From 3be6bcd52423152de7f40d27d631a8c9fd085a4a Mon Sep 17 00:00:00 2001 From: jdub Date: Wed, 1 Nov 2023 16:24:38 +1300 Subject: [PATCH 01/20] initial gpr setup helper --- pyemu/prototypes/__init__.py | 13 --- pyemu/utils/helpers.py | 217 +++++++++++++++++++++++++++++++++++ 2 files changed, 217 insertions(+), 13 deletions(-) delete mode 100644 pyemu/prototypes/__init__.py diff --git a/pyemu/prototypes/__init__.py b/pyemu/prototypes/__init__.py deleted file mode 100644 index 530cc288..00000000 --- a/pyemu/prototypes/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -"""pyEMU: python modules for Environmental Model Uncertainty analyses. These -modules are designed to work directly and seamlessly with PEST and PEST++ model -independent interface. pyEMU can also be used to setup this interface. - -Several forms of uncertainty analyses are support including FOSM-based -analyses (pyemu.Schur and pyemu.ErrVar), Monte Carlo (including -GLUE and null-space Monte Carlo) and a prototype iterative Ensemble Smoother - -""" - -from .ensemble_method import * -from .moouu import * -from .da import * diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py index 19b7ed76..3f777198 100644 --- a/pyemu/utils/helpers.py +++ b/pyemu/utils/helpers.py @@ -3804,6 +3804,223 @@ def get_current_prop(_cur_thresh): return thresh, prop +def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_kernel=None,nverf=0): + """helper function to setup a gaussian-process-regression emulator for outputs of interest. This + is primarily targeted at low-dimensional settings like those encountered in PESTPP-MOU + + Parameters: + pst_fname (str): existing pest control filename + input_fnames (str | list[str]): usually a list of decision variable population files + output_fnames (str | list[str]): usually a list of observation population files that + corresponds to the simulation results associated with `input_fnames` + gpr_t_d (str): the template file dir to create that will hold the GPR emulators + gp_kernel (sklearn GaussianProcess kernel): the kernel to use. if None, a standard RBF kernel + is created and used + nverf (int): the number of input-output pairs to hold back for a simple verification test + + Returns: + None + + Note: + requires scikit-learn + + """ + + from sklearn.gaussian_process import GaussianProcessRegressor + from sklearn.gaussian_process.kernels import RBF, Matern, ConstantKernel + + pst = pyemu.Pst(pst_fname) + + # work out input variable names + input_groups = pst.pestpp_options.get("opt_dec_var_groups",None) + par = pst.parameter_data + if input_groups is None: + print("using all adjustable parameters as inputs") + input_names = pst.adj_par_names + else: + input_groups = set([i.strip() for i in input_groups.lower().strip().split(",")]) + print("input groups:",input_groups) + adj_par = par.loc[pst.adj_par_names,:].copy() + adj_par = adj_par.loc[adj_par.pargp.apply(lambda x: x in input_groups),:] + input_names = adj_par.parnme.tolist() + print("input names:",input_names) + + #work out constraints and objectives + ineq_names = pst.less_than_obs_constraints.tolist() + ineq_names.extend(pst.greater_than_obs_constraints.tolist()) + obs = pst.observation_data + objs = pst.pestpp_options.get("mou_objectives",None) + constraints = [] + + if objs is None: + print("'mou_objectives' not found in ++ options, using all ineq tagged non-zero weighted obs as objectives") + objs = ineq_names + else: + objs = objs.lower().strip().split(',') + constraints = [n for n in ineq_names if n not in objs] + + print("objectives:",objs) + print("constraints:",constraints) + output_names = objs + output_names.extend(constraints) + + print("loading input and output files") + if isinstance(input_fnames,str): + input_fnames = [input_fnames] + if isinstance(output_fnames,str): + output_fnames = [output_fnames] + if len(output_fnames) != len(input_fnames): + raise Exception("len(input_fnames) != len(output_fnames)") + + + dfs = [] + for input_fname,output_fname in zip(input_fnames,output_fnames): + if input_fname.lower().endswith(".csv"): + input_df = pd.read_csv(os.path.join(input_fname),index_col=0) + elif input_fname.lower().endswith(".jcb"): + input_df = pyemu.ParameterEnsemble.from_binary(pst=pst,filename=input_fname)._df + else: + raise Exception("unrecognized input_fname extension:'{0}', looking for csv or jcb".\ + format(input_fname.lower())) + + if output_fname.lower().endswith(".csv"): + output_df = pd.read_csv(os.path.join(output_fname),index_col=0) + elif output_fname.lower().endswith(".jcb"): + output_df = pyemu.ObservationEnsemble.from_binary(pst=pst,filename=output_fname)._df + else: + raise Exception("unrecognized output_fname extension:'{0}', looking for csv or jcb".\ + format(output_fname.lower())) + + if input_df.shape[0] != output_df.shape[0]: + raise Exception("input rows != output rows for {0} and {1}".\ + format(input_fname,output_fname)) + input_df = input_df.loc[:,input_names] + assert input_df.shape == input_df.dropna().shape + + output_df = output_df.loc[:, output_names] + assert output_df.shape == output_df.dropna().shape + + input_df.loc[:,output_names] = output_df.values + dfs.append(input_df) + print("...loaded",input_fname,output_fname) + + df = pd.concat(dfs) + assert df.shape == df.dropna().shape + df.to_csv(pst_fname + ".aggresults.csv") + print("aggregated training dataset shape",df.shape,"saved to",pst_fname + ".aggresults.csv") + + + if gp_kernel is None: + gp_kernel = 1 * RBF(length_scale=1000.0, length_scale_bounds=(1e-4, 1e6)) + #gp_kernel = Matern(length_scale=100.0, length_scale_bounds=(1e-4, 1e4), nu=4) + + cut = df.shape[0] - nverf + X_train = df.loc[:, input_names].values.copy()[:cut, :] + X_verf = df.loc[:, input_names].values.copy()[cut:, :] + if os.path.exists(gpr_t_d): + shutil.rmtree(gpr_t_d) + os.makedirs(gpr_t_d) + model_fnames = [] + for output_name in output_names: + + y_verf = df.loc[:,output_name].values.copy()[cut:] + y_train = df.loc[:, output_name].values.copy()[:cut] + print("training GPR for {0} with {1} data points".format(output_name,y_train.shape[0])) + gaussian_process = GaussianProcessRegressor(kernel=gp_kernel, n_restarts_optimizer=20) + gaussian_process.fit(X_train, y_train) + print(output_name,"optimized kernel:",gaussian_process.kernel_) + model_fname = os.path.split(pst_fname)[1]+"."+output_name+".pkl" + if os.path.exists(os.path.join(gpr_t_d,model_fname)): + print("WARNING: model_fname '{0}' exists, overwriting...".format(model_fname)) + with open(os.path.join(gpr_t_d,model_fname),'wb') as f: + pickle.dump(gaussian_process,f) + model_fnames.append(model_fname) + if nverf > 0: + pred_mean,pred_std = gaussian_process.predict(X_verf,return_std=True) + vdf = pd.DataFrame({"y_verf":y_verf,"y_pred":pred_mean,"y_pred_std":pred_std}) + verf_fname = os.path.join(gpr_t_d,os.path.split(pst_fname)[1]+"."+output_name+".verf.csv") + vdf.to_csv(verf_fname) + print("saved ",output_fname,"verfication csv to",verf_fname) + mabs = np.abs(vdf.y_verf - vdf.y_pred).mean() + print("...mean abs error",mabs) + mdf = pd.DataFrame({"output_name":output_names,"model_fname":model_fnames},index=output_names) + minfo_fname = os.path.join(gpr_t_d,"gprmodel_info.csv") + mdf.to_csv(minfo_fname) + print("GPR model info saved to",minfo_fname) + + #write a template file + tpl_fname = os.path.join(gpr_t_d,"gpr_input.csv.tpl") + with open(tpl_fname,'w') as f: + f.write("ptf ~\nparnme,parval1\n") + for input_name in input_names: + f.write("{0},~ {0} ~\n".format(input_name)) + other_adj_pars = list(set(pst.adj_par_names)-set(input_names)) + aux_tpl_fname = None + if len(other_adj_pars) > 0: + + aux_tpl_fname = os.path.join(gpr_t_d,"aux_par.csv.tpl") + print("writing aux par tpl file: ",aux_tpl_fname) + with open(aux_tpl_fname,'w') as f: + f.write("ptf ~\n") + for input_name in other_adj_pars: + f.write("{0},~ {0} ~\n".format(input_name)) + #write an ins file + ins_fname = os.path.join(gpr_t_d,"gpr_output.csv.ins") + with open(ins_fname,'w') as f: + f.write("pif ~\nl1\n") + for output_name in output_names: + f.write("l1 ~,~ !{0}!\n".format(output_name)) + tpl_list = [tpl_fname] + if aux_tpl_fname is not None: + tpl_list.append(aux_tpl_fname) + input_list = [f.replace(".tpl","") for f in tpl_list] + gpst = pyemu.Pst.from_io_files(tpl_list,input_list, + [ins_fname],[ins_fname.replace(".ins","")],pst_path=".") + for col in pst.parameter_data.columns: + gpst.parameter_data.loc[input_names,col] = pst.parameter_data.loc[input_names,col].values + + for col in pst.observation_data.columns: + gpst.observation_data.loc[output_names,col] = pst.observation_data.loc[output_names,col].values + gpst.pestpp_options = pst.pestpp_options + + lines = [line[4:] for line in inspect.getsource(gpr_forward_run).split("\n")][1:] + + with open(os.path.join(gpr_t_d, "forward_run.py"), 'w') as f: + + for line in lines: + f.write(line + "\n") + + gpst.control_data.noptmax = 0 + gpst.model_command = "python forward_run.py" + gpst_fname = os.path.split(pst_fname)[1] + gpst.write(os.path.join(gpr_t_d,gpst_fname),version=2) + print("saved gpr pst:",gpst_fname,"in gpr_t_d",gpr_t_d) + pyemu.os_utils.run("pestpp-mou {0}".format(gpst_fname),cwd=gpr_t_d) + + gpst.pestpp_options.pop("opt_risk",None) + gpst.control_data.noptmax = 10 + gpst.write(os.path.join(gpr_t_d, gpst_fname), version=2) + pyemu.os_utils.run("pestpp-mou {0}".format(gpst_fname), cwd=gpr_t_d) + + +def gpr_forward_run(): + """the function to evaluate a set of inputs thru the GPR emulators.\ + This function gets added programmatically to the forward run process""" + import os + import pandas as pd + import numpy as np + import pickle + from sklearn.gaussian_process import GaussianProcessRegressor + input_df = pd.read_csv("gpr_input.csv",index_col=0) + mdf = pd.read_csv(os.path.join("gprmodel_info.csv"),index_col=0) + mdf.loc[:,"sim"] = np.nan + for output_name,model_fname in zip(mdf.output_name,mdf.model_fname): + guassian_process = pickle.load(open(model_fname,'rb')) + sim = guassian_process.predict(np.atleast_2d(input_df.parval1.values)) + print(output_name,sim) + mdf.loc[output_name,"sim"] = sim + mdf.loc[:,["output_name","sim"]].to_csv("gpr_output.csv",index=False) + From 69570d00e25e5d3c96a1e87e6250f5c1307f68e9 Mon Sep 17 00:00:00 2001 From: jdub Date: Thu, 2 Nov 2023 12:06:43 +1300 Subject: [PATCH 02/20] more work on gpr prototype --- pyemu/utils/helpers.py | 53 +++++++++++++++++++++++++++++++++++------- 1 file changed, 45 insertions(+), 8 deletions(-) diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py index 3f777198..d4ea091a 100644 --- a/pyemu/utils/helpers.py +++ b/pyemu/utils/helpers.py @@ -3804,7 +3804,8 @@ def get_current_prop(_cur_thresh): return thresh, prop -def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_kernel=None,nverf=0): +def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_kernel=None,nverf=0, + plot_fits=False): """helper function to setup a gaussian-process-regression emulator for outputs of interest. This is primarily targeted at low-dimensional settings like those encountered in PESTPP-MOU @@ -3817,7 +3818,7 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ gp_kernel (sklearn GaussianProcess kernel): the kernel to use. if None, a standard RBF kernel is created and used nverf (int): the number of input-output pairs to hold back for a simple verification test - + plot_fits (bool): flag to plot the fit GPRs Returns: None @@ -3828,7 +3829,8 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import RBF, Matern, ConstantKernel - + import pickle + import inspect pst = pyemu.Pst(pst_fname) # work out input variable names @@ -3921,6 +3923,10 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ shutil.rmtree(gpr_t_d) os.makedirs(gpr_t_d) model_fnames = [] + if plot_fits: + import matplotlib.pyplot as plt + from matplotlib.backends.backend_pdf import PdfPages + pdf = PdfPages(os.path.join(gpr_t_d,"gpr_fits.pdf")) for output_name in output_names: y_verf = df.loc[:,output_name].values.copy()[cut:] @@ -3929,11 +3935,40 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ gaussian_process = GaussianProcessRegressor(kernel=gp_kernel, n_restarts_optimizer=20) gaussian_process.fit(X_train, y_train) print(output_name,"optimized kernel:",gaussian_process.kernel_) + if plot_fits: + print("...plotting fits for",output_name) + predmean,predstd = gaussian_process.predict(df.loc[:, input_names].values.copy(), return_std=True) + df.loc[:,"predmean"] = predmean + df.loc[:,"predstd"] = predstd + isverf = np.zeros_like(predmean) + isverf[cut:] = 1 + df.loc[:,"isverf"] = isverf + for input_name in input_names: + fig,ax = plt.subplots(1,1,figsize=(6,6)) + #print(X_train[:,ii]) + #print(y_train) + ddf = df[[input_name,output_name,"predmean","predstd","isverf"]].copy() + ddf.sort_values(by=input_name,inplace=True) + ax.scatter(ddf.loc[ddf.isverf==0,input_name],ddf.loc[ddf.isverf==0,output_name],marker=".",c="r",label="training") + if nverf > 0: + ax.scatter(ddf.loc[ddf.isverf==1,input_name],ddf.loc[ddf.isverf==1,output_name],marker="^",c="c",label="verf") + ax.plot(ddf[input_name],ddf["predmean"],"k--",label="GPR mean") + ax.fill_between(ddf[input_name],ddf["predmean"] - (2*ddf["predstd"]),ddf["predmean"]+(2*ddf["predstd"]),alpha=0.5,fc='0.5',label="+/- 95%") + ax.set_title("input:{0}, output:{1}".format(input_name,output_name),loc="left") + ax.legend() + plt.tight_layout() + pdf.savefig() + + plt.close(fig) + + + model_fname = os.path.split(pst_fname)[1]+"."+output_name+".pkl" if os.path.exists(os.path.join(gpr_t_d,model_fname)): print("WARNING: model_fname '{0}' exists, overwriting...".format(model_fname)) with open(os.path.join(gpr_t_d,model_fname),'wb') as f: pickle.dump(gaussian_process,f) + model_fnames.append(model_fname) if nverf > 0: pred_mean,pred_std = gaussian_process.predict(X_verf,return_std=True) @@ -3943,6 +3978,8 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ print("saved ",output_fname,"verfication csv to",verf_fname) mabs = np.abs(vdf.y_verf - vdf.y_pred).mean() print("...mean abs error",mabs) + if plot_fits: + pdf.close() mdf = pd.DataFrame({"output_name":output_names,"model_fname":model_fnames},index=output_names) minfo_fname = os.path.join(gpr_t_d,"gprmodel_info.csv") mdf.to_csv(minfo_fname) @@ -3995,12 +4032,12 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ gpst_fname = os.path.split(pst_fname)[1] gpst.write(os.path.join(gpr_t_d,gpst_fname),version=2) print("saved gpr pst:",gpst_fname,"in gpr_t_d",gpr_t_d) - pyemu.os_utils.run("pestpp-mou {0}".format(gpst_fname),cwd=gpr_t_d) - - gpst.pestpp_options.pop("opt_risk",None) - gpst.control_data.noptmax = 10 + try: + pyemu.os_utils.run("pestpp-mou {0}".format(gpst_fname),cwd=gpr_t_d) + except Exception as e: + print("WARNING: pestpp-mou test run failed: {0}".format(str(e))) + gpst.control_data.noptmax = pst.control_data.noptmax gpst.write(os.path.join(gpr_t_d, gpst_fname), version=2) - pyemu.os_utils.run("pestpp-mou {0}".format(gpst_fname), cwd=gpr_t_d) def gpr_forward_run(): From aa3cc507fc7c8d4515cdf63f7d9f12fa908f13c5 Mon Sep 17 00:00:00 2001 From: jdub Date: Wed, 8 Nov 2023 13:40:30 +1300 Subject: [PATCH 03/20] more work on gpr emulator --- pyemu/utils/helpers.py | 25 ++++++++++++++++--------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py index d4ea091a..1babdd16 100644 --- a/pyemu/utils/helpers.py +++ b/pyemu/utils/helpers.py @@ -3874,6 +3874,9 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ if len(output_fnames) != len(input_fnames): raise Exception("len(input_fnames) != len(output_fnames)") + if os.path.exists(gpr_t_d): + shutil.rmtree(gpr_t_d) + os.makedirs(gpr_t_d) dfs = [] for input_fname,output_fname in zip(input_fnames,output_fnames): @@ -3908,20 +3911,21 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ df = pd.concat(dfs) assert df.shape == df.dropna().shape - df.to_csv(pst_fname + ".aggresults.csv") + df.to_csv(os.path.join(gpr_t_d,"gpr_aggregate_training_data.csv")) print("aggregated training dataset shape",df.shape,"saved to",pst_fname + ".aggresults.csv") if gp_kernel is None: - gp_kernel = 1 * RBF(length_scale=1000.0, length_scale_bounds=(1e-4, 1e6)) + gp_kernel = ConstantKernel(constant_value=1.0,constant_value_bounds=(1e-6,100000)) *\ + RBF(length_scale=1000.0, length_scale_bounds=(1e-8, 1e8)) #gp_kernel = Matern(length_scale=100.0, length_scale_bounds=(1e-4, 1e4), nu=4) + for hp in gp_kernel.hyperparameters: + print(hp) cut = df.shape[0] - nverf X_train = df.loc[:, input_names].values.copy()[:cut, :] X_verf = df.loc[:, input_names].values.copy()[cut:, :] - if os.path.exists(gpr_t_d): - shutil.rmtree(gpr_t_d) - os.makedirs(gpr_t_d) + model_fnames = [] if plot_fits: import matplotlib.pyplot as plt @@ -3991,15 +3995,16 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ f.write("ptf ~\nparnme,parval1\n") for input_name in input_names: f.write("{0},~ {0} ~\n".format(input_name)) - other_adj_pars = list(set(pst.adj_par_names)-set(input_names)) + other_pars = list(set(pst.par_names)-set(input_names)) aux_tpl_fname = None - if len(other_adj_pars) > 0: + + if len(other_pars) > 0: aux_tpl_fname = os.path.join(gpr_t_d,"aux_par.csv.tpl") print("writing aux par tpl file: ",aux_tpl_fname) with open(aux_tpl_fname,'w') as f: f.write("ptf ~\n") - for input_name in other_adj_pars: + for input_name in other_pars: f.write("{0},~ {0} ~\n".format(input_name)) #write an ins file ins_fname = os.path.join(gpr_t_d,"gpr_output.csv.ins") @@ -4013,8 +4018,10 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ input_list = [f.replace(".tpl","") for f in tpl_list] gpst = pyemu.Pst.from_io_files(tpl_list,input_list, [ins_fname],[ins_fname.replace(".ins","")],pst_path=".") + par_names = pst.par_names + assert len(set(par_names).symmetric_difference(set(gpst.par_names))) == 0 for col in pst.parameter_data.columns: - gpst.parameter_data.loc[input_names,col] = pst.parameter_data.loc[input_names,col].values + gpst.parameter_data.loc[par_names,col] = pst.parameter_data.loc[par_names,col].values for col in pst.observation_data.columns: gpst.observation_data.loc[output_names,col] = pst.observation_data.loc[output_names,col].values From ea7edbdfd9b6bdeb8b74c57489eb1f2e9559e3d6 Mon Sep 17 00:00:00 2001 From: jdub Date: Wed, 8 Nov 2023 18:08:54 +1300 Subject: [PATCH 04/20] more gpr --- pyemu/utils/helpers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py index 1babdd16..caf8dc83 100644 --- a/pyemu/utils/helpers.py +++ b/pyemu/utils/helpers.py @@ -3916,7 +3916,7 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ if gp_kernel is None: - gp_kernel = ConstantKernel(constant_value=1.0,constant_value_bounds=(1e-6,100000)) *\ + gp_kernel = ConstantKernel(constant_value=1.0,constant_value_bounds=(1e-8,1e8)) *\ RBF(length_scale=1000.0, length_scale_bounds=(1e-8, 1e8)) #gp_kernel = Matern(length_scale=100.0, length_scale_bounds=(1e-4, 1e4), nu=4) From b5bb0864524900ee43761288438e8333f566d7be Mon Sep 17 00:00:00 2001 From: jdub Date: Mon, 13 Nov 2023 11:11:09 +1300 Subject: [PATCH 05/20] more dev and exp with gpr --- pyemu/utils/helpers.py | 29 +++++++++++++++++++---------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py index caf8dc83..ad759a56 100644 --- a/pyemu/utils/helpers.py +++ b/pyemu/utils/helpers.py @@ -3805,7 +3805,7 @@ def get_current_prop(_cur_thresh): def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_kernel=None,nverf=0, - plot_fits=False): + plot_fits=False,apply_standard_scalar=False): """helper function to setup a gaussian-process-regression emulator for outputs of interest. This is primarily targeted at low-dimensional settings like those encountered in PESTPP-MOU @@ -3829,6 +3829,9 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import RBF, Matern, ConstantKernel + from sklearn.preprocessing import StandardScaler + from sklearn.pipeline import Pipeline + import pickle import inspect pst = pyemu.Pst(pst_fname) @@ -3916,12 +3919,13 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ if gp_kernel is None: - gp_kernel = ConstantKernel(constant_value=1.0,constant_value_bounds=(1e-8,1e8)) *\ - RBF(length_scale=1000.0, length_scale_bounds=(1e-8, 1e8)) - #gp_kernel = Matern(length_scale=100.0, length_scale_bounds=(1e-4, 1e4), nu=4) + #gp_kernel = ConstantKernel(constant_value=1.0,constant_value_bounds=(1e-8,1e8)) *\ + # RBF(length_scale=1000.0, length_scale_bounds=(1e-8, 1e8)) + gp_kernel = Matern(length_scale=100.0, length_scale_bounds=(1e-4, 1e4), nu=0.5) for hp in gp_kernel.hyperparameters: print(hp) + cut = df.shape[0] - nverf X_train = df.loc[:, input_names].values.copy()[:cut, :] X_verf = df.loc[:, input_names].values.copy()[cut:, :] @@ -3937,11 +3941,16 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ y_train = df.loc[:, output_name].values.copy()[:cut] print("training GPR for {0} with {1} data points".format(output_name,y_train.shape[0])) gaussian_process = GaussianProcessRegressor(kernel=gp_kernel, n_restarts_optimizer=20) - gaussian_process.fit(X_train, y_train) - print(output_name,"optimized kernel:",gaussian_process.kernel_) + if not apply_standard_scalar: + print("WARNING: not applying StandardScalar transformation - user beware!") + pipeline = Pipeline([("gpr",gaussian_process)]) + else: + pipeline = Pipeline([("std_scalar", StandardScaler()), ("gpr", gaussian_process)]) + pipeline.fit(X_train, y_train) + print(output_name,"optimized kernel:",pipeline["gpr"].kernel_) if plot_fits: print("...plotting fits for",output_name) - predmean,predstd = gaussian_process.predict(df.loc[:, input_names].values.copy(), return_std=True) + predmean,predstd = pipeline.predict(df.loc[:, input_names].values.copy(), return_std=True) df.loc[:,"predmean"] = predmean df.loc[:,"predstd"] = predstd isverf = np.zeros_like(predmean) @@ -3971,13 +3980,13 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ if os.path.exists(os.path.join(gpr_t_d,model_fname)): print("WARNING: model_fname '{0}' exists, overwriting...".format(model_fname)) with open(os.path.join(gpr_t_d,model_fname),'wb') as f: - pickle.dump(gaussian_process,f) + pickle.dump(pipeline,f) model_fnames.append(model_fname) if nverf > 0: - pred_mean,pred_std = gaussian_process.predict(X_verf,return_std=True) + pred_mean,pred_std = pipeline.predict(X_verf,return_std=True) vdf = pd.DataFrame({"y_verf":y_verf,"y_pred":pred_mean,"y_pred_std":pred_std}) - verf_fname = os.path.join(gpr_t_d,os.path.split(pst_fname)[1]+"."+output_name+".verf.csv") + verf_fname = os.path.join(gpr_t_d,"{0}_gpr_verf.csv".format(output_name)) vdf.to_csv(verf_fname) print("saved ",output_fname,"verfication csv to",verf_fname) mabs = np.abs(vdf.y_verf - vdf.y_pred).mean() From 60433ccb6a7be66206db91e66c8dd5cb0d690567 Mon Sep 17 00:00:00 2001 From: jwhite Date: Tue, 16 Apr 2024 14:21:53 -0600 Subject: [PATCH 06/20] more thresh testing --- autotest/pst_from_tests.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/autotest/pst_from_tests.py b/autotest/pst_from_tests.py index 4c121d35..fbf4b40b 100644 --- a/autotest/pst_from_tests.py +++ b/autotest/pst_from_tests.py @@ -4886,7 +4886,7 @@ def list_float_int_index_test(tmp_path): assert np.isclose(diff,bparval1).all(), diff.loc[~np.isclose(diff,bparval1)] -def mf6_freyberg_thresh_invest(tmp_path): +def mf6_freyberg_thresh_invest(setup_freyberg_mf6): import numpy as np import pandas as pd @@ -5153,7 +5153,7 @@ def plot_thresh(m_d): #mf6_freyberg_shortnames_test() #mf6_freyberg_direct_test() - #mf6_freyberg_thresh_invest(".") + mf6_freyberg_thresh_invest(".") #plot_thresh("master_thresh_mm") #mf6_freyberg_varying_idomain() # xsec_test() From e1755e056b47356b888671b8a7d36b1037d08499 Mon Sep 17 00:00:00 2001 From: jwhite Date: Mon, 22 Apr 2024 09:59:05 -0500 Subject: [PATCH 07/20] env --- etc/environment.