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tables.py
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136 lines (109 loc) · 6.04 KB
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import numpy as np
import pandas as pd
import simulation_utility as sutl
import matplotlib.pyplot as plt
from collections import Counter
types = ['No transition','Random transition', 'Sparse transition']
def table_1(latex=False):
res1 = pd.DataFrame(columns = ['Case No','replacement cost','transit mil', 'transition type',1,2,4,6])
res2 = res1.copy()
for diff_rep_cost in [True,False]:
for ml_tr_mode in [True, False]:
for q_trans_mod in [1,2,3]:
df = pd.read_pickle('data/simulation1_results/res_{}_{}_{}.pickle'.format(q_trans_mod, ml_tr_mode*1, diff_rep_cost*1))[[2,5,9,15]]
df = pd.DataFrame(data = np.concatenate(df.values.flatten()).reshape(-1,4))
res1.loc[len(res1)] = ["Case {}".format(len(res1)+1),diff_rep_cost*1, ml_tr_mode*1, q_trans_mod] + df.mean().round(3).tolist()
s = ['','','','']
quantiles = df.quantile([0.02,0.98]).round(3)
for i in range(4):
s.append("({}, {})".format(quantiles[i][0.02],quantiles[i][0.98]))
res2.loc[len(res2)] = s
combined = pd.DataFrame(columns = res1.columns)
for i in range(len(res1)):
combined.loc[len(combined)] = res1.loc[i]
combined.loc[len(combined)] = res2.loc[i]
for i in range(3):
combined['transition type'] = combined['transition type'].replace(i+1,types[i])
combined = combined.replace('0.0','---')
combined = combined.replace(' (0.0, 0.0)','---')
combined = combined.replace(True,'Dissimilar')
combined = combined.replace(False,'Similar')
if(latex):
return combined.to_latex(index=False,float_format="%.3f")
return combined
def table_2(latex=False):
res1 = pd.DataFrame(columns = ['Case No','replacement cost','transit mil', 'transition type','f0','f1','f2','f3','c_r'])
res2 = res1.copy()
for diff_rep_cost in [True,False]:
for ml_tr_mode in [True, False]:
for q_trans_mod in [1,2,3]:
df = pd.read_pickle('data/simulation1_results/res_{}_{}_{}.pickle'.format(q_trans_mod, ml_tr_mode*1, diff_rep_cost*1))[[10,11,12,13,3]]
res1.loc[len(res1)] = ["Case {}".format(len(res1)+1),diff_rep_cost*1, ml_tr_mode*1, q_trans_mod] + df.mean().round(3).tolist()
s = ['','','','']
quantiles = df.quantile([0.02,0.98]).round(3)
for i in [10,11,12,13,3]:
s.append("({}, {})".format(quantiles[i][0.02],quantiles[i][0.98]))
res2.loc[len(res2)] = s
combined = pd.DataFrame(columns = res1.columns)
for i in range(len(res1)):
combined.loc[len(combined)] = res1.loc[i]
combined.loc[len(combined)] = res2.loc[i]
for i in range(3):
combined['transition type'] = combined['transition type'].replace(i+1,types[i])
combined = combined.replace('0.0','---')
combined = combined.replace(' (0.0, 0.0)','---')
combined = combined.replace(True,'Dissimilar')
combined = combined.replace(False,'Similar')
if(latex):
return combined.to_latex(index=False, float_format="%.3f")
return combined
def table_3(N, dim_q, latex=False, expname='simulation2_results'):
df = pd.DataFrame(columns = ['k','transition_sparsity','affect transition','bus periods','lamb','value'])
for k in np.arange(N):
print("Reading results of replication {}".format(k))
partitions = pd.read_pickle("data/{}/partition_{}.pickle".format(expname, k))
for ttype_ext in [3,15]:
for m_trans in [False,True]:
for periods in [100,400]:
for lamb in [0,0.2,0.5,1,2,5,100]:
parts = pd.read_pickle("data/{}/parts_{}_{}_{}_{}_{}.pickle".format(expname, k,ttype_ext,m_trans*1,periods,lamb))
report = pd.read_pickle("data/{}/report_{}_{}_{}_{}_{}.pickle".format(expname, k,ttype_ext,m_trans*1,periods,lamb))
score = report.loc[len(report)-1].test_score
df.loc[len(df)]=[k, ttype_ext, m_trans, periods, lamb, score]
df['value'] = df['value'].astype(float)
tmp = df.groupby(['bus periods','transition_sparsity','affect transition','lamb'])['value'].mean().reset_index()
final = pd.pivot(tmp,index = ['bus periods','transition_sparsity','affect transition'],columns='lamb',values=['value']).reset_index()
final.transition_sparsity = final.transition_sparsity.replace([3,15],['Sparse','Random'])
final['affect transition'] = final['affect transition'].replace([False,True],['Yes','No'])
final['Case No'] = ['Case {}'.format(i) for i in range(1,len(final)+1)]
last_column = final.columns[-1]
new_columns_order = [last_column] + [col for col in final.columns if col != last_column]
print(new_columns_order)
final = final[new_columns_order]
if(latex):
return final.to_latex(index=False, float_format="%.0f")
return final
def figure_a3(N, dim_active_q, expname='simulation2_results'):
splits = []
f_dc = []
for k in np.arange(N):
print("Reading results of replication {}".format(k))
partitions = pd.read_pickle("data/{}/partition_{}.pickle".format(expname, k))
splits.extend(partitions.total_split.values.tolist())
partitions['f_dc'] = 0
for i in range(dim_active_q):
partitions['f_dc'] = partitions['f_dc'] - (partitions['q_{}_min'.format(i)] + partitions['q_{}_max'.format(i)]) / (2 * dim_active_q)
partitions['f_dc'] = (5 + partitions['f_dc']) * 2 - 5
f_dc.extend(partitions.f_dc.values.tolist())
# First plot
fig1, ax1 = plt.subplots()
ax1.hist(f_dc)
fig1.savefig("data/f_dc_hist.png")
# Second plot
counter = Counter(splits)
x = np.arange(min(splits), max(splits) + 1)
y = [counter[i] / len(splits) for i in x]
fig2, ax2 = plt.subplots()
ax2.bar(x, y)
fig2.savefig("data/total_split_hist.png")
return fig1, fig2 # Return the figure objects