def test_serialize_data_and_class_df():
sensor = 'DW'
raw_df, annotation_df = s_loader.load_smoking_df(session=6,
sensors=[
sensor,
],
kind='corrected')
seg_raw_df = s_segment.do_segmentation_on_raw(raw_df,
method='window',
paras={
'window_size': 200,
'overlap_rate': 0.5
})
seg_raw_df = s_feature.do_preprocess_on_segment_raw(seg_raw_df)
seg_annotation_df = s_segment.set_segmentation_on_annotation(
annotation_df, seg_raw_df)
feature_annotation_df = s_feature.get_info_from_segment_annotation(
seg_annotation_df, s_info.feature_info_dict)
class_df = assign_class_from_feature_annotation(feature_annotation_df)
feature_func_dict = s_info.feature_dataset_folder + "/feature_dict_" + sensor + '.json'
feature_raw_df = s_feature.get_features_from_segment_raw(
seg_raw_df, feature_func_dict)
data_df = get_data_df_from_feature_raw_df(feature_raw_df)
data_pkl_name, class_pkl_name = serialize_data_and_class_df(
data_df, class_df)
def unit_test():
raw_df, annotation_df = s_loader.load_smoking_df(session=5,
sensors=[
'DW',
],
kind='corrected')
seg_raw_df = do_segmentation_on_raw(raw_df,
method='window',
paras={
'window_size': 320,
'overlap_rate': 0.5
})
print "============test do windowing segmentation on raw==============="
print "=====test head======"
print seg_raw_df.head()
print "=====test joint====="
print seg_raw_df.iloc[315:325]
print "=====test tail======"
print seg_raw_df.tail()
seg_annotation_df = set_segmentation_on_annotation(annotation_df,
seg_raw_df)
print "============test set segmentation on annotation==============="
print seg_annotation_df
print "=====test head======"
print seg_annotation_df.head(10)
# print "=====test joint====="
# print seg_annotation_df.iloc[315:325]
print "=====test tail======"
print seg_annotation_df.tail(10)
def test_pre_exclude_rest_instances():
sensor = 'DW'
raw_df, annotation_df = s_loader.load_smoking_df(session=6,
sensors=[
sensor,
],
kind='corrected')
seg_raw_df = s_segment.do_segmentation_on_raw(raw_df,
method='window',
paras={
'window_size': 320,
'overlap_rate': 0.5
})
seg_raw_df = s_feature.do_preprocess_on_segment_raw(seg_raw_df)
seg_annotation_df = s_segment.set_segmentation_on_annotation(
annotation_df, seg_raw_df)
feature_annotation_df = s_feature.get_info_from_segment_annotation(
seg_annotation_df, s_info.feature_info_dict)
class_df = assign_class_from_feature_annotation(feature_annotation_df)
# test
class_df = pre_exclude_rest_instances(seg_raw_df, class_df=class_df)
print "============Test result of pre-exclusion================="
print "============class dataframe comparation================"
print class_df
print "=========rest segments==============="
print class_df[class_df[s_info.classnum_col] == -1]
print "=========not rest segments==========="
print class_df[class_df[s_info.classnum_col] != -1]
def test_do_preprocess_on_segment_raw():
sensor = 'DW'
raw_df, annotation_df = s_loader.load_smoking_df(session=1,
sensors=[
sensor,
],
kind='corrected')
seg_raw_df = s_segment.do_segmentation_on_raw(raw_df,
method='window',
paras={
'window_size': 320,
'overlap_rate': 0.5
})
pp_seg_raw_df = do_preprocess_on_segment_raw(seg_raw_df)
print "===============test results of preprocessing on segment raw=================="
print seg_raw_df.head()
print pp_seg_raw_df.head()
def test_get_info_from_segment_annotation():
sensor = 'DW'
raw_df, annotation_df = s_loader.load_smoking_df(session=1,
sensors=[
sensor,
],
kind='corrected')
seg_raw_df = s_segment.do_segmentation_on_raw(raw_df,
method='window',
paras={
'window_size': 320,
'overlap_rate': 0.5
})
seg_annotation_df = s_segment.set_segmentation_on_annotation(
annotation_df, seg_raw_df)
feature_annotation_df = get_info_from_segment_annotation(
seg_annotation_df, s_info.feature_info_dict)
print "===================test feature construction result=================="
print feature_annotation_df
print "===================feature dataframe head==========================="
print feature_annotation_df.head().T
print "===================feature dataframe tail==========================="
print feature_annotation_df.tail().T
def test_get_features_from_segment_raw():
sensor = 'DW'
raw_df, annotation_df = s_loader.load_smoking_df(session=1,
sensors=[
sensor,
],
kind='corrected')
