def main(args):
db = args[0]
date1 = args[1]
date2 = args[2]
date3 = args[3]
k = int(args[4])
basename = args[5]
reader = DBReader(db)
print("Getting uid")
uid = reader.uid()
print("Getting all the feature graphs")
feature_graphs = graphutils.get_feat_graphs(db, uid, None, date2)
print("Getting Gcollab_delta graph")
Gcollab_delta = graphutils.get_collab_graph(db, uid, date1, date2)
Gcollab_base = graphutils.get_collab_graph(db, uid, date3, date1)
base_graphs = graphutils.get_base_dict(Gcollab_base, feature_graphs)
graphutils.print_stats(base_graphs)
graphutils.print_graph_stats("Gcollab_delta", Gcollab_delta)
filepath = os.path.join(LEARNING_ROOT, basename + ".mat")
features_matrix_name = "%s_%s" % (basename, FEATURES)
labels_matrix_name = "%s_%s" % (basename, LABELS)
features = consolidateFeatures.consolidate_features_add(
base_graphs, k, Gcollab_delta)
#features = consolidateFeatures.consolidate_features(base_graphs, Gcollab_delta, k)
labels = consolidateFeatures.consolidate_labels(features, Gcollab_delta)
np_train, np_output = interface.matwrapTrain(features, labels)
interface.writeTrain(np_train, np_output, filepath, features_matrix_name,
labels_matrix_name)
# Add learning root to mlab path so that all .m functions are available as mlab attributes
mlab.path(mlab.path(), LEARNING_ROOT)
mlab.training(np_train, np_output)
def main(args):
db = args[0]
date1 = args[1]
date2 = args[2]
date3 = args[3]
k = int(args[4])
basename = args[5]
reader = DBReader(db)
print("Getting uid")
uid = reader.uid()
print("Getting all the feature graphs")
feature_graphs = graphutils.get_feat_graphs(db, uid, None, date2)
print("Getting Gcollab_delta graph")
Gcollab_delta = graphutils.get_collab_graph(db, uid, date1, date2)
Gcollab_base = graphutils.get_collab_graph(db, uid, date3, date1)
base_graphs = graphutils.get_base_dict(Gcollab_base, feature_graphs)
graphutils.print_stats(base_graphs)
graphutils.print_graph_stats("Gcollab_delta", Gcollab_delta)
filepath = os.path.join(LEARNING_ROOT, basename + ".mat")
features_matrix_name = "%s_%s"%(basename, FEATURES)
labels_matrix_name = "%s_%s"%(basename, LABELS)
features = consolidateFeatures.consolidate_features_add(base_graphs, k, Gcollab_delta)
#features = consolidateFeatures.consolidate_features(base_graphs, Gcollab_delta, k)
labels = consolidateFeatures.consolidate_labels(features, Gcollab_delta)
np_train, np_output = interface.matwrapTrain(features, labels)
interface.writeTrain(np_train, np_output, filepath, features_matrix_name, labels_matrix_name)
# Add learning root to mlab path so that all .m functions are available as mlab attributes
mlab.path(mlab.path(), LEARNING_ROOT)
mlab.training(np_train, np_output)
import path from db.interface import * from analysis import graphutils import utils db = utils.get_small_db() reader = DBReader(db) uid = reader.uid() rowid = uid.values()[0] print reader.get_users([rowid, rowid]) Gcollab = graphutils.get_collab_graph(db, uid) feature_graphs = graphutils.get_feat_graphs(db, uid) base_graphs = graphutils.get_base_dict(Gcollab, feature_graphs) graphutils.print_stats(base_graphs)