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.
Last modified 5mo ago