# Gooder AI Package
This package provides a streamlined way to ~evaluate~ valuate machine learning models on the **Gooder AI platform**. It exports a simple yet powerful function, **`valuate_model`**, which is designed to work with **scikit-learn-like models** and:
- **Authenticates** users with [Gooder.ai](https://latest.gooder.ai).
- **Validates and uploads** model configuration and dataset for secure storage and processing.
- **Creates or updates** a shared "view" on [Gooder.ai](https://latest.gooder.ai), allowing users to interactively visualize and analyze their model performance.
## Installation
Install the package using pip:
```bash
pip install gooder_ai
```
## Usage
Here's an example invocation of the `valuate_model` function:
```python
import numpy as np
from some_ml_library import SomeModel # Replace with an actual ML model library
import asyncio
# Define a dummy scikit-learn-like model
class DummyModel:
def predict_proba(self, X):
return np.random.rand(len(X), 2) # Simulate probabilities for 2 classes
classes_ = np.array([0, 1]) # Define class labels
# Instantiate the model
model = DummyModel()
# Create dummy dataset
x_data = np.random.rand(100, 5) # 100 samples, 5 features
y = np.random.randint(0, 2, 100) # Binary classification labels (0 or 1)
# Authentication credentials (optional)
auth_credentials = {
"email": "user@example.com",
"password": "securepassword123"
}
# Configuration dictionary
config = {
"param1": "value1",
"param2": "value2"
}
# View metadata
view_meta = {
"mode": "public",
"dataset_name": "test_dataset"
}
# Column names (optional)
column_names = {
"dataset_column_names": ["feature1", "feature2", "feature3", "feature4", "feature5"],
"score_column_names": ["prob_class_0", "prob_class_1"],
"dependent_variable_name": "target"
}
# AWS variables (optional)
aws_variables = {
"api_url": "https://api.gooder.ai",
"bucket_name": "gooder-ai-bucket"
}
# Model names (optional)
model_names = ["model1", "model2"]
# Run the async function
output = asyncio.run(valuate_model(
models=[model],
x_data=x_data,
y=y,
auth_credentials=auth_credentials,
config=config,
view_meta=view_meta,
model_names=model_names,
column_names=column_names,
aws_variables=aws_variables,
max_size_uploaded_data=10, # Limit uploaded data to 10MB
max_size_saved_data=1000 # Limit saved data to 1000MB
))
print(output) # Output: {'view_id': 'some-id', 'view_url': 'https://gooder.ai/some-id'}
```
## Function Parameters
### `valuate_model(**kwargs)`
The `valuate_model` function takes the following input arguments:
1. **`models: list[ScikitModel]`**
- Machine learning models that follow the `ScikitModel` protocol.
- It must have a scoring function (e.g., `predict_proba`), which is used to generate probability scores for classification.
- It must also have a `classes_` attribute, representing the possible target classes.
2. **`x_data: ndarray | DataFrame | list[str | int | float] | spmatrix`**
- A dataset containing the input features for evaluation.
- This is the dataset that will be fed into the model for prediction.
3. **`y: ndarray | DataFrame | list[str | int | float] | spmatrix`**
- A dataset representing the true target values (labels) corresponding to `x_data`.
- This helps in validating model performance.
4. **`auth_credentials: Credentials`**
- A dictionary with user authentication details for the Gooder AI platform.
- Structure:
```python
{
"email": str, # User's email
"password": str # User's password
}
```
- These credentials are used for authentication to upload the dataset and configuration. It is optional if `upload_data_to_gooder = False` and `upload_config_to_gooder = False`.
5. **`config: dict`**
- A dictionary containing model configuration settings.
- This is validated against Gooder AI's schema before being uploaded.
6. **`view_meta: ViewMeta`**
- A dictionary containing metadata about the "view" (shared result visualization) being created or updated.
- Structure:
```python
{
"mode": Optional["public" | "protected" | "private"], # Access control
"view_id": Optional[str], # ID of an existing view (if updating)
"dataset_name": Optional[str] # Name of the dataset (defaults to timestamp)
}
```
- If `view_id` is provided, an existing view is updated; otherwise, a new one is created.
