Optimizations to LINGER

code/lingergrn-1.106/LingerGRN/Benchmk.py

@@ -1,98 +1,114 @@
-import seaborn as sns
 import matplotlib.colors as mcolors
+import seaborn as sns
+
+
 def generate_colors(N):
     """
     Generate N visually appealing colors using seaborn color palette.
-    
+
     Args:
         N (int): The number of colors to generate.
-    
+
     Returns:
         list: A list of N RGB tuples representing the generated colors.
     """
     color_palette = sns.color_palette("husl", N)
     colors = [mcolors.rgb2hex(color_palette[i]) for i in range(N)]
     return colors
 
-def bm_trans(TFName,Method_name,Groundtruth,Infer_trans,outdir,filetype):
-    import pandas as pd
-    import numpy as np
+
+def bm_trans(TFName, Method_name, Groundtruth, Infer_trans, outdir, filetype):
     import matplotlib.pyplot as plt
-    from sklearn.metrics import roc_curve, roc_auc_score
+    import numpy as np
+    import pandas as pd
     import seaborn as sns
-# Set the working directory
-# Use the appropriate path for your system
+    from sklearn.metrics import roc_auc_score, roc_curve
+
+    # Set the working directory
+    # Use the appropriate path for your system
     import os
-    data0=pd.read_csv(Groundtruth,sep='\t', skiprows=5,header=0)
-    data1=data0.groupby(['symbol'])['score'].max()
-    label=data1.sort_values(axis=0, ascending=False)
-    label=label.reset_index()
+
+    data0 = pd.read_csv(Groundtruth, sep="\t", skiprows=5, header=0)
+    data1 = data0.groupby(["symbol"])["score"].max()
+    label = data1.sort_values(axis=0, ascending=False)
+    label = label.reset_index()
     N = 1000  # top 500 TG as the ground truth
-    label = label['symbol'].iloc[:N]
+    label = label["symbol"].iloc[:N]
     # Load data
-    colors=generate_colors(len(Infer_trans))
+    colors = generate_colors(len(Infer_trans))
     for i in range(len(Infer_trans)):
-        if filetype=='list':
-            data2 = pd.read_csv(Infer_trans[i], sep='\t')
-        #label = pd.read_csv('~/SC_NET/all_data/result/LL_sc/max/' + info0['id'][i] + '_gene_score_5fold.txt', header=True)
-            loc = data2[data2['TF'] == TFName].index
-            TGName = data2['TG'].values[loc].tolist()
+        if filetype == "list":
+            data2 = pd.read_csv(Infer_trans[i], sep="\t")
+            # label = pd.read_csv('~/SC_NET/all_data/result/LL_sc/max/' + info0['id'][i] + '_gene_score_5fold.txt', header=True)
+            loc = data2[data2["TF"] == TFName].index
+            TGName = data2["TG"].values[loc].tolist()
             TGset = TGName.copy()
-            Score = data2['score'].values[loc]
-        if filetype=='matrix':
-            data2=pd.read_csv(Infer_trans[i], sep='\t',header=0,index_col=0)
-            TGset=data2.index
-            Score=data2[TFName].values
+            Score = data2["score"].values[loc]
+        if filetype == "matrix":
+            data2 = pd.read_csv(Infer_trans[i], sep="\t", header=0, index_col=0)
+            TGset = data2.index
+            Score = data2[TFName].values
         d1 = np.zeros(len(TGset))
         loc = np.where(np.isin(TGset, label))[0]
         d1[loc] = 1
         fpr, tpr, thresholds = roc_curve(d1, Score)
-# Compute AUC
+        # Compute AUC
         auc = roc_auc_score(d1, Score)
-    # Plot the ROC curve
-        plt.plot(fpr, tpr, color=colors[i], label=Method_name[i]+': (AUC = %0.2f)' % auc)
-        plt.plot([0, 1], [0, 1], color='black', linestyle='--')
+        # Plot the ROC curve
+        plt.plot(
+            fpr, tpr, color=colors[i], label=Method_name[i] + ": (AUC = %0.2f)" % auc
+        )
+        plt.plot([0, 1], [0, 1], color="black", linestyle="--")
         plt.xlim([0.0, 1.0])
         plt.ylim([0.0, 1.05])
-        plt.xlabel('False Positive Rate')
-        plt.ylabel('True Positive Rate')
