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## This file contains the functions that uses the classes in generator.py to generate the synthetic data
from .generator import PSMGenerator, PSWGenerator, IVGenerator, RDDGenerator, RCTGenerator, DiDGenerator, MultiTreatRCTGenerator, FrontDoorGenerator
import pandas as pd
import numpy as np
from pathlib import Path
import logging
import logging.config
import json
from .util import export_info
Path("reproduce_results/logs").mkdir(parents=True, exist_ok=True)
logging.config.fileConfig('reproduce_results/log_config.ini')
def config_hyperparameters(base_seed, base_mean, base_cov_diag, max_cont, max_bin, n_obs,
max_obs, min_obs, max_treat=2, max_periods=5, cutoff_max=25):
"""
configure the hyperparameters for the data generation process.
Args:
base_seed (int): Base seed for random number generation
base_mean (np.ndarray): Base mean vector for the covariates
base_cov_diag (np.ndarray): Base (diagonal) covariance matrix for the covariates
max_cont (int): Maximum number of continuous covariates
max_bin (int): Maximum number of binary covariates
n_obs (int): Number of observations to generate
max_obs (int): Maximum number of observations to generate
min_obs (int): Minimum number of observations to generate
max_treat (int): Maximum number of treatment groups (default is 2)
max_periods (int): Maximum number of periods for DiD data (default is 5)
cutoff_max (int): Maximum value for the cutoff in RDD data (default is 25)
Returns:
dict: A dictionary containing the hyperparameters for data generation.
(str) attribute -> (int) value
"""
base_cov_mat = np.diag(base_cov_diag)
np.random.seed(base_seed)
n_treat = np.random.randint(2, max_treat + 1)
true_effect = np.random.uniform(1, 10)
true_effect_vec = np.array([0] + [np.random.uniform(1, 10) for i in range(n_treat)])
n_continuous = np.random.randint(2, max_cont + 1)
n_binary = np.random.randint(2, max_bin)
n_observations = np.random.randint(min_obs, max_obs + 1)
if n_obs is not None:
n_observations = n_obs
n_periods = np.random.randint(3, max_periods + 1)
cutoff = np.random.randint(2, cutoff_max + 1)
mean_vec = base_mean[0:n_continuous]
cov_mat = base_cov_mat[0:n_continuous, 0:n_continuous]
param_dict = {'tau': true_effect, 'continuous': n_continuous, 'binary': n_binary,
'obs': n_observations, 'mean': mean_vec, 'covar': cov_mat,
'tau_vec':true_effect_vec, "treat":n_treat, "periods": n_periods,
'cutoff':cutoff}
return param_dict
def generate_observational_data(base_mean, base_cov, dset_size, max_cont, max_bin, min_obs,
max_obs, data_save_loc, metadata_save_loc, n_obs=None):
"""
Generate observational data using the PSMGenerator class.
Args:
base_mean (np.ndarray): Base mean vector for the covariates
base_cov (np.ndarray): Base covariance matrix for the covariates
dset_size (int): Number of datasets to generate
max_cont (int): Maximum number of continuous covariates
max_bin (int): Maximum number of binary covariates
min_obs (int): Minimum number of observations to generate
max_obs (int): Maximum number of observations to generate
data_save_loc (str): Directory to save the generated data files
metadata_save_loc (str): Directory to save the metadata information
n_obs (int, None): number of observations. If None, it will be randomly
generated within the range of min_obs and max_obs.
