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Update tutorial
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‎doc/tutorial/climada_util_calibrate.ipynb‎

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"</div>"
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],
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"text/plain": [
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" country region_id cal_region2 year EM_ID ibtracsID \n",
752-
"326 MEX 484 NA1 2010 2010-0260 2010176N16278 \\\n",
751+
" country region_id cal_region2 year EM_ID ibtracsID \\\n",
752+
"326 MEX 484 NA1 2010 2010-0260 2010176N16278 \n",
753753
"331 ATG 28 NA1 2010 2010-0468 2010236N12341 \n",
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"334 MEX 484 NA1 2010 2010-0494 2010257N16282 \n",
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"339 LCA 662 NA1 2010 2010-0571 2010302N09306 \n",
@@ -777,8 +777,8 @@
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"464 DOM 214 NA1 2017 2017-0383 2017260N12310 \n",
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"465 PRI 630 NA1 2017 2017-0383 2017260N12310 \n",
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"\n",
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" emdat_impact reference_year emdat_impact_scaled climada_impact ... \n",
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"326 2.000000e+09 2014 2.485465e+09 2.478270e+09 ... \\\n",
780+
" emdat_impact reference_year emdat_impact_scaled climada_impact ... \\\n",
781+
"326 2.000000e+09 2014 2.485465e+09 2.478270e+09 ... \n",
782782
"331 1.260000e+07 2014 1.394594e+07 1.402875e+07 ... \n",
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"334 3.900000e+09 2014 4.846656e+09 4.857140e+09 ... \n",
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"339 5.000000e+05 2014 5.486675e+05 5.492871e+05 ... \n",
@@ -806,8 +806,8 @@
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"464 6.300000e+07 2014 5.481371e+07 5.493466e+07 ... \n",
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"465 6.800000e+10 2014 6.700905e+10 6.702718e+10 ... \n",
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"\n",
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" scale log_ratio unique_ID Associated_disaster Surge Rain Flood \n",
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"326 1.0 -0.002899 2010-0260MEX True False False True \\\n",
809+
" scale log_ratio unique_ID Associated_disaster Surge Rain Flood \\\n",
810+
"326 1.0 -0.002899 2010-0260MEX True False False True \n",
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"331 1.0 0.005920 2010-0468ATG True False False True \n",
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"334 1.0 0.002161 2010-0494MEX True False False True \n",
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"339 1.0 0.001129 2010-0571LCA True False False True \n",
@@ -1251,9 +1251,9 @@
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"</div>"
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],
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"text/plain": [
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"region_id 484 28 662 670 \n",
1254+
"region_id 484 28 662 670 \\\n",
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"ibtracsID \n",
1256-
"2010176N16278 2.485465e+09 0.000000e+00 0.0000 0.00 \\\n",
1256+
"2010176N16278 2.485465e+09 0.000000e+00 0.0000 0.00 \n",
12571257
"2010236N12341 0.000000e+00 1.394594e+07 0.0000 0.00 \n",
12581258
"2010257N16282 4.846656e+09 0.000000e+00 0.0000 0.00 \n",
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"2010302N09306 0.000000e+00 0.000000e+00 548667.5019 26706058.15 \n",
@@ -1271,9 +1271,9 @@
12711271
"2017242N16333 0.000000e+00 2.111764e+08 0.0000 0.00 \n",
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"2017260N12310 0.000000e+00 0.000000e+00 0.0000 0.00 \n",
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"\n",
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"region_id 44 214 630 388 \n",
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"region_id 44 214 630 388 \\\n",
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"ibtracsID \n",
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"2010176N16278 0.000000e+00 0.00 0.000000e+00 0.00 \\\n",
1276+
"2010176N16278 0.000000e+00 0.00 0.000000e+00 0.00 \n",
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"2010236N12341 0.000000e+00 0.00 0.000000e+00 0.00 \n",
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"2010257N16282 0.000000e+00 0.00 0.000000e+00 0.00 \n",
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"2010302N09306 0.000000e+00 0.00 0.000000e+00 0.00 \n",
@@ -1291,9 +1291,9 @@
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"2017242N16333 1.801876e+06 0.00 0.000000e+00 0.00 \n",
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"2017260N12310 0.000000e+00 54813712.03 6.700905e+10 0.00 \n",
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"\n",
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"region_id 132 192 659 796 \n",
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"region_id 132 192 659 796 \\\n",
