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Updates to example scripts #111

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May 16, 2024
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Merged

Updates to example scripts #111

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0a3b406
use not age specific, save all output to folder
jdebacker May 16, 2024
121b5e2
similar updates to CP example
jdebacker May 16, 2024
0b91c8a
format
jdebacker May 16, 2024
6383224
only upload codecov from ogusa repo
jdebacker May 16, 2024
78b5e75
style the plots
jdebacker May 16, 2024
13841dd
undo comment
jdebacker May 16, 2024
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2 changes: 1 addition & 1 deletion .github/workflows/build_and_test.yml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -54,7 +54,7 @@ jobs:
run: |
python -m pytest -m "not local" --cov=./ --cov-report=xml
- name: Upload coverage to Codecov
if: matrix.os == 'ubuntu-latest'
if: matrix.os == 'ubuntu-latest' && contains(github.repository, 'PSLmodels/OG-USA')
uses: codecov/codecov-action@v4
with:
files: ./coverage.xml
Expand Down
221 changes: 125 additions & 96 deletions examples/run_og_usa.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,13 +4,21 @@
import json
import time
from taxcalc import Calculator
import matplotlib.pyplot as plt
from ogusa.calibrate import Calibration
from ogcore.parameters import Specifications
from ogcore import output_tables as ot
from ogcore import output_plots as op
from ogcore.execute import runner
from ogcore.utils import safe_read_pickle

# Use a custom matplotlib style file for plots
style_file_url = (
"https://raw.githubusercontent.com/PSLmodels/OG-Core/"
+ "master/ogcore/OGcorePlots.mplstyle"
)
plt.style.use(style_file_url)


def main():
# Define parameters to use for multiprocessing
Expand All @@ -29,102 +37,104 @@ def main():
------------------------------------------------------------------------
"""
# Set up baseline parameterization
p = Specifications(
baseline=True,
num_workers=num_workers,
baseline_dir=base_dir,
output_base=base_dir,
)
# Update parameters for baseline from default json file
p.update_specifications(
json.load(
open(
os.path.join(
CUR_DIR, "..", "ogusa", "ogusa_default_parameters.json"
)
)
)
)
p.tax_func_type = "GS"
c = Calibration(p, estimate_tax_functions=True, client=client)
# close and delete client bc cache is too large
client.close()
del client
client = Client(n_workers=num_workers, threads_per_worker=1)
d = c.get_dict()
# # additional parameters to change
updated_params = {
"etr_params": d["etr_params"],
"mtrx_params": d["mtrx_params"],
"mtry_params": d["mtry_params"],
"mean_income_data": d["mean_income_data"],
"frac_tax_payroll": d["frac_tax_payroll"],
}
p.update_specifications(updated_params)
# Run model
start_time = time.time()
runner(p, time_path=True, client=client)
print("run time = ", time.time() - start_time)
# p = Specifications(
# baseline=True,
# num_workers=num_workers,
# baseline_dir=base_dir,
# output_base=base_dir,
# )
# # Update parameters for baseline from default json file
# p.update_specifications(
# json.load(
# open(
# os.path.join(
# CUR_DIR, "..", "ogusa", "ogusa_default_parameters.json"
# )
# )
# )
# )
# p.tax_func_type = "GS"
# p.age_specific = False
# c = Calibration(p, estimate_tax_functions=True, client=client)
# # close and delete client bc cache is too large
# client.close()
# del client
# client = Client(n_workers=num_workers, threads_per_worker=1)
# d = c.get_dict()
# # # additional parameters to change
# updated_params = {
# "etr_params": d["etr_params"],
# "mtrx_params": d["mtrx_params"],
# "mtry_params": d["mtry_params"],
# "mean_income_data": d["mean_income_data"],
# "frac_tax_payroll": d["frac_tax_payroll"],
# }
# p.update_specifications(updated_params)
# # Run model
# start_time = time.time()
# runner(p, time_path=True, client=client)
# print("run time = ", time.time() - start_time)

"""
------------------------------------------------------------------------
Run reform policy
------------------------------------------------------------------------
"""
# Grab a reform JSON file already in Tax-Calculator
# In this example the 'reform' is a change to 2017 law (the
# baseline policy is tax law in 2018)
reform_url = (
"github://PSLmodels:examples@main/psl_examples/"
+ "taxcalc/2017_law.json"
)
ref = Calculator.read_json_param_objects(reform_url, None)
iit_reform = ref["policy"]
# """
# ------------------------------------------------------------------------
# Run reform policy
# ------------------------------------------------------------------------
# """
# # Grab a reform JSON file already in Tax-Calculator
# # In this example the 'reform' is a change to 2017 law (the
# # baseline policy is tax law in 2018)
# reform_url = (
# "github://PSLmodels:examples@main/psl_examples/"
# + "taxcalc/2017_law.json"
# )
# ref = Calculator.read_json_param_objects(reform_url, None)
# iit_reform = ref["policy"]