-        plt.title('Receiver Operating Characteristic')
+        plt.xlabel("False Positive Rate")
+        plt.ylabel("True Positive Rate")
+        plt.title("Receiver Operating Characteristic")
         plt.legend(loc="lower right")
-# Display the plot in the notebook
-    plt.savefig(outdir+"trans_roc_curve"+TFName+".png", format='png', bbox_inches='tight')
+    # Display the plot in the notebook
+    plt.savefig(
+        outdir + "trans_roc_curve" + TFName + ".png", format="png", bbox_inches="tight"
+    )
     plt.show()
     plt.close()
-    from sklearn.metrics import precision_recall_curve
-    from sklearn.metrics import average_precision_score
+    from sklearn.metrics import average_precision_score, precision_recall_curve
+
     for i in range(len(Infer_trans)):
-        if filetype=='list':
-            data2 = pd.read_csv(Infer_trans[i], sep='\t')
-        #label = pd.read_csv('~/SC_NET/all_data/result/LL_sc/max/' + info0['id'][i] + '_gene_score_5fold.txt', header=True)
-            loc = data2[data2['TF'] == TFName].index
-            TGName = data2['TG'].values[loc].tolist()
+        if filetype == "list":
+            data2 = pd.read_csv(Infer_trans[i], sep="\t")
+            # label = pd.read_csv('~/SC_NET/all_data/result/LL_sc/max/' + info0['id'][i] + '_gene_score_5fold.txt', header=True)
+            loc = data2[data2["TF"] == TFName].index
+            TGName = data2["TG"].values[loc].tolist()
             TGset = TGName.copy()
-            Score = data2['score'].values[loc]
-        if filetype=='matrix':
-            data2=pd.read_csv(Infer_trans[i], sep='\t',header=0,index_col=0)
-            TGset=data2.index
-            Score=data2[TFName].values
+            Score = data2["score"].values[loc]
+        if filetype == "matrix":
+            data2 = pd.read_csv(Infer_trans[i], sep="\t", header=0, index_col=0)
+            TGset = data2.index
+            Score = data2[TFName].values
         d1 = np.zeros(len(TGset))
         loc = np.where(np.isin(TGset, label))[0]
         d1[loc] = 1
-# Assuming you have the true labels (y_true) and predicted probabilities (y_scores) for your classifier
-# Calculate the average precision score (AUPR
+        # Assuming you have the true labels (y_true) and predicted probabilities (y_scores) for your classifier
+        # Calculate the average precision score (AUPR
         aupr = average_precision_score(d1, Score)
-        auprr=aupr*len(d1)/sum(d1)
-# Assuming you have the true labels (y_true) and predicted probabilities (y_scores) for your classifier
-# Calculate precision and recall values
+        auprr = aupr * len(d1) / sum(d1)
+        # Assuming you have the true labels (y_true) and predicted probabilities (y_scores) for your classifier
+        # Calculate precision and recall values
         precision, recall, _ = precision_recall_curve(d1, Score)
-    # Plot precision-recall curve
-        plt.plot(recall, precision, color=colors[i], label=Method_name[i]+': (AUPR ratio = %0.2f)' % auprr)
+        # Plot precision-recall curve
+        plt.plot(
+            recall,
+            precision,
+            color=colors[i],
+            label=Method_name[i] + ": (AUPR ratio = %0.2f)" % auprr,
+        )
         plt.ylim([0.0, 0.6])
-        plt.xlabel('Recall')
-        plt.ylabel('Precision')
-        plt.title('Precision-Recall Curve')
-        plt.legend(loc='upper right')
-    plt.savefig(outdir+"trans_pr_curve"+TFName+".png", format='png', bbox_inches='tight')
+        plt.xlabel("Recall")
+        plt.ylabel("Precision")
+        plt.title("Precision-Recall Curve")
+        plt.legend(loc="upper right")
+    plt.savefig(
+        outdir + "trans_pr_curve" + TFName + ".png", format="png", bbox_inches="tight"
+    )
     plt.show()
-    plt.close()
+    plt.close()