def features_sentiment_manager_sharpness_preprocessor():
json_loader = l.JSONLoader()
preprocessor = l.Preprocessor()
preprocessor.with_pipeline('origin').set_loader(json_loader)
preprocessor.add_operation(l.DateTimeExtractor()).add_operation(
l.NewSimplePredictors())
preprocessor.add_operation(
l.LogTransform(['price_per_bedroom', 'price', 'price_per_bathroom']))
preprocessor.add_operation(
l.Selector([
'listing_id', 'bathrooms', u'bedrooms', 'latitude', 'longitude',
'price', 'month', 'day_of_month', 'hour', 'day_of_week',
'price_per_bathroom', 'price_per_bedroom', 'num_features',
'features_len', 'num_photos'
]))
merger = l.PandasColumnMerger(
['origin', 'features', 'sentiment', 'photo_stats'], on='listing_id')
preprocessor.set_consumer(merger)
preprocessor.with_pipeline('features').set_loader(
l.CSVLoader('data/features_train.csv', 'data/features_test.csv'))
preprocessor.set_consumer(merger)
preprocessor.with_pipeline('sentiment').set_loader(
l.CSVLoader('data/sentiment_train.csv', 'data/sentiment_test.csv'))
preprocessor.set_consumer(merger)
photo_url_merger = l.GetTopPhotoMerger('photo_stats_sharpness',
'photo_stats_photo_url')
preprocessor.with_pipeline('photo_stats_sharpness').set_loader(
l.CSVLoader('data/images_train.csv', 'data/images_test.csv'))
preprocessor.set_consumer(photo_url_merger)
preprocessor.with_pipeline('photo_stats_photo_url').set_loader(
json_loader.select_loader(['listing_id', 'photos']))
preprocessor.set_consumer(photo_url_merger)
preprocessor.with_pipeline('photo_stats').set_loader(
photo_url_merger).add_operation(l.ColumnDrop('photo_name'))
preprocessor.add_operation(
l.LogTransform(['avg_sharpness',
'cover_sharpness'])).set_consumer(merger)
preprocessor.with_pipeline('main').set_loader(merger).add_operation(
l.ColumnDrop('listing_id'))
preprocessor.add_operation(l.ToNdarray()).add_operation(
preprocessing.StandardScaler())
preprocessor.with_pipeline('response').set_loader(
json_loader.select_loader('interest_level'), only_train=True)
preprocessor.add_operation(
l.Dummifier(output_cols=['high', 'medium', 'low'])).add_operation(
l.ToNdarray())
preprocessor.with_pipeline('managers').set_loader(
json_loader.select_loader('manager_id'))
preprocessor.add_operation(l.CategoricalFilter(999)).add_operation(
l.ToNdarray(dtype=np.int64, outshape=(-1, 1)))
preprocessor.with_pipeline('ids').set_loader(
json_loader.select_loader('listing_id'))
preprocessor.add_operation(l.ToNdarray(dtype=np.int64))
return preprocessor
def cover_features_sentiment_manager_sharpness_preprocessor():
'''
Returns pipelines main (83 fields), managers (1 field), photo_cover_stats (3 fields), photo_cover (3*100*300), ids (1 field), response (3 fields)
'''
json_loader = l.JSONLoader(sample=20000)
preprocessor = l.Preprocessor()
preprocessor.with_pipeline('origin').set_loader(json_loader)
preprocessor.add_operation(l.DateTimeExtractor()).add_operation(
l.NewSimplePredictors())
preprocessor.add_operation(
l.LogTransform(['price_per_bedroom', 'price', 'price_per_bathroom']))
preprocessor.add_operation(
l.Selector([
'listing_id', 'bathrooms', u'bedrooms', 'latitude', 'longitude',
'price', 'month', 'day_of_month', 'hour', 'day_of_week',
'price_per_bathroom', 'price_per_bedroom', 'num_features',
'features_len', 'num_photos'
]))
merger = l.PandasColumnMerger(
['origin', 'features', 'sentiment', 'photo_stats'],
on='listing_id',
how='left')
preprocessor.set_consumer(merger)
preprocessor.with_pipeline('features').set_loader(
l.CSVLoader('data/features_train.csv', 'data/features_test.csv'))
preprocessor.set_consumer(merger)
preprocessor.with_pipeline('sentiment').set_loader(
l.CSVLoader('data/sentiment_train.csv', 'data/sentiment_test.csv'))
preprocessor.set_consumer(merger)
photo_url_merger = l.GetTopPhotoMerger('photo_stats_sharpness',
'photo_stats_photo_url')
preprocessor.with_pipeline('photo_stats_sharpness').set_loader(
l.CSVLoader('data/images_train.csv', 'data/images_test.csv'))
preprocessor.set_consumer(photo_url_merger)
preprocessor.with_pipeline('photo_stats_photo_url').set_loader(
json_loader.select_loader(['listing_id', 'photos']))
preprocessor.set_consumer(photo_url_merger)
preprocessor.with_pipeline('photo_stats').set_loader(photo_url_merger)
preprocessor.add_operation(
l.Selector(['listing_id', 'avg_width', 'avg_height', 'avg_sharpness']))
preprocessor.add_operation(l.LogTransform(['avg_sharpness'
])).set_consumer(merger)
preprocessor.with_pipeline('main').set_loader(merger).add_operation(
l.ColumnDrop('listing_id'))
preprocessor.add_operation(l.ToNdarray()).add_operation(
preprocessing.StandardScaler())
preprocessor.with_pipeline('response').set_loader(
json_loader.select_loader('interest_level'), only_train=True)
preprocessor.add_operation(
l.Dummifier(output_cols=['high', 'medium', 'low'])).add_operation(
l.ToNdarray())
preprocessor.with_pipeline('managers').set_loader(
json_loader.select_loader('manager_id'))
preprocessor.add_operation(l.CategoricalFilter(999)).add_operation(
l.ToNdarray(dtype=np.int64, outshape=(-1, 1)))
preprocessor.with_pipeline('photo_cover_stats').set_loader(
photo_url_merger)
preprocessor.add_operation(
l.Selector(['cover_width', 'cover_height', 'cover_sharpness']))
preprocessor.add_operation(l.LogTransform(['cover_sharpness']))
preprocessor.add_operation(l.ToNdarray()).add_operation(
preprocessing.StandardScaler())
preprocessor.with_pipeline('photo_cover_gather').set_loader(
photo_url_merger)
preprocessor.add_operation(l.Selector(['listing_id', 'photo_name']))
photo_loader = l.PhotoLoader(
) # using a loader in order to make different transformations depending on whether test or train
preprocessor.set_consumer(photo_loader)
preprocessor.with_pipeline('photo_cover').set_loader(photo_loader)
preprocessor.with_pipeline('ids').set_loader(
json_loader.select_loader('listing_id'))
preprocessor.add_operation(l.ToNdarray(dtype=np.int64))
return preprocessor