From 3be6bcd52423152de7f40d27d631a8c9fd085a4a Mon Sep 17 00:00:00 2001 From: jdub Date: Wed, 1 Nov 2023 16:24:38 +1300 Subject: [PATCH 01/56] 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 530cc2883..000000000 --- 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 19b7ed76d..3f7771988 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 02/56] 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 3f7771988..d4ea091a3 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 03/56] 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 d4ea091a3..1babdd166 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 04/56] 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 1babdd166..caf8dc83f 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 05/56] 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 caf8dc83f..ad759a564 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 06/56] 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 4c121d35d..fbf4b40b5 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 07/56] env --- etc/environment.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/etc/environment.yml b/etc/environment.yml index efa493d4a..396ff928e 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 08/56] 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 711459bb1..3433fed57 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 6dee76a9b1cad2f0b3e7e93f1ef5bb40ea187bb5 Mon Sep 17 00:00:00 2001 From: jwhite Date: Fri, 1 Nov 2024 10:23:33 -0500 Subject: [PATCH 09/56] starting on py worker --- pyemu/utils/os_utils.py | 119 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 119 insertions(+) diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index d95ee26ea..2c95d20e0 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -464,3 +464,122 @@ def start_workers( ret_val = master_p.returncode if ret_val != 0: raise Exception("start_workers() master returned non-zero: {0}".format(ret_val)) + + + +class NetPack(object): + netpack_type = { + 0: "UNKN", 1: "OK", 2: "CONFIRM_OK", 3: "READY", + 4: "REQ_RUNDIR", 5: "RUNDIR", 6: "REQ_LINPACK", + 7: "LINPACK", 8: "PAR_NAMES", 9: "OBS_NAMES", + 10: "START_RUN", 11: "RUN_FINISHED", 12: "RUN_FAILED", + 13: "RUN_KILLED", 14: "TERMINATE", 15: "PING", + 16: "REQ_KILL", 17: "IO_ERROR", 18: "CORRUPT_MESG", + 19: "DEBUG_LOOP", 20: "DEBUG_FAIL_FREEZE", + 21: "START_FILE_WRKR2MSTR", 22: "CONT_FILE_WRKR2MSTR", + 23: "FINISH_FILE_WRKR2MSTR"} + sec_message = [1, 3, 5, 7, 9] + def __init__(self): + self.header_size = 8 + 4 + 8 + 8 + 1001 + self.buf_size = None + self.buf_idx = (0, 8) + self.type_idx = (8, 12) + self.group_idx = (12, 20) + self.runid_idx = (20, 28) + self.desc_idx = (28, 1028) + self.data_idx = (1028,None) + + self.buf_size = None + self.mtype = None + self.group = None + self.runid = None + self.desc = None + self.data_pak = None + + # def recv(self,num_bytes): + # data = bytes() + # total = 0 + # bytes_left = num_bytes + # while total < num_bytes: + # data += s.recv() + + def recv(self,s): + recv_sec_message = None + while True: + data = s.recv(5) + if len(data) > 0: + recv_sec_message = [int(d) for d in data] + break + self._check_sec_message(recv_sec_message) + while True: + data = s.recv(self.header_size) + if len(data) > 0: + break + self.buf_size = int.from_bytes(data[self.buf_idx[0]:self.buf_idx[1]], "little") + self.mtype = int.from_bytes(data[self.type_idx[0]:self.type_idx[1]], "little") + self.group = int.from_bytes(data[self.group_idx[0]:self.group_idx[1]], "little") + self.runid = int.from_bytes(data[self.runid_idx[0]:self.runid_idx[1]], "little") + self.desc = data[self.desc_idx[0]:self.desc_idx[1]].decode().strip("\\0") + self.data_pak = data[self.data_idx[0]:] + + def send(self,s,mtype,group,runid,desc,data): + #buf = bytearray + buf_size = self.header_size + #buf.append() + + def _check_sec_message(self,recv_sec_message): + if recv_sec_message != self.sec_message: + raise Exception("recv'd security message {0} invalid, should be {1}".\ + format(recv_sec_message,self.sec_message)) + +class PyPestWorker(object): + + + def __init__(self, pst, host, port): + self.host = host + self.port = port + self._pst_arg = pst + self.s = None + self.net_pack = NetPack() + + def _process_pst(self): + pass + + def connect(self): + self.s = None + while True: + try: + self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + self.s.connect((self.host, self.port)) + print("-->connected to {0}:{1}".format(self.host,self.port)) + + break + except ConnectionRefusedError: + continue + except Exception as e: + continue + + def recv(self): + self.net_pack.recv(self.s) + + def send(self,mtype,group,runid,desc="",data=0): + self.net_pack.send(self.s,mtype,group,runid,desc,data) + + def initialize(self): + self.connect() + + #recv initial comms + self.recv() + print("recv'd message type:",NetPack.netpack_type[self.net_pack.mtype]) + if self.net_pack.mtype != 4: + raise Exception("unexpected net pack type, should be {0}, not {1}".\ + format(NetPack.netpack_type[4], + NetPack.netpack_type[self.net_pack.mtype])) + self.send(self,5,os.getcwd()) + +if __name__ == "__main__": + host = "localhost" + port = 4004 + + ppw = PyPestWorker(None,host,port) + ppw.initialize() From 542348a0c61c6ab69c1dc32fa8e87aa069795c81 Mon Sep 17 00:00:00 2001 From: jwhite Date: Fri, 1 Nov 2024 10:42:05 -0500 Subject: [PATCH 10/56] starting on py worker --- pyemu/utils/os_utils.py | 31 +++++++++++++++++++++++++++---- 1 file changed, 27 insertions(+), 4 deletions(-) diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index 2c95d20e0..7284cf33c 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -10,6 +10,9 @@ import socket import time from datetime import datetime + +import numpy as np + from ..pyemu_warnings import PyemuWarning ext = "" @@ -522,10 +525,29 @@ def recv(self,s): self.desc = data[self.desc_idx[0]:self.desc_idx[1]].decode().strip("\\0") self.data_pak = data[self.data_idx[0]:] + def _serialize_data(self,data): + if isinstance(data,str): + return data.encode() + elif isinstance(data,list): + return np.array(data).tobytes() + elif isinstance(data,np.ndarray): + return data.tobytes() + else: + raise Exception("can't serialize unknown 'data' type {0}".format(data)) + def send(self,s,mtype,group,runid,desc,data): - #buf = bytearray - buf_size = self.header_size - #buf.append() + buf = bytearray() + sdata = self._serialize_data(data) + "\x00".encode() + buf_size = self.header_size + len(sdata) + buf += buf_size.to_bytes(length=8,byteorder="little") + buf += mtype.to_bytes(length=4,byteorder="little") + buf += group.to_bytes(length=8, byteorder="little") + buf += runid.to_bytes(length=8, byteorder="little") + fill_desc = "\x00" * (1001 - len(desc)) + full_desc = desc + fill_desc + buf += full_desc.encode() + buf += sdata + s.send(buf) def _check_sec_message(self,recv_sec_message): if recv_sec_message != self.sec_message: @@ -575,7 +597,8 @@ def initialize(self): raise Exception("unexpected net pack type, should be {0}, not {1}".\ format(NetPack.netpack_type[4], NetPack.netpack_type[self.net_pack.mtype])) - self.send(self,5,os.getcwd()) + self.send(mtype=5,group=self.net_pack.group, + runid=self.net_pack.runid,desc="sending cwd",data=os.getcwd()) if __name__ == "__main__": host = "localhost" From a7818367dc8be31dc72502560d1f6434d3103a46 Mon Sep 17 00:00:00 2001 From: jwhite Date: Fri, 1 Nov 2024 12:25:40 -0500 Subject: [PATCH 11/56] more --- pyemu/utils/os_utils.py | 47 +++++++++++++++++++++++++++++++++-------- 1 file changed, 38 insertions(+), 9 deletions(-) diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index 7284cf33c..6ebf8c0a0 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -3,6 +3,7 @@ import os import sys import platform +import time import shutil import subprocess as sp import multiprocessing as mp @@ -482,7 +483,8 @@ class NetPack(object): 21: "START_FILE_WRKR2MSTR", 22: "CONT_FILE_WRKR2MSTR", 23: "FINISH_FILE_WRKR2MSTR"} sec_message = [1, 3, 5, 7, 9] - def __init__(self): + + def __init__(self,timeout=0.1): self.header_size = 8 + 4 + 8 + 8 + 1001 self.buf_size = None self.buf_idx = (0, 8) @@ -499,6 +501,13 @@ def __init__(self): self.desc = None self.data_pak = None + self.timeout = float(timeout) + + self.sec_message_buf = bytearray() + for sm in NetPack.sec_message: + self.sec_message_buf += sm.to_bytes(1,byteorder="little") + + # def recv(self,num_bytes): # data = bytes() # total = 0 @@ -509,21 +518,28 @@ def __init__(self): def recv(self,s): recv_sec_message = None while True: - data = s.recv(5) + data = s.recv(len(self.sec_message_buf)) if len(data) > 0: recv_sec_message = [int(d) for d in data] break + time.sleep(self.timeout) self._check_sec_message(recv_sec_message) while True: data = s.recv(self.header_size) if len(data) > 0: break + time.sleep(self.timeout) self.buf_size = int.from_bytes(data[self.buf_idx[0]:self.buf_idx[1]], "little") self.mtype = int.from_bytes(data[self.type_idx[0]:self.type_idx[1]], "little") self.group = int.from_bytes(data[self.group_idx[0]:self.group_idx[1]], "little") self.runid = int.from_bytes(data[self.runid_idx[0]:self.runid_idx[1]], "little") - self.desc = data[self.desc_idx[0]:self.desc_idx[1]].decode().strip("\\0") - self.data_pak = data[self.data_idx[0]:] + self.desc = data[self.desc_idx[0]:self.desc_idx[1]].decode() + data_len = self.buf_size - self.data_idx[0] + self.data_pak = None + if data_len > 0: + self.data_pak = s.recv(data_len) + #self.data_pak = data[self.data_idx[0]:] + print() def _serialize_data(self,data): if isinstance(data,str): @@ -537,6 +553,7 @@ def _serialize_data(self,data): def send(self,s,mtype,group,runid,desc,data): buf = bytearray() + buf += self.sec_message_buf sdata = self._serialize_data(data) + "\x00".encode() buf_size = self.header_size + len(sdata) buf += buf_size.to_bytes(length=8,byteorder="little") @@ -548,6 +565,8 @@ def send(self,s,mtype,group,runid,desc,data): buf += full_desc.encode() buf += sdata s.send(buf) + print() + def _check_sec_message(self,recv_sec_message): if recv_sec_message != self.sec_message: @@ -557,12 +576,13 @@ def _check_sec_message(self,recv_sec_message): class PyPestWorker(object): - def __init__(self, pst, host, port): + def __init__(self, pst, host, port, timeout=0.1): self.host = host self.port = port self._pst_arg = pst self.s = None - self.net_pack = NetPack() + self.timeout = float(timeout) + self.net_pack = NetPack(timeout=timeout) def _process_pst(self): pass @@ -574,8 +594,8 @@ def connect(self): self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.s.connect((self.host, self.port)) print("-->connected to {0}:{1}".format(self.host,self.port)) - break + time.sleep(self.timeout) except ConnectionRefusedError: continue except Exception as e: @@ -583,16 +603,19 @@ def connect(self): def recv(self): self.net_pack.recv(self.s) + print("recv'd message type:", NetPack.netpack_type[self.net_pack.mtype]) + def send(self,mtype,group,runid,desc="",data=0): self.net_pack.send(self.s,mtype,group,runid,desc,data) + print("sent message type:", NetPack.netpack_type[mtype]) def initialize(self): self.connect() - #recv initial comms + #request for cwd self.recv() - print("recv'd message type:",NetPack.netpack_type[self.net_pack.mtype]) + if self.net_pack.mtype != 4: raise Exception("unexpected net pack type, should be {0}, not {1}".\ format(NetPack.netpack_type[4], @@ -600,6 +623,12 @@ def initialize(self): self.send(mtype=5,group=self.net_pack.group, runid=self.net_pack.runid,desc="sending cwd",data=os.getcwd()) + # recv par names + self.recv() + print() + + + if __name__ == "__main__": host = "localhost" port = 4004 From 26965fdeb7f506092cfaa78aed64b97af01d2d7c Mon Sep 17 00:00:00 2001 From: jwhite Date: Fri, 1 Nov 2024 14:44:06 -0500 Subject: [PATCH 12/56] initailization rough'd in --- pyemu/utils/os_utils.py | 53 ++++++++++++++++++++++++++++++++++------- 1 file changed, 44 insertions(+), 9 deletions(-) diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index 6ebf8c0a0..3aebfb887 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -4,6 +4,7 @@ import sys import platform import time +import struct import shutil import subprocess as sp import multiprocessing as mp @@ -515,7 +516,7 @@ def __init__(self,timeout=0.1): # while total < num_bytes: # data += s.recv() - def recv(self,s): + def recv(self,s,dtype=None): recv_sec_message = None while True: data = s.recv(len(self.sec_message_buf)) @@ -533,28 +534,43 @@ def recv(self,s): self.mtype = int.from_bytes(data[self.type_idx[0]:self.type_idx[1]], "little") self.group = int.from_bytes(data[self.group_idx[0]:self.group_idx[1]], "little") self.runid = int.from_bytes(data[self.runid_idx[0]:self.runid_idx[1]], "little") - self.desc = data[self.desc_idx[0]:self.desc_idx[1]].decode() - data_len = self.buf_size - self.data_idx[0] + self.desc = data[self.desc_idx[0]:self.desc_idx[1]].decode().replace("\x00","") + data_len = self.buf_size - self.data_idx[0] - 1 self.data_pak = None if data_len > 0: - self.data_pak = s.recv(data_len) + self.data_pak = self.deserialize_data(s.recv(data_len),dtype) #self.data_pak = data[self.data_idx[0]:] print() - def _serialize_data(self,data): + def deserialize_data(self,data,dtype=None): + if dtype is not None and dtype in [int,float]: + return np.frombuffer(data,dtype=dtype) + ddata = np.array(data.decode().lower().replace('\x00',' ').split()) + if dtype is not None: + ddata = ddata.astype(dtype) + return ddata + + def serialize_data(self,data): + if isinstance(data,str): return data.encode() elif isinstance(data,list): return np.array(data).tobytes() elif isinstance(data,np.ndarray): return data.tobytes() + elif isinstance(data,int): + print("warning: casting int to float to serialize") + return struct.pack('d',float(data)) + elif isinstance(data,float): + return struct.pack('d', data) + else: raise Exception("can't serialize unknown 'data' type {0}".format(data)) def send(self,s,mtype,group,runid,desc,data): buf = bytearray() buf += self.sec_message_buf - sdata = self._serialize_data(data) + "\x00".encode() + sdata = self.serialize_data(data) + "\x00".encode() buf_size = self.header_size + len(sdata) buf += buf_size.to_bytes(length=8,byteorder="little") buf += mtype.to_bytes(length=4,byteorder="little") @@ -584,6 +600,9 @@ def __init__(self, pst, host, port, timeout=0.1): self.timeout = float(timeout) self.net_pack = NetPack(timeout=timeout) + self.par_names = None + self.obs_names = None + def _process_pst(self): pass @@ -601,8 +620,8 @@ def connect(self): except Exception as e: continue - def recv(self): - self.net_pack.recv(self.s) + def recv(self,dtype=None): + self.net_pack.recv(self.s,dtype=dtype) print("recv'd message type:", NetPack.netpack_type[self.net_pack.mtype]) @@ -623,8 +642,22 @@ def initialize(self): self.send(mtype=5,group=self.net_pack.group, runid=self.net_pack.runid,desc="sending cwd",data=os.getcwd()) - # recv par names + # par names self.recv() + # todo: check revc'd mtype + self.par_names = self.net_pack.data_pak.copy() + + # obs nanes + self.recv() + # todo check recv'd mtype + self.obs_names = self.net_pack.data_pak.copy() + + # lin pack request + self.recv() + + + self.send(7,self.net_pack.group,self.net_pack.runid,"fake linpack result",data=1) + print() @@ -635,3 +668,5 @@ def initialize(self): ppw = PyPestWorker(None,host,port) ppw.initialize() + + From 126bcfdddd1c909ea8e3b4e44507682a553a2e07 Mon Sep 17 00:00:00 2001 From: jwhite Date: Sat, 2 Nov 2024 10:23:10 -0500 Subject: [PATCH 13/56] more --- pyemu/utils/os_utils.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index 3aebfb887..c4f542e6f 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -16,6 +16,7 @@ import numpy as np from ..pyemu_warnings import PyemuWarning +from ..pst import pst_handler ext = "" bin_path = os.path.join("..", "bin") @@ -596,6 +597,7 @@ def __init__(self, pst, host, port, timeout=0.1): self.host = host self.port = port self._pst_arg = pst + self._pst = None self.s = None self.timeout = float(timeout) self.net_pack = NetPack(timeout=timeout) @@ -603,8 +605,17 @@ def __init__(self, pst, host, port, timeout=0.1): self.par_names = None self.obs_names = None + self._process_pst() + def _process_pst(self): - pass + if isinstance(self._pst_arg,str): + self._pst = pst_handler.Pst(self._pst_arg) + elif isinstance(self._pst_arg,pst_handler.Pst): + self._pst = self._pst_arg + else: + raise Exception("unrecognized 'pst' arg:{0}".