def get_all_features_for_id(userid, split_by, c):
""" get all summary features for watch data for one user """
features = ['timestamp', 'heart_rate', 'light', 'total_steps', 'walk_steps', 'run_steps', 'walk_freq', 'step_status', 'speed', 'distance', 'calories', 'accelx', 'accely', 'accelz', 'rotata', 'rotatb', 'rotatc']
# get data from mysql
breaks = tt.get_time_breaks(userid, split_by, c)
data = pd.read_sql_query("SELECT %s FROM user_data WHERE userid = %s;" % (", ".join(features), userid), c)
# split data and ensure correct format and data types
time_series = pd.concat([split_blob_series(data.iloc[i,:]) for i in range(data.shape[0])])
time_series.columns = features
time_series = time_series.convert_objects(convert_dates=True, convert_numeric=True)
time_series['timestamp'] = pd.to_datetime(time_series['timestamp'])
time_series = time_series.set_index('timestamp')
# convert cumulative data types
cum_dtypes = ['total_steps', 'walk_steps', 'run_steps', 'distance', 'calories']
#final_values = pd.concat([time_series[:b][cum_dtypes].iloc[-1] for b in breaks[1:]], axis=1).T
final_values = pd.concat([tt.get_time_ceiling(b, time_series[cum_dtypes]) for b in breaks[1:]], axis=1).T
time_series = time_series.join(time_series[cum_dtypes].diff(), lsuffix="", rsuffix="_diff")
time_series = time_series.drop(cum_dtypes, 1)
# convert acceleration and rotation coordinates to spherical
time_series = time_series.join(cart2sph(time_series[['accelx', 'accely', 'accelz']]))
time_series = time_series.drop(['accelx', 'accely', 'accelz'], 1)
# return combined data over all time series
data = pd.concat([tt.break_and_get_stats(time_series[x], breaks) for x in time_series.columns], axis=0).T
featurelist = []
for feat in time_series.columns:
featurelist.extend(['mean_%s' % feat, 'std_%s' % feat, 'freq_%s' % feat])
data.columns = featurelist
final_values.index = data.index
data = data.join(final_values)
return data
def get_var_by_user(var, user, split_by, c):
if split_by == 'hour':
df = pd.read_sql_query("SELECT time, %s FROM user_insights WHERE userid = %s" % (var, user), c)
df = df.set_index('time')
breaks = tt.get_time_breaks(user, split_by, c)
return pd.concat([tt.get_time_ceiling(t, df) for t in breaks], axis=1).values[0,1:]
df = pd.read_sql_query("SELECT %s FROM user_insights WHERE userid = %s" % (var, user), c)
return df.values.T[0]
def get_all_features_for_id(userid, split_by, c):
""" get all summary features for watch data for one user """
features = [
'timestamp', 'heart_rate', 'light', 'total_steps', 'walk_steps',
'run_steps', 'walk_freq', 'step_status', 'speed', 'distance',
'calories', 'accelx', 'accely', 'accelz', 'rotata', 'rotatb', 'rotatc'
]
# get data from mysql
breaks = tt.get_time_breaks(userid, split_by, c)
data = pd.read_sql_query(
"SELECT %s FROM user_data WHERE userid = %s;" %
(", ".join(features), userid), c)
# split data and ensure correct format and data types
time_series = pd.concat(
[split_blob_series(data.iloc[i, :]) for i in range(data.shape[0])])
time_series.columns = features
time_series = time_series.convert_objects(convert_dates=True,
convert_numeric=True)
time_series['timestamp'] = pd.to_datetime(time_series['timestamp'])
time_series = time_series.set_index('timestamp')
# convert cumulative data types
cum_dtypes = [
'total_steps', 'walk_steps', 'run_steps', 'distance', 'calories'
]
#final_values = pd.concat([time_series[:b][cum_dtypes].iloc[-1] for b in breaks[1:]], axis=1).T
final_values = pd.concat(
[tt.get_time_ceiling(b, time_series[cum_dtypes]) for b in breaks[1:]],
axis=1).T
time_series = time_series.join(time_series[cum_dtypes].diff(),
lsuffix="",
rsuffix="_diff")
time_series = time_series.drop(cum_dtypes, 1)
# convert acceleration and rotation coordinates to spherical
time_series = time_series.join(
cart2sph(time_series[['accelx', 'accely', 'accelz']]))
time_series = time_series.drop(['accelx', 'accely', 'accelz'], 1)
# return combined data over all time series
data = pd.concat([
tt.break_and_get_stats(time_series[x], breaks)
for x in time_series.columns
],
axis=0).T
featurelist = []
for feat in time_series.columns:
featurelist.extend(
['mean_%s' % feat,
'std_%s' % feat,
'freq_%s' % feat])
data.columns = featurelist
final_values.index = data.index
data = data.join(final_values)
return data
def get_var_by_user(var, user, split_by, c):
if split_by == 'hour':
df = pd.read_sql_query(
"SELECT time, %s FROM user_insights WHERE userid = %s" %
(var, user), c)
df = df.set_index('time')
breaks = tt.get_time_breaks(user, split_by, c)
return pd.concat([tt.get_time_ceiling(t, df) for t in breaks],
axis=1).values[0, 1:]
df = pd.read_sql_query(
"SELECT %s FROM user_insights WHERE userid = %s" % (var, user), c)
return df.values.T[0]
def get_keyboard_features(id, split_by, c):
""" get all features - both latency and press time - for one user """
if split_by not in ['user', 'insight', 'hour']:
raise ValueError("could not get split_by %s" % split_by)
if split_by == 'insight' or split_by == 'hour':
insights = get_insights_by_user(id, c).T.values[0]
keyboard = pd.concat([combine_keyboard_features(i,split_by,c) for i in insights])
if split_by == 'insight':
return keyboard
else:
breaks = tt.get_time_breaks(id, 'hour', c)[1:]
times = pd.Series(get_insight_times_by_user(id, c).values[:,0])
keyboard = keyboard.set_index(pd.to_datetime(times))
keyboard = pd.concat([tt.get_time_ceiling(t, keyboard) for t in breaks], axis=1).T
keyboard = keyboard.set_index(breaks)
return keyboard
else:
return combine_keyboard_features(id, split_by, c)
def get_keyboard_features(id, split_by, c):
""" get all features - both latency and press time - for one user """
if split_by not in ['user', 'insight', 'hour']:
raise ValueError("could not get split_by %s" % split_by)
if split_by == 'insight' or split_by == 'hour':
insights = get_insights_by_user(id, c).T.values[0]
keyboard = pd.concat(
[combine_keyboard_features(i, split_by, c) for i in insights])
if split_by == 'insight':
return keyboard
else:
breaks = tt.get_time_breaks(id, 'hour', c)[1:]
times = pd.Series(get_insight_times_by_user(id, c).values[:, 0])
keyboard = keyboard.set_index(pd.to_datetime(times))
keyboard = pd.concat(
[tt.get_time_ceiling(t, keyboard) for t in breaks], axis=1).T
keyboard = keyboard.set_index(breaks)
return keyboard
else:
return combine_keyboard_features(id, split_by, c)
