def nemenyi_unrolled_plot(data,
ylabel,
xlabel,
k_cv=1,
rot=0,
**set_fig_kwargs):
nem = pd.DataFrame(columns=['means', 'l_b', 'u_b'])
models = data.columns
for i in np.unique(data.index):
d_i = data.loc[data.index == i]
ranks_means, ranks_intervals, fr_p_val, fr_stat, are_identical = nemenyi(
d_i.values, k_cv=k_cv)
print('index {}, are identical: {}, Fr. p_val:{}'.format(
i, are_identical, fr_p_val))
nem_i_dat = np.hstack([ranks_means.reshape(-1, 1), ranks_intervals.T])
nem_i = pd.DataFrame(nem_i_dat,
columns=['means', 'l_b', 'u_b'],
index=np.tile(i, len(models)))
nem_i = pd.concat(
[nem_i, pd.Series(models, name='model', index=nem_i.index)],
axis=1)
nem = pd.concat([nem, nem_i], axis=0)
fig, ax = set_figure(**set_fig_kwargs)
cm = plt.get_cmap('plasma', len(models) * 2)
for i, ind in enumerate(np.unique(data.index)):
nem_i = nem.loc[nem.index == ind]
for j, m in enumerate(models):
nem_i_m = nem_i.loc[nem_i['model'] == m]
x = i + j / (len(models) * 1.2) - 0.5
w = 0.8 / len(models)
plt.bar(x,
nem_i_m['u_b'] - nem_i_m['l_b'],
bottom=nem_i_m['l_b'],
color=cm(j),
width=w,
alpha=0.3)
plt.scatter(x,
nem_i_m['means'],
marker='d',
color=cm(j),
s=w * 50 / len(np.unique(data.index)))
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_xticks(np.arange(len(np.unique(data.index))))
ax.set_xticklabels(np.unique(data.index), rotation=rot)
y_min = nem['l_b'].min() * (1 - np.sign(nem['l_b'].min()) * 0.1) - 0.2
y_max = np.max(nem['u_b'].max()) * (1 + np.sign(nem['u_b'].max()) * 0.1)
ax.set_ylim(y_min, y_max)
plt.legend(models, loc='upper right', fontsize='x-small')
return fig, ax
axis=1)
fourier_mins.columns = [
'mape w.r.t. mimo, Fourier', 'mape w.r.t. miso, Fourier',
'rmse w.r.t. mimo, Fourier', 'rmse w.r.t. miso, Fourier'
]
mape_comparison = pd.concat([
res.loc[:, ['mape wrt mimo', 'mape wrt miso']], fourier_mins.
loc[:, ['mape w.r.t. mimo, Fourier', 'mape w.r.t. miso, Fourier']]
])
rmse_comparison = pd.concat([
res.loc[:, ['rmse wrt mimo', 'rmse wrt miso']], fourier_mins.
loc[:, ['rmse w.r.t. mimo, Fourier', 'rmse w.r.t. miso, Fourier']]
])
fig, ax = set_figure(size, (2, 1),
font_scale=font_scale,
w=0,
h=0,
sharex=True)
sb.kdeplot(data=mape_comparison, ax=ax[0])
sb.kdeplot(data=rmse_comparison, ax=ax[1])
ax[0].vlines(1, *ax[0].get_ylim(), linewidth=1.5, color='k', linestyle='--')
ax[1].vlines(1, *ax[1].get_ylim(), linewidth=1.5, color='k', linestyle='--')
plt.savefig('figs/m4_regression.pdf')
plt.show()
lw = 1
s = 5
alpha = 0.1
# get terminal stages
pdfs, pdfs_idx = pd.DataFrame({}), pd.DataFrame({})
]).T / results_k['rmse_miso']
res_miso[k] = pd.concat([mape, rmse], axis=1)
mape = pd.DataFrame(results_k['mape'], index=['mape'
]).T / results_k['mape_mimo']
rmse = pd.DataFrame(results_k['rmse'], index=['rmse'
]).T / results_k['rmse_mimo']
res_mimo[k] = pd.concat([mape, rmse], axis=1)
rmse_miso[k] = results_k['rmse_miso']
mape_miso[k] = results_k['mape_miso']
rmse_mimo[k] = results_k['rmse_mimo']
mape_mimo[k] = results_k['mape_mimo']
fig, ax = set_figure(size, (2, 2), font_scale=font_scale, w=0, h=0)
lw = 2
s = 10
alpha = 0.3
t = np.asanyarray(list([res_mimo.values()][0])[0]['mape'].keys())
cm = plt.get_cmap('plasma', 256)
for k, v in res_mimo.items():
color = np.minimum(
