API Documentation

For the full Python API documentation, please go to https://api-docs.seclea.com.

If you just want to get straight into though, we have the basics here just for you!

Installation

To install the seclea_ai package, run the following command:

pip install seclea_ai

Initialisation

To use the seclea_ai API, first import the SecleaAI class and create an instance with your project and organisation details:

from seclea_ai import SecleaAI 

# NOTE - use the organisation name provided to you from which you received credentials. 
seclea = SecleaAI(project_name="Your AI Project Name", organization='')

Uploading Datasets

To upload a dataset to the Seclea Platform, use the upload_dataset() method, providing the dataset, dataset name, and metadata:

import pandas as pd 

# Load the data 
data = pd.read_csv('your_data.csv', index_col="index_column") 

# Define the metadata for the dataset. 
dataset_metadata = { 
# ... 
} 

seclea.upload_dataset(dataset=data, dataset_name="Your Dataset Name", metadata=dataset_metadata)

Uploading Datasets - as separate samples and labels

To upload dataset that is split into samples and labels, use the upload_dataset_split() method:

# Upload the train and test dataset splits
seclea.upload_dataset_split(
    X=X_train, 
    y=y_train, dataset_name="Your Dataset Name - Train", 
    metadata={}, 
    transformations=train_transformations
    ) 

Applying Dataset Transformations

To apply and record dataset transformations, use the DatasetTransformation class from the seclea_ai.transformations module:

from seclea_ai.transformations import DatasetTransformation 

# Define the updates to the metadata 
processed_metadata = { 
# ... 
} 

# Define the transformations to the dataset 
processing_transformations = [ 
    DatasetTransformation(
        # Define the dataset transformations
        ) 
] 

# Upload the processed datasets
seclea.upload_dataset(dataset=processed_data, 
                    dataset_name="Your Processed Dataset Name", 
                    metadata=processed_metadata, 
                    transformations=processing_transformations
                    )

Training and Uploading Models

To upload a model using the seclea_ai API, follow these steps:

from sklearn.model_selection import cross_val_score 
from sklearn.ensemble import RandomForestClassifier 

# Initialize a classifier 
classifier = RandomForestClassifier() 

# Cross-validate the classifier 
training_score = cross_val_score(classifier, X_train, y_train, cv=5) 

# Train the classifier on the full training set 
classifier.fit(X_train, y_train) 

# Upload the fully trained model
seclea.upload_training_run_split(model=classifier, 
                                X_train=X_train, 
                                y_train=y_train, 
                                X_test=X_test, 
                                y_test=y_test)

Note that this uses the upload_training_run_split function that takes datasets as samples and labels. If you prefer to reference a dataset that isn't split in this way you can use the upload_training_run function instead.

Conclusion

By following the steps outlined in this documentation, you can efficiently integrate the seclea_ai API into your AI project, enabling seamless data and model management, as well as regulatory compliance and risk management through the Seclea Platform.