def generate_fitness_mod_graph(grns, targets, perturbations, file_name):
fitness_values = []
mod_values = []
cur_idx = 0
for a_fit_grn in grns:
a_np_fit_grn = np.array(a_fit_grn).reshape(10, 10)
cur_targets = get_current_targets(cur_idx, target_thresholds, targets)
fitness_values.append(
fitness.evaluate_grn(a_np_fit_grn, cur_targets, perturbations))
a_grn_plotter = GRNPlotter()
mod_values.append(a_grn_plotter.get_modularity_value(a_fit_grn))
cur_idx += 1
fp.save_lists_graph([fitness_values, mod_values],
['Fitness', 'Modularity'],
path=working_path,
file_name=file_name)
def plot_dist_gen_curve(dist_dict,
dpi=300,
save_path=None,
save_name=None,
start_gen=0,
end_gen=10000,
is_running=False):
plt.rcParams.update({'font.size': 22})
dist_list = []
if isinstance(dist_dict, dict):
set_idxs = sorted(list([int(x) for x in dist_dict.keys()]))
for a_gen in set_idxs[start_gen:end_gen]:
if is_running:
dist_list.append(np.mean(dist_dict[int(a_gen)]))
else:
dist_list.append(np.mean(dist_dict[str(a_gen)]))
fp.plot_a_list_graph(dist_list,
'Avg dist',
dpi=dpi,
save_path=save_path,
save_name=save_name,
marker='x')
elif isinstance(dist_dict, list):
set_idxs = sorted(list([int(x) for x in dist_dict[0].keys()]))
for a_dist_dict in dist_dict:
a_dist_list = []
for a_gen in set_idxs[start_gen:end_gen]:
if is_running:
a_dist_list.append(np.mean(a_dist_dict[int(a_gen)]))
else:
a_dist_list.append(np.mean(a_dist_dict[str(a_gen)]))
dist_list.append(a_dist_list)
fp.save_lists_graph(dist_list, ['Symmetry', 'Asymmetry'],
path=save_path,
file_name=save_name,
dpi=dpi,
marker='x',
left_lim=start_gen)
if level_type == 'high':
file_name = target + '-' + selection_type + '-' + level_type + '.png'
value_tar = avg_dif_mut_num_fit_h_e
stdev_tar = avg_dif_mut_num_std_h_e
elif level_type == 'low':
file_name = target + '-' + selection_type + '-' + level_type + '.png'
value_tar = avg_dif_mut_num_fit_l_e
stdev_tar = avg_dif_mut_num_std_l_e
elif selection_type == 'mod':
if level_type == 'high':
file_name = target + '-' + selection_type + '-' + level_type + '.png'
value_tar = avg_dif_mut_num_fit_h_m
stdev_tar = avg_dif_mut_num_std_h_m
elif level_type == 'low':
file_name = target + '-' + selection_type + '-' + level_type + '.png'
value_tar = avg_dif_mut_num_fit_l_m
stdev_tar = avg_dif_mut_num_std_l_m
# fp.save_lists_graph([avg_fit_mut_num_fit_h_e, avg_fit_mut_num_fit_l_e, avg_fit_mut_num_fit_h_m, avg_fit_mut_num_fit_l_m],
# labels=['Hign Edge Density', 'Low Edge Density', 'High Mod', 'Low Mod'],
# ver_lines=None, path='/media/zhenyue-qin/New Volume/Temporary-Location/Untitled Folder', file_name='combined.png', marker='.', colors=None,
# dpi=500, to_normalize=False, error_bars=[avg_fit_mut_num_std_h_e, avg_fit_mut_num_std_l_e, avg_fit_mut_num_std_h_m, avg_fit_mut_num_std_l_m], leg_loc=0)
fp.save_lists_graph(
[value_tar],
labels=[label_name],
ver_lines=None, path='/media/zhenyue-qin/New Volume/Temporary-Location/Untitled Folder',
file_name=file_name, marker='.', colors=None,
dpi=500, to_normalize=False,
error_bars=[stdev_tar],
leg_loc=0)
prop_gen_edges, prop_gen_edges_stds = edge_num_tool.get_avg_edge_num_in_each_generation_of_an_exp(
prop_300, True, dir_num=100)
tour_gen_edges, tour_gen_edges_stds = edge_num_tool.get_avg_edge_num_in_each_generation_of_an_exp(
tour_dir, True, dir_num=100)
conv_p_t_gen_edges, conv_p_t_gen_edges_stds = edge_num_tool.get_avg_edge_num_in_each_generation_of_an_exp(
prop_to_tour_dir, True, dir_num=100)
conv_p_p_gen_edges, conv_p_p_gen_edges_stds = edge_num_tool.get_avg_edge_num_in_each_generation_of_an_exp(