"""
logger = logging.getLogger("observational_data_logger")
logger.info("Generating observational data")
metadata_dict = {}
base_seed = 31
for i in range(dset_size):
logger.info("Iteration: {}".format(i))
seed = (i + 1) * base_seed
params = config_hyperparameters(seed, base_mean, base_cov, max_cont, max_bin,
n_obs, max_obs, min_obs)
logger.info("n_observations:{}, n_continuous: {}, n_binary: {}".format(
params['obs'], params['continuous'], params['binary']))
logger.info("true_effect: {}".format(params['tau']))
mean_vec = params['mean']
cov_mat = params['covar']
gen = PSMGenerator(params['obs'], params['continuous'], n_binary_covars=params['binary'],
mean=mean_vec, covar=cov_mat, true_effect=params['tau'], seed=seed*2)
data = gen.generate_data()
name = "observational_data_{}.csv".format(i)
data_dict = {"true_effect": params['tau'], "observation": params['obs'], "continuous": params['continuous'],
"binary": params['binary'], "type": "observational"}
test_result = gen.test_data()
logger.info("Test result: {}\n".format(test_result))
metadata_dict[name] = data_dict
gen.save_data(data_save_loc, name)
export_info(metadata_dict, metadata_save_loc, "observational")
def generate_rct_data(base_mean, base_cov, dset_size, max_cont, max_bin, min_obs, max_obs,
data_save_loc, metadata_save_loc, n_obs=None):
"""
Generates RCT data
Args:
base_mean (np.ndarray): Base mean vector for the covariates
base_cov (np.ndarray): Base covariance matrix for the covariates
dset_size (int): Number of datasets to generate
max_cont (int): Maximum number of continuous covariates
max_bin (int): Maximum number of binary covariates
min_obs (int): Minimum number of observations to generate
max_obs (int): Maximum number of observations to generate
data_save_loc (str): Directory to save the generated data files
metadata_save_loc (str): Directory to save the metadata information
n_obs (int, None): number of observations. If None, it will be randomly
generated within the range of min_obs and max_obs.
"""
logger = logging.getLogger("rct_data_logger")
logger.info("Generating RCT data")
metadata_dict = {}
base_seed = 197
for i in range(dset_size):
logger.info("Iteration: {}".format(i))
seed = (i + 1) * base_seed
params = config_hyperparameters(seed, base_mean, base_cov, max_cont, max_bin, n_obs,
max_obs, min_obs)
logger.info("n_observations:{}, n_continuous: {}, n_binary: {}".format(
params['obs'], params['continuous'], params['binary']))
logger.info("true_effect: {}".format(params['tau']))
mean_vec = params['mean']
cov_mat = params['covar']
gen = RCTGenerator(params['obs'], params['continuous'], n_binary_covars=params['binary'],
mean=mean_vec, covar=cov_mat, true_effect=params['tau'], seed=seed)
data = gen.generate_data()
test_result = gen.test_data()
data_dict = {"true_effect": params['tau'], "observation": params['obs'], "continuous": params['continuous'],
"binary": params['binary'], "type": "rct"}
name = "rct_data_{}.csv".format(i)
logger.info("Test result: {}\n".format(test_result))
metadata_dict[name] = data_dict
gen.save_data(data_save_loc, name)
export_info(metadata_dict, metadata_save_loc, "rct")
def generate_multi_rct_data(base_mean, base_cov, dset_size, max_n_treat, max_cont, max_bin, min_obs, max_obs,
data_save_loc, metadata_save_loc, n_obs=None):
"""
Generate multi-treatment RCT data
Args:
base_mean (np.ndarray): Base mean vector for the covariates
base_cov (np.ndarray): Base covariance matrix for the covariates
dset_size (int): Number of datasets to generate
max_n_treat (int): Maximum number of treatment groups
max_cont (int): Maximum number of continuous covariates
max_bin (int): Maximum number of binary covariates
min_obs (int): Minimum number of observations to generate
max_obs (int): Maximum number of observations to generate
data_save_loc (str): Directory to save the generated data files
metadata_save_loc (str): Directory to save the metadata information
n_obs (int, None): number of observations. If None, it will be randomly
generated within the range of min_obs and max_obs.
