def generateTimeSeriesOutlierScores(inDir,
use_link_db=False,
robust=False,
num_pcs=10,
gamma=.5,
tol_perc=1e-06,
perc_missing_allowed=.05,
make_zscore_vid=False,
pool=DefaultPool()):
numpy.set_printoptions(linewidth=1000, precision=4)
#Read the time-series data from the file
logMsg("Reading files...")
stdout.flush()
if (use_link_db):
file_prefix = "link_"
#pace_timeseries, pace_grouped, weights_grouped, dates_grouped, trip_names, consistent_link_set = load_pace_data(
# num_trips_threshold=consistent_threshold, pool=pool)
pace_timeseries, pace_grouped, weights_grouped, dates_grouped, trip_names, consistent_link_set = load_from_file(
use_link_db)
else:
file_prefix = "coarse_"
(pace_timeseries, pace_grouped, dates_grouped,
trip_names) = readPaceData(inDir)
if (robust):
if (gamma == "tune"):
robustStr = "RPCAtune"
else:
robustStr = "RPCA%d" % int(gamma * 100)
else:
robustStr = "PCA"
file_prefix += "%s_%s_%dpcs_%dpercmiss" % (inDir, robustStr, num_pcs,
perc_missing_allowed * 100)
#pace_grouped = preprocess_data(pace_grouped, num_pcs,
# perc_missing_allowed=perc_missing_allowed)
pace_grouped, trip_names = remove_bad_dimensions_grouped(
pace_grouped, trip_names, perc_missing_allowed)
# logMsg(trip_names)
#Also get global pace information
if inDir != '':
global_pace_timeseries = readGlobalPace(inDir)
(expected_pace_timeseries,
sd_pace_timeseries) = getExpectedPace(global_pace_timeseries)
logMsg("Starting processes")
if (gamma == "tune"):
logMsg("Doing RPCA and tuning gamma")
else:
logMsg("Doing RPCA with gamma=%f, k=%d" % (gamma, num_pcs))
stdout.flush()
# Freeze the parameters of the computeMahalanobisDistances() function
mahalFunc = partial(computeMahalanobisDistances,
robust=robust,
k=num_pcs,
gamma=gamma,
tol_perc=tol_perc)
# Compute all mahalanobis distances
sorted_keys = sorted(pace_grouped)
groups = [(key, pace_grouped[key]) for key in sorted_keys]
outlier_scores = pool.map(
mahalFunc, groups
) #Run all of the groups, using as much parallel computing as possible
logMsg("Merging output")
#Merge outputs from all of the threads
entries = reduceOutlierScores(outlier_scores, sorted_keys, dates_grouped)
logMsg("Writing file")
#Output outlier scores to file
scoreWriter = csv.writer(
open("results/%s_robust_outlier_scores.csv" % file_prefix, "w"))
scoreWriter.writerow([
'date', 'hour', 'weekday', 'mahal5', 'mahal10', 'mahal20', 'mahal50',
'c_val', 'gamma', 'tol', 'pca_dim', 'num_guess', 'hi_pcs',
'global_pace', 'expected_pace', 'sd_pace'
])
for (date, hour, weekday, mahal5, mahal10, mahal20, mahal50, c_val,
z_scores, gamma, tol, n_pca_dim, n_guess, hi_pcs) in sorted(entries):
try:
gl_pace = global_pace_timeseries[(date, hour, weekday)]
exp_pace = expected_pace_timeseries[(date, hour, weekday)]
sd_pace = sd_pace_timeseries[(date, hour, weekday)]
except:
gl_pace = 0
exp_pace = 0
sd_pace = 0
scoreWriter.writerow([
date, hour, weekday, mahal5, mahal10, mahal20, mahal50, c_val,
gamma, tol, n_pca_dim, n_guess, hi_pcs, gl_pace, exp_pace, sd_pace
])
all_cvals = [