\ + format(type(self._pst_arg))) + def connect(self): self.s = None From 91a0f99d64f68d8a47c7571f78ce20ed17f7e6d4 Mon Sep 17 00:00:00 2001 From: jwhite Date: Sun, 3 Nov 2024 10:11:27 -0600 Subject: [PATCH 14/56] refactor to listen fxn, prep for threading --- pyemu/utils/os_utils.py | 159 +++++++++++++++++++++++++++++----------- 1 file changed, 116 insertions(+), 43 deletions(-) diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index c4f542e6f..b7dd91260 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -517,20 +517,44 @@ def __init__(self,timeout=0.1): # while total < num_bytes: # data += s.recv() + def nonblocking_recv(self,s,msg_len): + try: + msg = s.recv(msg_len) + except socket.timeout as e: + emess = e.args[0] + if emess == 'timed out': + return None + else: + raise Exception(e) + except socket.error as e: + # Something else happened, handle error, exit, etc. + raise Exception(e) + else: + if len(msg) == 0: + return None + else: + return msg + # got a message do something :) + def recv(self,s,dtype=None): recv_sec_message = None - while True: - data = s.recv(len(self.sec_message_buf)) - if len(data) > 0: - recv_sec_message = [int(d) for d in data] - break - time.sleep(self.timeout) + + #data = s.recv(len(self.sec_message_buf)) + data = self.nonblocking_recv(s,len(self.sec_message_buf)) + + #if len(data) == 0: + # return 0 + if data is None: + return 0 + + recv_sec_message = [int(d) for d in data] self._check_sec_message(recv_sec_message) - while True: - data = s.recv(self.header_size) - if len(data) > 0: - break - time.sleep(self.timeout) + #data = s.recv(self.header_size) + #if len(data) == 0: + # return -1 + data = self.nonblocking_recv(s,self.header_size) + if data is None: + raise Exception("didnt recv header after security message") self.buf_size = int.from_bytes(data[self.buf_idx[0]:self.buf_idx[1]], "little") self.mtype = int.from_bytes(data[self.type_idx[0]:self.type_idx[1]], "little") self.group = int.from_bytes(data[self.group_idx[0]:self.group_idx[1]], "little") @@ -539,9 +563,14 @@ def recv(self,s,dtype=None): data_len = self.buf_size - self.data_idx[0] - 1 self.data_pak = None if data_len > 0: - self.data_pak = self.deserialize_data(s.recv(data_len),dtype) - #self.data_pak = data[self.data_idx[0]:] - print() + raw_data = self.nonblocking_recv(s,data_len) + if raw_data is None: + raise Exception("didnt recv data pack after header of {0} bytes".format(data_len)) + if dtype is None and self.mtype == 10: + dtype = float + self.data_pak = self.deserialize_data(raw_data,dtype) + return len(data) + data_len + def deserialize_data(self,data,dtype=None): if dtype is not None and dtype in [int,float]: @@ -582,7 +611,6 @@ def send(self,s,mtype,group,runid,desc,data): buf += full_desc.encode() buf += sdata s.send(buf) - print() def _check_sec_message(self,recv_sec_message): @@ -593,7 +621,7 @@ def _check_sec_message(self,recv_sec_message): class PyPestWorker(object): - def __init__(self, pst, host, port, timeout=0.1): + def __init__(self, pst, host, port, timeout=0.25): self.host = host self.port = port self._pst_arg = pst @@ -605,7 +633,11 @@ def __init__(self, pst, host, port, timeout=0.1): self.par_names = None self.obs_names = None + self.par_values = None + self._process_pst() + self.connect() + def _process_pst(self): if isinstance(self._pst_arg,str): @@ -618,66 +650,107 @@ def _process_pst(self): def connect(self): + print("trying to connect to {0}:{1}...".format(self.host,self.port)) self.s = None + c = 0 while True: try: + time.sleep(self.timeout) + print(".", end='') + c += 1 + if c % 75 == 0: + print('') self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.s.connect((self.host, self.port)) print("-->connected to {0}:{1}".format(self.host,self.port)) break - time.sleep(self.timeout) + except ConnectionRefusedError: continue except Exception as e: continue def recv(self,dtype=None): - self.net_pack.recv(self.s,dtype=dtype) - print("recv'd message type:", NetPack.netpack_type[self.net_pack.mtype]) + n = self.net_pack.recv(self.s,dtype=dtype) + if n > 0: + print("recv'd message type:", NetPack.netpack_type[self.net_pack.mtype]) + return n def send(self,mtype,group,runid,desc="",data=0): self.net_pack.send(self.s,mtype,group,runid,desc,data) print("sent message type:", NetPack.netpack_type[mtype]) - def initialize(self): - self.connect() + def listen(self): + self.s.settimeout(self.timeout) + while True: + time.sleep(self.timeout) + + n = self.recv() + if n > 0: + # need to sync here + if self.net_pack.mtype == 10: + self.par_values = self.net_pack.data_pak.copy() - #request for cwd - self.recv() + # request cwd + elif self.net_pack.mtype == 4: + self.send(mtype=5, group=self.net_pack.group, + runid=self.net_pack.runid, + desc="sending cwd", data=os.getcwd()) - if self.net_pack.mtype != 4: - raise Exception("unexpected net pack type, should be {0}, not {1}".\ - format(NetPack.netpack_type[4], - NetPack.netpack_type[self.net_pack.mtype])) - self.send(mtype=5,group=self.net_pack.group, - runid=self.net_pack.runid,desc="sending cwd",data=os.getcwd()) + elif self.net_pack.mtype == 8: + self.par_names = self.net_pack.data_pak - # par names - self.recv() - # todo: check revc'd mtype - self.par_names = self.net_pack.data_pak.copy() + elif self.net_pack.mtype == 9: + self.obs_names = self.net_pack.data_pak - # obs nanes - self.recv() - # todo check recv'd mtype - self.obs_names = self.net_pack.data_pak.copy() + elif self.net_pack.mtype == 6: + self.send(7, self.net_pack.group, + self.net_pack.runid, + "fake linpack result", data=1) - # lin pack request - self.recv() + elif self.net_pack.mtype == 15: + self.send(15, self.net_pack.group, + self.net_pack.runid, + "ping back") - self.send(7,self.net_pack.group,self.net_pack.runid,"fake linpack result",data=1) - print() + # def initialize(self): + # self.connect() + # + # #request for cwd + # self.recv() + # + # if self.net_pack.mtype != 4: + # raise Exception("unexpected net pack type, should be {0}, not {1}".\ + # format(NetPack.netpack_type[4], + # NetPack.netpack_type[self.net_pack.mtype])) + # self.send(mtype=5,group=self.net_pack.group, + # runid=self.net_pack.runid,desc="sending cwd",data=os.getcwd()) + # + # # par names + # self.recv() + # # todo: check revc'd mtype + # self.par_names = self.net_pack.data_pak.copy() + # + # # obs nanes + # self.recv() + # # todo check recv'd mtype + # self.obs_names = self.net_pack.data_pak.copy() + # + # # lin pack request + # self.recv() + # + # self.send(7,self.net_pack.group,self.net_pack.runid,"fake linpack result",data=1) + # if __name__ == "__main__": host = "localhost" port = 4004 - ppw = PyPestWorker(None,host,port) - ppw.initialize() + #ppw.initialize() From c8b07747df30821fd9ccde59f303045ec5a8e9ce Mon Sep 17 00:00:00 2001 From: jwhite Date: Mon, 4 Nov 2024 10:48:04 -0700 Subject: [PATCH 15/56] more --- pyemu/utils/os_utils.py | 146 ++++++++++++++++++++++++++++++---------- 1 file changed, 112 insertions(+), 34 deletions(-) diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index b7dd91260..ba5dfacac 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -6,6 +6,7 @@ import time import struct import shutil +import threading import subprocess as sp import multiprocessing as mp import warnings @@ -14,6 +15,7 @@ from datetime import datetime import numpy as np +import pandas as pd from ..pyemu_warnings import PyemuWarning from ..pst import pst_handler @@ -637,7 +639,10 @@ def __init__(self, pst, host, port, timeout=0.25): self._process_pst() self.connect() - + self._lock = threading.Lock() + self._send_lock = threading.Lock() + self._listen_thread = threading.Thread(target=self.listen,args=(self._lock,self._send_lock)) + self._listen_thread.start() def _process_pst(self): if isinstance(self._pst_arg,str): @@ -650,7 +655,7 @@ def _process_pst(self): def connect(self): - print("trying to connect to {0}:{1}...".format(self.host,self.port)) + self.message("trying to connect to {0}:{1}...".format(self.host,self.port)) self.s = None c = 0 while True: @@ -662,7 +667,7 @@ def connect(self): print('') self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.s.connect((self.host, self.port)) - print("-->connected to {0}:{1}".format(self.host,self.port)) + self.message("connected to {0}:{1}".format(self.host,self.port)) break except ConnectionRefusedError: @@ -670,18 +675,22 @@ def connect(self): except Exception as e: continue + def message(self,msg): + print(str(datetime.now())+" : "+msg) + + def recv(self,dtype=None): n = self.net_pack.recv(self.s,dtype=dtype) if n > 0: - print("recv'd message type:", NetPack.netpack_type[self.net_pack.mtype]) + self.message("recv'd message type:{0}".format(NetPack.netpack_type[self.net_pack.mtype])) return n def send(self,mtype,group,runid,desc="",data=0): self.net_pack.send(self.s,mtype,group,runid,desc,data) - print("sent message type:", NetPack.netpack_type[mtype]) + self.message("sent message type:{0}".format(NetPack.netpack_type[mtype])) - def listen(self): + def listen(self,lock=None,send_lock=None): self.s.settimeout(self.timeout) while True: time.sleep(self.timeout) @@ -690,13 +699,21 @@ def listen(self): if n > 0: # need to sync here if self.net_pack.mtype == 10: + if lock is not None: + lock.acquire() self.par_values = self.net_pack.data_pak.copy() + if lock is not None: + lock.release() # request cwd elif self.net_pack.mtype == 4: + if self._send_lock is not None: + self._send_lock.acquire() self.send(mtype=5, group=self.net_pack.group, runid=self.net_pack.runid, desc="sending cwd", data=os.getcwd()) + if self._send_lock is not None: + self._send_lock.release() elif self.net_pack.mtype == 8: self.par_names = self.net_pack.data_pak @@ -705,45 +722,106 @@ def listen(self): self.obs_names = self.net_pack.data_pak elif self.net_pack.mtype == 6: + if self._send_lock is not None: + self._send_lock.acquire() self.send(7, self.net_pack.group, self.net_pack.runid, "fake linpack result", data=1) + if self._send_lock is not None: + self._send_lock.release() elif self.net_pack.mtype == 15: + if self._send_lock is not None: + self._send_lock.acquire() self.send(15, self.net_pack.group, self.net_pack.runid, "ping back") + if self._send_lock is not None: + self._send_lock.release() + else: + self.message("WARNING: unsupported request recieved: {0}".format(NetPack.netpack_type[self.net_pack.mtype])) - # def initialize(self): - # self.connect() - # - # #request for cwd - # self.recv() - # - # if self.net_pack.mtype != 4: - # raise Exception("unexpected net pack type, should be {0}, not {1}".\ - # format(NetPack.netpack_type[4], - # NetPack.netpack_type[self.net_pack.mtype])) - # self.send(mtype=5,group=self.net_pack.group, - # runid=self.net_pack.runid,desc="sending cwd",data=os.getcwd()) - # - # # par names - # self.recv() - # # todo: check revc'd mtype - # self.par_names = self.net_pack.data_pak.copy() - # - # # obs nanes - # self.recv() - # # todo check recv'd mtype - # self.obs_names = self.net_pack.data_pak.copy() - # - # # lin pack request - # self.recv() - # - # self.send(7,self.net_pack.group,self.net_pack.runid,"fake linpack result",data=1) - # + def get_parameters(self): + pars = None + while True: + self._lock.acquire() + if self.par_values is not None: + pars = self.par_values + self._lock.release() + break + self._lock.release() + time.sleep(self.timeout) + if len(pars) != len(self.par_names): + raise Exception("len(par vals) {0} != len(par names)".format(len(pars),len(self.par_names))) + return pd.Series(data=pars,index=self.par_names) + + def send_observations(self,obsvals,parvals=None,request_more_pars=True): + if len(obsvals) != len(self.obs_names): + raise Exception("len(obs vals) {0} != len(obs names)".format(len(obsvals), len(self.obs_names))) + if isinstance(obsvals,np.ndarray): + _obsvals = obsvals + elif isinstance(obsvals,pd.Series): + _obsvals = obsvals.loc[self.obs_names].values + elif isinstance(obsvals,list): + _obsvals = np.array(obsvals) + else: + raise Exception("unknown obsvals type {0}".format(type(obsvals))) + if np.any(np.isnan(_obsvals)): + raise Exception("nans in obsvals") + + if parvals is None: + _parvals = self.par_values + elif isinstance(parvals,np.ndarray): + _parvals = parvals + elif isinstance(parvals,pd.Series): + _parvals = parvals.loc[self.par_names].values + elif isinstance(parvals,list): + _parvals = np.array(parvals) + else: + raise Exception("unknown parvals type {0}".format(type(parvals))) + if np.any(np.isnan(_parvals)): + raise Exception("nans in parvals") + + #first reset par_values to None + self._lock.acquire() + self.par_values = None + self._lock.release() + + data = np.concatenate((_parvals,_obsvals)) + + self._send_lock.acquire() + # send the stack obsvals and par vals + self.send(11, self.net_pack.group, self.net_pack.runid, self.net_pack.desc, data=data) + + # now send the ready message + if request_more_pars: + self.send(3,0,0,"ready for next run",data=0) + self._send_lock.release() + + def request_more_pars(self): + self._send_lock.acquire() + self.send(3, 0, 0, "ready for next run", data=0.0) + self._send_lock.release() + + def send_failed_run(self,group=None,runid=None,desc="failed"): + if group is None: + group = self.net_pack.group + if runid is None: + runid = self.net_pack.runid + self._send_lock.acquire() + self.send(12, int(group), int(runid), desc, data=0.0) + self._send_lock.release() + + def send_killed_run(self,group=None,runid=None,desc="killed"): + if group is None: + group = self.net_pack.group + if runid is None: + runid = self.net_pack.runid + self._send_lock.acquire() + self.send(13, int(group), int(runid), desc, data=0.0) + self._send_lock.release() From 613385c812607bb1d115c819c334ef92796fe3da Mon Sep 17 00:00:00 2001 From: jwhite Date: Tue, 12 Nov 2024 14:54:58 -0700 Subject: [PATCH 16/56] 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 9f2b6bb24..0ede3d97e 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 17/56] 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 0ede3d97e..ef752cee9 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 041a68465..f4123587e 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 fe3609c4548387537d84177064ddb1fd8bc108fe Mon Sep 17 00:00:00 2001 From: jwhite Date: Mon, 18 Nov 2024 09:33:22 +0800 Subject: [PATCH 18/56] added initial placeholder pypestworker invest function --- autotest/utils_tests.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 39d894381..6ce56a68f 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2619,6 +2619,26 @@ def ppu_geostats_test(tmp_path): # plt.show() # exit() +def pypestworker_invest(): + + host = "localhost" + port = 4004 + + ppw = pyemu.os_utils.PyPestWorker("pest.pst",host,port) + obs = ppw._pst.observation_data + count = 0 + # todo: handle shutdown message + # todo: handle kill run message + while True: + parameters = ppw.get_parameters() + if count == 1: + ppw.send_failed_run(desc="sad") + ppw.par_values = None + ppw.request_more_pars() + else: + ppw.send_observations(obs.obsval,parvals=None) + count += 1 + if __name__ == "__main__": #ppu_geostats_test(".") From e725bfcb4302f9d5e1186682bdff1f303afb8ec2 Mon Sep 17 00:00:00 2001 From: jwhite Date: Tue, 19 Nov 2024 09:08:02 +0800 Subject: [PATCH 19/56] 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 20263f724..d73c6d812 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 000000000..45c8d6939 --- /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 000000000..f948ea6b2 --- /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 000000000..d6c871149 --- /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 000000000..03982ea0c --- /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 000000000..1ee6b2ed7 --- /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 000000000..534dcae96 --- /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 000000000..919332b7c --- /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 000000000..2b2a325f7 --- /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 000000000..6d7efddd6 --- /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 000000000..e105ab03a --- /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 000000000..4a84cd903 --- /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 000000000..95a3a3fd4 --- /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 000000000..f2e2a1b71 --- /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 000000000..d06a2b6d6 --- /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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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 39d894381..1a2c28be8 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 20/56] 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 1a2c28be8..0aa76f9f9 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 000000000..e36d26d65 --- /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 21/56] 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 e36d26d65..883bdaaef 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 22/56] 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 d73c6d812..b1cc8d671 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 000000000..ca5916f8a --- /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 000000000..e8e9cc5fa --- /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 000000000..c7f97f515 --- /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 000000000..ac9ebec51 --- /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 000000000..138fe8fe4 --- /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 000000000..155fbef66 --- /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 000000000..fba583782 --- /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 000000000..f2e2a1b71 --- /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 0aa76f9f9..4f2f4a094 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 883bdaaef..6bbb5619c 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 eecab5364..74a02b0ac 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 11a8992b0..9c5bb4479 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 23/56] 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 4f2f4a094..58ca19899 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 6bbb5619c..1037fbada 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 24/56] 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 58ca19899..8e2a754ae 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 25/56] 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 8e2a754ae..96c05eb75 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 26/56] 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 96c05eb75..58dc460e4 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 27/56] 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 58dc460e4..8e2c07378 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 28/56] 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 9c5bb4479..c7de9f913 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 From 61ba55c2c25ff78c8beb91bb3d0a20ebad534dc0 Mon Sep 17 00:00:00 2001 From: jwhite Date: Tue, 26 Nov 2024 22:11:29 +0800 Subject: [PATCH 29/56] more work on pypestworker, added test --- autotest/utils/zdt1_template/forward_run.py | 7 +- autotest/utils_tests.py | 82 ++++++++++++++++++--- pyemu/utils/os_utils.py | 24 ++++-- 3 files changed, 91 insertions(+), 22 deletions(-) diff --git a/autotest/utils/zdt1_template/forward_run.py b/autotest/utils/zdt1_template/forward_run.py index 4a84cd903..d5876a4ea 100644 --- a/autotest/utils/zdt1_template/forward_run.py +++ b/autotest/utils/zdt1_template/forward_run.py @@ -5,10 +5,11 @@ 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"]) +def helper(func=zdt1,pvals=None): + if pvals is None: + pvals = pd.read_csv("dv.dat",delim_whitespace=True,index_col=0, header=None, names=["parnme","parval1"]).values #obj1,obj2 = func(pdf.values) - objs,constrs = func(pdf.values) + objs,constrs = func(pvals) if os.path.exists("additive_par.dat"): obj1,obj2 = objs[0],objs[1] diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index dbf6ffd8f..c3d1b6331 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2653,25 +2653,84 @@ def ppu_geostats_test(tmp_path): # plt.show() # exit() -def pypestworker_invest(): - +def pypestworker_test(): + from datetime import datetime + import numpy as np + import subprocess as sp host = "localhost" port = 4004 + org_d = os.path.join("utils","zdt1_template") + t_d = "zdt1_ppw_template" + if os.path.exists(t_d): + shutil.rmtree(t_d) + shutil.copytree(org_d,t_d) + pst = pyemu.Pst(os.path.join(t_d,"zdt1.pst")) + pst.pestpp_options["mou_population_size"] = 20 + pst.control_data.noptmax = 5 + pst.write(os.path.join(t_d,"zdt1.pst"),version=2) + import sys + sys.path.insert(0,t_d) + from forward_run import helper as frun + + m_d = "zdt1_ppw_master" + + if os.path.exists(m_d): + shutil.rmtree(m_d) + shutil.copytree(t_d,m_d) + + start = datetime.now() + b_d = os.getcwd() + os.chdir(m_d) + p = sp.Popen([mou_exe_path,"zdt1.pst","/h",":{0}".format(port)]) + os.chdir(b_d) + #p.wait() + #return - ppw = pyemu.os_utils.PyPestWorker("pest.pst",host,port) + ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"zdt1.pst"),host,port,verbose=False) + obs = ppw._pst.observation_data count = 0 + par = pst.parameter_data + dpar = par.loc[par.parnme.str.startswith("dv"),:] + dpar["count"] = dpar.parnme.apply(lambda x: int(x.split('_')[1])) + dpar.sort_values(by="count",inplace=True) + pnames = dpar.parnme.tolist() + #pnames.extend(pst.par_names[-2:]) # todo: handle shutdown message # todo: handle kill run message while True: parameters = ppw.get_parameters() - if count == 1: - ppw.send_failed_run(desc="sad") - ppw.par_values = None - ppw.request_more_pars() - else: - ppw.send_observations(obs.obsval,parvals=None) - count += 1 + if parameters is None: + print("no more parameters...") + break + #print("parameters",parameters.loc[pnames]) + #print("got parameters:",parameters.values) + objs,constr = frun(pvals=parameters.loc[pnames].values) + #print("objs and constraints:",objs,constr) + obs.loc[["obj_1","obj_2"],"obsval"] = np.array(objs) + #print(obs) + ppw.send_observations(obs.obsval.loc[ppw.obs_names].values) + #input("press any key") + finish = datetime.now() + + print("all done, took",(finish-start).total_seconds()) + m_d2 = m_d+"_base" + start2 = datetime.now() + pyemu.os_utils.start_workers(t_d,mou_exe_path,"zdt1.pst",num_workers=10,worker_root='.',master_dir=m_d2) + finish2 = datetime.now() + print("ppw took",(finish-start).total_seconds()) + print("org took",(finish2-start2).total_seconds()) + arc1 = pd.read_csv(os.path.join(m_d,"zdt1.pareto.archive.summary.csv")) + arc2 = pd.read_csv(os.path.join(m_d2,"zdt1.pareto.archive.summary.csv")) + diff1 = np.abs((arc1["obj_1"] - arc2["obj_1"]).values) + diff2 = np.abs((arc1["obj_2"] - arc2["obj_2"]).values) + print(diff1.max()) + print(diff2.max()) + assert diff1.max() < 1.0e-6 + assert diff2.max() < 1.0e-6 + + + def gpr_compare_invest(): import numpy as np @@ -3086,7 +3145,8 @@ def gpr_zdt1_test(): if __name__ == "__main__": #ppu_geostats_test(".") #gpr_compare_invest() - gpr_constr_test() + #gpr_constr_test() + pypestworker_test() #gpr_zdt1_test() #while True: # thresh_pars_test() diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index ba5dfacac..743d468d5 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -488,7 +488,7 @@ class NetPack(object): 23: "FINISH_FILE_WRKR2MSTR"} sec_message = [1, 3, 5, 7, 9] - def __init__(self,timeout=0.1): + def __init__(self,timeout=0.1,verbose=True): self.header_size = 8 + 4 + 8 + 8 + 1001 self.buf_size = None self.buf_idx = (0, 8) @@ -506,6 +506,7 @@ def __init__(self,timeout=0.1): self.data_pak = None self.timeout = float(timeout) + self.verbose = bool(verbose) self.sec_message_buf = bytearray() for sm in NetPack.sec_message: @@ -591,7 +592,8 @@ def serialize_data(self,data): elif isinstance(data,np.ndarray): return data.tobytes() elif isinstance(data,int): - print("warning: casting int to float to serialize") + if self.verbose: + print("warning: casting int to float to serialize") return struct.pack('d',float(data)) elif isinstance(data,float): return struct.pack('d', data) @@ -623,15 +625,15 @@ def _check_sec_message(self,recv_sec_message): class PyPestWorker(object): - def __init__(self, pst, host, port, timeout=0.25): + def __init__(self, pst, host, port, timeout=0.01,verbose=True): self.host = host self.port = port self._pst_arg = pst self._pst = None self.s = None self.timeout = float(timeout) - self.net_pack = NetPack(timeout=timeout) - + self.net_pack = NetPack(timeout=timeout,verbose=verbose) + self.verbose = bool(verbose) self.par_names = None self.obs_names = None @@ -676,7 +678,8 @@ def connect(self): continue def message(self,msg): - print(str(datetime.now())+" : "+msg) + if self.verbose: + print(str(datetime.now())+" : "+msg) def recv(self,dtype=None): @@ -738,15 +741,20 @@ def listen(self,lock=None,send_lock=None): "ping back") if self._send_lock is not None: self._send_lock.release() + elif self.net_pack.mtype == 14: + print("recv'd terminate signal") + return else: - - self.message("WARNING: unsupported request recieved: {0}".format(NetPack.netpack_type[self.net_pack.mtype])) + print("WARNING: unsupported request recieved: {0}".format(NetPack.netpack_type[self.net_pack.mtype])) def get_parameters(self): pars = None while True: self._lock.acquire() + if self.net_pack.mtype == 14: + self._lock.release() + return None if self.par_values is not None: pars = self.par_values self._lock.release() From 8930216688da809b0bf067e793672c49103ead16 Mon Sep 17 00:00:00 2001 From: parallels Date: Wed, 27 Nov 2024 07:44:02 +0800 Subject: [PATCH 30/56] longer time --- autotest/utils_tests.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index c3d1b6331..86b227da8 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -6,6 +6,8 @@ # os.mkdir("temp") from pathlib import Path import pandas as pd +import sys +sys.path.append("..") import pyemu from pst_from_tests import _get_port @@ -2686,7 +2688,7 @@ def pypestworker_test(): #p.wait() #return - ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"zdt1.pst"),host,port,verbose=False) + ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"zdt1.pst"),host,port,verbose=False,timeout=0.05) obs = ppw._pst.observation_data count = 0 From 05383830bb7cff9fa7842c79061a736d869dc3cf Mon Sep 17 00:00:00 2001 From: jwhite Date: Wed, 27 Nov 2024 09:53:47 +0800 Subject: [PATCH 31/56] shortened timeouts again but added netpack.recv_all() to help --- autotest/utils_tests.py | 4 ++-- pyemu/utils/os_utils.py | 12 +++++++++++- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 86b227da8..302441c5c 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2667,7 +2667,7 @@ def pypestworker_test(): shutil.rmtree(t_d) shutil.copytree(org_d,t_d) pst = pyemu.Pst(os.path.join(t_d,"zdt1.pst")) - pst.pestpp_options["mou_population_size"] = 20 + pst.pestpp_options["mou_population_size"] = 200 pst.control_data.noptmax = 5 pst.write(os.path.join(t_d,"zdt1.pst"),version=2) import sys @@ -2688,7 +2688,7 @@ def pypestworker_test(): #p.wait() #return - ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"zdt1.pst"),host,port,verbose=False,timeout=0.05) + ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"zdt1.pst"),host,port,verbose=False,timeout=0.01) obs = ppw._pst.observation_data count = 0 diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index 743d468d5..5857249da 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -520,9 +520,19 @@ def __init__(self,timeout=0.1,verbose=True): # while total < num_bytes: # data += s.recv() + def recv_all(self,s,msg_len): + # Helper function to recv n bytes or return None if EOF is hit + data = bytearray() + while len(data) < msg_len: + packet = s.recv(msg_len - len(data)) + if not packet: + return None + data.extend(packet) + return data + def nonblocking_recv(self,s,msg_len): try: - msg = s.recv(msg_len) + msg = self.recv_all(s,msg_len) except socket.timeout as e: emess = e.args[0] if emess == 'timed out': From b8b4ef9283b21066f850893ba6ec13ac9fda6466 Mon Sep 17 00:00:00 2001 From: jwhite Date: Wed, 27 Nov 2024 10:25:34 +0800 Subject: [PATCH 32/56] added multiple pyworker test --- autotest/utils_tests.py | 74 ++++++++++++++++++++++++++--------------- 1 file changed, 47 insertions(+), 27 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 302441c5c..48e887bf1 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2655,10 +2655,40 @@ def ppu_geostats_test(tmp_path): # plt.show() # exit() +def ppw_worker(id_num,t_d,host,port,frun): + import numpy as np + ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"zdt1.pst"),host,port,verbose=False,timeout=0.01) + + obs = ppw._pst.observation_data + count = 0 + par = ppw._pst.parameter_data + dpar = par.loc[par.parnme.str.startswith("dv"),:] + dpar["count"] = dpar.parnme.apply(lambda x: int(x.split('_')[1])) + dpar.sort_values(by="count",inplace=True) + pnames = dpar.parnme.tolist() + print("starting worker",id_num) + while True: + parameters = ppw.get_parameters() + if parameters is None: + print("no more parameters...") + break + #print("parameters",parameters.loc[pnames]) + #print("got parameters:",parameters.values) + objs,constr = frun(pvals=parameters.loc[pnames].values) + #print("objs and constraints:",objs,constr) + obs.loc[["obj_1","obj_2"],"obsval"] = np.array(objs) + #print(obs) + ppw.send_observations(obs.obsval.loc[ppw.obs_names].values) + #input("press any key") + #print("worker",id_num,"finished run",ppw.net_pack.runid) + + + def pypestworker_test(): from datetime import datetime import numpy as np import subprocess as sp + import multiprocessing as mp host = "localhost" port = 4004 org_d = os.path.join("utils","zdt1_template") @@ -2668,6 +2698,11 @@ def pypestworker_test(): shutil.copytree(org_d,t_d) pst = pyemu.Pst(os.path.join(t_d,"zdt1.pst")) pst.pestpp_options["mou_population_size"] = 200 + #need these options bc the py workers run so fast, even slight + #delays show up as timeouts... + pst.pestpp_options["overdue_giveup_fac"] = 1e10 + pst.pestpp_options["overdue_resched_fac"] = 1e10 + pst.control_data.noptmax = 5 pst.write(os.path.join(t_d,"zdt1.pst"),version=2) import sys @@ -2687,35 +2722,20 @@ def pypestworker_test(): os.chdir(b_d) #p.wait() #return - - ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"zdt1.pst"),host,port,verbose=False,timeout=0.01) - - obs = ppw._pst.observation_data - count = 0 - par = pst.parameter_data - dpar = par.loc[par.parnme.str.startswith("dv"),:] - dpar["count"] = dpar.parnme.apply(lambda x: int(x.split('_')[1])) - dpar.sort_values(by="count",inplace=True) - pnames = dpar.parnme.tolist() - #pnames.extend(pst.par_names[-2:]) - # todo: handle shutdown message - # todo: handle kill run message - while True: - parameters = ppw.get_parameters() - if parameters is None: - print("no more parameters...") - break - #print("parameters",parameters.loc[pnames]) - #print("got parameters:",parameters.values) - objs,constr = frun(pvals=parameters.loc[pnames].values) - #print("objs and constraints:",objs,constr) - obs.loc[["obj_1","obj_2"],"obsval"] = np.array(objs) - #print(obs) - ppw.send_observations(obs.obsval.loc[ppw.obs_names].values) - #input("press any key") + + procs = [] + for i in range(10): + pp = mp.Process(target=ppw_worker,args=(i,t_d,host,port,frun)) + pp.start() + procs.append(pp) + for pp in procs: + pp.join() + + p.wait() finish = datetime.now() - print("all done, took",(finish-start).total_seconds()) + + m_d2 = m_d+"_base" start2 = datetime.now() pyemu.os_utils.start_workers(t_d,mou_exe_path,"zdt1.pst",num_workers=10,worker_root='.',master_dir=m_d2) From e5b191fb8bb5f87542204bd1593f6b6b600cd53c Mon Sep 17 00:00:00 2001 From: jwhite Date: Wed, 27 Nov 2024 11:11:34 +0800 Subject: [PATCH 33/56] more work on pyworker test --- autotest/utils/constr_template/forward_run.py | 11 +++--- autotest/utils/hosaki_template/forward_run.py | 30 ++++++++++------ autotest/utils/zdt1_template/forward_run.py | 6 ++-- autotest/utils_tests.py | 34 +++++++++++-------- 4 files changed, 48 insertions(+), 33 deletions(-) diff --git a/autotest/utils/constr_template/forward_run.py b/autotest/utils/constr_template/forward_run.py index 155fbef66..646e2b9ef 100644 --- a/autotest/utils/constr_template/forward_run.py +++ b/autotest/utils/constr_template/forward_run.py @@ -4,14 +4,15 @@ 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"]) +def helper(func=constr,pvals=None): + if pvals is None: + pvals = pd.read_csv("dv.dat", sep='\s+',index_col=0, header=None, names=["parnme","parval1"]).values #obj1,obj2 = func(pdf.values) - objs,constrs = func(pdf.values) + objs,constrs = func(pvals) 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"]) + cdf = pd.read_csv("additive_par.dat", sep='\s+', 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)): @@ -19,7 +20,7 @@ def helper(func): 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_{0} {1}\n".format(i+1,float(obj.item()))) #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))) diff --git a/autotest/utils/hosaki_template/forward_run.py b/autotest/utils/hosaki_template/forward_run.py index 45c8d6939..8c7cc1e37 100644 --- a/autotest/utils/hosaki_template/forward_run.py +++ b/autotest/utils/hosaki_template/forward_run.py @@ -1,12 +1,22 @@ +import os 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') + +def hosaki(x): + pval1 = x[0] + pval2 = x[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 + return sim + +def helper(func=hosaki,pvals=None): + if pvals is None: + pvals = pd.read_csv('par.csv',index_col=0).values + print(pvals) + sim = hosaki(pvals) + + with open('sim.csv','w') as f: + f.write('obsnme,obsval\n') + f.write('sim,'+str(sim)+'\n') diff --git a/autotest/utils/zdt1_template/forward_run.py b/autotest/utils/zdt1_template/forward_run.py index d5876a4ea..311e2a020 100644 --- a/autotest/utils/zdt1_template/forward_run.py +++ b/autotest/utils/zdt1_template/forward_run.py @@ -7,13 +7,13 @@ def zdt1(x): def helper(func=zdt1,pvals=None): if pvals is None: - pvals = pd.read_csv("dv.dat",delim_whitespace=True,index_col=0, header=None, names=["parnme","parval1"]).values + pvals = pd.read_csv("dv.dat",sep='\s+',index_col=0, header=None, names=["parnme","parval1"]).values #obj1,obj2 = func(pdf.values) objs,constrs = func(pvals) 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"]) + cdf = pd.read_csv("additive_par.dat", sep='\s+', 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)): @@ -21,7 +21,7 @@ def helper(func=zdt1,pvals=None): 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_{0} {1}\n".format(i+1,float(obj.item()))) #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))) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 48e887bf1..8c8223be9 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2655,14 +2655,15 @@ def ppu_geostats_test(tmp_path): # plt.show() # exit() -def ppw_worker(id_num,t_d,host,port,frun): +def ppw_worker(id_num,case,t_d,host,port,frun): import numpy as np - ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"zdt1.pst"),host,port,verbose=False,timeout=0.01) + ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"{0}.pst".format(case)),host,port,verbose=False,timeout=0.01) obs = ppw._pst.observation_data count = 0 par = ppw._pst.parameter_data - dpar = par.loc[par.parnme.str.startswith("dv"),:] + dpar = par.loc[par.parnme.str.startswith("dv"),:].copy() + assert len(dpar) > 0 dpar["count"] = dpar.parnme.apply(lambda x: int(x.split('_')[1])) dpar.sort_values(by="count",inplace=True) pnames = dpar.parnme.tolist() @@ -2691,25 +2692,26 @@ def pypestworker_test(): import multiprocessing as mp host = "localhost" port = 4004 - org_d = os.path.join("utils","zdt1_template") - t_d = "zdt1_ppw_template" + case = "constr" + org_d = os.path.join("utils","{0}_template".format(case)) + t_d = "{0}_ppw_template".format(case) if os.path.exists(t_d): shutil.rmtree(t_d) shutil.copytree(org_d,t_d) - pst = pyemu.Pst(os.path.join(t_d,"zdt1.pst")) - pst.pestpp_options["mou_population_size"] = 200 + pst = pyemu.Pst(os.path.join(t_d,"{0}.pst".format(case))) + pst.pestpp_options["mou_population_size"] = 20 #need these options bc the py workers run so fast, even slight #delays show up as timeouts... pst.pestpp_options["overdue_giveup_fac"] = 1e10 pst.pestpp_options["overdue_resched_fac"] = 1e10 pst.control_data.noptmax = 5 - pst.write(os.path.join(t_d,"zdt1.pst"),version=2) + pst.write(os.path.join(t_d,"{0}.pst".format(case)),version=2) import sys sys.path.insert(0,t_d) from forward_run import helper as frun - m_d = "zdt1_ppw_master" + m_d = "{0}_ppw_master".format(case) if os.path.exists(m_d): shutil.rmtree(m_d) @@ -2718,14 +2720,16 @@ def pypestworker_test(): start = datetime.now() b_d = os.getcwd() os.chdir(m_d) - p = sp.Popen([mou_exe_path,"zdt1.pst","/h",":{0}".format(port)]) + p = sp.Popen([mou_exe_path,"{0}.pst".format(case),"/h",":{0}".format(port)]) os.chdir(b_d) #p.wait() #return + + num_workers=10 procs = [] - for i in range(10): - pp = mp.Process(target=ppw_worker,args=(i,t_d,host,port,frun)) + for i in range(num_workers): + pp = mp.Process(target=ppw_worker,args=(i,case,t_d,host,port,frun)) pp.start() procs.append(pp) for pp in procs: @@ -2738,12 +2742,12 @@ def pypestworker_test(): m_d2 = m_d+"_base" start2 = datetime.now() - pyemu.os_utils.start_workers(t_d,mou_exe_path,"zdt1.pst",num_workers=10,worker_root='.',master_dir=m_d2) + pyemu.os_utils.start_workers(t_d,mou_exe_path,"{0}.pst".format(case),num_workers=num_workers,worker_root='.',master_dir=m_d2) finish2 = datetime.now() print("ppw took",(finish-start).total_seconds()) print("org took",(finish2-start2).total_seconds()) - arc1 = pd.read_csv(os.path.join(m_d,"zdt1.pareto.archive.summary.csv")) - arc2 = pd.read_csv(os.path.join(m_d2,"zdt1.pareto.archive.summary.csv")) + arc1 = pd.read_csv(os.path.join(m_d,"{0}.pareto.archive.summary.csv".format(case))) + arc2 = pd.read_csv(os.path.join(m_d2,"{0}.pareto.archive.summary.csv".format(case))) diff1 = np.abs((arc1["obj_1"] - arc2["obj_1"]).values) diff2 = np.abs((arc1["obj_2"] - arc2["obj_2"]).values) print(diff1.max()) From 04a16c56b7d93653e05c960cc6c1d25bc240b62b Mon Sep 17 00:00:00 2001 From: jwhite Date: Wed, 27 Nov 2024 12:22:09 +0800 Subject: [PATCH 34/56] timeout --- 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 8c8223be9..7a0071e7c 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2657,7 +2657,7 @@ def ppu_geostats_test(tmp_path): def ppw_worker(id_num,case,t_d,host,port,frun): import numpy as np - ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"{0}.pst".format(case)),host,port,verbose=False,timeout=0.01) + ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"{0}.pst".format(case)),host,port,verbose=False,timeout=0.1) obs = ppw._pst.observation_data count = 0 @@ -2699,7 +2699,7 @@ def pypestworker_test(): shutil.rmtree(t_d) shutil.copytree(org_d,t_d) pst = pyemu.Pst(os.path.join(t_d,"{0}.pst".format(case))) - pst.pestpp_options["mou_population_size"] = 20 + pst.pestpp_options["mou_population_size"] = 200 #need these options bc the py workers run so fast, even slight #delays show up as timeouts... pst.pestpp_options["overdue_giveup_fac"] = 1e10 From 699a6ead411ace7be6423dbbf792c156648cd558 Mon Sep 17 00:00:00 2001 From: jwhite Date: Wed, 27 Nov 2024 13:06:19 +0800 Subject: [PATCH 35/56] longer default timeout, removed start_workers from pyworker test to avoid conflicts with other tests --- autotest/utils_tests.py | 11 +++++++---- pyemu/utils/os_utils.py | 2 +- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 7a0071e7c..63c455aeb 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2657,7 +2657,8 @@ def ppu_geostats_test(tmp_path): def ppw_worker(id_num,case,t_d,host,port,frun): import numpy as np - ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"{0}.pst".format(case)),host,port,verbose=False,timeout=0.1) + ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"{0}.pst".format(case)), + host,port,verbose=False,timeout=0.1) obs = ppw._pst.observation_data count = 0 @@ -2699,7 +2700,7 @@ def pypestworker_test(): shutil.rmtree(t_d) shutil.copytree(org_d,t_d) pst = pyemu.Pst(os.path.join(t_d,"{0}.pst".format(case))) - pst.pestpp_options["mou_population_size"] = 200 + pst.pestpp_options["mou_population_size"] = 20 #need these options bc the py workers run so fast, even slight #delays show up as timeouts... pst.pestpp_options["overdue_giveup_fac"] = 1e10 @@ -2725,7 +2726,7 @@ def pypestworker_test(): #p.wait() #return - num_workers=10 + num_workers=20 procs = [] for i in range(num_workers): @@ -2742,7 +2743,9 @@ def pypestworker_test(): m_d2 = m_d+"_base" start2 = datetime.now() - pyemu.os_utils.start_workers(t_d,mou_exe_path,"{0}.pst".format(case),num_workers=num_workers,worker_root='.',master_dir=m_d2) + #pyemu.os_utils.start_workers(t_d,mou_exe_path,"{0}.pst".format(case),num_workers=num_workers,worker_root='.',master_dir=m_d2) + pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=t_d) + m_d2 = t_d finish2 = datetime.now() print("ppw took",(finish-start).total_seconds()) print("org took",(finish2-start2).total_seconds()) diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index 5857249da..5fd9e258c 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -635,7 +635,7 @@ def _check_sec_message(self,recv_sec_message): class PyPestWorker(object): - def __init__(self, pst, host, port, timeout=0.01,verbose=True): + def __init__(self, pst, host, port, timeout=0.1,verbose=True): self.host = host self.port = port self._pst_arg = pst From d53baf851d96203696aaaed1f3ffe2f2420bf1d2 Mon Sep 17 00:00:00 2001 From: jwhite Date: Wed, 27 Nov 2024 14:06:51 +0800 Subject: [PATCH 36/56] added try-catch-reconnect --- autotest/utils_tests.py | 8 +++++++- pyemu/utils/os_utils.py | 24 ++++++++++++++++++++++-- 2 files changed, 29 insertions(+), 3 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 63c455aeb..084a4882e 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2658,7 +2658,7 @@ def ppu_geostats_test(tmp_path): def ppw_worker(id_num,case,t_d,host,port,frun): import numpy as np ppw = pyemu.os_utils.PyPestWorker(os.path.join(t_d,"{0}.pst".format(case)), - host,port,verbose=False,timeout=0.1) + host,port,verbose=False) obs = ppw._pst.observation_data count = 0 @@ -3175,6 +3175,12 @@ def gpr_zdt1_test(): #ppu_geostats_test(".") #gpr_compare_invest() #gpr_constr_test() + # import sys + # t_d = "constr_ppw_template" + # case = "constr" + # sys.path.insert(0,t_d) + # from forward_run import helper as frun + # ppw_worker(0,case,t_d,"localhost",4004,frun) pypestworker_test() #gpr_zdt1_test() #while True: diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index 5fd9e258c..0ec4e75ac 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -543,6 +543,8 @@ def nonblocking_recv(self,s,msg_len): # Something else happened, handle error, exit, etc. raise Exception(e) else: + if msg is None: + return None if len(msg) == 0: return None else: @@ -707,8 +709,12 @@ def listen(self,lock=None,send_lock=None): self.s.settimeout(self.timeout) while True: time.sleep(self.timeout) + try: + n = self.recv() + except Exception as e: + print("WARNING: recv exception:"+str(e)+"...trying to reconnect...") + self.connect() - n = self.recv() if n > 0: # need to sync here if self.net_pack.mtype == 10: @@ -730,9 +736,18 @@ def listen(self,lock=None,send_lock=None): elif self.net_pack.mtype == 8: self.par_names = self.net_pack.data_pak - + diff = set(self.par_names).symmetric_difference(set(self._pst.par_names)) + if len(diff) > 0: + print("WARNING: pst par names != master par names") + self.message("WARNING: the following par names are not common\n"+\ + " between the control file and the master:{0}".format(','.join(diff))) elif self.net_pack.mtype == 9: self.obs_names = self.net_pack.data_pak + diff = set(self.obs_names).symmetric_difference(set(self._pst.obs_names)) + if len(diff) > 0: + print("WARNING: pst obs names != master obs names") + self.message("WARNING: the following obs names are not common\n"+\ + " between the control file and the master:{0}".format(','.join(diff))) elif self.net_pack.mtype == 6: if self._send_lock is not None: @@ -753,7 +768,12 @@ def listen(self,lock=None,send_lock=None): self._send_lock.release() elif self.net_pack.mtype == 14: print("recv'd terminate signal") + self.message("recv'd terminate signal") return + elif self.net_pack.mtype == 16: + print("master is requesting run kill...") + self.message("master is requesting run kill...") + else: print("WARNING: unsupported request recieved: {0}".format(NetPack.netpack_type[self.net_pack.mtype])) From f22d6a8415c5acefa6ee34c8c25c3d1e8b05d7dd Mon Sep 17 00:00:00 2001 From: jwhite Date: Wed, 27 Nov 2024 20:16:45 +0800 Subject: [PATCH 37/56] fix --- autotest/autotest_notebooks.py | 1 + autotest/utils/hosaki_template/forward_run.py | 3 +++ autotest/utils_tests.py | 9 ++++++++- examples/gpr_emulation_hosaki.ipynb | 12 ++++++------ 4 files changed, 18 insertions(+), 7 deletions(-) diff --git a/autotest/autotest_notebooks.py b/autotest/autotest_notebooks.py index d3138fe84..9c686db09 100644 --- a/autotest/autotest_notebooks.py +++ b/autotest/autotest_notebooks.py @@ -29,6 +29,7 @@ def run_notebook(fn): #pth = os.path.join(nbdir, fn) pth = fn cmd = 'jupyter ' + 'nbconvert ' + \ + '--ServerApp.iopub_data_rate_limit=1e10 ' + \ '--ExecutePreprocessor.kernel_name=python ' + \ '--ExecutePreprocessor.timeout=6000 ' + '--to ' + 'notebook ' + \ '--execute ' + '{} '.format(pth) + \ diff --git a/autotest/utils/hosaki_template/forward_run.py b/autotest/utils/hosaki_template/forward_run.py index 8c7cc1e37..38a44fb29 100644 --- a/autotest/utils/hosaki_template/forward_run.py +++ b/autotest/utils/hosaki_template/forward_run.py @@ -20,3 +20,6 @@ def helper(func=hosaki,pvals=None): with open('sim.csv','w') as f: f.write('obsnme,obsval\n') f.write('sim,'+str(sim)+'\n') + +if __name__ == "__main__": + helper() diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 084a4882e..d4dfd49fd 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2718,6 +2718,7 @@ def pypestworker_test(): shutil.rmtree(m_d) shutil.copytree(t_d,m_d) + # start the master start = datetime.now() b_d = os.getcwd() os.chdir(m_d) @@ -2727,15 +2728,21 @@ def pypestworker_test(): #return num_workers=20 - + + # looper over and start the workers - in this + # case they dont need unique dirs since they arent writing + # anything procs = [] for i in range(num_workers): pp = mp.Process(target=ppw_worker,args=(i,case,t_d,host,port,frun)) pp.start() procs.append(pp) + # if everyhing worked, the the workers should recieve the + # shutdown signal from the master and exit gracefully... for pp in procs: pp.join() + # wait for the master to finish...but should already be finished p.wait() finish = datetime.now() print("all done, took",(finish-start).total_seconds()) diff --git a/examples/gpr_emulation_hosaki.ipynb b/examples/gpr_emulation_hosaki.ipynb index 1037fbada..5a80a6a71 100644 --- a/examples/gpr_emulation_hosaki.ipynb +++ b/examples/gpr_emulation_hosaki.ipynb @@ -131,7 +131,7 @@ "\n", "pst.write(os.path.join(t_d,\"pest.pst\"))\n", "port = 5544\n", - "num_workers = 50\n", + "num_workers = 30\n", "\n", "pyemu.os_utils.start_workers(t_d,\"pestpp-ies\",\"pest.pst\",\n", " num_workers=num_workers,\n", @@ -421,7 +421,7 @@ "outputs": [], "source": [ "gpr_sweep_d = sweep_d+\"_gpr\"\n", - "num_workers = 50\n", + "num_workers = 30\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", @@ -686,7 +686,7 @@ "outputs": [], "source": [ "gpr_sweep_d = sweep_d+\"_gpr1\"\n", - "num_workers = 50\n", + "num_workers = 30\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", @@ -793,7 +793,7 @@ "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", + "num_workers = 30\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", @@ -870,7 +870,7 @@ "\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", + "num_workers = 30\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", @@ -954,7 +954,7 @@ "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", + "num_workers = 30\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", From aeff898472ebb8b6035988dceb8c12332d67f189 Mon Sep 17 00:00:00 2001 From: jwhite Date: Thu, 28 Nov 2024 05:52:36 +0800 Subject: [PATCH 38/56] fix --- autotest/utils/hosaki_template/forward_run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autotest/utils/hosaki_template/forward_run.py b/autotest/utils/hosaki_template/forward_run.py index 38a44fb29..8fb00b3a3 100644 --- a/autotest/utils/hosaki_template/forward_run.py +++ b/autotest/utils/hosaki_template/forward_run.py @@ -19,7 +19,7 @@ def helper(func=hosaki,pvals=None): with open('sim.csv','w') as f: f.write('obsnme,obsval\n') - f.write('sim,'+str(sim)+'\n') + f.write('sim,'+str(sim.item())+'\n') if __name__ == "__main__": helper() From 4a440e58c0b55b5755e523ece999c0f8c41958b5 Mon Sep 17 00:00:00 2001 From: jwhite Date: Thu, 28 Nov 2024 13:30:22 +0800 Subject: [PATCH 39/56] working on ppw gpr worker --- autotest/utils_tests.py | 14 ++++-- pyemu/utils/helpers.py | 106 +++++++++++++++++++++++++++++++++------- 2 files changed, 98 insertions(+), 22 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index d4dfd49fd..b69a0ebf1 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2746,7 +2746,6 @@ def pypestworker_test(): p.wait() finish = datetime.now() print("all done, took",(finish-start).total_seconds()) - m_d2 = m_d+"_base" start2 = datetime.now() @@ -3178,6 +3177,14 @@ def gpr_zdt1_test(): assert psum.obj_1.min() < 0.05 +def gpr_zdt1_ppw(): + t_d = "zdt1_gpr_template" + os.chdir(t_d) + pst_name = "zdt1.pst" + ppw = pyemu.helpers.gpr_pyworker(pst_name,"localhost",4004) + os.chdir("..") + + if __name__ == "__main__": #ppu_geostats_test(".") #gpr_compare_invest() @@ -3188,8 +3195,9 @@ def gpr_zdt1_test(): # sys.path.insert(0,t_d) # from forward_run import helper as frun # ppw_worker(0,case,t_d,"localhost",4004,frun) - pypestworker_test() - #gpr_zdt1_test() + #pypestworker_test() + gpr_zdt1_test() + #while True: # thresh_pars_test() #obs_ensemble_quantile_test() diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py index c7de9f913..9e1ca8cd7 100644 --- a/pyemu/utils/helpers.py +++ b/pyemu/utils/helpers.py @@ -27,7 +27,7 @@ pass import pyemu -from pyemu.utils.os_utils import run, start_workers +from pyemu.utils.os_utils import run, start_workers,PyPestWorker class Trie: @@ -4287,12 +4287,25 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ 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:] - + #lines = [line[4:] for line in inspect.getsource(gpr_forward_run).split("\n")][1:] + frun_lines = inspect.getsource(gpr_forward_run) + getfxn_lines = inspect.getsource(get_gpr_model_dict) + emulfxn_lines = inspect.getsource(emulate_with_gpr) with open(os.path.join(gpr_t_d, "forward_run.py"), 'w') as f: - - for line in lines: - f.write(line + "\n") + f.write("\n") + for import_name in ["pandas as pd","os","pickle","numpy as np"]: + f.write("import {0}\n".format(import_name)) + for line in getfxn_lines: + f.write(line) + f.write("\n") + for line in emulfxn_lines: + f.write(line) + f.write("\n") + for line in frun_lines: + f.write(line) + f.write("if __name__ == '__main__':\n") + f.write(" gpr_forward_run()\n") + gpst.control_data.noptmax = 0 gpst.model_command = "python forward_run.py" @@ -4307,26 +4320,81 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_ gpst.write(os.path.join(gpr_t_d, gpst_fname), version=2) -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 +def get_gpr_model_dict(mdf): 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) + gpr_model_dict = {} + for output_name,model_fname in zip(mdf.output_name,mdf.model_fname): + gaussian_process = pickle.load(open(model_fname,'rb')) + gpr_model_dict[output_name] = gaussian_process + return gpr_model_dict + + +def emulate_with_gpr(input_df,mdf,gpr_model_dict): 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),return_std=True) + for output_name,gaussian_process in gpr_model_dict.items(): + sim = gaussian_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] + return mdf + + +def gpr_pyworker(pst,host,port,input_df=None,mdf=None): + import os + import pandas as pd + import numpy as np + import pickle + if input_df is None: + input_df = pd.read_csv("gpr_input.csv",index_col=0) + if mdf is None: + mdf = pd.read_csv("gprmodel_info.csv",index_col=0) + gpr_model_dict = get_gpr_model_dict(mdf) + ppw = PyPestWorker(pst,host,port,verbose=False) + parameters = ppw.get_parameters() + if parameters is None: + return + obs = ppw._pst.observation_data.copy() + obs = obs.loc[ppw.obs_names,:] + par = ppw._pst.parameter_data.copy() + usepar = par.loc[par.parnme.isin(input_df.index.values),"parnme"].values + parameters = parameters.loc[usepar] + while True: + indf = input_df.copy() + indf.loc[parameters.index,"parval1"] = parameters.values + simdf = emulate_with_gpr(indf,mdf,gpr_model_dict) + obs.loc[simdf.index,"obsval"] = simdf.sim.values + obs.loc[simdf.index.map(lambda x: x+"_gprstd"),"obsval"] = simdf.sim_std.values + ppw.send_observations(obs.obsval.values) + parameters = ppw.get_parameters() + if parameters is None: + break + parameters = parameters.loc[usepar] + + + +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("gprmodel_info.csv",index_col=0) + gpr_model_dict = get_gpr_model_dict(mdf) + mdf = emulate_with_gpr(input_df,mdf,gpr_model_dict) + + # 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),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) - + return mdf def randrealgen_optimized(nreal, tol=1e-7, max_samples=1000000): """ From c222aaa51223db5697bc89c7de7aab9ccbb04f58 Mon Sep 17 00:00:00 2001 From: jwhite Date: Thu, 28 Nov 2024 13:52:06 +0800 Subject: [PATCH 40/56] added gpr ppw test: --- autotest/utils_tests.py | 54 ++++++++++++++++++++++++++++++++++++++--- 1 file changed, 50 insertions(+), 4 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index b69a0ebf1..1b9b97939 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -3112,6 +3112,9 @@ def gpr_constr_test(): def gpr_zdt1_test(): import numpy as np + import subprocess as sp + import multiprocessing as mp + from datetime import datetime from sklearn.gaussian_process import GaussianProcessRegressor case = "zdt1" use_chances = False @@ -3138,7 +3141,7 @@ def gpr_zdt1_test(): num_workers = 10 noptmax_full = 5 - port = 4554 + port = 4569 pst.control_data.noptmax = -1 pst.pestpp_options["mou_population_size"] = pop_size pst.pestpp_options["mou_save_population_every"] = 1 @@ -3146,8 +3149,11 @@ def gpr_zdt1_test(): #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) - + start = datetime.now() pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=t_d) + finish = datetime.now() + duration1 = (finish - start).total_seconds() + 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] @@ -3176,12 +3182,53 @@ def gpr_zdt1_test(): print(psum.obj_2.min()) assert psum.obj_1.min() < 0.05 + t = gpr_t_d + "_ppw" + if os.path.exists(t): + shutil.rmtree(t) + shutil.copytree(gpr_t_d,t) + gpr_t_d = t + start = datetime.now() + b_d = os.getcwd() + os.chdir(gpr_t_d) + p = sp.Popen([mou_exe_path,"{0}.pst".format(case),"/h",":{0}".format(port)]) + os.chdir(b_d) + #p.wait() + #return + + num_workers=1 + + # looper over and start the workers - in this + # case they dont need unique dirs since they arent writing + # anything + procs = [] + for i in range(num_workers): + pp = mp.Process(target=gpr_zdt1_ppw) + pp.start() + procs.append(pp) + # if everyhing worked, the the workers should recieve the + # shutdown signal from the master and exit gracefully... + for pp in procs: + pp.join() + + # wait for the master to finish...but should already be finished + p.wait() + finish = datetime.now() + print("ppw` took",(finish-start).total_seconds()) + print("org took",duration1) + + arcppw = pd.read_csv(os.path.join(gpr_t_d,"zdt1.archive.obs_pop.csv"),index_col=0) + arcorg = pd.read_csv(os.path.join(gpr_t_d.replace("_ppw",""),"zdt1.archive.obs_pop.csv"),index_col=0) + diff = np.abs(arcppw.values - arcorg.values) + print(diff.max()) + assert diff.max() < 1e-6 + + def gpr_zdt1_ppw(): t_d = "zdt1_gpr_template" os.chdir(t_d) pst_name = "zdt1.pst" - ppw = pyemu.helpers.gpr_pyworker(pst_name,"localhost",4004) + ppw = pyemu.helpers.gpr_pyworker(pst_name,"localhost",4569) os.chdir("..") @@ -3197,7 +3244,6 @@ def gpr_zdt1_ppw(): # ppw_worker(0,case,t_d,"localhost",4004,frun) #pypestworker_test() gpr_zdt1_test() - #while True: # thresh_pars_test() #obs_ensemble_quantile_test() From 1afb43dc2f6e5b6c0518465e27df576e51fae3fe Mon Sep 17 00:00:00 2001 From: jwhite Date: Fri, 29 Nov 2024 10:44:00 +0800 Subject: [PATCH 41/56] more work on gpr ppw fxn --- autotest/utils_tests.py | 12 ++++++----- pyemu/utils/helpers.py | 44 ++++++++++++++++++++++++++++++----------- 2 files changed, 40 insertions(+), 16 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 1b9b97939..8afaddb1e 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -3137,7 +3137,7 @@ def gpr_zdt1_test(): else: pst.pestpp_options["opt_risk"] = 0.5 - pop_size = 10 + pop_size = 20 num_workers = 10 noptmax_full = 5 @@ -3149,10 +3149,9 @@ def gpr_zdt1_test(): #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) - start = datetime.now() + pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=t_d) - finish = datetime.now() - duration1 = (finish - start).total_seconds() + m_d = t_d dv_pops = [os.path.join(m_d, "{0}.0.dv_pop.csv".format(case))] @@ -3172,7 +3171,10 @@ def gpr_zdt1_test(): 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) + start = datetime.now() pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=gpr_t_d) + finish = datetime.now() + duration1 = (finish - start).total_seconds() gpr_m_d = gpr_t_d psum_fname = os.path.join(gpr_m_d,case+".pareto.archive.summary.csv") @@ -3195,7 +3197,7 @@ def gpr_zdt1_test(): #p.wait() #return - num_workers=1 + num_workers=3 # looper over and start the workers - in this # case they dont need unique dirs since they arent writing diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py index 9e1ca8cd7..7847d07cf 100644 --- a/pyemu/utils/helpers.py +++ b/pyemu/utils/helpers.py @@ -4344,32 +4344,54 @@ def gpr_pyworker(pst,host,port,input_df=None,mdf=None): import pandas as pd import numpy as np import pickle + + # if explicit args werent passed, get the default ones... if input_df is None: input_df = pd.read_csv("gpr_input.csv",index_col=0) if mdf is None: mdf = pd.read_csv("gprmodel_info.csv",index_col=0) gpr_model_dict = get_gpr_model_dict(mdf) ppw = PyPestWorker(pst,host,port,verbose=False) + + # we can only get parameters once the worker has initialize and + # is ready to run, so getting the first of pars here + # essentially blocks until the worker is ready parameters = ppw.get_parameters() + # if its None, the master already quit... if parameters is None: return + obs = ppw._pst.observation_data.copy() - obs = obs.loc[ppw.obs_names,:] + # align the obsval series with the order sent from the master + obs = obs.loc[ppw.obs_names,"obsval"] + + # work out which par values sent from the master we need to run the emulator par = ppw._pst.parameter_data.copy() - usepar = par.loc[par.parnme.isin(input_df.index.values),"parnme"].values - parameters = parameters.loc[usepar] + usepar_idx = [] + ppw_par_names = list(ppw.par_names) + for i,pname in enumerate(input_df.index.values): + usepar_idx.append(ppw_par_names.index(pname)) + + while True: - indf = input_df.copy() - indf.loc[parameters.index,"parval1"] = parameters.values - simdf = emulate_with_gpr(indf,mdf,gpr_model_dict) - obs.loc[simdf.index,"obsval"] = simdf.sim.values - obs.loc[simdf.index.map(lambda x: x+"_gprstd"),"obsval"] = simdf.sim_std.values - ppw.send_observations(obs.obsval.values) + # map the current dv values in parameters into the + # df needed to run the emulator + input_df["parval1"] = parameters.values[usepar_idx] + # do the emulation + simdf = emulate_with_gpr(input_df,mdf,gpr_model_dict) + + # replace the emulated quantites in the obs series + obs.loc[simdf.index] = simdf.sim.values + obs.loc[simdf.index.map(lambda x: x+"_gprstd")] = simdf.sim_std.values + #send the obs series to the master + ppw.send_observations(obs.values) + + #try to get more pars parameters = ppw.get_parameters() + # if None, we are done if parameters is None: break - parameters = parameters.loc[usepar] - + def gpr_forward_run(): From cc99529d4a21b2d446cbb9bf7e7c123f5db0343e Mon Sep 17 00:00:00 2001 From: jwhite Date: Fri, 29 Nov 2024 12:33:58 +0800 Subject: [PATCH 42/56] added ppw options to start_workers --- autotest/utils_tests.py | 41 ++++++++++++------ pyemu/utils/helpers.py | 15 +------ pyemu/utils/os_utils.py | 92 ++++++++++++++++++++++++++--------------- 3 files changed, 88 insertions(+), 60 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 8afaddb1e..d564f612e 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -3139,7 +3139,7 @@ def gpr_zdt1_test(): pop_size = 20 num_workers = 10 - noptmax_full = 5 + noptmax_full = 1 port = 4569 pst.control_data.noptmax = -1 @@ -3176,7 +3176,9 @@ def gpr_zdt1_test(): finish = datetime.now() duration1 = (finish - start).total_seconds() gpr_m_d = gpr_t_d - + arcorg = pd.read_csv(os.path.join(gpr_m_d,"zdt1.archive.obs_pop.csv"),index_col=0) + + 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) @@ -3184,20 +3186,36 @@ def gpr_zdt1_test(): print(psum.obj_2.min()) assert psum.obj_1.min() < 0.05 - t = gpr_t_d + "_ppw" - if os.path.exists(t): - shutil.rmtree(t) - shutil.copytree(gpr_t_d,t) - gpr_t_d = t + gpr_t_d2 = gpr_t_d + "_ppw" + if os.path.exists(gpr_t_d2): + shutil.rmtree(gpr_t_d2) + shutil.copytree(gpr_t_d,gpr_t_d2) + + gpr_m_d2 = gpr_t_d2.replace("template","master") + + input_df = pd.read_csv(os.path.join(gpr_t_d2,"gpr_input.csv"),index_col=0) + mdf = pd.read_csv(os.path.join(gpr_t_d2,"gprmodel_info.csv"),index_col=0) + mdf["model_fname"] = mdf.model_fname.apply(lambda x: os.path.join(gpr_t_d2,x)) + pyemu.os_utils.start_workers(gpr_t_d2, mou_exe_path, case + ".pst", num_workers, worker_root=".", + master_dir=gpr_m_d2, verbose=True, port=port, + ppw_function=pyemu.helpers.gpr_pyworker, + ppw_kwargs={"input_df":input_df, + "mdf":mdf}) + + + arcppw = pd.read_csv(os.path.join(gpr_m_d2,"zdt1.archive.obs_pop.csv"),index_col=0) + diff = np.abs(arcppw.values - arcorg.values) + print(diff.max()) + assert diff.max() < 1e-6 + + start = datetime.now() b_d = os.getcwd() - os.chdir(gpr_t_d) + os.chdir(gpr_t_d2) p = sp.Popen([mou_exe_path,"{0}.pst".format(case),"/h",":{0}".format(port)]) os.chdir(b_d) #p.wait() #return - - num_workers=3 # looper over and start the workers - in this # case they dont need unique dirs since they arent writing @@ -3218,8 +3236,7 @@ def gpr_zdt1_test(): print("ppw` took",(finish-start).total_seconds()) print("org took",duration1) - arcppw = pd.read_csv(os.path.join(gpr_t_d,"zdt1.archive.obs_pop.csv"),index_col=0) - arcorg = pd.read_csv(os.path.join(gpr_t_d.replace("_ppw",""),"zdt1.archive.obs_pop.csv"),index_col=0) + arcppw = pd.read_csv(os.path.join(gpr_t_d2,"zdt1.archive.obs_pop.csv"),index_col=0) diff = np.abs(arcppw.values - arcorg.values) print(diff.max()) assert diff.max() < 1e-6 diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py index 7847d07cf..fbdf3bbce 100644 --- a/pyemu/utils/helpers.py +++ b/pyemu/utils/helpers.py @@ -4397,24 +4397,11 @@ def gpr_pyworker(pst,host,port,input_df=None,mdf=None): 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("gprmodel_info.csv",index_col=0) gpr_model_dict = get_gpr_model_dict(mdf) - mdf = emulate_with_gpr(input_df,mdf,gpr_model_dict) - - # 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),return_std=True) - # mdf.loc[output_name,"sim"] = sim[0] - # mdf.loc[output_name,"sim_std"] = sim[1] - + mdf = emulate_with_gpr(input_df,mdf,gpr_model_dict) mdf.loc[:,["output_name","sim","sim_std"]].to_csv("gpr_output.csv",index=False) return mdf diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index 0ec4e75ac..06062e638 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -268,7 +268,9 @@ def start_workers( verbose=False, silent_master=False, reuse_master=False, - restart=False + restart=False, + ppw_function=None, + ppw_kwargs=None ): """start a group of pest(++) workers on the local machine @@ -307,6 +309,14 @@ def start_workers( process started in it by this function. restart (`bool`): flag to add a restart flag to the master start. If `True`, this will include `/r` in the master call string. + ppw_function (function): a function pointer that uses PyPestWorker to execute model runs. + This is to help speed up pure-python and really fast running forward models and the + way its implemented in `start_workers` assumes each `PyPestWorker` instance does not need + a separate set of model+files, that is, these workers are assumed to execute entirely in + memory. The first three arguments to this function must be `pst_rel_path`, `host`, and `port`. + Default is None. + ppw_kwargs (dict): keyword arguments to pass to `ppw_function`. + Default is None. Notes: If all workers (and optionally master) exit gracefully, then the worker @@ -390,41 +400,51 @@ def start_workers( os.chdir(base_dir) except Exception as e: raise Exception("error starting master instance: {0}".format(str(e))) - time.sleep(1.5) # a few cycles to let the master get ready + time.sleep(0.5) # a few cycles to let the master get ready + - tcp_arg = "{0}:{1}".format(hostname, port) procs = [] worker_dirs = [] - for i in range(num_workers): - new_worker_dir = os.path.join(worker_root, "worker_{0}".format(i)) - if os.path.exists(new_worker_dir): - _try_remove_existing(new_worker_dir) - _try_copy_dir(worker_dir, new_worker_dir) - try: - if exe_verf: - # if rel_path is not None: - # exe_path = os.path.join(rel_path,exe_rel_path) - # else: - exe_path = exe_rel_path - else: - exe_path = exe_rel_path - args = [exe_path, pst_rel_path, "/h", tcp_arg] - # print("starting worker in {0} with args: {1}".format(new_worker_dir,args)) - if rel_path is not None: - cwd = os.path.join(new_worker_dir, rel_path) - else: - cwd = new_worker_dir - - os.chdir(cwd) - if verbose: - print("worker:{0} in {1}".format(" ".join(args), cwd)) - with open(os.devnull, "w") as f: - p = sp.Popen(args, stdout=f, stderr=f) + if ppw_function is not None: + args = (os.path.join(worker_dir,pst_rel_path),hostname,port) + for i in range(num_workers): + p = mp.Process(target=ppw_function,args=args,kwargs=ppw_kwargs) + p.start() procs.append(p) - os.chdir(base_dir) - except Exception as e: - raise Exception("error starting worker: {0}".format(str(e))) - worker_dirs.append(new_worker_dir) + + else: + tcp_arg = "{0}:{1}".format(hostname, port) + + for i in range(num_workers): + new_worker_dir = os.path.join(worker_root, "worker_{0}".format(i)) + if os.path.exists(new_worker_dir): + _try_remove_existing(new_worker_dir) + _try_copy_dir(worker_dir, new_worker_dir) + try: + if exe_verf: + # if rel_path is not None: + # exe_path = os.path.join(rel_path,exe_rel_path) + # else: + exe_path = exe_rel_path + else: + exe_path = exe_rel_path + args = [exe_path, pst_rel_path, "/h", tcp_arg] + # print("starting worker in {0} with args: {1}".format(new_worker_dir,args)) + if rel_path is not None: + cwd = os.path.join(new_worker_dir, rel_path) + else: + cwd = new_worker_dir + + os.chdir(cwd) + if verbose: + print("worker:{0} in {1}".format(" ".join(args), cwd)) + with open(os.devnull, "w") as f: + p = sp.Popen(args, stdout=f, stderr=f) + procs.append(p) + os.chdir(base_dir) + except Exception as e: + raise Exception("error starting worker: {0}".format(str(e))) + worker_dirs.append(new_worker_dir) if master_dir is not None: # while True: @@ -444,14 +464,18 @@ def start_workers( time.sleep(5) else: master_p.wait() - time.sleep(1.5) # a few cycles to let the workers end gracefully + time.sleep(0.5) # a few cycles to let the workers end gracefully # kill any remaining workers for p in procs: p.kill() # this waits for sweep to finish, but pre/post/model (sub)subprocs may take longer + for p in procs: - p.wait() + if ppw_function is not None: + p.join() + else: + p.wait() if cleanup: cleanit = 0 removed = set() From b23c72ba039e2f563fb2343a7715de64c362b772 Mon Sep 17 00:00:00 2001 From: jwhite Date: Fri, 29 Nov 2024 13:40:19 +0800 Subject: [PATCH 43/56] reworked gpr notebook to use ppw, omg sooo much better --- autotest/utils/hosaki_template/forward_run.py | 21 ++++- autotest/utils_tests.py | 7 +- examples/gpr_emulation_hosaki.ipynb | 87 ++++++++++++++----- pyemu/utils/os_utils.py | 6 +- 4 files changed, 92 insertions(+), 29 deletions(-) diff --git a/autotest/utils/hosaki_template/forward_run.py b/autotest/utils/hosaki_template/forward_run.py index 8fb00b3a3..e28a2cd91 100644 --- a/autotest/utils/hosaki_template/forward_run.py +++ b/autotest/utils/hosaki_template/forward_run.py @@ -14,12 +14,29 @@ def hosaki(x): def helper(func=hosaki,pvals=None): if pvals is None: pvals = pd.read_csv('par.csv',index_col=0).values - print(pvals) sim = hosaki(pvals) - with open('sim.csv','w') as f: f.write('obsnme,obsval\n') f.write('sim,'+str(sim.item())+'\n') + return sim + +def hosaki_ppw_worker(pst_name,host,port): + import pyemu + ppw = pyemu.os_utils.PyPestWorker(pst_name,host,port,verbose=False) + pvals = ppw.get_parameters() + if pvals is None: + return + pvals.sort_index(inplace=True) + + while True: + + sim = helper(pvals=pvals.values) + ppw.send_observations(np.array([sim])) + pvals = ppw.get_parameters() + if pvals is None: + break + pvals.sort_index(inplace=True) + if __name__ == "__main__": helper() diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index d564f612e..d77f1a40f 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -3169,13 +3169,14 @@ 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) + start = datetime.now() #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) - start = datetime.now() pyemu.os_utils.run("{0} {1}.pst".format(mou_exe_path,case),cwd=gpr_t_d) + gpr_m_d = gpr_t_d + finish = datetime.now() duration1 = (finish - start).total_seconds() - gpr_m_d = gpr_t_d arcorg = pd.read_csv(os.path.join(gpr_m_d,"zdt1.archive.obs_pop.csv"),index_col=0) @@ -3221,6 +3222,8 @@ def gpr_zdt1_test(): # case they dont need unique dirs since they arent writing # anything procs = [] + # try this test with 1 worker as an edge case + num_workers = 1 for i in range(num_workers): pp = mp.Process(target=gpr_zdt1_ppw) pp.start() diff --git a/examples/gpr_emulation_hosaki.ipynb b/examples/gpr_emulation_hosaki.ipynb index 5a80a6a71..4c200619e 100644 --- a/examples/gpr_emulation_hosaki.ipynb +++ b/examples/gpr_emulation_hosaki.ipynb @@ -20,6 +20,7 @@ "outputs": [], "source": [ "import os\n", + "import sys\n", "import shutil\n", "import pandas as pd\n", "import numpy as np\n", @@ -111,14 +112,14 @@ { "cell_type": "code", "execution_count": null, - "id": "a6e8e756-9513-4140-922c-76cc7a347a83", + "id": "83691702-4aab-4ff2-b147-fcdd3ed6efb4", "metadata": {}, "outputs": [], "source": [ "pvals = []\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", + "sweep_steps = 30\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", @@ -132,11 +133,12 @@ "pst.write(os.path.join(t_d,\"pest.pst\"))\n", "port = 5544\n", "num_workers = 30\n", - "\n", + "sys.path.insert(0,t_d)\n", + "from forward_run import hosaki_ppw_worker as ppw_function\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" + " port=port,ppw_function=ppw_function)" ] }, { @@ -219,11 +221,11 @@ "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", + "num_workers = 1\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" + " port=port,ppw_function=ppw_function)\n" ] }, { @@ -344,7 +346,11 @@ "outputs": [], "source": [ "gpr_t_d = t_d + \"_gpr\"\n", - "gpst = prep_for_gpr(training_dvpop_fnames,training_opop_fnames,gpr_t_d)" + "gpst = prep_for_gpr(training_dvpop_fnames,training_opop_fnames,gpr_t_d)\n", + "# we only need these files in memory so we can use the speed of PyPestWorker later....\n", + "input_df = pd.read_csv(os.path.join(gpr_t_d,\"gpr_input.csv\"),index_col=0)\n", + "mdf = pd.read_csv(os.path.join(gpr_t_d,\"gprmodel_info.csv\"),index_col=0)\n", + "mdf[\"model_fname\"] = mdf.model_fname.apply(lambda x: os.path.join(gpr_t_d,x))" ] }, { @@ -420,12 +426,15 @@ "metadata": {}, "outputs": [], "source": [ + "\n", "gpr_sweep_d = sweep_d+\"_gpr\"\n", "num_workers = 30\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" + " port=port,ppw_function=pyemu.helpers.gpr_pyworker,\n", + " ppw_kwargs={\"input_df\":input_df,\n", + " \"mdf\":mdf})\n" ] }, { @@ -507,7 +516,9 @@ "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)" + " port=port,ppw_function=pyemu.helpers.gpr_pyworker,\n", + " ppw_kwargs={\"input_df\":input_df,\n", + " \"mdf\":mdf})" ] }, { @@ -598,7 +609,7 @@ "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)" + " port=port,ppw_function=ppw_function)" ] }, { @@ -667,7 +678,11 @@ "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)" + "gpst = prep_for_gpr(training_dvpop_fnames,training_opop_fnames,gpr_t_d)\n", + "# we only need these files in memory so we can use the speed of PyPestWorker later....\n", + "input_df = pd.read_csv(os.path.join(gpr_t_d,\"gpr_input.csv\"),index_col=0)\n", + "mdf = pd.read_csv(os.path.join(gpr_t_d,\"gprmodel_info.csv\"),index_col=0)\n", + "mdf[\"model_fname\"] = mdf.model_fname.apply(lambda x: os.path.join(gpr_t_d,x))" ] }, { @@ -690,7 +705,9 @@ "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)" + " port=port,ppw_function=pyemu.helpers.gpr_pyworker,\n", + " ppw_kwargs={\"input_df\":input_df,\n", + " \"mdf\":mdf})" ] }, { @@ -736,7 +753,9 @@ "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", + " port=port,ppw_function=pyemu.helpers.gpr_pyworker,\n", + " ppw_kwargs={\"input_df\":input_df,\n", + " \"mdf\":mdf})\n", "plot_gpr_mou_results(gpr_m_d,gpr_sweep_d)" ] }, @@ -770,7 +789,7 @@ "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", + " port=port,ppw_function=ppw_function)\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", @@ -792,12 +811,18 @@ "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", + "# we only need these files in memory so we can use the speed of PyPestWorker later....\n", + "input_df = pd.read_csv(os.path.join(gpr_t_d,\"gpr_input.csv\"),index_col=0)\n", + "mdf = pd.read_csv(os.path.join(gpr_t_d,\"gprmodel_info.csv\"),index_col=0)\n", + "mdf[\"model_fname\"] = mdf.model_fname.apply(lambda x: os.path.join(gpr_t_d,x))\n", "gpr_sweep_d = sweep_d+\"_gpr{0}\".format(i)\n", "num_workers = 30\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", + " port=port,ppw_function=pyemu.helpers.gpr_pyworker,\n", + " ppw_kwargs={\"input_df\":input_df,\n", + " \"mdf\":mdf})\n", "\n", "plot_gpr_sweep_results(gpr_sweep_d,training_dvpop)\n", "\n" @@ -818,7 +843,9 @@ "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", + " port=port,ppw_function=pyemu.helpers.gpr_pyworker,\n", + " ppw_kwargs={\"input_df\":input_df,\n", + " \"mdf\":mdf})\n", "\n", "\n", "\n", @@ -847,7 +874,7 @@ "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", + " port=port,ppw_function=ppw_function)\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", @@ -869,12 +896,18 @@ "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", + "# we only need these files in memory so we can use the speed of PyPestWorker later....