1, mape_mimo[k] / np.quantile(list(mape_mimo.values()), 0.9))
ax[0, 0].plot(t, v['mape'], color=cm(color), alpha=alpha)
ax[0, 0].scatter(v.index[np.argmin(v['mape'])],
np.min(v['mape']),
color=cm(color),
marker='o',
s=s)
s = 5
alpha = 0.1
# get terminal stages
pdfs, pdfs_idx = pd.DataFrame({}), pd.DataFrame({})
pdfs['mape_mimo'] = mimo_mins.loc[(slice(None), 'mape'), 'min'].values
pdfs['mape_miso'] = miso_mins.loc[(slice(None), 'mape'), 'min'].values
pdfs['rmse_mimo'] = mimo_mins.loc[(slice(None), 'rmse'), 'min'].values
pdfs['rmse_miso'] = miso_mins.loc[(slice(None), 'rmse'), 'min'].values
pdfs_idx['mape_mimo'] = mimo_mins.loc[(slice(None), 'mape'), 'idx'].values
pdfs_idx['mape_miso'] = miso_mins.loc[(slice(None), 'mape'), 'idx'].values
pdfs_idx['rmse_mimo'] = mimo_mins.loc[(slice(None), 'rmse'), 'idx'].values
pdfs_idx['rmse_miso'] = miso_mins.loc[(slice(None), 'rmse'), 'idx'].values
fig, ax = set_figure((5, 4), (2,2),font_scale=font_scale, w=0.25, h=0.35, l=0.1, r=0.8, t=0.95,b=0.2)
# set distribution bars
distplot_width = 0.05
dax = []
iax = []
sb.set_style('whitegrid')
for i, a in enumerate(ax.ravel()):
x0, y0, b_w, b_h = a.get_position().bounds
dax.append(plt.axes([x0+b_w+0.01, y0, distplot_width, b_h]))
dax[-1].set_xticks([])
dax[-1].set_yticks([])
sb.histplot(data=pdfs, y=pdfs.columns[i], element='step')
sb.despine(ax=dax[-1])
dax[-1].hlines(1, *dax[-1].get_xlim()*np.array([0, 1.3]), linestyles='--', colors='gray', linewidth=lw)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb
from utils.cross_val import get_cv_results
from analysis.plot_utils import set_figure
size = (5, 4)
font_scale = 0.7
results = np.load('data/hierarchical_scores.npy', allow_pickle=True)
results = get_cv_results(results)
results['mbt'] = results['mgb']
name = 'reconciliation_RMSE'
f, ax = set_figure(size=size,
subplots=(2, 1),
w=0.3,
h=0.2,
b=0.2,
font_scale=font_scale)
results['bu']['type'] = results['bu'].index
results['rec']['type'] = results['rec'].index
results['mbt']['type'] = results['mgb'].index
results['bu']['method'] = 'bu'
results['rec']['method'] = 'rec'
results['mbt']['method'] = 'mbt'
res_k = pd.concat([results['bu'], results['rec'], results['mbt']])
sb.lineplot(x='sa', y='mape', data=res_k, hue='type', style='method', ax=ax[1])
ax[1].set_ylabel('MAPE [-]')
plt.legend(fontsize='small',
ncol=2,
results_meteo = get_cv_results(results_meteo)
# pretty rename
results['mbt lin meteo'] = results_meteo['mbt lin']
results['mbt meteo'] = results_meteo['mbt']
summary = OrderedDict({'rmse':{},'rmse_norm':{}})
for k,v in results.items():
if k in ['mbt', 'mbt meteo'] :
continue
summary['rmse'][k] = v['rmse']
summary['rmse_norm'][k] = v['norm_rmse']
f,ax = set_figure(size=size,w=0.3, h=0,b=0.2,font_scale=font_scale)
summary = pd.DataFrame(summary)
summary.reset_index(level=0, inplace=True)
summary.rename(columns={'index':'model'}, inplace=True)
summary = summary.melt('model',var_name='metric',value_name='RMSE [V]')
sb.set_color_codes("pastel")
sb.barplot(x='model', y='RMSE [V]', hue='metric', data=summary, linewidth=0.2)
plt.savefig('figs/vsc_rmse.pdf')
plt.show()
# noise analysis
results = np.load('data/vsc_results_meteo.npy', allow_pickle=True)
cm = plt.get_cmap('plasma',10)