c_val for (date, hour, weekday, mahal5, mahal10, mahal20, mahal50,
c_val, z_scores, gamma, tol, n_pca_dim, n_guess,
hi_pcs) in sorted(entries)
]
zscoreWriter = csv.writer(open("results/%s_zscore.csv" % file_prefix, "w"))
zscoreWriter.writerow(['Date', 'Hour', 'Weekday'] + trip_names)
#Output zscores to file
for (date, hour, weekday, mahal5, mahal10, mahal20, mahal50, c_val,
z_scores, gamma, tol, n_pca_dim, n_guess, hi_pcs) in sorted(entries):
std_vect = z_scores
zscoreWriter.writerow([date, hour, weekday] + ravel(std_vect).tolist())
#def make_video(tmp_folder, filename_base, pool=DefaultPool(), dates=None, speed_dicts=None)
if (make_zscore_vid):
logMsg("Making speed dicts")
#zscore_list = [zscores[key] for key in sorted(zscores)]
date_list = []
zscore_list = []
for (date, hour, weekday, mahal5, mahal10, mahal20, mahal50, c_val,
z_scores, gamma, tol, n_pca_dim, n_guess,
hi_pcs) in sorted(entries):
if (date >= '2014-06-01' and date < '2014-07-01'):
dt = datetime.strptime(date,
'%Y-%m-%d') + timedelta(hours=int(hour))
date_list.append(dt)
zscore_list.append(z_scores)
speed_dicts = build_speed_dicts(consistent_link_set, zscore_list)
logMsg("Making video with %d frames" % len(zscore_list))
with open('tmp_zscores.pickle', 'w') as f:
pickle.dump((date_list, speed_dicts), f)
make_video("tmp_vid",
"zscore_vid",
pool=pool,
dates=date_list,
speed_dicts=speed_dicts)
logMsg("Done.")
return all_cvals
def generateTimeSeriesOutlierScores(inDir, use_link_db=False, robust=False, num_pcs=10,
gamma=.5, tol_perc=1e-06, perc_missing_allowed=.05,
make_zscore_vid=False, pool = DefaultPool()):
numpy.set_printoptions(linewidth=1000, precision=4)
#Read the time-series data from the file
logMsg("Reading files...")
stdout.flush()
if(use_link_db):
file_prefix = "link_"
#pace_timeseries, pace_grouped, weights_grouped, dates_grouped, trip_names, consistent_link_set = load_pace_data(
# num_trips_threshold=consistent_threshold, pool=pool)
pace_timeseries, pace_grouped, weights_grouped, dates_grouped, trip_names, consistent_link_set = load_from_file(use_link_db)
else:
file_prefix = "coarse_"
(pace_timeseries, pace_grouped, dates_grouped, trip_names) = readPaceData(inDir)
if(robust):
if(gamma=="tune"):
robustStr = "RPCAtune"
else:
robustStr = "RPCA%d" % int(gamma*100)
else:
robustStr = "PCA"
file_prefix += "%s_%s_%dpcs_%dpercmiss" % (inDir, robustStr, num_pcs, perc_missing_allowed*100)
#pace_grouped = preprocess_data(pace_grouped, num_pcs,
# perc_missing_allowed=perc_missing_allowed)
pace_grouped, trip_names = remove_bad_dimensions_grouped(pace_grouped, trip_names, perc_missing_allowed)
logMsg(trip_names)
#Also get global pace information
global_pace_timeseries = readGlobalPace(inDir)
(expected_pace_timeseries, sd_pace_timeseries) = getExpectedPace(global_pace_timeseries)
logMsg("Starting processes")
if(gamma=="tune"):
logMsg("Doing RPCA and tuning gamma")
else:
logMsg("Doing RPCA with gamma=%f, k=%d" % (gamma, num_pcs))
stdout.flush()
# Freeze the parameters of the computeMahalanobisDistances() function
mahalFunc = partial(computeMahalanobisDistances, robust=robust, k=num_pcs,
