sweep_config_multi_games.py_example

import numpy as np
import games
from mcpy import metrics
from mcpy import plotting

def select_coord(x, dgp, method, metric, config):
    return x[dgp][method][metric][:, 0]

def plot_non_zero_histograms(param_estimates, metric_results, config):
    plotting.plot_subset_param_histograms(param_estimates, metric_results, config, np.concatenate((np.arange(config['dgp_opts']['kappa_gamma']), [config['dgp_opts']['n_dim']])))

CONFIG = {
        "dgps": {
            "one_player": games.stylized_data,
            #"two_player": games.equilibrium_data
        },
        "dgp_opts": {
            'n_samples': [5000, 10000], # samples used for estimation
            'n_dim': [1000, 2000, 5000], # dimension of the feature space
            'sigma_x': [1.0, 2.0, 3.0], # variance of the features
            'kappa_gamma': 1, # support size of the target parameter
            'kappa_gamma_aux': 3, # support size for opponent in stylized dgp
        },
        "methods": {
            #"OracleLG": games.two_stage_oracle,
            "2SLG": games.two_stage_non_orthogonal,
            "2SOrthoLG": games.two_stage_crossfit_orthogonal
        },
        "method_opts": {
            'lambda_coef': 0.5, # coeficient in front of the asymptotic rate for regularization lambda
            'n_splits': 3 # number of splits in each cross fitting
        },
        "metrics": {
            "$\\ell_2$ error": metrics.l2_error,
            "$\\ell_1$ error": metrics.l1_error,
            "bias": metrics.bias,
            "truth": metrics.truth,
            "estimates": metrics.raw_estimate,
            "nonzero_estimates": metrics.raw_estimate_nonzero_truth
        },
        "plots": {
            "hist": plot_non_zero_histograms,
            "iplot1": {'metrics': ["bias"], 'metric_transforms': {'coord[0]': select_coord}}
        },
        "sweep_plots": {
            "plot1": {'varying_params': [("sigma_x", "n_dim")], 'select_vals': {'n_samples': [5000]}, 'methods': ["2SLG"], 'metrics': ["$\\ell_2$ error"], 'metric_transforms': {'% decrease': metrics.transform_ratio} },
            "plot1b": {'varying_params': [("sigma_x", "n_dim")], 'select_vals': {'n_samples': [10000]}, 'methods': ["2SLG"], 'metrics': ["$\\ell_2$ error"], 'metric_transforms': {'% decrease': metrics.transform_ratio} },
            "plot1c": {'varying_params': [("sigma_x", "n_dim")], 'select_vals': {'n_samples': [5000]}, 'methods': ["2SLG", "2SOrthoLG"], 'metrics': ["$\\ell_2$ error"]},
            "plot1d": {'varying_params': [("sigma_x", "n_dim")], 'select_vals': {'n_samples': [10000]}, 'methods': ["2SLG", "2SOrthoLG"], 'metrics': ["$\\ell_2$ error"]},
            "plot1e": {'varying_params': ["n_dim"], 'select_vals': {'sigma_x': [2.0] ,'n_samples': [5000]},
                        'methods': ["2SLG"], 'metrics': ["$\\ell_2$ error"], 'metric_transforms': {'% decrease': metrics.transform_ratio}},
            "plot1f": {'varying_params': ["n_dim"], 'select_vals': {'sigma_x': [2.0] ,'n_samples': [10000]},
                        'methods': ["2SLG"], 'metrics': ["$\\ell_2$ error"], 'metric_transforms': {'% decrease': metrics.transform_ratio}}
        },
        "mc_opts": {
            'n_experiments': 100, # number of monte carlo experiments
            "seed": 123
        },
        "proposed_method": "2SOrthoLG",
        "target_dir": "sweep_results_games",
        "reload_results": True
    }