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plot_metrics_multi: add option to plot train/val separately

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Starbeamrainbowlabs 2023-07-20 15:49:09 +01:00
parent b5b26d980b
commit e44b5533b1
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
1 changed files with 27 additions and 6 deletions
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33
aimodel/src/plot_metrics_multi.py
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@ -33,15 +33,29 @@ def plot_metric(ax, train_list, val_list, metric_name, model_names, dir_output):
# plt.savefig(os.path.join(dir_output, f"{name}.png")) # plt.savefig(os.path.join(dir_output, f"{name}.png"))
# plt.close() # plt.close()
def plot_metric_single(ax, data_list, metric_name, model_names, dir_output):
i = 0
for item in data_list:
ax.plot(item, label=model_names[i], linewidth=1)
i += 1
ax.set_title(metric_name)
ax.set_xlabel("epoch")
ax.set_ylabel(metric_name)
# plt.savefig(os.path.join(dir_output, f"{name}.png"))
# plt.close()
def make_dfs(filepaths_input): def make_dfs(filepaths_input):
dfs = [] dfs = []
for filepath_input in filepaths_input: for filepath_input in filepaths_input:
print("DEBUG filepath_input", filepath_input) print("DEBUG filepath_input", filepath_input)
dfs = pd.read_csv(filepath_input, sep="\t") dfs = pd.read_csv(filepath_input, sep="\t")
def plot_metrics(filepaths_input, model_names, dirpath_output, resolution=1):
def plot_metrics(filepaths_input, model_names, dirpath_output, resolution=1, train_val_separate=False):
dfs = [ pd.read_csv(filepath_input, sep="\t") for filepath_input in filepaths_input ] dfs = [ pd.read_csv(filepath_input, sep="\t") for filepath_input in filepaths_input ]
matplotlib.rcParams.update({'font.size': 15*resolution}) matplotlib.rcParams.update({'font.size': 15*resolution*(0.5 if train_val_separate else 1)})
fig = plt.figure(figsize=(10*resolution, 14*resolution)) fig = plt.figure(figsize=(10*resolution, 14*resolution))
for i, colname in enumerate(filter(lambda colname: colname != "epoch" and not colname.startswith("val_"), dfs[0].columns.values.tolist())): for i, colname in enumerate(filter(lambda colname: colname != "epoch" and not colname.startswith("val_"), dfs[0].columns.values.tolist())):
train = [ df[colname] for df in dfs ] train = [ df[colname] for df in dfs ]
@ -50,9 +64,15 @@ def plot_metrics(filepaths_input, model_names, dirpath_output, resolution=1):
colname_display = colname.replace("metric_dice_coefficient", "dice coefficient") \ colname_display = colname.replace("metric_dice_coefficient", "dice coefficient") \
.replace("one_hot_mean_iou", "mean iou") .replace("one_hot_mean_iou", "mean iou")
ax = fig.add_subplot(3, 2, i+1) if train_val_separate:
ax = fig.add_subplot(3*2, 2*2, (i+1)*2 - 1)
plot_metric_single(ax, train, metric_name=colname_display, model_names=model_names, dir_output=dirpath_output)
ax = fig.add_subplot(3*2, 2*2, (i+1)*2)
plot_metric_single(ax, val, metric_name=f"val_{colname_display}", model_names=model_names, dir_output=dirpath_output)
else:
ax = fig.add_subplot(3 * 2, 2, i+1)
plot_metric(ax, train, val, metric_name=colname_display, model_names=model_names, dir_output=dirpath_output)
plot_metric(ax, train, val, metric_name=colname_display, model_names=model_names, dir_output=dirpath_output)
# Ref https://stackoverflow.com/a/57484812/1460422 # Ref https://stackoverflow.com/a/57484812/1460422
@ -91,7 +111,7 @@ Usage:
echo -e "filepathA\\nfilepathB..." | [OUTPUT="path/to/output_dir"] [REGEX_NAME=''] path/to/plot_metrics_multi.py echo -e "filepathA\\nfilepathB..." | [OUTPUT="path/to/output_dir"] [REGEX_NAME=''] path/to/plot_metrics_multi.py
""") """)
sys.exit() sys.exit()
TRAIN_VAL_SEPARATE = True if "TRAIN_VAL_SEPARATE" in os.environ else False
REGEX_NAME = os.environ["REGEX_NAME"] if "REGEX_NAME" in os.environ else None REGEX_NAME = os.environ["REGEX_NAME"] if "REGEX_NAME" in os.environ else None
if REGEX_NAME is None and len(sys.argv) >= 1: if REGEX_NAME is None and len(sys.argv) >= 1:
REGEX_NAME = sys.argv[1] REGEX_NAME = sys.argv[1]
@ -126,6 +146,7 @@ Usage:
FILEPATHS_INPUT, FILEPATHS_INPUT,
MODEL_NAMES, MODEL_NAMES,
DIRPATH_OUTPUT, DIRPATH_OUTPUT,
resolution=float(os.environ["RESOLUTION"]) if "RESOLUTION" in os.environ else 1 resolution=float(os.environ["RESOLUTION"]) if "RESOLUTION" in os.environ else 1,
train_val_separate=TRAIN_VAL_SEPARATE
) )