logging.info(
'================================================================================'
)
model = LambdaMART(
metric='NDCG@10',
max_leaf_nodes=7,
shrinkage=0.1,
estopping=30,
n_jobs=-1,
random_state=42,
use_pines=True,
pines_kwargs=dict(
switch_criterion=ObliviousCartSwitchCriterionType.OBLIVIOUS_WHILE_CAN,
tree_type=TreeType.OBLIVIOUS_CART,
max_n_splits=10,
min_samples_leaf=50,
max_depth=10,
))
model.fit(training_queries, validation_queries=validation_queries)
logging.info(
'================================================================================'
)
logging.info('%s on the test queries: %.8f' %
(model.metric, model.evaluate(test_queries, n_jobs=-1)))
model.save('LambdaMART_L7_S0.1_E50_' + model.metric)
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)
model = LambdaMART(metric='NDCG@10', max_leaf_nodes=7, shrinkage=0.1,
estopping=50, n_jobs=-1, min_samples_leaf=50,
random_state=42)
model.fit(training_queries, validation_queries=validation_queries)
logging.info('=' * 80)
logging.info('%s on the test queries: %.8f'
% (model.metric, model.evaluate(test_queries, n_jobs=-1)))
model.save('LambdaMART_L7_S0.1_E50_' + model.metric)
model = LambdaMART(metric='nDCG@38',
max_leaf_nodes=7,
shrinkage=0.1,
estopping=10,
n_jobs=-1,
min_samples_leaf=50,
random_state=42)
#TODO: do some crossval here?
model.fit(training_queries, validation_queries=test_queries)
logging.info(
'================================================================================'
)
logging.info('%s on the test queries: %.8f' %
(model.metric, model.evaluate(test_queries, n_jobs=-1)))
model.save('LambdaMART_L7_S0.1_E50_' + model.metric)
predicted_rankings = model.predict_rankings(test_queries)
test_df = pd.read_csv("../test_set_VU_DM_2014.csv",
header=0,
nrows=test_queries.document_count())
test_df['pred_position'] = np.concatenate(predicted_rankings)
sorted_df = test_df[['srch_id', 'prop_id', 'pred_position'
]].sort_values(['srch_id', 'pred_position'])
submission = pd.DataFrame({
'SearchId': sorted_df.srch_id,
'PropertyId': sorted_df.prop_id
})[['SearchId', 'PropertyId']]
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('================================================================================')
model = LambdaMART(metric='nDCG@38', max_leaf_nodes=7, shrinkage=0.1,
estopping=10, n_jobs=-1, min_samples_leaf=50,
random_state=42)
#TODO: do some crossval here?
model.fit(training_queries, validation_queries=test_queries)
logging.info('================================================================================')
logging.info('%s on the test queries: %.8f'
% (model.metric, model.evaluate(test_queries, n_jobs=-1)))
model.save('LambdaMART_L7_S0.1_E50_' + model.metric)
predicted_rankings = model.predict_rankings(test_queries)
test_df = pd.read_csv("../test_set_VU_DM_2014.csv", header=0, nrows = test_queries.document_count())
test_df['pred_position'] = np.concatenate(predicted_rankings)
sorted_df = test_df[['srch_id', 'prop_id', 'pred_position']].sort_values(['srch_id', 'pred_position'])
submission = pd.DataFrame({ 'SearchId': sorted_df.srch_id, 'PropertyId': sorted_df.prop_id })[['SearchId', 'PropertyId']]
submission.to_csv('model_%d2_%f.csv' % (test_queries.document_count(), model.evaluate(test_queries, n_jobs=-1)), index=False)