prop_to_prop_dir, True, dir_num=100)
tour_1_edges, tour_1_edges_stds = edge_num_tool.get_avg_edge_num_in_each_generation_of_an_exp(
tour_1_dir, True, dir_num=4)
tour_2_edges, tour_2_edges_stds = edge_num_tool.get_avg_edge_num_in_each_generation_of_an_exp(
tour_2_dir, True, dir_num=4)
elite_edge_lists = edge_num_tool.analyze_edge_nums_for_an_experiment(
tour_1_dir,
a_type='fit',
sample_size=100,
eva_type='list',
start_gen=0,
end_gen=300,
edge_type='original')
np_mean_elite_edge_lists = np.mean(elite_edge_lists, axis=0)
np_std_elite_edge_lists = np.std(elite_edge_lists, axis=0)
fp.save_lists_graph([tour_1_edges[:300], tour_2_edges[:300]],
error_bars=[tour_1_edges_stds, tour_2_edges_stds])
# fp.save_lists_graph([prop_gen_edges[:99], tour_gen_edges[:99]], dpi=100)
cur_idx = 0
for a_fit_grn in grns:
a_np_fit_grn = np.array(a_fit_grn).reshape(10, 10)
cur_targets = get_current_targets(cur_idx, target_thresholds, targets)
fitness_values.append(
fitness.evaluate_grn(a_np_fit_grn, cur_targets, perturbations))
a_grn_plotter = GRNPlotter()
mod_values.append(a_grn_plotter.get_modularity_value(a_fit_grn))
cur_idx += 1
fp.save_lists_graph([fitness_values, mod_values],
['Fitness', 'Modularity'],
path=working_path,
file_name=file_name)
mod_fit_list = csv_reader.get_entry_values_of_an_trial(working_path,
'MostModFitness')
mod_mod_list = csv_reader.get_entry_values_of_an_trial(working_path,
'MostModularity')
fit_fit_list = csv_reader.get_entry_values_of_an_trial(working_path, 'Best')
fit_mod_list = csv_reader.get_entry_values_of_an_trial(working_path,
'FittestModularity')
fp.save_lists_graph([mod_fit_list, mod_mod_list], ['Fitness', 'Modularity'],
path=working_path,
file_name='ModTrend.png')
fp.save_lists_graph([fit_fit_list, fit_mod_list], ['Fitness', 'Modularity'],
path=working_path,
file_name='FitTrend.png')
def plot_fit_lines(path_1,
labels,
save_path,
file_name,
sample_size=100,
gens=2000,
to_save=True):
csv_file_opener = CSVFileOpener()
target_column = None
if 'Fitness' in labels[0]:
if 'Stoc' in labels[0]:
if 'Sym' in labels[0]:
if 'Elite' in labels[0]:
values = fitness_warehouse.elite_stoc_p00_by_dist_p00_gen[:
gens]
value_std_1 = fitness_warehouse.elite_stoc_p00_by_dist_p00_stdev[:
gens]
else:
values = fitness_warehouse.stoc_p00_by_dist_p00_gen[:gens]
value_std_1 = fitness_warehouse.stoc_p00_by_dist_p00_stdev[:
gens]
elif 'Asym' in labels[0]:
if 'Elite' in labels[0]:
values = fitness_warehouse.elite_stoc_p01_by_dist_p01_gen[:
gens]
value_std_1 = fitness_warehouse.elite_stoc_p01_by_dist_p01_stdev[:
gens]
else:
values = fitness_warehouse.stoc_p01_by_dist_p01_gen[:gens]
value_std_1 = fitness_warehouse.stoc_p01_by_dist_p01_stdev[:
gens]
else:
target_column = 'Best'
values, value_std_1 = csv_file_opener. \
get_properties_of_each_generation_in_a_whole_experiment_with_stdev(path_1, target_column,
sample_size=sample_size)
elif 'Modularity' in labels[0]:
target_column = 'FittestModularity'
values, value_std_1 = csv_file_opener. \
get_mod_of_each_generation_in_a_whole_exp_with_stdev(path_1, target_column,
sample_size=sample_size)
elif 'Edge' in labels[0]:
values, value_std_1 = csv_file_opener. \
get_edge_num_of_each_generation_in_a_whole_exp_with_stdev(path_1, sample_size=sample_size)
# values, value_std_1 = csv_file_opener. \
# get_properties_of_each_generation_in_a_whole_experiment_with_stdev(path_1, target_column,
# sample_size=sample_size)
print('current label: ', labels)
print('final value: ', values[-1])
