-
Notifications
You must be signed in to change notification settings - Fork 48
/
monitoring.py
808 lines (645 loc) · 30 KB
/
monitoring.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
"""
The monitoring module contains the PerformanceMonitoring class used to run
quality control tests and store results. The module also contains individual
functions that can be used to run quality control tests.
"""
import pandas as pd
import numpy as np
import logging
none_list = ['','none','None','NONE', None, [], {}]
NoneType = type(None)
logger = logging.getLogger(__name__)
def _documented_by(original):
def wrapper(target):
docstring = original.__doc__
old = """
Parameters
----------
"""
new = """
Parameters
----------
data : pandas DataFrame
Data used in the quality control test, indexed by datetime
"""
new_docstring = docstring.replace(old, new) + \
"""
Returns
----------
dictionary
Results include cleaned data, mask, and test results summary
"""
target.__doc__ = new_docstring
return target
return wrapper
### Object-oriented approach
class PerformanceMonitoring(object):
def __init__(self):
"""
PerformanceMonitoring class
"""
self.df = pd.DataFrame()
self.trans = {}
self.tfilter = pd.Series()
self.test_results = pd.DataFrame(columns=['Variable Name',
'Start Time', 'End Time',
'Timesteps', 'Error Flag'])
@property
def mask(self):
"""
Boolean mask indicating data that failed a quality control test
Returns
--------
pandas DataFrame
Boolean values for each data point,
True = data point pass all tests,
False = data point did not pass at least one test (or data is NaN).
"""
if self.df.empty:
logger.info("Empty database")
return
mask = pd.DataFrame(True, index=self.df.index, columns=self.df.columns)
for i in self.test_results.index:
variable = self.test_results.loc[i, 'Variable Name']
start_date = self.test_results.loc[i, 'Start Time']
end_date = self.test_results.loc[i, 'End Time']
if variable in mask.columns:
try:
mask.loc[start_date:end_date,variable] = False
except:
pass
elif self.test_results.loc[i, 'Error Flag'] == 'Missing timestamp':
mask.loc[start_date:end_date,:] = False
return mask
@property
def cleaned_data(self):
"""
Cleaned data set
Returns
--------
pandas DataFrame
Cleaned data set, data that failed a quality control test are
replaced by NaN
"""
return self.df[self.mask]
def _setup_data(self, key):
"""
Setup data to use in the quality control test
"""
if self.df.empty:
logger.info("Empty database")
return
# Isolate subset if key is not None
if key is not None:
try:
df = self.df[self.trans[key]]
except:
logger.warning("Undefined key: " + key)
return
else:
df = self.df
return df
def _generate_test_results(self, df, bound, min_failures, error_prefix):
"""
Compare DataFrame to bounds to generate a True/False mask where
True = passed, False = failed. Append results to test_results.
"""
# Lower Bound
if bound[0] not in none_list:
mask = (df < bound[0])
error_msg = error_prefix+' < lower bound, '+str(bound[0])
self._append_test_results(mask, error_msg, min_failures)
# Upper Bound
if bound[1] not in none_list:
mask = (df > bound[1])
error_msg = error_prefix+' > upper bound, '+str(bound[1])
self._append_test_results(mask, error_msg, min_failures)
def _append_test_results(self, mask, error_msg, min_failures=1, use_mask_only=False):
"""
Append QC results to the PerformanceMonitoring object.
