def run_explicit_loop(): for problem in DATA.explicit_problems: prob, target = problem.split(";") df = DATA.read_datafile("explicit", prob) cols = [col for col in df.columns if not (col == target or col == "T" or (len(col) > 2 and col[:2] == "D_"))] ins = df[cols].as_matrix() outs = df[target].values for i in range(num_runs): seed = 23 * i print("\n\n", prob, target, ins.shape, outs.shape, "\n=======================\n") score, r2, gen = run_gp(ins, outs, seed, problem) print(i, problem, seed, gen, score, r2, file=fobj) print("\n", file=fobj) for problem in DATA.diffeq_problems: prob, target = problem.split(";") df = DATA.read_datafile("diffeq", prob) cols = [col for col in df.columns if not (col == target or col == "T" or (len(col) > 2 and col[:2] == "D_"))] ins = df[cols].as_matrix() outs = df[target].values for i in range(num_runs): seed = 23 * i + 1 print("\n\n", prob, target, ins.shape, outs.shape, "\n=======================\n") score, r2, gen = run_gp(ins, outs, seed, problem) print(i, problem, seed, gen, score, r2, file=fobj) print("\n", file=fobj)
def run_explicit_loop(): for problem in DATA.explicit_problems: prob,target = problem.split(";") df = DATA.read_datafile("explicit", prob) print("\n\n", prob, target, df.shape,"\n=======================\n") cols = [col for col in df.columns if not (col == target or col == "T" or (len(col)>2 and col[:2] == "D_"))] ins = df[cols].as_matrix() outs = df[target].values theline_model(ins,outs) elastic_model(ins,outs,0.1,0.7) for problem in DATA.diffeq_problems: prob,target = problem.split(";") df = DATA.read_datafile("diffeq", prob) print("\n\n", prob, target, df.shape,"\n=======================\n") cols = [col for col in df.columns if not (col == target or col == "T" or (len(col)>2 and col[:2] == "D_"))] ins = df[cols].as_matrix() outs = df[target].values theline_model(ins,outs) elastic_model(ins,outs,0.1,0.7)
def run_explicit_loop(): for problem in DATA.explicit_problems: prob, target = problem.split(";") df = DATA.read_datafile("explicit", prob) print("\n\n", prob, target, df.shape, "\n=======================\n") cols = [ col for col in df.columns if not (col == target or col == "T" or (len(col) > 2 and col[:2] == "D_")) ] ins = df[cols].as_matrix() outs = df[target].values theline_model(ins, outs) elastic_model(ins, outs, 0.1, 0.7) for problem in DATA.diffeq_problems: prob, target = problem.split(";") df = DATA.read_datafile("diffeq", prob) print("\n\n", prob, target, df.shape, "\n=======================\n") cols = [ col for col in df.columns if not (col == target or col == "T" or (len(col) > 2 and col[:2] == "D_")) ] ins = df[cols].as_matrix() outs = df[target].values theline_model(ins, outs) elastic_model(ins, outs, 0.1, 0.7)
def run_explicit_loop(): for problem in DATA.explicit_problems: prob,target = problem.split(";") df = DATA.read_datafile("explicit", prob) cols = [col for col in df.columns if not (col == target or col == "T" or (len(col)>2 and col[:2] == "D_"))] ins = df[cols].as_matrix() outs = df[target].values print("\n\n", prob, target, ins.shape, outs.shape, "\n=======================\n") models = ffx.run(ins,outs, ins,outs, cols) for model in models: print_model(model.complexity(), model, ins, outs) for problem in DATA.diffeq_problems: prob,target = problem.split(";") df = DATA.read_datafile("diffeq", prob) cols = [col for col in df.columns if not (col == target or col == "T" or (len(col)>2 and col[:2] == "D_"))] ins = df[cols].as_matrix() outs = df[target].values print("\n\n", prob, target, ins.shape, outs.shape, "\n=======================\n") models = ffx.run(ins,outs, ins,outs, cols) for model in models: print_model(model.complexity(), model, ins, outs)