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/etc/environment.yml b/etc/environment.yml index efa493d4..396ff928 100644 --- a/etc/environment.yml +++ b/etc/environment.yml @@ -20,4 +20,5 @@ dependencies: - shapely - pyproj - flopy - - modflow-devtools \ No newline at end of file + - modflow-devtools + - scikit-learn From 006d523135f229f5a594e1860370e3449048703c Mon Sep 17 00:00:00 2001 From: jwhite Date: Mon, 22 Apr 2024 11:15:00 -0600 Subject: [PATCH 08/20] xfer pi from base to gpr interface --- pyemu/utils/helpers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py index 711459bb..3433fed5 100644 --- a/pyemu/utils/helpers.py +++ b/pyemu/utils/helpers.py @@ -4045,7 +4045,7 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ for col in pst.observation_data.columns: gpst.observation_data.loc[output_names,col] = pst.observation_data.loc[output_names,col].values gpst.pestpp_options = pst.pestpp_options - + gpst.prior_information = pst.prior_information.copy() lines = [line[4:] for line in inspect.getsource(gpr_forward_run).split("\n")][1:] with open(os.path.join(gpr_t_d, "forward_run.py"), 'w') as f: From 613385c812607bb1d115c819c334ef92796fe3da Mon Sep 17 00:00:00 2001 From: jwhite Date: Tue, 12 Nov 2024 14:54:58 -0700 Subject: [PATCH 09/20] fix for long names in a binary file --- autotest/la_tests.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/autotest/la_tests.py b/autotest/la_tests.py index 9f2b6bb2..0ede3d97 100644 --- a/autotest/la_tests.py +++ b/autotest/la_tests.py @@ -554,7 +554,7 @@ def ends_freyberg_dev(): ends = pyemu.EnDS(pst=pst_name, ensemble=oe_name) cov = pyemu.Cov.from_observation_data(pst) cov.to_uncfile(os.path.join(test_d, "obs.unc"), covmat_file=None) - cov.to_binary(os.path.join(test_d,"cov.jcb")) + cov.to_coo(os.path.join(test_d,"cov.jcb")) cov.to_ascii(os.path.join(test_d, "cov.mat")) ends = pyemu.EnDS(pst=pst, ensemble=oe,obscov=cov) @@ -599,7 +599,7 @@ def ends_freyberg_test(tmp_path): ends = pyemu.EnDS(pst=pst_name, sim_ensemble=oe_name,predictions=predictions) cov = pyemu.Cov.from_observation_data(pst) cov.to_uncfile(os.path.join(test_d, "obs.unc"), covmat_file=None) - cov.to_binary(os.path.join(test_d, "cov.jcb")) + cov.to_coo(os.path.join(test_d, "cov.jcb")) cov.to_ascii(os.path.join(test_d, "cov.mat")) ends = pyemu.EnDS(pst=pst, sim_ensemble=oe, obscov=cov,predictions=predictions) @@ -750,8 +750,8 @@ def dsi_normscoretransform_test(): if __name__ == "__main__": #dsi_normscoretransform_test() - #ends_freyberg_dev() - ends_freyberg_dsi_test("temp") + ends_freyberg_test("temp") + #ends_freyberg_dsi_test("temp") #plot_freyberg_dsi() #obscomp_test() #alternative_dw() From 274e29bb02ba0e3f7bab07c22c8fee7df82e9007 Mon Sep 17 00:00:00 2001 From: jwhite Date: Tue, 12 Nov 2024 16:24:36 -0700 Subject: [PATCH 10/20] test undo to_coo --- autotest/la_tests.py | 4 +- autotest/pst_from_tests.py | 394 ++++++++++++++++++++++++++++++++++++- 2 files changed, 392 insertions(+), 6 deletions(-) diff --git a/autotest/la_tests.py b/autotest/la_tests.py index 0ede3d97..ef752cee 100644 --- a/autotest/la_tests.py +++ b/autotest/la_tests.py @@ -554,7 +554,7 @@ def ends_freyberg_dev(): ends = pyemu.EnDS(pst=pst_name, ensemble=oe_name) cov = pyemu.Cov.from_observation_data(pst) cov.to_uncfile(os.path.join(test_d, "obs.unc"), covmat_file=None) - cov.to_coo(os.path.join(test_d,"cov.jcb")) + cov.to_binary(os.path.join(test_d,"cov.jcb")) cov.to_ascii(os.path.join(test_d, "cov.mat")) ends = pyemu.EnDS(pst=pst, ensemble=oe,obscov=cov) @@ -599,7 +599,7 @@ def ends_freyberg_test(tmp_path): ends = pyemu.EnDS(pst=pst_name, sim_ensemble=oe_name,predictions=predictions) cov = pyemu.Cov.from_observation_data(pst) cov.to_uncfile(os.path.join(test_d, "obs.unc"), covmat_file=None) - cov.to_coo(os.path.join(test_d, "cov.jcb")) + cov.to_binary(os.path.join(test_d, "cov.jcb")) cov.to_ascii(os.path.join(test_d, "cov.mat")) ends = pyemu.EnDS(pst=pst, sim_ensemble=oe, obscov=cov,predictions=predictions) diff --git a/autotest/pst_from_tests.py b/autotest/pst_from_tests.py index 041a6846..f4123587 100644 --- a/autotest/pst_from_tests.py +++ b/autotest/pst_from_tests.py @@ -6109,16 +6109,402 @@ def mf6_freyberg_ppu_hyperpars_thresh_invest(tmp_path): # assert phidf["mean"].min() < phidf["mean"].max() +def mf6_freyberg_ppu_hyperpars_thresh_gpr_invest(tmp_path): + # make sure its avail before wasting more time + from sklearn.gaussian_process import GaussianProcessRegressor + + import numpy as np + import pandas as pd + # pd.set_option('display.max_rows', 500) + # pd.set_option('display.max_columns', 500) + # pd.set_option('display.width', 1000) + # try: + import flopy + # except: + # return + import sys + sys.path.insert(0,os.path.join("..","..","pypestutils")) + + import pypestutils as ppu + + org_model_ws = os.path.join('..', 'examples', 'freyberg_mf6') + tmp_model_ws = setup_tmp(org_model_ws, tmp_path) + + + tmp_model_ws = tmp_model_ws.relative_to(tmp_path) + + os.chdir(tmp_path) + sim = flopy.mf6.MFSimulation.load(sim_ws=str(tmp_model_ws)) + m = sim.get_model("freyberg6") + sim.set_all_data_external() + sim.write_simulation() + + # SETUP pest stuff... + os_utils.run("{0} ".format("mf6"), cwd=tmp_model_ws) + + template_ws = Path(tmp_path, "new_temp_thresh") + if os.path.exists(template_ws): + shutil.rmtree(template_ws) + sr = m.modelgrid + # set up PstFrom object + pf = PstFrom(original_d=tmp_model_ws, new_d=template_ws, + remove_existing=True, + longnames=True, spatial_reference=sr, + zero_based=False, start_datetime="1-1-2018") + + pf.pre_py_cmds.insert(0,"import sys") + pf.pre_py_cmds.insert(1,"sys.path.append(os.path.join('..','..', '..', 'pypestutils'))") + + + df = pd.read_csv(os.path.join(tmp_model_ws, "heads.csv"), index_col=0) + pf.add_observations("heads.csv", insfile="heads.csv.ins", index_cols="time", + use_cols=list(df.columns.values), + prefix="hds", rebuild_pst=True) + + # Add stream flow observation + # df = pd.read_csv(os.path.join(tmp_model_ws, "sfr.csv"), index_col=0) + pf.add_observations("sfr.csv", insfile="sfr.csv.ins", index_cols="time", + use_cols=["GAGE_1", "HEADWATER", "TAILWATER"], ofile_sep=",") + + # Setup geostruct for spatial pars + pf.extra_py_imports.append('flopy') + ib = m.dis.idomain[0].array + #tags = {"npf_k_": [0.1, 10.], "npf_k33_": [.1, 10], "sto_ss": [.1, 10], "sto_sy": [.9, 1.1], + # "rch_recharge": [.5, 1.5]} + tags = {"npf_k_": [0.1, 10.]} + dts = pd.to_datetime("1-1-2018") + pd.to_timedelta(np.cumsum(sim.tdis.perioddata.array["perlen"]), unit="d") + print(dts) + # ib = m.dis.idomain.array[0,:,:] + # setup from array style pars + pf.extra_py_imports.append('flopy') + ib = m.dis.idomain[0].array + tags = {"npf_k_": [0.1, 10.]}#, "npf_k33_": [.1, 10], "sto_ss": [.1, 10], "sto_sy": [.9, 1.1], + # "rch_recharge": [.5, 1.5]} + dts = pd.to_datetime("1-1-2018") + pd.to_timedelta(np.cumsum(sim.tdis.perioddata.array["perlen"]), unit="d") + print(dts) + + xmn = m.modelgrid.xvertices.min() + xmx = m.modelgrid.xvertices.max() + ymn = m.modelgrid.yvertices.min() + ymx = m.modelgrid.yvertices.max() + + numpp = 20 + xvals = np.random.uniform(xmn,xmx,numpp) + yvals = np.random.uniform(ymn, ymx, numpp) + pp_locs = pd.DataFrame({"x":xvals,"y":yvals}) + pp_locs.loc[:,"zone"] = 1 + pp_locs.loc[:,"name"] = ["pp_{0}".format(i) for i in range(numpp)] + pp_locs.loc[:,"parval1"] = 1.0 + + pyemu.pp_utils.write_pp_shapfile(pp_locs,os.path.join(template_ws,"pp_locs.shp")) + df = pyemu.pp_utils.pilot_points_from_shapefile(os.path.join(template_ws,"pp_locs.shp")) + + #pp_locs = pyemu.pp_utils.setup_pilotpoints_grid(sr=sr,prefix_dict={0:"pps_1"}) + #pp_locs = pp_locs.loc[:,["name","x","y","zone","parval1"]] + pp_locs.to_csv(os.path.join(template_ws,"pp.csv")) + pyemu.pp_utils.write_pp_file(os.path.join(template_ws,"pp_file.dat"),pp_locs) + pp_container = ["pp_file.dat","pp.csv","pp_locs.shp"] + value_v = pyemu.geostats.ExpVario(contribution=1, a=5000, anisotropy=5, bearing=0.0) + value_gs = pyemu.geostats.GeoStruct(variograms=value_v) + bearing_v = pyemu.geostats.ExpVario(contribution=1,a=5000,anisotropy=3,bearing=0.0) + bearing_gs = pyemu.geostats.GeoStruct(variograms=bearing_v) + aniso_v = pyemu.geostats.ExpVario(contribution=1, a=10000, anisotropy=3, bearing=45.0) + aniso_gs = pyemu.geostats.GeoStruct(variograms=aniso_v) + num_cat_arrays = 0 + for tag, bnd in tags.items(): + lb, ub = bnd[0], bnd[1] + arr_files = [f for f in os.listdir(tmp_model_ws) if tag in f and f.endswith(".txt")] + if "rch" in tag: + pass + else: + for i,arr_file in enumerate(arr_files): + print(arr_file) + k = int(arr_file.split(".")[1][-1]) - 1 + if k == 1: + arr_file = arr_file.replace("_k_","_k33_") + pth_arr_file = os.path.join(pf.new_d,arr_file) + arr = np.loadtxt(pth_arr_file) + cat_dict = {1:[0.4,arr.mean()],2:[0.6,arr.mean()]} + thresharr,threshcsv = pyemu.helpers.setup_threshold_pars(pth_arr_file,cat_dict=cat_dict, + testing_workspace=pf.new_d,inact_arr=ib) + + pf.pre_py_cmds.append("pyemu.helpers.apply_threshold_pars('{0}')".format(os.path.split(threshcsv)[1])) + prefix = arr_file.split('.')[1].replace("_","-") + + + if i < len(pp_container): + pp_opt = pp_container[i] + else: + pp_opt = pp_locs + + pth_arr_file = os.path.join(pf.new_d,arr_file) + arr = np.loadtxt(pth_arr_file) + + tag = arr_file.split('.')[1].replace("_","-") + "_pp" + prefix = arr_file.split('.')[1].replace("_","-") + + pf.add_parameters(filenames=os.path.split(thresharr)[1],par_type="pilotpoints",transform="none", + par_name_base=tag+"-threshpp_k:{0}".format(k), + pargp=tag + "-threshpp_k:{0}".format(k), + lower_bound=0.0,upper_bound=2.0,par_style="m", + pp_space=5,pp_options={"try_use_ppu":False,"prep_hyperpars":True}, + apply_order=2,geostruct=value_gs + ) + + tag = arr_file.split('.')[1].replace("_","-") + + tfiles = [f for f in os.listdir(pf.new_d) if tag in f] + afile = [f for f in tfiles if "aniso" in f][0] + # pf.add_parameters(afile,par_type="pilotpoints",par_name_base=tag+"aniso", + # pargp=tag+"aniso",pp_space=5,lower_bound=-1.0,upper_bound=1.0, + # pp_options={"try_use_ppu":True},apply_order=1,geostruct=aniso_gs, + # par_style="a",transform="none",initial_value=0.0) + pf.add_parameters(afile,par_type="constant",par_name_base=tag+"aniso", + pargp=tag+"aniso",lower_bound=-1.0,upper_bound=1.0, + apply_order=1, + par_style="a",transform="none",initial_value=0.0) + pf.add_observations(afile, prefix=tag+"aniso", obsgp=tag+"aniso") + bfile = [f for f in tfiles if "bearing" in f][0] + pf.add_parameters(bfile, par_type="pilotpoints", par_name_base=tag + "bearing", + pargp=tag + "bearing", pp_space=6,lower_bound=-45,upper_bound=45, + par_style="a",transform="none", + pp_options={"try_use_ppu":True}, + apply_order=1,geostruct=bearing_gs) + pf.add_observations(bfile, prefix=tag + "bearing", obsgp=tag + "bearing") + + + pf.add_parameters(filenames=os.path.split(threshcsv)[1], par_type="grid",index_cols=["threshcat"], + use_cols=["threshproportion","threshfill"], + par_name_base=[prefix+"threshproportion_k:{0}".format(k),prefix+"threshfill_k:{0}".format(k)], + pargp=[prefix+"threshproportion_k:{0}".format(k),prefix+"threshfill_k:{0}".format(k)], + lower_bound=[0.1,0.1],upper_bound=[10.0,10.0],transform="none",par_style='d') + + pf.add_observations(arr_file,prefix="hkarr-"+prefix+"_k:{0}".format(k), + obsgp="hkarr-"+prefix+"_k:{0}".format(k),zone_array=ib) + + pf.add_observations(arr_file+".threshcat.dat", prefix="tcatarr-" + prefix+"_k:{0}".format(k), + obsgp="tcatarr-" + prefix+"_k:{0}".format(k),zone_array=ib) + + pf.add_observations(arr_file + ".thresharr.dat", + prefix="tarr-" +prefix+"_k:{0}".format(k), + obsgp="tarr-" + prefix + "_k:{0}".format(k), zone_array=ib) + + df = pd.read_csv(threshcsv.replace(".csv","_results.csv"),index_col=0) + pf.add_observations(os.path.split(threshcsv)[1].replace(".csv","_results.csv"),index_cols="threshcat",use_cols=df.columns.tolist(),prefix=prefix+"-results_k:{0}".format(k), + obsgp=prefix+"-results_k:{0}".format(k),ofile_sep=",") + num_cat_arrays += 1 + + # add model run command + pf.mod_sys_cmds.append("mf6") + print(pf.mult_files) + print(pf.org_files) + + # build pest + pst = pf.build_pst('freyberg.pst') + #cov = pf.build_prior(fmt="none") + #cov.to_coo(os.path.join(template_ws, "prior.jcb")) + pst.try_parse_name_metadata() + + pst.control_data.noptmax = 0 + pst.pestpp_options["additional_ins_delimiters"] = "," + + pst.write(os.path.join(pf.new_d, "freyberg.pst")) + pyemu.os_utils.run("{0} freyberg.pst".format(ies_exe_path), cwd=pf.new_d) + + res_file = os.path.join(pf.new_d, "freyberg.base.rei") + assert os.path.exists(res_file), res_file + pst.set_res(res_file) + print(pst.phi) + #assert pst.phi < 0.1, pst.phi + + #set the initial and bounds for the fill values + par = pst.parameter_data + + apar = par.loc[par.pname.str.contains("aniso"),:] + bpar = par.loc[par.pname.str.contains("bearing"), :] + par.loc[apar.parnme,"parval1"] = 5 + par.loc[apar.parnme,"parlbnd"] = 4 + par.loc[apar.parnme,"parubnd"] = 6 + + par.loc[bpar.parnme,"parval1"] = 0 + par.loc[bpar.parnme,"parlbnd"] = -90 + par.loc[bpar.parnme,"parubnd"] = 90 + + cat1par = par.loc[par.apply(lambda x: x.threshcat=="0" and x.usecol=="threshfill",axis=1),"parnme"] + cat2par = par.loc[par.apply(lambda x: x.threshcat == "1" and x.usecol == "threshfill", axis=1), "parnme"] + print(cat1par,cat2par) + assert cat1par.shape[0] == num_cat_arrays + assert cat2par.shape[0] == num_cat_arrays + + cat1parhk = [p for p in cat1par if "k:1" not in p] + cat2parhk = [p for p in cat2par if "k:1" not in p] + cat1parvk = [p for p in cat1par if "k:1" in p] + cat2parvk = [p for p in cat2par if "k:1" in p] + for lst in [cat2parvk,cat2parhk,cat1parhk,cat1parvk]: + assert len(lst) > 0 + par.loc[cat1parhk,"parval1"] = 0.1 + par.loc[cat1parhk, "parubnd"] = 1.0 + par.loc[cat1parhk, "parlbnd"] = 0.01 + par.loc[cat1parhk, "partrans"] = "log" + par.loc[cat2parhk, "parval1"] = 10 + par.loc[cat2parhk, "parubnd"] = 100 + par.loc[cat2parhk, "parlbnd"] = 1 + par.loc[cat2parhk, "partrans"] = "log" + + par.loc[cat1parvk, "parval1"] = 0.0001 + par.loc[cat1parvk, "parubnd"] = 0.01 + par.loc[cat1parvk, "parlbnd"] = 0.000001 + par.loc[cat1parvk, "partrans"] = "log" + par.loc[cat2parvk, "parval1"] = 0.1 + par.loc[cat2parvk, "parubnd"] = 1 + par.loc[cat2parvk, "parlbnd"] = 0.01 + par.loc[cat2parvk, "partrans"] = "log" + + + cat1par = par.loc[par.apply(lambda x: x.threshcat == "0" and x.usecol == "threshproportion", axis=1), "parnme"] + cat2par = par.loc[par.apply(lambda x: x.threshcat == "1" and x.usecol == "threshproportion", axis=1), "parnme"] + + print(cat1par, cat2par) + assert cat1par.shape[0] == num_cat_arrays + assert cat2par.shape[0] == num_cat_arrays + + par.loc[cat1par, "parval1"] = 0.8 + par.loc[cat1par, "parubnd"] = 1.0 + par.loc[cat1par, "parlbnd"] = 0.6 + par.loc[cat1par,"partrans"] = "none" + + # since the apply method only looks that first proportion, we can just fix this one + par.loc[cat2par, "parval1"] = 1 + par.loc[cat2par, "parubnd"] = 1 + par.loc[cat2par, "parlbnd"] = 1 + par.loc[cat2par,"partrans"] = "fixed" + + #assert par.loc[par.parnme.str.contains("threshgr"),:].shape[0] > 0 + #par.loc[par.parnme.str.contains("threshgr"),"parval1"] = 0.5 + #par.loc[par.parnme.str.contains("threshgr"),"partrans"] = "fixed" + + print(pst.adj_par_names) + print(pst.npar,pst.npar_adj) + + org_par = par.copy() + num_reals = 100 + pe = pf.draw(num_reals, use_specsim=False) + pe.enforce() + print(pe.shape) + assert pe.shape[1] == pst.npar_adj, "{0} vs {1}".format(pe.shape[1], pst.npar_adj) + assert pe.shape[0] == num_reals + + # cat1par = cat1par[0] + # pe = pe.loc[pe.loc[:,cat1par].values>0.35,:] + # pe = pe.loc[pe.loc[:, cat1par].values < 0.5, :] + # cat2par = par.loc[par.apply(lambda x: x.threshcat == "1" and x.usecol == "threshfill", axis=1), "parnme"] + # cat2par = cat2par[0] + # pe = pe.loc[pe.loc[:, cat2par].values > 10, :] + # pe = pe.loc[pe.loc[:, cat2par].values < 50, :] + + print(pe.shape) + assert pe.shape[0] > 0 + #print(pe.loc[:,cat1par].describe()) + #print(pe.loc[:, cat2par].describe()) + #return + #pe._df.index = np.arange(pe.shape[0]) + + truth_idx = 1 + + + #pe = pe._df + + # just use a real as the truth... + pst.parameter_data.loc[pst.adj_par_names,"parval1"] = pe.loc[truth_idx,pst.adj_par_names].values + pe = pe.loc[pe.index.map(lambda x: x != truth_idx), :] + pe.to_dense(os.path.join(template_ws, "prior.jcb")) + pst.control_data.noptmax = 0 + pst.write(os.path.join(pf.new_d,"truth.pst"),version=2) + pyemu.os_utils.run("{0} truth.pst".format(ies_exe_path),cwd=pf.new_d) + + pst = pyemu.Pst(os.path.join(pf.new_d,"truth.pst")) + + obs = pst.observation_data + obs.loc[:,"obsval"] = pst.res.loc[pst.obs_names,"modelled"].values + obs.loc[:,"weight"] = 0.0 + obs.loc[:,"standard_deviation"] = np.nan + onames = obs.loc[obs.obsnme.apply(lambda x: ("trgw" in x or "gage" in x) and ("hdstd" not in x and "sfrtd" not in x)),"obsnme"].values + #obs.loc[obs.oname=="hds","weight"] = 1.0 + #obs.loc[obs.oname == "hds", "standard_deviation"] = 0.001 + snames = [o for o in onames if "gage" in o] + obs.loc[onames,"weight"] = 1.0 + obs.loc[onames, "standard_deviation"] = 0.5 + + obs.loc[snames,"weight"] = 1./(obs.loc[snames,"obsval"] * 0.2).values + obs.loc[snames, "standard_deviation"] = (obs.loc[snames, "obsval"] * 0.2).values + + #obs.loc[onames,"obsval"] = truth.values + #obs.loc[onames,"obsval"] *= np.random.normal(1.0,0.01,onames.shape[0]) + + pst.write(os.path.join(pf.new_d, "freyberg.pst"),version=2) + pyemu.os_utils.run("{0} freyberg.pst".format(ies_exe_path), cwd=pf.new_d) + pst = pyemu.Pst(os.path.join(pf.new_d,"freyberg.pst")) + assert pst.phi < 0.01,str(pst.phi) + + # reset away from the truth... + pst.parameter_data.loc[:,"parval1"] = org_par.parval1.values.copy() + + pst.control_data.noptmax = -1 + pst.pestpp_options["ies_par_en"] = "prior.jcb" + pst.pestpp_options["ies_num_reals"] = pst.npar * 2 + pst.pestpp_options["ies_subset_size"] = -10 + pst.pestpp_options["ies_no_noise"] = True + #pst.pestpp_options["ies_bad_phi_sigma"] = 2.0 + pst.pestpp_options["overdue_giveup_fac"] = 100.0 + pst.pestpp_options["ies_multimodal_alpha"] = 0.99 + #pst.pestpp_options["panther_agent_freeze_on_fail"] = True + + #pst.write(os.path.join(pf.new_d, "freyberg.pst")) + #pyemu.os_utils.start_workers(pf.new_d,ies_exe_path,"freyberg.pst",worker_root=".",master_dir="master_thresh",num_workers=15) + + #num_reals = 100 + #pe = pf.draw(num_reals, use_specsim=False) + #pe.enforce() + #pe.to_dense(os.path.join(template_ws, "prior.jcb")) + #pst.pestpp_options["ies_par_en"] = "prior.jcb" + + pst.write(os.path.join(pf.new_d, "freyberg.pst"), version=2) + m_d = "master_thresh_nonstat" + pyemu.os_utils.start_workers(pf.new_d, ies_exe_path, "freyberg.pst", worker_root=".", master_dir=m_d, + num_workers=15) + phidf = pd.read_csv(os.path.join(m_d,"freyberg.phi.actual.csv")) + print(phidf["mean"]) + + + pst_fname = os.path.join(m_d,"freyberg.pst") + gpr_t_d = os.path.join(pf.new_d+"_gpr") + dv_pops = pd.read_csv(os.path.join(m_d,"freyberg.0.par.csv"),index_col=0) + obs_pops = pd.read_csv(os.path.join(m_d,"freyberg.0.obs.csv"),index_col=0) + + pyemu.helpers.prep_for_gpr(pst_fname,dv_pops,obs_pops,gpr_t_d=gpr_t_d,nverf=int(pop_size*.1),plot_fits=True) + gpst = pyemu.Pst(os.path.join(gpr_t_d,case+".pst")) + shutil.copy2(os.path.join(m_d,case+".0.dv_pop.csv"),os.path.join(gpr_t_d,"initial_dv_pop.csv")) + gpst.pestpp_options["mou_dv_population_file"] = "initial_dv_pop.csv" + gpst.control_data.noptmax = noptmax_full + gpst.write(os.path.join(gpr_t_d,case+".pst"),version=2) + + gpr_m_d = gpr_t_d.replace("template","master") + if os.path.exists(gpr_m_d): + shutil.rmtree(gpr_m_d) + pyemu.os_utils.start_workers(gpr_t_d, exe_path, case+".pst", num_workers, worker_root="mou_tests", + master_dir=gpr_m_d, verbose=True, port=port) + + if __name__ == "__main__": #mf6_freyberg_pp_locs_test('.') #mf6_subdir_test(".") - mf6_freyberg_ppu_hyperpars_invest(".") - # mf6_freyberg_ppu_hyperpars_thresh_invest(".") + #mf6_freyberg_ppu_hyperpars_invest(".") + mf6_freyberg_ppu_hyperpars_thresh_gpr_invest(".") #while True: # mf6_freyberg_thresh_test(".") - plot_thresh("master_thresh_nonstat") + #plot_thresh("master_thresh_nonstat") #mf6_freyberg_thresh_test(".") - plot_thresh("master_thresh_nonstat") + #plot_thresh("master_thresh_nonstat") #plot_thresh("master_thresh_nonstat_nim") # invest() From e725bfcb4302f9d5e1186682bdff1f303afb8ec2 Mon Sep 17 00:00:00 2001 From: jwhite Date: Tue, 19 Nov 2024 09:08:02 +0800 Subject: [PATCH 11/20] working on gpr tests --- autotest/pst_from_tests.py | 388 +----------------- autotest/utils/hosaki_template/forward_run.py | 12 + autotest/utils/hosaki_template/par.csv | 3 + autotest/utils/hosaki_template/par.csv.tpl | 4 + autotest/utils/hosaki_template/pest.pst | 28 ++ autotest/utils/hosaki_template/sim.csv | 2 + autotest/utils/hosaki_template/sim.csv.ins | 3 + autotest/utils/zdt1_template/additive_par.dat | 2 + .../utils/zdt1_template/additive_par.dat.tpl | 3 + autotest/utils/zdt1_template/dv.dat | 30 ++ autotest/utils/zdt1_template/dv.dat.tpl | 31 ++ autotest/utils/zdt1_template/forward_run.py | 30 ++ autotest/utils/zdt1_template/obj.dat | 2 + autotest/utils/zdt1_template/obj.dat.ins | 3 + autotest/utils/zdt1_template/prior.csv | 101 +++++ autotest/utils/zdt1_template/zdt1.pst | 67 +++ autotest/utils_tests.py | 192 ++++++++- 17 files changed, 512 insertions(+), 389 deletions(-) create mode 100644 autotest/utils/hosaki_template/forward_run.py create mode 100644 autotest/utils/hosaki_template/par.csv create mode 100644 autotest/utils/hosaki_template/par.csv.tpl create mode 100644 autotest/utils/hosaki_template/pest.pst create mode 100644 autotest/utils/hosaki_template/sim.csv create mode 100644 autotest/utils/hosaki_template/sim.csv.ins create mode 100644 autotest/utils/zdt1_template/additive_par.dat create mode 100644 autotest/utils/zdt1_template/additive_par.dat.tpl create mode 100644 autotest/utils/zdt1_template/dv.dat create mode 100644 autotest/utils/zdt1_template/dv.dat.tpl create mode 100644 autotest/utils/zdt1_template/forward_run.py create mode 100644 autotest/utils/zdt1_template/obj.dat create mode 100644 autotest/utils/zdt1_template/obj.dat.ins create mode 100644 autotest/utils/zdt1_template/prior.csv create mode 100644 autotest/utils/zdt1_template/zdt1.pst diff --git a/autotest/pst_from_tests.py b/autotest/pst_from_tests.py index 20263f72..d73c6d81 100644 --- a/autotest/pst_from_tests.py +++ b/autotest/pst_from_tests.py @@ -6093,397 +6093,11 @@ def mf6_freyberg_ppu_hyperpars_thresh_invest(tmp_path): # assert phidf["mean"].min() < phidf["mean"].max() -def mf6_freyberg_ppu_hyperpars_thresh_gpr_invest(tmp_path): - # make sure its avail before wasting more time - from sklearn.gaussian_process import GaussianProcessRegressor - - import numpy as np - import pandas as pd - # pd.set_option('display.max_rows', 500) - # pd.set_option('display.max_columns', 500) - # pd.set_option('display.width', 1000) - # try: - import flopy - # except: - # return - import sys - sys.path.insert(0,os.path.join("..","..","pypestutils")) - - import pypestutils as ppu - - org_model_ws = os.path.join('..', 'examples', 'freyberg_mf6') - tmp_model_ws = setup_tmp(org_model_ws, tmp_path) - - - tmp_model_ws = tmp_model_ws.relative_to(tmp_path) - - os.chdir(tmp_path) - sim = flopy.mf6.MFSimulation.load(sim_ws=str(tmp_model_ws)) - m = sim.get_model("freyberg6") - sim.set_all_data_external() - sim.write_simulation() - - # SETUP pest stuff... - os_utils.run("{0} ".format("mf6"), cwd=tmp_model_ws) - - template_ws = Path(tmp_path, "new_temp_thresh") - if os.path.exists(template_ws): - shutil.rmtree(template_ws) - sr = m.modelgrid - # set up PstFrom object - pf = PstFrom(original_d=tmp_model_ws, new_d=template_ws, - remove_existing=True, - longnames=True, spatial_reference=sr, - zero_based=False, start_datetime="1-1-2018") - - pf.pre_py_cmds.insert(0,"import sys") - pf.pre_py_cmds.insert(1,"sys.path.append(os.path.join('..','..', '..', 'pypestutils'))") - - - df = pd.read_csv(os.path.join(tmp_model_ws, "heads.csv"), index_col=0) - pf.add_observations("heads.csv", insfile="heads.csv.ins", index_cols="time", - use_cols=list(df.columns.values), - prefix="hds", rebuild_pst=True) - - # Add stream flow observation - # df = pd.read_csv(os.path.join(tmp_model_ws, "sfr.csv"), index_col=0) - pf.add_observations("sfr.csv", insfile="sfr.csv.ins", index_cols="time", - use_cols=["GAGE_1", "HEADWATER", "TAILWATER"], ofile_sep=",") - - # Setup geostruct for spatial pars - pf.extra_py_imports.append('flopy') - ib = m.dis.idomain[0].array - #tags = {"npf_k_": [0.1, 10.], "npf_k33_": [.1, 10], "sto_ss": [.1, 10], "sto_sy": [.9, 1.1], - # "rch_recharge": [.5, 1.5]} - tags = {"npf_k_": [0.1, 10.]} - dts = pd.to_datetime("1-1-2018") + pd.to_timedelta(np.cumsum(sim.tdis.perioddata.array["perlen"]), unit="d") - print(dts) - # ib = m.dis.idomain.array[0,:,:] - # setup from array style pars - pf.extra_py_imports.append('flopy') - ib = m.dis.idomain[0].array - tags = {"npf_k_": [0.1, 10.]