def main(argv=None):
'''
Handles command line arguments and gets things started.
:param argv: List of arguments, as if specified on the command-line.
If None, ``sys.argv[1:]`` is used instead.
:type argv: list of str
'''
# Get command line arguments
parser = argparse.ArgumentParser(
description="Loads a trained model and outputs predictions based \
on input feature files.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
conflict_handler='resolve')
parser.add_argument('model_file',
help='Model file to load and use for generating \
predictions.')
parser.add_argument('input_file',
help='A csv file, json file, or megam file \
(with or without the label column), \
with the appropriate suffix.',
nargs='+')
parser.add_argument('-l', '--label_col',
help='Name of the column which contains the class \
labels in ARFF, CSV, or TSV files. For ARFF \
files, this must be the final column to count as\
the label.',
default='y')
parser.add_argument('-p', '--positive_class',
help="If the model is only being used to predict the \
probability of a particular class, this \
specifies the index of the class we're \
predicting. 1 = second class, which is default \
for binary classification. Keep in mind that \
classes are sorted lexicographically.",
default=1, type=int)
parser.add_argument('-q', '--quiet',
help='Suppress printing of "Loading..." messages.',
action='store_true')
parser.add_argument('-t', '--threshold',
help="If the model we're using is generating \
probabilities of the positive class, return 1 \
if it meets/exceeds the given threshold and 0 \
otherwise.",
type=float)
parser.add_argument('--version', action='version',
version='%(prog)s {0}'.format(__version__))
args = parser.parse_args(argv)
# Make warnings from built-in warnings module get formatted more nicely
logging.captureWarnings(True)
logging.basicConfig(format=('%(asctime)s - %(name)s - %(levelname)s - ' +
'%(message)s'))
# Create the classifier and load the model
predictor = Predictor(args.model_file,
positive_class=args.positive_class,
threshold=args.threshold)
for input_file in args.input_file:
data = load_examples(input_file, quiet=args.quiet,
label_col=args.label_col)
for pred in predictor.predict(data):
print(pred)
def main(argv=None):
'''
Handles command line arguments and gets things started.
:param argv: List of arguments, as if specified on the command-line.
If None, ``sys.argv[1:]`` is used instead.
:type argv: list of str
'''
# Get command line arguments
parser = argparse.ArgumentParser(
description="Loads a trained model and outputs predictions based \
on input feature files.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
conflict_handler='resolve')
parser.add_argument('model_file',
help='Model file to load and use for generating \
predictions.')
parser.add_argument('input_file',
help='A csv file, json file, or megam file \
(with or without the label column), \
with the appropriate suffix.',
nargs='+')
parser.add_argument('-l',
'--label_col',
help='Name of the column which contains the class \
labels in ARFF, CSV, or TSV files. For ARFF \
files, this must be the final column to count as\
the label.',
default='y')
parser.add_argument('-p',
'--positive_class',
help="If the model is only being used to predict the \
probability of a particular class, this \
specifies the index of the class we're \
predicting. 1 = second class, which is default \
for binary classification. Keep in mind that \
classes are sorted lexicographically.",
default=1,
type=int)
parser.add_argument('-q',
'--quiet',
help='Suppress printing of "Loading..." messages.',
action='store_true')
parser.add_argument('-t',
'--threshold',
help="If the model we're using is generating \
probabilities of the positive class, return 1 \
if it meets/exceeds the given threshold and 0 \
otherwise.",
type=float)
parser.add_argument('--version',
action='version',
version='%(prog)s {0}'.format(__version__))
args = parser.parse_args(argv)
# Make warnings from built-in warnings module get formatted more nicely
logging.captureWarnings(True)
logging.basicConfig(format=('%(asctime)s - %(name)s - %(levelname)s - ' +
'%(message)s'))
# Create the classifier and load the model
predictor = Predictor(args.model_file,
positive_class=args.positive_class,
threshold=args.threshold)
for input_file in args.input_file:
data = load_examples(input_file,
quiet=args.quiet,
label_col=args.label_col)
for pred in predictor.predict(data):
print(pred)
def main():
'''
Create directories and split CSV files into subsets.
'''
logging.basicConfig(format=('%(asctime)s - %(name)s - %(levelname)s - ' +
'%(message)s'), level=logging.INFO)
logger = logging.getLogger(__name__)
if not (os.path.exists('train.csv') and os.path.exists('test.csv')):
logger.error('This script requires the train.csv and test.csv files ' +
'from http://www.kaggle.com/c/titanic-gettingStarted/' +
'data to be in the current directory in order to work. ' +
'Please download them and try again.')
sys.exit(1)
# Create dictionary of subsets to use for creating split feature files
subset_dict = {'vitals': ['Sex', 'Age'],
'socioeconomic': ['Pclass', 'Fare'],
'family': ['SibSp', 'Parch'],
'misc': ['Embarked']}
# Create directories to store files
if not os.path.exists('titanic/train'):
logger.info('Creating titanic/train directory')
os.makedirs('titanic/train')
if not os.path.exists('titanic/dev'):
logger.info('Creating titanic/dev directory')
os.makedirs('titanic/dev')
if not os.path.exists('titanic/train+dev'):
logger.info('Creating titanic/train+dev directory')
os.makedirs('titanic/train+dev')
if not os.path.exists('titanic/test'):
logger.info('Creating titanic/test directory')
os.makedirs('titanic/test')
# Read and write training examples
train_examples = load_examples('train.csv', label_col='Survived',
quiet=False, sparse=False)
num_train_dev = len(train_examples.classes)
num_train = int((num_train_dev / 5) * 4)
train_ids = list(range(1, num_train_dev + 1))
write_feature_file('titanic/train/.csv',
train_ids[:num_train],
train_examples.classes[:num_train],
train_examples.features[:num_train, :],
feat_vectorizer=train_examples.feat_vectorizer,
subsets=subset_dict, label_col='Survived',
id_prefix='train_example')
# Write train+dev set for training model to use to generate predictions on test
write_feature_file('titanic/train+dev/.csv',
train_ids,
train_examples.classes,
train_examples.features,
feat_vectorizer=train_examples.feat_vectorizer,
subsets=subset_dict, label_col='Survived',
id_prefix='train_example')
# Write dev examples
write_feature_file('titanic/dev/.csv',
train_ids[num_train:],
train_examples.classes[num_train:],
train_examples.features[num_train:, :],
feat_vectorizer=train_examples.feat_vectorizer,
subsets=subset_dict, label_col='Survived',
id_prefix='dev_example')
# Read and write test examples
test_examples = load_examples('test.csv', label_col='Survived',
quiet=False, sparse=False)
num_test = len(test_examples.classes)
test_ids = list(range(num_train_dev + 1, num_test + num_train_dev + 1))
write_feature_file('titanic/test/.csv', test_ids,
test_examples.classes, test_examples.features,
feat_vectorizer=test_examples.feat_vectorizer,
subsets=subset_dict, label_col='Survived',
id_prefix='test_example')