"""
logger = logging.getLogger("multi_rct_data_logger")
logger.info("Generating multi-treatment RCT data")
metadata_dict = {}
base_seed = 173
for i in range(dset_size):
logger.info("Iteration: {}".format(i))
seed = (i+1) * base_seed
params = config_hyperparameters(seed, base_mean, base_cov, max_cont, max_bin, n_obs,
max_obs, min_obs, max_treat=max_n_treat)
n_treat = params['treat']
logger.info("n_observations:{}, n_continuous: {}, n_binary: {}, n_treat: {}".format(
params['obs'], params['continuous'], params['binary'], n_treat))
logger.info("true_effect: {}".format(params['tau_vec']))
mean_vec = params['mean']
cov_mat = params['covar']
gen = MultiTreatRCTGenerator(params['obs'], params['continuous'], params['treat'], n_binary_covars=params['binary'],
mean=mean_vec, covar=cov_mat, true_effect_vec=params['tau_vec'], seed=seed,
true_effect=params['tau'])
data = gen.generate_data()
test_result = gen.test_data()
data_dict = {"true_effect": list(params['tau_vec']), "observation": params['obs'], "continuous": params['continuous'],
"binary": params['binary'], "type": "multi_rct"}
name = "multi_rct_data_{}.csv".format(i)
logger.info("Test result: {}\n".format(test_result))
metadata_dict[name] = data_dict
gen.save_data(data_save_loc, name)
export_info(metadata_dict, metadata_save_loc, "multi_rct")
def generate_frontdoor_data(base_mean, base_cov, dset_size, max_cont, max_bin, min_obs, max_obs,
data_save_loc, metadata_save_loc, n_obs=None):
"""
Generates front-door data
Args:
base_mean (np.ndarray): Base mean vector for the covariates
base_cov (np.ndarray): Base covariance matrix for the covariates
dset_size (int): Number of datasets to generate
max_cont (int): Max number of continuous covariates
max_bin (int): Max number of binary covariates
min_obs (int): Minimum number of observations
max_obs (int): Maximum number of observations
data_save_loc (str): Folder to save generated CSV files
metadata_save_loc (str): Folder to save metadata JSON
n_obs (int or None): Fixed number of observations (if provided)
"""
logger = logging.getLogger("frontdoor_data_logger")
logger.info("Generating Front-Door synthetic data")
metadata_dict = {}
base_seed = 311
for i in range(dset_size):
logger.info(f"Iteration: {i}")
seed = (i + 1) * base_seed
params = config_hyperparameters(seed, base_mean, base_cov, max_cont, max_bin, n_obs,
max_obs, min_obs)
logger.info("n_observations: {}, n_continuous: {}, n_binary: {}".format(
params['obs'], params['continuous'], params['binary']))
logger.info("true_effect: {}".format(params['tau']))
mean_vec = params['mean']
cov_mat = params['covar']
gen = FrontDoorGenerator(
n_observations=params['obs'],
n_continuous_covars=params['continuous'],
n_binary_covars=params['binary'],
mean=mean_vec,
covar=cov_mat,
true_effect=params['tau'],
seed=seed
)
data = gen.generate_data()
test_result = gen.test_data()
logger.info("Test result: {}\n".format(test_result))
# Save CSV
filename = f"frontdoor_data_{i}.csv"
gen.save_data(data_save_loc, filename)
# Metadata
data_dict = {
"true_effect": params['tau'],
"observation": params['obs'],
"continuous": params['continuous'],
"binary": params['binary'],
"type": "frontdoor"
}
metadata_dict[filename] = data_dict
# Save metadata JSON
export_info(metadata_dict, metadata_save_loc, "frontdoor")
def generate_canonical_did_data(base_mean, base_cov, dset_size, max_cont, max_bin, min_obs, max_obs,
data_save_loc, metadata_save_loc, n_obs=None):
"""
Generate canonical DiD data
Args:
base_mean (np.ndarray): Base mean vector for the covariates
base_cov (np.ndarray): Base covariance matrix for the covariates
dset_size (int): Number of datasets to generate
max_cont (int): Maximum number of continuous covariates
max_bin (int): Maximum number of binary covariates
min_obs (int): Minimum number of observations to generate
max_obs (int): Maximum number of observations to generate
data_save_loc (str): Directory to save the generated data files
metadata_save_loc (str): Directory to save the metadata information
n_obs (int, None): number of observations. If None, it will be randomly
generated within the range of min_obs and max_obs.
"""
logger = logging.getLogger("did_data_logger")
logger.info("Generating canonical DiD data")
metadata_dict = {}
base_seed = 281
for i in range(dset_size):
logger.info("Iteration: {}".format(i))
seed = (i + 1) * base_seed
params = config_hyperparameters(seed, base_mean, base_cov, max_cont, max_bin, n_obs,
max_obs, min_obs)
logger.info("n_observations:{}, n_continuous: {}, n_binary: {}".format(
params['obs'], params['continuous'], params['binary']))
logger.info("true_effect: {}".format(params['tau']))
mean_vec = params['mean']
cov_mat = params['covar']
gen = DiDGenerator(params['obs'], params['continuous'], n_binary_covars=params['binary'],
mean=mean_vec, covar=cov_mat, true_effect=params['tau'], seed=seed)
data = gen.generate_data()
test_result = gen.test_data()
data_dict = {"true_effect": params['tau'], "observation": params['obs'], "continuous": params['continuous'],
"binary": params['binary'], "type": "did_canonical"}
name = "did_canonical_data_{}.csv".format(i)
logger.info("Test result: {}\n".format(test_result))
metadata_dict[name] = data_dict
gen.save_data(data_save_loc, name)
export_info(metadata_dict, metadata_save_loc, "did_canonical")
def generate_data_iv(base_mean, base_cov, dset_size, max_cont, max_bin, min_obs, max_obs,
data_save_loc, metadata_save_loc, n_obs=None):
"""
Generate IV data
Args:
base_mean (np.ndarray): Base mean vector for the covariates
base_cov (np.ndarray): Base covariance matrix for the covariates
dset_size (int): Number of datasets to generate
max_cont (int): Maximum number of continuous covariates
max_bin (int): Maximum number of binary covariates
min_obs (int): Minimum number of observations to generate
max_obs (int): Maximum number of observations to generate
data_save_loc (str): Directory to save the generated data files
metadata_save_loc (str): Directory to save the metadata information
n_obs (int, None): number of observations. If None, it will be randomly
generated within the range of min_obs and max_obs.
