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Simplify the color generation for cost vs run plots. #145

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Jun 13, 2022
+30 −38
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Simplify the color generation for cost vs run plots. #145

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9 changes: 0 additions & 9 deletions mloop/controllers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,15 +17,6 @@
import mloop.learners as mll
import mloop.interfaces as mli

controller_dict = {
'random': 1,
'nelder_mead': 2,
'gaussian_process': 3,
'differential_evolution': 4,
'neural_net': 5,
'third_party': 6,
}
number_of_controllers = len(controller_dict)
default_controller_archive_filename = 'controller_archive'
default_controller_archive_file_type = 'txt'

Expand Down
59 changes: 30 additions & 29 deletions mloop/visualizations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -280,24 +280,11 @@ def create_learner_visualizations(filename,
)
visualizer.create_visualizations(**learner_visualization_kwargs)

def _color_from_controller_name(controller_name):
def _color_list_from_num_options(num_of_params):
'''
Gives a color (as a number between zero an one) corresponding to each controller name string.
'''
global cmap
# If controller_name isn't in the mlc.controller_dict dictionary, assume it
# is the name of a third party controller provided by an external package.
if controller_name not in mlc.controller_dict:
controller_name = 'third_party'

# Determine the color.
index = mlc.controller_dict[controller_name]
fraction = float(index) / float(mlc.number_of_controllers)
return cmap(fraction)

def _color_list_from_num_of_params(num_of_params):
'''
Gives a list of colors based on the number of parameters.
Gives a list of colors based on a number of options.

A distinct color will be generated for each option.
'''
global cmap
return [cmap(float(x)/num_of_params) for x in range(num_of_params)]
Expand Down Expand Up @@ -413,8 +400,14 @@ def __init__(self, filename,
else:
self.finite_flag = False

self.unique_types = set(self.out_type)
self.cost_colors = [_color_from_controller_name(x) for x in self.out_type]
self.unique_types = list(set(self.out_type))
out_type_colors = _color_list_from_num_options(
len(self.unique_types),
)
self.out_type_color_mapping = {
self.unique_types[j]: out_type_colors[j] for j in range(len(self.unique_types))
}
self.cost_colors = [self.out_type_color_mapping[x] for x in self.out_type]
self.in_numbers = np.arange(1,self.num_in_costs+1)
self.out_numbers = np.arange(1,self.num_out_params+1)
self.param_numbers = np.arange(self.num_params)
Expand Down Expand Up @@ -485,7 +478,15 @@ def plot_cost_vs_run(self):
plt.title('Controller: Cost vs run number.')
artists = []
for ut in self.unique_types:
artists.append(plt.Line2D((0,1),(0,0), color=_color_from_controller_name(ut), marker='o', linestyle=''))
artists.append(
plt.Line2D(
(0,1),
(0,0),
color=self.out_type_color_mapping[ut],
marker='o',
linestyle='',
)
)
plt.legend(artists,self.unique_types,loc=legend_loc)

def _ensure_parameter_subset_valid(self, parameter_subset):
Expand Down Expand Up @@ -515,7 +516,7 @@ def plot_parameters_vs_run(self, parameter_subset=None):

# Generate set of distinct colors for plotting.
num_params = len(parameter_subset)
param_colors = _color_list_from_num_of_params(num_params)
param_colors = _color_list_from_num_options(num_params)

global figure_counter, run_label, scale_param_label, legend_loc
figure_counter += 1
Expand Down Expand Up @@ -576,7 +577,7 @@ def plot_parameters_vs_cost(self, parameter_subset=None):

# Generate set of distinct colors for plotting.
num_params = len(parameter_subset)
param_colors = _color_list_from_num_of_params(num_params)
param_colors = _color_list_from_num_options(num_params)

global figure_counter, run_label, run_label, scale_param_label, legend_loc
figure_counter += 1
Expand Down Expand Up @@ -718,7 +719,7 @@ def __init__(self, filename,
self.param_numbers = np.arange(self.num_params)

self.gen_numbers = np.arange(1,self.num_generations+1)
self.param_colors = _color_list_from_num_of_params(self.num_params)
self.param_colors = _color_list_from_num_options(self.num_params)
self.gen_plot = np.array([np.full(self.num_population_members, ind, dtype=int) for ind in self.gen_numbers]).flatten()

def create_visualizations(self,
Expand Down Expand Up @@ -795,7 +796,7 @@ def plot_params_vs_generations(self, parameter_subset=None):

# Generate set of distinct colors for plotting.
num_params = len(parameter_subset)
param_colors = _color_list_from_num_of_params(num_params)
param_colors = _color_list_from_num_options(num_params)

if self.params_generations.size == 0:
self.log.warning('Unable to plot DE: params vs generations as the initial generation did not complete.')
Expand Down Expand Up @@ -1125,7 +1126,7 @@ def plot_cross_sections(self, parameter_subset=None):

# Generate set of distinct colors for plotting.
num_params = len(parameter_subset)
param_colors = _color_list_from_num_of_params(num_params)
param_colors = _color_list_from_num_options(num_params)

global figure_counter, legend_loc
figure_counter += 1
Expand Down Expand Up @@ -1246,7 +1247,7 @@ def plot_hyperparameters_vs_fit(self, parameter_subset=None):

# Generate set of distinct colors for plotting.
num_params = len(parameter_subset)
param_colors = _color_list_from_num_of_params(num_params)
param_colors = _color_list_from_num_options(num_params)

global figure_counter, fit_label, legend_loc, log_length_scale_label
figure_counter += 1
Expand Down Expand Up @@ -1562,7 +1563,7 @@ def do_cross_sections(self, parameter_subset=None,

# Generate set of distinct colors for plotting.
num_params = len(parameter_subset)
param_colors = _color_list_from_num_of_params(num_params)
param_colors = _color_list_from_num_options(num_params)

# Generate labels for legends.
legend_labels = mlu._generate_legend_labels(
Expand Down Expand Up @@ -1702,7 +1703,7 @@ def plot_losses(self):

# Generate set of distinct colors for plotting.
num_nets = len(all_losses)
net_colors = _color_list_from_num_of_params(num_nets)
net_colors = _color_list_from_num_options(num_nets)

artists=[]
legend_labels=[]
Expand Down Expand Up @@ -1750,7 +1751,7 @@ def plot_regularization_history(self):

# Generate set of distinct colors for plotting.
num_nets = len(regularization_histories)
net_colors = _color_list_from_num_of_params(num_nets)
net_colors = _color_list_from_num_options(num_nets)

artists=[]
legend_labels=[]
Expand Down