def transform_to_rfv_one_user(user_id):
print "loading data for user "+str(user_id)
categorized_data = DataExtractor.load_json_data(user_id)
data = DataExtractor.complete_data(categorized_data)
metadata = DataExtractor.complete_metadata(categorized_data)
#order the data by the alphabetic name of the features
print "ordering data "+str(user_id)
data = collections.OrderedDict(sorted(data.items()))
#get the first date and the last date
print "getting first date and last date "
end_date = date_min
start_date = datetime.now()
for feature, feature_data in data.iteritems():
feature_data = collections.OrderedDict(sorted(feature_data.items()))
begin_date = DataExtractor.start_date_of_realization(feature_data.keys()[0])
if begin_date < start_date:
start_date = begin_date
last_date = DataExtractor.start_date_of_realization(feature_data.keys()[len(feature_data.keys())-1])
if last_date > end_date:
end_date = last_date
data[feature] = feature_data
#construct the values data
#I- construct the values for all the features
print "constructing the values data"
transformers = {}
features_name = []
records = []
values_name = {}
for feature, feature_date in data.iteritems():
if feature == "location":
transformers[feature] = ValuesFeatureTransformer(MatrixLocationFeatureTransformer, feature, data[feature], metadata[feature], start_date, end_date, coocurring_precision)
elif feature == "bluetoothSeen" or feature == "bluetoothPaired":
transformers[feature] = ValuesFeatureTransformer(MatrixBleutoothFeatureTransformer, feature, data[feature], metadata[feature], start_date, end_date, coocurring_precision)
else :
transformers[feature] = ValuesFeatureTransformer(MatrixFeatureTransformer, feature, data[feature], metadata[feature], start_date, end_date, coocurring_precision)
transformers[feature].transform()
features_name.append(feature)
values_name[features_name.index(feature)] = transformers[feature].values_labels
#construct the time feature
feature = "time"
timetrans = ValuesTimeFeatureTransformer(MatrixTimeFeatureTransformer, feature, start_date, end_date, coocurring_precision)
timetrans.transform()
transformers[ValuesTimeFeatureTransformer.day_label] = timetrans
transformers[ValuesTimeFeatureTransformer.hour_label] = timetrans
features_name.append(ValuesTimeFeatureTransformer.day_label)
values_name[features_name.index(ValuesTimeFeatureTransformer.day_label)] = timetrans.day_values_labels
features_name.append(ValuesTimeFeatureTransformer.hour_label)
values_name[features_name.index(ValuesTimeFeatureTransformer.hour_label)] = timetrans.time_values_labels
records_labels = timetrans.records_dates
records_nb = len(records_labels)
#make space for records
for r in range(records_nb):
records.append({})
#II-fill the records
for fid, fname in enumerate(features_name):
if fname == ValuesTimeFeatureTransformer.day_label:
for r in range(records_nb):
if transformers[fname].day_values_data[r]!= []: records[r][fid] = transformers[fname].day_values_data[r];
elif fname == ValuesTimeFeatureTransformer.hour_label:
for r in range(records_nb):
if transformers[fname].time_values_data[r]!= []: records[r][fid] = transformers[fname].time_values_data[r];
else:
for r in range(records_nb):
if transformers[fname].values_data[r]!= []: records[r][fid] = transformers[fname].values_data[r];
#remove the ones that only contain value for the time feature
for r in range(records_nb-1, -1, -1): #Decreasing loop over the records so that remove is possible
if len(records[r]) <= 2:
del records[r]
#for the remaining records, add non_present values for the non_persistant features that are not present in each record. non_persistant
for nf in nonpersistent_features: #add the non_present value as a value that can be taken by the non persistent features
if nf in features_name:
nfid = features_name.index(nf)
values_name[nfid].append(nonpresent_v)
rtv_data = {}
for idr, r in enumerate(records):
for nf in nonpersistent_features:
if nf in features_name:
nfid = features_name.index(nf)
if nfid not in r: r[nfid]=[values_name[nfid].index(nonpresent_v)];
rtv_data[idr]=r
print "first date of observation "+str(start_date)
print "first date of observation "+str(end_date)
print "features names "+str(features_name)
print "values names : "+str(values_name)
print "number of records "+str(len(rtv_data))
#write the data, the record dates, the feature names and the value names
RVFDataExtractor.save_rvf(user_id, rtv_data, features_name, values_name, records_labels)