"""
logger = logging.getLogger("iv_data_logger")
logger.info("Generating IV data")
metadata_dict = {}
base_seed = 343
for i in range(dset_size):
logger.info("Iteration: {}".format(i))
seed = (i + 1) * base_seed
params = config_hyperparameters(seed, base_mean, base_cov, max_cont, max_bin, n_obs,
max_obs, min_obs)
logger.info("n_observations:{}, n_continuous: {}, n_binary: {}".format(
params['obs'], params['continuous'], params['binary']))
logger.info("true_effect: {}".format(params['tau']))
mean_vec = params['mean']
cov_mat = params['covar']
gen = IVGenerator(params['obs'], params['continuous'], n_binary_covars=params['binary'],
mean=mean_vec, covar=cov_mat, true_effect=params['tau'], seed=seed)
data = gen.generate_data()
test_result = gen.test_data()
data_dict = {"true_effect": params['tau'], "observation": params['obs'], "continuous": params['continuous'],
"binary": params['binary'], "type": "IV"}
name = "iv_data_{}.csv".format(i)
logger.info("Test result: {}\n".format(test_result))
metadata_dict[name] = data_dict
gen.save_data(data_save_loc, name)
export_info(metadata_dict, metadata_save_loc, "iv")
def generate_twfe_did_data(base_mean, base_cov, dset_size, max_cont, max_bin, n_periods,
min_obs, max_obs, data_save_loc, metadata_save_loc, n_obs=None):
"""
Generate TWFE DiD data
Args:
base_mean (np.ndarray): Base mean vector for the covariates
base_cov (np.ndarray): Base covariance matrix for the covariates
dset_size (int): Number of datasets to generate
max_cont (int): Maximum number of continuous covariates
max_bin (int): Maximum number of binary covariates
n_periods (int): Number of periods for the DiD data
min_obs (int): Minimum number of observations to generate
max_obs (int): Maximum number of observations to generate
data_save_loc (str): Directory to save the generated data files
metadata_save_loc (str): Directory to save the metadata information
n_obs (int, None): number of observations. If None, it will be randomly
generated within the range of min_obs and max_obs.
"""
logger = logging.getLogger("did_data_logger")
logger.info("Generating TWFE DiD data")
metadata_dict = {}
base_seed = 447
print("preiods: ", n_periods)
for i in range(dset_size):
logger.info("Iteration: {}".format(i))
seed = (i + 1) * base_seed
params = config_hyperparameters(seed, base_mean, base_cov, max_cont, max_bin, n_obs,
max_obs, min_obs, max_periods=n_periods)
logger.info("n_observations:{}, n_continuous: {}, n_binary: {}, n_periods:{}".format(
params['obs'], params['continuous'], params['binary'], params['periods']))
logger.info("true_effect: {}".format(params['tau']))
mean_vec = params['mean']
cov_mat = params['covar']
gen = DiDGenerator(params['obs'], params['continuous'], n_binary_covars=params['binary'],
mean=mean_vec, covar=cov_mat, true_effect=params['tau'], seed=seed,
n_periods=n_periods)
data = gen.generate_data()
test_result = gen.test_data()
data_dict = {"true_effect": params['tau'], "observation": params['obs'], "continuous": params['continuous'],
"binary": params['binary'], "type": "did_twfe", "periods": params['periods']}
name = "did_twfe_data_{}.csv".format(i)
logger.info("Test result: {}\n".format(test_result))
metadata_dict[name] = data_dict
gen.save_data(data_save_loc, name)
export_info(metadata_dict, metadata_save_loc, "did_twfe")
def generate_encouragement_data(base_mean, base_cov, dset_size, max_cont, max_bin, min_obs, max_obs,
data_save_loc, metadata_save_loc, n_obs=None):
"""
Generate encouragement design data
Args:
base_mean (np.ndarray): Base mean vector for the covariates
base_cov (np.ndarray): Base covariance matrix for the covariates
dset_size (int): Number of datasets to generate
max_cont (int): Maximum number of continuous covariates
max_bin (int): Maximum number of binary covariates
min_obs (int): Minimum number of observations to generate
max_obs (int): Maximum number of observations to generate
data_save_loc (str): Directory to save the generated data files
metadata_save_loc (str): Directory to save the metadata information
n_obs (int, None): number of observations. If None, it will be randomly
generated within the range of min_obs and max_obs.
