GaussianCopula
class GaussianCopula(debug=False, correlation_method="kendall")
Learn/Build Gaussian Copula for multivariate data.
Parameters
debug: boolean, default False. Whether to print debug-related outputs to console.
correlation_method: str, default kendall. Method for computing covariance matrix
Notes
Examples
Please refer to the below pages for detailed examples:
| Example | Description |
|---|---|
| GaussianCopula | Demonstrates use of GaussianCopula to create multivariate synthetic data. |
Attributes
| Attribute | Description |
|---|---|
| debug | (boolean) whether to debug or not |
| var_names | (list) array of column names found in data dataframe |
| univariates | (dict) dictionary where the key is the variable name and the value is the fitted MarginalDist instances |
| correlation | (array) computed correlation matrix |
| fitted | (boolean) whether copula has been fitted |
Methods
| Method | Description |
|---|---|
| print_copula_params() | Display copula parameters |
| compute_correlation(data, [method, transform_to_normal]) | Compute the (pairwise) correlation matrix using input data method. Default: “kendall”, options include “kendall”, “spearman”, “pearson”. |
| fit(data, [marginal_dist_dict, ]) | Compute the distribution for each variable and then its covariance matrix |
| conditional_Gaussian(conditions) | Compute the parameters (mean, covariance) of a conditional multivariate normal distribution. (conditions is a pandas.series variable) |
| sample([size, conditions]) | Generates synthetic data from a fitted Gaussian Copula Model |