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gandlf_collectStats
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gandlf_collectStats
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#!usr/bin/env python
# -*- coding: utf-8 -*-
import os
import argparse
import ast
from pathlib import Path
import pandas as pd
from io import StringIO
import seaborn as sns
import matplotlib.pyplot as plt
from GANDLF.cli import copyrightMessage
def main():
parser = argparse.ArgumentParser(
prog="GANDLF_CollectStats",
formatter_class=argparse.RawTextHelpFormatter,
description="Collect statistics from different testing/validation combinations from output directory.\n\n"
+ copyrightMessage,
)
parser.add_argument(
"-m",
"--modeldir",
metavar="",
type=str,
help="Input directory which contains testing and validation models",
)
parser.add_argument(
"-o",
"--outputdir",
metavar="",
type=str,
help="Output directory to save stats and plot",
)
parser.add_argument(
"-c",
"--combinedplots",
metavar="",
default=False,
type=ast.literal_eval,
help="Overlays training and validation plots for both accuracy and loss (classification only).",
)
args = parser.parse_args()
inputDir = os.path.normpath(args.modeldir)
outputDir = os.path.normpath(args.outputdir)
Path(outputDir).mkdir(parents=True, exist_ok=True)
outputFile = os.path.join(outputDir, "data.csv") # data file name
outputPlot = os.path.join(outputDir, "plot.png") # plot file
combinedPlots = args.combinedplots
trainingLogs = os.path.join(inputDir, "logs_training.csv")
validationLogs = os.path.join(inputDir, "logs_validation.csv")
testingLogs = os.path.join(inputDir, "logs_testing.csv")
if os.path.exists(testingLogs):
testingLogsCSV = pd.read_csv(testingLogs)
# check for classification task
if len(testingLogsCSV) == 0:
print("Classification task detected, generating accuracy and loss plots.")
# check whether user wants training + validation overlaid plots
if combinedPlots:
df_training = pd.read_csv(trainingLogs)
df_validation = pd.read_csv(validationLogs)
epochs = len(df_training)
fig, axes = plt.subplots(nrows=1, ncols=2) # set plot properties
# ensure spacing between plots
plt.subplots_adjust(wspace=0.5, hspace=0.5)
# plot training accuracy data
splot = sns.lineplot(
data=df_training,
x="epoch_no",
y="train_balanced_accuracy",
ax=axes[0],
)
# plot validation accuracy data
splot = sns.lineplot(
data=df_validation,
x="epoch_no",
y="valid_balanced_accuracy",
ax=axes[0],
)
# set limits for x-axis for proper visualization
splot.set(xlim=(0, epochs - 1))
# set limits for y-axis for proper visualization
splot.set(ylim=(0, 1))
# add labels and title to plot
splot.set(xlabel="Epoch", ylabel="Accuracy", title="Accuracy Plot")
# add legend to plot
axes[0].legend(labels=["Training", "Validation"])
# plot training loss data
splot = sns.lineplot(
data=df_training, x="epoch_no", y="train_loss", ax=axes[1]
)
# plot validation loss data
splot = sns.lineplot(
data=df_validation, x="epoch_no", y="valid_loss", ax=axes[1]
)
# set limits for x-axis for proper visualization
splot.set(xlim=(0, epochs - 1))
# add labels and title to plot
splot.set(xlabel="Epoch", ylabel="Loss", title="Loss Plot")
# add legend to plot
axes[1].legend(labels=["Training", "Validation"])
# save plot
plt.savefig(outputPlot, dpi=600)
print("Plots saved successfully.")
