def setUp(self):
origin, dev, value = "origin", "dev", "value"
incrtab = trikit.load(dataset="raa")
# Create cumulative tabular data.
cumtab = incrtab.copy(deep=True).sort_values(
by=["origin", "dev"]).reset_index(drop=True)
cumtab["cum"] = cumtab.groupby(["origin"],
as_index=False)["value"].cumsum()
cumtab = cumtab.drop("value", axis=1).rename({"cum": "value"}, axis=1)
# Create incremental triangle data.
incrtri = incrtab[[origin, dev, value]]
incrtri = incrtri.groupby([origin, dev], as_index=False).sum()
incrtri = incrtri.sort_values(by=[origin, dev])
incrtri = incrtri.pivot(index=origin, columns=dev).rename_axis(None)
incrtri.columns = incrtri.columns.droplevel(0)
# Create cumulative triangle data.
cumtri = incrtab[[origin, dev, value]]
cumtri = cumtri.groupby([origin, dev], as_index=False).sum()
cumtri = cumtri.sort_values(by=[origin, dev])
cumtri = cumtri.pivot(index=origin, columns=dev).rename_axis(None)
cumtri.columns = cumtri.columns.droplevel(0)
self.incrtab = incrtab
self.cumtab = cumtab
self.incrtri = incrtri
self.cumtri = cumtri
self.incr_latest_ref = pd.DataFrame(
{
"origin":
list(range(1981, 1991, 1)),
"maturity":
list(range(10, 0, -1)),
"dev":
list(range(10, 0, -1)),
"latest": [
172.0, 535.0, 603.0, 984.0, 225.0, 2917.0, 1368.0, 6165.0,
2262.0, 2063.0
],
},
index=list(range(0, 10, 1)))
self.cum_latest_ref = pd.DataFrame(
{
"origin":
list(range(1981, 1991, 1)),
"maturity":
list(range(10, 0, -1)),
"dev":
list(range(10, 0, -1)),
"latest": [
18834.0, 16704.0, 23466.0, 27067.0, 26180.0, 15852.0,
12314.0, 13112.0, 5395.0, 2063.0
],
},
index=list(range(0, 10, 1)))
def setUp(self):
tri = trikit.load(dataset="raa", tri_type="cum")
# tri = trikit.totri(data, tri_type="cum", data_shape="tabular", data_format="incr")
# cl = base.BaseChainLadder(cumtri=tri)
# r_cl = cl()
r_cl = tri.base_cl()
dactual_raa = {
"ldfs_sum": 13.28018030198903,
"cldfs_sum": 21.59861048771567,
"latest_sum": 321974.0,
"ultimates_sum": 213122.22826121017,
"reserves_sum": 52135.228261210155,
}
self.ultimates_sum = r_cl.ultimate.drop("total").dropna().sum()
self.reserves_sum = r_cl.reserve.drop("total").dropna().sum()
self.latest_sum = r_cl.latest.dropna().sum()
self.cldfs_sum = r_cl.cldfs.dropna().sum()
self.ldfs_sum = r_cl.ldfs.dropna().sum()
self.dactual_raa = dactual_raa
self.tri = tri
self.custom_ldfs = np.asarray(
[2.75, 1.55, 1.50, 1.25, 1.15, 1.075, 1.03, 1.02, 1.01])
self.custom_ldfs_reserves_total = 145820.33941776195
def setUp(self):
