Virtualitics SDK

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### Example Usage of Explainer Class

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.explainer

Demonstrates how to prepare data and initialize the Explainer class using example datasets and a trained model. This snippet shows data loading, preprocessing for bounds calculation, and model retrieval, setting up the necessary components for explanation.

```python
# Imports
from virtualitics_sdk import Explainer
# Assuming store_interface and other necessary components are available

# Example usage
data_train = store_interface.get_dataset(label="example", name="data train")
train_mins = data_train.get_object().min()
train_maxs = data_train.get_object().max()
bounds = {key: [train_mins[key], train_maxs[key]] for key in data_train.get_object().columns}
data_test = store_interface.get_dataset(label="example", name="data test")
data_attributes = store_interface.get_input("Data Attributes")
graph_data = store_interface.get_dataset(label="example", name="graph data")
kmeans_anomaly = store_interface.get_model(label="example", name="kmeans")

# Further steps would involve initializing the Explainer with these objects
```

--------------------------------

### Create and Use DataSource Element (Python)

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.elements.data_source

Demonstrates how to instantiate a DataSource element with specified options and integrate it into a Card for a data upload step. This example requires the virtualitics_sdk library.

```Python
from virtualitics_sdk import DataSource
from virtualitics_sdk.elements import Card
from virtualitics_sdk.steps import Step

# Example usage
class DataUploadStep(Step):
    def run(self, flow_metadata):
        data_source = DataSource(
            title="Upload data here!",
            options=["csv"],
            description="Example datasource usage",
            required=True,
        )
        data_card = Card(title="Data Upload Card", content=[data_source])
        # Further steps would involve rendering the card and processing the uploaded data
```

--------------------------------

### Get Page

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.store.store_interface

Retrieves the most up-to-date version of the page for the current step. Can optionally use an existing outlink reference for performance.

```APIDOC
## GET /page

### Description
Get the most up to date version of the page for this step.

### Method
GET

### Endpoint
`/page`

### Parameters
#### Query Parameters
- **out_link** (any) - Optional - Optionally pass an existing outlink reference to prevent repeated calls to get_outlink() and to improve performance.

### Response
#### Success Response (200)
- **Page** (Page) - The most up to date Page for this step.

#### Response Example
```json
{
  "page_content": "..."
}
```
```

--------------------------------

### Initialize StepProgressTqdm with Custom Settings

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.utils.tqdm

Demonstrates how to initialize the StepProgressTqdm class with custom parameters such as total iterations, starting and target progress, step size, and a description. This allows for fine-grained control over the progress bar's behavior and appearance within the SDK.

```python
from virtualitics_sdk.utils.tqdm import StepProgressTqdm

# Example usage
# Assuming flow_metadata is available and other necessary setup is done

class ExStep:
    def run(self, flow_metadata):
        # ... other setup ...
        progress_bar = StepProgressTqdm(flow_metadata,
                                        starting_progress=0,
                                        target_progress=100,
                                        total=100,
                                        step_size=10,
                                        desc="My progress bar",
                                        init_update="Starting my progress bar...")
        # ... rest of the loop ...
        for i in range(100):
            progress_bar.update(1)

```

--------------------------------

### Get Explainer Asset

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.store.store_interface

Retrieves an asset with the 'Explainer' type. Assets can be filtered by label or name.

```APIDOC
## GET /assets/explainer

### Description
This is a convenience method for getting Assets that have an “Explainer” type.

### Method
GET

### Endpoint
`/assets/explainer`

### Parameters
#### Query Parameters
- **label** (str) - Optional - The label of the asset.
- **name** (str) - Optional - The name of the asset.

### Response
#### Success Response (200)
- **Explainer** (Explainer) - The Explainer asset.

#### Response Example
```json
{
  "explainer_details": "..."
}
```
```

--------------------------------

### Instantiate TfIdfBasedKG with StoreInterface (Python)

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.knowledge_graph

Demonstrates how to instantiate the TfIdfBasedKG class, which requires a StoreInterface object for managing its internal assets. This example shows the necessary import and the creation of the store interface using flow metadata.

```Python
from virtualitics_sdk import TfIdfBasedKG

# Assuming flow_metadata is defined elsewhere
# store_interface = StoreInterface(**flow_metadata)
# kg = TfIdfBasedKG(asset_label='my_kg', asset_name='MyKnowledgeGraph', store_interface=store_interface)

```

--------------------------------

### Instantiate and Use Model Asset with Scikit-learn

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.model

Demonstrates how to instantiate the Model asset with a scikit-learn model and fit it to data. This requires importing the Model class from virtualitics_sdk and a scikit-learn model like LogisticRegression. The example shows basic data preparation and model fitting.

```python
from virtualitics_sdk import Model
from sklearn.linear_model import LogisticRegression
import numpy as np

# Example usage
X = np.random.rand(10).reshape(-1, 1)
Y = np.random.randint(0, 2, size=(10, 1))
model = Model(LogisticRegression(), label="test", name="model")
model.fit(X, Y)
```

