import numpy as np
from utils.cross_val import get_cv_results
from analysis.plot_utils import plot_reliability, plot_reliability_tilted, plot_QS, plot_crsp, plot_reliability_diff, plot_QS_diff
font_scale = 0.7
size = (4.5,3)
# ----------------------------------- compare normal, refitted and squared-refitted -------------------------
results_all = np.load('data/cv_res_quantiles_all_squared_partial.npy', allow_pickle=True).item()
refit = get_cv_results(results_all['refit']['all'])
squared_refit = get_cv_results(results_all['suqared_refit']['all'])
results_all = get_cv_results(results_all['no_refit']['all'])
results_all['mbt'] = results_all['mgb']
del results_all['mgb']
results_all['mbt refit'] = refit['mgb']
results_all['mbt lin-quad refit'] = squared_refit['mgb']
plot_QS_diff(results_all,size,font_scale=font_scale)
plot_reliability_diff(results_all,size,font_scale=font_scale)
plot_crsp(results_all,size,font_scale=font_scale)
plot_QS(results_all,size,font_scale=font_scale)
plot_reliability(results_all,size,font_scale=font_scale)
plot_reliability_tilted(results_all,size,font_scale=font_scale)
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',
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
font_scale = 0.7
size = (4.5, 4)
results = np.load('data/cv_res_m4.npy', allow_pickle=True).item()
res_miso, res_mimo, rmse_miso, rmse_mimo, mape_mimo, mape_miso = [{}, {}, {},
{}, {}, {}]
res = []
for k in results.keys():
results_k = get_cv_results(results[k])
res.append(pd.DataFrame(results_k, index=[k]))
res = pd.concat(res)
res['mape wrt mimo'] = res['mape'] / res['mape_mimo']
res['mape wrt miso'] = res['mape'] / res['mape_miso']
res['rmse wrt mimo'] = res['rmse'] / res['rmse_mimo']
res['rmse wrt miso'] = res['rmse'] / res['rmse_miso']
results = np.load('data/cv_res_m4_fourier.npy', allow_pickle=True).item()
res_miso, res_mimo, rmse_miso, rmse_mimo, mape_mimo, mape_miso = [{}, {}, {},
{}, {}, {}]
for k in results.keys():
results_k = get_cv_results(results[k])
mape = pd.DataFrame(results_k['mape'], index=['mape'
]).T / results_k['mape_miso']
rmse = pd.DataFrame(results_k['rmse'], index=['rmse'
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
from collections import OrderedDict
from matplotlib.ticker import FormatStrFormatter
size = (4.5,3)
font_scale = 0.7
ns_control = [1]
results = np.load('data/vsc_results.npy', allow_pickle=True)
results = get_cv_results(results)
results_meteo = np.load('data/vsc_results_meteo.npy', allow_pickle=True)
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']