distance Levenshtein

requirements.txt

@@ -10,3 +10,6 @@ seaborn~=0.13.0
 pyopenms~=3.1.0
 dlomix~=0.0.6
 scikit-learn~=1.4.1.post1
+
+Levenshtein~=0.25.0
+keras~=2.15.0
\ No newline at end of file

res_viz.py

@@ -3,6 +3,8 @@ import matplotlib
 import numpy as np
 import matplotlib.pyplot as plt
 from dlomix.data import RetentionTimeDataset
+import pandas as pd
+import Levenshtein as lv
 epoch = 100
 number = 8
 BATCH_SIZE=64
@@ -10,6 +12,14 @@ BATCH_SIZE=64
 test_rtdata = RetentionTimeDataset(data_source='database/data_holdout.csv',
                                        seq_length=30, batch_size=BATCH_SIZE, test=True)
 
+rtdata = RetentionTimeDataset(data_source='database/data_train.csv',
+                                  seq_length=30, batch_size=BATCH_SIZE, val_ratio=0.2, test=False)
+
+data_train = pd.read_csv('database/data_train.csv')
+data_test = pd.read_csv('database/data_holdout.csv')
+train_seq = data_train['sequence']
+test_seq = data_test['sequence']
+
 target = test_rtdata.get_split_targets(split="test")
 index = random.sample(range(target.size), number)
 target_plot = target[index]
@@ -73,4 +83,30 @@ def compare_error():
     for i in range(number):
         plt.plot(error_plot_1[:,i], c = colors[i], ls =  'dashed')
         plt.plot(error_plot_2[:, i], c = colors[i])
-    plt.show()
\ No newline at end of file
+    plt.show()
+
+def get_worse_seq(n,epoch):
+    error = error_1[epoch,:]
+    ind = np.argpartition(error, n)[:n]
+    return test_seq[ind].to_list(),ind
+
+def get_nb_iteration(seq_list):
+    nb_list=[]
+    counts = train_seq.value_counts()
+    for seq in seq_list:
+        nb_list.append(counts[seq])
+
+
+def compute_min_distance(seq_list):
+    res=[]
+    for ref in seq_list :
+        min_dist = 10000
+        for seq in train_seq:
+            d = 1 - lv.ratio(seq,ref)
+            if d < min_dist:
+                min_dist = d
+        res.append(min_dist)
+    return res
+
+radom_1000 = index = random.sample(range(test_seq.size), 1000)
+