--------------------------------

### Get Element by Title

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.store.store_interface

Retrieves an element from a specific step using its title. Supports quiet mode to return None instead of an error if the element is not found.

```APIDOC
## GET /elements/{step_name}/{elem_title}

### Description
Get an element from any step by its title.

### Method
GET

### Endpoint
`/elements/{step_name}/{elem_title}`

### Parameters
#### Path Parameters
- **step_name** (str) - Required - The name of the step where the element was created.
- **elem_title** (str) - Required - The title of the element to lookup.

#### Query Parameters
- **quiet** (bool) - Optional - If True, return None instead of an error if the element does not exist. Defaults to False.

### Response
#### Success Response (200)
- **Element** (Element) - The retrieved Element object.

#### Response Example
```json
{
  "element_data": "..."
}
```
```

--------------------------------

### Create and Configure a Table with Cell and Text Colors (Python)

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.elements.table

Demonstrates how to create a `Table` object in the Virtualitics SDK with custom cell and text colors. It uses a Pandas DataFrame to define the colors and applies them to specific columns. This example requires the `virtualitics_sdk` and `pandas` libraries.

```python
from virtualitics_sdk import Table, PREDICT_ERROR_TEXT_COLOR
import pandas

# Assuming example_dataset is defined elsewhere
# example_dataset = ... 

point_per_cluster = 5 # Number of rows we want cell/text color to apply to 
cell_colors = pandas.DataFrame({"Y Feature": ["#39cd63"] * point_per_cluster})
text_colors = pandas.DataFrame({"X Feature": ["#ee2310"] * point_per_cluster})
table = Table(example_dataset, 
        title="Example Table", 
        description="This is a table showing cells/text color",
        downloadable=True, 
        cell_colors=cell_colors, 
        text_colors=text_colors)
```

--------------------------------

### Create and Use TextInput Element (Python)

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.elements.text_input

Demonstrates how to create a TextInput element using the Virtualitics SDK. This involves importing the class and instantiating it with various parameters to customize its appearance and behavior. The example shows setting initial values, titles, labels, descriptions, and placeholders.

```python
from virtualitics_sdk import TextInput

# Example usage within a Step class
class ExampleStep(Step):
  def run(self, flow_metadata):
    text_input = TextInput("Initial values can be set", 
                           title="Text Input", 
                           label="Some Text",
                           description="Of course we also 
                                        allow _open ended_ text 
                                        input such as this.",
                           placeholder='Type Something')
    # Further actions with text_input can be added here
```

--------------------------------

### Model Methods

Source: https://sdk.virtualitics.com/genindex

Methods for interacting with models, such as getting time-related information.

```APIDOC
## GET /model/time

### Description
Retrieves time-related information from the model.

### Method
GET

### Endpoint
/model/time

### Parameters
None

### Request Example
None

### Response
#### Success Response (200)
- **time_info** (object) - Information about the model's time properties.

#### Response Example
```json
{
  "time_info": {}
}
```
```

--------------------------------

### Virtualitics SDK - App Step Execution

Source: https://sdk.virtualitics.com/genindex

Documentation for the run method of the Step class in virtualitics_sdk.app.step.

```APIDOC
## run()

### Description
Executes a step within an application.

### Method
Step method

### Endpoint
N/A

### Parameters
None

### Request Example
None

### Response
#### Success Response (200)
- **result** (object) - The outcome of the step execution.

#### Response Example
```json
{
  "result": {
    "status": "completed",
    "data": "..."
  }
}
```
```

--------------------------------

### Visualization Utilities

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk

Functions for creating various types of plots and getting plot-related information.

```APIDOC
## Visualization Utilities

### Description
Provides a suite of functions for generating different types of plots (bar, histogram, line, scatter) and utility functions for determining plot axes domains and bounds.

### Functions
- `create_bar_plot()`: Creates a bar plot.
- `create_histogram_plot()`: Creates a histogram plot.
- `create_line_plot()`: Creates a line plot.
- `create_plot()`: Creates a generic plot.
- `create_scatter_plot()`: Creates a scatter plot.
- `get_axis_domain()`: Determines the domain for a plot axis.
- `get_bounds()`: Calculates the bounds for plot elements.
```

--------------------------------

### Utility and Type Methods

Source: https://sdk.virtualitics.com/genindex

Utility functions and methods for working with types, including getting valid enum values.

```APIDOC
## GET /utils/valid_enums

### Description
Retrieves a list of valid enumeration values.

### Method
GET

### Endpoint
/utils/valid_enums

### Parameters
None

### Request Example
None

### Response
#### Success Response (200)
- **enums** (array) - A list of valid enum values.

#### Response Example
```json
{
  "enums": []
}
```
```

--------------------------------

### Initialize Virtualitics SDK App

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.app.app

Demonstrates how to create an instance of the App class, which represents a workflow in the Virtualitics AI Platform. This involves providing a name, description, and optionally an image path and shareability settings.

```python
from virtualitics_sdk import App

tile_image_link = "https://example-image.jpeg"
example_flow = App(name="Example App", 
                          description="Simple example for using App class",
                          image_path=tile_image_link)
```

