@ -0,0 +1,1098 @@
+import { join } from '../../../util/path'
+import { ExperimentStatus, Row } from '../../../../experiments/webview/contract'
+
+const data: Row[] = [
+  {
+    id: 'workspace',
+    label: 'workspace',
+    outs: {
+      [join('data', 'raw', 'test.csv')]: {
+        hash: '029c9cd22461f6dbe8d9ab01def965c6',
+        size: 28629,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'raw', 'train.csv')]: {
+        hash: '61fdd54abdbf6a85b778e937122e1194',
+        size: 61194,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('reports', 'figures', 'data_dictionary.tex')]: {
+        hash: '10c5361db59b330722bd70b83ce0fcee',
+        size: 1521,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('reports', 'figures', 'table_one.tex')]: {
+        hash: '4581508bdb37e12d9b9b5ff03244390d',
+        size: 844,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'test_categorized.csv')]: {
+        hash: 'f0fcdcd7bb08c23d382a665ac1436034',
+        size: 10788,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'train_categorized.csv')]: {
+        hash: '5d06666c95fed743140b44190fb67c77',
+        size: 23884,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'test_nan_imputed.csv')]: {
+        hash: 'cbc38434c407b0761da80a422ba97cff',
+        size: 11136,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'train_nan_imputed.csv')]: {
+        hash: '9edd0421f46d2f0786ea6d82fdcf4e12',
+        size: 24592,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'test_featurized.csv')]: {
+        hash: '6879b369c8d9f93c8ddeff61baea9ada',
+        size: 59474,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'train_featurized.csv')]: {
+        hash: '980d370c7991c5b991bf8c47d13beb02',
+        size: 127169,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'processed', 'test_processed.csv')]: {
+        hash: '0cb34fc53024fa12b32a098a32870612',
+        size: 59004,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'processed', 'train_processed.csv')]: {
+        hash: '55fc818f9babfe04c7bd9a605e0f6240',
+        size: 126326,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'processed', 'split_train_dev.csv')]: {
+        hash: 'd4d2c3159380a986fc2f04a8bcffda08',
+        size: 56115,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('models', 'estimator.pkl')]: {
+        hash: 'a97b560743390021fc662ba0496e6237',
+        size: 31660351,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('results', 'test_predict_binary.csv')]: {
+        hash: '76577b506c3bc22a50d1aa61f3b940d0',
+        size: 2839,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('results', 'test_predict_proba.csv')]: {
+        hash: '84ac915213a1b4f510486a0d049f39df',
+        size: 10087,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      }
+    },
+    status: ExperimentStatus.SUCCESS,
+    executor: null,
+    metrics: {
+      [join('results', 'metrics.json')]: {
+        fit_time: 0.6337410688400269,
+        score_time: 0.07778854370117187,
+        accuracy: 0.8293632958801498,
+        balanced_accuracy: 0.8040020654726536,
+        f1: 0.7572265847252886,
+        gmpr: 0.7615174102573903,
+        jaccard: 0.6113136909663465,
+        precision: 0.8361572183378356,
+        recall: 0.695546218487395,
+        roc_auc: 0.8703211951447246
+      }
+    },
+    params: {
+      'params.yaml': {
+        classifier: 'random_forest',
+        drop_cols: ['Name', 'Cabin', 'Ticket'],
+        dtypes: {
+          Age: 'float',
+          Embarked: 'category',
+          Fare: 'float',
+          Parch: 'int',
+          Pclass: 'category',
+          Sex: 'category',
+          SibSp: 'int',
+          Survived: 'category'
+        },
+        feature_eng: { featurize: true },
+        imputation: { Age: 29.6991, Fare: 32.2042, method: 'mean' },
+        model_params: {
+          logistic_regression: null,
+          naive_bayes: null,
+          neural_network: null,