7. **`column_names: ColumnNames = {}`** *(optional)*
- A dictionary specifying the column names for the dataset and scores.
- Structure:
```python
{
"dataset_column_names": Optional[list[str]], # Feature names
"score_column_names": Optional[list[str]], # Column names for model scores
"dependent_variable_name": Optional[str] # Name of the target variable
}
```
8. **`included_columns: list[str] = []`** *(optional)*
- A list of names specifying which columns to include in the dataset before visualizing the scores on the Gooder platform.
9. **`aws_variables: AWSVariables = {}`** *(optional)*
- A dictionary containing AWS-related variables.
- Used for authentication and file uploads.
- Structure:
```python
{
"api_url": Optional[str],
"app_client_id": Optional[str],
"identity_pool_id": Optional[str],
"user_pool_id": Optional[str],
"bucket_name": Optional[str],
"base_url": Optional[str],
"validation_api_url": Optional[str]
}
```
- Defaults to global values if not provided.
10. **`model_names: list[str]`**
- This property is used to label the score columns in the output dataset and configuration.
- If not provided default names are generated based on model class names.
- Example: For a model that outputs binary classification scores, a column named "model1_score, model2_score" will be created.
11. **`max_size_uploaded_data: int = 10`** *(optional)*
- Defines the maximum allowed memory size (in megabytes, MB) for the combined dataset when uploading to Gooder AI.
- Before uploading, the function calculates the memory usage of the full dataset.
- If the dataset exceeds this threshold and `upload_data_to_gooder` is `True`, the operation is aborted and an exception is raised.
- This is a safety limit to prevent large uploads that could impact performance or exceed platform limits.
- Default value is 10MB, which is suitable for most use cases.
- Increase this value if you need to work with larger datasets, but be aware of potential performance implications.
12. **`max_size_saved_data: int = 1000`** *(optional)*
- Defines the maximum allowed memory size (in megabytes, MB) for the combined dataset when saving locally.
- Before saving, the function calculates the memory usage of the full dataset.
- If the dataset exceeds this threshold and `upload_data_to_gooder` is `False`, the operation is aborted and an exception is raised.
- This is a safety limit to prevent excessively large local files that could impact system performance.
- Default value is 1000MB (approximately 1GB), which allows for much larger local datasets compared to uploads.
- Increase this value if you need to work with very large datasets locally, but be aware of system memory constraints.
### Summary
- The function takes a **scikit-learn model**, **dataset**, **user credentials**, and **configuration details**.
- It authenticates with the Gooder AI platform, validates the config, and **uploads the dataset**.
- Then, it either **creates a new shared view or updates an existing one**.
- Finally, it **returns the view ID and URL**, allowing users to access model evaluation results.