--------------------------------

### Get Previous Step Name

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.store.store_interface

Retrieves the name of the previous step in the workflow.

```APIDOC
## GET /previous_step

### Description
Get the name of the previous step.

### Method
GET

### Endpoint
`/previous_step`

### Response
#### Success Response (200)
- **PreviousStepName** (Optional[str]) - The name of the previous step, or None if there is no previous step.

#### Response Example
```json
{
  "previous_step_name": "step_1"
}
```
```

--------------------------------

### Initialize Explainer with K-Means Anomaly Detection

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.explainer

Initializes an Explainer object for anomaly detection using K-Means. It specifies the model, training data, label, name, feature names, output names, and explanation mode. It also configures kernel width and enables SHAP for explanations.

```python
explainer = Explainer(model=kmeans_anomaly, training_data=data_train, 
                     label="example code", name="kmeans explainer",
                     feature_names=data_attributes['features'],
                     output_names=['Normal', 'Anomaly'], 
                     mode='classification', explain_class=1, kernel_width=0.5,
                     use_shap=True)
```

--------------------------------

### Data Upload Functions

Source: https://sdk.virtualitics.com/genindex

Functions related to data upload, including getting corpus names and default model configurations.

```APIDOC
## get_corpus_name()

### Description
Retrieves the name of the corpus associated with the data upload.

### Method
GET (assumed, as it's a retrieval function)

### Endpoint
/websites/sdk_virtualitics/virtualitics_sdk/nlp/data_upload

### Parameters
None specified.

### Request Example
None specified.

### Response
#### Success Response (200)
- **string** (string) - The name of the corpus.

#### Response Example
```json
{
  "example": "MyCorpusName"
}
```

## get_model_or_default()

### Description
Retrieves the model configuration for data upload, or a default configuration if not specified.

### Method
GET (assumed, as it's a retrieval function)

### Endpoint
/websites/sdk_virtualitics/virtualitics_sdk/nlp/data_upload

### Parameters
None specified.

### Request Example
None specified.

### Response
#### Success Response (200)
- **object** (object) - The model configuration object.

#### Response Example
```json
{
  "example": "Model configuration object"
}
```
```

--------------------------------

### Get Element by ID

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.store.store_interface

Retrieves an element from a specific step using its auto-generated ID.

```APIDOC
## GET /elements/{step_name}/{elem_id}

### Description
Get an element from any step by its auto-generated ID.

### Method
GET

### Endpoint
`/elements/{step_name}/{elem_id}`

### Parameters
#### Path Parameters
- **step_name** (str) - Required - The name of the step where the element was created.
- **elem_id** (str) - Required - The element ID.

### Response
#### Success Response (200)
- **Element** (Element) - An Element object.

#### Response Example
```json
{
  "element_data": "..."
}
```
```

--------------------------------

### Initialize Explainer Class with Parameters

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.explainer

Initializes the Explainer class with a machine learning model, training data, and explanation mode. It supports both classification and regression models and allows for optional feature and output name specifications. Dependencies include the model object, dataset, and specified explanation type.

```python
from virtualitics_sdk import Explainer

# Assuming 'model', 'training_data', and 'mode' are defined elsewhere
explainer = Explainer(
    model=model,
    training_data=training_data,
    mode='classification',  # or 'regression'
    label='my_explainer_label',
    name='my_explainer_name',
    feature_names=None,  # Optional: List of feature names
    output_names=None,   # Optional: List of output names
    explain_class=1,     # Optional: Class index to explain for classification
    kernel_width=None,   # Optional: Hyperparameter for LIME
    use_shap=False,      # Optional: Enable SHAP explainer
    use_lime=True,       # Optional: Enable LIME explainer
    n_classes=2,         # Optional: Number of classes for classification
    description=None,    # Optional: Asset description
    version=0,           # Optional: Asset version
    metadata={}
)
```

--------------------------------

### Explainer Class Initialization

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.explainer

Initializes the Explainer asset with a model, training data, and explanation mode. It supports both classification and regression tasks and can optionally use SHAP or LIME for explanations.

```APIDOC
## Initialize Explainer Asset

### Description
Initializes the Explainer class, which takes a machine learning model and a dataset to generate NLP and graphical explanations of the model's behavior, focusing on feature importance for specific predictions.

### Method
`__init__`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **model** (Model) - Required - A machine learning model with `predict_proba` (for classification) or `predict` (for regression) function.
- **training_data** (Dataset) - Required - The training dataset used by the explainer.
- **mode** (Union[str, ExplanationTypes]) - Required - The type of model and explanation, either 'classification' or 'regression'.
- **label** (str) - Required - Label for the Asset.
- **name** (str) - Required - Name for the Asset.
- **feature_names** (Optional[List[str]]) - Optional - List of feature names in the training data. Inferred from column names if None.
- **output_names** (Optional[List[str]]) - Optional - List of model output names. For classifiers, names of each class output; for regressors, a singleton list of the target name.
- **explain_class** (int) - Optional - The index of the model's output class to explain (used for classification).
- **kernel_width** (Optional[float]) - Optional - Hyperparameter for LimeTabularExplainer.
- **use_shap** (bool) - Optional - Boolean to initialize the SHAP explainer. Defaults to False.
- **use_lime** (bool) - Optional - Boolean to initialize the LIME explainer. Defaults to False.
- **n_classes** (int) - Optional - Number of output classes for classification explainers.
- **version** (int) - Optional - Version of the Asset.
- **metadata** (Optional[dict]) - Optional - Metadata for the Asset.

### Request Example
```python
# Imports
from virtualitics_sdk import Explainer