Parameters
----------
mask : pandas DataFrame
Result from quality control test, boolean values
error_msg : string
Error message to store with the QC results
min_failures : int (optional)
Minimum number of consecutive failures required for reporting,
default = 1
use_mask_only : boolean (optional)
When True, the mask is used directly to determine test
results and the variable name is not included in the
test_results. When False, the mask is used in combination with
pm.df to extract test results. Default = False
"""
if not self.tfilter.empty:
mask[~self.tfilter] = False
if mask.sum(axis=1).sum(axis=0) == 0:
return
if use_mask_only:
sub_df = mask
else:
sub_df = self.df[mask.columns]
# Find blocks
order = 'col'
if order == 'col':
mask = mask.T
np_mask = mask.values
start_nans_mask = np.hstack(
(np.resize(np_mask[:,0],(mask.shape[0],1)),
np.logical_and(np.logical_not(np_mask[:,:-1]), np_mask[:,1:])))
stop_nans_mask = np.hstack(
(np.logical_and(np_mask[:,:-1], np.logical_not(np_mask[:,1:])),
np.resize(np_mask[:,-1], (mask.shape[0],1))))
start_row_idx,start_col_idx = np.where(start_nans_mask)
stop_row_idx,stop_col_idx = np.where(stop_nans_mask)
if order == 'col':
temp = start_row_idx; start_row_idx = start_col_idx; start_col_idx = temp
temp = stop_row_idx; stop_row_idx = stop_col_idx; stop_col_idx = temp
#mask = mask.T
block = {'Start Row': list(start_row_idx),
'Start Col': list(start_col_idx),
'Stop Row': list(stop_row_idx),
'Stop Col': list(stop_col_idx)}
#if sub_df is None:
# sub_df = self.df
for i in range(len(block['Start Col'])):
length = block['Stop Row'][i] - block['Start Row'][i] + 1
if length >= min_failures:
if use_mask_only:
var_name = ''
else:
var_name = sub_df.iloc[:,block['Start Col'][i]].name #sub_df.icol(block['Start Col'][i]).name
frame = pd.DataFrame([var_name,
sub_df.index[block['Start Row'][i]],
sub_df.index[block['Stop Row'][i]],
length, error_msg],
index=['Variable Name', 'Start Time',
'End Time', 'Timesteps', 'Error Flag'])
self.test_results = self.test_results.append(frame.T, ignore_index=True)
def add_dataframe(self, data):
"""
Add data to the PerformanceMonitoring object
Parameters
-----------
data : pandas DataFrame
Data to add to the PerformanceMonitoring object, indexed by datetime
"""
assert isinstance(data, pd.DataFrame), 'data must be of type pd.DataFrame'
assert isinstance(data.index, pd.core.indexes.datetimes.DatetimeIndex), 'data.index must be a DatetimeIndex'
temp = data.copy()
if self.df is not None:
self.df = temp.combine_first(self.df)
else:
self.df = temp
# Add identity 1:1 translation dictionary
trans = {}
for col in temp.columns:
trans[col] = [col]
self.add_translation_dictionary(trans)
def add_translation_dictionary(self, trans):
"""
Add translation dictionary to the PerformanceMonitoring object
Parameters
-----------
trans : dictionary
Translation dictionary
"""
assert isinstance(trans, dict), 'trans must be of type dictionary'
for key, values in trans.items():
self.trans[key] = []
for value in values:
self.trans[key].append(value)
def add_time_filter(self, time_filter):
"""
Add a time filter to the PerformanceMonitoring object
Parameters
----------
time_filter : pandas DataFrame with a single column or pandas Series
Time filter containing boolean values for each time index
"""
assert isinstance(time_filter, (pd.Series, pd.DataFrame)), 'time_filter must be of type pd.Series or pd.DataFrame'
if isinstance(time_filter, pd.DataFrame):
self.tfilter = pd.Series(data = time_filter.values[:,0], index = self.df.index)
else:
self.tfilter = time_filter
def check_timestamp(self, frequency, expected_start_time=None,
expected_end_time=None, min_failures=1,
exact_times=True):
"""
Check time series for missing, non-monotonic and duplicate
timestamps
Parameters
----------
frequency : int or float
Expected time series frequency, in seconds
expected_start_time : Timestamp (optional)
Expected start time. If not specified, the minimum timestamp
is used
expected_end_time : Timestamp (optional)
Expected end time. If not specified, the maximum timestamp
is used
min_failures : int (optional)
Minimum number of consecutive failures required for
reporting, default = 1
exact_times : bool (optional)
Controls how missing times are checked.
If True, times are expected to occur at regular intervals
(specified in frequency) and the DataFrame is reindexed to match
the expected frequency.
If False, times only need to occur once or more within each
interval (specified in frequency) and the DataFrame is not
reindexed.