# Modify origin and development periods to test not sequentials.
df = trikit.load(dataset="ta83")
df["dev"] = df["dev"] * 12
df["origin"] = df["origin"] + 2000
tri = trikit.totri(df,
tri_type="cum",
data_shape="tabular",
data_format="incr")
mcl = mack.MackChainLadder(cumtri=tri)
r_lognorm = mcl(alpha=1, dist="lognorm")
r_norm = mcl(alpha=1, dist="norm")
dactual_ta83 = {
"norm_mu_sum": 18680869.054532073,
"norm_sigma_sum": 4771773.155719111,
"norm_75_sum": 21899381.138325423,
"norm_95_sum": 26529737.436706323,
"lognorm_mu_sum": 125.8539998696597,
"lognorm_sigma_sum": 2.6740386407158327,
"lognorm_75_sum": 21420867.75494642,
"lognorm_95_sum": 27371140.20920447,
"mse_sum": 4156154300629.1504,
"std_error_sum": 4771773.155719111,
"cv_sum": 2.80203003051732,
"process_error_sum": 3527957849338.302,
"parameter_error_sum": 628196451290.8485,
"ldfs_sum": 14.207460332760107,
"ultimates_sum": 53038959.05453208,
"reserves_sum": 18680869.054532073,
"devpvar_sum": 279118.8961841563,
"ldfvar_sum": 0.05702584091389985,
}
with np.errstate(invalid="ignore"):
self.norm_75_sum = pd.Series([
r_norm.rvs[ii].ppf(.75) for ii in r_norm.tri.index
]).dropna().sum()
self.norm_95_sum = pd.Series([
r_norm.rvs[ii].ppf(.95) for ii in r_norm.tri.index
]).dropna().sum()
self.lognorm_75_sum = pd.Series([
r_lognorm.rvs[ii].ppf(.75) for ii in r_lognorm.tri.index
]).dropna().sum()
self.lognorm_95_sum = pd.Series([
r_lognorm.rvs[ii].ppf(.95) for ii in r_lognorm.tri.index
]).dropna().sum()
self.mse_sum = r_lognorm.mse.drop("total").dropna().sum()
self.std_error_sum = r_lognorm.std_error.drop("total").dropna().sum()
self.cv_sum = r_lognorm.cv.drop("total").dropna().sum()
self.process_error_sum = r_lognorm.process_error.dropna().sum()
self.parameter_error_sum = r_lognorm.parameter_error.dropna().sum()
self.ldfs_sum = r_lognorm.ldfs.dropna().sum()
self.ultimates_sum = r_lognorm.ultimate.drop("total").dropna().sum()
self.reserves_sum = r_lognorm.reserve.drop("total").dropna().sum()
self.devpvar_sum = r_lognorm.devpvar.dropna().sum()
self.ldfvar_sum = r_lognorm.ldfvar.dropna().sum()
self.dactual_ta83 = dactual_ta83
def test_cy_effects_test(self):
ref_tt0 = (8.965613354894957, 16.78438664510504)
ref_tt1 = 14.
tri = trikit.totri(trikit.load("raa"))
tt = tri.mack_cl().cy_effects_test()
test1 = all([np.allclose(ii, jj) for ii, jj in zip(ref_tt0, tt[0])])
test2 = np.allclose(ref_tt1, tt[-1])
self.assertTrue(
test1 and test2,
"Non-equality between computed vs. reference cy effects test.")
def test_devp_corr_test(self):
ref_tt0 = (-0.12746658149149367, 0.12746658149149367)
ref_tt1 = 0.0695578231292517
tri = trikit.totri(trikit.load("raa"))
tt = tri.mack_cl().devp_corr_test()
test1 = all([np.allclose(ii, jj) for ii, jj in zip(ref_tt0, tt[0])])
test2 = np.allclose(ref_tt1, tt[-1])
self.assertTrue(
test1 and test2,
"Non-equality between computed vs. reference devp correlation test."
)
def setUp(self):
data = trikit.load(dataset="raa")
self.tri = trikit.totri(data=data, type_="incremental")
self.latest_ref = pd.DataFrame({
"origin":list(range(1981, 1991, 1)), "maturity":list(range(10, 0, -1)),
"dev":list(range(10, 0, -1)),
"latest":[
172.0, 535.0, 603.0, 984.0, 225.0, 2917.0, 1368.0,
6165.0, 2262.0, 2063.0
],
}, index=list(range(0, 10, 1))
)
def setUp(self):
tri = trikit.load(dataset="raa", tri_type="cum")
bcl = bootstrap.BootstrapChainLadder(tri)
r_bcl = bcl(random_state=516)
dactual_raa = {
"ldfs_sum": 13.28018030198903,
"cldfs_sum": 21.59861048771567,
"latest_sum": 321974.0,