"""
logger = logging.getLogger("iv_data_logger")
logger.info("Generating encouragement design data")
metadata_dict = {}
base_seed = 571
for i in range(dset_size):
logger.info("Iteration: {}".format(i))
seed = (i + 1) * base_seed
params = config_hyperparameters(seed, base_mean, base_cov, max_cont, max_bin, n_obs,
max_obs, min_obs)
logger.info("n_observations:{}, n_continuous: {}, n_binary: {}".format(
params['obs'], params['continuous'], params['binary']))
logger.info("true_effect: {}".format(params['tau']))
mean_vec = params['mean']
cov_mat = params['covar']
gen = IVGenerator(params['obs'], params['continuous'], n_binary_covars=params['binary'],
mean=mean_vec, covar=cov_mat, true_effect=params['tau'], seed=seed,
encouragement=True)
data = gen.generate_data()
test_result = gen.test_data()
data_dict = {"true_effect": params['tau'], "observation": params['obs'], "continuous": params['continuous'],
"binary": params['binary'], "type": "encouragement"}
name = "iv_encouragement_data_{}.csv".format(i)
logger.info("Test result: {}\n".format(test_result))
metadata_dict[name] = data_dict
gen.save_data(data_save_loc, name)
export_info(metadata_dict, metadata_save_loc, "iv_encouragement")
def generate_rdd_data(base_mean, base_cov, dset_size, max_cont, max_bin, max_cutoff,
min_obs, max_obs, data_save_loc, metadata_save_loc, n_obs=None):
"""
Generates (sharp) RDD data
Args:
base_mean (np.ndarray): Base mean vector for the covariates
base_cov (np.ndarray): Base covariance matrix for the covariates
dset_size (int): Number of datasets to generate
max_cont (int): Maximum number of continuous covariates
max_bin (int): Maximum number of binary covariates
max_cutoff (int): Maximum value for the cutoff in RDD data
min_obs (int): Minimum number of observations to generate
max_obs (int): Maximum number of observations to generate
data_save_loc (str): Directory to save the generated data files
metadata_save_loc (str): Directory to save the metadata information
n_obs (int, None): number of observations. If None, it will be randomly
generated within the range of min_obs and max_obs.
"""
logger = logging.getLogger("rdd_data_logger")
logger.info("Generating RDD data")
metadata_dict = {}
base_seed = 683
for i in range(dset_size):
logger.info("Iteration:{}".format(i))
seed = (i + 1) * base_seed
params = config_hyperparameters(seed, base_mean, base_cov, max_cont, max_bin, n_obs,
max_obs, min_obs, cutoff_max=max_cutoff)
logger.info("n_observations:{}, n_continuous: {}, n_binary: {}, cutoff:{}".format(
params['obs'], params['continuous'], params['binary'], params['cutoff']))
logger.info("true_effect: {}".format(params['tau']))
mean_vec = params['mean']
cov_mat = params['covar']
gen = RDDGenerator(params['obs'], params['continuous'], n_binary_covars=params['binary'],
mean=mean_vec, covar=cov_mat, true_effect=params['tau'], seed=seed,
cutoff=params['cutoff'], plot=True)
data = gen.generate_data()
test_result = gen.test_data()
data_dict = {"true_effect": params['tau'], "observation": params['obs'], "continuous": params['continuous'],
"binary": params['binary'], "type": "rdd", 'cutoff': params['cutoff']}
name = "rdd_data_{}.csv".format(i)
logger.info("Test result: {}\n".format(test_result))
metadata_dict[name] = data_dict
gen.save_data(data_save_loc, name)
export_info(metadata_dict, metadata_save_loc, "rdd")
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