Project for fitting a multiscale model as described in Kolaczyk et al 2000.

Python 3.7.2

Required Packages:

1. pandas 0.24.1
2. random
3. numpy 1.16.1
4. anytree 2.6.0
5. scipy 1.2.1
6. sklearn 0.20.2

Recovering parameters via hierarchical model

In order to recover the means of the finest level measurements for either the Gaussian or Poisson hierarchical model, run:

python -i py/recover_synthetic_gaussian_parameters.py

or

python -i py/recover_synthetic_poisson_parameters.py

The true parameters can be found in the 'region_param_frame' pandas dataframe, the estimated parameters can be found in the 'region_param_estimated' pandas dataframe.

Creating synthetic data

To create a csv file containing heirarchical synthetic data, run:

python py/synthesise_hierarchical_gaussian_data.py

or

python py/synthesise_hierarchical_poisson_data.py

The files will be saved in the 'csv' directory.

Creating synthetic data with given correlations

It is possible to create synthetic hierarchical data with correlations chosen by the user. Just run:

python py/synthesise_hierarchical_correlated_gaussian_data.py --covariance_type manual --correlation_values 0.5 0.3 0.1 --save_prefix man_

The csv files will be saved in the 'csv' directory and the 'save_prefix' will be fixed to the beginning of the files' names. The three 'correlation_values' correspond to intraprovincial correlation, intracountry (but extraprovincial) correlation, and extracountry correlation.

Examples with plots

For data from independent regions, run:

python py/synthesise_hierarchical_correlated_gaussian_data.py --covariance_type independent --save_prefix indy_ --num_samples 1000
python -i py/recover_synthetic_gaussian_parameters.py --plot_mean_accuracy --plot_correlation --plot_variance_accuracy --csv_file_prefix indy_ --num_samples 1000

For data with manually controlled correlations, run:

python py/synthesise_hierarchical_correlated_gaussian_data.py --covariance_type manual --correlation_values 0.5 0.3 0.1 --save_prefix man_ --num_samples 1000
python -i py/recover_synthetic_gaussian_parameters.py --plot_mean_accuracy --plot_correlation --plot_variance_accuracy --csv_file_prefix man_ --num_samples 1000

Using different trees

Examples of trees can be found in py/true_tree.py and py/false_tree.py. At the moment, every valid tree must have 2 countries, 6 provinces, and 24 regions. The tree used to create synthetic data is true_tree.py, the 'false' tree can be used to measure differences created by using an incorrect tree. To make use of a given tree, use the --tree_file command line argument for the recover_synthetic_gaussian_parameters.py script.

TODO:

• Save and load tree using YAML.
• calculate marginal likelihood of more than one hierarchy of partitions
• A lot of variance is pushed into the 'leftover' regions. Why is that?