import numpy as np import matplotlib.pyplot as plt import os from os import path from get_pareto import Point, ParetoSet from RPN_to_pytorch import RPN_to_pytorch from RPN_to_eq import RPN_to_eq from S_NN_train import NN_train from S_NN_eval import NN_eval from S_symmetry import * from S_separability import * from S_change_output import * from S_brute_force import brute_force from S_combine_pareto import combine_pareto from S_get_number_DL import get_number_DL from sympy.parsing.sympy_parser import parse_expr from sympy import preorder_traversal, count_ops from S_polyfit import polyfit from S_get_symbolic_expr_error import get_symbolic_expr_error from S_add_snap_expr_on_pareto import add_snap_expr_on_pareto from S_add_sym_on_pareto import add_sym_on_pareto from S_run_bf_polyfit import run_bf_polyfit from S_final_gd import final_gd from S_add_bf_on_numbers_on_pareto import add_bf_on_numbers_on_pareto from dimensionalAnalysis import dimensionalAnalysis PA = ParetoSet() def run_AI_all(pathdir,filename,BF_try_time=60,BF_ops_file_type="14ops", polyfit_deg=3, NN_epochs=4000, PA=PA): try: os.mkdir("results/") except: pass # load the data for different checks data = np.loadtxt(pathdir+filename) # Run bf and polyfit PA = run_bf_polyfit(pathdir,pathdir,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) # Run bf and polyfit on modified output PA = get_acos(pathdir,"results/mystery_world_acos/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_asin(pathdir,"results/mystery_world_asin/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_atan(pathdir,"results/mystery_world_atan/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_cos(pathdir,"results/mystery_world_cos/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_exp(pathdir,"results/mystery_world_exp/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_inverse(pathdir,"results/mystery_world_inverse/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_log(pathdir,"results/mystery_world_log/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_sin(pathdir,"results/mystery_world_sin/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_sqrt(pathdir,"results/mystery_world_sqrt/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_squared(pathdir,"results/mystery_world_squared/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_tan(pathdir,"results/mystery_world_tan/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) ############################################################################################################################# # check if the NN is trained. If it is not, train it on the data. print("Checking for symmetry \n", filename) if len(data[0])<3: print("Just one variable!") pass elif path.exists("results/NN_trained_models/models/" + filename + ".h5"):# or len(data[0])<3: print("NN already trained \n") print("NN loss: ", NN_eval(pathdir,filename), "\n") elif path.exists("results/NN_trained_models/models/" + filename + "_pretrained.h5"): print("Found pretrained NN \n") NN_train(pathdir,filename,NN_epochs/2,lrs=1e-3,N_red_lr=3,pretrained_path="results/NN_trained_models/models/" + filename + "_pretrained.h5") print("NN loss after training: ", NN_eval(pathdir,filename), "\n") else: print("Training a NN on the data... \n") NN_train(pathdir,filename,NN_epochs) print("NN loss: ", NN_eval(pathdir,filename), "\n") # Check which symmetry/separability is the best # Symmetries symmetry_minus_result = check_translational_symmetry_minus(pathdir,filename) symmetry_divide_result = check_translational_symmetry_divide(pathdir,filename) symmetry_multiply_result = check_translational_symmetry_multiply(pathdir,filename) symmetry_plus_result = check_translational_symmetry_plus(pathdir,filename) # Separabilities separability_plus_result = check_separability_plus(pathdir,filename) separability_multiply_result = check_separability_multiply(pathdir,filename) if symmetry_plus_result[0]==-1: idx_min = -1 else: idx_min = np.argmin(np.array([symmetry_plus_result[0], symmetry_minus_result[0], symmetry_multiply_result[0], symmetry_divide_result[0], separability_plus_result[0], separability_multiply_result[0]])) # Apply the best symmetry/separability and rerun the main function on this new file if idx_min == 0: new_pathdir, new_filename = do_translational_symmetry_plus(pathdir,filename,symmetry_plus_result[1],symmetry_plus_result[2]) PA1_ = ParetoSet() PA1 = run_AI_all(new_pathdir,new_filename,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA1_) PA = add_sym_on_pareto(pathdir,filename,PA1,symmetry_plus_result[1],symmetry_plus_result[2],PA,"+") return PA elif idx_min == 1: new_pathdir, new_filename = do_translational_symmetry_minus(pathdir,filename,symmetry_minus_result[1],symmetry_minus_result[2]) PA1_ = ParetoSet() PA1 = run_AI_all(new_pathdir,new_filename,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA1_) PA = add_sym_on_pareto(pathdir,filename,PA1,symmetry_minus_result[1],symmetry_minus_result[2],PA,"-") return PA elif idx_min == 2: new_pathdir, new_filename = do_translational_symmetry_multiply(pathdir,filename,symmetry_multiply_result[1],symmetry_multiply_result[2]) PA1_ = ParetoSet() PA1 = run_AI_all(new_pathdir,new_filename,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA1_) PA = add_sym_on_pareto(pathdir,filename,PA1,symmetry_multiply_result[1],symmetry_multiply_result[2],PA,"*") return PA elif idx_min == 3: new_pathdir, new_filename = do_translational_symmetry_divide(pathdir,filename,symmetry_divide_result[1],symmetry_divide_result[2]) PA1_ = ParetoSet() PA1 = run_AI_all(new_pathdir,new_filename,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA1_) PA = add_sym_on_pareto(pathdir,filename,PA1,symmetry_divide_result[1],symmetry_divide_result[2],PA,"/") return PA elif idx_min == 4: new_pathdir1, new_filename1, new_pathdir2, new_filename2, = do_separability_plus(pathdir,filename,separability_plus_result[1],separability_plus_result[2]) PA1_ = ParetoSet() PA1 = run_AI_all(new_pathdir1,new_filename1,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA1_) PA2_ = ParetoSet() PA2 = run_AI_all(new_pathdir2,new_filename2,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA2_) PA = combine_pareto(pathdir,filename,PA1,PA2,separability_plus_result[1],separability_plus_result[2],PA,"+") return PA elif idx_min == 5: new_pathdir1, new_filename1, new_pathdir2, new_filename2, = do_separability_multiply(pathdir,filename,separability_multiply_result[1],separability_multiply_result[2]) PA1_ = ParetoSet() PA1 = run_AI_all(new_pathdir1,new_filename1,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA1_) PA2_ = ParetoSet() PA2 = run_AI_all(new_pathdir2,new_filename2,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA2_) PA = combine_pareto(pathdir,filename,PA1,PA2,separability_multiply_result[1],separability_multiply_result[2],PA,"*") return PA else: return PA # this runs snap on the output of aifeynman def run_aifeynman(pathdir,filename,BF_try_time,BF_ops_file_type, polyfit_deg=3, NN_epochs=4000, vars_name=[],test_percentage=20): # If the variable names are passed, do the dimensional analysis first filename_orig = filename try: if vars_name!=[]: dimensionalAnalysis(pathdir,filename,vars_name) DR_file = filename + "_dim_red_variables.txt" filename = filename + "_dim_red" else: DR_file = "" except: DR_file = "" # Split the data into train and test set input_data = np.loadtxt(pathdir+filename) sep_idx = np.random.permutation(len(input_data)) train_data = input_data[sep_idx[0:(100-test_percentage)*len(input_data)//100]] test_data = input_data[sep_idx[test_percentage*len(input_data)//100:len(input_data)]] np.savetxt(pathdir+filename+"_train",train_data) if test_data.size != 0: np.savetxt(pathdir+filename+"_test",test_data) PA = ParetoSet() # Run the code on the train data PA = run_AI_all(pathdir,filename+"_train",BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA=PA) PA_list = PA.get_pareto_points() # Run bf snap on the resulted equations for i in range(len(PA_list)): try: PA = add_bf_on_numbers_on_pareto(pathdir,filename,PA,PA_list[i][-1]) except: continue PA_list = PA.get_pareto_points() np.savetxt("results/solution_before_snap_%s.txt" %filename,PA_list,fmt="%s") # Run zero, integer and rational snap on the resulted equations for j in range(len(PA_list)): PA = add_snap_expr_on_pareto(pathdir,filename,PA_list[j][-1],PA, "") PA_list = PA.get_pareto_points() np.savetxt("results/solution_first_snap_%s.txt" %filename,PA_list,fmt="%s") # Run gradient descent on the data one more time for i in range(len(PA_list)): try: gd_update = final_gd(pathdir,filename,PA_list[i][-1]) PA.add(Point(x=gd_update[1],y=gd_update[0],data=gd_update[2])) except: continue PA_list = PA.get_pareto_points() for j in range(len(PA_list)): PA = add_snap_expr_on_pareto(pathdir,filename,PA_list[j][-1],PA, DR_file) list_dt = np.array(PA.get_pareto_points()) data_file_len = len(np.loadtxt(pathdir+filename)) log_err = [] log_err_all = [] for i in range(len(list_dt)): log_err = log_err + [np.log2(float(list_dt[i][1]))] log_err_all = log_err_all + [data_file_len*np.log2(float(list_dt[i][1]))] log_err = np.array(log_err) log_err_all = np.array(log_err_all) # Try the found expressions on the test data if DR_file=="" and test_data.size != 0: test_errors = [] for i in range(len(list_dt)): test_errors = test_errors + [get_symbolic_expr_error(pathdir,filename+"_test",str(list_dt[i][-1]))] test_errors = np.array(test_errors) # Save all the data to file save_data = np.column_stack((test_errors,log_err,log_err_all,list_dt)) else: save_data = np.column_stack((log_err,log_err_all,list_dt)) np.savetxt("results/solution_%s" %filename_orig,save_data,fmt="%s")