+          random_forest: {
+            criterion: 'gini',
+            max_depth: 15,
+            max_features: 'auto',
+            min_samples_leaf: 6,
+            min_samples_split: 9,
+            n_estimators: 460
+          },
+          support_vector_machine: null,
+          xgboost: null
+        },
+        normalize: null,
+        param_tuning: {
+          logistic_regression: null,
+          naive_bayes: null,
+          neural_network: null,
+          num_eval: 100,
+          random_forest: {
+            criterion: 'gini',
+            max_depth: 15,
+            max_features: 'auto',
+            min_samples_leaf: 6,
+            min_samples_split: 9,
+            n_estimators: 460
+          },
+          scoring: 'accuracy',
+          support_vector_machine: null
+        },
+        predict: { js_estimator: true },
+        random_seed: 12345,
+        train_test_split: {
+          n_split: 10,
+          shuffle: true,
+          target_class: 'Survived'
+        }
+      }
+    },
+    deps: {
+      [join('src', 'data', 'make_dataset.py')]: {
+        changes: false,
+        value: '4f66b01'
+      },
+      [join('data', 'raw', 'test.csv')]: { changes: false, value: '029c9cd' },
+      [join('data', 'raw', 'train.csv')]: { changes: false, value: '61fdd54' },
+      [join('src', 'data', 'encode_labels.py')]: {
+        changes: false,
+        value: '71c20e2'
+      },
+      [join('data', 'interim', 'test_categorized.csv')]: {
+        changes: false,
+        value: 'f0fcdcd'
+      },
+      [join('data', 'interim', 'train_categorized.csv')]: {
+        changes: false,
+        value: '5d06666'
+      },
+      [join('src', 'data', 'replace_nan.py')]: {
+        changes: false,
+        value: 'a292443'
+      },
+      [join('data', 'interim', 'test_nan_imputed.csv')]: {
+        changes: false,
+        value: 'cbc3843'
+      },
+      [join('data', 'interim', 'train_nan_imputed.csv')]: {
+        changes: false,
+        value: '9edd042'
+      },
+      [join('src', 'features', 'build_features.py')]: {
+        changes: false,
+        value: '15a0db1'
+      },
+      [join('data', 'interim', 'test_featurized.csv')]: {
+        changes: false,
+        value: '6879b36'
+      },
+      [join('data', 'interim', 'train_featurized.csv')]: {
+        changes: false,
+        value: '980d370'
+      },
+      [join('src', 'features', 'normalize.py')]: {
+        changes: false,
+        value: '06e7d4f'
+      },
+      [join('data', 'processed', 'train_processed.csv')]: {
+        changes: false,
+        value: '55fc818'
+      },
+      [join('src', 'data', 'split_train_dev.py')]: {
+        changes: false,
+        value: '3ccd2f1'
+      },
+      [join('data', 'processed', 'split_train_dev.csv')]: {
+        changes: false,
+        value: 'd4d2c31'
+      },
+      [join('src', 'models', 'train_model.py')]: {
+        changes: false,
+        value: '3edee7d'
+      },
+      [join('data', 'processed', 'test_processed.csv')]: {
+        changes: false,
+        value: '0cb34fc'
+      },
+      [join('models', 'estimator.pkl')]: { changes: false, value: 'a97b560' },
+      [join('src', 'models', 'metrics.py')]: {
+        changes: false,
+        value: '71807a3'
+      },
+      [join('src', 'models', 'predict.py')]: {
+        changes: false,
+        value: 'ffcea00'
+      }
+    },
+    displayColor: '#945dd6',
+    selected: true,
+    starred: false
+  },
+  {
+    id: 'master',
+    label: 'master',
+    outs: {
+      [join('data', 'raw', 'test.csv')]: {
+        hash: '029c9cd22461f6dbe8d9ab01def965c6',
+        size: 28629,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'raw', 'train.csv')]: {
+        hash: '61fdd54abdbf6a85b778e937122e1194',
+        size: 61194,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('reports', 'figures', 'data_dictionary.tex')]: {
+        hash: '10c5361db59b330722bd70b83ce0fcee',
