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feat(AutoML): implemented block function evaluation
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# take input image and ground truth | ||
# take input pred csv | ||
#==============================================================================# | ||
# Author: Dominik Müller # | ||
# Copyright: 2022 IT-Infrastructure for Translational Medical Research, # | ||
# University of Augsburg # | ||
# # | ||
# This program is free software: you can redistribute it and/or modify # | ||
# it under the terms of the GNU General Public License as published by # | ||
# the Free Software Foundation, either version 3 of the License, or # | ||
# (at your option) any later version. # | ||
# # | ||
# This program is distributed in the hope that it will be useful, # | ||
# but WITHOUT ANY WARRANTY; without even the implied warranty of # | ||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # | ||
# GNU General Public License for more details. # | ||
# # | ||
# You should have received a copy of the GNU General Public License # | ||
# along with this program. If not, see <http://www.gnu.org/licenses/>. # | ||
#==============================================================================# | ||
#-----------------------------------------------------# | ||
# Library imports # | ||
#-----------------------------------------------------# | ||
# External libraries | ||
import os | ||
import pandas as pd | ||
import numpy as np | ||
# Internal libraries | ||
from aucmedi import * | ||
from aucmedi.evaluation import evaluate_performance | ||
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#-----------------------------------------------------# | ||
# Building Blocks for Evaluation # | ||
#-----------------------------------------------------# | ||
def block_evaluate(config): | ||
""" Internal code block for AutoML evaluation. | ||
This function is called by the Command-Line-Interface (CLI) of AUCMEDI. | ||
Args: | ||
config (dict): Configuration dictionary containing all required | ||
parameters for performing an AutoML evaluation. | ||
The following attributes are stored in the `config` dictionary: | ||
Attributes: | ||
interface (str): String defining format interface for loading/storing data (`csv` or `dictionary`). | ||
path_imagedir (str): Path to the directory containing the images. | ||
path_data (str): Path to the index/class annotation file if required. (csv/json). | ||
input (str): Path to the input file in which predicted csv file is stored. | ||
output (str): Path to the directory in which evaluation figures and tables should be stored. | ||
ohe (bool): Boolean option whether annotation data is sparse categorical or one-hot encoded. | ||
""" | ||
# Peak into the dataset via the input interface | ||
ds = input_interface(config["interface"], | ||
config["path_imagedir"], | ||
path_data=config["path_data"], | ||
training=True, | ||
ohe=config["ohe"], | ||
image_format=None) | ||
(index_list, class_ohe, class_n, class_names, image_format) = ds | ||
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# Create output directory | ||
if not os.path.exists(config["output"]) : os.mkdir(config["output"]) | ||
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# Read prediction csv | ||
df_pred = pd.read_csv(config["input"]) | ||
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# Create ground truth pandas dataframe | ||
df_index = pd.DataFrame(data={"SAMPLE": index_list}) | ||
df_gt_data = pd.DataFrame(data=class_ohe, columns=class_names) | ||
df_gt = pd.concat([df_index, df_gt_data], axis=1, sort=False) | ||
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# Merge dataframes to verify correct order | ||
df_merged = df_pred.merge(df_gt, on="SAMPLE", suffixes=("_pd", "_gt")) | ||
# Extract pd and gt again to NumPy | ||
data_pd = df_merged.iloc[:, 1:(class_n+1)].to_numpy() | ||
data_gt = df_merged.iloc[:, (class_n+1):9].to_numpy() | ||
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# Identify task (multi-class vs multi-label) | ||
if np.sum(data_pd) > (class_ohe.shape[0] + 1.5) : multi_label = True | ||
else : multi_label = False | ||
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# Evaluate performance via AUCMEDI evaluation submodule | ||
evaluate_performance(data_pd, data_gt, | ||
out_path=config["output"], | ||
class_names=class_names, | ||
multi_label=multi_label, | ||
metrics_threshold=0.5, | ||
suffix=None, | ||
store_csv=True, | ||
plot_barplot=True, | ||
plot_confusion_matrix=True, | ||
plot_roc_curve=True) |