def main():
initial_partition = PA_partition()
chain = BasicChain(initial_partition, total_steps=10000)
scores = {
'Mean-Median':
functools.partial(mean_median, proportion_column_name='VoteA%'),
'Mean-Thirdian':
functools.partial(mean_thirdian, proportion_column_name='VoteA%'),
'Efficiency Gap':
functools.partial(efficiency_gap, col1='VoteA', col2='VoteB'),
}
initial_scores = {
key: score(initial_partition)
for key, score in scores.items()
}
table = pipe_to_table(chain, scores)
return {
key: p_value_report(key, table[key], initial_scores[key])
for key in scores
}
def main():
initial_partition = PA_partition(
'../rundmcmc/testData/PA_graph_with_data.json')
chain = BasicChain(initial_partition, total_steps=10000)
scores = {
'Mean-Median':
functools.partial(mean_median, proportion_column_name='VoteA%'),
'Mean-Thirdian':
functools.partial(mean_thirdian, proportion_column_name='VoteA%'),
'Efficiency Gap':
functools.partial(efficiency_gap, col1='VoteA', col2='VoteB'),
'L1 Reciprocal Polsby-Popper':
L1_reciprocal_polsby_popper
}
initial_scores = {
key: score(initial_partition)
for key, score in scores.items()
}
table = pipe_to_table(chain, scores)
fig, axes = plt.subplots(2, 2)
quadrants = {
'Mean-Median': (0, 0),
'Mean-Thirdian': (0, 1),
'Efficiency Gap': (1, 0),
'L1 Reciprocal Polsby-Popper': (1, 1)
}
for key in scores:
quadrant = quadrants[key]
axes[quadrant].hist(table[key], bins=50)
axes[quadrant].set_title(key)
axes[quadrant].axvline(x=initial_scores[key], color='r')
plt.show()
col2=election_columns[i][1])
}
scores_for_plots.append(vscores)
scores = {**scores, **vscores}
# Compute the values of the intial state and the chain
initial_scores = {
key: score(initial_partition)
for key, score in scores.items()
}
table = pipe_to_table(chain,
scores,
display=True,
number_to_display=number_to_display,
bin_interval=bin_interval)
print(steps, " Steps in ", time.time() - start_time, " Seconds")
pscores = dict(scores)
pscores.pop("Node Flipped:")
pscores.pop("Flipped to:")
pscores.pop("All Flips:")
# P-value reports
pv_dict = {
key: p_value_report(key, table[key], initial_scores[key])
for key in pscores
functools.partial(how_many_seats_value,
col1=election_columns[i][0],
col2=election_columns[i][1])
}
scores_for_plots.append(vscores)
scores = {**scores, **vscores}
# Compute the values of the intial state and the chain
initial_scores = {
key: score(initial_partition)
for key, score in scores.items()
}
table = pipe_to_table(chain, scores, display=True, number_to_display=10)
results_df = table.to_dataframe()
for name in election_names:
print(results_df["demseats" + name].describe())
print()
# P-value reports
pv_dict = {
key: p_value_report(key, table[key], initial_scores[key])
for key in scores
}
# print(pv_dict)
with open(newdir + 'pvals_report_multi.json', 'w') as fp:
json.dump(pv_dict, fp)
'Mean-Thirdian':
functools.partial(mean_thirdian, proportion_column_name=vote_col1 + "%"),
'Efficiency Gap':
functools.partial(efficiency_gap, col1=vote_col1, col2=vote_col2),
'L1 Reciprocal Polsby-Popper':
L1_reciprocal_polsby_popper
}
initial_scores = {
key: score(initial_partition)
for key, score in scores.items()
}
table = pipe_to_table(chain,
scores,
display=True,
display_frequency=100,
bin_frequency=1)
# Histogram Plotting
hist_path = "chain_histogram31.png"
hist_of_table_scores(table, scores, outputFile=hist_path, num_bins=50)
print("plotted histograms")
# Trace Plotting
trace_path = "chain_traces31.png"
trace_of_table_scores(table, scores, outputFile=trace_path)
def test_pipe_to_table_records_all_scores_by_default(self):
mock_chain, handlers, values = self.setup()
table = pipe_to_table(mock_chain, handlers, display=False)
assert all(table[key] == values[key] for key in values)