+        size: 1521,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('reports', 'figures', 'table_one.tex')]: {
+        hash: '4581508bdb37e12d9b9b5ff03244390d',
+        size: 844,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'test_categorized.csv')]: {
+        hash: 'f0fcdcd7bb08c23d382a665ac1436034',
+        size: 10788,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'train_categorized.csv')]: {
+        hash: '5d06666c95fed743140b44190fb67c77',
+        size: 23884,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'test_nan_imputed.csv')]: {
+        hash: 'cbc38434c407b0761da80a422ba97cff',
+        size: 11136,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'train_nan_imputed.csv')]: {
+        hash: '9edd0421f46d2f0786ea6d82fdcf4e12',
+        size: 24592,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'test_featurized.csv')]: {
+        hash: '6879b369c8d9f93c8ddeff61baea9ada',
+        size: 59474,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'train_featurized.csv')]: {
+        hash: '980d370c7991c5b991bf8c47d13beb02',
+        size: 127169,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'processed', 'test_processed.csv')]: {
+        hash: '0cb34fc53024fa12b32a098a32870612',
+        size: 59004,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'processed', 'train_processed.csv')]: {
+        hash: '55fc818f9babfe04c7bd9a605e0f6240',
+        size: 126326,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'processed', 'split_train_dev.csv')]: {
+        hash: 'd4d2c3159380a986fc2f04a8bcffda08',
+        size: 56115,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('models', 'estimator.pkl')]: {
+        hash: 'a97b560743390021fc662ba0496e6237',
+        size: 31660351,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('results', 'test_predict_binary.csv')]: {
+        hash: '76577b506c3bc22a50d1aa61f3b940d0',
+        size: 2839,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('results', 'test_predict_proba.csv')]: {
+        hash: '84ac915213a1b4f510486a0d049f39df',
+        size: 10087,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      }
+    },
+    status: ExperimentStatus.SUCCESS,
+    executor: null,
+    name: 'master',
+    sha: '3d5adcb974bb2c85917a5d61a489b933adaa2b7f',
+    Created: '2021-07-16T19:54:42',
+    metrics: {
+      [join('results', 'metrics.json')]: {
+        fit_time: 0.6337410688400269,
+        score_time: 0.07778854370117187,
+        accuracy: 0.8293632958801498,
+        balanced_accuracy: 0.8040020654726536,
+        f1: 0.7572265847252886,
+        gmpr: 0.7615174102573903,
+        jaccard: 0.6113136909663465,
+        precision: 0.8361572183378356,
+        recall: 0.695546218487395,
+        roc_auc: 0.8703211951447246
+      }
+    },
+    params: {
+      'params.yaml': {
+        classifier: 'random_forest',
+        drop_cols: ['Name', 'Cabin', 'Ticket'],
+        dtypes: {
+          Age: 'float',
+          Embarked: 'category',
+          Fare: 'float',
+          Parch: 'int',
+          Pclass: 'category',
+          Sex: 'category',
+          SibSp: 'int',
+          Survived: 'category'
+        },
+        feature_eng: { featurize: true },
+        imputation: { Age: 29.6991, Fare: 32.2042, method: 'mean' },
+        model_params: {
+          logistic_regression: null,
+          naive_bayes: null,
+          neural_network: null,
+          random_forest: {
+            criterion: 'gini',
+            max_depth: 15,
+            max_features: 'auto',
+            min_samples_leaf: 6,
+            min_samples_split: 9,
+            n_estimators: 460
+          },
+          support_vector_machine: null,
+          xgboost: null
+        },
+        normalize: null,
+        param_tuning: {
+          logistic_regression: null,
+          naive_bayes: null,
+          neural_network: null,
+          num_eval: 100,
+          random_forest: {
