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Merge pull request #84 from ClimateImpactLab/equity_tests
Reenabling equity tests
2 parents d9bdae3 + e3f9778 commit 74737f5

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-124
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3 files changed

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‎tests/conftest.py‎

Lines changed: 2 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -4,6 +4,7 @@
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from dscim.menu.simple_storage import Climate, EconVars
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from dscim.menu.baseline import Baseline
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from dscim.menu.risk_aversion import RiskAversionRecipe
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from dscim.menu.equity import EquityRecipe
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from pathlib import Path
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from itertools import product
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import numpy as np
@@ -61,7 +62,7 @@ def discount_types(request):
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return request.param
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64-
all_menu_classes = [Baseline, RiskAversionRecipe]
65+
all_menu_classes = [Baseline, RiskAversionRecipe, EquityRecipe]
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@pytest.fixture(params=all_menu_classes, scope="module")

‎tests/data/menu_results.zip‎

8.18 MB
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‎tests/test_equity.py‎

Lines changed: 88 additions & 123 deletions
Original file line numberDiff line numberDiff line change
@@ -1,123 +1,88 @@
1-
# import os
2-
# import pytest
3-
# import pandas as pd
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# import xarray as xr
5-
6-
# from pathlib import Path
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# from pandas.testing import assert_frame_equal
8-
# from xarray.testing import assert_equal, assert_allclose
9-
10-
# from dscim.tests import open_example_dataset, open_zipped_results
11-
# from dscim.menu.equity import EquityRecipe
12-
13-
14-
# @pytest.fixture(
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# params=[
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# "constant",
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# # "constant_model_collapsed", # not used
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# "naive_ramsey",
19-
# "euler_ramsey",
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# "naive_gwr",
21-
# "gwr_gwr",
22-
# "euler_gwr",
23-
# ]
24-
# )
25-
# def discount_types(request):
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# return request.param
27-
28-
29-
# @pytest.fixture
30-
# def equity(discount_types, econ, climate):
31-
# datadir = os.path.join(os.path.dirname(__file__), "data")
32-
33-
# recipe = EquityRecipe(
34-
# sector_path=[{"dummy_sector": os.path.join(datadir, "damages")}],
35-
# save_path=None,
36-
# econ_vars=econ,
37-
# climate_vars=climate,
38-
# fit_type="ols",
39-
# variable=[{"dummy_sector": "damages"}],
40-
# sector="dummy_sector",
41-
# discounting_type=discount_types,
42-
# ext_method="global_c_ratio",
43-
# ce_path= os.path.join(datadir, "CEs"),
44-
45-
# subset_dict={
46-
# "ssp": ["SSP2", "SSP3", "SSP4"],
47-
# "region": [
48-
# "IND.21.317.1249",
49-
# "CAN.2.33.913",
50-
# "USA.14.608",
51-
# "EGY.11",
52-
# "SDN.4.11.50.164",
53-
# "NGA.25.510",
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# "SAU.7",
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# "RUS.16.430.430",
56-
# "SOM.2.5",
57-
# ],
58-
# },
59-
# fair_aggregation=["ce", "median_params", "mean"],
60-
# extrap_formula=None,
61-
# formula="damages ~ -1 + anomaly + np.power(anomaly, 2)",
62-
# )
63-
64-
# yield recipe
65-
66-
# @pytest.mark.xfail
67-
# def test_equity_points(equity, discount_types):
68-
# path = f"equity_{discount_types}_eta{equity.eta}_rho{equity.rho}_damage_function_points.csv"
69-
# expected = open_zipped_results(path)
70-
# actual = equity.damage_function_points
71-
# assert_frame_equal(expected, actual, rtol=1e-4, atol=1e-4)
72-
73-
# @pytest.mark.xfail
74-
# def test_equity_coefficients(equity, discount_types):
75-
# path = f"equity_{discount_types}_eta{equity.eta}_rho{equity.rho}_damage_function_coefficients.nc4"
76-
# expected = open_zipped_results(path)
77-
# actual = equity.damage_function_coefficients
78-
# assert_allclose(
79-
# expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
80-
# actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
81-
# )
82-
83-
# @pytest.mark.xfail
84-
# def test_equity_fit(equity, discount_types):
85-
# path = f"equity_{discount_types}_eta{equity.eta}_rho{equity.rho}_damage_function_fit.nc4"
86-
