added seedvar script

cami_src/evaluation_scripts/seed_variation_script.py

+import matplotlib.pyplot as plt
+import seaborn as sb
+import pandas as pd
+import os
+import random
+from cami_suite import cami
+import utils.comparison_matrix as comparison_matrix
+import numpy as np
+from utils import kolmogorov_smirnoff
+
+def predict_and_make_consensus(cami, vis=False):
+    result_sets = cami.make_predictions()
+    cami.create_consensus(result_sets, save_output=False)
+    if vis:
+        n_results = len(cami.result_gene_sets)
+        cami.visualize_and_save_comparison_matrix()
+        if vis:
+            cami.use_nvenn(download=True)
+            
+def make_seedvariation(cami, n_iterations, removal_frac=0.2, vis=False, plot=True):
+    identifier = cami.uid
+    base_seeds = cami.origin_seed_lst
+    original_seeds = [cami.ppi_vertex2gene[seed] for seed in base_seeds]
+    print(f'All given seeds:{original_seeds}')
+    
+    random.seed(50)
+    removal_frac = removal_frac
+    nof_iterations = int(n_iterations)
+    used_tools = list(cami.result_gene_sets.keys())
+    prediction_tools = cami.prediction_tools
+    nof_seeds = len(base_seeds)
+    nof_removals = max([int(nof_seeds * removal_frac), 1])
+    
+    redisc_seeds_file = f'{cami.output_dir}/00_seedvariation_rediscovered_seeds.tsv'
+    result_table_file = f'{cami.output_dir}/00_seedvariation_result_table.tsv'
+    n_results = len(cami.result_gene_sets)
+
+    redisc_intersection_matrix = pd.DataFrame([[0 for _ in range(n_results)] for __ in range(n_results)],
+                                                columns = list(cami.result_gene_sets.keys()),
+                                                index = list(cami.result_gene_sets.keys()),
+                                                dtype=int)
+    
+    with open(redisc_seeds_file, 'w') as redisc_table:
+        with open(result_table_file, 'w') as res_table:
+            redisc_table.write('id')
+            for tool in used_tools:
+                redisc_table.write(f'\t{tool}')                    
+            redisc_table.write('\n')
+            res_table.write('tool\trdr\trdr_std\tsensitivity\tsensitivity_std\tprecision\tprecision_std')
+            for tool in prediction_tools:
+                res_table.write(f'\t{tool}_rdr_ks_pvalue')
+            for tool in prediction_tools:
+                res_table.write(f'\t{tool}_msr_ks_pvalue')
+            
+            with open(os.path.join(cami.tmp_dir, f'{used_tools[0]}_{cami.uid}_relevance_scores.tsv)'), 'r') as f:
+                for line in f:
+                    val_name = line.split('\t')[0]
+                    redisc_table.write(f'\t{val_name}')
+            res_table.write('\n')
+            # result dictionaries of the form {tool:list(value for each iteration)}
+            
+            tp_rate_dict = {k:list() for k in used_tools}
+            redisc_rate_dict = {k:list() for k in used_tools}
+            module_size_dict = {k:list() for k in used_tools}
+            
+            # removed and used seeds per iteration
+            all_removed_seeds = list()
+            all_used_seeds = list()
+            
+            all_redisc_seeds = []
+            
+            for ident in range(nof_iterations):
+                redisc_table.write(f'{ident}')
+                # update uid
+                new_identifier = identifier + f'_{ident}'
+                # reset cami
+                cami.reset_cami(new_uid=new_identifier)
+#               cami.ppi_graph = original_ppi
+                
+                #remove seeds (again)
+                print(f'Removing {nof_removals} seeds from the original seed list...')