+            criterion: 'gini',
+            max_depth: 15,
+            max_features: 'auto',
+            min_samples_leaf: 6,
+            min_samples_split: 9,
+            n_estimators: 460
+          },
+          scoring: 'accuracy',
+          support_vector_machine: null
+        },
+        predict: { js_estimator: true },
+        random_seed: 12345,
+        train_test_split: {
+          n_split: 10,
+          shuffle: true,
+          target_class: 'Survived'
+        }
+      }
+    },
+    deps: {
+      [join('src', 'data', 'make_dataset.py')]: {
+        changes: false,
+        value: '4f66b01'
+      },
+      [join('data', 'raw', 'test.csv')]: { changes: false, value: '029c9cd' },
+      [join('data', 'raw', 'train.csv')]: { changes: false, value: '61fdd54' },
+      [join('src', 'data', 'encode_labels.py')]: {
+        changes: false,
+        value: '71c20e2'
+      },
+      [join('data', 'interim', 'test_categorized.csv')]: {
+        changes: false,
+        value: 'f0fcdcd'
+      },
+      [join('data', 'interim', 'train_categorized.csv')]: {
+        changes: false,
+        value: '5d06666'
+      },
+      [join('src', 'data', 'replace_nan.py')]: {
+        changes: false,
+        value: 'a292443'
+      },
+      [join('data', 'interim', 'test_nan_imputed.csv')]: {
+        changes: false,
+        value: 'cbc3843'
+      },
+      [join('data', 'interim', 'train_nan_imputed.csv')]: {
+        changes: false,
+        value: '9edd042'
+      },
+      [join('src', 'features', 'build_features.py')]: {
+        changes: false,
+        value: '15a0db1'
+      },
+      [join('data', 'interim', 'test_featurized.csv')]: {
+        changes: false,
+        value: '6879b36'
+      },
+      [join('data', 'interim', 'train_featurized.csv')]: {
+        changes: false,
+        value: '980d370'
+      },
+      [join('src', 'features', 'normalize.py')]: {
+        changes: false,
+        value: '06e7d4f'
+      },
+      [join('data', 'processed', 'train_processed.csv')]: {
+        changes: false,
+        value: '55fc818'
+      },
+      [join('src', 'data', 'split_train_dev.py')]: {
+        changes: false,
+        value: '3ccd2f1'
+      },
+      [join('data', 'processed', 'split_train_dev.csv')]: {
+        changes: false,
+        value: 'd4d2c31'
+      },
+      [join('src', 'models', 'train_model.py')]: {
+        changes: false,
+        value: '3edee7d'
+      },
+      [join('data', 'processed', 'test_processed.csv')]: {
+        changes: false,
+        value: '0cb34fc'
+      },
+      [join('models', 'estimator.pkl')]: { changes: false, value: 'a97b560' },
+      [join('src', 'models', 'metrics.py')]: {
+        changes: false,
+        value: '71807a3'
+      },
+      [join('src', 'models', 'predict.py')]: {
+        changes: false,
+        value: 'ffcea00'
+      }
+    },
+    displayColor: '#13adc7',
+    selected: true,
+    starred: false
+  },
+  {
+    id: 'a49e03966a1f9f1299ec222ebc4bed8625d2c54d',
+    label: 'a49e039',
+    outs: {
+      [join('data', 'raw', 'test.csv')]: {
+        hash: '029c9cd22461f6dbe8d9ab01def965c6',
+        size: 28629,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'raw', 'train.csv')]: {
+        hash: '61fdd54abdbf6a85b778e937122e1194',
+        size: 61194,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('reports', 'figures', 'data_dictionary.tex')]: {
+        hash: '10c5361db59b330722bd70b83ce0fcee',
+        size: 1521,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('reports', 'figures', 'table_one.tex')]: {
+        hash: '4581508bdb37e12d9b9b5ff03244390d',
+        size: 844,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'test_categorized.csv')]: {
+        hash: 'f0fcdcd7bb08c23d382a665ac1436034',
+        size: 10788,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'train_categorized.csv')]: {
+        hash: '5d06666c95fed743140b44190fb67c77',