gamma=gamma, tol_perc=tol_perc)
# Compute all mahalanobis distances
sorted_keys = sorted(pace_grouped)
groups = [(key,pace_grouped[key]) for key in sorted_keys]
outlier_scores = pool.map(mahalFunc, groups) #Run all of the groups, using as much parallel computing as possible
logMsg("Merging output")
#Merge outputs from all of the threads
entries = reduceOutlierScores(outlier_scores, sorted_keys, dates_grouped)
logMsg("Writing file")
#Output outlier scores to file
scoreWriter = csv.writer(open("results/%s_robust_outlier_scores.csv"%file_prefix, "w"))
scoreWriter.writerow(['date','hour','weekday', 'mahal5', 'mahal10', 'mahal20',
'mahal50' ,'c_val', 'gamma', 'tol', 'pca_dim', 'num_guess',
'hi_pcs', 'global_pace', 'expected_pace', 'sd_pace'])
for (date, hour, weekday, mahal5, mahal10, mahal20, mahal50, c_val, z_scores, gamma, tol,
n_pca_dim, n_guess, hi_pcs) in sorted(entries):
try:
gl_pace = global_pace_timeseries[(date, hour, weekday)]
exp_pace = expected_pace_timeseries[(date, hour, weekday)]
sd_pace = sd_pace_timeseries[(date, hour, weekday)]
except:
gl_pace = 0
exp_pace = 0
sd_pace = 0
scoreWriter.writerow([date, hour, weekday, mahal5, mahal10, mahal20, mahal50,
c_val, gamma, tol, n_pca_dim, n_guess, hi_pcs,
gl_pace, exp_pace, sd_pace])
all_cvals = [c_val for(date, hour, weekday, mahal5, mahal10, mahal20, mahal50,
c_val, z_scores, gamma, tol,n_pca_dim, n_guess, hi_pcs) in sorted(entries)]
zscoreWriter= csv.writer(open("results/%s_zscore.csv"%file_prefix, "w"))
zscoreWriter.writerow(['Date','Hour','Weekday'] + trip_names)
#Output zscores to file
for (date, hour, weekday, mahal5, mahal10, mahal20, mahal50, c_val, z_scores, gamma, tol,
n_pca_dim, n_guess, hi_pcs) in sorted(entries):
std_vect = z_scores
zscoreWriter.writerow([date, hour, weekday] + ravel(std_vect).tolist())
#def make_video(tmp_folder, filename_base, pool=DefaultPool(), dates=None, speed_dicts=None)
if(make_zscore_vid):
logMsg("Making speed dicts")
#zscore_list = [zscores[key] for key in sorted(zscores)]
date_list = []
zscore_list = []
for (date, hour, weekday, mahal5, mahal10, mahal20, mahal50, c_val, z_scores, gamma, tol,
n_pca_dim, n_guess, hi_pcs) in sorted(entries):
if(date >= '2012-10-21' and date < '2012-11-11'):
dt = datetime.strptime(date, '%Y-%m-%d') + timedelta(hours=int(hour))
date_list.append(dt)
zscore_list.append(z_scores)
speed_dicts = build_speed_dicts(consistent_link_set, zscore_list)
logMsg("Making video with %d frames" % len(zscore_list))
with open('tmp_zscores.pickle', 'w') as f:
pickle.dump((date_list, speed_dicts), f)
make_video("tmp_vid", "zscore_vid", pool=pool, dates=date_list, speed_dicts=speed_dicts)
logMsg("Done.")
return all_cvals
def generateTimeSeriesOutlierScores(inDir, use_link_db=False, robust=False, num_pcs=10,
gamma=.5, perc_missing_allowed=.05, make_zscore_vid=False,
pool = DefaultPool()):
numpy.set_printoptions(linewidth=1000, precision=4)
#Read the time-series data from the file
logMsg("Reading files...")
