
from sklearn.cross_validation import train_test_split

from dataset_meta import DATASET_META

from spam.common.utils import get_file_path_list


file_path_list = get_file_path_list(DATASET_META)
path, classification = zip(*file_path_list)

unlabeled_path, labeled_path, \
    unlabeled_class, labeled_class = train_test_split(
        path,
        classification,
        test_size=0.1,
    )

print(len(unlabeled_path))
print(len(unlabeled_class))
print(len(labeled_path))
print(len(labeled_class))


from sklearn.cross_validation import train_test_split

from dataset_meta import DATASET_META

from spam.common.utils import get_file_path_list


file_path_list = get_file_path_list(DATASET_META)

# transform list of tuple into two list
# e.g. [('/path/to/file', 'spam')] ==> ['path/to/file'], ['spam']
path, classification = zip(*file_path_list)

# split the data into unlabeled labeled
unlabeled_path, labeled_path, \
    unlabeled_class, labeled_class = train_test_split(
        path,
        classification,
        test_size=0.1,
        random_state=0,
    )

Set random state to 0, add comments and remove print.
Set random state to 0, add comments and remove print.

  
  from sklearn.cross_validation import train_test_split
  
  from dataset_meta import DATASET_META
  
  from spam.common.utils import get_file_path_list
  
  
  file_path_list = get_file_path_list(DATASET_META)
+ 
+ # transform list of tuple into two list
+ # e.g. [('/path/to/file', 'spam')] ==> ['path/to/file'], ['spam']
  path, classification = zip(*file_path_list)
  
+ # split the data into unlabeled labeled
  unlabeled_path, labeled_path, \
      unlabeled_class, labeled_class = train_test_split(
          path,
          classification,
          test_size=0.1,
+         random_state=0,
      )
  
- print(len(unlabeled_path))
- print(len(unlabeled_class))
- print(len(labeled_path))
- print(len(labeled_class))
- 
Set random state to 0, add comments and remove print.
## Code Before:

from sklearn.cross_validation import train_test_split

from dataset_meta import DATASET_META

from spam.common.utils import get_file_path_list


file_path_list = get_file_path_list(DATASET_META)
path, classification = zip(*file_path_list)

unlabeled_path, labeled_path, \
    unlabeled_class, labeled_class = train_test_split(
        path,
        classification,
        test_size=0.1,
    )

print(len(unlabeled_path))
print(len(unlabeled_class))
print(len(labeled_path))
print(len(labeled_class))

## Instruction:
Set random state to 0, add comments and remove print.
## Code After:

from sklearn.cross_validation import train_test_split

from dataset_meta import DATASET_META

from spam.common.utils import get_file_path_list


file_path_list = get_file_path_list(DATASET_META)

# transform list of tuple into two list
# e.g. [('/path/to/file', 'spam')] ==> ['path/to/file'], ['spam']
path, classification = zip(*file_path_list)

# split the data into unlabeled labeled
unlabeled_path, labeled_path, \
    unlabeled_class, labeled_class = train_test_split(
        path,
        classification,
        test_size=0.1,
        random_state=0,
    )