+        size: 23884,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'test_nan_imputed.csv')]: {
+        hash: 'cbc38434c407b0761da80a422ba97cff',
+        size: 11136,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'train_nan_imputed.csv')]: {
+        hash: '9edd0421f46d2f0786ea6d82fdcf4e12',
+        size: 24592,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'test_featurized.csv')]: {
+        hash: '6879b369c8d9f93c8ddeff61baea9ada',
+        size: 59474,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'train_featurized.csv')]: {
+        hash: '980d370c7991c5b991bf8c47d13beb02',
+        size: 127169,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'processed', 'test_processed.csv')]: {
+        hash: '0cb34fc53024fa12b32a098a32870612',
+        size: 59004,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'processed', 'train_processed.csv')]: {
+        hash: '55fc818f9babfe04c7bd9a605e0f6240',
+        size: 126326,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'processed', 'split_train_dev.csv')]: {
+        hash: 'd4d2c3159380a986fc2f04a8bcffda08',
+        size: 56115,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('models', 'estimator.pkl')]: {
+        hash: 'a97b560743390021fc662ba0496e6237',
+        size: 31660351,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('results', 'test_predict_binary.csv')]: {
+        hash: '76577b506c3bc22a50d1aa61f3b940d0',
+        size: 2839,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('results', 'test_predict_proba.csv')]: {
+        hash: '84ac915213a1b4f510486a0d049f39df',
+        size: 10087,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      }
+    },
+    status: ExperimentStatus.SUCCESS,
+    executor: null,
+    sha: 'a49e03966a1f9f1299ec222ebc4bed8625d2c54d',
+    Created: '2021-07-16T19:50:39',
+    metrics: {
+      [join('results', 'metrics.json')]: {
+        fit_time: 0.6337410688400269,
+        score_time: 0.07778854370117187,
+        accuracy: 0.8293632958801498,
+        balanced_accuracy: 0.8040020654726536,
+        f1: 0.7572265847252886,
+        gmpr: 0.7615174102573903,
+        jaccard: 0.6113136909663465,
+        precision: 0.8361572183378356,
+        recall: 0.695546218487395,
+        roc_auc: 0.8703211951447246
+      }
+    },
+    params: {
+      'params.yaml': {
+        classifier: 'random_forest',
+        drop_cols: ['Name', 'Cabin', 'Ticket'],
+        dtypes: {
+          Age: 'float',
+          Embarked: 'category',
+          Fare: 'float',
+          Parch: 'int',
+          Pclass: 'category',
+          Sex: 'category',
+          SibSp: 'int',
+          Survived: 'category'
+        },
+        feature_eng: { featurize: true },
+        imputation: { Age: 29.6991, Fare: 32.2042, method: 'mean' },
+        model_params: {
+          logistic_regression: null,
+          naive_bayes: null,
+          neural_network: null,
+          random_forest: {
+            criterion: 'gini',
+            max_depth: 15,
+            max_features: 'auto',
+            min_samples_leaf: 6,
+            min_samples_split: 9,
+            n_estimators: 460
+          },
+          support_vector_machine: null,
+          xgboost: null
+        },
+        normalize: null,
+        param_tuning: {
+          logistic_regression: null,
+          naive_bayes: null,
+          neural_network: null,
+          num_eval: 100,
+          random_forest: {
+            criterion: 'gini',
+            max_depth: 15,
+            max_features: 'auto',
+            min_samples_leaf: 6,
+            min_samples_split: 9,
+            n_estimators: 460
+          },
+          scoring: 'accuracy',
+          support_vector_machine: null
+        },
+        predict: { js_estimator: true },
+        random_seed: 12345,
+        train_test_split: {
+          n_split: 10,
+          shuffle: true,
+          target_class: 'Survived'
+        }
+      }
