added PR curve figure

generate_figures.py

@@ -3,7 +3,7 @@
 
 from matplotlib import pyplot as plt
 import numpy as np
-from sklearn.metrics import auc, roc_curve
+from sklearn.metrics import auc, precision_recall_curve, roc_curve
 
 
 def read_file(filepath: str) -> dict:
@@ -33,6 +33,15 @@ def get_roc_data(data_df: dict) -> list:
     return fpr, tpr, threshold, roc_auc
 
 
+def get_pr_data(data_df: dict) -> list:
+    y = np.array(data_df["y_true"])
+    scores = np.array(data_df["y_score"])
+    precision, recall , _= precision_recall_curve(y, scores)
+    pr_auc = auc(recall, precision)
+
+    return precision, recall, pr_auc
+
+
 def create_plot(ax, x_data: list, y_data: list, auc: float, type: str, color) -> None:
     ax.plot(
         x_data,
@@ -46,21 +55,18 @@ def create_plot(ax, x_data: list, y_data: list, auc: float, type: str, color) ->
 def main():
     print("Generating figures")
     species_list = ["elegans", "fly", "bsub", "yeast", "zfish"]
+    file_directory = "./results/final-non-inferred-complete/"
     final_data = defaultdict(list)
 
     for species in species_list:
         overlapping_path = Path(
-            f"./results/final-inferred-complete/{species}/overlapping_neighbor_data.csv"
+            file_directory, f"{species}/overlapping_neighbor_data.csv"
         )
         hypergeometric_path = Path(
-            f"./results/final-inferred-complete/{species}/hypergeometric_distribution.csv"
-        )
-        degree_path = Path(
-            f"./results/final-inferred-complete/{species}/protein_degree_v3_data.csv"
-        )
-        rw_path = Path(
-            f"./results/final-inferred-complete/{species}/random_walk_data_v2.csv"
+            file_directory, f"{species}/hypergeometric_distribution.csv"
         )
+        degree_path = Path(file_directory, f"{species}/protein_degree_v3_data.csv")
+        rw_path = Path(file_directory, f"{species}/random_walk_data_v2.csv")
 
         species_path = [overlapping_path, hypergeometric_path, degree_path, rw_path]
 
@@ -74,6 +80,9 @@ def main():
         tpr_list = []
         threshold_list = []
         roc_auc_list = []
+        precision_list = []
+        recall_list = []
+        pr_auc_list = []
 
         for data in methods:
             fpr, tpr, threshold, roc_auc = get_roc_data(data)
@@ -82,10 +91,18 @@ def main():
             threshold_list.append(threshold)
             roc_auc_list.append(roc_auc)
 
+            precision, recall, pr_auc = get_pr_data(data)
+            precision_list.append(precision)
+            recall_list.append(recall)
+            pr_auc_list.append(pr_auc)
+
         species_data = {
             "fpr": fpr_list,
             "tpr": tpr_list,
             "roc": roc_auc_list,
+            "precision": precision_list,
+            "recall": recall_list,
+            "pr": pr_auc_list,
             "method": ["Overlapping", "Hypergeometric", "Degree", "RW"],
         }
         final_data[species].append(species_data)
@@ -105,17 +122,50 @@ def main():
                 final_data[species][0]["tpr"][i],
                 final_data[species][0]["roc"][i],
                 final_data[species][0]["method"][i],
-                colors[i]
+                colors[i],
             )
 
         ax.set_xlim([0.0, 1.0])
         ax.set_ylim([0.0, 1.05])
         ax.set_xlabel("False Positive Rate")
         ax.set_ylabel("True Positive Rate")
-        ax.set_title(f"ROC Curve for {species.capitalize()}")
+        ax.set_title(f"{species.capitalize()}")
+        ax.legend(loc="lower right")
+
+    axes[5].set_visible(False)
+    fig.suptitle("ROC Curve for All Species w/ Complete Inferred Networks", fontsize=20)
+    plt.tight_layout()
+    plt.show()
+
+    fig, axes = plt.subplots(2, 3, figsize=(18, 10))  # Create a 2x3 grid of subplots
+    axes = axes.flatten()
+    colors = ["red", "green", "blue", "orange", "purple"]
+
+    for idx, species in enumerate(species_list):
+        ax = axes[idx]  # Get the subplot axis for the current species
+
+        for i in range(len(final_data[species][0]["method"])):
+            create_plot(
+                ax,
+                final_data[species][0]["precision"][i],
+                final_data[species][0]["recall"][i],
+                final_data[species][0]["pr"][i],
+                final_data[species][0]["method"][i],
+                colors[i],
+            )
+
+        ax.set_xlim([0.0, 1.0])
+        ax.set_ylim([0.0, 1.05])
+        ax.set_xlabel("Precision")
+        ax.set_ylabel("Recall")
+        ax.set_title(f"{species.capitalize()}")
         ax.legend(loc="lower right")
 
     axes[5].set_visible(False)
+    fig.suptitle(
+        "Precision/Recall Curve for All Species w/ Complete Inferred Networks",
+        fontsize=20,
+    )
     plt.tight_layout()
     plt.show()