"""
assert isinstance(frequency, (int, float)), 'frequency must be of type int or float'
assert isinstance(expected_start_time, (NoneType, pd.Timestamp)), 'expected_start_time must be None or of type pd.Timestamp'
assert isinstance(expected_end_time, (NoneType, pd.Timestamp)), 'expected_end_time must be None or of type pd.Timestamp'
assert isinstance(min_failures, int), 'min_failures must be of type int'
assert isinstance(exact_times, bool), 'exact_times must be of type bool'
logger.info("Check timestamp")
if self.df.empty:
logger.info("Empty database")
return
if expected_start_time is None:
expected_start_time = min(self.df.index)
if expected_end_time is None:
expected_end_time = max(self.df.index)
rng = pd.date_range(start=expected_start_time, end=expected_end_time,
freq=str(int(frequency*1e3)) + 'ms') # milliseconds
# Check to see if timestamp is monotonic
# mask = pd.TimeSeries(self.df.index).diff() < 0
mask = pd.Series(self.df.index).diff() < pd.Timedelta('0 days 00:00:00')
mask.index = self.df.index
mask[mask.index[0]] = False
mask = pd.DataFrame(mask)
mask.columns = [0]
self._append_test_results(mask, 'Nonmonotonic timestamp',
use_mask_only=True,
min_failures=min_failures)
# If not monotonic, sort df by timestamp
if not self.df.index.is_monotonic:
self.df = self.df.sort_index()
# Check for duplicate timestamps
# mask = pd.TimeSeries(self.df.index).diff() == 0
mask = pd.Series(self.df.index).diff() == pd.Timedelta('0 days 00:00:00')
mask.index = self.df.index
mask[mask.index[0]] = False
mask = pd.DataFrame(mask)
mask.columns = [0]
mask['TEMP'] = mask.index # remove duplicates in the mask
mask.drop_duplicates(subset='TEMP', keep='last', inplace=True)
del mask['TEMP']
# Drop duplicate timestamps (this has to be done before the
# results are appended)
self.df['TEMP'] = self.df.index
#self.df.drop_duplicates(subset='TEMP', take_last=False, inplace=True)
self.df.drop_duplicates(subset='TEMP', keep='first', inplace=True)
self._append_test_results(mask, 'Duplicate timestamp',
use_mask_only=True,
min_failures=min_failures)
del self.df['TEMP']
if exact_times:
temp = pd.Index(rng)
missing = temp.difference(self.df.index).tolist()
# reindex DataFrame
self.df = self.df.reindex(index=rng)
mask = pd.DataFrame(data=self.df.shape[0]*[False],
index=self.df.index)
mask.loc[missing] = True
self._append_test_results(mask, 'Missing timestamp',
use_mask_only=True,
min_failures=min_failures)
else:
# uses pandas >= 0.18 resample syntax
df_index = pd.DataFrame(index=self.df.index)
df_index[0]=1 # populate with placeholder values
mask = df_index.resample(str(int(frequency*1e3))+'ms').count() == 0 # milliseconds
self._append_test_results(mask, 'Missing timestamp',
use_mask_only=True,
min_failures=min_failures)
def check_range(self, bound, key=None, min_failures=1):
"""
Check for data that is outside expected range
Parameters
----------
bound : list of floats
[lower bound, upper bound], None can be used in place of a lower
or upper bound
key : string (optional)
Data column name or translation dictionary key. If not specified,
all columns are used in the test.
min_failures : int (optional)
Minimum number of consecutive failures required for reporting,
default = 1
"""
assert isinstance(bound, list), 'bound must be of type list'
assert isinstance(key, (NoneType, str)), 'key must be None or of type string'
assert isinstance(min_failures, int), 'min_failures must be of type int'
logger.info("Check for data outside expected range")
df = self._setup_data(key)
if df is None:
return
error_prefix = 'Data'
self._generate_test_results(df, bound, min_failures, error_prefix)
def check_increment(self, bound, key=None, increment=1, absolute_value=True,
min_failures=1):
"""
Check data increments using the difference between values
Parameters
----------
bound : list of floats
[lower bound, upper bound], None can be used in place of a lower
or upper bound
key : string (optional)
Data column name or translation dictionary key. If not specified,
all columns are used in the test.