# create new Specifications object for reform simulation
p2 = Specifications(
baseline=False,
num_workers=num_workers,
baseline_dir=base_dir,
output_base=reform_dir,
)
# Update parameters for baseline from default json file
p2.update_specifications(
json.load(
open(
os.path.join(
CUR_DIR, "..", "ogusa", "ogusa_default_parameters.json"
)
)
)
)
p2.tax_func_type = "GS"
# Use calibration class to estimate reform tax functions from
# Tax-Calculator, specifying reform for Tax-Calculator in iit_reform
c2 = Calibration(
p2, iit_reform=iit_reform, estimate_tax_functions=True, client=client
)
# close and delete client bc cache is too large
client.close()
del client
client = Client(n_workers=num_workers, threads_per_worker=1)
# update tax function parameters in Specifications Object
d = c2.get_dict()
# # additional parameters to change
updated_params = {
"cit_rate": [[0.35]],
"etr_params": d["etr_params"],
"mtrx_params": d["mtrx_params"],
"mtry_params": d["mtry_params"],
"mean_income_data": d["mean_income_data"],
"frac_tax_payroll": d["frac_tax_payroll"],
}
p2.update_specifications(updated_params)
# Run model
start_time = time.time()
runner(p2, time_path=True, client=client)
print("run time = ", time.time() - start_time)
client.close()
# # create new Specifications object for reform simulation
# p2 = Specifications(
# baseline=False,
# num_workers=num_workers,
# baseline_dir=base_dir,
# output_base=reform_dir,
# )
# # Update parameters for baseline from default json file
# p2.update_specifications(
# json.load(
# open(
# os.path.join(
# CUR_DIR, "..", "ogusa", "ogusa_default_parameters.json"
# )
# )
# )
# )
# p2.tax_func_type = "GS"
# p2.age_specific = False
# # Use calibration class to estimate reform tax functions from
# # Tax-Calculator, specifying reform for Tax-Calculator in iit_reform
# c2 = Calibration(
# p2, iit_reform=iit_reform, estimate_tax_functions=True, client=client
# )
# # close and delete client bc cache is too large
# client.close()
# del client
# client = Client(n_workers=num_workers, threads_per_worker=1)
# # update tax function parameters in Specifications Object
# d = c2.get_dict()
# # # additional parameters to change
# updated_params = {
# "cit_rate": [[0.35]],
# "etr_params": d["etr_params"],
# "mtrx_params": d["mtrx_params"],
# "mtry_params": d["mtry_params"],
# "mean_income_data": d["mean_income_data"],
# "frac_tax_payroll": d["frac_tax_payroll"],
# }
# p2.update_specifications(updated_params)
# # Run model
# start_time = time.time()
# runner(p2, time_path=True, client=client)
# print("run time = ", time.time() - start_time)
# client.close()

"""
------------------------------------------------------------------------
Expand Down Expand Up @@ -152,12 +162,31 @@ def main():

# create plots of output
op.plot_all(
base_dir, reform_dir, os.path.join(CUR_DIR, "OG-USA_example_plots")
base_dir,
reform_dir,
os.path.join(CUR_DIR, "OG-USA_example_plots_tables"),
)
# Create CSV file with output
ot.tp_output_dump_table(
base_params,
base_tpi,
reform_params,
reform_tpi,
table_format="csv",
path=os.path.join(
CUR_DIR,
"OG-USA_example_plots_tables",
"macro_time_series_output.csv",
),
)

print("Percentage changes in aggregates:", ans)
# save percentage change output to csv file
ans.to_csv("ogusa_example_output.csv")
ans.to_csv(
os.path.join(
CUR_DIR, "OG-USA_example_plots_tables", "ogusa_example_output.csv"
)
)


if __name__ == "__main__":
Expand Down
36 changes: 34 additions & 2 deletions examples/run_og_usa_current_policy_baseline.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import json
import time
from taxcalc import Calculator
import matplotlib.pyplot as plt
from ogusa.calibrate import Calibration
from ogcore.parameters import Specifications
from ogcore import output_tables as ot
Expand All @@ -13,6 +14,14 @@
from ogcore.utils import safe_read_pickle


# Use a custom matplotlib style file for plots
style_file_url = (
"https://raw.githubusercontent.com/PSLmodels/OG-Core/"
+ "master/ogcore/OGcorePlots.mplstyle"
)
plt.style.use(style_file_url)


def main():
# Define parameters to use for multiprocessing
num_workers = min(multiprocessing.cpu_count(), 7)
Expand Down Expand Up @@ -47,6 +56,7 @@ def main():
)
)
p.tax_func_type = "GS"
p.age_specific = False
# get current policy JSON file
base_url = (
"github://PSLmodels:Tax-Calculator@master/taxcalc/"
Expand Down Expand Up @@ -111,6 +121,7 @@ def main():
)
)
p2.tax_func_type = "GS"
p.age_specific = False
# Use calibration class to estimate reform tax functions from
# Tax-Calculator, specifying reform for Tax-Calculator in iit_reform
c2 = Calibration(
Expand Down Expand Up @@ -168,12 +179,33 @@ def main():

# create plots of output
op.plot_all(
base_dir, reform_dir, os.path.join(CUR_DIR, "OG-USA_example_plots")
base_dir,
reform_dir,
os.path.join(CUR_DIR, "OG-USA_current_policy_example_plots_tables"),
)
# Create CSV file with output
ot.tp_output_dump_table(
base_params,
base_tpi,
reform_params,
reform_tpi,
table_format="csv",
path=os.path.join(
CUR_DIR,
"OG-USA_example_plots_tables",
"macro_time_series_output.csv",
),
)

print("Percentage changes in aggregates:", ans)
# save percentage change output to csv file
ans.to_csv("ogusa_example_output.csv")
ans.to_csv(
os.path.join(
CUR_DIR,
"OG-USA_current_policy_example_plots_tables",
"ogusa_example_output.csv",
)
)


if __name__ == "__main__":
Expand Down
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