+    },
+    deps: {
+      [join('src', 'data', 'make_dataset.py')]: {
+        changes: false,
+        value: '4f66b01'
+      },
+      [join('data', 'raw', 'test.csv')]: { changes: false, value: '029c9cd' },
+      [join('data', 'raw', 'train.csv')]: { changes: false, value: '61fdd54' },
+      [join('src', 'data', 'encode_labels.py')]: {
+        changes: false,
+        value: '71c20e2'
+      },
+      [join('data', 'interim', 'test_categorized.csv')]: {
+        changes: false,
+        value: 'f0fcdcd'
+      },
+      [join('data', 'interim', 'train_categorized.csv')]: {
+        changes: false,
+        value: '5d06666'
+      },
+      [join('src', 'data', 'replace_nan.py')]: {
+        changes: false,
+        value: 'e1a2e28'
+      },
+      [join('data', 'interim', 'test_nan_imputed.csv')]: {
+        changes: false,
+        value: 'cbc3843'
+      },
+      [join('data', 'interim', 'train_nan_imputed.csv')]: {
+        changes: false,
+        value: '9edd042'
+      },
+      [join('src', 'features', 'build_features.py')]: {
+        changes: false,
+        value: '15a0db1'
+      },
+      [join('data', 'interim', 'test_featurized.csv')]: {
+        changes: false,
+        value: '6879b36'
+      },
+      [join('data', 'interim', 'train_featurized.csv')]: {
+        changes: false,
+        value: '980d370'
+      },
+      [join('src', 'features', 'normalize.py')]: {
+        changes: false,
+        value: '06e7d4f'
+      },
+      [join('data', 'processed', 'train_processed.csv')]: {
+        changes: false,
+        value: '55fc818'
+      },
+      [join('src', 'data', 'split_train_dev.py')]: {
+        changes: false,
+        value: '3ccd2f1'
+      },
+      [join('data', 'processed', 'split_train_dev.csv')]: {
+        changes: false,
+        value: 'd4d2c31'
+      },
+      [join('src', 'models', 'train_model.py')]: {
+        changes: false,
+        value: '3edee7d'
+      },
+      [join('data', 'processed', 'test_processed.csv')]: {
+        changes: false,
+        value: '0cb34fc'
+      },
+      [join('models', 'estimator.pkl')]: { changes: false, value: 'a97b560' },
+      [join('src', 'models', 'metrics.py')]: {
+        changes: false,
+        value: '71807a3'
+      },
+      [join('src', 'models', 'predict.py')]: {
+        changes: false,
+        value: 'ffcea00'
+      }
+    },
+    displayColor: '#f46837',
+    selected: true,
+    starred: false
+  },
+  {
+    id: '4f7b50c3d171a11b6cfcd04416a16fc80b61018d',
+    label: '4f7b50c',
+    outs: {
+      [join('data', 'raw', 'test.csv')]: {
+        hash: '029c9cd22461f6dbe8d9ab01def965c6',
+        size: 28629,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'raw', 'train.csv')]: {
+        hash: '61fdd54abdbf6a85b778e937122e1194',
+        size: 61194,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('reports', 'figures', 'data_dictionary.tex')]: {
+        hash: '10c5361db59b330722bd70b83ce0fcee',
+        size: 1521,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('reports', 'figures', 'table_one.tex')]: {
+        hash: '4581508bdb37e12d9b9b5ff03244390d',
+        size: 844,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'test_categorized.csv')]: {
+        hash: 'f0fcdcd7bb08c23d382a665ac1436034',
+        size: 10788,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'train_categorized.csv')]: {
+        hash: '5d06666c95fed743140b44190fb67c77',
+        size: 23884,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'test_nan_imputed.csv')]: {
+        hash: 'cbc38434c407b0761da80a422ba97cff',
+        size: 11136,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'train_nan_imputed.csv')]: {
+        hash: '9edd0421f46d2f0786ea6d82fdcf4e12',
+        size: 24592,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'test_featurized.csv')]: {
+        hash: '6879b369c8d9f93c8ddeff61baea9ada',
