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107 lines
2.9 KiB
Python
107 lines
2.9 KiB
Python
import pandas as pd
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from collections import Counter
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class Stats:
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'''
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Interface for all stats. It is not necessary, but it is useful
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if you don't plan to implement all the methods.
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'''
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def __init__(self, simulation):
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self.simulation = simulation
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def start(self):
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'''Method to call when the simulation starts'''
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pass
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def end(self):
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'''Method to call when the simulation ends'''
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return {}
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def trial(self, env):
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'''Method to call when a trial ends'''
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return {}
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class distribution(Stats):
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'''
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Calculate the distribution of agent states at the end of each trial,
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the mean value, and its deviation.
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'''
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def start(self):
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self.means = []
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self.counts = []
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def trial(self, env):
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df = env[None, None, None].df()
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df = df.drop('SEED', axis=1)
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ix = df.index[-1]
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attrs = df.columns.get_level_values(0)
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vc = {}
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stats = {
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'mean': {},
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'count': {},
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}
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for a in attrs:
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t = df.loc[(ix, a)]
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try:
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stats['mean'][a] = t.mean()
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self.means.append(('mean', a, t.mean()))
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except TypeError:
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pass
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for name, count in t.value_counts().iteritems():
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if a not in stats['count']:
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stats['count'][a] = {}
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stats['count'][a][name] = count
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self.counts.append(('count', a, name, count))
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return stats
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def end(self):
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dfm = pd.DataFrame(self.means, columns=['metric', 'key', 'value'])
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dfc = pd.DataFrame(self.counts, columns=['metric', 'key', 'value', 'count'])
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count = {}
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mean = {}
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if self.means:
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res = dfm.groupby(by=['key']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
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mean = res['value'].to_dict()
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if self.counts:
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res = dfc.groupby(by=['key', 'value']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
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for k,v in res['count'].to_dict().items():
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if k not in count:
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count[k] = {}
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for tup, times in v.items():
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subkey, subcount = tup
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if subkey not in count[k]:
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count[k][subkey] = {}
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count[k][subkey][subcount] = times
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return {'count': count, 'mean': mean}
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class defaultStats(Stats):
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def trial(self, env):
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c = Counter()
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c.update(a.__class__.__name__ for a in env.network_agents)
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c2 = Counter()
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c2.update(a['id'] for a in env.network_agents)
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return {
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'network ': {
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'n_nodes': env.G.number_of_nodes(),
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'n_edges': env.G.number_of_edges(),
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},
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'agents': {
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'model_count': dict(c),
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'state_count': dict(c2),
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}
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}
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