# Assuming store_interface, data_train, data_test, etc. are defined elsewhere
# data_train = store_interface.get_dataset(label="example", name="data train")
# kmeans_anomaly = store_interface.get_model(label="example", name="kmeans")

# explainer = Explainer(
#     model=kmeans_anomaly,
#     training_data=data_train,
#     mode='classification',
#     label='my_explainer_label',
#     name='my_explainer_name',
#     feature_names=['feature1', 'feature2'],
#     output_names=['class0', 'class1'],
#     explain_class=1,
#     use_shap=True
# )
```

### Response
#### Success Response (200)
An instance of the Explainer class is created.

#### Response Example
```json
{
  "message": "Explainer asset initialized successfully."
}
```
```

--------------------------------

### Get Schema Asset

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.store.store_interface

Retrieves an asset with the 'Schema' type. Assets can be filtered by label or name.

```APIDOC
## GET /assets/schema

### Description
This is a convenience method for getting Assets that have a “Schema” type.

### Method
GET

### Endpoint
`/assets/schema`

### Parameters
#### Query Parameters
- **label** (str) - Optional - The label of the asset.
- **name** (str) - Optional - The name of the asset.

### Response
#### Success Response (200)
- **Schema** (Schema) - The Schema asset.

#### Response Example
```json
{
  "schema_details": "..."
}
```
```

--------------------------------

### Get Model Asset

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.store.store_interface

Retrieves an asset with the 'Model' type. Assets can be filtered by label or name.

```APIDOC
## GET /assets/model

### Description
This is a convenience method for getting Assets that have a “Model” type.

### Method
GET

### Endpoint
`/assets/model`

### Parameters
#### Query Parameters
- **label** (str) - Optional - The label of the asset.
- **name** (str) - Optional - The name of the asset.

### Response
#### Success Response (200)
- **Model** (Model) - The Model asset.

#### Response Example
```json
{
  "model_details": "..."
}
```
```

--------------------------------

### Explain Instances using Manual or Smart Selection

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.explainer

The explain method generates explanations for given instances. It supports both manual selection of instances and smart instance selection based on data subsets. Various parameters control the explanation process, including the number of features, NLP explanation method (SHAP or LIME), and output format (plots, cards, or images).

```python
explain(_data =None_, _indices =None_, _method ='manual'_, _n =10_, _encoding =None_, _titles =None_, _instance_sets =[]_, _num_features_explain =3_, _nlp_explanation_method ='shap'_, _return_as ='plots'_, _waterfall_positive =None_, _waterfall_negative =None_, _expected_title =None_, _predicted_title =None_, _top_n =None_, _show_title =True_, _show_description =True_)
```