print('value_std_1: ', value_std_1[499])
print('max value: ', np.max(values))
print('min value: ', np.min(values))
print('len of values: ', len(values))
if to_save:
if 'Stoc' in labels[0]:
the_colors = [1, 0, 2, 3, 4, 5]
else:
the_colors = None
fp.save_lists_graph([values],
labels=labels,
ver_lines=[500],
path=save_path,
file_name=file_name,
marker='.',
colors=the_colors,
dpi=500,
to_normalize=False,
x_gap=20,
error_bars=[value_std_1],
leg_loc='lower right')
return values, labels, value_std_1
file_name=label_key_2 + '.png',
sample_size=sample_size,
to_save=False)
# stat_tool_kil = StatisticsToolkit()
# print stat_tool_kil.calculate_statistical_significances(values_1[501:], values_2[501:])
to_save_name = path_key_1 + ' ' + path_key_2 + ' ' + value_type
to_save_name = to_save_name.replace(' ', '_')
fp.save_lists_graph([values_1[1:2001], values_2[1:2001]],
labels=[path_key_1, path_key_2],
ver_lines=[500],
path=save_path_selection,
file_name=to_save_name,
marker='.',
colors=[0, 1],
dpi=500,
to_normalize=False,
x_gap=20,
error_bars=[stdevs_1[1:2001], stdevs_2[1:2001]],
leg_loc='upper left')
# # fits_avg_1 = csv_file_opener.get_properties_of_each_generation_in_a_whole_experiment(path_1, 'Median')
# # fits_avg_2 = csv_file_opener.get_properties_of_each_generation_in_a_whole_experiment(path_2, 'Median')
#
# mod_1 = csv_file_opener.get_properties_of_each_generation_in_a_whole_experiment(path_1, 'FittestModularity')
# mod_2 = csv_file_opener.get_properties_of_each_generation_in_a_whole_experiment(path_2, 'FittestModularity')
#
# fp.save_lists_graph([mod_1, mod_2], labels=['Distributional', 'Stochastic'], ver_lines=[500], path=path_1, file_name='avg_mod_comp_esw_new_p01_balanced.png', marker='.',
# colors=None, dpi=500, to_normalize=False)
#
prefix_path = os.path.expanduser("~")
fitness_plotter = GRNCSVReader()
path_1 = '/Volumes/Qin-Warehouse/Warehouse-Data/Modularity-Data/Maotai-Project-Symmetry-Breaking/generated-outputs/fixed-record-zhenyue-balanced-combinations-p00'
path_2 = '/Volumes/Qin-Warehouse/Warehouse-Data/Modularity-Data/Maotai-Project-Symmetry-Breaking/generated-outputs/record-esw-balanced-combinations-p00'
# path_1 = '/Volumes/Qin-Warehouse/Warehouse-Data/Modularity-Data/Maotai-Project-Symmetry-Breaking/generated-outputs/fixed-record-zhenyue-balanced-combinations-elite-p00'
#
# from GRNPlotter import GRNPlotter
#
# grn_plotter = GRNPlotter()
# mod_1 = grn_plotter.get_avg_module_values_for_each_generation_of_an_experiment(path_1, no_self_edge=True)
# # mod_2 = grn_plotter.get_avg_module_values_for_each_generation_of_an_experiment(path_2, no_self_edge=True)
#
# print('mod 1: ', len(mod_1))
# fp.save_lists_graph([mod_1], labels=['Modularity'], ver_lines=[500], path=path_1, file_name='avg_mod_no_self_edge.png', marker='.',
# colors=None, dpi=500, to_normalize=False)
sample_size = 100
most_mod_values_1 = fitness_plotter.get_fittest_modularities_of_an_experiment(path_1, 500)[:sample_size]
most_mod_values_2 = fitness_plotter.get_fittest_modularities_of_an_experiment(path_1, -1)[:sample_size]
mods_1 = csv_file_opener.get_properties_of_each_generation_in_a_whole_experiment(path_1, 'FittestModularity')
mods_2 = csv_file_opener.get_properties_of_each_generation_in_a_whole_experiment(path_1, 'FittestModularity')
#
# print StatisticsToolkit.calculate_statistical_significances(most_mod_values_1, most_mod_values_2)
fp.save_lists_graph([mods_1], labels=['Modularity'], ver_lines=[500], path=path_1, file_name='modularity.png', marker='.',
colors=[0], dpi=500, to_normalize=False)