# expected = open_zipped_results(path)
87-
# actual = equity.damage_function_fit
88-
# assert_allclose(
89-
# expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
90-
# actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
91-
# )
92-
93-
# @pytest.mark.xfail
94-
# def test_equity_global_consumption(equity, discount_types):
95-
# path = f"equity_{discount_types}_eta{equity.eta}_rho{equity.rho}_global_consumption.nc4"
96-
# expected = open_zipped_results(path)
97-
# actual = equity.global_consumption.squeeze()
98-
# # Small format hack from I/O
99-
# if isinstance(expected, xr.Dataset):
100-
# expected = expected.to_array().squeeze().drop("variable")
101-
102-
# assert_allclose(
103-
# expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
104-
# actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
105-
# )
106-
107-
108-
# @pytest.mark.xfail
109-
# def test_equity_scc(equity, discount_types):
110-
# path = f"equity_{discount_types}_eta{equity.eta}_rho{equity.rho}_scc.nc4"
111-
# expected = open_zipped_results(path)
112-
# actual = equity.calculate_scc.squeeze()
113-
114-
# # Small format hack from I/O
115-
# if isinstance(expected, xr.Dataset):
116-
# expected = expected.to_array().squeeze().drop("variable")
117-
118-
# assert_allclose(
119-
# expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
120-
# actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
121-
# rtol=1.5e-4,
122-
# atol=1e-4,
123-
# )
1+
import pandas
2+
import xarray as xr
3+
from pandas.testing import assert_frame_equal
4+
from xarray.testing import assert_allclose
5+
import pytest
6+
7+
from . import open_zipped_results
8+
from dscim.menu.equity import EquityRecipe
9+
10+
11+
@pytest.mark.parametrize("menu_class", [EquityRecipe], indirect=True)
12+
def test_equity_points(menu_instance, discount_types):
13+
path = f"equity_{discount_types}_eta{menu_instance.eta}_rho{menu_instance.rho}_damage_function_points.csv"
14+
expected = open_zipped_results(path)
15+
actual = menu_instance.damage_function_points
16+
assert_frame_equal(
17+
expected,
18+
actual,
19+
rtol=1e-4,
20+
atol=1e-4,
21+
)
22+
23+
24+
@pytest.mark.parametrize("menu_class", [EquityRecipe], indirect=True)
25+
def test_equity_coefficients(menu_instance, discount_types):
26+
path = f"equity_{discount_types}_eta{menu_instance.eta}_rho{menu_instance.rho}_damage_function_coefficients.nc4"
27+
expected = open_zipped_results(path)
28+
actual = menu_instance.damage_function_coefficients
29+
assert_allclose(
30+
expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
31+
actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
32+
)
33+
34+
35+
@pytest.mark.parametrize("menu_class", [EquityRecipe], indirect=True)
36+
def test_equity_fit(menu_instance, discount_types):
37+
path = f"equity_{discount_types}_eta{menu_instance.eta}_rho{menu_instance.rho}_damage_function_fit.nc4"
38+
expected = open_zipped_results(path)
39+
actual = menu_instance.damage_function_fit
40+
assert_allclose(
41+
expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
42+
actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
43+
)
44+
45+
46+
@pytest.mark.parametrize("menu_class", [EquityRecipe], indirect=True)
47+
def test_equity_global_consumption(menu_instance, discount_types):
48+
path = f"equity_{discount_types}_eta{menu_instance.eta}_rho{menu_instance.rho}_global_consumption.nc4"
49+
expected = open_zipped_results(path)
50+
actual = menu_instance.global_consumption.squeeze()
51+
# Small format hack from I/O
52+
if isinstance(expected, xr.Dataset):
53+
expected = expected.to_array().squeeze().drop("variable")
54+
55+
assert_allclose(
56+
expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
57+
actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
58+
)
59+
60+
61+
@pytest.mark.parametrize("menu_class", [EquityRecipe], indirect=True)
62+
def test_equity_scc(menu_instance, discount_types):
63+
path = (
64+
f"equity_{discount_types}_eta{menu_instance.eta}_rho{menu_instance.rho}_scc.nc4"
65+
)
66+
expected = open_zipped_results(path)
67+
actual = menu_instance.calculate_scc.squeeze()
68+
69+
# Small format hack from I/O
70+
if isinstance(expected, xr.Dataset):
71+
expected = expected.to_array().squeeze().drop("variable")
72+
73+
assert_allclose(
74+
expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
75+
actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
76+
rtol=1e-4,
77+
atol=1e-4,
78+
)
79+
80+
81+
@pytest.mark.parametrize("discount_types", ["euler_ramsey"], indirect=True)
82+
@pytest.mark.parametrize("menu_class", [EquityRecipe], indirect=True)
83+
def test_global_damages_calculation(menu_instance):
84+
global_damages = menu_instance.global_damages_calculation()
85+
assert (
86+
isinstance(global_damages, pandas.DataFrame)
87+
and "region" not in global_damages.columns
88+
)

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