--------------------------------

### Expl

Source: https://sdk.virtualitics.com/genindex

No description

--------------------------------

### Virtualitics SDK - NLP Advanced Configurations

Source: https://sdk.virtualitics.com/genindex

Documentation for various classes within the virtualitics_sdk.nlp module related to advanced configurations and analytics.

```APIDOC
## KGAdvancedConfig (method)

### Description
Configuration for advanced Knowledge Graph settings.

### Method
Method

### Endpoint
N/A

### Parameters
- **config_params** (object) - Optional - Parameters for advanced configuration.

### Request Example
```json
{
  "config_params": {
    "depth": 5,
    "similarity_threshold": 0.7
  }
}
```

### Response
#### Success Response (200)
- **status** (string) - Indicates the success of the configuration.

#### Response Example
```json
{
  "status": "KG_advanced_config_applied"
}
```

## HistogramInsightsStep (method)

### Description
Step for generating histogram insights.

### Method
Method

### Endpoint
N/A

### Parameters
- **data** (object) - Required - The data for generating insights.

### Request Example
```json
{
  "data": {
    "column_name": "age",
    "bins": 10
  }
}
```

### Response
#### Success Response (200)
- **insights** (object) - The generated histogram insights.

#### Response Example
```json
{
  "insights": {
    "mean": 35.5,
    "median": 33.0,
    "histogram_data": [...] 
  }
}
```

## LouvainCommunityEntities (method)

### Description
Method for identifying communities using the Louvain algorithm.

### Method
Method

### Endpoint
N/A

### Parameters
- **graph_data** (object) - Required - The graph data.

### Request Example
```json
{
  "graph_data": {
    "nodes": [...],
    "edges": [...] 
  }
}
```

### Response
#### Success Response (200)
- **communities** (array) - A list of identified communities.

#### Response Example
```json
{
  "communities": [
    {"id": 1, "nodes": [...]},
    {"id": 2, "nodes": [...]}
  ]
}
```

## DataUpload (method)

### Description
Handles the upload of data.

### Method
Method

### Endpoint
N/A

### Parameters
- **file_path** (string) - Required - The path to the data file.
- **format** (string) - Optional - The format of the data file (e.g., 'csv', 'json').

### Request Example
```json
{
  "file_path": "/path/to/data.csv",
  "format": "csv"
}
```

### Response
#### Success Response (200)
- **status** (string) - Indicates the success of the data upload.
- **dataset_id** (string) - The ID of the uploaded dataset.

#### Response Example
```json
{
  "status": "data_uploaded",
  "dataset_id": "ds_12345"
}
```

## FeatureExploration (method)

### Description
Performs feature exploration on a dataset.

### Method
Method

### Endpoint
N/A

### Parameters
- **dataset_id** (string) - Required - The ID of the dataset.
- **features** (array) - Optional - List of features to explore.

### Request Example
```json
{
  "dataset_id": "ds_12345",
  "features": ["age", "income"]
}
```

### Response
#### Success Response (200)
- **exploration_results** (object) - Results of the feature exploration.

#### Response Example
```json
{
  "exploration_results": {
    "feature_summary": {...},
    "correlations": {...}
  }
}
```

## SimplifiedRelevantEntities (method)

### Description
Identifies simplified relevant entities.

### Method
Method

### Endpoint
N/A

### Parameters
- **text** (string) - Required - The input text.

### Request Example
```json
{
  "text": "The quick brown fox jumps over the lazy dog."
}
```

### Response
#### Success Response (200)
- **entities** (array) - A list of simplified relevant entities.

#### Response Example
```json
{
  "entities": ["fox", "dog"]
}
```

## DataDiagnostic (method)

### Description
Performs diagnostic checks on the data.

### Method
Method

### Endpoint
N/A

### Parameters
- **dataset_id** (string) - Required - The ID of the dataset.

### Request Example
```json
{
  "dataset_id": "ds_12345"
}
```

### Response
#### Success Response (200)
- **diagnostics** (object) - The results of the data diagnostics.

#### Response Example
```json
{
  "diagnostics": {
    "missing_values": 0.05,
    "outliers": 10
  }
}
```

## DateTimeFiltering (method)

### Description
Applies date and time filtering to the data.

### Method
Method

### Endpoint
N/A

### Parameters
- **dataset_id** (string) - Required - The ID of the dataset.
- **start_date** (string) - Optional - The start date for filtering.
- **end_date** (string) - Optional - The end date for filtering.

### Request Example
```json
{
  "dataset_id": "ds_12345",
  "start_date": "2023-01-01",
  "end_date": "2023-12-31"
}
```

### Response
#### Success Response (200)
- **status** (string) - Indicates the success of the filtering.
- **filtered_dataset_id** (string) - The ID of the filtered dataset.

#### Response Example
```json
{
  "status": "datetime_filtered",
  "filtered_dataset_id": "ds_67890"
}
```

## ShowGraph (method)

### Description
Displays a graph.

### Method
Method

### Endpoint
N/A

### Parameters
- **graph_data** (object) - Required - The data to display in the graph.

### Request Example
```json
{
  "graph_data": {
    "nodes": [...],
    "edges": [...] 
  }
}
```

### Response
#### Success Response (200)
- **display_status** (string) - Indicates the status of graph display.

#### Response Example
```json
{
  "display_status": "graph_displayed"
}
```

## PipelineConfigurationStep (method)

### Description
Configures a processing pipeline.

### Method
Method

### Endpoint
N/A

### Parameters
- **pipeline_config** (object) - Required - The configuration for the pipeline.

### Request Example
```json
{
  "pipeline_config": {
    "steps": ["preprocess", "analyze", "visualize"]
  }
}
```

### Response
#### Success Response (200)
- **status** (string) - Indicates the success of the pipeline configuration.

#### Response Example
```json
{
  "status": "pipeline_configured"
}
```

## SimilarityThresholdExperiments (method)

### Description
Runs experiments based on similarity thresholds.

### Method
Method

### Endpoint
N/A

### Parameters
- **data** (object) - Required - The input data for experiments.
- **threshold** (float) - Required - The similarity threshold.

### Request Example
```json
{
  "data": {...},
  "threshold": 0.8
}
```

### Response
#### Success Response (200)
- **experiment_results** (object) - The results of the similarity threshold experiments.

#### Response Example
```json
{
  "experiment_results": {
    "matches_found": 50,
    "performance": "high"
  }
}
```
```

--------------------------------

### Get Document Numbers

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.language_processor

Returns a list of all document names (or identifiers) that have been processed or are available within the LanguageProcessor instance.

```python
get_doc_numbers()
```

--------------------------------

### Custom Components Initialization

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk

Function to initialize custom components.

```APIDOC
## `init_custom_components()`

### Description
Initializes custom components within the application.

### Method
`init_custom_components()`
```

