stelar-synth-corr-data 0.1.0

STELAR Synthetic Correlation Data

STELAR Synthetic Correlated Data Generator

Work in progress.

Overview

This module generates synthetic data with the same correlations as a given dataset.

Installation

python3 -m pip install --index-url https://test.pypi.org/simple/ --no-deps stelar_synth_corr_data

Usage outside STELAR KLMS

import pandas as pd
from stelar_synth_corr_data import generate_synthetic_data

# Load your dataset
data = pd.read_csv('your_dataset.csv')
# Generate synthetic data
synthetic_data = generate_synthetic_data(data, num_samples=1000)

# Save the synthetic data to a CSV file
synthetic_data.to_csv('synthetic_data.csv', index=False)

Parameters

• data: The input dataset (Pandas DataFrame) from which to derive correlations.
• num_samples: The number of samples to generate in the synthetic dataset.
• random_state: An optional seed for reproducibility.

Output

• A Pandas DataFrame containing the synthetic data with the same correlations as the input dataset.
• The synthetic data will have the same column names and data types as the input dataset.
• The synthetic data will be saved to a CSV file with the specified filename.

Usage inside STELAR KLMS

Load dataset from KLMS, create synthetic data and upload as resource:

from stelar_synth_corr_data import stelar
datasetname = 'your_dataset_name'
resource_name = 'filename.csv'
data = stelar.generate_synthetic_data_from_klms_to_klms(
    datasetname, resource_name, num_samples=1000, credentials
)

Use own dataset, create synthetic data and upload as resource:

import pandas as pd
from stelar_synth_corr_data import stelar
df = pd.read_csv('your_dataset.csv')
resource_name = 'filename.csv'
data = stelar.generate_synthetic_data_from_own_to_klms(
    df, resource_name, num_samples=1000, credentials
)

Parameters

• data: The input dataset (Pandas DataFrame) from which to derive correlations.
• num_samples: The number of samples to generate in the synthetic dataset.
• random_state: An optional seed for reproducibility.
• datasetname: The name of the dataset in KLMS (if using KLMS).
• resource_name: The name of the resource to save the synthetic data to in KLMS (if using KLMS).
• credentials: A dictionary containing the credentials for accessing KLMS (if using KLMS).

To use this module inside STELAR KLMS, you need to have the following credentials:

• username: Your STELAR KLMS username.
• password: Your STELAR KLMS password.
• url: The URL of the STELAR KLMS instance.
• bucket: The name of the bucket where the dataset is stored.

For example:

credentials = {
    'username': 'your_username',
    'password': 'your_password',
    'url': 'https://klms.stelar.gr/stelar',
    'bucket': 'klms-bucket'
}

Output

• A Pandas DataFrame containing the synthetic data with the same correlations as the input dataset.
• The synthetic data will have the same column names and data types as the input dataset.
• The synthetic data will be saved to a CSV file with the specified filename.