## Config
# Chart Definition Configuration Example
Given below is an example chart/dashboard configuration for the Gooder AI app:
```typescript
const config = {
"activeFilter": null, // Optional: ID of the active filter (default: null)
"customChartLayoutMap": {
"xl": [ // Layout for extra-large screens
{
"i": "d292c59c-e1f6-4f7e-ac53-525b7b6e6a56", // Chart ID
"x": 0, // X position (grid units)
"y": 0, // Y position (grid units)
"w": 11, // Width (grid units)
"h": 9, // Height (grid units)
"moved": false, // Optional: Whether the item has been moved (default: false)
"static": false // Optional: Whether the item is static (default: false)
},
// ... other xl layouts
],
"lg": [ // Layout for large screens
// ... similar structure as xl
],
"md": [ // Layout for medium screens
// ... similar structure as xl
],
"sm": [ // Layout for small screens
{
"i": "2db67e79-3c40-40c5-ae42-7d426fa37597", // Chart ID
"x": 0,
"y": 36,
"w": 12,
"h": 12
}
],
"xs": [ // Layout for extra-small screens
// ... similar structure as sm
]
},
"customCharts": [
{
"id": "74a92600-9334-4e57-afe5-2dbb61526547", // Required: Unique chart ID (UUID)
"dataInitializationCode": "data init script", // Required: Script to initialize chart data
"metricCalculationCode": "data calculate script", // Required: Script to calculate metrics
"metric": "Num responses", // Required: Metric name (displayed on Y-axis)
"title": "Response deciles", // Optional: Chart title (default: null)
"resolution": 10, // Required: Number of data points/bins
"unit": "none", // Required: Y-axis unit (from UnitTypes enum)
"decimalPlaces": 2, // Optional: Decimal precision (default: 2)
"minRange": 0, // Required: Minimum zoom range (0-100)
"maxRange": 100, // Required: Maximum zoom range (0-100)
"seriesType": "bar", // Required: Chart type (line/bar from SeriesTypes)
"showRangeSlider": false, // Optional: Shows zoom slider on dashboard (default: false)
"showInDashboard": true, // Required: Display chart on dashboard
"showInMetricTable": true, // Required: Show metric in the table
"showBaseline": false, // Optional: Display baseline (default: false)
"description": null, // Optional: Chart description (default: null)
"aspectRatio": null, // Optional: Aspect ratio (default: null)
"scaleMetric": true, // Optional: Scale Y-axis with projected population (default: true)
"suppressBar": true, // Optional: Hide the first bar for bar series (default: false)
"suppressXAxisLabel": true, // Optional: Hide X-axis labels (default: false)
"showThreshold": true, // Optional: Show threshold line (default: false)
"reverseXAxis": false, // Optional: Reverse X-axis for bar series (default: false)
"aiPrompt": null, // Optional: Custom AI prompt (default: null)
"highlightType": "none", // Optional: Highlight type (from HighlightType, default: "none")
"optimizeResolution": false // Optional: Optimize resolution (default: true)
}
],
"datasetID": "", // Optional: Dataset ID (format: dataset_name/Sheet1)
"dependentVariable": "TARGET", // Required: Dependent variable column name
"filters": null, // Optional: Array of rule IDs (default: null)
"isOffline": false, // Optional: Offline mode toggle (default: false)
"numberOfRowsSentInAIPrompt": 10, // Optional: Rows sent to AI (default: 10)
"percentTreated": null, // Optional: Current % treated value (default: null)
"positivePolarityValues": ["1"], // Required: Positive values of dependent variable
"projectedPopulation": null, // Optional: Population scaling value (default: null)
"rules": [ // Optional: Array of rules (default: null)
{
"id": "29a827d4-35ac-410e-9a31-45203dc1bb18", // Required: Rule ID (UUID)
"value": "abcd", // Required: Value (string/number/boolean/array)
"operator": ">" // Required: Operator (from Operators enum)
}
],
"scores": [
{
"fieldName": "P (TARGET=1) Min", // Required: Model field name
"fieldLabel": "Model 3", // Optional: Display name (default: fieldName)
"sortOrder": "desc" // Optional: Sorting (asc/desc, default: "desc")
}
],
"showFiltersOnDashboard": false, // Optional: Show filter controls (default: false)
"sliderAssociations": [
{
"sliderID": "29a827d4-35ac-410e-9a31-45203dc1bb18", // Required: Slider ID
"uswID": "d292c59c-e1f6-4f7e-ac53-525b7b6e6a56", // Required: Chart ID
"showInDashboard": false // Optional: Show slider on dashboard (default: false)
}
],
"sliders": [
{
"id": "29a827d4-35ac-410e-9a31-45203dc1bb18", // Required: Slider ID (UUID)
"name": "Cost of contact", // Required: Slider name
"description": null, // Optional: Description (default: null)
"min": 0, // Required: Minimum value
"max": 100, // Required: Maximum value
"value": 10, // Required: Current value
"label": "coc" // Optional: Display label (default: null)
}
],
"title": "Targeted Marketing", // Optional: Dashboard title (default: null)
"variableSelectors": null, // Optional: Variable aliases (default: null)
"version": "2.3", // Optional: Config version (latest: 2.3, default: 1.0)
"xAxisName": "Percent treated" // Optional: X-axis label (default: "Percent treated")
};
```
## **Common Issues**
1. **Mismatch in column names:** Ensure that the number of column names matches the dataset shape.