--------------------------------

### trigger_flow_execution

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.trigger.trigger

Triggers the execution of another app (to be run headless). This is useful for orchestrating app workflows, such as starting one app upon the completion of another.

```APIDOC
## trigger_flow_execution

### Description
A utility function to trigger the execution of another app (to be run headless). For example, trigger the execution of another app upon the completion of a different app.

### Method
`async` (Python function, implies asynchronous execution)

### Endpoint
N/A (This is an SDK function, not a direct HTTP endpoint)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **flow_name** (`str`) - Required - The name of the app to trigger. App names are usually CamelCaseNames.
* **store_interface** (`StoreInterface`) - Required - The StoreInterface to pass metadata about the app execution environment.
* **input_parameters** (`Optional[dict]`) - Optional - Additional input parameters to pass to the triggered app

### Request Example
```python
# Assuming you have a flow_name, store_interface, and optional input_parameters
await virtualitics_sdk.trigger.trigger.trigger_flow_execution(
    flow_name="MyOtherApp",
    store_interface=my_store_interface,
    input_parameters={"key": "value"}
)
```

### Response
#### Success Response
This function does not return a specific value upon successful triggering. Its success is indicated by the absence of errors during the triggering process.

#### Response Example
None (Indicates successful execution by not raising an exception)
```

--------------------------------

### Get S3 Asset

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.store.store_interface

Retrieves an asset from a pre-specified S3 bucket. The store interface must be initialized with the bucket name.

```APIDOC
## GET /s3_asset

### Description
Retrieve an asset from a pre-specified bucket. In order to use, please initialize the store interface with the bucket_name parameter.

### Method
GET

### Endpoint
`/s3_asset`

### Parameters
#### Query Parameters
- **path** (str) - Required - The path to the asset in the S3 bucket.

### Response
#### Success Response (200)
- **Asset** (any) - Returns the asset from S3.

#### Response Example
```json
{
  "asset_content": "..."
}
```
```

--------------------------------

### Instantiate Asset Object

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.asset

Demonstrates how to create an instance of the Asset class, specifying the object to be stored, its label, type, and optional metadata, name, description, and version.

```python
from virtualitics_sdk import Asset, AssetType

# Example usage:
my_object = {"key": "value"}
asset = Asset(object=my_object, label="My Data", type=AssetType.OTHER, name="Sample Asset", description="A sample asset for demonstration.", version=1)
```

--------------------------------

### Explainer.get_instance_hash

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.explainer

Generates a hash for a given instance, useful for uniquely identifying instances.

```APIDOC
## GET /websites/sdk_virtualitics/explainer/get_instance_hash

### Description
Generates a unique hash for a given instance.

### Method
GET

### Endpoint
/websites/sdk_virtualitics/explainer/get_instance_hash

### Parameters
#### Query Parameters
- **instance** (object) - Required - The instance for which to generate a hash.
- **encoding** (Union[str, DataEncoding, None]) - Optional - The encoding of the instance. If None, assumes the same encoding as the training dataset. Defaults to None.

### Response
#### Success Response (200)
- **str** - The unique hash of the instance.

#### Response Example
```json
{
  "hash": "a1b2c3d4e5f67890"
}
```
```

--------------------------------

### Get Element Value

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.store.store_interface

Retrieves the value of an element that a user interacted with on the Virtualitics AI Platform. Supports quiet mode.

```APIDOC
## GET /elements/{step_name}/{elem_title}/value

### Description
Get the value of an element the user interacted with in the Virtualitics AI Platform.

### Method
GET

### Endpoint
`/elements/{step_name}/{elem_title}/value`

### Parameters
#### Path Parameters
- **step_name** (str) - Required - The name of the step the element was in.
- **elem_title** (str) - Required - The title of the element to select.

#### Query Parameters
- **quiet** (bool) - Optional - If True, return None instead of an error if the element does not exist. Defaults to False.

### Response
#### Success Response (200)
- **Value** (any) - The value of that element interaction. This will differ by input element type.

#### Response Example
```json
{
  "value": "user_input_data"
}
```
```

--------------------------------

### InstanceSelectionMethod Enum

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.explainer

Defines methods for selecting instances for explanation.

```APIDOC
## InstanceSelectionMethod Enum

### Description
Enumeration for specifying the method used to select instances for explanation analysis.

### Members
- `InstanceSelectionMethod.MANUAL`: Manual selection of instances.
- `InstanceSelectionMethod.SMART`: Automatic, intelligent selection of instances.
```

--------------------------------

### Get Input Value

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.store.store_interface

Retrieves a previously stored input value. Can specify a step name to retrieve data from a particular step.

```APIDOC
## GET /inputs/{name}

### Description
Get a previously stored input value stored with save_output.

### Method
GET

### Endpoint
`/inputs/{name}`

### Parameters
#### Path Parameters
- **name** (str) - Required - The input name.

#### Query Parameters
- **step_name** (str) - Optional - The step name to retrieve the input data from.

### Raises
- **ValueError**: If the name is not found in the database.

### Response
#### Success Response (200)
- **Data** (any) - The previously saved data.

#### Response Example
```json
{
  "saved_data": "..."
}
```
```