else:
df_training = pd.read_csv(trainingLogs)
df_validation = pd.read_csv(validationLogs)
epochs = len(df_training)
# set plot properties
fig, axes = plt.subplots(nrows=2, ncols=2)
plt.subplots_adjust(wspace=0.5, hspace=0.5)
# plot the data
splot = sns.lineplot(
data=df_training,
x="epoch_no",
y="train_balanced_accuracy",
ax=axes[0, 0],
)
splot.set(xlim=(0, epochs - 1))
splot.set(ylim=(0, 1)) # set limits for y-axis for proper visualization
# set labels
splot.set(
xlabel="Epoch", ylabel="Accuracy", title="Training Accuracy Plot"
)
# plot the data
splot = sns.lineplot(
data=df_validation,
x="epoch_no",
y="valid_balanced_accuracy",
ax=axes[0, 1],
)
splot.set(xlim=(0, epochs - 1))
splot.set(ylim=(0, 1)) # set limits for y-axis for proper visualization
# set labels
splot.set(
xlabel="Epoch", ylabel="Accuracy", title="Validation Accuracy Plot"
)
# plot the data
splot = sns.lineplot(
data=df_training, x="epoch_no", y="train_loss", ax=axes[1, 0]
)
splot.set(xlim=(0, epochs - 1))
# set labels
splot.set(xlabel="Epoch", ylabel="Loss", title="Training Loss Plot")
# plot the data
splot = sns.lineplot(
data=df_validation, x="epoch_no", y="valid_loss", ax=axes[1, 1]
)
splot.set(xlim=(0, epochs - 1))
# set labels
splot.set(xlabel="Epoch", ylabel="Loss", title="Validation Loss Plot")
plt.savefig(outputPlot, dpi=600)
print("Plots saved successfully.")
else:
print("Segmentation task detected, generating dice and loss plots.")
final_stats = "Epoch,Train_Loss,Train_Dice,Val_Loss,Val_Dice,Testing_Loss,Testing_Dice\n" # the columns that need to be present in final output; epoch is always removed
# loop through output directory
for dirs in os.listdir(inputDir):
currentTestingDir = os.path.join(inputDir, dirs)
if os.path.isdir(currentTestingDir): # go in only if it is a directory
if "testing_" in dirs: # ensure it is part of the testing structure
# loop through all validation directories
for val in os.listdir(currentTestingDir):
currentValidationDir = os.path.join(currentTestingDir, val)
if os.path.isdir(currentValidationDir):
# get all files in each directory
filesInDir = os.listdir(currentValidationDir)
for i, n in enumerate(filesInDir):
# when the log has been found, collect the final numbers
if "trainingScores_log" in n:
log_file = os.path.join(currentValidationDir, n)
with open(log_file) as f:
for line in f:
pass
final_stats = final_stats + line
data_string = StringIO(final_stats)
data_full = pd.read_csv(data_string, sep=",")
del data_full["Epoch"] # no need for epoch
data_full.to_csv(outputFile, index=False) # save updated data
# perform deep copy
data_loss = data_full.copy()
data_dice = data_full.copy()
# set the datasets that need to be plotted
cols = [
"Train",
"Val",
"Testing",
]
for i in cols:
del data_dice[i + "_Loss"] # keep only dice
del data_loss[i + "_Dice"] # keep only loss
# rename the columns
data_loss.rename(columns={i + "_Loss": i}, inplace=True)
# rename the columns
data_dice.rename(columns={i + "_Dice": i}, inplace=True)
# set plot properties
fig, axes = plt.subplots(nrows=1, ncols=2, constrained_layout=True)
# plot the data
bplot = sns.boxplot(
data=data_dice, width=0.5, palette="colorblind", ax=axes[0]
)
# set limits for y-axis for proper visualization
bplot.set(ylim=(0, 1))
# set labels
bplot.set(xlabel="Dataset", ylabel="Dice", title="Dice plot")
# rotate so that everything is visible
bplot.set_xticklabels(bplot.get_xticklabels(), rotation=15, ha="right")
# plot the data
bplot = sns.boxplot(
data=data_loss, width=0.5, palette="colorblind", ax=axes[1]
)
# set limits for y-axis for proper visualization
bplot.set(ylim=(0, 1))
# set labels
bplot.set(xlabel="Dataset", ylabel="Loss", title="Loss plot")
# rotate so that everything is visible
bplot.set_xticklabels(bplot.get_xticklabels(), rotation=15, ha="right")
plt.savefig(outputPlot, dpi=600)
print("Plots saved successfully.")
# main function
if __name__ == "__main__":
main()