Python find_constant_features Examples

Example #1

File: gridsearch_lambdarandomforest_example.py Project: deronnek/rankpy

validation_queries.save('data/MQ2007/Fold1/validation')
test_queries.save('data/MQ2007/Fold1/test')

# ... because loading them will be then faster.
training_queries = Queries.load('data/MQ2007/Fold1/training')
validation_queries = Queries.load('data/MQ2007/Fold1/validation')
test_queries = Queries.load('data/MQ2007/Fold1/test')

logging.info('=' * 80)

# Set this to True in order to remove queries containing all documents
# of the same relevance score -- these are useless for LambdaMART.
remove_useless_queries = False

# Find constant query-document features.
cfs = find_constant_features(
    [training_queries, validation_queries, test_queries])

# Get rid of constant features and (possibly) remove useless queries.
training_queries.adjust(remove_features=cfs, purge=remove_useless_queries)
validation_queries.adjust(remove_features=cfs, purge=remove_useless_queries)
test_queries.adjust(remove_features=cfs)

# Print basic info about query datasets.
logging.info('Train queries: %s' % training_queries)
logging.info('Valid queries: %s' % validation_queries)
logging.info('Test queries: %s' % test_queries)

logging.info('=' * 80)

param_grid = {
    'metric': ['nDCG@10'],

Example #2

File: gridsearch_example.py Project: dmitru/rankpy

test_queries.save('data/MQ2007/Fold1/test')

# ... because loading them will be then faster.
training_queries = Queries.load('data/MQ2007/Fold1/training')
validation_queries = Queries.load('data/MQ2007/Fold1/validation')
test_queries = Queries.load('data/MQ2007/Fold1/test')

logging.info('=' * 80)

# Set this to True in order to remove queries containing all documents
# of the same relevance score -- these are useless for LambdaMART.
remove_useless_queries = False

# Find constant query-document features.
cfs = find_constant_features([training_queries,
                              validation_queries,
                              test_queries])

# Get rid of constant features and (possibly) remove useless queries.
training_queries.adjust(remove_features=cfs, purge=remove_useless_queries)
validation_queries.adjust(remove_features=cfs, purge=remove_useless_queries)
test_queries.adjust(remove_features=cfs)

# Print basic info about query datasets.
logging.info('Train queries: %s' % training_queries)
logging.info('Valid queries: %s' % validation_queries)
logging.info('Test queries: %s' % test_queries)

logging.info('=' * 80)

param_grid = {'metric':              ['NDCG@10'],