+        size: 59474,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'interim', 'train_featurized.csv')]: {
+        hash: '980d370c7991c5b991bf8c47d13beb02',
+        size: 127169,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'processed', 'test_processed.csv')]: {
+        hash: '0cb34fc53024fa12b32a098a32870612',
+        size: 59004,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'processed', 'train_processed.csv')]: {
+        hash: '55fc818f9babfe04c7bd9a605e0f6240',
+        size: 126326,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('data', 'processed', 'split_train_dev.csv')]: {
+        hash: 'd4d2c3159380a986fc2f04a8bcffda08',
+        size: 56115,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('models', 'estimator.pkl')]: {
+        hash: 'a97b560743390021fc662ba0496e6237',
+        size: 31660351,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('results', 'test_predict_binary.csv')]: {
+        hash: '76577b506c3bc22a50d1aa61f3b940d0',
+        size: 2839,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      },
+      [join('results', 'test_predict_proba.csv')]: {
+        hash: '84ac915213a1b4f510486a0d049f39df',
+        size: 10087,
+        nfiles: null,
+        use_cache: true,
+        is_data_source: false
+      }
+    },
+    status: ExperimentStatus.SUCCESS,
+    executor: null,
+    sha: '4f7b50c3d171a11b6cfcd04416a16fc80b61018d',
+    Created: '2021-07-16T19:48:45',
+    metrics: {
+      [join('results', 'metrics.json')]: {
+        fit_time: 0.6337410688400269,
+        score_time: 0.07778854370117187,
+        accuracy: 0.8293632958801498,
+        balanced_accuracy: 0.8040020654726536,
+        f1: 0.7572265847252886,
+        gmpr: 0.7615174102573903,
+        jaccard: 0.6113136909663465,
+        precision: 0.8361572183378356,
+        recall: 0.695546218487395,
+        roc_auc: 0.8703211951447246
+      }
+    },
+    params: {
+      'params.yaml': {
+        classifier: 'random_forest',
+        drop_cols: ['Name', 'Cabin', 'Ticket'],
+        dtypes: {
+          Age: 'float',
+          Embarked: 'category',
+          Fare: 'float',
+          Parch: 'int',
+          Pclass: 'category',
+          Sex: 'category',
+          SibSp: 'int',
+          Survived: 'category'
+        },
+        feature_eng: { featurize: true },
+        imputation: { Age: 29.6991, Fare: 32.2042, method: 'mean' },
+        model_params: {
+          logistic_regression: null,
+          naive_bayes: null,
+          neural_network: null,
+          random_forest: {
+            criterion: 'gini',
+            max_depth: 15,
+            max_features: 'auto',
+            min_samples_leaf: 6,
+            min_samples_split: 9,
+            n_estimators: 460
+          },
+          support_vector_machine: null,
+          xgboost: null
+        },
+        normalize: null,
+        param_tuning: {
+          logistic_regression: null,
+          naive_bayes: null,
+          neural_network: null,
+          num_eval: 100,
+          random_forest: {
+            criterion: 'gini',
+            max_depth: 15,
+            max_features: 'auto',
+            min_samples_leaf: 6,
+            min_samples_split: 9,
+            n_estimators: 460
+          },
+          scoring: 'accuracy',
+          support_vector_machine: null
+        },
+        predict: { js_estimator: true },
+        random_seed: 12345,
+        train_test_split: {
+          n_split: 10,
+          shuffle: true,
+          target_class: 'Survived'
+        }
+      }
+    },
+    deps: {
+      [join('src', 'data', 'make_dataset.py')]: {
+        changes: false,
+        value: '4f66b01'
+      },
+      [join('data', 'raw', 'test.csv')]: { changes: false, value: '029c9cd' },
+      [join('data', 'raw', 'train.csv')]: { changes: false, value: '61fdd54' },
+      [join('src', 'data', 'encode_labels.py')]: {
+        changes: false,
+        value: '71c20e2'
+      },
+      [join('data', 'interim', 'test_categorized.csv')]: {