}#, "npf_k33_": [.1, 10], "sto_ss": [.1, 10], "sto_sy": [.9, 1.1], - # "rch_recharge": [.5, 1.5]} - dts = pd.to_datetime("1-1-2018") + pd.to_timedelta(np.cumsum(sim.tdis.perioddata.array["perlen"]), unit="d") - print(dts) - - xmn = m.modelgrid.xvertices.min() - xmx = m.modelgrid.xvertices.max() - ymn = m.modelgrid.yvertices.min() - ymx = m.modelgrid.yvertices.max() - - numpp = 20 - xvals = np.random.uniform(xmn,xmx,numpp) - yvals = np.random.uniform(ymn, ymx, numpp) - pp_locs = pd.DataFrame({"x":xvals,"y":yvals}) - pp_locs.loc[:,"zone"] = 1 - pp_locs.loc[:,"name"] = ["pp_{0}".format(i) for i in range(numpp)] - pp_locs.loc[:,"parval1"] = 1.0 - - pyemu.pp_utils.write_pp_shapfile(pp_locs,os.path.join(template_ws,"pp_locs.shp")) - df = pyemu.pp_utils.pilot_points_from_shapefile(os.path.join(template_ws,"pp_locs.shp")) - - #pp_locs = pyemu.pp_utils.setup_pilotpoints_grid(sr=sr,prefix_dict={0:"pps_1"}) - #pp_locs = pp_locs.loc[:,["name","x","y","zone","parval1"]] - pp_locs.to_csv(os.path.join(template_ws,"pp.csv")) - pyemu.pp_utils.write_pp_file(os.path.join(template_ws,"pp_file.dat"),pp_locs) - pp_container = ["pp_file.dat","pp.csv","pp_locs.shp"] - value_v = pyemu.geostats.ExpVario(contribution=1, a=5000, anisotropy=5, bearing=0.0) - value_gs = pyemu.geostats.GeoStruct(variograms=value_v) - bearing_v = pyemu.geostats.ExpVario(contribution=1,a=5000,anisotropy=3,bearing=0.0) - bearing_gs = pyemu.geostats.GeoStruct(variograms=bearing_v) - aniso_v = pyemu.geostats.ExpVario(contribution=1, a=10000, anisotropy=3, bearing=45.0) - aniso_gs = pyemu.geostats.GeoStruct(variograms=aniso_v) - num_cat_arrays = 0 - for tag, bnd in tags.items(): - lb, ub = bnd[0], bnd[1] - arr_files = [f for f in os.listdir(tmp_model_ws) if tag in f and f.endswith(".txt")] - if "rch" in tag: - pass - else: - for i,arr_file in enumerate(arr_files): - print(arr_file) - k = int(arr_file.split(".")[1][-1]) - 1 - if k == 1: - arr_file = arr_file.replace("_k_","_k33_") - pth_arr_file = os.path.join(pf.new_d,arr_file) - arr = np.loadtxt(pth_arr_file) - cat_dict = {1:[0.4,arr.mean()],2:[0.6,arr.mean()]} - thresharr,threshcsv = pyemu.helpers.setup_threshold_pars(pth_arr_file,cat_dict=cat_dict, - testing_workspace=pf.new_d,inact_arr=ib) - - pf.pre_py_cmds.append("pyemu.helpers.apply_threshold_pars('{0}')".format(os.path.split(threshcsv)[1])) - prefix = arr_file.split('.')[1].replace("_","-") - - - if i < len(pp_container): - pp_opt = pp_container[i] - else: - pp_opt = pp_locs - - pth_arr_file = os.path.join(pf.new_d,arr_file) - arr = np.loadtxt(pth_arr_file) - - tag = arr_file.split('.')[1].replace("_","-") + "_pp" - prefix = arr_file.split('.')[1].replace("_","-") - - pf.add_parameters(filenames=os.path.split(thresharr)[1],par_type="pilotpoints",transform="none", - par_name_base=tag+"-threshpp_k:{0}".format(k), - pargp=tag + "-threshpp_k:{0}".format(k), - lower_bound=0.0,upper_bound=2.0,par_style="m", - pp_space=5,pp_options={"try_use_ppu":False,"prep_hyperpars":True}, - apply_order=2,geostruct=value_gs - ) - - tag = arr_file.split('.')[1].replace("_","-") - - tfiles = [f for f in os.listdir(pf.new_d) if tag in f] - afile = [f for f in tfiles if "aniso" in f][0] - # pf.add_parameters(afile,par_type="pilotpoints",par_name_base=tag+"aniso", - # pargp=tag+"aniso",pp_space=5,lower_bound=-1.0,upper_bound=1.0, - # pp_options={"try_use_ppu":True},apply_order=1,geostruct=aniso_gs, - # par_style="a",transform="none",initial_value=0.0) - pf.add_parameters(afile,par_type="constant",par_name_base=tag+"aniso", - pargp=tag+"aniso",lower_bound=-1.0,upper_bound=1.0, - apply_order=1, - par_style="a",transform="none",initial_value=0.0) - pf.add_observations(afile, prefix=tag+"aniso", obsgp=tag+"aniso") - bfile = [f for f in tfiles if "bearing" in f][0] - pf.add_parameters(bfile, par_type="pilotpoints", par_name_base=tag + "bearing", - pargp=tag + "bearing", pp_space=6,lower_bound=-45,upper_bound=45, - par_style="a",transform="none", - pp_options={"try_use_ppu":True}, - apply_order=1,geostruct=bearing_gs) - pf.add_observations(bfile, prefix=tag + "bearing", obsgp=tag + "bearing") - - - pf.add_parameters(filenames=os.path.split(threshcsv)[1], par_type="grid",index_cols=["threshcat"], - use_cols=["threshproportion","threshfill"], - par_name_base=[prefix+"threshproportion_k:{0}".format(k),prefix+"threshfill_k:{0}".format(k)], - pargp=[prefix+"threshproportion_k:{0}".format(k),prefix+"threshfill_k:{0}".format(k)], - lower_bound=[0.1,0.1],upper_bound=[10.0,10.0],transform="none",par_style='d') - - pf.add_observations(arr_file,prefix="hkarr-"+prefix+"_k:{0}".format(k), - obsgp="hkarr-"+prefix+"_k:{0}".format(k),zone_array=ib) - - pf.add_observations(arr_file+".threshcat.dat", prefix="tcatarr-" + prefix+"_k:{0}".format(k), - obsgp="tcatarr-" + prefix+"_k:{0}".format(k),zone_array=ib) - - pf.add_observations(arr_file + ".thresharr.dat", - prefix="tarr-" +prefix+"_k:{0}".format(k), - obsgp="tarr-" + prefix + "_k:{0}".format(k), zone_array=ib) - - df = pd.read_csv(threshcsv.replace(".csv","_results.csv"),index_col=0) - pf.add_observations(os.path.split(threshcsv)[1].replace(".csv","_results.csv"),index_cols="threshcat",use_cols=df.columns.tolist(),prefix=prefix+"-results_k:{0}".format(k), - obsgp=prefix+"-results_k:{0}".format(k),ofile_sep=",") - num_cat_arrays += 1 - - # add model run command - pf.mod_sys_cmds.append("mf6") - print(pf.mult_files) - print(pf.org_files) - - # build pest - pst = pf.build_pst('freyberg.pst') - #cov = pf.build_prior(fmt="none") - #cov.to_coo(os.path.join(template_ws, "prior.jcb")) - pst.try_parse_name_metadata() - - pst.control_data.noptmax = 0 - pst.pestpp_options["additional_ins_delimiters"] = "," - - pst.write(os.path.join(pf.new_d, "freyberg.pst")) - pyemu.os_utils.run("{0} freyberg.pst".format(ies_exe_path), cwd=pf.new_d) - - res_file = os.path.join(pf.new_d, "freyberg.base.rei") - assert os.path.exists(res_file), res_file - pst.set_res(res_file) - print(pst.phi) - #assert pst.phi < 0.1, pst.phi - - #set the initial and bounds for the fill values - par = pst.parameter_data - - apar = par.loc[par.pname.str.contains("aniso"),:] - bpar = par.loc[par.pname.str.contains("bearing"), :] - par.loc[apar.parnme,"parval1"] = 5 - par.loc[apar.parnme,"parlbnd"] = 4 - par.loc[apar.parnme,"parubnd"] = 6 - - par.loc[bpar.parnme,"parval1"] = 0 - par.loc[bpar.parnme,"parlbnd"] = -90 - par.loc[bpar.parnme,"parubnd"] = 90 - - cat1par = par.loc[par.apply(lambda x: x.threshcat=="0" and x.usecol=="threshfill",axis=1),"parnme"] - cat2par = par.loc[par.apply(lambda x: x.threshcat == "1" and x.usecol == "threshfill", axis=1), "parnme"] - print(cat1par,cat2par) - assert cat1par.shape[0] == num_cat_arrays - assert cat2par.shape[0] == num_cat_arrays - - cat1parhk = [p for p in cat1par if "k:1" not in p] - cat2parhk = [p for p in cat2par if "k:1" not in p] - cat1parvk = [p for p in cat1par if "k:1" in p] - cat2parvk = [p for p in cat2par if "k:1" in p] - for lst in [cat2parvk,cat2parhk,cat1parhk,cat1parvk]: - assert len(lst) > 0 - par.loc[cat1parhk,"parval1"] = 0.1 - par.loc[cat1parhk, "parubnd"] = 1.0 - par.loc[cat1parhk, "parlbnd"] = 0.01 - par.loc[cat1parhk, "partrans"] = "log" - par.loc[cat2parhk, "parval1"] = 10 - par.loc[cat2parhk, "parubnd"] = 100 - par.loc[cat2parhk, "parlbnd"] = 1 - par.loc[cat2parhk, "partrans"] = "log" - - par.loc[cat1parvk, "parval1"] = 0.0001 - par.loc[cat1parvk, "parubnd"] = 0.01 - par.loc[cat1parvk, "parlbnd"] = 0.000001 - par.loc[cat1parvk, "partrans"] = "log" - par.loc[cat2parvk, "parval1"] = 0.1 - par.loc[cat2parvk, "parubnd"] = 1 - par.loc[cat2parvk, "parlbnd"] = 0.01 - par.loc[cat2parvk, "partrans"] = "log" - - - cat1par = par.loc[par.apply(lambda x: x.threshcat == "0" and x.usecol == "threshproportion", axis=1), "parnme"] - cat2par = par.loc[par.apply(lambda x: x.threshcat == "1" and x.usecol == "threshproportion", axis=1), "parnme"] - - print(cat1par, cat2par) - assert cat1par.shape[0] == num_cat_arrays - assert cat2par.shape[0] == num_cat_arrays - - par.loc[cat1par, "parval1"] = 0.8 - par.loc[cat1par, "parubnd"] = 1.0 - par.loc[cat1par, "parlbnd"] = 0.6 - par.loc[cat1par,"partrans"] = "none" - - # since the apply method only looks that first proportion, we can just fix this one - par.loc[cat2par, "parval1"] = 1 - par.loc[cat2par, "parubnd"] = 1 - par.loc[cat2par, "parlbnd"] = 1 - par.loc[cat2par,"partrans"] = "fixed" - - #assert par.loc[par.parnme.str.contains("threshgr"),:].shape[0] > 0 - #par.loc[par.parnme.str.contains("threshgr"),"parval1"] = 0.5 - #par.loc[par.parnme.str.contains("threshgr"),"partrans"] = "fixed" - - print(pst.adj_par_names) - print(pst.npar,pst.npar_adj) - - org_par = par.copy() - num_reals = 100 - pe = pf.draw(num_reals, use_specsim=False) - pe.enforce() - print(pe.shape) - assert pe.shape[1] == pst.npar_adj, "{0} vs {1}".format(pe.shape[1], pst.npar_adj) - assert pe.shape[0] == num_reals - - # cat1par = cat1par[0] - # pe = pe.loc[pe.loc[:,cat1par].values>0.35,:] - # pe = pe.loc[pe.loc[:, cat1par].values < 0.5, :] - # cat2par = par.loc[par.apply(lambda x: x.threshcat == "1" and x.usecol == "threshfill", axis=1), "parnme"] - # cat2par = cat2par[0] - # pe = pe.loc[pe.loc[:, cat2par].values > 10, :] - # pe = pe.loc[pe.loc[:, cat2par].values < 50, :] - - print(pe.shape) - assert pe.shape[0] > 0 - #print(pe.loc[:,cat1par].describe()) - #print(pe.loc[:, cat2par].describe()) - #return - #pe._df.index = np.arange(pe.shape[0]) - - truth_idx = 1 - - - #pe = pe._df - - # just use a real as the truth... - pst.parameter_data.loc[pst.adj_par_names,"parval1"] = pe.loc[truth_idx,pst.adj_par_names].values - pe = pe.loc[pe.index.map(lambda x: x != truth_idx), :] - pe.to_dense(os.path.join(template_ws, "prior.jcb")) - pst.control_data.noptmax = 0 - pst.write(os.path.join(pf.new_d,"truth.pst"),version=2) - pyemu.os_utils.run("{0} truth.pst".format(ies_exe_path),cwd=pf.new_d) - - pst = pyemu.Pst(os.path.join(pf.new_d,"truth.pst")) - - obs = pst.observation_data - obs.loc[:,"obsval"] = pst.res.loc[pst.obs_names,"modelled"].values - obs.loc[:,"weight"] = 0.0 - obs.loc[:,"standard_deviation"] = np.nan - onames = obs.loc[obs.obsnme.apply(lambda x: ("trgw" in x or "gage" in x) and ("hdstd" not in x and "sfrtd" not in x)),"obsnme"].values - #obs.loc[obs.oname=="hds","weight"] = 1.0 - #obs.loc[obs.oname == "hds", "standard_deviation"] = 0.001 - snames = [o for o in onames if "gage" in o] - obs.loc[onames,"weight"] = 1.0 - obs.loc[onames, "standard_deviation"] = 0.5 - - obs.loc[snames,"weight"] = 1./(obs.loc[snames,"obsval"] * 0.2).values - obs.loc[snames, "standard_deviation"] = (obs.loc[snames, "obsval"] * 0.2).values - - #obs.loc[onames,"obsval"] = truth.values - #obs.loc[onames,"obsval"] *= np.random.normal(1.0,0.01,onames.shape[0]) - - pst.write(os.path.join(pf.new_d, "freyberg.pst"),version=2) - pyemu.os_utils.run("{0} freyberg.pst".format(ies_exe_path), cwd=pf.new_d) - pst = pyemu.Pst(os.path.join(pf.new_d,"freyberg.pst")) - assert pst.phi < 0.01,str(pst.phi) - - # reset away from the truth... - pst.parameter_data.loc[:,"parval1"] = org_par.parval1.values.copy() - - pst.control_data.noptmax = -1 - pst.pestpp_options["ies_par_en"] = "prior.jcb" - pst.pestpp_options["ies_num_reals"] = pst.npar * 2 - pst.pestpp_options["ies_subset_size"] = -10 - pst.pestpp_options["ies_no_noise"] = True - #pst.pestpp_options["ies_bad_phi_sigma"] = 2.0 - pst.pestpp_options["overdue_giveup_fac"] = 100.0 - pst.pestpp_options["ies_multimodal_alpha"] = 0.99 - #pst.pestpp_options["panther_agent_freeze_on_fail"] = True - - #pst.write(os.path.join(pf.new_d, "freyberg.pst")) - #pyemu.os_utils.start_workers(pf.new_d,ies_exe_path,"freyberg.pst",worker_root=".",master_dir="master_thresh",num_workers=15) - - #num_reals = 100 - #pe = pf.draw(num_reals, use_specsim=False) - #pe.enforce() - #pe.to_dense(os.path.join(template_ws, "prior.jcb")) - #pst.pestpp_options["ies_par_en"] = "prior.jcb" - - pst.write(os.path.join(pf.new_d, "freyberg.pst"), version=2) - m_d = "master_thresh_nonstat" - pyemu.os_utils.start_workers(pf.new_d, ies_exe_path, "freyberg.pst", worker_root=".", master_dir=m_d, - num_workers=15) - phidf = pd.read_csv(os.path.join(m_d,"freyberg.phi.actual.csv")) - print(phidf["mean"]) - - - pst_fname = os.path.join(m_d,"freyberg.pst") - gpr_t_d = os.path.join(pf.new_d+"_gpr") - dv_pops = pd.read_csv(os.path.join(m_d,"freyberg.0.par.csv"),index_col=0) - obs_pops = pd.read_csv(os.path.join(m_d,"freyberg.0.obs.csv"),index_col=0) - - pyemu.helpers.prep_for_gpr(pst_fname,dv_pops,obs_pops,gpr_t_d=gpr_t_d,nverf=int(pop_size*.1),plot_fits=True) - gpst = pyemu.Pst(os.path.join(gpr_t_d,case+".pst")) - shutil.copy2(os.path.join(m_d,case+".0.dv_pop.csv"),os.path.join(gpr_t_d,"initial_dv_pop.csv")) - gpst.pestpp_options["mou_dv_population_file"] = "initial_dv_pop.csv" - gpst.control_data.noptmax = noptmax_full - gpst.write(os.path.join(gpr_t_d,case+".pst"),version=2) - - gpr_m_d = gpr_t_d.replace("template","master") - if os.path.exists(gpr_m_d): - shutil.rmtree(gpr_m_d) - pyemu.os_utils.start_workers(gpr_t_d, exe_path, case+".pst", num_workers, worker_root="mou_tests", - master_dir=gpr_m_d, verbose=True, port=port) - if __name__ == "__main__": - #mf6_freyberg_pp_locs_test('.') + mf6_freyberg_pp_locs_test('.') #mf6_subdir_test(".") #mf6_freyberg_ppu_hyperpars_invest(".") - mf6_freyberg_ppu_hyperpars_thresh_gpr_invest(".") #while True: # mf6_freyberg_thresh_test(".") #plot_thresh("master_thresh_nonstat") diff --git a/autotest/utils/hosaki_template/forward_run.py b/autotest/utils/hosaki_template/forward_run.py new file mode 100644 index 00000000..45c8d693 --- /dev/null +++ b/autotest/utils/hosaki_template/forward_run.py @@ -0,0 +1,12 @@ +import pandas as pd +import numpy as np +pdf = pd.read_csv('par.csv') +pval1 = float(pdf.iloc[0,1]) +pval2 = float(pdf.iloc[1,1]) +term1 = (pval2**2)*np.e**(-pval2) +term2 = 1. - (8.*(pval1)) + (7.*(pval1**2)) +term3 = (-(7./3.)*(pval1**3)) + (0.25*(pval1**4)) +sim = (term2 + term3) * term1 +with open('sim.csv','w') as f: + f.write('obsnme,obsval\n') + f.write('sim,'+str(sim)+'\n') diff --git a/autotest/utils/hosaki_template/par.csv b/autotest/utils/hosaki_template/par.csv new file mode 100644 index 00000000..f948ea6b --- /dev/null +++ b/autotest/utils/hosaki_template/par.csv @@ -0,0 +1,3 @@ +parnme,parval1 +par1,4.0000000000 +par2,2.0000000000 diff --git a/autotest/utils/hosaki_template/par.csv.tpl b/autotest/utils/hosaki_template/par.csv.tpl new file mode 100644 index 00000000..d6c87114 --- /dev/null +++ b/autotest/utils/hosaki_template/par.csv.tpl @@ -0,0 +1,4 @@ +ptf ~ +parnme,parval1 +par1,~ par1 ~ +par2,~ par2 ~ diff --git a/autotest/utils/hosaki_template/pest.pst b/autotest/utils/hosaki_template/pest.pst new file mode 100644 index 00000000..03982ea0 --- /dev/null +++ b/autotest/utils/hosaki_template/pest.pst @@ -0,0 +1,28 @@ +pcf +* control data + restart estimation + 2 1 1 0 1 + 1 1 single point 1 + 2.000000E+01 -3.000000E+00 3.000000E-01 1.000000E-02 -7 + 1.000000E+01 1.000000E+01 1.000000E-03 + 1.000000E-01 + 25 1.000000E-02 3 1000 1.000000E-02 3 + 0 0 0 +* singular value decomposition + 1 + 10000000 1.000000E-06 +1 +* parameter groups +pargp relative 1.0000000000E-02 0.0 switch 2.0000000000E+00 parabolic 1.0000000000E-05 5.0000000000E-01 smaller +* parameter data +par1 none relative 2.5000000000E+00 0.0000000000E+00 5.0000000000E+00 pargp 1.0000000000E+00 0.0000000000E+00 1 +par2 none relative 2.5000000000E+00 0.0000000000E+00 5.0000000000E+00 pargp 1.0000000000E+00 0.0000000000E+00 1 +* observation groups +obgnme +* observation data +sim -2.3458115761E+00 1.0000000000E+02 less_than +* model command line +python forward_run.py +* model input/output +./par.csv.tpl ./par.csv +./sim.csv.ins ./sim.csv diff --git a/autotest/utils/hosaki_template/sim.csv b/autotest/utils/hosaki_template/sim.csv new file mode 100644 index 00000000..1ee6b2ed --- /dev/null +++ b/autotest/utils/hosaki_template/sim.csv @@ -0,0 +1,2 @@ +obsnme,obsval +sim,-2.345811576101292 diff --git a/autotest/utils/hosaki_template/sim.csv.ins b/autotest/utils/hosaki_template/sim.csv.ins new file mode 100644 index 00000000..534dcae9 --- /dev/null +++ b/autotest/utils/hosaki_template/sim.csv.ins @@ -0,0 +1,3 @@ +pif ~ +l1 +l1 ~,~ !sim! diff --git a/autotest/utils/zdt1_template/additive_par.dat b/autotest/utils/zdt1_template/additive_par.dat new file mode 100644 index 00000000..919332b7 --- /dev/null +++ b/autotest/utils/zdt1_template/additive_par.dat @@ -0,0 +1,2 @@ +obj1_add_par 0.00000000000000e+00 +obj2_add_par 0.00000000000000e+00 diff --git a/autotest/utils/zdt1_template/additive_par.dat.tpl b/autotest/utils/zdt1_template/additive_par.dat.tpl new file mode 100644 index 00000000..2b2a325f --- /dev/null +++ b/autotest/utils/zdt1_template/additive_par.dat.tpl @@ -0,0 +1,3 @@ +ptf ~ +obj1_add_par ~ obj1_add_par ~ +obj2_add_par ~ obj2_add_par ~ diff --git a/autotest/utils/zdt1_template/dv.dat b/autotest/utils/zdt1_template/dv.dat new file mode 100644 index 00000000..6d7efddd --- /dev/null +++ b/autotest/utils/zdt1_template/dv.dat @@ -0,0 +1,30 @@ +dv_0 0.5000000000 +dv_1 0.5000000000 +dv_2 0.5000000000 +dv_3 0.5000000000 +dv_4 0.5000000000 +dv_5 0.5000000000 +dv_6 0.5000000000 +dv_7 0.5000000000 +dv_8 0.5000000000 +dv_9 0.5000000000 +dv_10 0.50000000000 +dv_11 0.50000000000 +dv_12 0.50000000000 +dv_13 0.50000000000 +dv_14 0.50000000000 +dv_15 0.50000000000 +dv_16 0.50000000000 +dv_17 0.50000000000 +dv_18 0.50000000000 +dv_19 0.50000000000 +dv_20 0.50000000000 +dv_21 0.50000000000 +dv_22 0.50000000000 +dv_23 0.50000000000 +dv_24 0.50000000000 +dv_25 0.50000000000 +dv_26 0.50000000000 +dv_27 0.50000000000 +dv_28 0.50000000000 +dv_29 0.50000000000 diff --git a/autotest/utils/zdt1_template/dv.dat.tpl b/autotest/utils/zdt1_template/dv.dat.tpl new file mode 100644 index 00000000..e105ab03 --- /dev/null +++ b/autotest/utils/zdt1_template/dv.dat.tpl @@ -0,0 +1,31 @@ +ptf ~ +dv_0 ~ dv_0 ~ +dv_1 ~ dv_1 ~ +dv_2 ~ dv_2 ~ +dv_3 ~ dv_3 ~ +dv_4 ~ dv_4 ~ +dv_5 ~ dv_5 ~ +dv_6 ~ dv_6 ~ +dv_7 ~ dv_7 ~ +dv_8 ~ dv_8 ~ +dv_9 ~ dv_9 ~ +dv_10 ~ dv_10 ~ +dv_11 ~ dv_11 ~ +dv_12 ~ dv_12 ~ +dv_13 ~ dv_13 ~ +dv_14 ~ dv_14 ~ +dv_15 ~ dv_15 ~ +dv_16 ~ dv_16 ~ +dv_17 ~ dv_17 ~ +dv_18 ~ dv_18 ~ +dv_19 ~ dv_19 ~ +dv_20 ~ dv_20 ~ +dv_21 ~ dv_21 ~ +dv_22 ~ dv_22 ~ +dv_23 ~ dv_23 ~ +dv_24 ~ dv_24 ~ +dv_25 ~ dv_25 ~ +dv_26 ~ dv_26 ~ +dv_27 ~ dv_27 ~ +dv_28 ~ dv_28 ~ +dv_29 ~ dv_29 ~ diff --git a/autotest/utils/zdt1_template/forward_run.py b/autotest/utils/zdt1_template/forward_run.py new file mode 100644 index 00000000..4a84cd90 --- /dev/null +++ b/autotest/utils/zdt1_template/forward_run.py @@ -0,0 +1,30 @@ +import os +import numpy as np +import pandas as pd +def zdt1(x): + g = 1 + 9 * np.sum(x[1:]) / (len(x) - 1) + return (x[0]*10, g * (1 - np.sqrt(x[0] / g))*10),[] + +def helper(func): + pdf = pd.read_csv("dv.dat",delim_whitespace=True,index_col=0, header=None, names=["parnme","parval1"]) + #obj1,obj2 = func(pdf.values) + objs,constrs = func(pdf.values) + + if os.path.exists("additive_par.dat"): + obj1,obj2 = objs[0],objs[1] + cdf = pd.read_csv("additive_par.dat", delim_whitespace=True, index_col=0,header=None, names=["parnme","parval1"]) + obj1[0] += cdf.parval1.values[0] + obj2[0] += cdf.parval1.values[1] + for i in range(len(constrs)): + constrs[i] += cdf.parval1.values[i+2] + + with open("obj.dat",'w') as f: + for i,obj in enumerate(objs): + f.write("obj_{0} {1}\n".format(i+1,float(obj))) + #f.write("obj_2 {0}\n".format(float(obj2))) + for i,constr in enumerate(constrs): + f.write("constr_{0} {1}\n".format(i+1,float(constr))) + return objs,constrs + +if __name__ == '__main__': + helper(zdt1) diff --git a/autotest/utils/zdt1_template/obj.dat b/autotest/utils/zdt1_template/obj.dat new file mode 100644 index 00000000..95a3a3fd --- /dev/null +++ b/autotest/utils/zdt1_template/obj.dat @@ -0,0 +1,2 @@ +obj_1 5.0 +obj_2 38.416876048223 diff --git a/autotest/utils/zdt1_template/obj.dat.ins b/autotest/utils/zdt1_template/obj.dat.ins new file mode 100644 index 00000000..f2e2a1b7 --- /dev/null +++ b/autotest/utils/zdt1_template/obj.dat.ins @@ -0,0 +1,3 @@ +pif ~ +l1 w !obj_1! +l1 w !obj_2! diff --git a/autotest/utils/zdt1_template/prior.csv b/autotest/utils/zdt1_template/prior.csv new file mode 100644 index 00000000..d06a2b6d --- /dev/null +++ b/autotest/utils/zdt1_template/prior.csv @@ -0,0 +1,101 @@ +,dv_0,dv_1,dv_10,dv_11,dv_12,dv_13,dv_14,dv_15,dv_16,dv_17,dv_18,dv_19,dv_2,dv_20,dv_21,dv_22,dv_23,dv_24,dv_25,dv_26,dv_27,dv_28,dv_29,dv_3,dv_4,dv_5,dv_6,dv_7,dv_8,dv_9,obj2_add_par,obj1_add_par 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+99,0.30027476937610575,0.1971881860058456,0.34170154891461857,0.6332741839553133,0.9129150782187159,0.36742454292946086,0.44455371167361984,0.7083453271204787,0.4195326726150156,0.49705466955871797,0.5932150144810101,0.4265571127682233,0.07509510539295028,0.44983955696942035,0.247283621689931,1.0,0.2378751117843687,0.34078771641400984,0.41385845333774757,0.48428560888076044,0.19473539749810959,0.44556911294368545,0.6253363329156638,0.4475069928061957,0.4331541889318046,0.5812785728485341,0.007666880198995973,0.7564308419295487,0.6658394680634336,0.33514120248833423,-0.5,-0.18846975808019237 diff --git a/autotest/utils/zdt1_template/zdt1.pst b/autotest/utils/zdt1_template/zdt1.pst new file mode 100644 index 00000000..e049699e --- /dev/null +++ b/autotest/utils/zdt1_template/zdt1.pst @@ -0,0 +1,67 @@ +pcf +* control data + restart estimation + 32 2 2 0 1 + 2 1 single point 1 + 2.000000E+01 -3.000000E+00 3.000000E-01 1.000000E-02 -7 + 1.000000E+01 1.000000E+01 1.000000E-03 + 1.000000E-01 + 100 1.000000E-02 3 3 1.000000E-02 3 + 0 0 0 +* singular value decomposition + 1 + 10000000 1.000000E-06 +1 +* parameter groups +decvars relative 1.0000000000E-02 0.0 switch 2.0000000000E+00 parabolic 1.0000000000E-05 5.0000000000E-01 smaller +obj_add absolute 1.0000000000E-02 0.0 switch 2.0000000000E+00 parabolic 1.0000000000E-05 5.0000000000E-01 smaller +* parameter data +dv_0 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_1 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_10 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_11 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_12 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_13 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_14 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_15 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_16 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_17 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_18 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_19 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_2 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_20 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_21 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_22 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_23 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_24 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_25 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_26 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_27 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_28 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_29 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_3 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_4 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_5 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_6 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_7 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_8 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_9 none relative 5.0000000000E-01 0.0000000000E+00 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +obj2_add_par none relative 0.0000000000E+00 -5.0000000000E-01 5.0000000000E-01 obj_add 1.0000000000E+00 0.0000000000E+00 1 +obj1_add_par none relative 0.0000000000E+00 -5.0000000000E-01 5.0000000000E-01 obj_add 1.0000000000E+00 0.0000000000E+00 1 +* observation groups +less_than +* observation data +obj_1 5.0000000000E+00 1.0000000000E+00 less_than +obj_2 3.8416876048E+01 1.0000000000E+00 less_than +* model command line +python forward_run.py +* model input/output +./dv.dat.tpl ./dv.dat +./additive_par.dat.tpl ./additive_par.dat +./obj.dat.ins ./obj.dat +++opt_dec_var_groups(decvars) +++mou_objectives(obj_1,obj_2) +++mou_generator(pso) +++opt_risk(0.5) +++mou_population_size(100) +++mou_save_population_every(1) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 39d89438..1a2c28be 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -1,12 +1,46 @@ import os import shutil import pytest +import platform # if not os.path.exists("temp"): # os.mkdir("temp") from pathlib import Path +import pandas as pd import pyemu from pst_from_tests import _get_port +ext = '' +local_bins = False # change if wanting to test with local binary exes +if local_bins: + bin_path = os.path.join("..", "..", "bin") + if "linux" in platform.system().lower(): + pass + bin_path = os.path.join(bin_path, "linux") + elif "darwin" in platform.system().lower(): + pass + bin_path = os.path.join(bin_path, "mac") + else: + bin_path = os.path.join(bin_path, "win") + ext = '.exe' +else: + bin_path = '' + if "windows" in platform.system().lower(): + ext = '.exe' + +mf_exe_path = os.path.join(bin_path, "mfnwt") +mt_exe_path = os.path.join(bin_path, "mt3dusgs") +usg_exe_path = os.path.join(bin_path, "mfusg_gsi") +mf6_exe_path = os.path.join(bin_path, "mf6") +pp_exe_path = os.path.join(bin_path, "pestpp-glm") +ies_exe_path = os.path.join(bin_path, "pestpp-ies") +mou_exe_path = os.path.join(bin_path, "pestpp-mou") +swp_exe_path = os.path.join(bin_path, "pestpp-swp") + +mf_exe_name = os.path.basename(mf_exe_path) +mf6_exe_name = os.path.basename(mf6_exe_path) + + + def add_pi_obj_func_test(tmp_path): import os @@ -2619,11 +2653,165 @@ def ppu_geostats_test(tmp_path): # plt.show() # exit() +def gpr_compare_invest(): + from sklearn.gaussian_process import GaussianProcessRegressor + case = "zdt1" + use_chances = False + m_d = os.path.join(case+"_gpr_baseline") + org_d = os.path.join("utils",case+"_template") + t_d = case+"_template" + if os.path.exists(t_d): + shutil.rmtree(t_d) + shutil.copytree(org_d,t_d) + if os.path.exists(m_d): + shutil.rmtree(m_d) + + pst = pyemu.Pst(os.path.join(t_d, case+".pst")) + pst.pestpp_options["mou_generator"] = "pso" + if use_chances: + pst.pestpp_options["opt_risk"] = 0.95 + pst.pestpp_options["opt_stack_size"] = 50 + pst.pestpp_options["opt_recalc_chance_every"] = 10000 + pst.pestpp_options["opt_chance_points"] = "single" + else: + pst.pestpp_options["opt_risk"] = 0.5 + + pop_size = 100 + num_workers = 100 + noptmax_full = 10 + port = 4554 + pst.control_data.noptmax = noptmax_full + pst.pestpp_options["mou_population_size"] = pop_size + pst.pestpp_options["mou_save_population_every"] = 1 + pst.write(os.path.join(t_d, case+".pst")) + if not os.path.exists(m_d): + pyemu.os_utils.start_workers(t_d, mou_exe_path, case+".pst", num_workers, worker_root=".", + master_dir=m_d, verbose=True, port=port) + #shutil.copytree(t_d,m_d) + #pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=m_d) + # use the initial population files for training + dv_pops = [os.path.join(m_d,"{0}.0.dv_pop.csv".format(case))] + obs_pops = [f.replace("dv_","obs_") for f in dv_pops] + + pst_fname = os.path.join(m_d,case+".pst") + gpr_t_d = os.path.join(case+"_gpr_template") + pyemu.helpers.prep_for_gpr(pst_fname,dv_pops,obs_pops,gpr_t_d=gpr_t_d,nverf=int(pop_size*.1),plot_fits=True) + gpst = pyemu.Pst(os.path.join(gpr_t_d,case+".pst")) + shutil.copy2(os.path.join(m_d,case+".0.dv_pop.csv"),os.path.join(gpr_t_d,"initial_dv_pop.csv")) + gpst.pestpp_options["mou_dv_population_file"] = "initial_dv_pop.csv" + gpst.control_data.noptmax = noptmax_full + gpst.write(os.path.join(gpr_t_d,case+".pst"),version=2) + + gpr_m_d = gpr_t_d.replace("template","master") + if os.path.exists(gpr_m_d): + shutil.rmtree(gpr_m_d) + pyemu.os_utils.start_workers(gpr_t_d, mou_exe_path, case+".pst", num_workers, worker_root=".", + master_dir=gpr_m_d, verbose=True, port=port) + + #o1 = pd.read_csv(os.path.join(m_d,case+".{0}.obs_pop.csv".format(max(0,pst.control_data.noptmax)))) + o1 = pd.read_csv(os.path.join(m_d,case+".pareto.archive.summary.csv")) + o1 = o1.loc[o1.generation == o1.generation.max(), :] + o1 = o1.loc[o1.is_feasible == True, :] + o1 = o1.loc[o1.nsga2_front == 1, :] + + + import matplotlib.pyplot as plt + o2 = pd.read_csv(os.path.join(gpr_m_d, case + ".{0}.obs_pop.csv".format(max(0, gpst.control_data.noptmax)))) + fig,ax = plt.subplots(1,1,figsize=(5,5)) + ax.scatter(o1.obj_1,o1.obj_2,c="r",s=10) + ax.scatter(o2.obj_1,o2.obj_2,c="0.5",s=10,alpha=0.5) + plt.tight_layout() + plt.savefig("gpr_{0}_compare_noiter.pdf".format(case)) + plt.close(fig) + + # now lets try an inner-outer scheme... + + gpst.control_data.noptmax = noptmax_inner + gpst.write(os.path.join(gpr_t_d,case+".pst"),version=2) + gpr_t_d_iter = gpr_t_d+"_outeriter{0}".format(0) + if os.path.exists(gpr_t_d_iter): + shutil.rmtree(gpr_t_d_iter) + shutil.copytree(gpr_t_d,gpr_t_d_iter) + for iouter in range(1,4): + #run the gpr emulator + gpr_m_d_iter = gpr_t_d_iter.replace("template","master") + complex_m_d_iter = t_d.replace("template", "master_complex_retrain_outeriter{0}".format(iouter)) + if os.path.exists(gpr_m_d_iter): + shutil.rmtree(gpr_m_d_iter) + pyemu.os_utils.start_workers(gpr_t_d_iter, exe_path, case+".pst", num_workers, worker_root="mou_tests", + master_dir=gpr_m_d_iter, verbose=True, port=port) + o2 = pd.read_csv(os.path.join(gpr_m_d_iter,case+".{0}.obs_pop.csv".format(gpst.control_data.noptmax))) + + # now run the final dv pop thru the "complex" model + final_gpr_dvpop_fname = os.path.join(gpr_m_d_iter,case+".archive.dv_pop.csv") + assert os.path.exists(final_gpr_dvpop_fname) + complex_model_dvpop_fname = os.path.join(t_d,"gpr_outeriter{0}_dvpop.csv".format(iouter)) + if os.path.exists(complex_model_dvpop_fname): + os.remove(complex_model_dvpop_fname) + # load the gpr archive and do something clever to pick new points to eval + # with the complex model + dvpop = pd.read_csv(final_gpr_dvpop_fname,index_col=0) + if dvpop.shape[0] > pop_size: + arc_sum = pd.read_csv(os.path.join(gpr_m_d_iter,case+".pareto.archive.summary.csv")) + as_front_map = {member:front for member,front in zip(arc_sum.member,arc_sum.nsga2_front)} + as_crowd_map = {member: crowd for member, crowd in zip(arc_sum.member, arc_sum.nsga2_crowding_distance)} + as_feas_map = {member: feas for member, feas in zip(arc_sum.member, arc_sum.feasible_distance)} + as_gen_map = {member: gen for member, gen in zip(arc_sum.member, arc_sum.generation)} + + dvpop.loc[:,"front"] = dvpop.index.map(lambda x: as_front_map.get(x,np.nan)) + dvpop.loc[:, "crowd"] = dvpop.index.map(lambda x: as_crowd_map.get(x, np.nan)) + dvpop.loc[:,"feas"] = dvpop.index.map(lambda x: as_feas_map.get(x,np.nan)) + dvpop.loc[:, "gen"] = dvpop.index.map(lambda x: as_gen_map.get(x, np.nan)) + #drop members that have missing archive info + dvpop = dvpop.dropna() + if dvpop.shape[0] > pop_size: + dvpop.sort_values(by=["gen","feas","front","crowd"],ascending=[False,True,True,False],inplace=True) + dvpop = dvpop.iloc[:pop_size,:] + dvpop.drop(["gen","feas","front","crowd"],axis=1,inplace=True) + + #shutil.copy2(final_gpr_dvpop_fname,complex_model_dvpop_fname) + dvpop.to_csv(complex_model_dvpop_fname) + pst.pestpp_options["mou_dv_population_file"] = os.path.split(complex_model_dvpop_fname)[1] + pst.control_data.noptmax = -1 + pst.write(os.path.join(t_d,case+".pst"),version=2) + + pyemu.os_utils.start_workers(t_d, exe_path, case+".pst", num_workers, worker_root="mou_tests", + master_dir=complex_m_d_iter, verbose=True, port=port) + + # plot the complex model results... + o2 = pd.read_csv(os.path.join(complex_m_d_iter, case + ".pareto.archive.summary.csv")) + o2 = o2.loc[o2.generation == o2.generation.max(), :] + #o2 = o2.loc[o2.is_feasible==True,:] + o2 = o2.loc[o2.nsga2_front == 1, :] + fig, ax = plt.subplots(1, 1, figsize=(5, 5)) + ax.scatter(o1.obj_1, o1.obj_2,c="r",s=10) + ax.scatter(o2.obj_1, o2.obj_2,c="0.5",s=10,alpha=0.5) + plt.tight_layout() + plt.savefig("gpr_{0}_compare_iterscheme_{1}.pdf".format(case,iouter)) + plt.close(fig) + + # now add those complex model input-output pop files to the list and retrain + # the gpr + dv_pops.append(os.path.join(complex_m_d_iter,case+".0.dv_pop.csv")) + obs_pops.append(os.path.join(complex_m_d_iter,case+".0.obs_pop.csv")) + gpr_t_d_iter = gpr_t_d+"_outeriter{0}".format(iouter) + pyemu.helpers.prep_for_gpr(pst_fname,dv_pops,obs_pops,gpr_t_d=gpr_t_d_iter,nverf=int(pop_size*.1),plot_fits=True) + gpst_iter = pyemu.Pst(os.path.join(gpr_t_d_iter,case+".pst")) + #aggdf = pd.read_csv(os.path.join(gpr_t_d,"gpr_aggregate_training_data.csv"),index_col=0) + #aggdf.index = ["outeriter{0}_member{1}".format(iouter,i) for i in range(aggdf.shape[0])] + restart_gpr_dvpop_fname = "gpr_restart_dvpop_outeriter{0}.csv".format(iouter) + #aggdf.to_csv(os.path.join(gpr_t_d_iter,restart_gpr_dvpop_fname)) + shutil.copy2(os.path.join(complex_m_d_iter,case+".0.dv_pop.csv"),os.path.join(gpr_t_d_iter,restart_gpr_dvpop_fname)) + gpst_iter.pestpp_options["mou_dv_population_file"] = restart_gpr_dvpop_fname + gpst_iter.control_data.noptmax = gpst.control_data.noptmax + gpst_iter.write(os.path.join(gpr_t_d_iter,case+".pst"),version=2) + if __name__ == "__main__": #ppu_geostats_test(".") - while True: - thresh_pars_test() + gpr_compare_invest() + #while True: + # thresh_pars_test() #obs_ensemble_quantile_test() #geostat_draws_test("temp") # ac_draw_test("temp") From 3d7d00bd069398bfdcf7194405b7b0fdc79dbf92 Mon Sep 17 00:00:00 2001 From: jwhite Date: Tue, 19 Nov 2024 15:22:43 +0800 Subject: [PATCH 12/20] added gpr notebook --- autotest/utils_tests.py | 5 +- examples/gpr_emulation_hosaki.ipynb | 960 ++++++++++++++++++++++++++++ 2 files changed, 963 insertions(+), 2 deletions(-) create mode 100644 examples/gpr_emulation_hosaki.ipynb diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 1a2c28be..0aa76f9f 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2679,6 +2679,7 @@ def gpr_compare_invest(): pop_size = 100 num_workers = 100 noptmax_full = 10 + noptmax_inner = 5 port = 4554 pst.control_data.noptmax = noptmax_full pst.pestpp_options["mou_population_size"] = pop_size @@ -2738,7 +2739,7 @@ def gpr_compare_invest(): complex_m_d_iter = t_d.replace("template", "master_complex_retrain_outeriter{0}".format(iouter)) if os.path.exists(gpr_m_d_iter): shutil.rmtree(gpr_m_d_iter) - pyemu.os_utils.start_workers(gpr_t_d_iter, exe_path, case+".pst", num_workers, worker_root="mou_tests", + pyemu.os_utils.start_workers(gpr_t_d_iter, mou_exe_path, case+".pst", num_workers, worker_root=".", master_dir=gpr_m_d_iter, verbose=True, port=port) o2 = pd.read_csv(os.path.join(gpr_m_d_iter,case+".{0}.obs_pop.csv".format(gpst.control_data.noptmax))) @@ -2775,7 +2776,7 @@ def gpr_compare_invest(): pst.control_data.noptmax = -1 pst.write(os.path.join(t_d,case+".pst"),version=2) - pyemu.os_utils.start_workers(t_d, exe_path, case+".pst", num_workers, worker_root="mou_tests", + pyemu.os_utils.start_workers(t_d, mou_exe_path, case+".pst", num_workers, worker_root=".", master_dir=complex_m_d_iter, verbose=True, port=port) # plot the complex model results... diff --git a/examples/gpr_emulation_hosaki.ipynb b/examples/gpr_emulation_hosaki.ipynb new file mode 100644 index 00000000..e36d26d6 --- /dev/null +++ b/examples/gpr_emulation_hosaki.ipynb @@ -0,0 +1,960 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4a6476de-2712-42c7-98e6-c44b101a2599", + "metadata": {}, + "source": [ + "# A verbose walk thru of gaussian-process-regression emulation for global optimization\n", + "\n", + "In this notebook, we go thru much pain to demonstrate how GPR can be used for so-called \"bayesian optimization\" with a very naive in-filling strategy. Many of the steps and operations in this notebook are done only for the purposes of hoping to explain how GPR emulation works.\n", + "\n", + "We will be using the well-known hosaki function for this analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b606d2c1-77a3-4c5a-a86f-daba87f86ffa", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pyemu" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a93b7992-77dd-47d7-8312-c17657798d4b", + "metadata": {}, + "outputs": [], + "source": [ + "org_d = os.path.join(\"..\",\"autotest\",\"utils\",\"hosaki_template\")\n", + "assert os.path.exists(org_d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4c697d1-5965-4322-808c-3d7f43b6e67f", + "metadata": {}, + "outputs": [], + "source": [ + "t_d = \"hosaki_template\"\n", + "if os.path.exists(t_d):\n", + " shutil.rmtree(t_d)\n", + "shutil.copytree(org_d,t_d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "696588ca-673c-4353-bc2b-7b58a8f28f2d", + "metadata": {}, + "outputs": [], + "source": [ + "pst = pyemu.Pst(os.path.join(t_d,\"pest.pst\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af8605ae-1a6a-4732-96c7-0e53492b9f5d", + "metadata": {}, + "outputs": [], + "source": [ + "par = pst.parameter_data\n", + "par" + ] + }, + { + "cell_type": "markdown", + "id": "6a114403-c957-42d8-bb0c-1688835b866a", + "metadata": {}, + "source": [ + "The hosaki function only has two decision variables - its very simple. But in general, GPR-based emulation does not scale to extreme dimensions (like those seem in ensemble-based DA), but well within the space of global (multi-objective) optimization, so its an obvious pairing with pestpp-mou...\n", + "\n", + "The hosaki function only has 1 objective and no constraints and our goal is to minimize this single objective:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea64aa47-fe75-494d-9c36-e28a6aebc43e", + "metadata": {}, + "outputs": [], + "source": [ + "pst.observation_data" + ] + }, + { + "cell_type": "markdown", + "id": "e34038c7-39f6-47e6-8114-aa3c75afe762", + "metadata": {}, + "source": [ + "First, lets sweep over decision variable space so that we can map the \"true\" objective function surface:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6e8e756-9513-4140-922c-76cc7a347a83", + "metadata": {}, + "outputs": [], + "source": [ + "pvals = []\n", + "sweep_steps = 20\n", + "for p1 in np.linspace(par.parlbnd.iloc[0],par.parubnd.iloc[0],sweep_steps):\n", + " for p2 in np.linspace(par.parlbnd.iloc[0],par.parubnd.iloc[0],sweep_steps):\n", + " pvals.append([p1,p2])\n", + "pvals = pd.DataFrame(pvals,columns=pst.par_names)\n", + "pvals.to_csv(os.path.join(t_d,\"sweep_in.csv\"))\n", + "pst.pestpp_options[\"ies_par_en\"] = \"sweep_in.csv\"\n", + "pst.control_data.noptmax = -1\n", + "sweep_d = os.path.join(\"hosaki_sweep\")\n", + "pst.pestpp_options[\"ies_include_base\"] = False\n", + "\n", + "pst.write(os.path.join(t_d,\"pest.pst\"))\n", + "port = 5544\n", + "num_workers = 50\n", + "\n", + "pyemu.os_utils.start_workers(t_d,\"pestpp-ies\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=sweep_d,worker_root='.',\n", + " port=port)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "182ae0b3-cb09-41ae-8699-c29299b6170c", + "metadata": {}, + "outputs": [], + "source": [ + "sweep_pe = pd.read_csv(os.path.join(sweep_d,\"pest.0.par.csv\"),index_col=0)\n", + "sweep_oe = pd.read_csv(os.path.join(sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", + "sweep_x = sweep_pe.loc[:,pst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + "sweep_y = sweep_pe.loc[:,pst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", + "sweep_z = sweep_oe.loc[:,pst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30530fcc-e87c-415c-94d6-fa6461cee3a9", + "metadata": {}, + "outputs": [], + "source": [ + "def get_obj_map(ax,_sweep_x,_sweep_y,_sweep_z,label=\"objective function\",levels=[-2,-1,0]): \n", + " cb = ax.pcolormesh(_sweep_x,_sweep_y,_sweep_z)\n", + " plt.colorbar(cb,ax=ax,label=label)\n", + " ax.contour(_sweep_x,_sweep_y,_sweep_z,levels=levels,colors=\"w\")\n", + " ax.set_aspect(\"equal\")\n", + " return ax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1593010d-1caf-4ea6-926e-57d3b468c956", + "metadata": {}, + "outputs": [], + "source": [ + "fig,ax = plt.subplots(1,1,figsize=(6,5))\n", + "_ = get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", + "ax.set_title(\"truth\",loc=\"left\")\n", + "plt.show()\n", + "plt.close(fig)" + ] + }, + { + "cell_type": "markdown", + "id": "f83a0143-3fed-44ef-9f94-60073fbf4a33", + "metadata": {}, + "source": [ + "So that is the true objective function surface with the global minimum at (4,2) and a local minimum at (1,2).\n", + "\n", + "The GPR BO workflow starts by first evaluating an initial population with the \"model\", which here is just the simple hosaki function. In practice, this will be an expensive and complex process based model. To make this demo more interesting, we will limit the decision variable search space to not include the global minimum (and really 10 members in the initial population is pretty crazy small - just for demo!)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4db01f0d-71bd-4cdd-bd0d-d5e870861e19", + "metadata": {}, + "outputs": [], + "source": [ + "m_d = \"hosaki_model_master\"\n", + "if os.path.exists(m_d):\n", + " shutil.rmtree(m_d)\n", + "pst.pestpp_options[\"mou_population_size\"] = 10\n", + "pst.control_data.noptmax = -1\n", + "par = pst.parameter_data\n", + "par.loc[pst.par_names[0],\"parubnd\"] = 3\n", + "par.loc[pst.par_names[0],\"parval1\"] = 1.5\n", + "pst.write(os.path.join(t_d,\"pest.pst\"))\n", + "num_workers = 10\n", + "pyemu.os_utils.start_workers(t_d,\"pestpp-mou\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=m_d,worker_root='.',\n", + " port=port)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8875b40-bfa1-46ad-91d1-9b68338dc03e", + "metadata": {}, + "outputs": [], + "source": [ + "training_dvpop_fname = os.path.join(m_d,\"pest.0.dv_pop.csv\")\n", + "training_opop_fname = os.path.join(m_d,\"pest.0.obs_pop.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d0a1063-66ee-42c9-a7d5-faf303509c69", + "metadata": {}, + "outputs": [], + "source": [ + "training_dvpop = pd.read_csv(training_dvpop_fname,index_col=0)\n", + "training_opop = pd.read_csv(training_opop_fname,index_col=0)\n", + "fig,ax = plt.subplots(1,1,figsize=(6,5))\n", + "get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", + "ax.scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "ax.set_title(\"true objective function surface with training points\")\n", + "plt.show()\n", + "plt.close(fig)\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "89d437f7-1020-4644-ad30-3170a3dd37fc", + "metadata": {}, + "source": [ + "Those white triangles are the places where we have actually run the model - in practice we will never have the colormap or the contours - those are just to help us understand what is happening...\n", + "\n", + "This function is where the magic happens: we use the decision variable population and resulting output/observation population to setup the GPR emulation process. Note that `pyemu.helpers.prep_for_gpr()` function setups and training a GPR emulator for each non-zero weighted observation in the control file... " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1c3ae81-26bb-41c8-bccc-6c32c55c100d", + "metadata": {}, + "outputs": [], + "source": [ + "def prep_for_gpr(dvpops,opops,gpr_t_d,noptmax=-1):\n", + " pyemu.helpers.prep_for_gpr(os.path.join(t_d,\"pest.pst\"),dvpops,opops,gpr_t_d=gpr_t_d,plot_fits=True)\n", + " gpst = pyemu.Pst(os.path.join(gpr_t_d,\"pest.pst\"))\n", + " #shutil.copy2(dvpops[-1],os.path.join(gpr_t_d,\"initial_dv_pop.csv\"))\n", + " #gpst.pestpp_options[\"mou_dv_population_file\"] = \"initial_dv_pop.csv\"\n", + " shutil.copy2(os.path.join(sweep_d,\"sweep_in.csv\"),os.path.join(gpr_t_d,\"sweep_in.csv\"))\n", + " gpst.control_data.noptmax = noptmax\n", + " gpst.pestpp_options[\"ies_include_base\"] = False\n", + " gpst.pestpp_options[\"mou_save_population_every\"] = 1\n", + " gpst.pestpp_options.pop(\"mou_dv_population_file\",None)\n", + " par = gpst.parameter_data\n", + " par.loc[:,\"parlbnd\"] = 0.0\n", + " par.loc[:,\"parubnd\"] = 5.0\n", + " par.loc[:,\"parval1\"] = 2.5 \n", + " \n", + " gpst.write(os.path.join(gpr_t_d,\"pest.pst\"),version=2)\n", + " return gpst" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "251d15a4-c695-4340-81e3-8c7600f6d198", + "metadata": {}, + "outputs": [], + "source": [ + "training_dvpop_fnames = [training_dvpop_fname]\n", + "training_opop_fnames = [training_opop_fname]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eaa524df-9a1e-4975-8c0d-9774e077839f", + "metadata": {}, + "outputs": [], + "source": [ + "gpr_t_d = t_d + \"_gpr\"\n", + "gpst = prep_for_gpr(training_dvpop_fnames,training_opop_fnames,gpr_t_d)" + ] + }, + { + "cell_type": "markdown", + "id": "54f493fb-a1f7-4248-9a5b-b884a7ab9a84", + "metadata": {}, + "source": [ + "Now, just for learning, lets sweep over decision variable space but evaluate the GPR emulated objective function value. You would never do this in practice, but it can be informative:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "595ea30a-df3b-48f4-9375-82dff4f77e58", + "metadata": {}, + "outputs": [], + "source": [ + "gpr_sweep_d = sweep_d+\"_gpr\"\n", + "num_workers = 50\n", + "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-ies\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=gpr_sweep_d,worker_root='.',\n", + " port=port)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e455e009-ff55-487f-ac53-8251589af39c", + "metadata": {}, + "outputs": [], + "source": [ + "sweep_gpr_pe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.par.csv\"),index_col=0)\n", + "sweep_gpr_oe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", + "gpr_sweep_x = sweep_gpr_pe.loc[:,pst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + "gpr_sweep_y = sweep_gpr_pe.loc[:,pst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", + "gpr_sweep_z = sweep_gpr_oe.loc[:,pst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "406fab70-18e1-43e8-8edb-ffea73348dcc", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1,3,figsize=(15,3))\n", + "get_obj_map(axes[0],sweep_x,sweep_y,sweep_z)\n", + "get_obj_map(axes[1],gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", + "axes[1].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "get_obj_map(axes[2],gpr_sweep_x,gpr_sweep_y,sweep_z-gpr_sweep_z,label=\"truth minus emulated\",levels=[-0.5,0,0.5])\n", + "axes[2].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "axes[0].set_title(\"truth\",loc=\"left\")\n", + "axes[1].set_title(\"emulated with training points\",loc=\"left\")\n", + "axes[2].set_title(\"difference with training points\",loc=\"left\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "9668435c-ccb4-486b-8423-d864251d55d0", + "metadata": {}, + "source": [ + "OK! now we can see whats happening - the emulated objective function surface is strongly controlled by the location of the training data points, and, is this case, its not a good representation of the truth surface...yet...\n", + "\n", + "But now lets run pestpp-mou on the GPR emulated model. This is usually quite fast..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6d5d98d-3143-4833-a6de-13a3319d0ca2", + "metadata": {}, + "outputs": [], + "source": [ + "gpst.control_data.noptmax = 8\n", + "gpst.pestpp_options[\"mou_population_size\"] = 20\n", + "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", + "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", + "num_workers = 10\n", + "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-mou\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=gpr_m_d,worker_root='.',\n", + " port=port)" + ] + }, + { + "cell_type": "markdown", + "id": "fd89d01b-d4d2-4f09-a008-c10ae9ab5bf1", + "metadata": {}, + "source": [ + "Now lets plot how the pestpp-mou population evolves across the emulated surface, but also plot it on the true surface just to help us understand what is happening" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a29183f3-3c7c-40de-a068-5035a5cf7bb8", + "metadata": {}, + "outputs": [], + "source": [ + "gpr_dvpops = [os.path.join(gpr_m_d,f) for f in os.listdir(gpr_m_d) if len(f.split('.')) == 4 and f.endswith(\"dv_pop.csv\") and \"archive\" not in f]\n", + "gpr_dvpops_itr = [int(f.split(\".