finish script to plot generic metrics

aimodel/src/plot_metrics.py

@@ -1,19 +1,40 @@
+#!/usr/bin/env python3
+
 import os
 import seaborn as sns
+import matplotlib.pyplot as plt
 import pandas as pd
 
-def plot_metric(train, val, name, dir_output):
-	plt.plot(train, label=f"train_{name}")
-	plt.plot(val, label=f"val_{name}")
-	plt.title(name)
-	plt.xlabel("epoch")
-	plt.ylabel(name)
-	plt.savefig(os.path.join(dir_output, f"{name}.png"))
-	plt.close()
+def plot_metric(ax, train, val, name, dir_output):
+	ax.plot(train, label=f"train_{name}")
+	ax.plot(val, label=f"val_{name}")
+	ax.set_title(name)
+	ax.set_xlabel("epoch")
+	ax.set_ylabel(name)
+	# plt.savefig(os.path.join(dir_output, f"{name}.png"))
+	# plt.close()
 
 FILEPATH_INPUT = os.environ["INPUT"]
 DIRPATH_OUTPUT = os.environ["OUTPUT"] if "OUTPUT" in os.environ else os.getcwd()
 
 
-df = pd.read_csv(FILEPATH_INPUT)
+df = pd.read_csv(FILEPATH_INPUT, sep="\t")
 
+fig = plt.figure(figsize=(10,13))
+for i, colname in enumerate(filter(lambda colname: colname != "epoch" and not colname.startswith("val_"), df.columns.values.tolist())):
+	train = df[colname]
+	val = df[f"val_{colname}"]
+	
+	colname_display = colname.replace("metric_dice_coefficient", "dice coefficient") \
+		.replace("one_hot_mean_iou", "mean iou")
+	
+	ax = fig.add_subplot(3, 2, i+1)
+	
+	plot_metric(ax, train, val, name=colname_display, dir_output=DIRPATH_OUTPUT)
+
+fig.tight_layout()
+
+target=os.path.join(DIRPATH_OUTPUT, f"metrics.png")
+plt.savefig(target)
+
+print(f">>> Saved to {target}")