--------------------------------

### Create and Display Image using SDK

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.elements.image

Demonstrates how to create an Image element using the virtualitics_sdk.Image class. It shows how to load image content (e.g., from BytesIO and PIL.Image) and display it with different sizes and titles. This snippet requires the PIL library for image manipulation and the virtualitics_sdk for the Image class.

```python
from io import BytesIO
from PIL import Image as PILImage
from virtualitics_sdk import Image, ImageSize

# Assume store_interface is available and can fetch S3 assets
# Example usage:
# jpeg_content = PILImage.open(BytesIO(store_interface.get_s3_asset("ex_path/image/ex.jpg")))
# For demonstration, let's create a dummy image:
jpeg_content = PILImage.new('RGB', (60, 30), color = 'red')

jpeg_1 = Image(content=jpeg_content, size=ImageSize.SMALL, title="This is a small image")
jpeg_2 = Image(content=jpeg_content, size=ImageSize.MEDIUM, title="This is a medium image")
jpeg_3 = Image(content=jpeg_content, size=ImageSize.LARGE, title="This is a large image")

# These Image objects can then be added to a virtualitics app or flow.
```

--------------------------------

### Create and Add a Card to a Section (Python)

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.page.card

Demonstrates how to instantiate a Card with a title and content, and then add it to a specified section on a page. This is a basic usage pattern for displaying content within the Virtualitics framework.

```python
from virtualitics_sdk import Card

# Assuming example_element is defined elsewhere
# example_element = ...

# Example usage
# class ExStep(Step):
#   def run(self, flow_metadata):
#     store_interface = StoreInterface(**flow_metadata)
#     page = store_interface.get_page()
#     ...
card = Card(title="Example Card", content=[example_element])
page.add_card_to_section(card, "")
```

--------------------------------

### Get Raw Data Source Data

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.store.store_interface

Retrieves raw data from a data source, identified by step name, element ID, or element title.

```APIDOC
## GET /raw_data

### Description
Retrieve raw data from a data source.

### Method
GET

### Endpoint
`/raw_data`

### Parameters
#### Query Parameters
- **step_name** (str) - Optional - The name of the step.
- **element_id** (str) - Optional - The ID of the element.
- **element_title** (str) - Optional - The title of the element.

### Response
#### Success Response (200)
- **Data** (BytesIO) - The raw data from the source.

#### Response Example
```binary
[Binary data content]
```
```

--------------------------------

### Compare Platform Version (Python)

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.utils

Compares the platform version with a given semver string. Supports partial version matching with the '=' operator. Requires the platform version to be accessible.

```Python
from virtualitics_sdk.utils import compare_platform_version

platform_version = (1, 24, 0)
print(compare_platform_version("1.24.0", ">="))
print(compare_platform_version("1.24", "="))
print(compare_platform_version("1", "="))
print(compare_platform_version("1.23.0", ">="))
print(compare_platform_version("1.23.0", ">"))
print(compare_platform_version("1.24.0", "<="))
print(compare_platform_version("1.26.0", "<"))
```

--------------------------------

### Get Dataset Asset in Python

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.store.store_interface

This Python function, `get_dataset`, is a convenience method for retrieving Assets specifically of type 'Dataset'. It can filter by label and name.

```python
get_dataset(_label =None_, _name =None_)
```

--------------------------------

### Get Ranked NLP Features

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.knowledge_graph

Retrieves NLP features ranked based on their importance or relevance. This function requires feature index, a mapping of features to vectors, and feature names.

```python
virtualitics_sdk.assets.knowledge_graph.get_ranked_nlp_features(_feature_index_ , _feature_vector_mapping_ , _feature_names_)
```

--------------------------------

### Initialize XAIDashboard

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.elements.xai_dashboard

Initializes an XAIDashboard object with model, explainer, data, and display configurations. It allows customization of titles, descriptions, colors, and data encoding for interactive scenario exploration.

```python
from virtualitics_sdk import XAIDashboard
from virtualitics_sdk.utils import DataEncoding

# Assuming xgb, explainer, prediction_dataset, explain_dataset, and bounds are defined elsewhere

class ExampleStep(Step):
  def run(self, flow_metadata):
    # ... other code ...
    dash = XAIDashboard(
         xgb,
         explainer,
         prediction_dataset,
         "LandContour",
         "PredictedPrice",
         "PredictedPrice",
         title="",
         bounds=bounds,
         description=XAIDashboard.xai_dashboard_description(),
         train_data=explain_dataset,
         expected_title="Avg. Listed Price",
         predicted_title="Predicted Price",
         encoding=DataEncoding.ONE_HOT,
     )
    # ... other code ...

```

--------------------------------

### Get Categorical Column Names

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.dataset

Retrieves the names of categorical columns within the dataset. The `get_categorical_names` function can optionally return only those categorical columns that are also designated as prediction columns.

```python
get_categorical_names(_predict_cols =False_)

Returns the names of the categorical columns of this dataset. Can also optionally return only categorical columns which are also prediction columns. 

Parameters:
    
**predict_cols** (`bool`) – Whether to reduce the set of categorical columns returned to just prediction columns. Defaults to False. 

Return type:
    
`List`[`str`] 

Returns:
    
The list of categorical columns. 
```

--------------------------------

### Get Document Node Attributes

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.language_processor

Retrieves the fundamental information or attributes associated with a specific document number. The output is a dictionary containing feature-value pairs for the document.

```python
get_doc_node_attributes(_doc_number_)
```

