Plotting codes for model evaluation

model_eval.py

@@ -0,0 +1,29 @@
+# -- coding: utf-8 -*-
+"""
+This script takes the model metric results stored as csv and generates plots to
+visualize the performance based on the choice of hyperparameters.
+"""
+
+import pandas as pd
+
+from visualize import plot
+
+def main():
+    metric = pd.read_csv("/app/workdir/metrics.csv")
+    print(metric.head())
+    hyperparam_keys = metric.columns.tolist()
+    metric_keys = ["test_accuracy", "test_loss"]
+    [hyperparam_keys.remove(key) for key in metric_keys]
+
+    for key in hyperparam_keys:
+        for metric_key in metric_keys:
+            plot.lineplot(
+                data=metric,
+                x=key,
+                y=metric_key,
+            )
+
+if __name__ == "__main__":
+    main()
+
+# EOF

visualize/plot.py

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+# --*- coding: utf-8 -*-
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def lineplot(data=None, x=None, y=None):
+    if data is None or x is None or y is None:
+        raise ValueError("Data, x, and y parameters must be provided.")
+
+    sns.lineplot(data=data, x=x, y=y)
+    plt.title(f"{y} by {x}")
+    plt.xlabel(x)
+    plt.ylabel(y)
+    plt.savefig(f"/app/workdir/figures/lineplot_{y}_by_{x}.png")
+    plt.close()
+
+
+# EOF