2. **Invalid model type:** Ensure that the model conforms to the `ScikitModel` interface and implements a scoring function e.g `predict_proba` method.
3. **Authentication failure:** Double-check credentials and the Gooder AI endpoint URL.
4. **Dataset size limits:** If you encounter size-related errors, adjust the `max_size_uploaded_data` or `max_size_saved_data` parameters.
5. **Model naming issues:** Ensure that the `model_names` list has the same length as the `models` list to avoid default naming.
## Loading specific configs:
```python
from gooder_ai.configs import load_uber_config
config = load_uber_config()
```
This config can be used to create view
Raw data
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"home_page": null,
"name": "gooder-ai",
"maintainer": null,
"docs_url": null,
"requires_python": "<4.0,>=3.11",
"maintainer_email": null,
"keywords": "MLvaluation, modelvaluation, ML valuation, model valuation",
"author": "Eric Siegel",
"author_email": "eric@predictionimpact.com",
"download_url": "https://files.pythonhosted.org/packages/59/bf/e3d9cb0dd5c23ff2aa29698f4c702813ded998a4eba17df5dbccf22973df/gooder_ai-1.2.6.tar.gz",
"platform": null,
"description": "# Gooder AI Package\n\nThis package provides a streamlined way to ~evaluate~ valuate machine learning models on the **Gooder AI platform**. It exports a simple yet powerful function, **`valuate_model`**, which is designed to work with **scikit-learn-like models** and:\n\n- **Authenticates** users with [Gooder.ai](https://latest.gooder.ai). \n- **Validates and uploads** model configuration and dataset for secure storage and processing.\n- **Creates or updates** a shared \"view\" on [Gooder.ai](https://latest.gooder.ai), allowing users to interactively visualize and analyze their model performance.\n\n## Installation\nInstall the package using pip:\n\n```bash\npip install gooder_ai\n```\n\n## Usage\n\nHere's an example invocation of the `valuate_model` function:\n\n```python\nimport numpy as np\nfrom some_ml_library import SomeModel # Replace with an actual ML model library\nimport asyncio\n\n# Define a dummy scikit-learn-like model\nclass DummyModel:\n def predict_proba(self, X):\n return np.random.rand(len(X), 2) # Simulate probabilities for 2 classes\n \n classes_ = np.array([0, 1]) # Define class labels\n\n# Instantiate the model\nmodel = DummyModel()\n\n# Create dummy dataset\nx_data = np.random.rand(100, 5) # 100 samples, 5 features\ny = np.random.randint(0, 2, 100) # Binary classification labels (0 or 1)\n\n# Authentication credentials (optional)\nauth_credentials = {\n \"email\": \"user@example.com\",\n \"password\": \"securepassword123\"\n}\n\n# Configuration dictionary\nconfig = {\n \"param1\": \"value1\",\n \"param2\": \"value2\"\n}\n\n# View metadata\nview_meta = {\n \"mode\": \"public\",\n \"dataset_name\": \"test_dataset\"\n}\n\n# Column names (optional)\ncolumn_names = {\n \"dataset_column_names\": [\"feature1\", \"feature2\", \"feature3\", \"feature4\", \"feature5\"],\n \"score_column_names\": [\"prob_class_0\", \"prob_class_1\"],\n \"dependent_variable_name\": \"target\"\n}\n\n# AWS variables (optional)\naws_variables = {\n \"api_url\": \"https://api.gooder.ai\",\n \"bucket_name\": \"gooder-ai-bucket\"\n}\n\n# Model names (optional)\nmodel_names = [\"model1\", \"model2\"]\n\n# Run the async function\noutput = asyncio.run(valuate_model(\n models=[model],\n x_data=x_data,\n y=y,\n auth_credentials=auth_credentials,\n config=config,\n view_meta=view_meta,\n model_names=model_names,\n column_names=column_names,\n aws_variables=aws_variables,\n max_size_uploaded_data=10, # Limit uploaded data to 10MB\n max_size_saved_data=1000 # Limit saved data to 1000MB\n))\n\nprint(output) # Output: {'view_id': 'some-id', 'view_url': 'https://gooder.ai/some-id'}\n```\n\n## Function Parameters\n\n### `valuate_model(**kwargs)`\nThe `valuate_model` function takes the following input arguments:\n\n1. **`models: list[ScikitModel]`** \n - Machine learning models that follow the `ScikitModel` protocol. \n - It must have a scoring function (e.g., `predict_proba`), which is used to generate probability scores for classification. \n - It must also have a `classes_` attribute, representing the possible target classes.