+                removed_seeds_idx = random.sample(list(range(nof_seeds)), nof_removals)
+                removed_seeds = cami.remove_seeds(removed_seeds_idx)
+                rem_seeds = [cami.ppi_vertex2gene[seed] for seed in removed_seeds]
+                print(f'Removed: {rem_seeds} from the seed list')
+                print('Updating tools and repeat CAMI')
+                # reinitialize tools
+                cami.initialize_all_tools()
+                
+                # repeat consensus
+                if ident%20==0:     
+                    predict_and_make_consensus(cami)
+                else:
+                    predict_and_make_consensus(cami)
+                
+                used_seeds = [cami.ppi_vertex2gene[seed] for seed in cami.seed_lst]
+                
+                redisc_seeds_dict = {}
+                result_dict = cami.result_gene_sets
+                
+                for tool in result_dict:
+                    nof_predictions = len(result_dict[tool]) + len(used_seeds)
+                    redisc_seeds = set(result_dict[tool]).intersection(set(rem_seeds))
+                    redisc_prev = len(redisc_seeds)
+                    redisc_rate = redisc_prev / nof_removals
+                    redisc_rate_dict[tool].append(redisc_rate)
+                    redisc_seeds_dict[tool] = redisc_seeds
+                    tp_rate = redisc_prev / len(removed_seeds)
+                    tp_rate_dict[tool].append(tp_rate)
+                    module_size_frac = redisc_prev / nof_predictions
+                    assert module_size_frac <= 1
+                    module_size_dict[tool].append(module_size_frac)
+                    redisc_table.write('\t')
+                    for idx,seed in enumerate(redisc_seeds):
+                        if idx == 0:
+                            redisc_table.write(f'{list(redisc_seeds)[0]}')
+                        else:
+                            redisc_table.write(f',{seed}') 
+                    print(f'{tool} rediscovered {redisc_seeds} after removing {rem_seeds}.')
+                all_redisc_seeds.append(redisc_seeds_dict)
+                redisc_table.write('\n')
+                all_used_seeds.append(used_seeds)
+                all_removed_seeds.append(rem_seeds)
+                for algo1 in redisc_seeds_dict:
+                    for algo2 in redisc_seeds_dict:
+                        redisc_intersection_matrix.loc[algo1,algo2] += len(redisc_seeds_dict[algo1].intersection(redisc_seeds_dict[algo2]))
+
+            for tool in redisc_rate_dict:
+                res_table.write(f'{tool}\t')
+                res_table.write(f'{np.mean(redisc_rate_dict[tool])}\t')
+                res_table.write(f'{np.std(redisc_rate_dict[tool])}\t')
+                res_table.write(f'{np.mean(tp_rate_dict[tool])}\t')
+                res_table.write(f'{np.std(tp_rate_dict[tool])}\t')
+                res_table.write(f'{np.mean(module_size_dict[tool])}\t')
+                res_table.write(f'{np.std(module_size_dict[tool])}')
+                for pred_tool in prediction_tools:
+                    p_val = kolmogorov_smirnoff.calculate_ks_p_value(list(redisc_rate_dict[tool]), 
+                                                                     list(redisc_rate_dict[pred_tool]))
+                    res_table.write(f'\t{p_val}')
+                for pred_tool in prediction_tools:
+                    p_val = kolmogorov_smirnoff.calculate_ks_p_value(list(module_size_dict[tool]), 
+                                                                     list(module_size_dict[pred_tool]))
+                    res_table.write(f'\t{p_val}')
+                    
+                with open(os.path.join(cami.tmp_dir, f'{tool}_{cami.uid}_relevance_scores.tsv)'), 'r') as f:
+                    for line in f:
+                        rel_score = line.split('\t')[1].strip()
+                        res_table.write(f'\t{rel_score}')
+                res_table.write('\n')
+    
+    print(f'Result tables are saved in the following locations:')
+    
+    fig1,ax1, fig2,ax2 = comparison_matrix.plot_comparison_matrix(redisc_intersection_matrix, n_rows=cami.nof_tools,
+                                                                  title=f'number of times algorithms rediscovered the same seeds after removing {nof_removals} seeds')
+    fig1.savefig(f'{cami.output_dir}/same_rediscs_{identifier}_comparison_matrix.png')
+    fig2.savefig(f'{cami.output_dir}/same_rediscs_{identifier}_comparison_matrix_normalized.png')
+    # print(variation_results)
+    # print(rediscovery_rates_results)
