removed additional codacy issues

tests/test_khmer_features.py

@@ -1,5 +1,4 @@
 import vica
-import khmer
 import tempfile
 import os
 import filecmp

vica/get_features.py

@@ -2,16 +2,14 @@
 """prodigal.py: a module to call genes with prodigal then count codon usage
 and transform into centered log ratio returning values as a CSV"""
 
-import argparse
+
 import tempfile
 import os
-import glob
 import shutil
 import logging
 import time
 import datetime
 
-import pyfaidx
 import yaml
 
 import vica
@@ -30,7 +28,7 @@ def run(infile, output, label, minhashlocal=None, configpath=vica.CONFIG_PATH):
     t0 = time.perf_counter()
     # read config
     with open(configpath) as cf:
-        config = yaml.load(cf)
+        config = yaml.safe_load(cf)
     # Set up temp directory
     if config["get_features"]["tempdir"]:
         if not os.path.exists(config["get_features"]["tempdir"]):

vica/train_eval.py

@@ -12,7 +12,6 @@
 import shutil
 
 import yaml
-import numpy as np
 import tensorflow as tf
 import csv
 # from google.protobuf.json_format import MessageToDict
@@ -157,7 +156,7 @@ def train(infiles, out, modeldir, n_classes, configpath):
         logging.exception("While exporting the model after tensorflow training the following exception occured:")
 
 
-def eval(infiles, out, modeldir, n_classes, configpath):
+def evaluate(infiles, out, modeldir, n_classes, configpath):
     """ Main evaluation function called by vica_cli. Load a model from a model directory
     returning a file of predictions. In the fiture it will have other evaluation datd.
 
@@ -197,10 +196,10 @@ def eval(infiles, out, modeldir, n_classes, configpath):
             os.mkdir(out)
         predictions = os.path.join(out, "modelpredictions.txt")
         idlist = _ids_from_tfrecords(infile)
-        header = ["ID", "Class", "Class_id"] + ["Prob_class_" + str(i) for i in range(int(n_classes))]
-        csv_writer_instance.writerow(header)
         with open(predictions, "w") as outfile:
             csv_writer_instance = csv.writer(outfile, lineterminator='\n')
+            header = ["ID", "Class", "Class_id"] + ["Prob_class_" + str(i) for i in range(int(n_classes))]
+            csv_writer_instance.writerow(header)
             for idrec, rec in zip(idlist,preds):
                 plist = rec['probabilities']
                 pliststr = [str(x) for x in plist]

vica/vica_cli.py

@@ -8,6 +8,7 @@
 import os
 import sys
 import logging
+import ast
 
 import yaml
 
@@ -204,7 +205,7 @@ def main():
     try:
         with open(args.config) as cf:
             global config
-            config = yaml.load(cf)
+            config = yaml.save_load(cf)
     except:
         print("Could not parse the configuration file.")
         raise SystemExit(1)
@@ -230,7 +231,7 @@ def main():
                 n_per_class=args.n_per_class,
                 testfrac =args.testfrac,
                 splitlevel=args.level,
-                classes=eval(args.classes),
+                classes=ast.literal_eval(args.classes),
                 configpath = args.config)
         elif args.whichmethod == 'get_features':
             vica.get_features.run(infile=args.infile,
@@ -245,7 +246,7 @@ def main():
                 n_classes= args.n_classes,
                 configpath= args.config)
         elif args.whichmethod == 'evaluate':
-            vica.train_eval.eval(infiles=args.infile,
+            vica.train_eval.evaluate(infiles=args.infile,
                 out= args.out,
                 modeldir= args.modeldir,
                 n_classes= args.n_classes,