\n", + "input_df = pd.read_csv(os.path.join(gpr_t_d,\"gpr_input.csv\"),index_col=0)\n", + "mdf = pd.read_csv(os.path.join(gpr_t_d,\"gprmodel_info.csv\"),index_col=0)\n", + "mdf[\"model_fname\"] = mdf.model_fname.apply(lambda x: os.path.join(gpr_t_d,x))\n", "gpr_sweep_d = sweep_d+\"_gpr{0}\".format(i)\n", "num_workers = 30\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", + " port=port,ppw_function=pyemu.helpers.gpr_pyworker,\n", + " ppw_kwargs={\"input_df\":input_df,\n", + " \"mdf\":mdf})\n", "\n", "plot_gpr_sweep_results(gpr_sweep_d,training_dvpop)\n", "\n" @@ -895,7 +928,9 @@ "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", + " port=port,ppw_function=pyemu.helpers.gpr_pyworker,\n", + " ppw_kwargs={\"input_df\":input_df,\n", + " \"mdf\":mdf})\n", "\n", "\n", "\n", @@ -932,7 +967,7 @@ "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", + " port=port,ppw_function=ppw_function)\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", @@ -953,12 +988,18 @@ "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", + "# we only need these files in memory so we can use the speed of PyPestWorker later....\n", + "input_df = pd.read_csv(os.path.join(gpr_t_d,\"gpr_input.csv\"),index_col=0)\n", + "mdf = pd.read_csv(os.path.join(gpr_t_d,\"gprmodel_info.csv\"),index_col=0)\n", + "mdf[\"model_fname\"] = mdf.model_fname.apply(lambda x: os.path.join(gpr_t_d,x))\n", "gpr_sweep_d = sweep_d+\"_gpr{0}\".format(i)\n", "num_workers = 30\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", + " port=port,ppw_function=pyemu.helpers.gpr_pyworker,\n", + " ppw_kwargs={\"input_df\":input_df,\n", + " \"mdf\":mdf})\n", "\n", "plot_gpr_sweep_results(gpr_sweep_d,training_dvpop)\n", "\n" @@ -979,7 +1020,9 @@ "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", + " port=port,ppw_function=pyemu.helpers.gpr_pyworker,\n", + " ppw_kwargs={\"input_df\":input_df,\n", + " \"mdf\":mdf})\n", "\n", "plot_gpr_mou_results(gpr_m_d,gpr_sweep_d)" ] diff --git a/pyemu/utils/os_utils.py b/pyemu/utils/os_utils.py index 06062e638..4d98ec3a5 100644 --- a/pyemu/utils/os_utils.py +++ b/pyemu/utils/os_utils.py @@ -270,7 +270,7 @@ def start_workers( reuse_master=False, restart=False, ppw_function=None, - ppw_kwargs=None + ppw_kwargs={} ): """start a group of pest(++) workers on the local machine @@ -316,7 +316,7 @@ def start_workers( memory. The first three arguments to this function must be `pst_rel_path`, `host`, and `port`. Default is None. ppw_kwargs (dict): keyword arguments to pass to `ppw_function`. - Default is None. + Default is empty dict. Notes: If all workers (and optionally master) exit gracefully, then the worker @@ -791,7 +791,7 @@ def listen(self,lock=None,send_lock=None): if self._send_lock is not None: self._send_lock.release() elif self.net_pack.mtype == 14: - print("recv'd terminate signal") + #print("recv'd terminate signal") self.message("recv'd terminate signal") return elif self.net_pack.mtype == 16: From 56a26a263582b22bcf95f2a8daf4027d67e1be80 Mon Sep 17 00:00:00 2001 From: jwhite Date: Fri, 29 Nov 2024 13:42:29 +0800 Subject: [PATCH 44/56] even longer timeout for ends tests --- autotest/la_tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autotest/la_tests.py b/autotest/la_tests.py index fdc11461c..cb1be63ef 100644 --- a/autotest/la_tests.py +++ b/autotest/la_tests.py @@ -624,7 +624,7 @@ def ends_run_freyberg_dsi(tmp_d, nst=False, nst_extrap=None, ztz=False, energy=1 os.remove(filename) pst = pyemu.Pst(os.path.join(t_d,"dsi.pst")) pst.control_data.noptmax = -1 - pst.pestpp_options["overdue_giveup_fac"] = 1000 + pst.pestpp_options["overdue_giveup_fac"] = 100000000 pst.write(os.path.join(t_d,"dsi.pst"),version=2) #pyemu.os_utils.run("pestpp-ies dsi.pst",cwd=t_d) m_d = t_d.replace("template","master") From a27144d9ae9715c95de406680e897869b6c48b49 Mon Sep 17 00:00:00 2001 From: jwhite Date: Fri, 29 Nov 2024 14:18:14 +0800 Subject: [PATCH 45/56] a few tweaks to the gpr hosaki notebook --- examples/gpr_emulation_hosaki.ipynb | 38 ++++++++++++++++++++--------- 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/examples/gpr_emulation_hosaki.ipynb b/examples/gpr_emulation_hosaki.ipynb index 4c200619e..fc29a1c96 100644 --- a/examples/gpr_emulation_hosaki.ipynb +++ b/examples/gpr_emulation_hosaki.ipynb @@ -117,8 +117,6 @@ "outputs": [], "source": [ "pvals = []\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 = 30\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", @@ -509,7 +507,7 @@ "outputs": [], "source": [ "gpst.control_data.noptmax = 8\n", - "gpst.pestpp_options[\"mou_population_size\"] = 20\n", + "gpst.pestpp_options[\"mou_population_size\"] = 40\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", "num_workers = 20\n", @@ -591,7 +589,8 @@ "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", + "# only use the first 10 members of the emulated population to evaluate in the model to make things more interesting\n", + "gpr_dvpops[max(gpr_dvpops_itr)].iloc[:10].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" @@ -746,7 +745,7 @@ "outputs": [], "source": [ "gpst.control_data.noptmax = 8\n", - "gpst.pestpp_options[\"mou_population_size\"] = 20\n", + "gpst.pestpp_options[\"mou_population_size\"] = 40\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", "num_workers = 20\n", @@ -780,7 +779,8 @@ "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", + "# only use the first 10 members of the emulated population to evaluate in the model to make things more interesting\n", + "gpr_dvpops[max(gpr_dvpops_itr)].iloc[:10,:].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", @@ -865,7 +865,8 @@ "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", + "# only use the first 10 members of the emulated population to evaluate in the model to make things more interesting\n", + "gpr_dvpops[max(gpr_dvpops_itr)].iloc[:10,:].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", @@ -958,7 +959,8 @@ "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", + "# only use the first 10 members of the emulated population to evaluate in the model to make things more interesting\n", + "gpr_dvpops[max(gpr_dvpops_itr)].iloc[:10,:].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", @@ -1012,8 +1014,8 @@ "metadata": {}, "outputs": [], "source": [ - "gpst.control_data.noptmax = 15\n", - "gpst.pestpp_options[\"mou_population_size\"] = 20\n", + "gpst.control_data.noptmax = 20\n", + "gpst.pestpp_options[\"mou_population_size\"] = 40\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "\n", "num_workers = 20\n", @@ -1032,7 +1034,7 @@ "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. In this simple and naive demo, we've only run the true \"complex\" model at the points listed here:" ] }, { @@ -1041,7 +1043,9 @@ "id": "31eeeee5-f54e-4dfd-8ba7-717108066370", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "training_dvpop" + ] }, { "cell_type": "code", @@ -1049,6 +1053,16 @@ "id": "0d58eb64-1e67-4d01-b7c9-2d024b0806a0", "metadata": {}, "outputs": [], + "source": [ + "print(\"The complex model has only been run {0} times!!!\".format(training_dvpop.shape[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "355b93a9-17f8-4bd8-ab93-4f13a10fea3a", + "metadata": {}, + "outputs": [], "source": [] } ], From 76083215406559928c91c2a91e0b7024ce179909 Mon Sep 17 00:00:00 2001 From: jwhite Date: Fri, 29 Nov 2024 17:21:19 +0800 Subject: [PATCH 46/56] fix for notebook with really short runs being timed out --- autotest/autotest_notebooks.py | 2 +- autotest/utils/hosaki_template/pest.pst | 2 + examples/MODFLOW_to_PEST_even_more_boss.ipynb | 440 --------- examples/gpr_emulation_hosaki.ipynb | 30 +- examples/modflow_to_pest_like_a_boss.ipynb | 865 ------------------ 5 files changed, 18 insertions(+), 1321 deletions(-) delete mode 100644 examples/MODFLOW_to_PEST_even_more_boss.ipynb delete mode 100644 examples/modflow_to_pest_like_a_boss.ipynb diff --git a/autotest/autotest_notebooks.py b/autotest/autotest_notebooks.py index 9c686db09..41048487f 100644 --- a/autotest/autotest_notebooks.py +++ b/autotest/autotest_notebooks.py @@ -51,5 +51,5 @@ def test_notebooks_test(): if __name__ == '__main__': shutil.copy2(os.path.join("..","examples","helpers.py"),"helpers.py") files = get_notebooks() - for fn in files: + for fn in files: run_notebook(fn) diff --git a/autotest/utils/hosaki_template/pest.pst b/autotest/utils/hosaki_template/pest.pst index 03982ea0c..60d1bd4ca 100644 --- a/autotest/utils/hosaki_template/pest.pst +++ b/autotest/utils/hosaki_template/pest.pst @@ -26,3 +26,5 @@ python forward_run.py * model input/output ./par.csv.tpl ./par.csv ./sim.csv.ins ./sim.csv +++overdue_giveup_fac(1e10) +++overdue_resched_fac(1e10) diff --git a/examples/MODFLOW_to_PEST_even_more_boss.ipynb b/examples/MODFLOW_to_PEST_even_more_boss.ipynb deleted file mode 100644 index 5f625ab10..000000000 --- a/examples/MODFLOW_to_PEST_even_more_boss.ipynb +++ /dev/null @@ -1,440 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### \"What just happened???\"\n", - "\n", - "Here we take an existing modflow model and setup a very complex parameterization system for arrays and boundary conditions. All parameters are setup as multpliers: the original inputs from the modflow model are saved in separate files and during the forward run, they are multplied by the parameters to form new model inputs. the forward run script (\"forward_run.py\") is also written. And somewhat meaningful prior covariance matrix is constructed from geostatistical structures with out any additional arguements...oh yeah!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import os\n", - "import platform\n", - "import shutil\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import flopy\n", - "import pyemu\n", - "from pathlib import Path\n", - "from pyemu.legacy import PstFromFlopyModel" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Sandbox (this is mostly for test running safety)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "org_model_ws = \"freyberg_sfr_update\"\n", - "egpath = Path(\".\").absolute()\n", - "while egpath.name != 'examples':\n", - " os.chdir('..')\n", - " egpath = Path(\".\").absolute()\n", - "\n", - "model_ws = Path(org_model_ws).absolute()\n", - "tmp_path = Path(\"MODFLOW_to_PEST_even_more_bosslike\").absolute()\n", - "\n", - "EXE_DIR = Path(\"..\",\"bin\").absolute()\n", - "if \"window\" in platform.platform().lower():\n", - " EXE_DIR = Path(EXE_DIR,\"win\")\n", - "elif \"darwin\" in platform.platform().lower() or \"macos\" in platform.platform().lower():\n", - " EXE_DIR = Path(EXE_DIR,\"mac\")\n", - "else:\n", - " EXE_DIR = Path(EXE_DIR,\"linux\")\n", - " \n", - "basename = Path(model_ws).name\n", - "new_d = Path(tmp_path, basename)\n", - "if new_d.exists():\n", - " shutil.rmtree(new_d)\n", - "Path(tmp_path).mkdir(exist_ok=True)\n", - "# creation functionality\n", - "shutil.copytree(model_ws, new_d)\n", - "\n", - "os.chdir(tmp_path)\n", - "print(Path.cwd())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nam_file = \"freyberg.nam\"\n", - "temp_model_ws = \"temp\"\n", - "new_model_ws = \"template\"\n", - "# load the model, change dir and run once just to make sure everthing is working\n", - "m = flopy.modflow.Modflow.load(nam_file,model_ws=org_model_ws,check=False, exe_name=\"mfnwt\",\n", - " forgive=False,verbose=True)\n", - "m.change_model_ws(temp_model_ws,reset_external=True)\n", - "m.write_input()\n", - "[shutil.copy2(os.path.join(EXE_DIR,f),os.path.join(temp_model_ws,f)) for f in os.listdir(EXE_DIR)]\n", - "\n", - "try:\n", - " pyemu.os_utils.run(f\"mfnwt {nam_file}\", cwd=m.model_ws) \n", - "except():\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You want some pilot points? We got that...how about one set of recharge multiplier pilot points applied to all stress periods? and sy in layer 1?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.get_package_list()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parameterization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pp_props = [[\"upw.sy\",0], [\"rch.rech\",None]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You want some constants (uniform value multipliers)? We got that too...." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "const_props = []\n", - "for iper in range(m.nper): # recharge for past and future\n", - " const_props.append([\"rch.rech\",iper])\n", - "for k in range(m.nlay):\n", - " const_props.append([\"upw.hk\",k])\n", - " const_props.append([\"upw.ss\",k])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You want grid-scale parameter flexibility for hk in all layers? We got that too...and how about sy in layer 1 and vka in layer 2 while we are at it" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grid_props = [[\"upw.sy\",0],[\"upw.vka\",1]]\n", - "for k in range(m.nlay):\n", - " grid_props.append([\"upw.hk\",k])\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some people like using zones...so we have those too" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "zn_array = np.loadtxt(os.path.join(egpath, \"Freyberg_Truth\",\"hk.zones\"))\n", - "plt.imshow(zn_array)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "zone_props = [[\"upw.ss\",0], [\"rch.rech\",0],[\"rch.rech\",1]]\n", - "k_zone_dict = {k:zn_array for k in range(m.nlay)}\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But wait, boundary conditions are uncertain too...Can we add some parameter to represent that uncertainty? You know it!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bc_props = []\n", - "for iper in range(m.nper):\n", - " bc_props.append([\"wel.flux\",iper])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Observations\n", - "\n", - "Since observations are \"free\", we can carry lots of them around..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# here were are building a list of stress period, layer pairs (zero-based) that we will use\n", - "# to setup obserations from every active model cell for a given pair\n", - "hds_kperk = []\n", - "for iper in range(m.nper):\n", - " for k in range(m.nlay):\n", - " hds_kperk.append([iper,k])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Here it goes...\n", - "Now we will use all these args to construct a complete PEST interface - template files, instruction files, control file and even the forward run script! All parameters are setup as multiplers against the existing inputs in the modflow model - the existing inputs are extracted (with flopy) and saved in a sub directory for safe keep and for multiplying against during a forward model run. The constructor will also write a full (covariates included) prior parameter covariance matrix, which is needed for all sorts of important analyses.|" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mfp_boss = PstFromFlopyModel(nam_file,new_model_ws,org_model_ws=temp_model_ws,\n", - " pp_props=pp_props,spatial_list_props=bc_props,\n", - " zone_props=zone_props,grid_props=grid_props,\n", - " const_props=const_props,k_zone_dict=k_zone_dict,\n", - " remove_existing=True,pp_space=4,sfr_pars=True,\n", - " sfr_obs=True,hds_kperk=hds_kperk)\n", - "\n", - "[shutil.copy2(os.path.join(EXE_DIR,f),os.path.join(new_model_ws,f)) for f in os.listdir(EXE_DIR)]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``mpf_boss`` instance containts a ``pyemu.Pst`` object (its already been saved to a file, but you may want to manipulate it more)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst = mfp_boss.pst" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.npar,pst.nobs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That was crazy easy - this used to take me weeks to get a PEST interface setup with level of complexity" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.template_files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.instruction_files" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets look at that important prior covariance matrix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cov = pyemu.Cov.from_ascii(os.path.join(new_model_ws,m.name+\".pst.prior.cov\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cov = cov.x\n", - "cov[cov==0] = np.nan\n", - "plt.imshow(cov)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### adjusting parameter bounds\n", - "Let's say you don't like the parameter bounds in the new control file (note you can pass a par_bounds arg to the constructor)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.parameter_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's change the ``welflux`` pars" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "par = pst.parameter_data #get a ref to the parameter data dataframe\n", - "wpars = par.pargp==\"welflux_k02\"\n", - "par.loc[wpars]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "par.loc[wpars,\"parubnd\"] = 1.1\n", - "par.loc[wpars,\"parlbnd\"] = 0.9\n", - "pst.parameter_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# now we need to rebuild the prior parameter covariance matrix\n", - "cov = mfp_boss.build_prior()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Boom!