\n\n2. **`x_data: ndarray | DataFrame | list[str | int | float] | spmatrix`** \n - A dataset containing the input features for evaluation. \n - This is the dataset that will be fed into the model for prediction.\n\n3. **`y: ndarray | DataFrame | list[str | int | float] | spmatrix`** \n - A dataset representing the true target values (labels) corresponding to `x_data`. \n - This helps in validating model performance.\n\n4. **`auth_credentials: Credentials`** \n - A dictionary with user authentication details for the Gooder AI platform. \n - Structure:\n ```python\n {\n \"email\": str, # User's email\n \"password\": str # User's password\n }\n ```\n - These credentials are used for authentication to upload the dataset and configuration. It is optional if `upload_data_to_gooder = False` and `upload_config_to_gooder = False`.\n\n5. **`config: dict`** \n - A dictionary containing model configuration settings. \n - This is validated against Gooder AI's schema before being uploaded.\n\n6. **`view_meta: ViewMeta`** \n - A dictionary containing metadata about the \"view\" (shared result visualization) being created or updated. \n - Structure:\n ```python\n {\n \"mode\": Optional[\"public\" | \"protected\" | \"private\"], # Access control\n \"view_id\": Optional[str], # ID of an existing view (if updating)\n \"dataset_name\": Optional[str] # Name of the dataset (defaults to timestamp)\n }\n ```\n - If `view_id` is provided, an existing view is updated; otherwise, a new one is created.\n\n7. **`column_names: ColumnNames = {}`** *(optional)* \n - A dictionary specifying the column names for the dataset and scores. \n - Structure:\n ```python\n {\n \"dataset_column_names\": Optional[list[str]], # Feature names\n \"score_column_names\": Optional[list[str]], # Column names for model scores\n \"dependent_variable_name\": Optional[str] # Name of the target variable\n }\n ```\n\n8. **`included_columns: list[str] = []`** *(optional)* \n - A list of names specifying which columns to include in the dataset before visualizing the scores on the Gooder platform. \n\n9. **`aws_variables: AWSVariables = {}`** *(optional)* \n - A dictionary containing AWS-related variables. \n - Used for authentication and file uploads.\n - Structure:\n ```python\n {\n \"api_url\": Optional[str],\n \"app_client_id\": Optional[str],\n \"identity_pool_id\": Optional[str],\n \"user_pool_id\": Optional[str],\n \"bucket_name\": Optional[str],\n \"base_url\": Optional[str],\n \"validation_api_url\": Optional[str]\n }\n ```\n - Defaults to global values if not provided.\n\n10. **`model_names: list[str]`**\n - This property is used to label the score columns in the output dataset and configuration.\n - If not provided default names are generated based on model class names.\n - Example: For a model that outputs binary classification scores, a column named \"model1_score, model2_score\" will be created.\n\n11. **`max_size_uploaded_data: int = 10`** *(optional)* \n - Defines the maximum allowed memory size (in megabytes, MB) for the combined dataset when uploading to Gooder AI.\n - Before uploading, the function calculates the memory usage of the full dataset.\n - If the dataset exceeds this threshold and `upload_data_to_gooder` is `True`, the operation is aborted and an exception is raised.