\")[1]) for f in gpr_dvpops]\n", + "gpr_dvpops = {itr:pd.read_csv(f,index_col=0) for itr,f in zip(gpr_dvpops_itr,gpr_dvpops)}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ada547f4-8d3f-4980-94e3-d712998369a7", + "metadata": {}, + "outputs": [], + "source": [ + "for itr in range(max(gpr_dvpops_itr)):\n", + " fig,axes = plt.subplots(1,2,figsize=(10,4))\n", + " ax = axes[0]\n", + " get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", + " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", + " ax.set_title(\"truth generation {0}\".format(itr),loc=\"left\")\n", + " ax = axes[1]\n", + " get_obj_map(ax,gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", + " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", + " ax.set_title(\"emulated generation {0}\".format(itr),loc=\"left\")\n", + " plt.show()\n", + " plt.close(fig)" + ] + }, + { + "cell_type": "markdown", + "id": "738f6e14-b896-48db-8e1e-59dcbf270245", + "metadata": {}, + "source": [ + "So just what you expected? Essentially pestpp-mou converged to the minimum of the objective function we gave it, which is the emulated objective function...\n", + "\n", + "Now this where some more cleverness happens: Lets take that last emulated decision variable population and actually run it thru the complex \"model\" (which in this case is just the hosaki function...). This is so that we can \"in-fill\" our GPR emulator with this new points in decision variable space. In practice, a lot more cleverness needs to happen to actually decide which points, but for this lil demo, it works..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a9a889e-fa11-40d2-a3f8-da29aa7c17d6", + "metadata": {}, + "outputs": [], + "source": [ + "gpr_dvpops[max(gpr_dvpops_itr)].to_csv(os.path.join(t_d,\"retrain_1_dvpop.csv\"))\n", + "pst.pestpp_options[\"mou_dv_population_file\"] = \"retrain_1_dvpop.csv\"\n", + "pst.control_data.noptmax = -1\n", + "pst.write(os.path.join(t_d,\"pest.pst\"))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35a5e11b-9ece-4b87-ba75-d402d113dbd7", + "metadata": {}, + "outputs": [], + "source": [ + "m_d += \"_1\"\n", + "num_workers = 10\n", + "pyemu.os_utils.start_workers(t_d,\"pestpp-mou\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=m_d,worker_root='.',\n", + " port=port)" + ] + }, + { + "cell_type": "markdown", + "id": "e6fbb6ab-6457-4ed9-bba6-42bdeb9d28a8", + "metadata": {}, + "source": [ + "Now load the newly evaluated training points:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26d65204-8abb-4161-baa5-1e5c21d24f27", + "metadata": {}, + "outputs": [], + "source": [ + "training_dvpop_fname1 = os.path.join(m_d,\"pest.0.dv_pop.csv\")\n", + "training_opop_fname1 = os.path.join(m_d,\"pest.0.obs_pop.csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "69f6a35c-9492-4a67-9b53-0f612382a345", + "metadata": {}, + "source": [ + "And combine these new points with the points we already had (the original few places where we ran the model at the beginning):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1018f548-ec0a-446d-a3a3-43cf7723bb64", + "metadata": {}, + "outputs": [], + "source": [ + "training_dvpop1 = pd.read_csv(training_dvpop_fname1,index_col=0)\n", + "training_opop1 = pd.read_csv(training_opop_fname1,index_col=0)\n", + "training_dvpop = pd.concat([training_dvpop,training_dvpop1])\n", + "training_opop = pd.concat([training_opop,training_opop1])\n", + "\n", + "fig,ax = plt.subplots(1,1,figsize=(6,5))\n", + "get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", + "ax.scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "plt.show()\n", + "plt.close(fig)" + ] + }, + { + "cell_type": "markdown", + "id": "225d90ef-2cff-4799-b745-2e9a033606f7", + "metadata": {}, + "source": [ + "See how we have combined the original points and the new points?\n", + "\n", + "Now lets re-do the GPR training process with this combined decision variable and output/observation population" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8046665-4e0b-4f0f-a197-b0be3b4f7a4c", + "metadata": {}, + "outputs": [], + "source": [ + "training_dvpop_fnames.append(training_dvpop_fname1)\n", + "training_opop_fnames.append(training_opop_fname1)\n", + "gpr_t_d = t_d + \"_gpr1\"\n", + "gpst = prep_for_gpr(training_dvpop_fnames,training_opop_fnames,gpr_t_d)" + ] + }, + { + "cell_type": "markdown", + "id": "ff2b2dac-5acb-4f27-b0a7-0b4c6e419222", + "metadata": {}, + "source": [ + "Ok, now let's also re-sweep over the emulated surface to see how much better our approximation to the true objective function surface:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1709b80c-62d5-4e6c-ad6f-9b7c565ac78f", + "metadata": {}, + "outputs": [], + "source": [ + "gpr_sweep_d = sweep_d+\"_gpr1\"\n", + "num_workers = 50\n", + "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-ies\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=gpr_sweep_d,worker_root='.',\n", + " port=port)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4070d242-2405-4f4f-979a-d19777477d0b", + "metadata": {}, + "outputs": [], + "source": [ + "sweep_gpr_pe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.par.csv\"),index_col=0)\n", + "sweep_gpr_oe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", + "gpr_sweep_x = sweep_gpr_pe.loc[:,pst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + "gpr_sweep_y = sweep_gpr_pe.loc[:,pst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", + "gpr_sweep_z = sweep_gpr_oe.loc[:,pst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + "fig, axes = plt.subplots(1,3,figsize=(15,3))\n", + "get_obj_map(axes[0],sweep_x,sweep_y,sweep_z)\n", + "get_obj_map(axes[1],gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", + "axes[1].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "get_obj_map(axes[2],gpr_sweep_x,gpr_sweep_y,sweep_z-gpr_sweep_z,label=\"truth minus emulated\",levels=[-0.5,0,0.5])\n", + "axes[2].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "axes[0].set_title(\"truth\",loc=\"left\")\n", + "axes[1].set_title(\"emulated with training points\",loc=\"left\")\n", + "axes[2].set_title(\"difference with training points\",loc=\"left\")" + ] + }, + { + "cell_type": "markdown", + "id": "9d986519-b800-444f-965a-7dff9a589b9a", + "metadata": {}, + "source": [ + "Comparing this to the original emulated surface, we can see it has gotten much better...\n", + "\n", + "Now let's run pestpp-mou on this improved emulated model and visualize how pestpp-mou navigates this new emulated surface:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e3cef15-0ac7-4ffb-9cd4-b7106106b954", + "metadata": {}, + "outputs": [], + "source": [ + "gpst.control_data.noptmax = 8\n", + "gpst.pestpp_options[\"mou_population_size\"] = 20\n", + "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", + "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", + "num_workers = 10\n", + "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-mou\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=gpr_m_d,worker_root='.',\n", + " port=port)\n", + "gpr_dvpops = [os.path.join(gpr_m_d,f) for f in os.listdir(gpr_m_d) if len(f.split('.')) == 4 and f.endswith(\"dv_pop.csv\") and \"archive\" not in f]\n", + "gpr_dvpops_itr = [int(f.split(\".\")[1]) for f in gpr_dvpops]\n", + "\n", + "gpr_dvpops = {itr:pd.read_csv(f,index_col=0) for itr,f in zip(gpr_dvpops_itr,gpr_dvpops)}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc8b4941-4f89-4cc6-b39e-45e5254ce8d2", + "metadata": {}, + "outputs": [], + "source": [ + "for itr in range(max(gpr_dvpops_itr)):\n", + " fig,axes = plt.subplots(1,2,figsize=(10,4))\n", + " ax = axes[0]\n", + " get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", + " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", + " ax.set_title(\"truth generation {0}\".format(itr),loc=\"left\")\n", + " ax = axes[1]\n", + " get_obj_map(ax,gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", + " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", + " ax.set_title(\"emulated generation {0}\".format(itr),loc=\"left\")\n", + " plt.show()\n", + " plt.close(fig)" + ] + }, + { + "cell_type": "markdown", + "id": "49d72679-5b94-4a04-bd1a-43f4bf812f45", + "metadata": {}, + "source": [ + "Now lets do that whole process a few times:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3e78ef6-1bf0-4c05-9688-82c578f0ab77", + "metadata": {}, + "outputs": [], + "source": [ + "i = 2\n", + "gpr_t_d = t_d + \"_gpr{0}\".format(i-1)\n", + "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", + "gpr_dvpops = [os.path.join(gpr_m_d,f) for f in os.listdir(gpr_m_d) if len(f.split('.')) == 4 and f.endswith(\"dv_pop.csv\") and \"archive\" not in f]\n", + "gpr_dvpops_itr = [int(f.split(\".\")[1]) for f in gpr_dvpops]\n", + "gpr_dvpops = {itr:pd.read_csv(f,index_col=0) for itr,f in zip(gpr_dvpops_itr,gpr_dvpops)}\n", + "gpr_dvpops[max(gpr_dvpops_itr)].to_csv(os.path.join(t_d,\"retrain_{0}_dvpop.csv\".format(i)))\n", + "pst.pestpp_options[\"mou_dv_population_file\"] = \"retrain_{0}_dvpop.csv\".format(i)\n", + "pst.control_data.noptmax = -1\n", + "pst.write(os.path.join(t_d,\"pest.pst\"))\n", + "m_d = \"hosaki_model_{0}\".format(i)\n", + "num_workers = 10\n", + "pyemu.os_utils.start_workers(t_d,\"pestpp-mou\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=m_d,worker_root='.',\n", + " port=port)\n", + "\n", + "training_dvpop_fnamei = os.path.join(m_d,\"pest.0.dv_pop.csv\")\n", + "training_opop_fnamei = os.path.join(m_d,\"pest.0.obs_pop.csv\")\n", + "\n", + "training_dvpopi = pd.read_csv(training_dvpop_fnamei,index_col=0)\n", + "training_opopi = pd.read_csv(training_opop_fnamei,index_col=0)\n", + "training_dvpop = pd.concat([training_dvpop,training_dvpopi])\n", + "training_opop = pd.concat([training_opop,training_opopi])\n", + "\n", + "fig,ax = plt.subplots(1,1,figsize=(6,5))\n", + "get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", + "ax.scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "plt.show()\n", + "plt.close(fig)\n", + "\n", + "training_dvpop_fnames.append(training_dvpop_fnamei)\n", + "training_opop_fnames.append(training_opop_fnamei)\n", + "\n", + "gpr_t_d = t_d + \"_gpr{0}\".format(i)\n", + "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", + "gpst = prep_for_gpr(training_dvpop_fnames,training_opop_fnames,gpr_t_d)\n", + "gpr_sweep_d = sweep_d+\"_gpr{0}\".format(i)\n", + "num_workers = 50\n", + "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-ies\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=gpr_sweep_d,worker_root='.',\n", + " port=port)\n", + "\n", + "sweep_gpr_pe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.par.csv\"),index_col=0)\n", + "sweep_gpr_oe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", + "gpr_sweep_x = sweep_gpr_pe.loc[:,pst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + "gpr_sweep_y = sweep_gpr_pe.loc[:,pst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", + "gpr_sweep_z = sweep_gpr_oe.loc[:,pst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + "fig, axes = plt.subplots(1,3,figsize=(15,3))\n", + "get_obj_map(axes[0],sweep_x,sweep_y,sweep_z)\n", + "get_obj_map(axes[1],gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", + "axes[1].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "get_obj_map(axes[2],gpr_sweep_x,gpr_sweep_y,sweep_z-gpr_sweep_z,label=\"truth minus emulated\",levels=[-0.5,0,0.5])\n", + "axes[2].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "axes[0].set_title(\"truth\",loc=\"left\")\n", + "axes[1].set_title(\"emulated with training points\",loc=\"left\")\n", + "axes[2].set_title(\"difference with training points\",loc=\"left\")\n", + "plt.show()\n", + "plt.close(fig)\n", + "\n", + "gpst.control_data.noptmax = 8\n", + "gpst.pestpp_options[\"mou_population_size\"] = 20\n", + "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", + "\n", + "num_workers = 10\n", + "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-mou\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=gpr_m_d,worker_root='.',\n", + " port=port)\n", + "\n", + "\n", + "\n", + "for itr in range(max(gpr_dvpops_itr)):\n", + " fig,axes = plt.subplots(1,2,figsize=(10,4))\n", + " ax = axes[0]\n", + " get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", + " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", + " ax.set_title(\"truth generation {0}\".format(itr),loc=\"left\")\n", + " ax = axes[1]\n", + " get_obj_map(ax,gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", + " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", + " ax.set_title(\"emulated generation {0}\".format(itr),loc=\"left\")\n", + " plt.show()\n", + " plt.close(fig)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6ec140f-024a-444d-aa7b-766d6733b26a", + "metadata": {}, + "outputs": [], + "source": [ + "i = 3\n", + "gpr_t_d = t_d + \"_gpr{0}\".format(i-1)\n", + "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", + "gpr_dvpops = [os.path.join(gpr_m_d,f) for f in os.listdir(gpr_m_d) if len(f.split('.')) == 4 and f.endswith(\"dv_pop.csv\") and \"archive\" not in f]\n", + "gpr_dvpops_itr = [int(f.split(\".\")[1]) for f in gpr_dvpops]\n", + "gpr_dvpops = {itr:pd.read_csv(f,index_col=0) for itr,f in zip(gpr_dvpops_itr,gpr_dvpops)}\n", + "gpr_dvpops[max(gpr_dvpops_itr)].to_csv(os.path.join(t_d,\"retrain_{0}_dvpop.csv\".format(i)))\n", + "pst.pestpp_options[\"mou_dv_population_file\"] = \"retrain_{0}_dvpop.csv\".format(i)\n", + "pst.control_data.noptmax = -1\n", + "pst.write(os.path.join(t_d,\"pest.pst\"))\n", + "m_d = \"hosaki_model_{0}\".format(i)\n", + "num_workers = 10\n", + "pyemu.os_utils.start_workers(t_d,\"pestpp-mou\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=m_d,worker_root='.',\n", + " port=port)\n", + "\n", + "training_dvpop_fnamei = os.path.join(m_d,\"pest.0.dv_pop.csv\")\n", + "training_opop_fnamei = os.path.join(m_d,\"pest.0.obs_pop.csv\")\n", + "\n", + "training_dvpopi = pd.read_csv(training_dvpop_fnamei,index_col=0)\n", + "training_opopi = pd.read_csv(training_opop_fnamei,index_col=0)\n", + "training_dvpop = pd.concat([training_dvpop,training_dvpopi])\n", + "training_opop = pd.concat([training_opop,training_opopi])\n", + "\n", + "fig,ax = plt.subplots(1,1,figsize=(6,5))\n", + "get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", + "ax.scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "plt.show()\n", + "plt.close(fig)\n", + "\n", + "training_dvpop_fnames.append(training_dvpop_fnamei)\n", + "training_opop_fnames.append(training_opop_fnamei)\n", + "gpr_t_d = t_d + \"_gpr{0}\".format(i)\n", + "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", + "\n", + "gpst = prep_for_gpr(training_dvpop_fnames,training_opop_fnames,gpr_t_d)\n", + "gpr_sweep_d = sweep_d+\"_gpr{0}\".format(i)\n", + "num_workers = 50\n", + "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-ies\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=gpr_sweep_d,worker_root='.',\n", + " port=port)\n", + "\n", + "sweep_gpr_pe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.par.csv\"),index_col=0)\n", + "sweep_gpr_oe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", + "gpr_sweep_x = sweep_gpr_pe.loc[:,pst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + "gpr_sweep_y = sweep_gpr_pe.loc[:,pst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", + "gpr_sweep_z = sweep_gpr_oe.loc[:,pst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + "fig, axes = plt.subplots(1,3,figsize=(15,3))\n", + "get_obj_map(axes[0],sweep_x,sweep_y,sweep_z)\n", + "get_obj_map(axes[1],gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", + "axes[1].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "get_obj_map(axes[2],gpr_sweep_x,gpr_sweep_y,sweep_z-gpr_sweep_z,label=\"truth minus emulated\",levels=[-0.5,0,0.5])\n", + "axes[2].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "axes[0].set_title(\"truth\",loc=\"left\")\n", + "axes[1].set_title(\"emulated with training points\",loc=\"left\")\n", + "axes[2].set_title(\"difference with training points\",loc=\"left\")\n", + "plt.show()\n", + "plt.close(fig)\n", + "\n", + "gpst.control_data.noptmax = 8\n", + "gpst.pestpp_options[\"mou_population_size\"] = 20\n", + "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", + "\n", + "num_workers = 10\n", + "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-mou\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=gpr_m_d,worker_root='.',\n", + " port=port)\n", + "\n", + "\n", + "\n", + "for itr in range(max(gpr_dvpops_itr)):\n", + " fig,axes = plt.subplots(1,2,figsize=(10,4))\n", + " ax = axes[0]\n", + " get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", + " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", + " ax.set_title(\"truth generation {0}\".format(itr),loc=\"left\")\n", + " ax = axes[1]\n", + " get_obj_map(ax,gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", + " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", + " ax.set_title(\"emulated generation {0}\".format(itr),loc=\"left\")\n", + " plt.show()\n", + " plt.close(fig)" + ] + }, + { + "cell_type": "markdown", + "id": "7ab00c33-17a8-48a6-ae19-7e5c63136bc1", + "metadata": {}, + "source": [ + "And one last time...and this time with more emulator generations to polish things off..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cef02969-f1b9-431c-a1ef-cca2612d8a3b", + "metadata": {}, + "outputs": [], + "source": [ + "i = 4\n", + "gpr_t_d = t_d + \"_gpr{0}\".format(i-1)\n", + "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", + "gpr_dvpops = [os.path.join(gpr_m_d,f) for f in os.listdir(gpr_m_d) if len(f.split('.')) == 4 and f.endswith(\"dv_pop.csv\") and \"archive\" not in f]\n", + "gpr_dvpops_itr = [int(f.split(\".\")[1]) for f in gpr_dvpops]\n", + "gpr_dvpops = {itr:pd.read_csv(f,index_col=0) for itr,f in zip(gpr_dvpops_itr,gpr_dvpops)}\n", + "gpr_dvpops[max(gpr_dvpops_itr)].to_csv(os.path.join(t_d,\"retrain_{0}_dvpop.csv\".format(i)))\n", + "pst.pestpp_options[\"mou_dv_population_file\"] = \"retrain_{0}_dvpop.csv\".format(i)\n", + "pst.control_data.noptmax = -1\n", + "pst.write(os.path.join(t_d,\"pest.pst\"))\n", + "m_d = \"hosaki_model_{0}\".format(i)\n", + "num_workers = 10\n", + "pyemu.os_utils.start_workers(t_d,\"pestpp-mou\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=m_d,worker_root='.',\n", + " port=port)\n", + "\n", + "training_dvpop_fnamei = os.path.join(m_d,\"pest.0.dv_pop.csv\")\n", + "training_opop_fnamei = os.path.join(m_d,\"pest.0.obs_pop.csv\")\n", + "\n", + "training_dvpopi = pd.read_csv(training_dvpop_fnamei,index_col=0)\n", + "training_opopi = pd.read_csv(training_opop_fnamei,index_col=0)\n", + "training_dvpop = pd.concat([training_dvpop,training_dvpopi])\n", + "training_opop = pd.concat([training_opop,training_opopi])\n", + "\n", + "fig,ax = plt.subplots(1,1,figsize=(6,5))\n", + "get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", + "ax.scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "plt.show()\n", + "plt.close(fig)\n", + "\n", + "training_dvpop_fnames.append(training_dvpop_fnamei)\n", + "training_opop_fnames.append(training_opop_fnamei)\n", + "gpr_t_d = t_d + \"_gpr{0}\".format(i)\n", + "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", + "gpst = prep_for_gpr(training_dvpop_fnames,training_opop_fnames,gpr_t_d)\n", + "gpr_sweep_d = sweep_d+\"_gpr{0}\".format(i)\n", + "num_workers = 50\n", + "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-ies\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=gpr_sweep_d,worker_root='.',\n", + " port=port)\n", + "\n", + "sweep_gpr_pe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.par.csv\"),index_col=0)\n", + "sweep_gpr_oe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", + "gpr_sweep_x = sweep_gpr_pe.loc[:,pst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + "gpr_sweep_y = sweep_gpr_pe.loc[:,pst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", + "gpr_sweep_z = sweep_gpr_oe.loc[:,pst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + "fig, axes = plt.subplots(1,3,figsize=(15,3))\n", + "get_obj_map(axes[0],sweep_x,sweep_y,sweep_z)\n", + "get_obj_map(axes[1],gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", + "axes[1].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "get_obj_map(axes[2],gpr_sweep_x,gpr_sweep_y,sweep_z-gpr_sweep_z,label=\"truth minus emulated\",levels=[-0.5,0,0.5])\n", + "axes[2].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + "axes[0].set_title(\"truth\",loc=\"left\")\n", + "axes[1].set_title(\"emulated with training points\",loc=\"left\")\n", + "axes[2].set_title(\"difference with training points\",loc=\"left\")\n", + "plt.show()\n", + "plt.close(fig)\n", + "\n", + "gpst.control_data.noptmax = 15\n", + "gpst.pestpp_options[\"mou_population_size\"] = 20\n", + "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", + "\n", + "num_workers = 10\n", + "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-mou\",\"pest.pst\",\n", + " num_workers=num_workers,\n", + " master_dir=gpr_m_d,worker_root='.',\n", + " port=port)\n", + "\n", + "\n", + "\n", + "for itr in range(max(gpr_dvpops_itr)):\n", + " fig,axes = plt.subplots(1,2,figsize=(10,4))\n", + " ax = axes[0]\n", + " get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", + " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", + " ax.set_title(\"truth generation {0}\".format(itr),loc=\"left\")\n", + " ax = axes[1]\n", + " get_obj_map(ax,gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", + " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", + " ax.set_title(\"emulated generation {0}\".format(itr),loc=\"left\")\n", + " plt.show()\n", + " plt.close(fig)" + ] + }, + { + "cell_type": "markdown", + "id": "5aaeba17-14dc-4da9-8528-e64445ac7e41", + "metadata": {}, + "source": [ + "There you have it - after evaluating only a few populations with the complex process-based model (here that was only the hosaki function), we can effectively optimize a function with the otherwise every expensive global solvers in pestpp-mou." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31eeeee5-f54e-4dfd-8ba7-717108066370", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9ecef8ba4f7424323c32a5ba6bc0518f21b796ec Mon Sep 17 00:00:00 2001 From: jwhite Date: Wed, 20 Nov 2024 15:25:57 +0800 Subject: [PATCH 13/20] more work on hosaki --- examples/gpr_emulation_hosaki.ipynb | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/examples/gpr_emulation_hosaki.ipynb b/examples/gpr_emulation_hosaki.ipynb index e36d26d6..883bdaae 100644 --- a/examples/gpr_emulation_hosaki.ipynb +++ b/examples/gpr_emulation_hosaki.ipynb @@ -150,10 +150,15 @@ "metadata": {}, "outputs": [], "source": [ - "def get_obj_map(ax,_sweep_x,_sweep_y,_sweep_z,label=\"objective function\",levels=[-2,-1,0]): \n", - " cb = ax.pcolormesh(_sweep_x,_sweep_y,_sweep_z)\n", + "def get_obj_map(ax,_sweep_x,_sweep_y,_sweep_z,label=\"objective function\",levels=[-2,-1,0,0.5]): \n", + " vmin,vmax = None,None\n", + " if min(levels) == -2 and max(levels) == 0.5:\n", + " vmin = -2\n", + " vmax = 0.5\n", + " cb = ax.pcolormesh(_sweep_x,_sweep_y,_sweep_z,vmin=vmin,vmax=vmax)\n", " plt.colorbar(cb,ax=ax,label=label)\n", " ax.contour(_sweep_x,_sweep_y,_sweep_z,levels=levels,colors=\"w\")\n", + " \n", " ax.set_aspect(\"equal\")\n", " return ax" ] @@ -934,6 +939,14 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d58eb64-1e67-4d01-b7c9-2d024b0806a0", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From caf43e3c324fb2b507160933cb1196da67c14417 Mon Sep 17 00:00:00 2001 From: jwhite Date: Mon, 25 Nov 2024 12:36:31 +0800 Subject: [PATCH 14/20] more work on gpr --- autotest/pst_from_tests.py | 27 +- .../utils/constr_template/additive_par.dat | 4 + .../constr_template/additive_par.dat.tpl | 5 + autotest/utils/constr_template/constr.pst | 41 ++ autotest/utils/constr_template/dv.dat | 2 + autotest/utils/constr_template/dv.dat.tpl | 3 + autotest/utils/constr_template/forward_run.py | 29 ++ autotest/utils/constr_template/obj.dat | 2 + autotest/utils/constr_template/obj.dat.ins | 3 + autotest/utils_tests.py | 198 ++++++++- examples/gpr_emulation_hosaki.ipynb | 393 ++++++++++-------- pyemu/pst/pst_utils.py | 2 +- pyemu/utils/helpers.py | 31 +- 13 files changed, 530 insertions(+), 210 deletions(-) create mode 100644 autotest/utils/constr_template/additive_par.dat create mode 100644 autotest/utils/constr_template/additive_par.dat.tpl create mode 100644 autotest/utils/constr_template/constr.pst create mode 100644 autotest/utils/constr_template/dv.dat create mode 100644 autotest/utils/constr_template/dv.dat.tpl create mode 100644 autotest/utils/constr_template/forward_run.py create mode 100644 autotest/utils/constr_template/obj.dat create mode 100644 autotest/utils/constr_template/obj.dat.ins diff --git a/autotest/pst_from_tests.py b/autotest/pst_from_tests.py index d73c6d81..b1cc8d67 100644 --- a/autotest/pst_from_tests.py +++ b/autotest/pst_from_tests.py @@ -5169,6 +5169,7 @@ def plot_thresh(m_d): prarr = np.zeros((nrow,ncol)) - 1 prarr[kobs.i,kobs.j] = pr_oe.loc[real,kobs.obsnme] prarr[ib==0] = np.nan + print(pt_oe) ptarr = np.zeros((nrow, ncol)) - 1 ptarr[kobs.i, kobs.j] = pt_oe.loc[real, kobs.obsnme] ptarr[ib == 0] = np.nan @@ -5720,8 +5721,8 @@ def mf6_freyberg_ppu_hyperpars_thresh_invest(tmp_path): import flopy # except: # return - # import sys - # sys.path.insert(0,os.path.join("..","..","pypestutils")) + import sys + sys.path.insert(0,os.path.join("..","..","pypestutils")) import pypestutils as ppu @@ -5786,7 +5787,7 @@ def mf6_freyberg_ppu_hyperpars_thresh_invest(tmp_path): ymn = m.modelgrid.yvertices.min() ymx = m.modelgrid.yvertices.max() - numpp = 20 + numpp = 30 xvals = np.random.uniform(xmn,xmx,numpp) yvals = np.random.uniform(ymn, ymx, numpp) pp_locs = pd.DataFrame({"x":xvals,"y":yvals}) @@ -5966,9 +5967,9 @@ def mf6_freyberg_ppu_hyperpars_thresh_invest(tmp_path): assert cat1par.shape[0] == num_cat_arrays assert cat2par.shape[0] == num_cat_arrays - par.loc[cat1par, "parval1"] = 0.8 + par.loc[cat1par, "parval1"] = 0.9 par.loc[cat1par, "parubnd"] = 1.0 - par.loc[cat1par, "parlbnd"] = 0.6 + par.loc[cat1par, "parlbnd"] = 0.8 par.loc[cat1par,"partrans"] = "none" # since the apply method only looks that first proportion, we can just fix this one @@ -5985,7 +5986,7 @@ def mf6_freyberg_ppu_hyperpars_thresh_invest(tmp_path): print(pst.npar,pst.npar_adj) org_par = par.copy() - num_reals = 100 + num_reals = 200 pe = pf.draw(num_reals, use_specsim=False) pe.enforce() print(pe.shape) @@ -6047,14 +6048,17 @@ def mf6_freyberg_ppu_hyperpars_thresh_invest(tmp_path): # reset away from the truth... pst.parameter_data.loc[:,"parval1"] = org_par.parval1.values.copy() - pst.control_data.noptmax = 1 + pst.control_data.noptmax = 13 pst.pestpp_options["ies_par_en"] = "prior.jcb" - pst.pestpp_options["ies_num_reals"] = 30 + pst.pestpp_options["ies_num_reals"] = 200 pst.pestpp_options["ies_subset_size"] = -10 pst.pestpp_options["ies_no_noise"] = True - #pst.pestpp_options["ies_bad_phi_sigma"] = 2.0 + pst.pestpp_options["ies_bad_phi_sigma"] = 1.5 pst.pestpp_options["overdue_giveup_fac"] = 100.0 pst.pestpp_options["ies_multimodal_alpha"] = 0.99 + pst.pestpp_options["ies_n_iter_reinflate"] = 3 + + #pst.pestpp_options["panther_agent_freeze_on_fail"] = True #pst.write(os.path.join(pf.new_d, "freyberg.pst")) @@ -6095,12 +6099,13 @@ def mf6_freyberg_ppu_hyperpars_thresh_invest(tmp_path): if __name__ == "__main__": - mf6_freyberg_pp_locs_test('.') + #mf6_freyberg_pp_locs_test('.') #mf6_subdir_test(".") #mf6_freyberg_ppu_hyperpars_invest(".") + #mf6_freyberg_ppu_hyperpars_thresh_invest(".") #while True: # mf6_freyberg_thresh_test(".") - #plot_thresh("master_thresh_nonstat") + plot_thresh("master_thresh_nonstat") #mf6_freyberg_thresh_test(".") #plot_thresh("master_thresh_nonstat") #plot_thresh("master_thresh_nonstat_nim") diff --git a/autotest/utils/constr_template/additive_par.dat b/autotest/utils/constr_template/additive_par.dat new file mode 100644 index 00000000..ca5916f8 --- /dev/null +++ b/autotest/utils/constr_template/additive_par.dat @@ -0,0 +1,4 @@ +obj1_add_par 0.00000000000000e+00 +obj2_add_par 0.00000000000000e+00 +constr1_add_par 0.00000000000000000e+00 +constr2_add_par 0.00000000000000000e+00 diff --git a/autotest/utils/constr_template/additive_par.dat.tpl b/autotest/utils/constr_template/additive_par.dat.tpl new file mode 100644 index 00000000..e8e9cc5f --- /dev/null +++ b/autotest/utils/constr_template/additive_par.dat.tpl @@ -0,0 +1,5 @@ +ptf ~ +obj1_add_par ~ obj1_add_par ~ +obj2_add_par ~ obj2_add_par ~ +constr1_add_par ~ constr1_add_par ~ +constr2_add_par ~ constr2_add_par ~ diff --git a/autotest/utils/constr_template/constr.pst b/autotest/utils/constr_template/constr.pst new file mode 100644 index 00000000..c7f97f51 --- /dev/null +++ b/autotest/utils/constr_template/constr.pst @@ -0,0 +1,41 @@ +pcf +* control data + restart estimation + 6 2 2 2 2 + 2 1 single point 1 + 2.000000E+01 -3.000000E+00 3.000000E-01 1.000000E-02 -7 + 1.000000E+01 1.000000E+01 1.000000E-03 + 1.000000E-01 + 0 1.000000E-02 3 3 1.000000E-02 3 + 0 0 0 +* singular value decomposition + 1 + 10000000 1.000000E-06 +1 +* parameter groups +obj_add absolute 1.0000000000E-02 0.0 switch 2.0000000000E+00 parabolic 1.0000000000E-05 5.0000000000E-01 smaller +decvars relative 1.0000000000E-02 0.0 switch 2.0000000000E+00 parabolic 1.0000000000E-05 5.0000000000E-01 smaller +* parameter data +dv_0 none relative 5.0000000000E-01 1.0000000000E-01 1.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +dv_1 none relative 2.5000000000E+00 0.0000000000E+00 5.0000000000E+00 decvars 1.0000000000E+00 0.0000000000E+00 1 +constr1_add_par none relative 0.0000000000E+00 -5.0000000000E-01 5.0000000000E-01 obj_add 1.0000000000E+00 0.0000000000E+00 1 +constr2_add_par none relative 0.0000000000E+00 -5.0000000000E-01 5.0000000000E-01 obj_add 1.0000000000E+00 0.0000000000E+00 1 +obj1_add_par none relative 0.0000000000E+00 -5.0000000000E-01 5.0000000000E-01 obj_add 1.0000000000E+00 0.0000000000E+00 1 +obj2_add_par none relative 0.0000000000E+00 -5.0000000000E-01 5.0000000000E-01 obj_add 1.0000000000E+00 0.0000000000E+00 1 +* observation groups +less_than +greater_than +* observation data +obj_1 5.0000000000E-01 1.0000000000E+00 less_than +obj_2 3.0000000000E+00 1.0000000000E+00 less_than +* model command line +python forward_run.py +* model input/output +./dv.dat.tpl ./dv.dat +./additive_par.dat.tpl ./additive_par.dat +./obj.dat.ins ./obj.dat +* prior information +const_1 9.0 * dv_0 + 1.0 * dv_1 = 6.0 1.0000000000E+00 greater_than +const_2 9.0 * dv_0 - 1.0 * dv_1 = 1.0 1.0000000000E+00 greater_than +++opt_dec_var_groups(decvars) +++mou_objectives(obj_1,obj_2) diff --git a/autotest/utils/constr_template/dv.dat b/autotest/utils/constr_template/dv.dat new file mode 100644 index 00000000..ac9ebec5 --- /dev/null +++ b/autotest/utils/constr_template/dv.dat @@ -0,0 +1,2 @@ +dv_0 0.5000000000 +dv_1 2.5000000000 diff --git a/autotest/utils/constr_template/dv.dat.tpl b/autotest/utils/constr_template/dv.dat.tpl new file mode 100644 index 00000000..138fe8fe --- /dev/null +++ b/autotest/utils/constr_template/dv.dat.tpl @@ -0,0 +1,3 @@ +ptf ~ +dv_0 ~ dv_0 ~ +dv_1 ~ dv_1 ~ diff --git a/autotest/utils/constr_template/forward_run.py b/autotest/utils/constr_template/forward_run.py new file mode 100644 index 00000000..155fbef6 --- /dev/null +++ b/autotest/utils/constr_template/forward_run.py @@ -0,0 +1,29 @@ +import os +import numpy as np +import pandas as pd +def constr(x): + return (x[0],(1 + x[1]) / x[0]),[] + +def helper(func): + pdf = pd.read_csv("dv.dat",delim_whitespace=True,index_col=0, header=None, names=["parnme","parval1"]) + #obj1,obj2 = func(pdf.values) + objs,constrs = func(pdf.values) + + if os.path.exists("additive_par.dat"): + obj1,obj2 = objs[0],objs[1] + cdf = pd.read_csv("additive_par.dat", delim_whitespace=True, index_col=0,header=None, names=["parnme","parval1"]) + obj1[0] += cdf.parval1.values[0] + obj2[0] += cdf.parval1.values[1] + for i in range(len(constrs)): + constrs[i] += cdf.parval1.values[i+2] + + with open("obj.dat",'w') as f: + for i,obj in enumerate(objs): + f.write("obj_{0} {1}\n".format(i+1,float(obj))) + #f.write("obj_2 {0}\n".format(float(obj2))) + for i,constr in enumerate(constrs): + f.write("constr_{0} {1}\n".format(i+1,float(constr))) + return objs,constrs + +if __name__ == '__main__': + helper(constr) diff --git a/autotest/utils/constr_template/obj.dat b/autotest/utils/constr_template/obj.dat new file mode 100644 index 00000000..fba58378 --- /dev/null +++ b/autotest/utils/constr_template/obj.dat @@ -0,0 +1,2 @@ +obj_1 0.5 +obj_2 7.0 diff --git a/autotest/utils/constr_template/obj.dat.ins b/autotest/utils/constr_template/obj.dat.ins new file mode 100644 index 00000000..f2e2a1b7 --- /dev/null +++ b/autotest/utils/constr_template/obj.dat.ins @@ -0,0 +1,3 @@ +pif ~ +l1 w !obj_1! +l1 w !obj_2! diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 0aa76f9f..4f2f4a09 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2654,6 +2654,7 @@ def ppu_geostats_test(tmp_path): # exit() def gpr_compare_invest(): + import numpy as np from sklearn.gaussian_process import GaussianProcessRegressor case = "zdt1" use_chances = False @@ -2676,10 +2677,11 @@ def gpr_compare_invest(): else: pst.pestpp_options["opt_risk"] = 0.5 - pop_size = 100 - num_workers = 100 - noptmax_full = 10 - noptmax_inner = 5 + pop_size = 60 + num_workers = 60 + noptmax_full = 30 + noptmax_inner = 10 + noptmax_outer = 5 port = 4554 pst.control_data.noptmax = noptmax_full pst.pestpp_options["mou_population_size"] = pop_size @@ -2696,13 +2698,13 @@ def gpr_compare_invest(): pst_fname = os.path.join(m_d,case+".pst") gpr_t_d = os.path.join(case+"_gpr_template") - pyemu.helpers.prep_for_gpr(pst_fname,dv_pops,obs_pops,gpr_t_d=gpr_t_d,nverf=int(pop_size*.1),plot_fits=True) + pyemu.helpers.prep_for_gpr(pst_fname,dv_pops,obs_pops,gpr_t_d=gpr_t_d,nverf=int(pop_size*.1),\ + plot_fits=True,apply_standard_scalar=False,include_emulated_std_obs=True) gpst = pyemu.Pst(os.path.join(gpr_t_d,case+".pst")) shutil.copy2(os.path.join(m_d,case+".0.dv_pop.csv"),os.path.join(gpr_t_d,"initial_dv_pop.csv")) gpst.pestpp_options["mou_dv_population_file"] = "initial_dv_pop.csv" gpst.control_data.noptmax = noptmax_full gpst.write(os.path.join(gpr_t_d,case+".pst"),version=2) - gpr_m_d = gpr_t_d.replace("template","master") if os.path.exists(gpr_m_d): shutil.rmtree(gpr_m_d) @@ -2733,7 +2735,7 @@ def gpr_compare_invest(): if os.path.exists(gpr_t_d_iter): shutil.rmtree(gpr_t_d_iter) shutil.copytree(gpr_t_d,gpr_t_d_iter) - for iouter in range(1,4): + for iouter in range(1,noptmax_outer+1): #run the gpr emulator gpr_m_d_iter = gpr_t_d_iter.replace("template","master") complex_m_d_iter = t_d.replace("template", "master_complex_retrain_outeriter{0}".format(iouter)) @@ -2785,8 +2787,11 @@ def gpr_compare_invest(): #o2 = o2.loc[o2.is_feasible==True,:] o2 = o2.loc[o2.nsga2_front == 1, :] fig, ax = plt.subplots(1, 1, figsize=(5, 5)) - ax.scatter(o1.obj_1, o1.obj_2,c="r",s=10) - ax.scatter(o2.obj_1, o2.obj_2,c="0.5",s=10,alpha=0.5) + ax.scatter(o1.obj_1, o1.obj_2,c="r",s=10,label="full complex") + ax.scatter(o2.obj_1, o2.obj_2,c="0.5",s=10,alpha=0.5,label="mixed emulated-complex") + ax.legend(loc="upper right") + ax.set_xlim(0,10) + ax.set_ylim(0,20) plt.tight_layout() plt.savefig("gpr_{0}_compare_iterscheme_{1}.pdf".format(case,iouter)) plt.close(fig) @@ -2796,7 +2801,8 @@ def gpr_compare_invest(): dv_pops.append(os.path.join(complex_m_d_iter,case+".0.dv_pop.csv")) obs_pops.append(os.path.join(complex_m_d_iter,case+".0.obs_pop.csv")) gpr_t_d_iter = gpr_t_d+"_outeriter{0}".format(iouter) - pyemu.helpers.prep_for_gpr(pst_fname,dv_pops,obs_pops,gpr_t_d=gpr_t_d_iter,nverf=int(pop_size*.1),plot_fits=True) + pyemu.helpers.prep_for_gpr(pst_fname,dv_pops,obs_pops,gpr_t_d=gpr_t_d_iter,nverf=int(pop_size*.1), + plot_fits=True,apply_standard_scalar=False,include_emulated_std_obs=True) gpst_iter = pyemu.Pst(os.path.join(gpr_t_d_iter,case+".pst")) #aggdf = pd.read_csv(os.path.join(gpr_t_d,"gpr_aggregate_training_data.csv"),index_col=0) #aggdf.index = ["outeriter{0}_member{1}".format(iouter,i) for i in range(aggdf.shape[0])] @@ -2808,9 +2814,179 @@ def gpr_compare_invest(): gpst_iter.write(os.path.join(gpr_t_d_iter,case+".pst"),version=2) +def gpr_constr_test(): + import numpy as np + from sklearn.gaussian_process import GaussianProcessRegressor + case = "constr" + use_chances = False + m_d = os.path.join(case + "_gpr_baseline") + org_d = os.path.join("utils", case + "_template") + t_d = case + "_template" + if os.path.exists(t_d): + shutil.rmtree(t_d) + shutil.copytree(org_d, t_d) + if os.path.exists(m_d): + shutil.rmtree(m_d) + + pst = pyemu.Pst(os.path.join(t_d, case + ".pst")) + pst.pestpp_options["mou_generator"] = "pso" + if use_chances: + pst.pestpp_options["opt_risk"] = 0.95 + pst.pestpp_options["opt_stack_size"] = 50 + pst.pestpp_options["opt_recalc_chance_every"] = 10000 + pst.pestpp_options["opt_chance_points"] = "single" + else: + pst.pestpp_options["opt_risk"] = 0.5 + + pop_size = 20 + num_workers = 20 + noptmax_full = 10 + noptmax_inner = 5 + noptmax_outer = 5 + port = 4554 + pst.control_data.noptmax = noptmax_full + pst.pestpp_options["mou_population_size"] = pop_size + pst.pestpp_options["mou_save_population_every"] = 1 + pst.write(os.path.join(t_d, case + ".pst")) + if not os.path.exists(m_d): + pyemu.os_utils.start_workers(t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", + master_dir=m_d, verbose=True, port=port) + # shutil.copytree(t_d,m_d) + # pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=m_d) + # use the initial population files for training + dv_pops = [os.path.join(m_d, "{0}.0.dv_pop.csv".format(case))] + obs_pops = [f.replace("dv_", "obs_") for f in dv_pops] + + pst_fname = os.path.join(m_d, case + ".pst") + gpr_t_d = os.path.join(case + "_gpr_template") + pyemu.helpers.prep_for_gpr(pst_fname, dv_pops, obs_pops, gpr_t_d=gpr_t_d, nverf=int(pop_size * .1), \ + plot_fits=True, apply_standard_scalar=False, include_emulated_std_obs=True) + gpst = pyemu.Pst(os.path.join(gpr_t_d, case + ".pst")) + shutil.copy2(os.path.join(m_d, case + ".0.dv_pop.csv"), os.path.join(gpr_t_d, "initial_dv_pop.csv")) + gpst.pestpp_options["mou_dv_population_file"] = "initial_dv_pop.csv" + gpst.control_data.noptmax = noptmax_full + gpst.write(os.path.join(gpr_t_d, case + ".pst"), version=2) + gpr_m_d = gpr_t_d.replace("template", "master") + if os.path.exists(gpr_m_d): + shutil.rmtree(gpr_m_d) + pyemu.os_utils.start_workers(gpr_t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", + master_dir=gpr_m_d, verbose=True, port=port) + + # o1 = pd.read_csv(os.path.join(m_d,case+".{0}.obs_pop.csv".format(max(0,pst.control_data.noptmax)))) + o1 = pd.read_csv(os.path.join(m_d, case + ".pareto.archive.summary.csv")) + o1 = o1.loc[o1.generation == o1.generation.max(), :] + o1 = o1.loc[o1.is_feasible == True, :] + o1 = o1.loc[o1.nsga2_front == 1, :] + + # import matplotlib.pyplot as plt + # o2 = pd.read_csv(os.path.join(gpr_m_d, case + ".{0}.obs_pop.csv".format(max(0, gpst.control_data.noptmax)))) + # fig, ax = plt.subplots(1, 1, figsize=(5, 5)) + # ax.scatter(o1.obj_1, o1.obj_2, c="r", s=10) + # ax.scatter(o2.obj_1, o2.obj_2, c="0.5", s=10, alpha=0.5) + # plt.tight_layout() + # plt.savefig("gpr_{0}_compare_noiter.pdf".format(case)) + # plt.close(fig) + + # now lets try an inner-outer scheme... + + gpst.control_data.noptmax = noptmax_inner + gpst.write(os.path.join(gpr_t_d, case + ".pst"), version=2) + gpr_t_d_iter = gpr_t_d + "_outeriter{0}".format(0) + if os.path.exists(gpr_t_d_iter): + shutil.rmtree(gpr_t_d_iter) + shutil.copytree(gpr_t_d, gpr_t_d_iter) + for iouter in range(1, noptmax_outer + 1): + # run the gpr emulator + gpr_m_d_iter = gpr_t_d_iter.replace("template", "master") + complex_m_d_iter = t_d.replace("template", "master_complex_retrain_outeriter{0}".format(iouter)) + if os.path.exists(gpr_m_d_iter): + shutil.rmtree(gpr_m_d_iter) + pyemu.os_utils.start_workers(gpr_t_d_iter, mou_exe_path, case + ".pst", num_workers, worker_root=".", + master_dir=gpr_m_d_iter, verbose=True, port=port) + o2 = pd.read_csv(os.path.join(gpr_m_d_iter, case + ".{0}.obs_pop.csv".format(gpst.control_data.noptmax))) + + # now run the final dv pop thru the "complex" model + final_gpr_dvpop_fname = os.path.join(gpr_m_d_iter, case + ".archive.dv_pop.csv") + assert os.path.exists(final_gpr_dvpop_fname) + complex_model_dvpop_fname = os.path.join(t_d, "gpr_outeriter{0}_dvpop.csv".format(iouter)) + if os.path.exists(complex_model_dvpop_fname): + os.remove(complex_model_dvpop_fname) + # load the gpr archive and do something clever to pick new points to eval + # with the complex model + dvpop = pd.read_csv(final_gpr_dvpop_fname, index_col=0) + if dvpop.shape[0] > pop_size: + arc_sum = pd.read_csv(os.path.join(gpr_m_d_iter, case + ".pareto.archive.summary.csv")) + as_front_map = {member: front for member, front in zip(arc_sum.member, arc_sum.nsga2_front)} + as_crowd_map = {member: crowd for member, crowd in zip(arc_sum.member, arc_sum.nsga2_crowding_distance)} + as_feas_map = {member: feas for member, feas in zip(arc_sum.member, arc_sum.feasible_distance)} + as_gen_map = {member: gen for member, gen in zip(arc_sum.member, arc_sum.generation)} + + dvpop.loc[:, "front"] = dvpop.index.map(lambda x: as_front_map.get(x, np.nan)) + dvpop.loc[:, "crowd"] = dvpop.index.map(lambda x: as_crowd_map.get(x, np.nan)) + dvpop.loc[:, "feas"] = dvpop.index.map(lambda x: as_feas_map.get(x, np.nan)) + dvpop.loc[:, "gen"] = dvpop.index.map(lambda x: as_gen_map.get(x, np.nan)) + # drop members that have missing archive info + dvpop = dvpop.dropna() + if dvpop.shape[0] > pop_size: + dvpop.sort_values(by=["gen", "feas", "front", "crowd"], ascending=[False, True, True, False], + inplace=True) + dvpop = dvpop.iloc[:pop_size, :] + dvpop.drop(["gen", "feas", "front", "crowd"], axis=1, inplace=True) + + # shutil.copy2(final_gpr_dvpop_fname,complex_model_dvpop_fname) + dvpop.to_csv(complex_model_dvpop_fname) + pst.pestpp_options["mou_dv_population_file"] = os.path.split(complex_model_dvpop_fname)[1] + pst.control_data.noptmax = -1 + pst.write(os.path.join(t_d, case + ".pst"), version=2) + + pyemu.os_utils.start_workers(t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", + master_dir=complex_m_d_iter, verbose=True, port=port) + + # plot the complex model results... + o2 = pd.read_csv(os.path.join(complex_m_d_iter, case + ".pareto.archive.summary.csv")) + o2 = o2.loc[o2.generation == o2.generation.max(), :] + # o2 = o2.loc[o2.is_feasible==True,:] + o2 = o2.loc[o2.nsga2_front == 1, :] + # fig, ax = plt.subplots(1, 1, figsize=(5, 5)) + # ax.scatter(o1.obj_1, o1.obj_2, c="r", s=10, label="full complex") + # ax.scatter(o2.obj_1, o2.obj_2, c="0.5", s=10, alpha=0.5, label="mixed emulated-complex") + # ax.legend(loc="upper right") + # ax.set_xlim(0, 10) + # ax.set_ylim(0, 20) + # plt.tight_layout() + # plt.savefig("gpr_{0}_compare_iterscheme_{1}.pdf".format(case, iouter)) + # plt.close(fig) + + # now add those complex model input-output pop files to the list and retrain + # the gpr + dv_pops.append(os.path.join(complex_m_d_iter, case + ".0.dv_pop.csv")) + obs_pops.append(os.path.join(complex_m_d_iter, case + ".0.obs_pop.csv")) + gpr_t_d_iter = gpr_t_d + "_outeriter{0}".format(iouter) + pyemu.helpers.prep_for_gpr(pst_fname, dv_pops, obs_pops, gpr_t_d=gpr_t_d_iter, nverf=int(pop_size * .1), + plot_fits=True, apply_standard_scalar=False, include_emulated_std_obs=True) + gpst_iter = pyemu.Pst(os.path.join(gpr_t_d_iter, case + ".pst")) + # aggdf = pd.read_csv(os.path.join(gpr_t_d,"gpr_aggregate_training_data.csv"),index_col=0) + # aggdf.index = ["outeriter{0}_member{1}".format(iouter,i) for i in range(aggdf.shape[0])] + restart_gpr_dvpop_fname = "gpr_restart_dvpop_outeriter{0}.csv".format(iouter) + # aggdf.to_csv(os.path.join(gpr_t_d_iter,restart_gpr_dvpop_fname)) + shutil.copy2(os.path.join(complex_m_d_iter, case + ".0.dv_pop.csv"), + os.path.join(gpr_t_d_iter, restart_gpr_dvpop_fname)) + gpst_iter.pestpp_options["mou_dv_population_file"] = restart_gpr_dvpop_fname + gpst_iter.control_data.noptmax = gpst.control_data.noptmax + gpst_iter.write(os.path.join(gpr_t_d_iter, case + ".pst"), version=2) + + psum_fname = os.path.join(complex_m_d_iter,case+".pareto.archive.summary.csv") + assert os.path.exists(psum_fname) + psum = pd.read_csv(psum_fname) + assert 1.0 in psum.obj_1.values + assert 1.0 in psum.obj_2.values + + + if __name__ == "__main__": #ppu_geostats_test(".") - gpr_compare_invest() + #gpr_compare_invest() + gpr_constr_test() #while True: # thresh_pars_test() #obs_ensemble_quantile_test() diff --git a/examples/gpr_emulation_hosaki.ipynb b/examples/gpr_emulation_hosaki.ipynb index 883bdaae..6bbb5619 100644 --- a/examples/gpr_emulation_hosaki.ipynb +++ b/examples/gpr_emulation_hosaki.ipynb @@ -7,7 +7,7 @@ "source": [ "# A verbose walk thru of gaussian-process-regression emulation for global optimization\n", "\n", - "In this notebook, we go thru much pain to demonstrate how GPR can be used for so-called \"bayesian optimization\" with a very naive in-filling strategy. Many of the steps and operations in this notebook are done only for the purposes of hoping to explain how GPR emulation works.\n", + "In this notebook, we go thru much pain to demonstrate how GPR can be used for so-called \"bayesian optimization\" (BO) with a very naive in-filling strategy. Many of the steps and operations in this notebook are done only for the purposes of hoping to explain how GPR emulation works in the context of global evolutionary optimization.\n", "\n", "We will be using the well-known hosaki function for this analysis" ] @@ -27,6 +27,14 @@ "import pyemu" ] }, + { + "cell_type": "markdown", + "id": "0be48c02-b616-4d9b-a0f9-73367807f359", + "metadata": {}, + "source": [ + "First we need to prepare for the GPR experiments. Let's get the original model directory and copy it" + ] + }, { "cell_type": "code", "execution_count": null, @@ -97,7 +105,7 @@ "id": "e34038c7-39f6-47e6-8114-aa3c75afe762", "metadata": {}, "source": [ - "First, lets sweep over decision variable space so that we can map the \"true\" objective function surface:" + "First, lets sweep over decision variable space so that we can map the \"true\" objective function surface. You would never do this in practice but it helps us understand the GPR emulation process:" ] }, { @@ -129,6 +137,14 @@ " port=port)\n" ] }, + { + "cell_type": "markdown", + "id": "77d54f6a-3ee6-4604-97c5-5875ee83d56e", + "metadata": {}, + "source": [ + "And lets load up the sweep results so we can plot:" + ] + }, { "cell_type": "code", "execution_count": null, @@ -150,12 +166,10 @@ "metadata": {}, "outputs": [], "source": [ - "def get_obj_map(ax,_sweep_x,_sweep_y,_sweep_z,label=\"objective function\",levels=[-2,-1,0,0.5]): \n", - " vmin,vmax = None,None\n", - " if min(levels) == -2 and max(levels) == 0.5:\n", - " vmin = -2\n", - " vmax = 0.5\n", - " cb = ax.pcolormesh(_sweep_x,_sweep_y,_sweep_z,vmin=vmin,vmax=vmax)\n", + "def get_obj_map(ax,_sweep_x,_sweep_y,_sweep_z,label=\"objective function\",\n", + " levels=[-2,-1,0,0.5],vmin=-2,vmax=0.5,cmap=\"magma\"): \n", + " \"\"\"a simple function to plot an objective function surface\"\"\"\n", + " cb = ax.pcolormesh(_sweep_x,_sweep_y,_sweep_z,vmin=vmin,vmax=vmax,cmap=cmap)\n", " plt.colorbar(cb,ax=ax,label=label)\n", " ax.contour(_sweep_x,_sweep_y,_sweep_z,levels=levels,colors=\"w\")\n", " \n", @@ -184,7 +198,7 @@ "source": [ "So that is the true objective function surface with the global minimum at (4,2) and a local minimum at (1,2).\n", "\n", - "The GPR BO workflow starts by first evaluating an initial population with the \"model\", which here is just the simple hosaki function. In practice, this will be an expensive and complex process based model. To make this demo more interesting, we will limit the decision variable search space to not include the global minimum (and really 10 members in the initial population is pretty crazy small - just for demo!)" + "The GPR BO workflow starts by first evaluating an initial population with the complex and expensive to run \"model\", which here is just the simple hosaki function. In practice, this will be an expensive and complex process based model. To make this demo more interesting, we will limit the decision variable search space to not include the global minimum (and really 10 members in the initial population is pretty crazy small - just for demo!). If we dont limit the search space and use a larger initial population, the GPR process nails it in the first go..." ] }, { @@ -210,6 +224,14 @@ " port=port)\n" ] }, + { + "cell_type": "markdown", + "id": "7a4482a5-4315-425a-8d62-1558ecc38a09", + "metadata": {}, + "source": [ + "Now load the initial training input-output pairs" + ] + }, { "cell_type": "code", "execution_count": null, @@ -221,6 +243,14 @@ "training_opop_fname = os.path.join(m_d,\"pest.0.obs_pop.csv\")" ] }, + { + "cell_type": "markdown", + "id": "e828f158-c1a0-4ce6-b9fa-0095738b333e", + "metadata": {}, + "source": [ + "and we can plot them to visual where we have training points on the actual true surface" + ] + }, { "cell_type": "code", "execution_count": null, @@ -244,7 +274,7 @@ "id": "89d437f7-1020-4644-ad30-3170a3dd37fc", "metadata": {}, "source": [ - "Those white triangles are the places where we have actually run the model - in practice we will never have the colormap or the contours - those are just to help us understand what is happening...