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"ibtracsID \n",
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"2010176N16278 0.000 0.000000e+00 0.00 0.0 \\\n",
1296+
"2010176N16278 0.000 0.000000e+00 0.00 0.0 \n",
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@@ -1419,7 +1419,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2023-06-09 12:52:52,649 - climada.hazard.tc_tracks - WARNING - The cached IBTrACS data set dates from 2022-03-08 23:23:51 (older than 180 days). Very likely, a more recent version is available. Consider manually removing the file /Users/ldr.riedel/climada/data/IBTrACS.ALL.v04r00.nc and re-running this function, which will download the most recent version of the IBTrACS data set from the official URL.\n"
1422+
"2023-06-13 16:36:05,304 - climada.hazard.tc_tracks - WARNING - The cached IBTrACS data set dates from 2022-03-08 23:23:51 (older than 180 days). Very likely, a more recent version is available. Consider manually removing the file /Users/ldr.riedel/climada/data/IBTrACS.ALL.v04r00.nc and re-running this function, which will download the most recent version of the IBTrACS data set from the official URL.\n"
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]
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},
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{
@@ -1534,8 +1534,9 @@
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"import numpy as np\n",
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"from climada.engine import Impact\n",
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"\n",
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"# TODO: Dont\n",
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"# Make sure that Hazard.event_id matches indexes of 'data'\n",
1538-
"hazard.event_id = np.asarray(hazard.event_name)\n",
1539+
"# hazard.event_id = np.asarray(hazard.event_name)\n",
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"\n",
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"def cost_rmse(impact: Impact, data: pd.DataFrame):\n",
15411542
" \"\"\"A cost function computing the RMSE\"\"\"\n",
@@ -1624,12 +1625,14 @@
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" cost_func=cost_rmse,\n",
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" impact_func_gen=impact_func_tc,\n",
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" bounds=bounds,\n",
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" align=False,\n",
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")\n",
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"exposure.assign_centroids(hazard)\n",
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"\n",
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"# Create and run the optimizer\n",
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"opt = BayesianOptimizer(input)\n",
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"output = opt.run()\n",
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"output.params # The optimal parameters"
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"bayesian_output = opt.run()\n",
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"bayesian_output.params # The optimal parameters"
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]
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},
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{
@@ -1670,9 +1673,7 @@
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}
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],
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"source": [
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"from climada.entity import ImpfTropCyclone\n",
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"\n",
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"ImpfTropCyclone.from_emanuel_usa(**output.params).plot()"
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"impact_func_tc(**bayesian_output.params).plot()"
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]
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},
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{
@@ -1683,7 +1684,7 @@
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"The Bayesian Optimizer returns the entire paramter space it sampled.\n",
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"We can find out a lot about the relation of the fitted parameters by investigating how the target function value depends on them.\n",
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"Since we only calibrated two values, we can plot them in a scatter plot.\n",
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"To that end, retrieving the parameter space data via `BayesianOptimizerOutput.param_space_to_dataframe` will help.\n",
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"To that end, retrieving the parameter space data via `BayesianOptimizerOutput.p_space_to_dataframe` will help.\n",
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"Notice that the optimal parameter set is not necessarily the last entry in the parameter space!"