seg_raw_df = s_segment.do_segmentation_on_raw(raw_df,
method='window',
paras={
'window_size': 320,
'overlap_rate': 0.5
})
feature_func_dict = s_info.feature_dataset_folder + "/feature_dict_" + sensor + '.json'
feature_raw_df = get_features_from_segment_raw(seg_raw_df,
feature_func_dict)
print "===================test feature construction result=================="
print seg_raw_df
print feature_raw_df
print "===================feature dataframe head==========================="
print feature_raw_df.head().T
print "===================feature dataframe tail==========================="
print feature_raw_df.tail().T
def test_assign_class_from_feature_annotation():
sensor = 'DW'
raw_df, annotation_df = s_loader.load_smoking_df(session=1,
sensors=[
sensor,
],
kind='corrected')
seg_raw_df = s_segment.do_segmentation_on_raw(raw_df,
method='window',
paras={
'window_size': 40,
'overlap_rate': 0.5
})
seg_annotation_df = s_segment.set_segmentation_on_annotation(
annotation_df, seg_raw_df)
feature_annotation_df = s_feature.get_info_from_segment_annotation(
seg_annotation_df, s_info.feature_info_dict)
class_df = assign_class_from_feature_annotation(feature_annotation_df)
print "============Test result of assigning class================="
print class_df
print "============class dataframe head================="
print class_df.head().T
print "============class dataframe tail================="
print class_df.tail().T
def build_whole_dataset_each_sensor_from_csv(sessions=[],
sensors=[],
raw_type="corrected",
paras={
'window_size': 320,
'overlap_rate': 0.5
}):
""" It will load raw data and annotation from csv files, compute features and merge different sessions
them into a large dataset
Load:
1. Read in raw and annotation dataset
2. Do segmentation
3. Do feature construction and information computation
4. Do class assignment and pre-exclusion
5. Serialization
Connect:
1. Load
Args:
sessions: sessions in array to be used
sensors: sensor code in array to be used
raw_type: "raw", "clean" and "corrected"
paras: hyperparameters: window_size and overlap_rate
Return:
data_pkl_names, class_pkl_names
"""
print "================refresh pkls=============================================================="
print "sessions: %s, sensors: %s, raw_type: %s, paras: %s" % (
sessions, sensors, raw_type, paras)
if len(sessions) == 0 or len(sensors) == 0:
return None
data_pkl_names = {}
class_pkl_names = {}
for sensor in sensors:
data_pkl_names[sensor] = []
class_pkl_names[sensor] = []
# load from csv, get data and
for session in sessions:
raw_df, annotation_df = s_loader.load_smoking_df(session=session,
sensors=sensors,
kind=raw_type)
# groupby sensors
grouped_raw = raw_df.groupby(s_info.sensor_col)
grouped_annotation = annotation_df.groupby(s_info.sensor_col)
for sensor, group_raw in grouped_raw:
group_annotation = grouped_annotation.get_group(sensor)
# do segmentation on raw
seg_raw_df = s_segment.do_segmentation_on_raw(group_raw,
method='window',
paras=paras)
# do preprocessing on raw
seg_raw_df = s_feature.do_preprocess_on_segment_raw(seg_raw_df)
# do segmentation on annotation
seg_annotation_df = s_segment.set_segmentation_on_annotation(
group_annotation, seg_raw_df)
# do info construction on annotation
feature_annotation_df = s_feature.get_info_from_segment_annotation(
seg_annotation_df, s_info.feature_info_dict)
# do class assignment on annotation
class_df = s_prepare.assign_class_from_feature_annotation(
feature_annotation_df)
# do pre-exclusion on class dataframe
class_df = s_prepare.pre_exclude_rest_instances(seg_raw_df,
class_df=class_df)
# do feature construction on segment raw
feature_func_dict = s_info.feature_dataset_folder + "/feature_dict_" + sensor + '.json'
feature_raw_df = s_feature.get_features_from_segment_raw(
seg_raw_df, feature_func_dict)
data_df = s_prepare.get_data_df_from_feature_raw_df(feature_raw_df)
# serialize data and class df
data_pkl, class_pkl = s_prepare.serialize_data_and_class_df(
data_df, class_df)
data_pkl_names[sensor].append(data_pkl)
class_pkl_names[sensor].append(class_pkl)
print ""
# connect together
if len(sessions) > 1:
data_pkl_names, class_pkl_names = build_whole_dataset_each_sensor_from_pkl(
sessions, sensors, paras['window_size'])
return data_pkl_names, class_pkl_names