+    tools = [tool for tool in redisc_rate_dict.keys()]
+    tool_labels = tools.copy()
+    
+    for idx,tool in enumerate(tools):
+        if '_' in tool:
+            # find the index of the second occurrence of the character
+            second_occurrence_index = tool.find('_', tool.find('_') + 1)
+            if second_occurrence_index > -1:
+                # replace the character at that index with the replacement character
+                tool_name = tool[:second_occurrence_index] + '\n' + tool[second_occurrence_index + 1:]
+                tool_labels[idx] = tool_name        
+    if plot:
+        #PLOT
+        # Create a figure instance
+        #print(sys.getrecursionlimit())
+        fig1, (ax1, ax5, ax4) = plt.subplots(3, 1, figsize=(20,20))
+        fig1.subplots_adjust(left=0.2)
+        # Extract Figure and Axes instance
+
+        # Create a plot
+        violins1 = ax1.violinplot([redisc_rate_dict[tool] for tool in tools], showmeans=True, showextrema=True)
+            
+        for violinpart in list(violins1.keys())[2:]:
+            violins1[violinpart].set_color('k')
+        
+        for violin, tool in zip(violins1['bodies'], tools):
+            if tool in [tw.name for tw in cami.tool_wrappers]:        
+                violin.set_facecolor('saddlebrown')
+            elif tool == 'first_neighbors':
+                violin.set_facecolor('orange')
+            elif tool in ['union', 'intersection']:
+                violin.set_facecolor('peachpuff')
+            else:
+                violin.set_facecolor('red')
+            
+        # Add title
+        ax1.set_title(f'Rediscovery rate after randomly removing {nof_removals} seeds {nof_iterations} times from {identifier} seeds.', wrap=True, fontsize=14)
+
+        ax1.set_xticks(list(range(1,len(tools)+1)))
+        ax1.set_xticklabels(tool_labels)
+        ax1.tick_params(axis='x', labelsize=11)
+
+        ax1.set_ylabel('Rediscovery rate (<rediscovered seeds>/<removed seeds>)', wrap=True, fontsize=14)
+
+        violins2 = ax4.violinplot([tp_rate_dict[tool] for tool in tools], showmeans=True, showextrema=True)
+        for violinpart in list(violins2.keys())[2:]:
+            violins2[violinpart].set_color('k')
+        for violin, tool in zip(violins2['bodies'], tools):
+            if tool in [tw.name for tw in cami.tool_wrappers]:        
+                violin.set_facecolor('tan')
+            elif tool == 'first_neighbors':
+                violin.set_facecolor('peachpuff')
+            elif tool in ['union', 'intersection']:
+                violin.set_facecolor('orange')
+            else:
+                violin.set_facecolor('darkorange')
+        # Add title
+        ax4.set_title(f'True positive rates after randomly removing {nof_removals} seeds {nof_iterations} times from {identifier} seeds.', wrap=True, fontsize=14)
+
+        ax4.set_xticks(list(range(1,len(tools)+1)))
+        ax4.set_xticklabels(tool_labels)
+        ax4.tick_params(axis='x', labelsize=11)
+
+        ax4.set_ylabel('Sensitivity (TP/TP + FN)', wrap=True, fontsize=14)
+
+        violins3 = ax5.violinplot([module_size_dict[tool] for tool in tools], showmeans=True, showextrema=True)
+        # Add title
+        for violinpart in list(violins3.keys())[2:]:
+            violins3[violinpart].set_color('k')
+            
+        for violin, tool in zip(violins3['bodies'], tools):
+            if tool in [tw.name for tw in cami.tool_wrappers]:        
+                violin.set_facecolor('midnightblue')
+            elif tool == 'first_neighbors':
+                violin.set_facecolor('mediumblue')
+            elif tool in ['union', 'intersection']:
+                violin.set_facecolor('lightsteelblue')
+            else:
+                violin.set_facecolor('royalblue')
+                
+        ax5.set_title(f'Ratio of number of rediscovered seeds and predicted module size after removing {nof_removals} seeds {nof_iterations} times from {identifier} seeds.', wrap=True, fontsize=14)
+
+        ax5.set_xticks(list(range(1,len(tools)+1)))
+        ax5.set_xticklabels(tool_labels)
+
+        ax5.set_ylabel('precision (<rediscovered seeds>/<module size>)', fontsize=14)
+        ax5.tick_params(axis='x', labelsize=11)
+        fig1.tight_layout()
+        fig1.savefig(f'{cami.output_dir}/00_{identifier}_seed_variation_result.png', bbox_inches="tight")
+        plt.close(fig1)
+        print(f'Violin plot saved under: 00_{identifier}_seed_variation_result.png')
+    return cami
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