+        changes: false,
+        value: 'f0fcdcd'
+      },
+      [join('data', 'interim', 'train_categorized.csv')]: {
+        changes: false,
+        value: '5d06666'
+      },
+      [join('src', 'data', 'replace_nan.py')]: {
+        changes: false,
+        value: 'e1a2e28'
+      },
+      [join('data', 'interim', 'test_nan_imputed.csv')]: {
+        changes: false,
+        value: 'cbc3843'
+      },
+      [join('data', 'interim', 'train_nan_imputed.csv')]: {
+        changes: false,
+        value: '9edd042'
+      },
+      [join('src', 'features', 'build_features.py')]: {
+        changes: false,
+        value: '15a0db1'
+      },
+      [join('data', 'interim', 'test_featurized.csv')]: {
+        changes: false,
+        value: '6879b36'
+      },
+      [join('data', 'interim', 'train_featurized.csv')]: {
+        changes: false,
+        value: '980d370'
+      },
+      [join('src', 'features', 'normalize.py')]: {
+        changes: false,
+        value: '06e7d4f'
+      },
+      [join('data', 'processed', 'train_processed.csv')]: {
+        changes: false,
+        value: '55fc818'
+      },
+      [join('src', 'data', 'split_train_dev.py')]: {
+        changes: false,
+        value: '3ccd2f1'
+      },
+      [join('data', 'processed', 'split_train_dev.csv')]: {
+        changes: false,
+        value: 'd4d2c31'
+      },
+      [join('src', 'models', 'train_model.py')]: {
+        changes: false,
+        value: '3edee7d'
+      },
+      [join('data', 'processed', 'test_processed.csv')]: {
+        changes: false,
+        value: '0cb34fc'
+      },
+      [join('models', 'estimator.pkl')]: { changes: false, value: 'a97b560' },
+      [join('src', 'models', 'metrics.py')]: {
+        changes: false,
+        value: '71807a3'
+      },
+      [join('src', 'models', 'predict.py')]: {
+        changes: false,
+        value: 'ffcea00'
+      }
+    },
+    displayColor: '#48bb78',
+    selected: true,
+    starred: false
+  }
+]
+
+export default data
diff --git a/extension/src/test/fixtures/expShow/survival/tableData.ts b/extension/src/test/fixtures/expShow/survival/tableData.ts
new file mode 100644
index 0000000000..7b0b2b427c
--- /dev/null
+++ b/extension/src/test/fixtures/expShow/survival/tableData.ts
@@ -0,0 +1,19 @@
+import { TableData } from '../../../../experiments/webview/contract'
+import rowsFixture from './rows'
+import columnsFixture from './columns'
+
+const data: TableData = {
+  filteredCounts: { experiments: 0, checkpoints: 0 },
+  rows: rowsFixture,
+  columns: columnsFixture,
+  filters: [],
+  hasCheckpoints: true,
+  hasRunningExperiment: true,
+  hasColumns: true,
+  sorts: [],
+  changes: [],
+  columnOrder: [],
+  columnWidths: {}
+}
+
+export default data
diff --git a/webview/src/stories/Table.stories.tsx b/webview/src/stories/Table.stories.tsx
index 2f81adb8e8..2d20977fc3 100644
--- a/webview/src/stories/Table.stories.tsx
+++ b/webview/src/stories/Table.stories.tsx
@@ -7,6 +7,7 @@ import columnsFixture from 'dvc/src/test/fixtures/expShow/base/columns'
 import workspaceChangesFixture from 'dvc/src/test/fixtures/expShow/base/workspaceChanges'
 import deeplyNestedTableData from 'dvc/src/test/fixtures/expShow/deeplyNested/tableData'
 import dataTypesTableFixture from 'dvc/src/test/fixtures/expShow/dataTypes/tableData'
+import survivalTableData from 'dvc/src/test/fixtures/expShow/survival/tableData'
 import { timestampColumn } from 'dvc/src/experiments/columns/constants'
 import {
   ExperimentStatus,
@@ -126,6 +127,11 @@ const Template: Story<{ tableData: TableDataState }> = ({ tableData }) => {
 
 export const WithData = Template.bind({})
 
+export const WithSurvivalData = Template.bind({})
+WithSurvivalData.args = {
+  tableData: { ...survivalTableData, hasData: true }
+}
+
 export const WithMiddleStates = Template.bind({})
 const tableDataWithSomeSelectedExperiments = setExperimentsAsSelected(
   tableData,