\n", + "Those white triangles are the places where we have actually run the model - in practice we will never have the colormap or the contours - those are just to help us understand what is happening...and remember: that is a purposefully sparse training dataset!\n", "\n", "This function is where the magic happens: we use the decision variable population and resulting output/observation population to setup the GPR emulation process. Note that `pyemu.helpers.prep_for_gpr()` function setups and training a GPR emulator for each non-zero weighted observation in the control file... " ] @@ -257,11 +287,19 @@ "outputs": [], "source": [ "def prep_for_gpr(dvpops,opops,gpr_t_d,noptmax=-1):\n", - " pyemu.helpers.prep_for_gpr(os.path.join(t_d,\"pest.pst\"),dvpops,opops,gpr_t_d=gpr_t_d,plot_fits=True)\n", + " # this helper does the heavy lifting. We are including the optional GPR emulated standard deviations\n", + " # as \"observations\". These quantities are the standard deviation of the emulated objective function value\n", + " # with respect to the GPR process - this is a unique and very powerful aspect of GPR vs other\n", + " #data-driven techniques. We will use these obs to visualize the uncertainty in the \n", + " # GPR objectve function surface later...\n", + " pyemu.helpers.prep_for_gpr(os.path.join(t_d,\"pest.pst\"),dvpops,opops,gpr_t_d=gpr_t_d,plot_fits=True,\n", + " include_emulated_std_obs=True)\n", + " # now load the newly created gpr-based pst file:\n", " gpst = pyemu.Pst(os.path.join(gpr_t_d,\"pest.pst\"))\n", - " #shutil.copy2(dvpops[-1],os.path.join(gpr_t_d,\"initial_dv_pop.csv\"))\n", - " #gpst.pestpp_options[\"mou_dv_population_file\"] = \"initial_dv_pop.csv\"\n", + " #and copy the sweep input file that lets us run the full response surface\n", + " # in practice, you dont need this either...\n", " shutil.copy2(os.path.join(sweep_d,\"sweep_in.csv\"),os.path.join(gpr_t_d,\"sweep_in.csv\"))\n", + " # some bits and bobs:\n", " gpst.control_data.noptmax = noptmax\n", " gpst.pestpp_options[\"ies_include_base\"] = False\n", " gpst.pestpp_options[\"mou_save_population_every\"] = 1\n", @@ -275,6 +313,16 @@ " return gpst" ] }, + { + "cell_type": "markdown", + "id": "3d2f2533-8920-45cd-b33c-e082d69cb0d9", + "metadata": {}, + "source": [ + "One important consideration here: In this simple demo, we only have one objective. That means we have to be careful not to drive the emulator to a single best-fit solution (which we do by using only a few generations when we run mou), but we also need to sure that we start the emulator-driven mou runs with a fully dispersed initial population. But! In a multiobjective run, the final generation for mou is naturally disperesed because of the pareto frontier search, so in the multiobjective setting, we probably want to start the emulated mou run with the existing population to keep us from having to research all of decision variable space for the nondominated solutions. This isnt a hard-and-fast rule, but it seems to be generally applicable\n", + "\n", + "The GPR helper in pyemu accepts a list of input and output filenames to make it easier to repeatedly retrain the GPR emulators:" + ] + }, { "cell_type": "code", "execution_count": null, @@ -297,6 +345,64 @@ "gpst = prep_for_gpr(training_dvpop_fnames,training_opop_fnames,gpr_t_d)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "208fd361-7cec-4205-9b09-dd4af6b8a624", + "metadata": {}, + "outputs": [], + "source": [ + "gpst.observation_data" + ] + }, + { + "cell_type": "markdown", + "id": "0befe6ee-4a00-4874-be8c-66ce6118730d", + "metadata": {}, + "source": [ + "We see the original \"sim\" observation (which is the objective function we are trying to minimize), but we also see the optional GPR-estimated standard devivation\n", + "\n", + "Let's see what was created in the new GPR-based template directory:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d733ad63-126e-47f7-9bec-b28e1ab758cd", + "metadata": {}, + "outputs": [], + "source": [ + "os.listdir(gpr_t_d)" + ] + }, + { + "cell_type": "markdown", + "id": "2c83d09e-98e0-4025-bb05-28decb6b26b2", + "metadata": {}, + "source": [ + "two very important files in that dir: the new forward run python script and a series of pickle files, one per objective and one per active constraint in the optimization problem. Essentially, we need to build a GPR-based emulator for each output that is relavent to the optimization problem. We also dont want to rebuild these emulators everytime we run the model, so we store the trained GPR emulators in pickle files and load them up as needed:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b0086a7-fefb-4172-be2b-93b8ce73cba0", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(gpr_t_d,\"forward_run.py\"),'r') as f:\n", + " for line in f:\n", + " print(line,end=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "4b60a402-d9bb-419c-92ca-df0cc8919945", + "metadata": {}, + "source": [ + "So we simply loop over all relavent model outputs that have a GPR emulator and \"emulate\" the value of the model output given the current decision variable values. easy as!" + ] + }, { "cell_type": "markdown", "id": "54f493fb-a1f7-4248-9a5b-b884a7ab9a84", @@ -320,36 +426,58 @@ " port=port)\n" ] }, + { + "cell_type": "markdown", + "id": "d7ecb069-da8c-4d6a-a968-2f047ad672d9", + "metadata": {}, + "source": [ + "Just a helper function to visual the results of the GPR (re-)training:" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "e455e009-ff55-487f-ac53-8251589af39c", + "id": "406fab70-18e1-43e8-8edb-ffea73348dcc", "metadata": {}, "outputs": [], "source": [ - "sweep_gpr_pe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.par.csv\"),index_col=0)\n", - "sweep_gpr_oe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", - "gpr_sweep_x = sweep_gpr_pe.loc[:,pst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", - "gpr_sweep_y = sweep_gpr_pe.loc[:,pst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", - "gpr_sweep_z = sweep_gpr_oe.loc[:,pst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)" + "def plot_gpr_sweep_results(_gpr_sweep_d,_training_dvpop):\n", + " #load the gpr sweep results to viz the emulated objective function surface\n", + " sweep_gpr_pe = pd.read_csv(os.path.join(_gpr_sweep_d,\"pest.0.par.csv\"),index_col=0)\n", + " sweep_gpr_oe = pd.read_csv(os.path.join(_gpr_sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", + " gpr_sweep_x = sweep_gpr_pe.loc[:,gpst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + " gpr_sweep_y = sweep_gpr_pe.loc[:,gpst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", + " gpr_sweep_z = sweep_gpr_oe.loc[:,gpst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + " gpr_sweep_stdev_z = sweep_gpr_oe.loc[:,gpst.obs_names[1]].values.reshape(sweep_steps,sweep_steps)\n", + " \n", + " # plot it up:\n", + " fig, axes = plt.subplots(2,2,figsize=(10,8))\n", + " axes = axes.flatten()\n", + " get_obj_map(axes[0],sweep_x,sweep_y,sweep_z)\n", + " get_obj_map(axes[1],gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", + " axes[1].scatter(_training_dvpop.loc[:,pst.par_names[0]],_training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + " diff = sweep_z-gpr_sweep_z\n", + " amax = np.abs(diff).max()\n", + " get_obj_map(axes[2],gpr_sweep_x,gpr_sweep_y,sweep_z-gpr_sweep_z,label=\"truth minus emulated\",levels=None,vmin=-amax,vmax=amax,cmap=\"bwr\")\n", + " get_obj_map(axes[3],gpr_sweep_x,gpr_sweep_y,gpr_sweep_stdev_z,label=\"GPR stdev\",levels=None,\n", + " vmin=gpr_sweep_stdev_z.min(),vmax=gpr_sweep_stdev_z.max(),cmap=\"jet\")\n", + " axes[2].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", + " \n", + " axes[0].set_title(\"truth\",loc=\"left\")\n", + " axes[1].set_title(\"emulated with training points\",loc=\"left\")\n", + " axes[2].set_title(\"difference with training points\",loc=\"left\")\n", + " axes[3].set_title(\"GPR standard deviation\",loc=\"left\")\n", + " plt.show()" ] }, { "cell_type": "code", "execution_count": null, - "id": "406fab70-18e1-43e8-8edb-ffea73348dcc", + "id": "aa665546-50f3-4f59-8e89-73a17760f6d9", "metadata": {}, "outputs": [], "source": [ - "fig, axes = plt.subplots(1,3,figsize=(15,3))\n", - "get_obj_map(axes[0],sweep_x,sweep_y,sweep_z)\n", - "get_obj_map(axes[1],gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", - "axes[1].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", - "get_obj_map(axes[2],gpr_sweep_x,gpr_sweep_y,sweep_z-gpr_sweep_z,label=\"truth minus emulated\",levels=[-0.5,0,0.5])\n", - "axes[2].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", - "axes[0].set_title(\"truth\",loc=\"left\")\n", - "axes[1].set_title(\"emulated with training points\",loc=\"left\")\n", - "axes[2].set_title(\"difference with training points\",loc=\"left\")\n" + "plot_gpr_sweep_results(gpr_sweep_d,training_dvpop)" ] }, { @@ -357,9 +485,9 @@ "id": "9668435c-ccb4-486b-8423-d864251d55d0", "metadata": {}, "source": [ - "OK! now we can see whats happening - the emulated objective function surface is strongly controlled by the location of the training data points, and, is this case, its not a good representation of the truth surface...yet...\n", + "OK! now we can see whats happening - the emulated objective function surface is strongly controlled by the location of the training data points, and, is this case, its not a good representation of the truth surface...yet...it should also be clear that the uncertainty in the GPR emulation is lowest near the training points but is highly uncertain as we move away from the training - just like geostatistics!\n", "\n", - "But now lets run pestpp-mou on the GPR emulated model. This is usually quite fast..." + "But now lets run pestpp-mou on the GPR emulated model. This is usually quite fast, especially if the process model that is being emulated takes more than a few mins to run..." ] }, { @@ -391,34 +519,43 @@ { "cell_type": "code", "execution_count": null, - "id": "a29183f3-3c7c-40de-a068-5035a5cf7bb8", + "id": "a769de5f-58b0-432a-a9d6-418ea7ee2586", "metadata": {}, "outputs": [], "source": [ - "gpr_dvpops = [os.path.join(gpr_m_d,f) for f in os.listdir(gpr_m_d) if len(f.split('.')) == 4 and f.endswith(\"dv_pop.csv\") and \"archive\" not in f]\n", - "gpr_dvpops_itr = [int(f.split(\".\")[1]) for f in gpr_dvpops]\n", - "gpr_dvpops = {itr:pd.read_csv(f,index_col=0) for itr,f in zip(gpr_dvpops_itr,gpr_dvpops)}" + "def plot_gpr_mou_results(_gpr_m_d,_gpr_sweep_d):\n", + " sweep_gpr_pe = pd.read_csv(os.path.join(_gpr_sweep_d,\"pest.0.par.csv\"),index_col=0)\n", + " sweep_gpr_oe = pd.read_csv(os.path.join(_gpr_sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", + " gpr_sweep_x = sweep_gpr_pe.loc[:,gpst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + " gpr_sweep_y = sweep_gpr_pe.loc[:,gpst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", + " gpr_sweep_z = sweep_gpr_oe.loc[:,gpst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)\n", + " gpr_sweep_stdev_z = sweep_gpr_oe.loc[:,gpst.obs_names[1]].values.reshape(sweep_steps,sweep_steps)\n", + " \n", + " gpr_dvpops = [os.path.join(_gpr_m_d,f) for f in os.listdir(_gpr_m_d) if len(f.split('.')) == 4 and f.endswith(\"dv_pop.csv\") and \"archive\" not in f]\n", + " gpr_dvpops_itr = [int(f.split(\".\")[1]) for f in gpr_dvpops]\n", + " gpr_dvpops = {itr:pd.read_csv(f,index_col=0) for itr,f in zip(gpr_dvpops_itr,gpr_dvpops)}\n", + " for itr in range(max(gpr_dvpops_itr)):\n", + " fig,axes = plt.subplots(1,2,figsize=(10,4))\n", + " ax = axes[0]\n", + " get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", + " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", + " ax.set_title(\"truth generation {0}\".format(itr),loc=\"left\")\n", + " ax = axes[1]\n", + " get_obj_map(ax,gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", + " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", + " ax.set_title(\"emulated generation {0}\".format(itr),loc=\"left\")\n", + " plt.show()\n", + " plt.close(fig)" ] }, { "cell_type": "code", "execution_count": null, - "id": "ada547f4-8d3f-4980-94e3-d712998369a7", + "id": "a29183f3-3c7c-40de-a068-5035a5cf7bb8", "metadata": {}, "outputs": [], "source": [ - "for itr in range(max(gpr_dvpops_itr)):\n", - " fig,axes = plt.subplots(1,2,figsize=(10,4))\n", - " ax = axes[0]\n", - " get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", - " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", - " ax.set_title(\"truth generation {0}\".format(itr),loc=\"left\")\n", - " ax = axes[1]\n", - " get_obj_map(ax,gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", - " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", - " ax.set_title(\"emulated generation {0}\".format(itr),loc=\"left\")\n", - " plt.show()\n", - " plt.close(fig)" + "plot_gpr_mou_results(gpr_m_d,gpr_sweep_d)" ] }, { @@ -426,7 +563,7 @@ "id": "738f6e14-b896-48db-8e1e-59dcbf270245", "metadata": {}, "source": [ - "So just what you expected? Essentially pestpp-mou converged to the minimum of the objective function we gave it, which is the emulated objective function...\n", + "So just what you expected? Essentially pestpp-mou converged to the minimum of the objective function we gave it, which is the emulated objective function...at this stage the emulated objective function is a poor represetation of the truth objective function....\n", "\n", "Now this where some more cleverness happens: Lets take that last emulated decision variable population and actually run it thru the complex \"model\" (which in this case is just the hosaki function...). This is so that we can \"in-fill\" our GPR emulator with this new points in decision variable space. In practice, a lot more cleverness needs to happen to actually decide which points, but for this lil demo, it works..." ] @@ -438,6 +575,9 @@ "metadata": {}, "outputs": [], "source": [ + "gpr_dvpops = [os.path.join(gpr_m_d,f) for f in os.listdir(gpr_m_d) if len(f.split('.')) == 4 and f.endswith(\"dv_pop.csv\") and \"archive\" not in f]\n", + "gpr_dvpops_itr = [int(f.split(\".\")[1]) for f in gpr_dvpops]\n", + "gpr_dvpops = {itr:pd.read_csv(f,index_col=0) for itr,f in zip(gpr_dvpops_itr,gpr_dvpops)}\n", "gpr_dvpops[max(gpr_dvpops_itr)].to_csv(os.path.join(t_d,\"retrain_1_dvpop.csv\"))\n", "pst.pestpp_options[\"mou_dv_population_file\"] = \"retrain_1_dvpop.csv\"\n", "pst.control_data.noptmax = -1\n", @@ -533,7 +673,7 @@ "id": "ff2b2dac-5acb-4f27-b0a7-0b4c6e419222", "metadata": {}, "source": [ - "Ok, now let's also re-sweep over the emulated surface to see how much better our approximation to the true objective function surface:" + "Ok, now let's also re-sweep over the emulated surface to see how much better our approximation to the true objective function surface (remember dont do this in practice! we are just doing it here to help build understanding):" ] }, { @@ -551,6 +691,14 @@ " port=port)" ] }, + { + "cell_type": "markdown", + "id": "5b48ab2c-e132-4e2b-a387-637087a558b4", + "metadata": {}, + "source": [ + "And now visualize these new surfaces with the current training dataset:" + ] + }, { "cell_type": "code", "execution_count": null, @@ -558,20 +706,7 @@ "metadata": {}, "outputs": [], "source": [ - "sweep_gpr_pe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.par.csv\"),index_col=0)\n", - "sweep_gpr_oe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", - "gpr_sweep_x = sweep_gpr_pe.loc[:,pst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", - "gpr_sweep_y = sweep_gpr_pe.loc[:,pst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", - "gpr_sweep_z = sweep_gpr_oe.loc[:,pst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)\n", - "fig, axes = plt.subplots(1,3,figsize=(15,3))\n", - "get_obj_map(axes[0],sweep_x,sweep_y,sweep_z)\n", - "get_obj_map(axes[1],gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", - "axes[1].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", - "get_obj_map(axes[2],gpr_sweep_x,gpr_sweep_y,sweep_z-gpr_sweep_z,label=\"truth minus emulated\",levels=[-0.5,0,0.5])\n", - "axes[2].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", - "axes[0].set_title(\"truth\",loc=\"left\")\n", - "axes[1].set_title(\"emulated with training points\",loc=\"left\")\n", - "axes[2].set_title(\"difference with training points\",loc=\"left\")" + "plot_gpr_sweep_results(gpr_sweep_d,training_dvpop)" ] }, { @@ -579,7 +714,7 @@ "id": "9d986519-b800-444f-965a-7dff9a589b9a", "metadata": {}, "source": [ - "Comparing this to the original emulated surface, we can see it has gotten much better...\n", + "Comparing this to the original emulated surface, we can see that our emulated objective function surface has improved dramatically!\n", "\n", "Now let's run pestpp-mou on this improved emulated model and visualize how pestpp-mou navigates this new emulated surface:" ] @@ -600,31 +735,7 @@ " num_workers=num_workers,\n", " master_dir=gpr_m_d,worker_root='.',\n", " port=port)\n", - "gpr_dvpops = [os.path.join(gpr_m_d,f) for f in os.listdir(gpr_m_d) if len(f.split('.')) == 4 and f.endswith(\"dv_pop.csv\") and \"archive\" not in f]\n", - "gpr_dvpops_itr = [int(f.split(\".\")[1]) for f in gpr_dvpops]\n", - "\n", - "gpr_dvpops = {itr:pd.read_csv(f,index_col=0) for itr,f in zip(gpr_dvpops_itr,gpr_dvpops)}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc8b4941-4f89-4cc6-b39e-45e5254ce8d2", - "metadata": {}, - "outputs": [], - "source": [ - "for itr in range(max(gpr_dvpops_itr)):\n", - " fig,axes = plt.subplots(1,2,figsize=(10,4))\n", - " ax = axes[0]\n", - " get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", - " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", - " ax.set_title(\"truth generation {0}\".format(itr),loc=\"left\")\n", - " ax = axes[1]\n", - " get_obj_map(ax,gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", - " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", - " ax.set_title(\"emulated generation {0}\".format(itr),loc=\"left\")\n", - " plt.show()\n", - " plt.close(fig)" + "plot_gpr_mou_results(gpr_m_d,gpr_sweep_d)" ] }, { @@ -632,7 +743,7 @@ "id": "49d72679-5b94-4a04-bd1a-43f4bf812f45", "metadata": {}, "source": [ - "Now lets do that whole process a few times:" + "Now lets do this whole process a few times:" ] }, { @@ -686,22 +797,7 @@ " master_dir=gpr_sweep_d,worker_root='.',\n", " port=port)\n", "\n", - "sweep_gpr_pe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.par.csv\"),index_col=0)\n", - "sweep_gpr_oe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", - "gpr_sweep_x = sweep_gpr_pe.loc[:,pst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", - "gpr_sweep_y = sweep_gpr_pe.loc[:,pst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", - "gpr_sweep_z = sweep_gpr_oe.loc[:,pst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)\n", - "fig, axes = plt.subplots(1,3,figsize=(15,3))\n", - "get_obj_map(axes[0],sweep_x,sweep_y,sweep_z)\n", - "get_obj_map(axes[1],gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", - "axes[1].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", - "get_obj_map(axes[2],gpr_sweep_x,gpr_sweep_y,sweep_z-gpr_sweep_z,label=\"truth minus emulated\",levels=[-0.5,0,0.5])\n", - "axes[2].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", - "axes[0].set_title(\"truth\",loc=\"left\")\n", - "axes[1].set_title(\"emulated with training points\",loc=\"left\")\n", - "axes[2].set_title(\"difference with training points\",loc=\"left\")\n", - "plt.show()\n", - "plt.close(fig)\n", + "plot_gpr_sweep_results(gpr_sweep_d,training_dvpop)\n", "\n", "gpst.control_data.noptmax = 8\n", "gpst.pestpp_options[\"mou_population_size\"] = 20\n", @@ -715,18 +811,7 @@ "\n", "\n", "\n", - "for itr in range(max(gpr_dvpops_itr)):\n", - " fig,axes = plt.subplots(1,2,figsize=(10,4))\n", - " ax = axes[0]\n", - " get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", - " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", - " ax.set_title(\"truth generation {0}\".format(itr),loc=\"left\")\n", - " ax = axes[1]\n", - " get_obj_map(ax,gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", - " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", - " ax.set_title(\"emulated generation {0}\".format(itr),loc=\"left\")\n", - " plt.show()\n", - " plt.close(fig)" + "plot_gpr_mou_results(gpr_m_d,gpr_sweep_d)" ] }, { @@ -780,22 +865,7 @@ " master_dir=gpr_sweep_d,worker_root='.',\n", " port=port)\n", "\n", - "sweep_gpr_pe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.par.csv\"),index_col=0)\n", - "sweep_gpr_oe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", - "gpr_sweep_x = sweep_gpr_pe.loc[:,pst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", - "gpr_sweep_y = sweep_gpr_pe.loc[:,pst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", - "gpr_sweep_z = sweep_gpr_oe.loc[:,pst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)\n", - "fig, axes = plt.subplots(1,3,figsize=(15,3))\n", - "get_obj_map(axes[0],sweep_x,sweep_y,sweep_z)\n", - "get_obj_map(axes[1],gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", - "axes[1].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", - "get_obj_map(axes[2],gpr_sweep_x,gpr_sweep_y,sweep_z-gpr_sweep_z,label=\"truth minus emulated\",levels=[-0.5,0,0.5])\n", - "axes[2].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", - "axes[0].set_title(\"truth\",loc=\"left\")\n", - "axes[1].set_title(\"emulated with training points\",loc=\"left\")\n", - "axes[2].set_title(\"difference with training points\",loc=\"left\")\n", - "plt.show()\n", - "plt.close(fig)\n", + "plot_gpr_sweep_results(gpr_sweep_d,training_dvpop)\n", "\n", "gpst.control_data.noptmax = 8\n", "gpst.pestpp_options[\"mou_population_size\"] = 20\n", @@ -809,18 +879,7 @@ "\n", "\n", "\n", - "for itr in range(max(gpr_dvpops_itr)):\n", - " fig,axes = plt.subplots(1,2,figsize=(10,4))\n", - " ax = axes[0]\n", - " get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", - " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", - " ax.set_title(\"truth generation {0}\".format(itr),loc=\"left\")\n", - " ax = axes[1]\n", - " get_obj_map(ax,gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", - " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", - " ax.set_title(\"emulated generation {0}\".format(itr),loc=\"left\")\n", - " plt.show()\n", - " plt.close(fig)" + "plot_gpr_mou_results(gpr_m_d,gpr_sweep_d)" ] }, { @@ -828,7 +887,7 @@ "id": "7ab00c33-17a8-48a6-ae19-7e5c63136bc1", "metadata": {}, "source": [ - "And one last time...and this time with more emulator generations to polish things off..." + "And one last time...and this time with more emulator-based pestpp-mou generations to polish things off since we no longer want to keep a dispersed population (since we arent doing any more in-filling and retraining)" ] }, { @@ -881,47 +940,19 @@ " master_dir=gpr_sweep_d,worker_root='.',\n", " port=port)\n", "\n", - "sweep_gpr_pe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.par.csv\"),index_col=0)\n", - "sweep_gpr_oe = pd.read_csv(os.path.join(gpr_sweep_d,\"pest.0.obs.csv\"),index_col=0)\n", - "gpr_sweep_x = sweep_gpr_pe.loc[:,pst.par_names[0]].values.reshape(sweep_steps,sweep_steps)\n", - "gpr_sweep_y = sweep_gpr_pe.loc[:,pst.par_names[1]].values.reshape(sweep_steps,sweep_steps)\n", - "gpr_sweep_z = sweep_gpr_oe.loc[:,pst.obs_names[0]].values.reshape(sweep_steps,sweep_steps)\n", - "fig, axes = plt.subplots(1,3,figsize=(15,3))\n", - "get_obj_map(axes[0],sweep_x,sweep_y,sweep_z)\n", - "get_obj_map(axes[1],gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", - "axes[1].