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]
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},
@@ -1717,6 +1718,12 @@
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" <th>v_half</th>\n",
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" <th>target</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Iteration</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
@@ -1791,18 +1798,19 @@
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"</div>"
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],
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"text/plain": [
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" scale v_half target\n",
1795-
"0 0.422852 115.236335 2.405716e-10\n",
1796-
"1 0.010113 63.279939 2.280614e-10\n",
1797-
"2 0.155288 37.177687 3.057439e-10\n",
1798-
"3 0.194398 68.653198 2.637483e-10\n",
1799-
"4 0.402800 92.674920 2.571778e-10\n",
1800-
".. ... ... ...\n",
1801-
"195 0.980632 47.015299 3.576957e-10\n",
1802-
"196 0.943001 147.501934 2.396817e-10\n",
1803-
"197 0.999377 70.913361 6.412774e-10\n",
1804-
"198 0.997574 64.547339 9.480593e-10\n",
1805-
"199 0.982757 39.948848 2.226480e-10\n",
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" scale v_half target\n",
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"Iteration \n",
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"0 0.422852 115.236335 2.405716e-10\n",
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"1 0.010113 63.279939 2.280614e-10\n",
1805+
"2 0.155288 37.177687 3.057439e-10\n",
1806+
"3 0.194398 68.653198 2.637483e-10\n",
1807+
"4 0.402800 92.674920 2.571778e-10\n",
1808+
"... ... ... ...\n",
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"195 0.980632 47.015299 3.576957e-10\n",
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"196 0.943001 147.501934 2.396817e-10\n",
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"197 0.999377 70.913361 6.412774e-10\n",
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"198 0.997574 64.547339 9.480593e-10\n",
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"199 0.982757 39.948848 2.226480e-10\n",
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"\n",
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"[200 rows x 3 columns]"
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]
@@ -1813,7 +1821,7 @@
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}
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],
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"source": [
1816-
"p_space_df = output.p_space_to_dataframe()\n",
1824+
"p_space_df = bayesian_output.p_space_to_dataframe()\n",
18171825
"p_space_df"
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]
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},
@@ -1855,6 +1863,12 @@
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" <th>v_half</th>\n",
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" <th>target</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Iteration</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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"</div>"
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],
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"text/plain": [
1932-
" scale v_half target\n",
1933-
"116 0.990320 61.511633 1.013992e-09\n",
1934-
"108 0.988234 61.959510 1.013962e-09\n",
1935-
"110 0.982902 61.693392 1.013046e-09\n",
1936-
"170 0.978972 61.694825 1.011628e-09\n",
1937-
"119 0.997161 61.119598 1.007215e-09\n",
1938-
".. ... ... ...\n",
1939-
"91 0.755228 33.970485 2.088355e-10\n",
1940-
"194 0.980303 38.162301 1.980600e-10\n",
1941-
"66 0.565420 28.017858 1.792657e-10\n",
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"74 0.564120 27.260708 1.737898e-10\n",
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"174 0.968588 35.695063 1.692841e-10\n",
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" scale v_half target\n",
1947+
"Iteration \n",
1948+
"116 0.990320 61.511633 1.013992e-09\n",
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"108 0.988234 61.959510 1.013962e-09\n",
1950+
"110 0.982902 61.693392 1.013046e-09\n",
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"170 0.978972 61.694825 1.011628e-09\n",
1952+
"119 0.997161 61.119598 1.007215e-09\n",
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"... ... ... ...\n",
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"91 0.755228 33.970485 2.088355e-10\n",
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"194 0.980303 38.162301 1.980600e-10\n",
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"66 0.565420 28.017858 1.792657e-10\n",
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"74 0.564120 27.260708 1.737898e-10\n",
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"174 0.968588 35.695063 1.692841e-10\n",
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"\n",
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"[200 rows x 3 columns]"
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]
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}
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],
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"source": [
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"from climada.entity import ImpactFuncSet, ImpfTropCyclone\n",
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"from climada.engine import ImpactCalc\n",
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"\n",
2428-
"impf = ImpactFuncSet([ImpfTropCyclone.from_emanuel_usa(**output.params)])\n",
2442+
"impf = impact_func_tc(**bayesian_output.params)\n",
24292443
"impact = ImpactCalc(exposure, impf, hazard).impact(assign_centroids=False)\n",
24302444
"impact_data = impact.impact_at_reg(exposure.gdf[\"region_id\"])\n",
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"impact_data"
@@ -2988,7 +3002,9 @@
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"\n",
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"So we present some ideas on how to continue and/or improve the calibration:\n",
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"1. Use a different cost function\n",
2991-
"2. Employ the `ScipyMinimizeOptimizer` instead of the `BayesianOptimizer`"
3005+
"2. Also calibrate the `v_thresh` parameter. This requires adding constraints, because `v_thresh` < `v_half`.\n",
3006+
"3. Calibrate different impact functions for houses in Mexico and Puerto Rico within the same optimization task.\n",
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"4. Employ the `ScipyMinimizeOptimizer` instead of the `BayesianOptimizer`"
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]
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},
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{

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