def h5d_auc_table(h5d_fl1, h5d_fl2, kfolds, params_od, mix, is_ttbl, strata, trec):
"""Area Under the Curve(AUC) paired with table of parameters
# # # Make proper Definition here # # #
"""
# Beginning AUC-Params table building.
res_lst = list()
# Loading data in a convenient form.
for params_lst, params_path in zip(
param_comb.ParamGridIter(params_od, 'list'),
param_comb.ParamGridIter(params_od, 'path')):
# Defining list for AUC values storage. For this loop.
auc_values = list()
if params_lst[0] > params_lst[1]:
if mix:
pred_scores, expd_y, pred_y = get_predictions_mix(
h5d_fl1, h5d_fl2, kfolds, params_path, params_lst[2],
genre_tag=None, binary=is_ttbl, strata=strata
)
else:
pred_scores, expd_y, pred_y = get_predictions(
h5d_fl1, kfolds, params_path, genre_tag=None, binary=is_ttbl, strata=strata
)
if is_ttbl:
# NOTE:Option is_truth_tbl is critical to be selected correctly depending...
# ...on the input.
prec, recl, t = mx.pr_curve(
expd_y, pred_scores, full_curve=True, is_truth_tbl=is_ttbl
)
# Interpolated at 11-Recall-Levels.
prec, recl = mx.reclev11_max(prec, recl, trec=trec)
try:
auc_values.append(mx.auc(recl, prec))
except:
print "Warning:", params_path, "AUC is for these params has set to 0.0"
auc_values.append(0.0)
else:
# Finding unique genres.
gnr_tgs = np.unique(expd_y)
# Calculating AUC per genre tag.
for gnr in gnr_tgs:
# Converting expected Y to binary format.
expd_y_bin = np.where((expd_y == gnr), 1, 0)
# NOTE:Option is_truth_tbl is critical to be selected correctly depending...
# ...on the input.
prec, recl, t = mx.pr_curve(
expd_y_bin, pred_scores, full_curve=True, is_truth_tbl=is_ttbl
)
# Interpolated at 11-Recall-Levels.
prec, recl = mx.reclev11_max(prec, recl, trec=trec)
try:
auc_values.append(auc(recl, prec))
except:
print "Warning:", params_path, "AUC is for these params has setted to 0.0"
auc_values.append(0.0)
# Extending parameters list with AUC(s).
params_lst.extend(auc_values)
# Appending the parameters list together with their respective AUC(s).
res_lst.append(params_lst)
# Stacking and returning the data collected in a 2D array. Last column contain the AUC for...
# ...every parameters values possible combination.
return np.vstack(res_lst)
fg1 = plt.figure(num=1, figsize=(12, 8), dpi=80, facecolor='w', edgecolor='k')
ax1 = fg1.add_subplot(111)
#fg2 = plt.figure(num=2, figsize=(30, 8), dpi=80, facecolor='w', edgecolor='k')
#ax2 = fg2.add_subplot(111)
i = 0
bar_width = 0.15
for params_lst, params_path in \
zip(param_comb.ParamGridIter(params_od, 'list'),
param_comb.ParamGridIter(params_od, 'path')):
if params_lst[0] > params_lst[1]:
pred_scores, expd_y, pred_y = get_predictions(
h5d_fl1, kfolds, params_path, genre_tag=None, binary=True, strata=None
#(10, 1000)
)
"""
pred_scores, expd_y, pred_y = get_predictions_mix(
h5d_fl1, h5d_fl2, kfolds, params_path, params_lst[2],
genre_tag=None, binary=True, strata=None
#(10, 1000)
)
"""
#pred_scores, expd_y, pred_y = get_ocsvme(res_h5file, kfolds, params_path, genre_tag=None)
#y, x, t = roc_curve(expd_y, pred_scores, full_curve=False)
