def createConfusionMatrix(self, otbmatrix):
"""Create a multi-level confusion matrix (pandas DataFrame)
otbmatrix : csv file
an OTB confusionMatrix file
Return
------
pandas.core.frame.DataFrame
vector or raster QML style file
"""
mat = fu.confCoordinatesCSV(otbmatrix)
d = defaultdict(list)
for k, v in mat:
d[k].append(v)
csv_f = list(d.items())
# get matrix from csv
matrix = fu.gen_confusionMatrix(csv_f, self.getCode(self.level))
# MultiIndex Pandas dataframe
cmdf = DataFrame(matrix, index=self.HierarchicalNomenclature, \
columns=self.HierarchicalNomenclature)
cmdf.sort_index(level=self.level - 1, inplace=True, axis=1)
cmdf.sort_index(level=self.level - 1, inplace=True, axis=0)
return cmdf
def merge_confusions(csv_in, labels, csv_out):
"""
from a list of confusion matrix, generate an unique one
Parameters
----------
csv_in : list
paths to csv confusion matrix
labels : list
all possible labels
csv_out : string
output path
"""
csv = fu.confCoordinatesCSV(csv_in)
csv_f = fu.sortByFirstElem(csv)
conf_mat = fu.gen_confusionMatrix(csv_f, labels)
write_csv(conf_mat, labels, csv_out)
def confusion_fusion(input_vector: str, data_field: str, csv_out: str,
txt_out: str, csv_path: str, runs: int, crop_mix: bool,
annual_crop: List[str],
annual_crop_label_replacement: int) -> None:
"""merge otb tile confusion matrix
Parameters
----------
input_vector: str
input database
data_field: str
data field
csv_out: str
output csv file which will contains the merge of matrix
txt_out: str
diretory which will contains the resulting file of merged matrix
csv_path: str
path to the directory which contains all *.csv files to merge
runs: int
number of random learning/validation samples-set
crop_mix: bool
inform if cropMix workflow is enable
annual_crop: list
list of annual labels
annual_crop_label_replacement: int
replace annual labels by annual_crop_label_replacement
"""
for seed in range(runs):
#Recherche de toute les classes possible
all_class = []
all_class = fu.getFieldElement(input_vector, "ESRI Shapefile",
data_field, "unique")
all_class = sorted(all_class)
#Initialisation de la matrice finale
all_conf = fu.FileSearch_AND(csv_path, True,
"seed_" + str(seed) + ".csv")
csv = fu.confCoordinatesCSV(all_conf)
csv_f = fu.sortByFirstElem(csv)
conf_mat = fu.gen_confusionMatrix(csv_f, all_class)
if crop_mix:
write_csv(
conf_mat, all_class,
csv_out + "/MatrixBeforeClassMerge_" + str(seed) + ".csv")
conf_mat, all_class = replace_annual_crop_in_conf_mat(
conf_mat, all_class, annual_crop,
annual_crop_label_replacement)
write_csv(conf_mat, all_class,
csv_out + "/Classif_Seed_" + str(seed) + ".csv")
else:
write_csv(conf_mat, all_class,
csv_out + "/Classif_Seed_" + str(seed) + ".csv")
nbr_good = conf_mat.trace()
nbr_sample = conf_mat.sum()
if nbr_sample > 1:
overall_acc = float(nbr_good) / float(nbr_sample)
else:
overall_acc = 0.0
kappa = compute_kappa(conf_mat)
precision = compute_precision_by_class(conf_mat, all_class)
recall = compute_recall_by_class(conf_mat, all_class)
f_score = compute_fscore_by_class(precision, recall, all_class)
write_results(
f_score, recall, precision, kappa, overall_acc, all_class,
txt_out + "/ClassificationResults_seed_" + str(seed) + ".txt")