"ultimates_sum": 214989.29310411066,
"reserves_sum": 54002.29310411069,
"se_sum": 44279.591512336876,
"dof": 56,
"scale_param": 632.3368030912758,
"fitted_cum_sum": 707622.0,
"fitted_incr_sum": 160987.0,
"resid_us_sum": 4.7274165831925234,
"resid_adj_sum": 4.68501737172304,
"sampling_dist_sum": 4.68501737172304,
"bs_samples_sum": 16198534.554200275,
"bs_ldfs_sum": 1245.7029695928195,
"bs_forecasts_sum": 21528898.436414644,
"bs_process_error_sum": 156451006.23791158,
"bs_reserves_sum": 5227886.663640983,
}
self.ultimates_sum = r_bcl.ultimate.drop("total").dropna().sum()
self.reserves_sum = r_bcl.reserve.drop("total").dropna().sum()
self.se_sum = r_bcl.std_error.dropna().sum()
self.latest_sum = r_bcl.latest.dropna().sum()
self.cldfs_sum = r_bcl.cldfs.dropna().sum()
self.ldfs_sum = r_bcl.ldfs.dropna().sum()
self.dof = r_bcl.dof
self.tri_fit_cum = bcl._tri_fit_cum(r_bcl.ldfs)
self.tri_fit_incr = bcl._tri_fit_incr(self.tri_fit_cum)
self.resid_us = bcl._resid_us(self.tri_fit_incr)
self.scale_param = bcl._scale_param(self.resid_us)
self.resid_adj = bcl._resid_adj(self.resid_us)
self.sampling_dist = bcl._sampling_dist(self.resid_adj)
self.bs_samples = bcl._bs_samples(self.sampling_dist,
self.tri_fit_incr,
sims=100,
random_state=516)
self.bs_ldfs = bcl._bs_ldfs(self.bs_samples)
self.bs_forecasts = bcl._bs_forecasts(self.bs_samples, self.bs_ldfs,
self.scale_param)
self.bs_process_error = bcl._bs_process_error(self.bs_forecasts,
self.scale_param,
procdist="gamma",
random_state=516)
self.bs_reserves = bcl._bs_reserves(self.bs_process_error)
self.dactual_raa = dactual_raa
def setUp(self):
data = trikit.load(dataset="raa")
self.tri = trikit.totri(data=data, tri_type="incremental")
self.latest_ref = pd.DataFrame(
{
"origin":
list(range(1981, 1991, 1)),
"maturity":
list(range(10, 0, -1)),
"dev":
list(range(10, 0, -1)),
"latest": [
172.0, 535.0, 603.0, 984.0, 225.0, 2917.0, 1368.0, 6165.0,
2262.0, 2063.0
],
},
index=list(range(0, 10, 1)))
self.offset_1 = np.asarray(
[54., 673., 649., 2658., 3786., 1233., 6926., 5596., 3133.])
self.offset_2 = np.asarray(
[599., -103., 3479., 2159., 6333., 5257., 3463., 1351.])
self.offset_7 = np.asarray([2638., 4179., 3410.])
def setUp(self):
raa = trikit.load(dataset="raa")
self.tri = trikit.totri(raa, type_="cumulative")
self.latest_ref = pd.DataFrame({
"origin":list(range(1981, 1991, 1)), "maturity":list(range(10, 0, -1)),
"dev":list(range(10, 0, -1)),
"latest":[18834.0, 16704.0, 23466.0, 27067.0, 26180.0, 15852.0, 12314.0, 13112.0, 5395.0, 2063.0],
}, index=list(range(0, 10, 1))
)
self.a2aref = pd.DataFrame({
1:[1.64984, 40.42453, 2.63695, 2.04332, 8.75916, 4.25975, 7.21724, 5.14212, 1.72199],
2:[1.31902, 1.25928, 1.54282, 1.36443, 1.65562, 1.81567, 2.72289, 1.88743, np.NaN],
3:[1.08233, 1.97665, 1.16348, 1.34885, 1.39991, 1.10537, 1.12498, np.NaN, np.NaN],
4:[1.14689, 1.29214, 1.16071, 1.10152, 1.17078, 1.22551, np.NaN, np.NaN, np.NaN],
5:[1.19514, 1.13184, 1.1857 , 1.11347, 1.00867,np.NaN, np.NaN, np.NaN, np.NaN],
6:[1.11297, 0.9934 , 1.02922, 1.03773, np.NaN, np.NaN,np.NaN, np.NaN, np.NaN],
7:[1.03326, 1.04343, 1.02637,np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN],
8:[1.0029 , 1.03309,np.NaN, np.NaN, np.NaN, np.NaN,np.NaN, np.NaN, np.NaN],
9:[1.00922,np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN],
}, index=list(range(1981, 1990))
)
def test_raa2incr(self):
self.assertTrue(
isinstance(trikit.load("raa", tri_type="incr"),
trikit.triangle.IncrTriangle),
"RAA dataset not coerced to incremental triangle object.")