stdout.flush()
if(use_link_db):
file_prefix = "link_"
#pace_timeseries, pace_grouped, weights_grouped, dates_grouped, trip_names, consistent_link_set = load_pace_data(
# num_trips_threshold=consistent_threshold, pool=pool)
pace_timeseries, pace_grouped, weights_grouped, dates_grouped, trip_names, consistent_link_set = load_from_file('tmp_vectors.pickle')
else:
file_prefix = "coarse_"
(pace_timeseries, pace_grouped, dates_grouped, trip_names) = readPaceData(inDir)
if(robust):
robustStr = "RPCA%d" % int(gamma*100)
else:
robustStr = "PCA"
file_prefix += "%s_%s_%dpcs_%dpercmiss" % (inDir, robustStr, num_pcs, perc_missing_allowed*100)
#pace_grouped = preprocess_data(pace_grouped, num_pcs,
# perc_missing_allowed=perc_missing_allowed)
pace_grouped = remove_bad_dimensions_grouped(pace_grouped, perc_missing_allowed)
#Also get global pace information
global_pace_timeseries = readGlobalPace(inDir)
(expected_pace_timeseries, sd_pace_timeseries) = getExpectedPace(global_pace_timeseries)
logMsg("Starting processes")
logMsg("Doing RPCA with gamma=%f, k=%d" % (gamma, num_pcs))
stdout.flush()
# Freeze the parameters of the computeMahalanobisDistances() function
mahalFunc = partial(computeMahalanobisDistances, robust=robust, k=num_pcs,
gamma=gamma)
# Compute all mahalanobis distances
sorted_keys = sorted(pace_grouped)
groups = [pace_grouped[key] for key in sorted_keys]
outlier_scores = pool.map(mahalFunc, groups) #Run all of the groups, using as much parallel computing as possible
logMsg("Merging output")
#Merge outputs from all of the threads
entries = reduceOutlierScores(outlier_scores, sorted_keys, dates_grouped)
logMsg("Writing file")
#Output outlier scores to file
scoreWriter = csv.writer(open("results/%s_robust_outlier_scores.csv"%file_prefix, "w"))
scoreWriter.writerow(['date','hour','weekday', 'mahal' ,'c_val','global_pace', 'expected_pace', 'sd_pace'])
for (date, hour, weekday, mahal, c_val) in sorted(entries):
try:
gl_pace = global_pace_timeseries[(date, hour, weekday)]
exp_pace = expected_pace_timeseries[(date, hour, weekday)]
sd_pace = sd_pace_timeseries[(date, hour, weekday)]
except:
gl_pace = 0
exp_pace = 0
sd_pace = 0
scoreWriter.writerow([date, hour, weekday, mahal, c_val, gl_pace, exp_pace, sd_pace])
"""
zscoreWriter= csv.writer(open("results/%szscore.csv"%file_prefix, "w"))
zscoreWriter.writerow(['Date','Hour','Weekday'] + trip_names)
#Output zscores to file
for (date, hour, weekday) in sorted(zscores):
std_vect = zscores[date, hour, weekday]
zscoreWriter.writerow([date, hour, weekday] + ravel(std_vect).tolist())
"""
#def make_video(tmp_folder, filename_base, pool=DefaultPool(), dates=None, speed_dicts=None)
if(make_zscore_vid):
logMsg("Making speed dicts")
#zscore_list = [zscores[key] for key in sorted(zscores)]
date_list = dates = [datetime(2012,10,21) + timedelta(hours=1)*x for x in range(168*3)]
weekday_names = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
zscore_list = [zscores[str(d.date()), d.hour, weekday_names[d.weekday()]] for d in date_list]
speed_dicts = build_speed_dicts(consistent_link_set, zscore_list)
logMsg("Making video")
make_video("tmp_vid", "zscore_vid", pool=pool, dates=date_list, speed_dicts=speed_dicts)
logMsg("Done.")