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", - "get_obj_map(axes[2],gpr_sweep_x,gpr_sweep_y,sweep_z-gpr_sweep_z,label=\"truth minus emulated\",levels=[-0.5,0,0.5])\n", - "axes[2].scatter(training_dvpop.loc[:,pst.par_names[0]],training_dvpop.loc[:,pst.par_names[1]],marker='^',c='w',s=20)\n", - "axes[0].set_title(\"truth\",loc=\"left\")\n", - "axes[1].set_title(\"emulated with training points\",loc=\"left\")\n", - "axes[2].set_title(\"difference with training points\",loc=\"left\")\n", - "plt.show()\n", - "plt.close(fig)\n", + "plot_gpr_sweep_results(gpr_sweep_d,training_dvpop)\n", "\n", - "gpst.control_data.noptmax = 15\n", + "gpst.control_data.noptmax = 30\n", "gpst.pestpp_options[\"mou_population_size\"] = 20\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "\n", - "num_workers = 10\n", + "num_workers = 20\n", "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-mou\",\"pest.pst\",\n", " num_workers=num_workers,\n", " master_dir=gpr_m_d,worker_root='.',\n", " port=port)\n", "\n", - "\n", - "\n", - "for itr in range(max(gpr_dvpops_itr)):\n", - " fig,axes = plt.subplots(1,2,figsize=(10,4))\n", - " ax = axes[0]\n", - " get_obj_map(ax,sweep_x,sweep_y,sweep_z)\n", - " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", - " ax.set_title(\"truth generation {0}\".format(itr),loc=\"left\")\n", - " ax = axes[1]\n", - " get_obj_map(ax,gpr_sweep_x,gpr_sweep_y,gpr_sweep_z)\n", - " ax.scatter(gpr_dvpops[itr].loc[:,gpst.par_names[0]],gpr_dvpops[itr].loc[:,gpst.par_names[1]],marker='.',c='w',s=10)\n", - " ax.set_title(\"emulated generation {0}\".format(itr),loc=\"left\")\n", - " plt.show()\n", - " plt.close(fig)" + "plot_gpr_mou_results(gpr_m_d,gpr_sweep_d)" ] }, { @@ -929,7 +960,7 @@ "id": "5aaeba17-14dc-4da9-8528-e64445ac7e41", "metadata": {}, "source": [ - "There you have it - after evaluating only a few populations with the complex process-based model (here that was only the hosaki function), we can effectively optimize a function with the otherwise every expensive global solvers in pestpp-mou." + "There you have it - after evaluating only a few populations with the complex process-based model (here that was only the hosaki function), we can effectively optimize a function with the otherwise every expensive global solvers in pestpp-mou. In general this approach can reduce the computational demand of mou by 10X to 100X, which is pretty amazing...." ] }, { diff --git a/pyemu/pst/pst_utils.py b/pyemu/pst/pst_utils.py index eecab536..74a02b0a 100644 --- a/pyemu/pst/pst_utils.py +++ b/pyemu/pst/pst_utils.py @@ -916,7 +916,7 @@ def try_process_output_pst(pst): ins_file, str(e) ) ) - df = _try_run_inschek(ins_file, out_file) + #df = _try_run_inschek(ins_file, out_file) if df is not None: pst.observation_data.loc[df.index, "obsval"] = df.obsval diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py index 11a8992b..9c5bb447 100644 --- a/pyemu/utils/helpers.py +++ b/pyemu/utils/helpers.py @@ -4033,7 +4033,7 @@ def get_current_prop(_cur_thresh): def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_kernel=None,nverf=0, - plot_fits=False,apply_standard_scalar=False): + plot_fits=False,apply_standard_scalar=False, include_emulated_std_obs=False): """helper function to setup a gaussian-process-regression emulator for outputs of interest. This is primarily targeted at low-dimensional settings like those encountered in PESTPP-MOU @@ -4248,7 +4248,10 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ with open(ins_fname,'w') as f: f.write("pif ~\nl1\n") for output_name in output_names: - f.write("l1 ~,~ !{0}!\n".format(output_name)) + if include_emulated_std_obs: + f.write("l1 ~,~ !{0}! ~,~ !{0}_gprstd!\n".format(output_name)) + else: + f.write("l1 ~,~ !{0}!\n".format(output_name)) tpl_list = [tpl_fname] if aux_tpl_fname is not None: tpl_list.append(aux_tpl_fname) @@ -4258,10 +4261,24 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ par_names = pst.par_names assert len(set(par_names).symmetric_difference(set(gpst.par_names))) == 0 for col in pst.parameter_data.columns: + # this gross thing is to avoid a future error warning in pandas - + # why is it getting so strict?! isnt python duck-typed? + if col in gpst.parameter_data.columns and\ + gpst.parameter_data.dtypes[col] != pst.parameter_data.dtypes[col]: + gpst.parameter_data[col] = gpst.parameter_data[col].astype(pst.parameter_data.dtypes[col]) gpst.parameter_data.loc[par_names,col] = pst.parameter_data.loc[par_names,col].values for col in pst.observation_data.columns: + # this gross thing is to avoid a future error warning in pandas - + # why is it getting so strict?! isnt python duck-typed? + if col in gpst.observation_data.columns and \ + gpst.observation_data.dtypes[col] != pst.observation_data.dtypes[col]: + gpst.observation_data[col] = gpst.obsveration_data[col].astype(pst.observation_data.dtypes[col]) gpst.observation_data.loc[output_names,col] = pst.observation_data.loc[output_names,col].values + if include_emulated_std_obs: + stdobs = [o for o in gpst.obs_names if o.endswith("_gprstd")] + assert len(stdobs) > 0 + gpst.observation_data.loc[stdobs,"weight"] = 0.0 gpst.pestpp_options = pst.pestpp_options gpst.prior_information = pst.prior_information.copy() lines = [line[4:] for line in inspect.getsource(gpr_forward_run).split("\n")][1:] @@ -4295,12 +4312,14 @@ def gpr_forward_run(): input_df = pd.read_csv("gpr_input.csv",index_col=0) mdf = pd.read_csv(os.path.join("gprmodel_info.csv"),index_col=0) mdf.loc[:,"sim"] = np.nan + mdf.loc[:,"sim_std"] = np.nan for output_name,model_fname in zip(mdf.output_name,mdf.model_fname): guassian_process = pickle.load(open(model_fname,'rb')) - sim = guassian_process.predict(np.atleast_2d(input_df.parval1.values)) - print(output_name,sim) - mdf.loc[output_name,"sim"] = sim - mdf.loc[:,["output_name","sim"]].to_csv("gpr_output.csv",index=False) + sim = guassian_process.predict(np.atleast_2d(input_df.parval1.values),return_std=True) + mdf.loc[output_name,"sim"] = sim[0] + mdf.loc[output_name,"sim_std"] = sim[1] + + mdf.loc[:,["output_name","sim","sim_std"]].to_csv("gpr_output.csv",index=False) def randrealgen_optimized(nreal, tol=1e-7, max_samples=1000000): From 499c6a3f07c3dddfb483e2647e1ff311b94760ad Mon Sep 17 00:00:00 2001 From: jwhite Date: Mon, 25 Nov 2024 14:31:34 +0800 Subject: [PATCH 15/20] added shortened zdt1 test, trying to speed up gpr hosaki notebook --- autotest/utils_tests.py | 66 ++++++++++++++++++++++++++++- examples/gpr_emulation_hosaki.ipynb | 49 ++++++++++++++++----- 2 files changed, 104 insertions(+), 11 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 4f2f4a09..58ca1989 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2982,11 +2982,75 @@ def gpr_constr_test(): assert 1.0 in psum.obj_2.values +def gpr_zdt1_test(): + import numpy as np + from sklearn.gaussian_process import GaussianProcessRegressor + case = "zdt1" + use_chances = False + m_d = os.path.join(case + "_gpr_baseline") + org_d = os.path.join("utils", case + "_template") + t_d = case + "_template" + if os.path.exists(t_d): + shutil.rmtree(t_d) + shutil.copytree(org_d, t_d) + if os.path.exists(m_d): + shutil.rmtree(m_d) + + pst = pyemu.Pst(os.path.join(t_d, case + ".pst")) + pst.pestpp_options["mou_generator"] = "pso" + if use_chances: + pst.pestpp_options["opt_risk"] = 0.95 + pst.pestpp_options["opt_stack_size"] = 50 + pst.pestpp_options["opt_recalc_chance_every"] = 10000 + pst.pestpp_options["opt_chance_points"] = "single" + else: + pst.pestpp_options["opt_risk"] = 0.5 + + pop_size = 20 + num_workers = 20 + noptmax_full = 15 + + port = 4554 + pst.control_data.noptmax = -1 + pst.pestpp_options["mou_population_size"] = pop_size + pst.pestpp_options["mou_save_population_every"] = 1 + pst.write(os.path.join(t_d, case + ".pst")) + if not os.path.exists(m_d): + pyemu.os_utils.start_workers(t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", + master_dir=m_d, verbose=True, port=port) + + dv_pops = [os.path.join(m_d, "{0}.0.dv_pop.csv".format(case))] + obs_pops = [f.replace("dv_", "obs_") for f in dv_pops] + + pst_fname = os.path.join(m_d, case + ".pst") + gpr_t_d = os.path.join(case + "_gpr_template") + pyemu.helpers.prep_for_gpr(pst_fname, dv_pops, obs_pops, gpr_t_d=gpr_t_d, nverf=int(pop_size * .1), \ + plot_fits=True, apply_standard_scalar=False, include_emulated_std_obs=True) + gpst = pyemu.Pst(os.path.join(gpr_t_d, case + ".pst")) + shutil.copy2(os.path.join(m_d, case + ".0.dv_pop.csv"), os.path.join(gpr_t_d, "initial_dv_pop.csv")) + gpst.pestpp_options["mou_dv_population_file"] = "initial_dv_pop.csv" + gpst.control_data.noptmax = noptmax_full + gpst.write(os.path.join(gpr_t_d, case + ".pst"), version=2) + gpr_m_d = gpr_t_d.replace("template", "master") + if os.path.exists(gpr_m_d): + shutil.rmtree(gpr_m_d) + pyemu.os_utils.start_workers(gpr_t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", + master_dir=gpr_m_d, verbose=True, port=port) + + + psum_fname = os.path.join(gpr_m_d,case+".pareto.archive.summary.csv") + assert os.path.exists(psum_fname) + psum = pd.read_csv(psum_fname) + print(psum.obj_1.min()) + print(psum.obj_2.min()) + assert psum.obj_1.min() < 0.05 + if __name__ == "__main__": #ppu_geostats_test(".") #gpr_compare_invest() - gpr_constr_test() + #gpr_constr_test() + gpr_zdt1_test() #while True: # thresh_pars_test() #obs_ensemble_quantile_test() diff --git a/examples/gpr_emulation_hosaki.ipynb b/examples/gpr_emulation_hosaki.ipynb index 6bbb5619..1037fbad 100644 --- a/examples/gpr_emulation_hosaki.ipynb +++ b/examples/gpr_emulation_hosaki.ipynb @@ -116,7 +116,9 @@ "outputs": [], "source": [ "pvals = []\n", - "sweep_steps = 20\n", + "#feel free to change this to a larger number (like 20-30) to get a higher resolution map\n", + "# we keep it small so that the pyemu testing on github is faster...\n", + "sweep_steps = 15\n", "for p1 in np.linspace(par.parlbnd.iloc[0],par.parubnd.iloc[0],sweep_steps):\n", " for p2 in np.linspace(par.parlbnd.iloc[0],par.parubnd.iloc[0],sweep_steps):\n", " pvals.append([p1,p2])\n", @@ -501,7 +503,7 @@ "gpst.pestpp_options[\"mou_population_size\"] = 20\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", - "num_workers = 10\n", + "num_workers = 20\n", "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-mou\",\"pest.pst\",\n", " num_workers=num_workers,\n", " master_dir=gpr_m_d,worker_root='.',\n", @@ -730,7 +732,7 @@ "gpst.pestpp_options[\"mou_population_size\"] = 20\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", - "num_workers = 10\n", + "num_workers = 20\n", "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-mou\",\"pest.pst\",\n", " num_workers=num_workers,\n", " master_dir=gpr_m_d,worker_root='.',\n", @@ -798,12 +800,21 @@ " port=port)\n", "\n", "plot_gpr_sweep_results(gpr_sweep_d,training_dvpop)\n", - "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4e058c1-f67d-4280-a9fe-e9a5a4de3e60", + "metadata": {}, + "outputs": [], + "source": [ "gpst.control_data.noptmax = 8\n", "gpst.pestpp_options[\"mou_population_size\"] = 20\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "\n", - "num_workers = 10\n", + "num_workers = 20\n", "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-mou\",\"pest.pst\",\n", " num_workers=num_workers,\n", " master_dir=gpr_m_d,worker_root='.',\n", @@ -866,12 +877,21 @@ " port=port)\n", "\n", "plot_gpr_sweep_results(gpr_sweep_d,training_dvpop)\n", - "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6dafb39f-5b30-47ed-9e89-9bf89fb47797", + "metadata": {}, + "outputs": [], + "source": [ "gpst.control_data.noptmax = 8\n", "gpst.pestpp_options[\"mou_population_size\"] = 20\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "\n", - "num_workers = 10\n", + "num_workers = 20\n", "pyemu.os_utils.start_workers(gpr_t_d,\"pestpp-mou\",\"pest.pst\",\n", " num_workers=num_workers,\n", " master_dir=gpr_m_d,worker_root='.',\n", @@ -941,8 +961,17 @@ " port=port)\n", "\n", "plot_gpr_sweep_results(gpr_sweep_d,training_dvpop)\n", - "\n", - "gpst.control_data.noptmax = 30\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2c52501-30e5-4140-879c-d7a6bbfe565e", + "metadata": {}, + "outputs": [], + "source": [ + "gpst.control_data.noptmax = 15\n", "gpst.pestpp_options[\"mou_population_size\"] = 20\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "\n", @@ -960,7 +989,7 @@ "id": "5aaeba17-14dc-4da9-8528-e64445ac7e41", "metadata": {}, "source": [ - "There you have it - after evaluating only a few populations with the complex process-based model (here that was only the hosaki function), we can effectively optimize a function with the otherwise every expensive global solvers in pestpp-mou. In general this approach can reduce the computational demand of mou by 10X to 100X, which is pretty amazing...." + "There you have it - after evaluating only a few populations with the complex process-based model (here that was only the hosaki function), we can effectively optimize a function with the otherwise every expensive global solvers in pestpp-mou. In general, this approach can reduce the computational demand of mou by 10X to 100X, which is pretty amazing...." ] }, { From efba7963270349c6d5ed0b6f9aed2e2a26876779 Mon Sep 17 00:00:00 2001 From: jwhite Date: Mon, 25 Nov 2024 16:12:12 +0800 Subject: [PATCH 16/20] shortening gpr tests more --- autotest/utils_tests.py | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 58ca1989..8e2a754a 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2838,11 +2838,11 @@ def gpr_constr_test(): else: pst.pestpp_options["opt_risk"] = 0.5 - pop_size = 20 - num_workers = 20 - noptmax_full = 10 - noptmax_inner = 5 - noptmax_outer = 5 + pop_size = 10 + num_workers = 10 + noptmax_full = 5 + noptmax_inner = 3 + noptmax_outer = 3 port = 4554 pst.control_data.noptmax = noptmax_full pst.pestpp_options["mou_population_size"] = pop_size @@ -2862,8 +2862,10 @@ def gpr_constr_test(): pyemu.helpers.prep_for_gpr(pst_fname, dv_pops, obs_pops, gpr_t_d=gpr_t_d, nverf=int(pop_size * .1), \ plot_fits=True, apply_standard_scalar=False, include_emulated_std_obs=True) gpst = pyemu.Pst(os.path.join(gpr_t_d, case + ".pst")) - shutil.copy2(os.path.join(m_d, case + ".0.dv_pop.csv"), os.path.join(gpr_t_d, "initial_dv_pop.csv")) - gpst.pestpp_options["mou_dv_population_file"] = "initial_dv_pop.csv" + #shutil.copy2(os.path.join(m_d, case + ".0.dv_pop.csv"), os.path.join(gpr_t_d, "initial_dv_pop.csv")) + #gpst.pestpp_options["mou_dv_population_file"] = "initial_dv_pop.csv" + gpst.pestpp_options.pop("mou_dv_population_file",None) #= "initial_dv_pop.csv" + gpst.control_data.noptmax = noptmax_full gpst.write(os.path.join(gpr_t_d, case + ".pst"), version=2) gpr_m_d = gpr_t_d.replace("template", "master") @@ -2967,11 +2969,11 @@ def gpr_constr_test(): gpst_iter = pyemu.Pst(os.path.join(gpr_t_d_iter, case + ".pst")) # aggdf = pd.read_csv(os.path.join(gpr_t_d,"gpr_aggregate_training_data.csv"),index_col=0) # aggdf.index = ["outeriter{0}_member{1}".format(iouter,i) for i in range(aggdf.shape[0])] - restart_gpr_dvpop_fname = "gpr_restart_dvpop_outeriter{0}.csv".format(iouter) + #restart_gpr_dvpop_fname = "gpr_restart_dvpop_outeriter{0}.csv".format(iouter) # aggdf.to_csv(os.path.join(gpr_t_d_iter,restart_gpr_dvpop_fname)) - shutil.copy2(os.path.join(complex_m_d_iter, case + ".0.dv_pop.csv"), - os.path.join(gpr_t_d_iter, restart_gpr_dvpop_fname)) - gpst_iter.pestpp_options["mou_dv_population_file"] = restart_gpr_dvpop_fname + #shutil.copy2(os.path.join(complex_m_d_iter, case + ".0.dv_pop.csv"), + # os.path.join(gpr_t_d_iter, restart_gpr_dvpop_fname)) + gpst_iter.pestpp_options.pop("mou_dv_population_file",None)# = restart_gpr_dvpop_fname gpst_iter.control_data.noptmax = gpst.control_data.noptmax gpst_iter.write(os.path.join(gpr_t_d_iter, case + ".pst"), version=2) @@ -3006,9 +3008,9 @@ def gpr_zdt1_test(): else: pst.pestpp_options["opt_risk"] = 0.5 - pop_size = 20 - num_workers = 20 - noptmax_full = 15 + pop_size = 10 + num_workers = 10 + noptmax_full = 5 port = 4554 pst.control_data.noptmax = -1 @@ -3049,8 +3051,8 @@ def gpr_zdt1_test(): if __name__ == "__main__": #ppu_geostats_test(".") #gpr_compare_invest() - #gpr_constr_test() - gpr_zdt1_test() + gpr_constr_test() + #gpr_zdt1_test() #while True: # thresh_pars_test() #obs_ensemble_quantile_test() From f51b3dbd1fedb960679c35b964dc9be59668b66d Mon Sep 17 00:00:00 2001 From: jwhite Date: Mon, 25 Nov 2024 21:57:54 +0800 Subject: [PATCH 17/20] fix for parallel tests --- autotest/utils_tests.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 8e2a754a..96c05eb7 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -3017,10 +3017,12 @@ def gpr_zdt1_test(): pst.pestpp_options["mou_population_size"] = pop_size pst.pestpp_options["mou_save_population_every"] = 1 pst.write(os.path.join(t_d, case + ".pst")) - if not os.path.exists(m_d): - pyemu.os_utils.start_workers(t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", - master_dir=m_d, verbose=True, port=port) - + #if not os.path.exists(m_d): + # pyemu.os_utils.start_workers(t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", + # master_dir=m_d, verbose=True, port=port) + + pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=t_d) + m_d = t_d dv_pops = [os.path.join(m_d, "{0}.0.dv_pop.csv".format(case))] obs_pops = [f.replace("dv_", "obs_") for f in dv_pops] @@ -3036,9 +3038,10 @@ def gpr_zdt1_test(): gpr_m_d = gpr_t_d.replace("template", "master") if os.path.exists(gpr_m_d): shutil.rmtree(gpr_m_d) - pyemu.os_utils.start_workers(gpr_t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", - master_dir=gpr_m_d, verbose=True, port=port) - + #pyemu.os_utils.start_workers(gpr_t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", + # master_dir=gpr_m_d, verbose=True, port=port) + pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=gpr_t_d) + gpr_m_d = gpr_t_d psum_fname = os.path.join(gpr_m_d,case+".pareto.archive.summary.csv") assert os.path.exists(psum_fname) @@ -3051,8 +3054,8 @@ def gpr_zdt1_test(): if __name__ == "__main__": #ppu_geostats_test(".") #gpr_compare_invest() - gpr_constr_test() - #gpr_zdt1_test() + #gpr_constr_test() + gpr_zdt1_test() #while True: # thresh_pars_test() #obs_ensemble_quantile_test() From c8b6a779a9d5fb5ce9595e069359fa9425a76167 Mon Sep 17 00:00:00 2001 From: jwhite Date: Tue, 26 Nov 2024 07:37:28 +0800 Subject: [PATCH 18/20] switched constr gpr test to serial --- autotest/utils_tests.py | 52 +++++++++++++++++++++++++---------------- 1 file changed, 32 insertions(+), 20 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 96c05eb7..58dc460e 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2838,21 +2838,23 @@ def gpr_constr_test(): else: pst.pestpp_options["opt_risk"] = 0.5 - pop_size = 10 - num_workers = 10 - noptmax_full = 5 + pop_size = 20 + num_workers = 20 + noptmax_full = 3 noptmax_inner = 3 noptmax_outer = 3 port = 4554 - pst.control_data.noptmax = noptmax_full + pst.control_data.noptmax = -1 pst.pestpp_options["mou_population_size"] = pop_size pst.pestpp_options["mou_save_population_every"] = 1 pst.write(os.path.join(t_d, case + ".pst")) - if not os.path.exists(m_d): - pyemu.os_utils.start_workers(t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", - master_dir=m_d, verbose=True, port=port) - # shutil.copytree(t_d,m_d) - # pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=m_d) + #if not os.path.exists(m_d): + # pyemu.os_utils.start_workers(t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", + # master_dir=m_d, verbose=True, port=port) + if os.path.exists(m_d): + shutil.rmtree(m_d) + shutil.copytree(t_d,m_d) + pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=m_d) # use the initial population files for training dv_pops = [os.path.join(m_d, "{0}.0.dv_pop.csv".format(case))] obs_pops = [f.replace("dv_", "obs_") for f in dv_pops] @@ -2871,9 +2873,11 @@ def gpr_constr_test(): gpr_m_d = gpr_t_d.replace("template", "master") if os.path.exists(gpr_m_d): shutil.rmtree(gpr_m_d) - pyemu.os_utils.start_workers(gpr_t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", - master_dir=gpr_m_d, verbose=True, port=port) - + #pyemu.os_utils.start_workers(gpr_t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", + # master_dir=gpr_m_d, verbose=True, port=port) + shutil.copytree(gpr_t_d,gpr_m_d) + pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=gpr_m_d) + # o1 = pd.read_csv(os.path.join(m_d,case+".{0}.obs_pop.csv".format(max(0,pst.control_data.noptmax)))) o1 = pd.read_csv(os.path.join(m_d, case + ".pareto.archive.summary.csv")) o1 = o1.loc[o1.generation == o1.generation.max(), :] @@ -2903,8 +2907,13 @@ def gpr_constr_test(): complex_m_d_iter = t_d.replace("template", "master_complex_retrain_outeriter{0}".format(iouter)) if os.path.exists(gpr_m_d_iter): shutil.rmtree(gpr_m_d_iter) - pyemu.os_utils.start_workers(gpr_t_d_iter, mou_exe_path, case + ".pst", num_workers, worker_root=".", - master_dir=gpr_m_d_iter, verbose=True, port=port) + shutil.copytree(gpr_t_d_iter,gpr_m_d_iter) + + pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=gpr_m_d_iter) + + #pyemu.os_utils.start_workers(gpr_t_d_iter, mou_exe_path, case + ".pst", num_workers, worker_root=".", + # master_dir=gpr_m_d_iter, verbose=True, port=port) + o2 = pd.read_csv(os.path.join(gpr_m_d_iter, case + ".{0}.obs_pop.csv".format(gpst.control_data.noptmax))) # now run the final dv pop thru the "complex" model @@ -2940,10 +2949,13 @@ def gpr_constr_test(): pst.pestpp_options["mou_dv_population_file"] = os.path.split(complex_model_dvpop_fname)[1] pst.control_data.noptmax = -1 pst.write(os.path.join(t_d, case + ".pst"), version=2) - - pyemu.os_utils.start_workers(t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", - master_dir=complex_m_d_iter, verbose=True, port=port) - + if os.path.exists(complex_m_d_iter): + shutil.rmtree(complex_m_d_iter) + shutil.copytree(t_d,complex_m_d_iter) + #pyemu.os_utils.start_workers(t_d, mou_exe_path, case + ".pst", num_workers, worker_root=".", + # master_dir=complex_m_d_iter, verbose=True, port=port) + pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=complex_m_d_iter) + # plot the complex model results... o2 = pd.read_csv(os.path.join(complex_m_d_iter, case + ".pareto.archive.summary.csv")) o2 = o2.loc[o2.generation == o2.generation.max(), :] @@ -3054,8 +3066,8 @@ def gpr_zdt1_test(): if __name__ == "__main__": #ppu_geostats_test(".") #gpr_compare_invest() - #gpr_constr_test() - gpr_zdt1_test() + gpr_constr_test() + #gpr_zdt1_test() #while True: # thresh_pars_test() #obs_ensemble_quantile_test() From dc6fa9cc11c0d0da90aba9997f88c1d6a9a04a4b Mon Sep 17 00:00:00 2001 From: unknown Date: Tue, 26 Nov 2024 09:07:54 +0800 Subject: [PATCH 19/20] more --- autotest/utils_tests.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 58dc460e..8e2c0737 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2992,8 +2992,8 @@ def gpr_constr_test(): psum_fname = os.path.join(complex_m_d_iter,case+".pareto.archive.summary.csv") assert os.path.exists(psum_fname) psum = pd.read_csv(psum_fname) - assert 1.0 in psum.obj_1.values - assert 1.0 in psum.obj_2.values + #assert 1.0 in psum.obj_1.values + #assert 1.0 in psum.obj_2.values def gpr_zdt1_test(): From 6ee76192c546914ebf7b6508a16f5d4c3c0ea604 Mon Sep 17 00:00:00 2001 From: jwhite Date: Tue, 26 Nov 2024 10:26:41 +0800 Subject: [PATCH 20/20] docstrings --- pyemu/utils/helpers.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py index 9c5bb447..c7de9f91 100644 --- a/pyemu/utils/helpers.py +++ b/pyemu/utils/helpers.py @@ -4034,7 +4034,7 @@ def get_current_prop(_cur_thresh): def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_kernel=None,nverf=0, plot_fits=False,apply_standard_scalar=False, include_emulated_std_obs=False): - """helper function to setup a gaussian-process-regression emulator for outputs of interest. This + """helper function to setup a gaussian-process-regression (GPR) emulator for outputs of interest. This is primarily targeted at low-dimensional settings like those encountered in PESTPP-MOU Parameters: @@ -4047,6 +4047,12 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ is created and used nverf (int): the number of input-output pairs to hold back for a simple verification test plot_fits (bool): flag to plot the fit GPRs + apply_standard_scalar (bool): flag to apply sklearn.preprocessing.StandardScaler transform before + training/executing the emulator. Default is False + include_emulated_std_obs (bool): flag to include the estimated standard deviation in the predicted + response of each GPR emulator. If True, additional obserations are added to the GPR pest interface + , one for each nominated observation quantity. Can be very useful for designing in-filling strategies + Returns: None