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = cov.x\n", - "x[x==0.0] = np.nan\n", - "plt.imshow(x)" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "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": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/gpr_emulation_hosaki.ipynb b/examples/gpr_emulation_hosaki.ipynb index fc29a1c96..1c1037f96 100644 --- a/examples/gpr_emulation_hosaki.ipynb +++ b/examples/gpr_emulation_hosaki.ipynb @@ -130,7 +130,7 @@ "\n", "pst.write(os.path.join(t_d,\"pest.pst\"))\n", "port = 5544\n", - "num_workers = 30\n", + "num_workers = 10\n", "sys.path.insert(0,t_d)\n", "from forward_run import hosaki_ppw_worker as ppw_function\n", "pyemu.os_utils.start_workers(t_d,\"pestpp-ies\",\"pest.pst\",\n", @@ -426,7 +426,7 @@ "source": [ "\n", "gpr_sweep_d = sweep_d+\"_gpr\"\n", - "num_workers = 30\n", + "num_workers = 10\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", @@ -510,7 +510,7 @@ "gpst.pestpp_options[\"mou_population_size\"] = 40\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", - "num_workers = 20\n", + "num_workers = 3\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", @@ -604,7 +604,7 @@ "outputs": [], "source": [ "m_d += \"_1\"\n", - "num_workers = 10\n", + "num_workers = 1\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", @@ -700,7 +700,7 @@ "outputs": [], "source": [ "gpr_sweep_d = sweep_d+\"_gpr1\"\n", - "num_workers = 30\n", + "num_workers = 10\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", @@ -748,7 +748,7 @@ "gpst.pestpp_options[\"mou_population_size\"] = 40\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "gpr_m_d = gpr_t_d.replace(\"template\",\"master\")\n", - "num_workers = 20\n", + "num_workers = 3\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", @@ -785,7 +785,7 @@ "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", + "num_workers = 1\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", @@ -816,7 +816,7 @@ "mdf = pd.read_csv(os.path.join(gpr_t_d,\"gprmodel_info.csv\"),index_col=0)\n", "mdf[\"model_fname\"] = mdf.model_fname.apply(lambda x: os.path.join(gpr_t_d,x))\n", "gpr_sweep_d = sweep_d+\"_gpr{0}\".format(i)\n", - "num_workers = 30\n", + "num_workers = 10\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", @@ -839,7 +839,7 @@ "gpst.pestpp_options[\"mou_population_size\"] = 20\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "\n", - "num_workers = 20\n", + "num_workers = 3\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", @@ -871,7 +871,7 @@ "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", + "num_workers = 1\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", @@ -902,7 +902,7 @@ "mdf = pd.read_csv(os.path.join(gpr_t_d,\"gprmodel_info.csv\"),index_col=0)\n", "mdf[\"model_fname\"] = mdf.model_fname.apply(lambda x: os.path.join(gpr_t_d,x))\n", "gpr_sweep_d = sweep_d+\"_gpr{0}\".format(i)\n", - "num_workers = 30\n", + "num_workers = 10\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", @@ -925,7 +925,7 @@ "gpst.pestpp_options[\"mou_population_size\"] = 20\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "\n", - "num_workers = 20\n", + "num_workers = 3\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", @@ -965,7 +965,7 @@ "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", + "num_workers = 1\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", @@ -995,7 +995,7 @@ "mdf = pd.read_csv(os.path.join(gpr_t_d,\"gprmodel_info.csv\"),index_col=0)\n", "mdf[\"model_fname\"] = mdf.model_fname.apply(lambda x: os.path.join(gpr_t_d,x))\n", "gpr_sweep_d = sweep_d+\"_gpr{0}\".format(i)\n", - "num_workers = 30\n", + "num_workers = 10\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", @@ -1018,7 +1018,7 @@ "gpst.pestpp_options[\"mou_population_size\"] = 40\n", "gpst.write(os.path.join(gpr_t_d,\"pest.pst\"))\n", "\n", - "num_workers = 20\n", + "num_workers = 5\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", diff --git a/examples/modflow_to_pest_like_a_boss.ipynb b/examples/modflow_to_pest_like_a_boss.ipynb deleted file mode 100644 index 0e377f25c..000000000 --- a/examples/modflow_to_pest_like_a_boss.ipynb +++ /dev/null @@ -1,865 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Freyberg Model PEST setup example\n", - "Herein, we will show users how to use pyEMU to setup a groundwater model for use in pest. We will cover the following topics:\n", - "- setup pilot points as parameters, including 1st-order tikhonov regularization\n", - "- setup other model inputs as parameters\n", - "- setup simulated water levels as observations\n", - "- setup simulated water budget components as observations (or forecasts)\n", - "- create a pest control file and adjust observation weights to balance the objective function\n", - "\n", - "Note that, in addition to `pyemu`, this notebook relies on `flopy`. `flopy` can be obtained (along with installation instructions) at https://github.com/modflowpy/flopy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import os\n", - "import shutil\n", - "import platform\n", - "import numpy as np\n", - "import pandas as pd\n", - "from matplotlib.patches import Rectangle as rect\n", - "import matplotlib.pyplot as plt\n", - "import warnings\n", - "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - "import matplotlib as mpl\n", - "newparams = {'legend.fontsize':10, 'axes.labelsize':10,\n", - " 'xtick.labelsize':10, 'ytick.labelsize':10,\n", - " 'font.family':'Univers 57 Condensed', \n", - " 'pdf.fonttype':42}\n", - "plt.rcParams.update(newparams)\n", - "import pyemu\n", - "from pathlib import Path" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some pathing trickery to make sure we can run this whole nb in a sandbox (and to make sure we dont break paths if we keep running this cell!)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "egpath = Path(\".\").absolute()\n", - "while egpath.name != 'examples':\n", - " os.chdir('..')\n", - " egpath = Path(\".\").absolute()\n", - "\n", - "model_ws = Path(\"Freyberg_transient\").absolute()\n", - "tmp_path = Path(\"modflow_to_pest_like_a_boss\").absolute()\n", - "\n", - "EXE_DIR = Path(\"..\",\"bin\").absolute()\n", - "if \"window\" in platform.platform().lower():\n", - " EXE_DIR = Path(EXE_DIR,\"win\")\n", - "elif \"darwin\" in platform.platform().lower() or \"macos\" in platform.platform().lower():\n", - " EXE_DIR = Path(EXE_DIR,\"mac\")\n", - "else:\n", - " EXE_DIR = Path(EXE_DIR,\"linux\")\n", - " \n", - "basename = Path(model_ws).name\n", - "new_d = Path(tmp_path, basename)\n", - "if new_d.exists():\n", - " shutil.rmtree(new_d)\n", - "Path(tmp_path).mkdir(exist_ok=True)\n", - "# creation functionality\n", - "shutil.copytree(model_ws, new_d)\n", - "\n", - "os.chdir(tmp_path)\n", - "print(Path.cwd())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model background\n", - "This example is based on the synthetic classroom model of Freyberg(1988). The model is a 2-dimensional MODFLOW model with 1 layer, 40 rows, and 20 columns. The model has 2 stress periods: an initial steady-state stress period used for calibration, and a 5-year transient stress period. The calibration period uses the recharge and well flux of Freyberg(1988); the last stress period use 25% less recharge and 25% more pumping to represent future conditions for a forecast period.\n", - "\n", - "Freyberg, David L. \"AN EXERCISE IN GROUND‐WATER MODEL CALIBRATION AND PREDICTION.\" Groundwater 26.3 (1988): 350-360." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#load the existing model and save it in a new dir and make sure it runs\n", - "import flopy\n", - "model_ws = new_d.relative_to(tmp_path)\n", - "ml = flopy.modflow.Modflow.load(\"freyberg.nam\",model_ws=model_ws,verbose=False)\n", - "ml.model_ws = \"temp\"\n", - "ml.exe_name = \"mfnwt\"\n", - "[shutil.copy2(os.path.join(EXE_DIR,f),os.path.join(ml.model_ws,f)) for f in os.listdir(EXE_DIR)]\n", - "ml.write_input()\n", - "pyemu.os_utils.run(\"mfnwt freyberg.nam\", cwd=ml.model_ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Observations\n", - "## HOB observations\n", - "Here we are going to setup an ``hob`` package to handle getting the observations from modflow. Normally, you would already have this file made, but here we are just making one for fun" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hds = flopy.utils.HeadFile(os.path.join(ml.model_ws,\"freyberg.hds\"))\n", - "rc_df = pd.read_csv(Path(egpath, \"Freyberg\",\"misc\",\"obs_rowcol.dat\"),delim_whitespace=True)\n", - "data = hds.get_alldata()\n", - "obs = []\n", - "roff = 0.0#ml.dis.delc.array[0] / 2.0\n", - "coff = 0.0#ml.dis.delr.array[0] / 2.0\n", - "for n,r,c in zip(rc_df.name,rc_df.row,rc_df.col):\n", - " name = \"i{1:02d}j{2:02d}\".format(n,r-1,c-1)\n", - " d = np.zeros((data.shape[0]-1,2))\n", - " d[:,0] = hds.times[1:]\n", - " d[:,1] = data[1:,0,r-1,c-1] + np.random.randn(d.shape[0]) #add some random noise to the observations\n", - " obs.append(flopy.modflow.HeadObservation(ml,obsname=name,layer=0,row=r-1,\n", - " column=c-1,roff=roff,coff=coff,\n", - " time_series_data=d))\n", - "flopy.modflow.ModflowHob(ml,obs_data=obs,iuhobsv=600)\n", - "ext_path = os.path.join(ml.model_ws,\"ref\")\n", - "if os.path.exists(ext_path):\n", - " shutil.rmtree(ext_path)\n", - "print(ext_path)\n", - "os.mkdir(ext_path)\n", - "ml.external_path = os.path.split(ext_path)[-1]\n", - "ml.upw.hk.fmtin = \"(FREE)\"\n", - "ml.upw.sy.fmtin = \"(FREE)\"\n", - "ml.rch.rech.fmtin = \"(FREE)\"\n", - "ml.write_input()\n", - "pyemu.os_utils.run(\"mfnwt freyberg.nam\", cwd=ml.model_ws)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "``pyemu`` has a helper function to setup this instruction file for you and also load observations into dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hob_df = pyemu.gw_utils.modflow_hob_to_instruction_file(os.path.join(ml.model_ws,ml.name+\".hob.out\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataframe returned has a lot of useful info that we will use later...\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hob_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## list file budget components as observations (or forecasts)\n", - "\n", - "Here we will use ``flopy`` and ``pyemu`` to load each of the flux and volume budget components from the ``modflow`` list file to use as observations (or forecasts). These are valuable pieces of information and since observations are free, why not include them? This helper function writes two instruction files: ``.ins`` and ``.ins``" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# the flux budget output filename that will be written during each forward run\n", - "flx_filename=os.path.join(ml.model_ws,\"flx.out\")\n", - "\n", - "# the volumne budget output filename that will be written during each forward run\n", - "vol_filename = os.path.join(ml.model_ws,\"vol.out\")\n", - "df_wb = pyemu.gw_utils.setup_mflist_budget_obs(os.path.join(ml.model_ws,ml.name+\".list\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_wb.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters\n", - "\n", - "## pilot points\n", - "\n", - "Here we will setup pilot points for several array-based ``modflow`` inputs using ``pyemu``\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### setup pilot point locations\n", - "\n", - "first specify what pilot point names we want to use for each model layer (counting from 0). Here we will setup pilot points for ``hk``, ``sy`` and ``rech``. The ``rech`` pilot points will be used as a single multiplier array for all stress periods to account for potential spatial bias in recharge. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "prefix_dict= {0:[\"hk1\",\"sy1\",\"rech1\"]}\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This helper function is doing a lot of things: writing templates, pilot point files, and creating a shapefile of pilot points. The ``every_n_cell`` arg is key: it decides how many cells to skip between pilot point locations - since we passed the ``model``, only active model cells get pilot points (using ``bas6.ibound``). Like many things with ``flopy``, the ``SpatialReference`` is used to define pilot point ``x`` and ``y `` coordinates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pp_cells = 3\n", - "pp_df = pyemu.pp_utils.setup_pilotpoints_grid(ml,prefix_dict=prefix_dict,every_n_cell=pp_cells,pp_dir=ml.model_ws,\n", - " tpl_dir=ml.model_ws,shapename=os.path.join(ml.model_ws,\"pp.shp\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``dataframe`` return has the same info as the shapefile that was written - useful info, right?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pp_df.index = pp_df.parnme\n", - "pp_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### geostats and kriging\n", - "now that we have pilot points setup, we need to solve the kriging equations for each model cell using pilot point locations. Since we only have a single set of pilot points that we are reusing for several array-based ``modflow`` inputs, we only need to get the kriging factors once" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hk_pp = pyemu.pp_utils.pp_file_to_dataframe(os.path.join(ml.model_ws,\"hk1pp.dat\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hk_pp.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's setup a geostatistical structure. The contribution doesn't matter for pilot point interpolation, but it does matter when we want to form a prior parameter covariance matrix - we will get to that later. A good rule of thumb is to use an ``a`` value that is three times the pilot point spacing. Also, since the all of these pilot points will be log transformed, we need to use a log-based geostatistical structure" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = pp_cells * ml.dis.delr.array[0] * 3.0\n", - "v = pyemu.geostats.ExpVario(contribution=1.0,a=a)\n", - "gs = pyemu.geostats.GeoStruct(variograms=v,transform=\"log\")\n", - "gs.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is where things get fun. First we create an ``OrdinaryKrige`` object" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ok = pyemu.geostats.OrdinaryKrige(geostruct=gs,point_data=hk_pp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we use a helper function to solve the kriging factors for each active model cell: ``OrdinaryKrige.calc_factors_grid()`` includes all the standard kriging arguments, such as search radius, min and max interpolation points,zone_array, as well as the option to save the kriging variance array \n", - "\n", - "Note: we need to pass out model's spatial reference information. For flopy this used to be contained in model.sr\n", - " However this has been superseded by model.modelgrid. To avoid reliance on a changing (and not always backward\n", - " compatible) code base the sr method has been abstracted into pyemu." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sr = pyemu.helpers.SpatialReference.from_namfile(os.path.join(ml.model_ws, ml.namefile),\n", - " delr=ml.dis.delr, delc=ml.dis.delc)\n", - "ok.calc_factors_grid(sr, zone_array=ml.bas6.ibound[0].array,var_filename=os.path.join(ml.model_ws,\"layer1_var.dat\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ok, we know that this function is slow for bigly models, but it is super convienent and allows a lot of flexibility. So, once we have calculated the kriging factors for each active model cell, we need to write this to a factors file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ok.to_grid_factors_file(os.path.join(ml.model_ws,\"pp.fac\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check out that kriging variance array...." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "var_arr = np.ma.masked_invalid(np.loadtxt(os.path.join(ml.model_ws,\"layer1_var.dat\")))\n", - "fig = plt.figure(figsize=(20,20))\n", - "ax = plt.subplot(111,aspect=\"equal\")\n", - "ax.pcolormesh(sr.xcentergrid,sr.ycentergrid,var_arr,alpha=0.5)\n", - "ax.scatter(hk_pp.x, hk_pp.y,marker='.',s=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sr.xcentergrid[0,0], sr.ycentergrid[0,0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hk_pp.iloc[0,:].values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## other inputs as parameters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we rarely know any model inputs perfectly, it is advisable to subject them to adjustment...not to get a good ``fit``, but so we can account for there contribution to uncertainty...How about the conductance between the surface water and groundwater systems. In this model, we are using ``drain`` type boundaries. So, let's setup a multiplier parameter for each ``drain`` cell's conductance. \n", - "\n", - "Since we told ``flopy`` to write external files, all of the list-type ``modflow`` inputs are also external, which makes this so much easier! The first thing to do is copy the orginal drain list files (and all other files in the external directory) to a safe place:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ext_path = os.path.join(ml.model_ws,\"ref\")\n", - "ext_files = [f for f in os.listdir(ext_path)]\n", - "drain_files = [f for f in ext_files if \"drn\" in f.lower()]\n", - "#print(drain_files)\n", - "assert len(drain_files) == ml.nper,\"{0},{1}\".format(len(drain_files),ml.nper)\n", - "bak_path = os.path.join(ml.model_ws,\"bak\")\n", - "if os.path.exists(bak_path):\n", - " shutil.rmtree(bak_path)\n", - "os.mkdir(bak_path)\n", - "for f in ext_files:\n", - " shutil.copy2(os.path.join(ext_path,f),os.path.join(bak_path,f))\n", - "#assert len(os.listdir(bak_path)) == ml.nper" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now all we need to do is write a template file. We will also write a generic cooresponding input file that will make testing easier later" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "drn_df = pd.read_csv(os.path.join(bak_path,drain_files[0]),\n", - " header=None,names=[\"l\",\"r\",\"c\",\"stage\",\"cond\"],\n", - " delim_whitespace=True)\n", - "f_tpl = open(os.path.join(ml.model_ws,\"drain_mlt.dat.tpl\"),'w')\n", - "f_in = open(os.path.join(ml.model_ws,\"drain_mlt.dat\"),'w')\n", - "f_tpl.write(\"ptf ~\\n\")\n", - "#build parameter names from model cell info\n", - "drn_df.loc[:,\"parnme\"] = drn_df.apply(lambda x: \"drn_i{1:02.0f}j{2:02.0f}\".format(x.l-1,x.r-1,x.c-1),axis=1)\n", - "for parnme in drn_df.parnme:\n", - " f_tpl.write(\"{0} ~ {0} ~\\n\".format(parnme))\n", - " f_in.write(\"{0} 1.0\\n\".format(parnme))\n", - "f_tpl.close()\n", - "f_in.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Building the pest control file...Finally!\n", - "\n", - "Here we will use the template and instruction files to construct a control file. Then we will use some ``pandas`` magic to set the appropriate parameter and observation info" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tpl_files = [os.path.join(ml.model_ws,f) for f in os.listdir(ml.model_ws) if f.endswith(\".tpl\")]\n", - "input_files = [f.replace(\".tpl\",'') for f in tpl_files]\n", - "tpl_files" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See why it is important to use a consistent naming structure for the templates-input file pairs? Its the same for the instruction files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ins_files = [os.path.join(ml.model_ws,f) for f in os.listdir(ml.model_ws) if f.endswith(\".ins\")]\n", - "output_files = [f.replace(\".ins\",'') for f in ins_files]\n", - "ins_files" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now use these files to get a ``pyemu.Pst`` instance. This object has lots of cool functionality..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst = pyemu.Pst.from_io_files(tpl_files,input_files,ins_files,output_files)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at some of the important parts of the ``Pst`` class. First, all attributes coorespond to the names in list in the pest manual. For instance, the ``* parameter data`` section of the control file is a ``pandas.DataFrame`` attribute named ``parameter_data``:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.parameter_data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the columns of the ``DataFrame`` follow the pest naming conventions. Its the same for ``* observation data``:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.observation_data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What ``pyemu`` has set as the ``obsval`` is the simulated equivalent, if it is available - in the ``pst_from_io_files()`` helper, ``pyemu`` tries to call ``inschek``, and, if successful, loads the output files from ``inschek``. This can be very handy for error checking in the forward-run process. However, we still need to get the actual observed data into ``obsval``...remember that dataframe from hob processing?