fixed PR curve and added images folder

generate_figures.py

@@ -55,119 +55,132 @@ 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_category_data = defaultdict(list)
-
-    for species in species_list:
-        overlapping_path = Path(
-            file_directory, f"{species}/overlapping_neighbor_data.csv"
-        )
-        hypergeometric_path = Path(
-            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]
-
-        methods = []
-        for path in species_path:
-            data = read_file(path)
-            methods.append(data)
-
-        # calculate AUC values
-        fpr_list = []
-        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)
-            fpr_list.append(fpr)
-            tpr_list.append(tpr)
-            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_category_data[species].append(species_data)
-
-    # Create a figure with 2 subplots (one for each species)
-    fig, axes = plt.subplots(2, 3, figsize=(18, 10))  # Create a 2x3 grid of subplots
-    axes = axes.flatten()
-    colors = ["red", "green", "blue", "orange", "purple"]
+    file_directories = [
+        "./results/final-non-inferred-complete/",
+        "./results/final-inferred-complete/",
+    ]
+    subplot_titles = ["Complete Non Inferred Networks", "Complete Inferred Networks"]
+    k = 0
+    for directory in file_directories:
+        final_category_data = defaultdict(list)
 
-    for idx, species in enumerate(species_list):
-        ax = axes[idx]  # Get the subplot axis for the current species
-
-        for i in range(len(final_category_data[species][0]["method"])):
-            create_plot(
-                ax,
-                final_category_data[species][0]["fpr"][i],
-                final_category_data[species][0]["tpr"][i],
-                final_category_data[species][0]["roc"][i],
-                final_category_data[species][0]["method"][i],
-                colors[i],
+        for species in species_list:
+            overlapping_path = Path(
+                directory, f"{species}/overlapping_neighbor_data.csv"
             )
+            hypergeometric_path = Path(
+                directory, f"{species}/hypergeometric_distribution.csv"
+            )
+            degree_path = Path(directory, f"{species}/protein_degree_v3_data.csv")
+            rw_path = Path(directory, f"{species}/random_walk_data_v2.csv")
 
-        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"{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()
+            species_path = [overlapping_path, hypergeometric_path, degree_path, rw_path]
 
-    fig, axes = plt.subplots(2, 3, figsize=(18, 10))  # Create a 2x3 grid of subplots
-    axes = axes.flatten()
-    colors = ["red", "green", "blue", "orange", "purple"]
+            methods = []
+            for path in species_path:
+                data = read_file(path)
+                methods.append(data)
 
-    for idx, species in enumerate(species_list):
-        ax = axes[idx]  # Get the subplot axis for the current species
-
-        for i in range(len(final_category_data[species][0]["method"])):
-            create_plot(
-                ax,
-                final_category_data[species][0]["precision"][i],
-                final_category_data[species][0]["recall"][i],
-                final_category_data[species][0]["pr"][i],
-                final_category_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")
+            # calculate AUC values
+            fpr_list = []
+            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)
+                fpr_list.append(fpr)
+                tpr_list.append(tpr)
+                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)
 
-    axes[5].set_visible(False)
-    fig.suptitle(
-        "Precision/Recall Curve for All Species w/ Complete Inferred Networks",
-        fontsize=20,
-    )
-    plt.tight_layout()
-    plt.show()
+            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_category_data[species].append(species_data)
+
+        # Create a figure with 2 subplots (one for each species)
+        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_category_data[species][0]["method"])):
+                create_plot(
+                    ax,
+                    final_category_data[species][0]["fpr"][i],
+                    final_category_data[species][0]["tpr"][i],
+                    final_category_data[species][0]["roc"][i],
+                    final_category_data[species][0]["method"][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"{species.capitalize()}")
+            ax.legend(loc="lower right")
+
+        axes[5].set_visible(False)
+        fig.suptitle("ROC Curve for All Species w/ " + subplot_titles[k], fontsize=20)
+        plt.savefig(Path("./results/images/", f"roc_{subplot_titles[k].lower().replace(" ", "_")}"))
+        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_category_data[species][0]["method"])):
+                create_plot(
+                    ax,
+                    final_category_data[species][0]["recall"][i],
+                    final_category_data[species][0]["precision"][i],
+                    final_category_data[species][0]["pr"][i],
+                    final_category_data[species][0]["method"][i],
+                    colors[i],
+                )
+
+            ax.set_xlim([0.0, 1.0])
+            ax.set_ylim([0.0, 1.05])
+            ax.set_xlabel("Recall")
+            ax.set_ylabel("Precision")
+            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/ " + subplot_titles[k],
+            fontsize=20,
+        )
+        plt.tight_layout()
+        plt.savefig(Path("./results/images/", f"pr_{subplot_titles[k].lower().replace(" ", "_")}"))
+        plt.show()
+        k += 1
 
     # generate RW figures
 
@@ -178,6 +191,12 @@ def main():
         "./results/final-rw-non-inferred-regular/",
         "./results/final-rw-non-inferred-pro-go/",
     ]
+    subplot_titles = [
+        "Inferred Complete Network",
+        "Inferred ProGo Network",
+        "Non Inferred Complete Network",
+        "Non Inferred ProGo Network",
+    ]
     final_rw_data = defaultdict(list)
 
     # Load data for each directory and species
@@ -222,10 +241,15 @@ def main():
         ax.set_ylim([0.0, 1.05])
         ax.set_xlabel("False Positive Rate")
         ax.set_ylabel("True Positive Rate")
-        ax.set_title(f"{directory}")
+        ax.set_title(f"{subplot_titles[idx]}")
         ax.legend(loc="lower right")
 
     # Adjust layout and show the plot
+    fig.suptitle(
+        "ROC Curve for RandomWalk Configuration",
+        fontsize=20,
+    )
+    plt.savefig(Path("./results/images/rw_roc.png"))
     plt.tight_layout()
     plt.show()
 
@@ -239,21 +263,26 @@ def main():
         ax = axs[idx]  # Get the corresponding subplot
         for i, species in enumerate(species_list):
             ax.plot(
-                final_rw_data[species][idx]["precision"],
                 final_rw_data[species][idx]["recall"],
+                final_rw_data[species][idx]["precision"],
                 color=colors[i],
                 lw=2,
                 label=f"{species} (area = %0.2f)" % final_rw_data[species][idx]["roc"],
             )
 
         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"{directory}")
+        ax.set_xlabel("Recall")
+        ax.set_ylabel("Precision")
+        ax.set_title(f"{subplot_titles[idx]}")
         ax.legend(loc="lower right")
 
     # Adjust layout and show the plot
+    fig.suptitle(
+        "Precision/Recall Curve for RandomWalk Configuration",
+        fontsize=20,
+    )
+    plt.savefig(Path("./results/images/rw_pr.png"))
     plt.tight_layout()
     plt.show()