Upload DNAEncoder.py

DNAEncoder.py

@@ -0,0 +1,126 @@
+import sys
+import time
+import traceback
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+import os
+from tqdm.auto import tqdm
+os.system("cls")
+
+import warnings
+warnings.filterwarnings("ignore")
+
+class ConvertDNALabelEncoder(object):
+    """
+        convert dna sequence string csv file to dna label encoder csv file and viceverse
+    """
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def convert_dna_string_to_dna_labelencoder(dna_string_csv_path, seq_column,label_column):
+        """
+            convert dna sequence string csv file to dna label encoder csv file
+        args:
+            dna_string_csv_path (string): dna string csv file path
+            dna_labelencoder_csv_path (string): dna label encoder csv file path
+        returns:
+            none
+        """
+        try:
+            df_dna_string = pd.read_csv(filepath_or_buffer=dna_string_csv_path) #.iloc[:10,:]
+            #df_dna_string.loc[df_dna_string['labels']!='Homo sapiens','labels'] ='Other Choredate Host'
+            #df_dna_string['labels']=df_dna_string.loc[df_dna_string['labels']!='Homo sapiens','label'] #='Other Choredate Host'
+            #print(len(df_dna_string))
+            label_encoder = LabelEncoder()
+            dna_string_list = []
+            for row in tqdm(df_dna_string.itertuples()):
+                #print(row[2])
+                #dna_string_row = row[3] #.Sequence
+                dna_string_row = getattr(row, seq_column)
+                dna_string_row=dna_string_row.replace('S','').replace('W','').replace('Y','').replace('H','').replace('R','').replace('K','').replace('V','').replace('M','').replace('D','').replace('B','').replace('I','').replace('J','')
+                dna_string_nparray = np.array(list(dna_string_row))
+                sample=['A','T','C','G','N']
+                label_encoder.fit(sample)
+
+                dna_labelencoder_row = label_encoder.transform(dna_string_nparray)
+                dna_string_list.append(dna_labelencoder_row.astype(np.int8))
+            #df_dna_labelencoder = pd.DataFrame(dna_string_list)
+            #df_dna_labelencoder.to_csv(path_or_buf=dna_labelencoder_csv_path, index=False, header=None)
+            return dna_string_list, df_dna_string[label_column]
+        except:
+            print("An error occurred. {}".format(ConvertDNALabelEncoder.get_exception_stack_trace()))
+            return "nothing"
+
+    
+    @staticmethod
+    def convert_dna_labelencoder_to_dna_string(dna_labelencoder_list):
+    	"""
+        	Convert DNA sequence label encoder CSV file to DNA string CSV file.
+    	Args:
+        	dna_labelencoder_csv_path (string): DNA label encoder CSV file path
+        	dna_string_csv_path (string): DNA string CSV file path
+    """
+    	try:
+        # Read the label-encoded DNA sequences
+        		#df_dna_labelencoder = pd.read_csv(filepath_or_buffer=dna_labelencoder_csv_path, header=None)
+        		dna_labelencoder_list = dna_labelencoder_list #df_dna_labelencoder.values.tolist()
+
+        # Initialize the LabelEncoder and fit it to the DNA bases
+       			label_encoder = LabelEncoder()
+        		sample = ['A', 'T', 'C', 'G', 'N']  # DNA bases
+        		label_encoder.fit(sample)
+
+        # Use the inverse transform to decode label encodings back to DNA strings
+        		dna_string_list = []
+        		for encoded_sequence in dna_labelencoder_list:
+            			encoded_array = np.array(encoded_sequence, dtype=np.int8)  # Ensure it's a NumPy array
+            			decoded_sequence = label_encoder.inverse_transform(encoded_array)  # Decode back to DNA bases
+            			dna_string_list.append(''.join(decoded_sequence))  # Join decoded bases into a string
+
+        # Save the decoded DNA strings to a CSV file
+        		df_dna_string = pd.DataFrame(dna_string_list)
+        		#df_dna_string.to_csv(path_or_buf=dna_string_csv_path, index=False, header=None)
+
+    	except Exception as e:
+		        print("An error occurred. {}".format(ConvertDNALabelEncoder.get_exception_stack_trace()))
+
+    @staticmethod
+    def get_exception_stack_trace():
+        """
+            get exception stack trace
+        args:
+            none
+        returns:
+            exception_stack_trace (string): exception stack trace parameters
+        """
+        try:
+            exception_type, exception_value, exception_traceback = sys.exc_info()
+            file_name, line_number, procedure_name, line_code = traceback.extract_tb(exception_traceback)[-1]
+            exception_stack_trace = ''.join('[Time Stamp]: ' + str(time.strftime('%d-%m-%Y %I:%M:%S %p')) + '' + '[File Name]: ' + str(file_name) + ' '
+            + '[Procedure Name]: ' + str(procedure_name) + ' '
+            + '[Error Message]: ' + str(exception_value) + ' '
+            + '[Error Type]: ' + str(exception_type) + ' '
+            + '[Line Number]: ' + str(line_number) + ' '
+            + '[Line Code]: ' + str(line_code))
+        except:
+            print("An error occurred in {}".format("get_exception_stack_trace() function"))
+        return exception_stack_trace
+
+    @staticmethod
+    def get_program_running(start_time):
+        """
+            calculate program running
+        args:
+            start_time (string): start time program runtime
+        returns:
+            none
+        """
+        try:
+            end_time = time.time()
+            diff_time = end_time - start_time
+            result = time.strftime("%H:%M:%S", time.gmtime(diff_time))
+            print("program runtime: {}".format(result))
+        except:
+            print("An error occurred. {}".format(ConvertDNALabelEncoder.get_exception_stack_trace()))