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[FEAT] INT-2427 Blueprint model actions (#452) (#454)
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* update use cases test with new fields

* update json
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@thesteady
thesteady authored Oct 30, 2024
1 parent f752b65 commit e9ef3f6
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36 changes: 36 additions & 0 deletions test_use_cases.py
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Expand Up @@ -16,6 +16,42 @@
"type": "string",
"enum": ["New", "Beta", "Preview", "Popular", "Deprecated","Labs"],
},
"modelActions": {
"type": "array",
"items": {
"type": "object",
"properties": {
"modelCategory": {
"type": "string",
"enum": [
"synthetics",
"transform",
"classify",
"evaluate",
],
},
"modelType": {
"type": "string",
"enum": [
"actgan",
"navigator_ft",
"amplify",
"classify",
"ctgan",
"evaluate",
"gpt_x",
"lstm",
"synthetics",
"timeseries_dgan",
"transform",
"transform_v2",
"tabular_dp",
],
},
"defaultConfig": {"type": "string"},
}
}
},
"modelType": {
"type": "string",
"enum": [
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91 changes: 91 additions & 0 deletions use_cases/gretel.json
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Expand Up @@ -27,6 +27,13 @@
"modelType": "navigator_ft",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/navigator-ft.yml",
"modelActions": [
{
"modelType": "navigator_ft",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/navigator-ft.yml"
}
],
"button1": {
"label": "SDK Notebook",
"link": "https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/docs/notebooks/demo/navigator-fine-tuning-intro-tutorial.ipynb"
Expand All @@ -49,6 +56,13 @@
"modelType": "transform_v2",
"modelCategory": "transform",
"defaultConfig": "config_templates/gretel/transform/default.yml",
"modelActions": [
{
"modelType": "transform_v2",
"modelCategory": "transform",
"defaultConfig": "config_templates/gretel/transform/default.yml"
}
],
"sampleDataset": {
"fileName": "patients.csv",
"description": "This patient dataset contains names, addresses and other personally identifiable information, which needs to be redacted before the dataset can be shared or used to train ML models.",
Expand All @@ -69,6 +83,13 @@
"modelType": "gpt_x",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/natural-language-differential-privacy.yml",
"modelActions": [
{
"modelType": "gpt_x",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/natural-language-differential-privacy.yml"
}
],
"button1": {
"label": "SDK Notebook",
"link": "https://colab.research.google.com/github/gretelai/gretel-blueprints/blob/main/docs/notebooks/generate_differentially_private_synthetic_text.ipynb"
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"modelType": "synthetics",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/tabular-actgan.yml",
"modelActions": [
{
"modelType": "synthetics",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/tabular-actgan.yml"
}
],
"sampleDataset": {
"fileName": "sample-synthetic-healthcare.csv",
"description": "Use this sample electronic health records (EHR) dataset to synthesize an entirely new set of statistically equivalent records.",
Expand All @@ -134,6 +162,13 @@
"modelType": "tabular_dp",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/tabular-differential-privacy.yml",
"modelActions": [
{
"modelType": "tabular_dp",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/tabular-differential-privacy.yml"
}
],
"sampleDataset": {
"fileName": "bank_marketing_small.csv",
"description": "This dataset contains direct marketing campaign details (phone calls) from a Portuguese financial institution. It has sensitive information such as demographics and financials, which can benefit from privacy preserving techniques before sharing. ",
Expand All @@ -153,6 +188,13 @@
"modelType": "gpt_x",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/natural-language.yml",
"modelActions": [
{
"modelType": "gpt_x",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/natural-language.yml"
}
],
"sampleDataset": {
"fileName": "sample-banking-questions-intents.csv",
"description": "Create realistic banking-related questions and intent labels using this sample dataset.",
Expand All @@ -179,6 +221,13 @@
"modelType": "timeseries_dgan",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/time-series.yml",
"modelActions": [
{
"modelType": "timeseries_dgan",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/time-series.yml"
}
],
"sampleDataset": {
"fileName": "daily-website-visitors.csv",
"description": "Safely synthesize a dataset of daily website visitors while maintaining correlations and data patterns.",
Expand All @@ -197,6 +246,13 @@
"modelType": "transform_v2",
"modelCategory": "transform",
"defaultConfig": "config_templates/gretel/transform/transform_v2.yml",
"modelActions": [
{
"modelType": "transform_v2",
"modelCategory": "transform",
"defaultConfig": "config_templates/gretel/transform/transform_v2.yml"
}
],
"sampleConnection": {
"id": "sample_mysql_telecom",
"type": "mysql",
Expand All @@ -217,6 +273,13 @@
"modelType": "evaluate",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/tabular-lstm-evaluate.yml",
"modelActions": [
{
"modelType": "evaluate",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/tabular-lstm-evaluate.yml"
}
],
"sampleDataset": {
"fileName": "bank_marketing_small.csv",
"description": "Create synthetic data based on the publicly available dataset predicting opting in or out of bank marketing.",
Expand All @@ -235,6 +298,13 @@
"modelType": "amplify",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/amplify.yml",
"modelActions": [
{
"modelType": "amplify",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/amplify.yml"
}
],
"sampleDataset": {
"fileName": "safe-driver-prediction.csv",
"description": "Use this dataset to predict if a driver will file an insurance claim in the following year. Specify an output size in the config. By default, the model will create as many records as the input dataset.",
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"modelType": "actgan",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/tabular-actgan.yml",
"modelActions": [
{
"modelType": "actgan",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/tabular-actgan.yml"
}
],
"sampleDataset": {
"fileName": "monthly-customer-payments.csv",
"description": "This dataset of monthly customer charges contains sensitive information and more than 20 columns.",
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"modelType": "gpt_x",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/natural-language.yml",
"modelActions": [
{
"modelType": "gpt_x",
"modelCategory": "synthetics",
"defaultConfig": "config_templates/gretel/synthetics/natural-language.yml"
}
],
"sampleDataset": {
"fileName": "taylor-swift-lyrics-up.csv",
"description": "Create believable song lyrics using this dataset of Taylor Swift hits.",
Expand All @@ -353,6 +437,13 @@
"modelType": "transform_v2",
"modelCategory": "transform",
"defaultConfig": "config_templates/gretel/transform/default.yml",
"modelActions": [
{
"modelType": "transform_v2",
"modelCategory": "transform",
"defaultConfig": "config_templates/gretel/transform/default.yml"
}
],
"sampleDataset": {
"fileName": "sample-transform-emails.csv",
"description": "Unstructured text datasets are useful for training chatbots or other models that need large amounts of data. The emails in this public dataset need to be de-identified before they can be used to train ML models.",
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