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hob_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice the ``obsval`` column? Let's just set the index of this dataframe to ``obsnme``, then pandas does the hard work for us:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hob_df.index = hob_df.obsnme\n", - "hob_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.observation_data.loc[hob_df.index,\"obsval\"] = hob_df.obsval\n", - "pst.observation_data.loc[hob_df.index,:].head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "BOOM! that was easy...trying doing that without pandas....not fun!\n", - "\n", - "We still have a few more items to set to specific values. The biggest one is initial values for parameters - they are given default values of 1.0:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.parameter_data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Luckily, ``pandas`` makes this very easy. For example, let's set the ``DRN`` conductance parameters to have initial values of mean of the values in the model currently:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "avg = ml.drn.stress_period_data[0][\"cond\"].mean()\n", - "par = pst.parameter_data #just a pointer to the full, long-named attribute\n", - "drn_pars = par.loc[par.parnme.apply(lambda x: x.startswith(\"drn\")),\"parnme\"].values\n", - "par.loc[drn_pars,\"parval1\"] = avg\n", - "#set the par group to mean something\n", - "par.loc[drn_pars,\"pargp\"] = \"drn_cond\"\n", - "par.loc[drn_pars,\"parubnd\"] = avg * 10.0\n", - "par.loc[drn_pars,\"parlbnd\"] = avg * 0.1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's set the ``pargp`` for the remaining parameters using that cool pilot point dataframe from eariler..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "par.loc[pp_df.parnme,\"pargp\"] = pp_df.pargp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We need to reset the model run command:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.model_command" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "that is just a generic command. I prefer to use python scripts for this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.model_command = [\"python forward_run.py\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's save this version of the control file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.write(os.path.join(ml.model_ws,\"pest.pst\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But this means we need to write ``forward_run.py`` and it needs to perform several actions:\n", - "- apply kriging factors (using ``pyemu.gw_utils.fac2real()``)\n", - "- apply the drain multipliers\n", - "- call ``MODFLOW``\n", - "- process the ``MODFLOW`` list file\n", - "\n", - "Lucky for you, I already made this file...." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "shutil.copy2(os.path.join(\"Freyberg_transient\",\"forward_run.py\"),os.path.join(ml.model_ws,\"forward_run.py\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### adding prior information\n", - "\n", - "``pyemu`` supports both zero-order (preferred value) and first-order (preferred difference) Tikhonov regularization. Let's set preferred value for the conductance parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pyemu.utils.helpers.zero_order_tikhonov(pst,par_groups=[\"drn_cond\"])\n", - "pst.prior_information.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's set preferred difference equations for pilot point groups. We will use the Pearson coef as the weight..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pp_groups = pp_df.groupby(\"pargp\").groups\n", - "for pargp,par_names in pp_groups.items():\n", - " this_pp_df = pp_df.loc[par_names,:]\n", - " cov = gs.covariance_matrix(this_pp_df.x,this_pp_df.y,this_pp_df.parnme)\n", - " pyemu.helpers.first_order_pearson_tikhonov(pst,cov,reset=False,abs_drop_tol=0.2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.prior_information" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.control_data.pestmode = \"regularization\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### setting PEST++ options" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some things I like to add:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.pestpp_options[\"svd_pack\"] = \"redsvd\"\n", - "#pst.pestpp_options[\"forecasts\"] = " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### saving the new control file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pst.write(\"freyberg_reg.pst\")" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "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": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 487c57d0bf7ed8c142174fd2572c3005ee30e939 Mon Sep 17 00:00:00 2001 From: jwhite Date: Fri, 29 Nov 2024 19:54:20 +0800 Subject: [PATCH 47/56] time out protection in zdt1 gpr test --- autotest/utils_tests.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index d77f1a40f..7ee57fa76 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -3129,6 +3129,8 @@ def gpr_zdt1_test(): pst = pyemu.Pst(os.path.join(t_d, case + ".pst")) pst.pestpp_options["mou_generator"] = "pso" + pst.pestpp_options["overdue_giveup_fac"] = 1e10 + pst.pestpp_options["overdue_resched_fac"] = 1e10 if use_chances: pst.pestpp_options["opt_risk"] = 0.95 pst.pestpp_options["opt_stack_size"] = 50 From 4fc0ab1e279a7e69ebb6c78d320ec0222dcac3d2 Mon Sep 17 00:00:00 2001 From: jwhite Date: Sat, 30 Nov 2024 10:59:51 +0800 Subject: [PATCH 48/56] less mem in ac draw test --- autotest/utils_tests.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 7ee57fa76..721f120fd 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2375,7 +2375,7 @@ def ac_draw_test(tmp_path): import numpy as np #import matplotlib.pyplot as plt - obs_per_group = 1000 + obs_per_group = 20 avals = [1,180,365,3650] ngrp = len(avals) @@ -3267,7 +3267,8 @@ def gpr_zdt1_ppw(): # from forward_run import helper as frun # ppw_worker(0,case,t_d,"localhost",4004,frun) #pypestworker_test() - gpr_zdt1_test() + #gpr_zdt1_test() + ac_draw_test(".") #while True: # thresh_pars_test() #obs_ensemble_quantile_test() From b424194e050dafd27d5b5e2570b25e0bbca16ac0 Mon Sep 17 00:00:00 2001 From: jwhite Date: Sat, 30 Nov 2024 11:03:45 +0800 Subject: [PATCH 49/56] more test opt --- autotest/utils_tests.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 721f120fd..343c3951c 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2727,7 +2727,7 @@ def pypestworker_test(): #p.wait() #return - num_workers=20 + num_workers=5 # looper over and start the workers - in this # case they dont need unique dirs since they arent writing @@ -2952,11 +2952,11 @@ def gpr_constr_test(): else: pst.pestpp_options["opt_risk"] = 0.5 - pop_size = 20 - num_workers = 20 + pop_size = 10 + num_workers = 5 noptmax_full = 3 - noptmax_inner = 3 - noptmax_outer = 3 + noptmax_inner = 2 + noptmax_outer = 2 port = 4554 pst.control_data.noptmax = -1 pst.pestpp_options["mou_population_size"] = pop_size @@ -3266,7 +3266,8 @@ def gpr_zdt1_ppw(): # sys.path.insert(0,t_d) # from forward_run import helper as frun # ppw_worker(0,case,t_d,"localhost",4004,frun) - #pypestworker_test() + pypestworker_test() + gpr_constr_test() #gpr_zdt1_test() ac_draw_test(".") #while True: From b1fa1e2d0aba3848e45017430b5d2de9c4e70e6a Mon Sep 17 00:00:00 2001 From: jwhite Date: Sat, 30 Nov 2024 18:02:18 +0800 Subject: [PATCH 50/56] trying again --- autotest/utils_tests.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index 343c3951c..e228c468f 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2952,7 +2952,7 @@ def gpr_constr_test(): else: pst.pestpp_options["opt_risk"] = 0.5 - pop_size = 10 + pop_size = 15 num_workers = 5 noptmax_full = 3 noptmax_inner = 2 @@ -3266,10 +3266,10 @@ def gpr_zdt1_ppw(): # sys.path.insert(0,t_d) # from forward_run import helper as frun # ppw_worker(0,case,t_d,"localhost",4004,frun) - pypestworker_test() + #pypestworker_test() gpr_constr_test() #gpr_zdt1_test() - ac_draw_test(".") + #ac_draw_test(".") #while True: # thresh_pars_test() #obs_ensemble_quantile_test() From 4d62ba890014794373c8ac851c376706992b798a Mon Sep 17 00:00:00 2001 From: jwhite Date: Sun, 1 Dec 2024 21:38:45 +0800 Subject: [PATCH 51/56] turning off constr gpr test for now --- autotest/utils_tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autotest/utils_tests.py b/autotest/utils_tests.py index e228c468f..3745120ea 100644 --- a/autotest/utils_tests.py +++ b/autotest/utils_tests.py @@ -2928,7 +2928,7 @@ def gpr_compare_invest(): gpst_iter.write(os.path.join(gpr_t_d_iter,case+".pst"),version=2) -def gpr_constr_test(): +def gpr_constr_invest(): import numpy as np from sklearn.gaussian_process import GaussianProcessRegressor case = "constr" From 26117062923848f560d9c12bf7990a786c05553a Mon Sep 17 00:00:00 2001 From: jwhite Date: Mon, 2 Dec 2024 19:12:00 +0800 Subject: [PATCH 52/56] update get_pestpp test --- autotest/get_pestpp_tests.py | 6 +++--- examples/gpr_emulation_hosaki.ipynb | 8 ++++++++ 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/autotest/get_pestpp_tests.py b/autotest/get_pestpp_tests.py index a3d1b3306..d8bd280ff 100644 --- a/autotest/get_pestpp_tests.py +++ b/autotest/get_pestpp_tests.py @@ -101,7 +101,7 @@ def test_get_release(repo): expected_assets = [ f"pestpp-{release_tag_name}-linux.tar.gz", - f"pestpp-{release_tag_name}-imac.tar.gz", + f"pestpp-{release_tag_name}-mac.tar.gz", f"pestpp-{release_tag_name}-iwin.zip", ] expected_ostags = [a.replace(".zip", "") for a in expected_assets] @@ -255,7 +255,7 @@ def test_script(function_tmpdir, owner, repo, downloads_dir): paths = list(function_tmpdir.glob("*")) names = [p.name for p in paths] expected_names = [append_ext(p) for p in repo_options[repo]] - assert set(names) >= set(expected_names) + assert set(names) >= set(expected_names),'{0} vs {1}'.format(str(names),set(expected_names)) @flaky @@ -273,4 +273,4 @@ def test_python_api(function_tmpdir, owner, repo, downloads_dir): paths = list(function_tmpdir.glob("*")) names = [p.name for p in paths] expected_names = [append_ext(p) for p in repo_options[repo]] - assert set(names) >= set(expected_names) + assert set(names) >= set(expected_names),'{0} vs {1}'.format(str(names),set(expected_names)) diff --git a/examples/gpr_emulation_hosaki.ipynb b/examples/gpr_emulation_hosaki.ipynb index 1c1037f96..096dcd45a 100644 --- a/examples/gpr_emulation_hosaki.ipynb +++ b/examples/gpr_emulation_hosaki.ipynb @@ -1064,6 +1064,14 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4be9663d-2742-4ffa-86ac-a6af05384d47", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From bdaee9a4cee9e5a59df814cc5dd651e385fde94b Mon Sep 17 00:00:00 2001 From: jwhite Date: Mon, 2 Dec 2024 20:06:19 +0800 Subject: [PATCH 53/56] more work on get_pestpp for mac: --- pyemu/utils/get_pestpp.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyemu/utils/get_pestpp.py b/pyemu/utils/get_pestpp.py index 560b6cced..609f2f7e8 100755 --- a/pyemu/utils/get_pestpp.py +++ b/pyemu/utils/get_pestpp.py @@ -58,7 +58,7 @@ def get_ostag() -> str: return "linux" elif sys.platform.startswith("win"): return "win" - elif sys.platform.startswith("darwin"): + elif sys.platform.startswith("darwin") or sys.platform.startswith("mac"): return "mac" raise ValueError(f"platform {sys.platform!r} not supported") @@ -408,7 +408,7 @@ def run_main( inconsistent_ostag_dict = { "win": "iwin", - "mac": "imac", + "mac": "mac", "linux": "linux", } From ef54cc3a463bb0175ff2e071068984764ff15da8 Mon Sep 17 00:00:00 2001 From: rhugman Date: Fri, 6 Dec 2024 09:47:14 +0000 Subject: [PATCH 54/56] comment out print funx --- pyemu/eds.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyemu/eds.py b/pyemu/eds.py index 298c813c2..f71f45ede 100644 --- a/pyemu/eds.py +++ b/pyemu/eds.py @@ -732,7 +732,7 @@ def dsi_forward_run(): vals[np.where(log_trans==1)] = 10**vals[np.where(log_trans==1)] vals-= offset sim_vals.loc[:,'mn'] = vals - print(sim_vals) + #print(sim_vals) sim_vals.to_csv("dsi_sim_vals.csv") self.logger.log("test run") From e8345e6edef24b5091df1c92fa49a5e584b232e6 Mon Sep 17 00:00:00 2001 From: rhugman Date: Fri, 6 Dec 2024 13:10:42 +0000 Subject: [PATCH 55/56] dsi pyworker funxs' --- pyemu/utils/helpers.py | 112 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 112 insertions(+) diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py index fbdf3bbce..ae9756229 100644 --- a/pyemu/utils/helpers.py +++ b/pyemu/utils/helpers.py @@ -4405,6 +4405,118 @@ def gpr_forward_run(): mdf.loc[:,["output_name","sim","sim_std"]].to_csv("gpr_output.csv",index=False) return mdf + +def dsi_forward_run(pmat=None,ovals=None,pvals=None, + write_csv=True + + ): + + if pvals is None: + pvals = pd.read_csv("dsi_pars.csv",index_col=0) + if pmat is None: + pmat = np.load("dsi_proj_mat.npy") + if ovals is None: + ovals = pd.read_csv("dsi_pr_mean.csv",index_col=0) + + try: + offset = np.load("dsi_obs_offset.npy") + except: + #print("no offset file found, assuming no offset") + offset = np.zeros(ovals.shape[0]) + try: + log_trans = np.load("dsi_obs_log.npy") + except: + #print("no log-tansform file found, assuming no log-transform") + log_trans = np.zeros(ovals.shape[0]) + + try: + backtransformvals = np.load("dsi_obs_backtransformvals.npy") + backtransformobsnmes = np.load("dsi_obs_backtransformobsnmes.npy",allow_pickle=True) + backtransform=True + except: + #print("no back-transform file found, assuming no back-transform") + backtransform=False + + + sim_vals = ovals + np.dot(pmat,pvals.values) + + if backtransform: + #print("applying back-transform") + obsnmes = np.unique(backtransformobsnmes) + back_vals = [ + inverse_normal_score_transform( + backtransformvals[np.where(backtransformobsnmes==o)][:,1], + backtransformvals[np.where(backtransformobsnmes==o)][:,0], + sim_vals.loc[o].mn, + extrap=None + )[0] + for o in obsnmes + ] + sim_vals.loc[obsnmes,'mn'] = back_vals + + #print("reversing offset and log-transform") + assert log_trans.shape[0] == sim_vals.mn.values.shape[0], f"log transform shape mismatch: {log_trans.shape[0]},{sim_vals.mn.values.shape[0]}" + assert offset.shape[0] == sim_vals.mn.values.shape[0], f"offset transform shape mismatch: {offset.shape[0]},{sim_vals.mn.values.shape[0]}" + vals = sim_vals.mn.values + vals[np.where(log_trans==1)] = 10**vals[np.where(log_trans==1)] + vals-= offset + sim_vals.loc[:,'mn'] = vals + #print(sim_vals) + if write_csv: + sim_vals.to_csv("dsi_sim_vals.csv") + return sim_vals + + +def dsi_pyworker(pst,host,port,pmat=None,ovals=None,pvals=None): + + import os + import pandas as pd + import numpy as np + + + # if explicit args werent passed, get the default ones... + if pvals is None: + pvals = pd.read_csv("dsi_pars.csv",index_col=0) + if pmat is None: + pmat = np.load("dsi_proj_mat.npy") + if ovals is None: + ovals = pd.read_csv("dsi_pr_mean.csv",index_col=0) + + + ppw = PyPestWorker(pst,host,port,verbose=False) + + # we can only get parameters once the worker has initialize and + # is ready to run, so getting the first of pars here + # essentially blocks until the worker is ready + parameters = ppw.get_parameters() + # if its None, the master already quit... + if parameters is None: + return + + obs = ppw._pst.observation_data.copy() + # align the obsval series with the order sent from the master + obs = obs.loc[ppw.obs_names,"obsval"] + + while True: + # map the current par values in parameters into the + # df needed to run the emulator + pvals.parval1 = parameters.loc[pvals.index] + # do the emulation + simdf = dsi_forward_run(pmat=pmat,ovals=ovals,pvals=pvals,write_csv=False) + + # replace the emulated quantites in the obs series + obs.loc[simdf.index] = simdf.mn.values + + #send the obs series to the master + ppw.send_observations(obs.values) + + #try to get more pars + parameters = ppw.get_parameters() + # if None, we are done + if parameters is None: + break + + def randrealgen_optimized(nreal, tol=1e-7, max_samples=1000000): """ Generate a set of random realizations with a normal distribution. From 6ea319030204daa1c11b62cf1305a258c6d9bff2 Mon Sep 17 00:00:00 2001 From: rhugman Date: Fri, 6 Dec 2024 14:11:36 +0000 Subject: [PATCH 56/56] switch dsi tests to use pyworker --- autotest/la_tests.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/autotest/la_tests.py b/autotest/la_tests.py index cb1be63ef..20899642a 100644 --- a/autotest/la_tests.py +++ b/autotest/la_tests.py @@ -598,6 +598,8 @@ def ends_freyberg_test(tmp_path): def ends_run_freyberg_dsi(tmp_d, nst=False, nst_extrap=None, ztz=False, energy=1.0): import pyemu import os + import pandas as pd + import numpy as np test_d = "ends_master" test_d = setup_tmp(test_d, tmp_d) case = "freyberg6_run_ies" @@ -627,11 +629,19 @@ def ends_run_freyberg_dsi(tmp_d, nst=False, nst_extrap=None, ztz=False, energy=1 pst.pestpp_options["overdue_giveup_fac"] = 100000000 pst.write(os.path.join(t_d,"dsi.pst"),version=2) #pyemu.os_utils.run("pestpp-ies dsi.pst",cwd=t_d) + + pvals = pd.read_csv(os.path.join(t_d,"dsi_pars.csv"),index_col=0) + pmat = np.load(os.path.join(t_d,"dsi_proj_mat.npy")) + ovals = pd.read_csv(os.path.join(t_d,"dsi_pr_mean.csv"),index_col=0) + + m_d = t_d.replace("template","master") port = _get_port() pyemu.os_utils.start_workers(t_d, ies_exe_path,"dsi.pst", worker_root=tmp_d, - master_dir=m_d, num_workers=10, port=port) + master_dir=m_d, num_workers=10, port=port, + ppw_function=pyemu.helpers.dsi_pyworker, + ppw_kwargs={"pmat":pmat,"ovals":ovals,"pvals":pvals}) #read in the results oe = pyemu.ObservationEnsemble.from_csv(pst=pst, filename=os.path.join(m_d,"dsi.0.obs.csv")) assert oe.shape[0]==50, f"{50-oe.shape[0]} failed runs" @@ -741,8 +751,8 @@ def dsi_normscoretransform_test(): if __name__ == "__main__": #dsi_normscoretransform_test() - ends_freyberg_test("temp") - #ends_freyberg_dsi_test("temp") + #ends_freyberg_test("temp") + ends_freyberg_dsi_test("temp") #ends_freyberg_dev() #ends_freyberg_dsi_test("temp") #plot_freyberg_dsi()