added comparison_matrix utils

cami_src/utils/comparison_matrix.py

+import pandas as pd
+import seaborn as sb
+import matplotlib.pyplot as plt
+
+
+def make_comparison_matrix(result_gene_sets):
+    n_results = len(result_gene_sets)
+    comparison_matrix = pd.DataFrame([[int(0) for _ in range(n_results)] for __ in range(n_results)],
+                                      columns = list(result_gene_sets.keys()),
+                                      index = list(result_gene_sets.keys()),
+                                      dtype=int)
+    for algo1 in result_gene_sets:
+        for algo2 in result_gene_sets:
+            comparison_matrix.loc[algo1,algo2] = int(len(result_gene_sets[algo1].intersection(result_gene_sets[algo2])))
+    return comparison_matrix
+
+def plot_comparison_matrix(comparison_matrix, title='', n_rows=-1):
+    """plot the comparison matrix
+
+    Args:
+        comparison_matrix (DataFrame): comparison matrix that compares the result_gene_sets of all algorithms to each other
+        n_rows (int, optional): number of rows to plot. Defaults to -1.
+    Returns:
+        fig1, ax2: matplotlib figure and axis of the comparison matrix
+        fig2, ax2: matplotlib figure and axis of the normalized comparison matrix
+    """
+    comparison_matrix_slice = comparison_matrix.iloc[:n_rows]
+    fig1, ax1 = plt.subplots(figsize=(20,10))
+    ax1 = sb.heatmap(comparison_matrix_slice, annot=True, fmt='g')
+    ax1.set_title(f'{title.capitalize()}')
+    
+    fig2, ax2 = plt.subplots(figsize=(20,20))
+    comparison_matrix_slice_normalized = comparison_matrix_slice.apply(lambda row: row/row.max(), axis=1)
+    ax2 = sb.heatmap(comparison_matrix_slice_normalized, annot=True, fmt='.2f')
+    ax2.set_title(f'Normalized {title}')
+    return fig1,ax1, fig2,ax2
+