\n - This is a safety limit to prevent large uploads that could impact performance or exceed platform limits.\n - Default value is 10MB, which is suitable for most use cases.\n - Increase this value if you need to work with larger datasets, but be aware of potential performance implications.\n\n12. **`max_size_saved_data: int = 1000`** *(optional)* \n - Defines the maximum allowed memory size (in megabytes, MB) for the combined dataset when saving locally.\n - Before saving, the function calculates the memory usage of the full dataset.\n - If the dataset exceeds this threshold and `upload_data_to_gooder` is `False`, the operation is aborted and an exception is raised.\n - This is a safety limit to prevent excessively large local files that could impact system performance.\n - Default value is 1000MB (approximately 1GB), which allows for much larger local datasets compared to uploads.\n - Increase this value if you need to work with very large datasets locally, but be aware of system memory constraints.\n\n### Summary\n- The function takes a **scikit-learn model**, **dataset**, **user credentials**, and **configuration details**.\n- It authenticates with the Gooder AI platform, validates the config, and **uploads the dataset**.\n- Then, it either **creates a new shared view or updates an existing one**.\n- Finally, it **returns the view ID and URL**, allowing users to access model evaluation results.\n\n## Config\n\n# Chart Definition Configuration Example\n\nGiven below is an example chart/dashboard configuration for the Gooder AI app:\n\n```typescript\nconst config = {\n \"activeFilter\": null, // Optional: ID of the active filter (default: null)\n \"customChartLayoutMap\": {\n \"xl\": [ // Layout for extra-large screens\n {\n \"i\": \"d292c59c-e1f6-4f7e-ac53-525b7b6e6a56\", // Chart ID\n \"x\": 0, // X position (grid units)\n \"y\": 0, // Y position (grid units)\n \"w\": 11, // Width (grid units)\n \"h\": 9, // Height (grid units)\n \"moved\": false, // Optional: Whether the item has been moved (default: false)\n \"static\": false // Optional: Whether the item is static (default: false)\n },\n // ... other xl layouts\n ],\n \"lg\": [ // Layout for large screens\n // ... similar structure as xl\n ],\n \"md\": [ // Layout for medium screens\n // ... similar structure as xl\n ],\n \"sm\": [ // Layout for small screens\n {\n \"i\": \"2db67e79-3c40-40c5-ae42-7d426fa37597\", // Chart ID\n \"x\": 0,\n \"y\": 36,\n \"w\": 12,\n \"h\": 12\n }\n ],\n \"xs\": [ // Layout for extra-small screens\n // ... similar structure as sm\n ]\n },\n \"customCharts\": [\n {\n \"id\": \"74a92600-9334-4e57-afe5-2dbb61526547\", // Required: Unique chart ID (UUID)\n \"dataInitializationCode\": \"data init script\", // Required: Script to initialize chart data\n \"metricCalculationCode\": \"data calculate script\", // Required: Script to calculate metrics\n \"metric\": \"Num responses\", // Required: Metric name (displayed on Y-axis)\n \"title\": \"Response deciles\", // Optional: Chart title (default: null)\n \"resolution\": 10, // Required: Number of data points/bins\n \"unit\": \"none\", // Required: Y-axis unit (from UnitTypes enum)\n \"decimalPlaces\": 2, // Optional: Decimal precision (default: 2)\n \"minRange\": 0, // Required: Minimum zoom range (0-100)\n \"maxRange\": 100, // Required: Maximum zoom range (0-100)\n \"seriesType\": \"bar\", // Required: Chart type (line/bar from SeriesTypes)\n \"showRangeSlider\": false, // Optional: Shows zoom slider on dashboard (default: false)\n \"showInDashboard\": true, // Required: Display chart on dashboard\n \"showInMetricTable\": true, // Required: Show metric in the table\n \"showBaseline\": false, // Optional: Display