From 649975f0c878e6e08b9223858ef5af1d61904e4c Mon Sep 17 00:00:00 2001
From: Daniel Yang <t5wol3yv@duck.com>
Date: Thu, 20 Mar 2025 09:10:03 +0100
Subject: [PATCH] refactored code
---
code/machine_learning_models/utilities.py | 7 ++++---
1 file changed, 4 insertions(+), 3 deletions(-)
diff --git a/code/machine_learning_models/utilities.py b/code/machine_learning_models/utilities.py
index cfbca48..5c409f0 100644
--- a/code/machine_learning_models/utilities.py
+++ b/code/machine_learning_models/utilities.py
@@ -12,7 +12,7 @@ show_plots = False
# Plots
-def heat_map(df, model_name=None):
+def heat_map(df: pd.DataFrame, model_name=None):
"""
Generates a heatmap of the correlation matrix for numerical features in the DataFrame.
@@ -39,7 +39,7 @@ def plot_counts(model_name=None):
if show_plots:
plt.show()
-def plot_xy(df, x, y, model_name=None):
+def plot_xy(df: pd.DataFrame, x, y, model_name=None):
"""
Creates a scatter plot for two numerical columns.
@@ -113,8 +113,9 @@ def plot_precision_recall_curve(precision, recall, model_name=None):
A good curve is mostly at the top and right.
"""
+ prc_auc = auc(recall, precision)
plt.figure(figsize=(8, 6))
- plt.plot(recall, precision, marker='.', label="Precision-Recall Curve")
+ plt.plot(recall, precision, color = 'darkorange', marker = '.', lw = 2, label = f"Precision Recall Curve (area = {prc_auc:.2f})")
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.title("Precision-Recall Curve")
--
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