def test_create_pivot_plot(self):
x, outcomes = utilities.load_flu_data()
y = outcomes['deceased population region 1'][:, -1] > 1000000
dimensional_stacking.create_pivot_plot(x, y, 2)
dimensional_stacking.create_pivot_plot(x, y, 2, labels=False,
bin_labels=True )
dimensional_stacking.create_pivot_plot(x, y, 1, labels=False)
plt.draw()
plt.close('all')
''' This file illustrated the use of the workbench for using dimensional stacking for scenario discovery .. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl> ''' import matplotlib.pyplot as plt from ema_workbench import ema_logging, load_results from ema_workbench.analysis import dimensional_stacking ema_logging.log_to_stderr(level=ema_logging.INFO) # load data fn = './data/1000 flu cases no policy.tar.gz' x, outcomes = load_results(fn) y = outcomes['deceased population region 1'][:, -1] > 1000000 fig = dimensional_stacking.create_pivot_plot(x, y, 2, bin_labels=True) plt.show()
x = experiments1.iloc[:, 0:50]
y = pd.DataFrame.from_dict(outcomes1)
fs = feature_scoring.get_feature_scores_all(x, y)
sns.heatmap(fs, cmap='viridis', annot=True)
plt.show()
plt.savefig('feature scoring.png')
#%% dimensional stacking
from ema_workbench.analysis import dimensional_stacking
x = experiments1.iloc[:, 0:50]
outcomesdf = pd.DataFrame.from_dict(outcomes1)
y = outcomesdf['Expected Number of Deaths'] < 1.0
dimensional_stacking.create_pivot_plot(x, y, 2, nbins=3)
plt.show()
plt.savefig('dimensional stacking.png')
#%% sensitivity analysis
from ema_workbench.analysis import regional_sa
from numpy.lib import recfunctions as rf
sns.set_style('white')
# model is the same across experiments
x = experiments1.iloc[:, 0:50]
y = outcomesdf['Expected Number of Deaths'] < 1.0
fig = regional_sa.plot_cdfs(x, y)
sns.despine()
a PRIM analysis.
The data was generated using a system dynamics models implemented in Vensim.
See flu_example.py for the code. The dataset was generated using 32 bit Python.
Therefore, this example will not work if you are running 64 bit Python.
.. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
chamarat <c.hamarat (at) tudelft (dot) nl>
'''
from __future__ import (division, print_function, unicode_literals)
import matplotlib.pyplot as plt
from ema_workbench import ema_logging, load_results
from ema_workbench.analysis import dimensional_stacking
ema_logging.log_to_stderr(level=ema_logging.INFO)
# load data
fn = './data/1000 flu cases no policy.tar.gz'
x, outcomes = load_results(fn)
y = outcomes['deceased population region 1'][:, -1] > 1000000
fig = dimensional_stacking.create_pivot_plot(x, y, 2)
fig.set_size_inches(6, 6)
plt.show()
'''
This file illustrated the use of the workbench for doing
a PRIM analysis.
The data was generated using a system dynamics models implemented in Vensim.
See flu_example.py for the code. The dataset was generated using 32 bit Python.
Therefore, this example will not work if you are running 64 bit Python.
.. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
chamarat <c.hamarat (at) tudelft (dot) nl>
'''
from __future__ import (division, print_function, unicode_literals)
import matplotlib.pyplot as plt
from ema_workbench import ema_logging, load_results
from ema_workbench.analysis import dimensional_stacking
ema_logging.log_to_stderr(level=ema_logging.INFO)
# load data
fn = './data/1000 flu cases no policy.tar.gz'
x, outcomes = load_results(fn)
y = outcomes['deceased population region 1'][:, -1] > 1000000
dimensional_stacking.create_pivot_plot(x, y, 2)
plt.show()