baseline (default: false)\n \"description\": null, // Optional: Chart description (default: null)\n \"aspectRatio\": null, // Optional: Aspect ratio (default: null)\n \"scaleMetric\": true, // Optional: Scale Y-axis with projected population (default: true)\n \"suppressBar\": true, // Optional: Hide the first bar for bar series (default: false)\n \"suppressXAxisLabel\": true, // Optional: Hide X-axis labels (default: false)\n \"showThreshold\": true, // Optional: Show threshold line (default: false)\n \"reverseXAxis\": false, // Optional: Reverse X-axis for bar series (default: false)\n \"aiPrompt\": null, // Optional: Custom AI prompt (default: null)\n \"highlightType\": \"none\", // Optional: Highlight type (from HighlightType, default: \"none\")\n \"optimizeResolution\": false // Optional: Optimize resolution (default: true)\n }\n ],\n \"datasetID\": \"\", // Optional: Dataset ID (format: dataset_name/Sheet1)\n \"dependentVariable\": \"TARGET\", // Required: Dependent variable column name\n \"filters\": null, // Optional: Array of rule IDs (default: null)\n \"isOffline\": false, // Optional: Offline mode toggle (default: false)\n \"numberOfRowsSentInAIPrompt\": 10, // Optional: Rows sent to AI (default: 10)\n \"percentTreated\": null, // Optional: Current % treated value (default: null)\n \"positivePolarityValues\": [\"1\"], // Required: Positive values of dependent variable\n \"projectedPopulation\": null, // Optional: Population scaling value (default: null)\n \"rules\": [ // Optional: Array of rules (default: null)\n {\n \"id\": \"29a827d4-35ac-410e-9a31-45203dc1bb18\", // Required: Rule ID (UUID)\n \"value\": \"abcd\", // Required: Value (string/number/boolean/array)\n \"operator\": \">\" // Required: Operator (from Operators enum)\n }\n ],\n \"scores\": [\n {\n \"fieldName\": \"P (TARGET=1) Min\", // Required: Model field name\n \"fieldLabel\": \"Model 3\", // Optional: Display name (default: fieldName)\n \"sortOrder\": \"desc\" // Optional: Sorting (asc/desc, default: \"desc\")\n }\n ],\n \"showFiltersOnDashboard\": false, // Optional: Show filter controls (default: false)\n \"sliderAssociations\": [\n {\n \"sliderID\": \"29a827d4-35ac-410e-9a31-45203dc1bb18\", // Required: Slider ID\n \"uswID\": \"d292c59c-e1f6-4f7e-ac53-525b7b6e6a56\", // Required: Chart ID\n \"showInDashboard\": false // Optional: Show slider on dashboard (default: false)\n }\n ],\n \"sliders\": [\n {\n \"id\": \"29a827d4-35ac-410e-9a31-45203dc1bb18\", // Required: Slider ID (UUID)\n \"name\": \"Cost of contact\", // Required: Slider name\n \"description\": null, // Optional: Description (default: null)\n \"min\": 0, // Required: Minimum value\n \"max\": 100, // Required: Maximum value\n \"value\": 10, // Required: Current value\n \"label\": \"coc\" // Optional: Display label (default: null)\n }\n ],\n \"title\": \"Targeted Marketing\", // Optional: Dashboard title (default: null)\n \"variableSelectors\": null, // Optional: Variable aliases (default: null)\n \"version\": \"2.3\", // Optional: Config version (latest: 2.3, default: 1.0)\n \"xAxisName\": \"Percent treated\" // Optional: X-axis label (default: \"Percent treated\")\n};\n```\n\n## **Common Issues**\n1. **Mismatch in column names:** Ensure that the number of column names matches the dataset shape. \n2. **Invalid model type:** Ensure that the model conforms to the `ScikitModel` interface and implements a scoring function e.g `predict_proba` method. \n3. **Authentication failure:** Double-check credentials and the Gooder AI endpoint URL.\n4. **Dataset size limits:** If you encounter size-related errors, adjust the `max_size_uploaded_data` or `max_size_saved_data` parameters.\n5. **Model naming 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