def test_raa2cum(self):
self.assertTrue(
isinstance(trikit.load("raa", tri_type="cum"),
trikit.triangle.CumTriangle),
"RAA dataset not coerced to cumulative triangle object.")
def test_amw09(self):
self.assertEqual(
trikit.load("amw09").value.sum(), self.dactual["amw09"],
"Issue detected with amw09 sample dataset.")
def test_raa2df(self):
self.assertTrue(
trikit.load("raa", tri_type=None).value.sum() == self.raa_incr_sum,
"Non-equality between computed vs. raa losses.")
def test_singproperty(self):
self.assertEqual(
trikit.load("singproperty").value.sum(),
self.dactual["singproperty"],
"Issue detected with singproperty sample dataset.")
def test_raa(self):
self.assertEqual(
trikit.load("raa").value.sum(), self.dactual["raa"],
"Issue detected with raa sample dataset.")
def test_glre(self):
self.assertEqual(
trikit.load("glre").value.sum(), self.dactual["glre"],
"Issue detected with glre sample dataset.")
def test_autoliab(self):
self.assertEqual(
trikit.load("autoliab").value.sum(), self.dactual["autoliab"],
"Issue detected with autoliab sample dataset.")
def test_ta83(self):
self.assertEqual(
trikit.load("ta83").value.sum(), self.dactual["ta83"],
"Issue detected with ta83 sample dataset.")
def setUp(self):
data = trikit.load(dataset="raa")
tri = trikit.totri(data,
type_="cum",
data_shape="tabular",
data_format="incr")
self.cl = trikit.chainladder.BaseChainLadder(cumtri=tri).__call__(
sel="all-weighted", tail=1.)
raa_cl_ref = pd.DataFrame({
"origin": [
1981,
1982,
1983,
1984,
1985,
1986,
1987,
1988,
1989,
1990,
],
"maturity": [
'10',
'9',
'8',
'7',
'6',
'5',
'4',
'3',
'2',
'1',
],
"cldf": [
1.,
1.00922,
1.02631,
1.06045,
1.10492,
1.2302,
1.44139,
1.83185,
2.97405,
8.92023,
],
"latest": [
18834.,
16704.,
23466.,
27067.,
26180.,
15852.,
12314.,
13112.,
5395.,
2063.,
],
"ultimate": [
18834.,
16857.95392,
24083.37092,
28703.14216,
28926.73634,
19501.10318,
17749.30259,
24019.19251,
16044.9841,
18402.44253,
],
"reserve": [
0.,
153.95392,
617.37092,
1636.14216,
2746.73634,
3649.10318,
5435.30259,
10907.19251,
10649.9841,
16339.44253,
]
})
ref_ldfs = pd.Series([
2.99936,
1.62352,
1.27089,
1.17167,
1.11338,
1.04193,
1.03326,
1.01694,
1.00922,
1.,
],
dtype=np.float)
ref_cldfs = np.asarray([
8.92023, 2.97405, 1.83185, 1.44139, 1.2302, 1.10492, 1.06045,
1.02631, 1.00922, 1.
],
dtype=np.float)
self.raa_cl_ref = raa_cl_ref #[raa_cl_ref.index!="total"]
self.ref_ldfs = ref_ldfs
self.ref_cldfs = ref_cldfs
c1 = t1.cum
c2 = _CumTriangle(trisize=10, incr=False)
c3 = _CumTriangle(incr=False)
# Convert incremental triangle to cumulative.
i2c = _CumTriangle(i1)
# Convert cumulative triangle to incremental.
c2i = _IncrTriangle(c1)
ctri = _Triangle(ds0, origin='ORIGIN',dev='DEV',value='VALUE').cumulative
ta = trikit.load("ta83")
sf = trikit.load("sf")
sf1 = trikit.load("sf",lob="com_auto")
# ChainLadder tests =>
# cl = trikit.ChainLadder(ds0,origin='ORIGIN',dev='DEV',value='VALUE', tail_fact=1.05)
#
# # Get age to ultimate factors
# cl.age2ult
#
# cl.squared_tri