--------------------------------

### Virtualitics SDK - Assets Explainer

Source: https://sdk.virtualitics.com/genindex

Documentation for the ExplanationTypes attribute and the Explainer method within the virtualitics_sdk.assets.explainer module.

```APIDOC
## REGRESSION

### Description
Represents a regression type within the explainer.

### Method
Attribute

### Endpoint
N/A

### Parameters
None

### Request Example
None

### Response
#### Success Response (200)
- **REGRESSION** (string) - The regression type identifier.

#### Response Example
```json
{
  "type": "REGRESSION"
}
```

## replace_dataset()

### Description
Replaces the dataset associated with an explainer.

### Method
Explainer method

### Endpoint
N/A

### Parameters
None

### Request Example
None

### Response
#### Success Response (200)
- **status** (string) - Indicates the success of the operation.

#### Response Example
```json
{
  "status": "dataset_replaced"
}
```
```

--------------------------------

### Virtualitics SDK - NLP Utilities

Source: https://sdk.virtualitics.com/genindex

Documentation for utility functions within the virtualitics_sdk.nlp module.

```APIDOC
## round_to_multiple()

### Description
Rounds a number to the nearest multiple of a specified value.

### Method
Function

### Endpoint
N/A

### Parameters
- **number** (float) - Required - The number to round.
- **multiple** (float) - Required - The multiple to round to.

### Request Example
None

### Response
#### Success Response (200)
- **rounded_number** (float) - The rounded number.

#### Response Example
```json
{
  "rounded_number": 10.0
}
```
```

--------------------------------

### Get Document NLP Features

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.language_processor

Extracts computed NLP features from all documents processed by the LanguageProcessor. It returns both the list of document names and their corresponding extracted features.

```python
get_doc_nlp_features()
```

--------------------------------

### Create Virtualitics Table with Subtables in ExampleStep

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.elements.table

This Python code demonstrates how to create a Virtualitics Table element within an ExampleStep. It utilizes the pandas library to create a DataFrame and then initializes a Table object, passing the DataFrame and the `generate_subtable` function to the `subtables` parameter. This allows for dynamic subtables to be generated for each row when the table is interacted with.

```python
import pandas
from virtualitics_sdk.elements.table import Table
from virtualitics_sdk.elements.steps import Step

# Assume generate_subtable is defined as above

class ExampleStep(Step):
   def run(self, flow_metadata):
     # ... existing code ...
     pandas_df = pandas.DataFrame(...)
     new_table = Table(pandas_df, 
                       title="Example Table", 
                       data_grid=True,
                       subtables=generate_subtable)
```

--------------------------------

### Get DataSourceDropdown Value

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.elements.dropdown_data_sources

Retrieves the current value of the DataSourceDropdown element. If the user has interacted with the element, this method returns the updated value; otherwise, it returns the default value.

```python
from virtualitics_sdk import DataSourceDropdown

# Assuming api_key_selection is an instance of DataSourceDropdown
# selected_value = api_key_selection.get_value()
```

--------------------------------

### Store Interface Methods

Source: https://sdk.virtualitics.com/genindex

Methods for the store interface, including checking PyVIP connection status.

```APIDOC
## GET /store/is_pyvip_connected

### Description
Checks if the PyVIP connection is active.

### Method
GET

### Endpoint
/store/is_pyvip_connected

### Parameters
None

### Request Example
None

### Response
#### Success Response (200)
- **is_connected** (boolean) - True if PyVIP is connected, False otherwise.

#### Response Example
```json
{
  "is_connected": true
}
```
```

--------------------------------

### Get Platform Version Info (Python)

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.utils

Retrieves the current platform version as a tuple of integers representing major, minor, and patch versions. This function does not require any arguments.

```Python
from virtualitics_sdk.utils import get_platform_version_info

version_info = get_platform_version_info()
print(version_info)
```

--------------------------------

### Compute NLP Feature Vectors

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.knowledge_graph

Creates a TF-IDF model and feature vectors from features stored in a LanguageProcessor object. This function can be used independently by setting 'return_out' to True to get the model and vectors.

```python
compute_nlp_feature_vectors(_nlp_module_ , _return_out =False_)
```

--------------------------------

### Get Internal Table Data

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.language_processor

Allows access to specific internal data tables managed by the LanguageProcessor, such as 'doc_data' or 'entities'. This is useful for inspecting raw or intermediate data.

```python
get_table(_table_)
```

--------------------------------

### Initialize Section with Cards

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.page.section

Demonstrates how to create a Section object, providing a title, content (a list of Card objects), and optional subtitle and description. This is fundamental for structuring content within a page.

```python
from virtualitics_sdk import Card, Section

# Assuming example_content is defined elsewhere
# example_content = [...] 

ex_card = Card(title="Example Card", content=[example_content])
ex_section = Section(title="Here's the card!", content=[ex_card])
```

--------------------------------

### Get Document IDs and Dates

Source: https://sdk.virtualitics.com/modules/virtualitics_sdk.assets.language_processor

Retrieves a list of tuples, where each tuple contains a document number and its associated date. This is useful for time-series analysis or filtering documents by date.

```python
get_doc_ids_and_dates()
```
Tokens:
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