def plot_rfDim_waterwheel(base_dir, plot_dir, eval_dict=EVAL_DICT_ALL):
output_dir = os.path.join(base_dir, 'l63_WATERWHEEL_v5_rfDim_validateAll')
full_summary_df_name = os.path.join(output_dir, 'full_summary_df.pickle')
with open(full_summary_df_name, "rb") as file:
summary_df = pickle.load(file)
# add paper names
summary_df['Model'] = summary_df.modelType
summary_df['$f_0$'] = 'None'
summary_df.loc[(summary_df.doResidual == 0) &
(summary_df.stateType == 'stateAndPred'),
'$f_0$'] = 'Augmented'
summary_df.loc[(summary_df.doResidual == 1) &
(summary_df.stateType == 'stateAndPred'),
'$f_0$'] = 'Augmented residual'
summary_df.loc[(summary_df.doResidual == 1) &
(summary_df.stateType == 'state'), '$f_0$'] = 'Residual'
# rhsname_list = ['rhs w/ diff=TrueDeriv, costInt=datagrid', 'rhs w/ diff=Spline, costInt=datagrid', 'f0only', 'Psi']
# papername_list = ['rhs w/ diff=Spline, costInt=datagrid', 'f0only', 'Psi']
papername_list = ['rhs w/ diff=Spline, costInt=datagrid', 'Psi']
fid = 'hifi'
# f0eps=3.0
# dt=0.001
# # f0eps=0.2
# # dt=0.01
# tTrain=100
# NEW SETTINGS
f0eps = 0
dt = 0.001
tTrain = 100
paper_df = summary_df[
(summary_df.rhsname.isin(papername_list))
& (summary_df.fidelity == fid) & (summary_df.f0eps == f0eps) &
(summary_df.dt == dt) & (summary_df.tTrain == tTrain) &
(summary_df.eval_pickle_fname.str.contains("test_eval.pickle"))]
# for nm in papername_list:
# paper_df.loc[(paper_df.rhsname == nm) & (paper_df.stateType=='stateAndPred'), "Model"] += ' Augmented Inputs'
for key in eval_dict:
fig_path = os.path.join(plot_dir,
'WATERWHEEL_rfDim_quality_{}'.format(key))
new_summary(df=paper_df,
fig_path=fig_path,
x="rfDim",
y=key,
xlabel='$D_r$',
ylabel=eval_dict[key],
estimator=np.mean,
hue='$f_0$',
style='Model',
ci='sd',
title='Model complexity needed for hybrid learning',
legloc='best',
figsize=(12, 10))
def plot_rfDim(base_dir, plot_dir, eval_dict=EVAL_DICT_SHORT):
output_dir = os.path.join(base_dir, 'l63eps_v5_rfDim_validateReg')
full_summary_df_name = os.path.join(output_dir, 'full_summary_df.pickle')
with open(full_summary_df_name, "rb") as file:
summary_df = pickle.load(file)
# add paper names
summary_df['Model'] = summary_df.modelType
summary_df['Uses $f_0$'] = 'No'
summary_df.loc[summary_df.usef0 == 1, 'Uses $f_0$'] = 'Yes'
# rhsname_list = ['rhs w/ diff=TrueDeriv, costInt=datagrid', 'rhs w/ diff=Spline, costInt=datagrid', 'f0only', 'Psi']
# papername_list = ['rhs w/ diff=Spline, costInt=datagrid', 'f0only', 'Psi']
papername_list = ['rhs w/ diff=Spline, costInt=datagrid', 'Psi']
fid = 'hifi'
# f0eps=3.0
# dt=0.001
# # f0eps=0.2
# # dt=0.01
# tTrain=100
# NEW SETTINGS
f0eps = 0.05
dt = 0.001
tTrain = 100
paper_df = summary_df[
(summary_df.rhsname.isin(papername_list))
& (summary_df.stateType != 'stateAndPred') &
(summary_df.fidelity == fid) & (summary_df.f0eps == f0eps) &
(summary_df.dt == dt) & (summary_df.tTrain == tTrain) &
(summary_df.eval_pickle_fname.str.contains("test_eval.pickle"))]
fig_path = os.path.join(plot_dir, 'rfDim_quality')
for key in eval_dict:
fig_path = os.path.join(plot_dir, 'rfDim_quality_{}'.format(key))
new_summary(df=paper_df,
fig_path=fig_path,
x="rfDim",
y=key,
xlabel='$D_r$',
ylabel=eval_dict[key],
estimator=np.mean,
ci='sd',
title='Model complexity needed for hybrid learning',
legloc='best',
figsize=(12, 10))
def plot_dt(base_dir, plot_dir, eval_dict=EVAL_DICT_ALL):
output_dir = os.path.join(base_dir, 'l63eps_v5_dt_validateReg2')
full_summary_df_name = os.path.join(output_dir, 'full_summary_df.pickle')
with open(full_summary_df_name, "rb") as file:
summary_df = pickle.load(file)
# add paper names
summary_df['Model'] = summary_df.modelType
summary_df['Uses $f_0$'] = 'No'
summary_df.loc[summary_df.usef0 == 1, 'Uses $f_0$'] = 'Yes'
# rhsname_list = ['rhs w/ diff=TrueDeriv, costInt=datagrid', 'rhs w/ diff=Spline, costInt=datagrid', 'f0only', 'Psi']
papername_list = ['rhs w/ diff=Spline, costInt=datagrid', 'f0only', 'Psi']
fid = 'hifi'
f0eps = 0.05
# rfDim=200
tTrain = 1000
paper_df = summary_df[
(summary_df.rhsname.isin(papername_list))
& (summary_df.stateType != 'stateAndPred') &
(summary_df.fidelity == fid) & (summary_df.f0eps == f0eps) &
# (summary_df.rfDim==rfDim) &
(summary_df.tTrain == tTrain) &
(summary_df.eval_pickle_fname.str.contains("test_eval.pickle"))]
for key in eval_dict:
fig_path = os.path.join(plot_dir, 'dt_quality_{}'.format(key))
new_summary(df=paper_df,
fig_path=fig_path,
x="dt",
y=key,
xlabel='$\Delta t$',
ylabel=eval_dict[key],
estimator=np.mean,
ci='sd',
title='Effects of sampling rate on learning methods',
legloc='upper right',
figsize=(12, 10))