from sklearn.cross_validation import ShuffleSplit from sklearn.grid_search import RandomizedSearchCV from sklearn.pipeline import Pipeline from sklearn.preprocessing import MinMaxScaler from nnet.oldstyle.base_optimize import name_from_file from nnet.prepare import LogTransform from nnet.score_logging import get_logloss_loggingscorer from settings import OPTIMIZE_RESULTS_DIR from nnet.scikit import NNet from settings import LOGS_DIR, VERBOSITY, SUBMISSIONS_DIR, PRIORS from utils.features import PositiveSparseFeatureGenerator, PositiveSparseRowFeatureGenerator from utils.ioutil import makeSubmission from utils.loading import get_preproc_data from utils.postprocess import scale_to_priors train, labels, test = get_preproc_data(None, expand_confidence=None) cpus = max(cpu_count() - 1, 1) random = RandomState() opt = RandomizedSearchCV( estimator=Pipeline([ ('row', PositiveSparseRowFeatureGenerator()), ('gen23', PositiveSparseFeatureGenerator(difficult_classes=(2, 3), extra_features=40)), ('gen234', PositiveSparseFeatureGenerator(difficult_classes=(2, 3, 4), extra_features=40)), ('gen19', PositiveSparseFeatureGenerator(difficult_classes=(1, 9),
from nnet.oldstyle.base_optimize import name_from_file from nnet.prepare import LogTransform from nnet.scikit import NNet from utils.features import PositiveSparseRowFeatureGenerator, PositiveSparseFeatureGenerator from nndist.distance import DistanceFeatureGenerator from utils.loading import get_preproc_data train, labels, test = get_preproc_data(Pipeline([ ('row', PositiveSparseRowFeatureGenerator()), ('distp31', DistanceFeatureGenerator(n_neighbors=3, distance_p=1)), ('distp52', DistanceFeatureGenerator(n_neighbors=5, distance_p=2)), ('gen23', PositiveSparseFeatureGenerator(difficult_classes=(2, 3), extra_features=40)), ('gen234', PositiveSparseFeatureGenerator(difficult_classes=(2, 3, 4), extra_features=40)), ('gen19', PositiveSparseFeatureGenerator(difficult_classes=(1, 9), extra_features=40)), ('log', LogTransform()), ('scale03', MinMaxScaler(feature_range=(0, 3))), ]), expand_confidence=0.9) net = NNet( name=name_from_file(), dense1_nonlinearity='rectify', dense1_init='glorot_normal', auto_stopping=True, max_epochs=1000,
from sklearn.grid_search import RandomizedSearchCV from sklearn.metrics.scorer import log_loss_scorer from sklearn.pipeline import Pipeline from sklearn.preprocessing import MinMaxScaler from nnet.oldstyle.base_optimize import name_from_file from nnet.prepare import LogTransform from nnet.scikit import NNet from settings import LOGS_DIR, VERBOSITY, SUBMISSIONS_DIR from utils.features import PositiveSparseFeatureGenerator, PositiveSparseRowFeatureGenerator, DistanceFeatureGenerator from utils.ioutil import makeSubmission from utils.loading import get_preproc_data, get_training_data, get_testing_data train, labels, test = get_preproc_data( Pipeline([ ('row', PositiveSparseRowFeatureGenerator()), #('distp31', DistanceFeatureGenerator(n_neighbors = 3, distance_p = 1)), #('distp52', DistanceFeatureGenerator(n_neighbors = 5, distance_p = 2)), ]), expand_confidence=0.9) #train, labels = get_training_data()[:2] #test = get_testing_data()[0] #cpus = max(cpu_count() - 1, 1) #random = RandomState() opt = RandomizedSearchCV( estimator=Pipeline([ ('gen23', PositiveSparseFeatureGenerator(difficult_classes=(2, 3), extra_features=40)), ('gen234',
# Due to this error: # ValueError: Loading weights from a list of parameter values is no longer supported. # Please send me something like the return value of 'net.get_all_param_values()' instead. # testing new method import warnings from sklearn.pipeline import Pipeline from sklearn.preprocessing import MinMaxScaler from nnet.prepare import LogTransform from nnet.scikit import NNet from utils.loading import get_preproc_data warnings.filterwarnings("ignore") train, labels, test = get_preproc_data(Pipeline([ ('log', LogTransform()), ('scale03', MinMaxScaler(feature_range=(0, 3))), ]), expand_confidence=0.9) nn = NNet(max_epochs=1) nn.fit(train, labels) nn.save(filepath='/tmp/test') nn = NNet.load(filepath='/tmp/test') w = nn.net.get_all_params_values() print w nn.net.load_params_from(w)