def test_parameter_sweep_function_validation_level_val(self, mgmt_data):
"""
This testcase executes a parameter sweep and captures any warnings
generated during the construction of the bokeh figure object.
:param mgmt_data: Use management department data
:type mgmt_data: dictionary
:return: Pass/Fail
:rtype: Boolean
"""
modlist = list([Mod_Stoch_FBHP(**mgmt_data)])
c = Comparison(modlist)
plot_settings = {
'plottype':
'parameter sweep gender percentage',
'intervals':
'empirical',
'number_of_runs':
10,
# number simulations to average over
'target':
0.25,
'xlabels': [
'Hiring Rate for Women', 'Hiring Rate for Women',
'Hiring Rate for Women', 'Hiring Rate for Women',
'Hiring Rate for Women', 'Hiring Rate for Women'
],
'ylabels': [
'Proportion Women', 'Proportion Women', 'Proportion Women',
'Proportion Women', 'Proportion Women', 'Proportion Women'
],
'model_legend_label':
['Model 1, Hire-Promote', 'Model '
'2, '
'Promote-Hire'],
'parameter_sweep_param':
'bf1',
'parameter_ubound':
0.6,
'parameter_lbound':
0.05,
'number_of_steps':
5
}
show(c.plot_comparison_level_chart(**plot_settings))
def test_bokeh_comparison_plot_sweep_bylevel(mgmt_data):
modlist = list([Model3GenderDiversity(**mgmt_data)])
# modlist = list([Model2GenderDiversity(**mgmt_data),
# Mod_Stoch_FBPH(**mgmt_data)])
modlist[0].init_default_hiring_rate()
c = Comparison(modlist)
plot_settings = {
'plottype':
'parameter sweep percentage',
'intervals':
'empirical',
'number_of_runs':
100,
'target':
0.25,
'line_width':
2,
'model_legend_label': ['new model', 'model2, Promote-Hire'],
'legend_location':
'top right',
'height_':
300,
'width_':
300,
# main plot axis labels
'xlabels': ['Years', 'Years', 'Years', 'Years', 'Years', 'Years'],
'ylabels': [
'Percentage of Women', 'Percentage of Women',
'Percentage of Women', 'Percentage of Men', 'Percentage of Men',
'Percentage of Men'
],
'titles': ['f1', 'f2', 'f3', 'm1', 'm2', 'm3'],
'parameter_sweep_param':
'bf1',
'parameter_ubound':
0.6,
'parameter_lbound':
0.05,
'number_of_steps':
5
}
show(c.plot_comparison_level_chart(**plot_settings))
def test_parameter_sweep_function_validation_level_val(self, mgmt_data):
"""
This testcase executes a parameter sweep and captures any warnings
generated during the construction of the bokeh figure object.
:param mgmt_data: Use management department data
:type mgmt_data: dictionary
:return: Pass/Fail
:rtype: Boolean
"""
modlist = list([Mod_Stoch_FBHP(**mgmt_data)])
c = Comparison(modlist)
plot_settings = {'plottype': 'parameter sweep gender percentage',
'intervals': 'empirical',
'number_of_runs': 10,
# number simulations to average over
'target': 0.25,
'xlabels': ['Hiring Rate for Women',
'Hiring Rate for Women',
'Hiring Rate for Women',
'Hiring Rate for Women',
'Hiring Rate for Women',
'Hiring Rate for Women'],
'ylabels': ['Proportion Women',
'Proportion Women',
'Proportion Women',
'Proportion Women',
'Proportion Women',
'Proportion Women'],
'model_legend_label': ['Model 1, Hire-Promote',
'Model '
'2, '
'Promote-Hire'],
'parameter_sweep_param': 'bf1',
'parameter_ubound': 0.6,
'parameter_lbound': 0.05,
'number_of_steps': 5
}
show(c.plot_comparison_level_chart(**plot_settings))
def test_bokeh_comparison_plot_probability_bylevel(mgmt_data):
modlist = list([Model3GenderDiversity(**mgmt_data)])
# modlist = list([Model2GenderDiversity(**mgmt_data),
# Mod_Stoch_FBPH(**mgmt_data)])
modlist[0].init_default_hiring_rate()
c = Comparison(modlist)
plot_settings = {
'plottype':
'probability proportion',
'intervals':
'empirical',
'number_of_runs':
100,
'target':
0.25,
'line_width':
2,
'model_legend_label': ['new model', 'model2, Promote-Hire'],
'legend_location':
'top right',
'height_':
300,
'width_':
300,
# main plot axis labels
'xlabels': ['Years', 'Years', 'Years', 'Years', 'Years', 'Years'],
'ylabels': [
'Probability of Target', 'Probability of Target',
'Probability of Target', 'Probability of Target',
'Probability of Target', 'Probability of Target'
],
'titles': ['f1', 'f2', 'f3', 'm1', 'm2', 'm3'],
}
show(c.plot_comparison_level_chart(**plot_settings))
def test_bokeh_comparison_plot_bylevel(mgmt_data):
modlist = list([
Model3GenderDiversity(**mgmt_data),
ModelGenderDiversityLinearGrowth(**mgmt_data),
ModelGenderDiversityGrowthForecast(**mgmt_data)
])
modlist[0].init_default_hiring_rate()
modlist[1].init_default_hiring_rate()
modlist[1].init_growth_rate(0.05)
modlist[2].init_default_hiring_rate()
modlist[2].init_growth_rate([73, 78, 83, 88])
c = Comparison(modlist)
plot_settings = {
'plottype':
'gender number',
'intervals':
'empirical',
'number_of_runs':
100,
'target':
0.25,
'line_width':
2,
'model_legend_label':
['Model 3 No Growth', 'Model 3 Lin Growth', 'Model 3 Forecast'],
'transparency':
0.25,
'legend_location':
'top right',
'height_':
400,
'width_':
400,
# main plot axis labels
'xlabels': ['Years', 'Years', 'Years', 'Years', 'Years', 'Years'],
'ylabels': [
'Number of Women', 'Number of Women', 'Number of Women',
'Number of Men', 'Number of Men', 'Number of Men'
],
'titles': ['f1', 'f2', 'f3', 'm1', 'm2', 'm3'],
# target plot settings
'target_plot':
True,
'target_color':
'red',
'target_plot_linewidth':
2,
'target_number_labels':
['Target Model 3 NG', 'Target Model 3 LG', 'Target Model 3 FG'],
# percent plot settings
'percent_line_plot':
False,
'percent_line_value':
0.5,
'color_percent_line':
'red',
'percent_linewidth':
2,
'percent_legend_label':
'Reference Line'
}
show(c.plot_comparison_level_chart(**plot_settings))