def run_explicit_loop(): for problem in DATA.explicit_problems: prob,target = problem.split(";") df = DATA.read_datafile("explicit", prob) cols = [col for col in df.columns if not (col == target or col == "T" or (len(col)>2 and col[:2] == "D_"))] ins = df[cols].as_matrix() outs = df[target].values print("\n\n", prob, target, ins.shape, outs.shape, "\n=======================\n") svr_rbf = svm.SVR(kernel='rbf', C=1e3, gamma=0.1) svr_rbf.fit(ins,outs) print_model("svr - rbf", svr_rbf, ins,outs) for problem in DATA.diffeq_problems: prob,target = problem.split(";") df = DATA.read_datafile("diffeq", prob) cols = [col for col in df.columns if not (col == target or col == "T" or (len(col)>2 and col[:2] == "D_"))] ins = df[cols].as_matrix() outs = df[target].values print("\n\n", prob, target, ins.shape, outs.shape, "\n=======================\n") svr_rbf = svm.SVR(kernel='rbf', C=1e3, gamma=0.1) svr_rbf.fit(ins,outs) print_model("svr - rbf", svr_rbf, ins,outs)
def run_explicit_loop(): for problem in DATA.explicit_problems: prob, target = problem.split(";") df = DATA.read_datafile("explicit", prob) cols = [ col for col in df.columns if not (col == target or col == "T" or (len(col) > 2 and col[:2] == "D_")) ] ins = df[cols].as_matrix() outs = df[target].values for i in range(num_runs): seed = 23 * i print("\n\n", prob, target, ins.shape, outs.shape, "\n=======================\n") score, r2, gen = run_gp(ins, outs, seed, problem) print(i, problem, seed, gen, score, r2, file=fobj) print("\n", file=fobj) for problem in DATA.diffeq_problems: prob, target = problem.split(";") df = DATA.read_datafile("diffeq", prob) cols = [ col for col in df.columns if not (col == target or col == "T" or (len(col) > 2 and col[:2] == "D_")) ] ins = df[cols].as_matrix() outs = df[target].values for i in range(num_runs): seed = 23 * i + 1 print("\n\n", prob, target, ins.shape, outs.shape, "\n=======================\n") score, r2, gen = run_gp(ins, outs, seed, problem) print(i, problem, seed, gen, score, r2, file=fobj) print("\n", file=fobj)
def run_explicit_loop(): for problem in DATA.explicit_problems: prob, target = problem.split(";") df = DATA.read_datafile("explicit", prob) cols = [ col for col in df.columns if not (col == target or col == "T" or (len(col) > 2 and col[:2] == "D_")) ] ins = df[cols].as_matrix() outs = df[target].values print("\n\n", prob, target, ins.shape, outs.shape, "\n=======================\n") svr_rbf = svm.SVR(kernel='rbf', C=1e3, gamma=0.1) svr_rbf.fit(ins, outs) print_model("svr - rbf", svr_rbf, ins, outs) for problem in DATA.diffeq_problems: prob, target = problem.split(";") df = DATA.read_datafile("diffeq", prob) cols = [ col for col in df.columns if not (col == target or col == "T" or (len(col) > 2 and col[:2] == "D_")) ] ins = df[cols].as_matrix() outs = df[target].values print("\n\n", prob, target, ins.shape, outs.shape, "\n=======================\n") svr_rbf = svm.SVR(kernel='rbf', C=1e3, gamma=0.1) svr_rbf.fit(ins, outs) print_model("svr - rbf", svr_rbf, ins, outs)
def run_explicit_loop(): for problem in DATA.explicit_problems: prob, target = problem.split(";") df = DATA.read_datafile("explicit", prob) cols = [ col for col in df.columns if not (col == target or col == "T" or (len(col) > 2 and col[:2] == "D_")) ] ins = df[cols].as_matrix() outs = df[target].values print("\n\n", prob, target, ins.shape, outs.shape, "\n=======================\n") models = ffx.run(ins, outs, ins, outs, cols) for model in models: print_model(model.complexity(), model, ins, outs) for problem in DATA.diffeq_problems: prob, target = problem.split(";") df = DATA.read_datafile("diffeq", prob) cols = [ col for col in df.columns if not (col == target or col == "T" or (len(col) > 2 and col[:2] == "D_")) ] ins = df[cols].as_matrix() outs = df[target].values print("\n\n", prob, target, ins.shape, outs.shape, "\n=======================\n") models = ffx.run(ins, outs, ins, outs, cols) for model in models: print_model(model.complexity(), model, ins, outs)