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fununifrac/benchmarking_trees.py

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+import sys, os
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(ROOT_DIR)
+import glob
+import pandas as pd
+from src.objects.func_tree import FuncTreeEmduInput
+import src.factory.make_tree as make_tree
+import src.factory.make_emd_input as make_emd_input
+from src.algorithms.emd_unifrac import EarthMoverDistanceUniFracSolver
+import numpy as np
+from sklearn.metrics import silhouette_score
+
+
+RAND_TREE = 'reproducibility/data/kegg_trees/fununifrac_edge_lengths_kegg_ko00001_randomized_method.csv'
+UNIFORM_TREE = 'reproducibility/data/kegg_trees/kegg_ko00001_edge_length_1.txt'
+DETERMINISTIC_TREE = 'reproducibility/data/kegg_trees/kegg_ko00001_scaled_10_k_5_assigned_positivity_enforced.txt'
+BRITE = 'ko00001'
+
+metadata_file = 'reproducibility/data/simulated_data/simulated_metadata.csv'
+trees = {
+    RAND_TREE: 'randomized_tree',
+    UNIFORM_TREE: 'uniform_tree',
+    DETERMINISTIC_TREE: 'deterministic_tree',
+}
+input_dir = 'data/simulated_data'
+similarity_levels = ['high', 'medium', 'low']
+meta = pd.read_csv(metadata_file)
+meta_dict = dict(zip(meta['sample'], meta['env']))
+print(meta_dict)
+
+def make_fununifrac_inputs(raw_P, input, normalize=True):
+    #convert an array into one that's suitable for use
+    EMDU_index_2_node = input.idx_to_node
+    node_2_EMDU_index = {v: k for k, v in EMDU_index_2_node.items()}
+    if normalize:
+        raw_P = raw_P/raw_P.sum()
+    P = np.zeros(len(EMDU_index_2_node))
+    for ko in raw_P.index:
+        if ko not in node_2_EMDU_index:
+            print(f"Warning: {ko} not found in EMDU index, skipping.")
+        else:
+            P_index = node_2_EMDU_index[ko]
+            P[P_index] = raw_P[ko]
+    return P
+
+def compute_pw_fununifrac(tree_path, dataframe_file):
+    #compute pw_fununifrac of 1 file
+    solver = EarthMoverDistanceUniFracSolver()
+    tree = make_tree.import_graph(tree_path, directed=True)
+    input: FuncTreeEmduInput = make_emd_input.tree_to_EMDU_input(tree, BRITE)
+    sample_df = pd.read_csv(dataframe_file, index_col='name')
+    Ps_pushed = {}
+    for col in sample_df.columns:
+        P = make_fununifrac_inputs(sample_df[col], input)
+        P_pushed = solver.push_up_L1(P, input)
+        Ps_pushed[col] = P_pushed
+    dists, diffabs_sparse = solver.pairwise_computation(Ps_pushed, sample_df.columns, input, False, False)
+    return dists, sample_df.columns
+
+for sim in similarity_levels:
+    df_dict = {
+        'tree': [],
+        'score': [],
+    }
+    files = glob.glob(f"sim_*{sim}.csv")
+    for tree in trees:
+        for file in files:
+            dist_matrix, sample_ids = compute_pw_fununifrac(tree, file)
+            labels = [meta_dict[i] for i in sample_ids]
+            sil_score = silhouette_score(dist_matrix, labels, metric='precomputed')
+            df_dict['tree'].append(trees[tree])
+            df_dict['score'].append(sil_score)
+    df = pd.DataFrame.from_dict(df_dict)
+    print(df)
+    out_file_name = f"reproducibility/data/simulated_data/df_{sim}_{trees[tree]}"
+    df.to_csv(out_file_name, sep='\t')
+
+
+
+