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feat(Evaluation): Implemented fitting curve plotting
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#==============================================================================# | ||
# 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 numpy as np | ||
import pandas as pd | ||
import os | ||
from plotnine import * | ||
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#-----------------------------------------------------# | ||
# Evaluation - Plot Fitting # | ||
#-----------------------------------------------------# | ||
def evaluate_fitting(keras_history, out_path, monitor=["loss"], | ||
prefix_split=".", suffix=None): | ||
""" blabla | ||
Args: | ||
monitor (list of str): List of metrics which should be visualized in the fitting plot. | ||
suffix (str): Special suffix to add in the created figure filename. | ||
""" | ||
# Convert to pandas dataframe | ||
dt = pd.DataFrame.from_dict(keras_history, orient="columns") | ||
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# Identify all selected colums | ||
selected_cols = [] | ||
for key in keras_history: | ||
for m in monitor: | ||
if m in key: | ||
selected_cols.append(key) | ||
break | ||
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# Add epoch column | ||
dt["epoch"] = dt.index + 1 | ||
# Melt dataframe | ||
dt_melted = dt.melt(id_vars=["epoch"], | ||
value_vars=selected_cols, | ||
var_name="metric", | ||
value_name="score") | ||
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# Handle special prefix tags (if split-able by '.') | ||
if prefix_split is not None: | ||
for c in selected_cols: | ||
valid_split = True | ||
if prefix_split not in c: | ||
valid_split = False | ||
break | ||
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if valid_split: | ||
dt_melted[["prefix", "metric"]] = dt_melted["metric"].str.split(".", | ||
expand=True) | ||
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# Preprocess dataframe | ||
dt_melted["subset"] = np.where(dt_melted["metric"].str.startswith("val_"), | ||
"validation", "training") | ||
dt_melted["metric"] = dt_melted["metric"].apply(remove_val_tag) | ||
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# Plot results via plotnine | ||
fig = (ggplot(dt_melted, aes("epoch", "score", color="subset")) | ||
+ geom_line(size=1) | ||
+ ggtitle("Fitting Curve during Training Process") | ||
+ xlab("Epoch") | ||
+ ylab("Score") | ||
+ scale_colour_discrete(name="Subset") | ||
+ theme_bw() | ||
+ theme(subplots_adjust={'wspace':0.15})) | ||
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if prefix_split is not None and valid_split: | ||
fig += facet_grid("prefix ~ metric") | ||
else : fig += facet_wrap("metric", scales="free_y") | ||
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# Store figure to disk | ||
fig.save(filename="plot.fitting_course." + str(suffix) + ".png", | ||
path=out_path, dpi=200, limitsize=False) | ||
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#-----------------------------------------------------# | ||
# Subroutines # | ||
#-----------------------------------------------------# | ||
def remove_val_tag(x): | ||
if x.startswith("val_") : return x[4:] | ||
else : return x |