increment : int (optional)
Time step shift used to compute difference, default = 1
absolute_value : boolean (optional)
Use the absolute value of the increment data, default = True
min_failures : int (optional)
Minimum number of consecutive failures required for reporting,
default = 1
"""
assert isinstance(bound, list), 'bound must be of type list'
assert isinstance(key, (NoneType, str)), 'key must be None or of type string'
assert isinstance(increment, int), 'increment must be of type int'
assert isinstance(absolute_value, bool), 'absolute_value must be of type bool'
assert isinstance(min_failures, int), 'min_failures must be of type int'
logger.info("Check for data increment outside expected range")
df = self._setup_data(key)
if df is None:
return
if df.isnull().all().all():
logger.warning("Check increment range failed (all data is Null): " + key)
return
# Compute interval
if absolute_value:
df = np.abs(df.diff(periods=increment))
else:
df = df.diff(periods=increment)
if absolute_value:
error_prefix = '|Increment|'
else:
error_prefix = 'Increment'
self._generate_test_results(df, bound, min_failures, error_prefix)
def check_delta(self, bound, key=None, window=3600, direction=None,
min_failures=1):
"""
Check for stagnant data and/or abrupt changes in the data using the
difference between max and min values (delta) within a rolling window
Parameters
----------
bound : list of floats
[lower bound, upper bound], None can be used in place of a lower
or upper bound
key : string (optional)
Data column name or translation dictionary key. If not specified,
all columns are used in the test.
window : int or float (optional)
Size of the rolling window (in seconds) used to compute delta,
default = 3600
direction : str (optional)
Options = 'positive', 'negative', or None
* If direction is positive, then only identify positive deltas
(the min occurs before the max)
* If direction is negative, then only identify negative deltas
(the max occurs before the min)
* If direction is None, then identify both positive and negative
deltas
min_failures : int (optional)
Minimum number of consecutive failures required for reporting,
default = 1
"""
assert isinstance(bound, list), 'bound must be of type list'
assert isinstance(key, (NoneType, str)), 'key must be None or of type string'
assert isinstance(window, (int, float)), 'window must be of type int or float'
assert direction in [None, 'positive', 'negative'], "direction must None or the string 'positive' or 'negative'"
assert isinstance(min_failures, int), 'min_failures must be of type int'
assert self.df.index.is_monotonic, 'index must be monotonic'
logger.info("Check for stagant data and/or abrupt changes using delta (max-min) within a rolling window")
df = self._setup_data(key)
if df is None:
return
window_str = str(int(window*1e3)) + 'ms' # milliseconds
min_df = df.rolling(window_str, min_periods=2, closed='both').min()
max_df = df.rolling(window_str, min_periods=2, closed='both').max()
diff_df = max_df - min_df
diff_df.loc[diff_df.index[0]:diff_df.index[0]+pd.Timedelta(window_str),:] = None
def update_mask(mask1, df, window_str, bound, direction):
# While the mask flags data at the time at which the failure occurs,
# the actual timespan betwen the min and max should be flagged so that
# the final results include actual data points that caused the failure.
# This function uses numpy arrays to improve performance and returns
# a mask DataFrame.
mask2 = np.zeros((len(mask1.index), len(mask1.columns)), dtype=bool)
index = mask1.index
# Loop over t, col in mask1 where condition is True
for t,col in list(mask1[mask1 > 0].stack().index):
icol = mask1.columns.get_loc(col)
it = mask1.index.get_loc(t)
t1 = t-pd.Timedelta(window_str)
if (bound == 'lower') and (direction is None):
# set the entire time interval to True
mask2[(index >= t1) & (index <= t),icol] = True
else:
# extract the min and max time
min_time = df.loc[t1:t,col].idxmin()
max_time = df.loc[t1:t,col].idxmax()
if bound == 'lower': # bound = upper, direction = positive or negative
# set the entire time interval to True
if (direction == 'positive') and (min_time <= max_time):
mask2[(index >= t1) & (index <= t),icol] = True
elif (direction == 'negative') and (min_time >= max_time):
mask2[(index >= t1) & (index <= t),icol] = True
elif bound == 'upper': # bound = upper, direction = None, positive or negative
# set the initially flaged location to False
mask2[it,icol] = False
# set the time between max/min or min/max to true
if min_time < max_time and (direction is None or direction == 'positive'):
mask2[(index >= min_time) & (index <= max_time),icol] = True
elif min_time > max_time and (direction is None or direction == 'negative'):
mask2[(index >= max_time) & (index <= min_time),icol] = True
elif min_time == max_time:
mask2[it,icol] = True
mask2 = pd.DataFrame(mask2, columns=mask1.columns, index=mask1.index)
return mask2
if direction == 'positive':
error_prefix = 'Delta (+)'
elif direction == 'negative':
error_prefix = 'Delta (-)'
else:
error_prefix = 'Delta'
# Lower Bound
if bound[0] not in none_list:
mask = (diff_df < bound[0])
error_msg = error_prefix+' < lower bound, '+str(bound[0])
if not self.tfilter.empty:
mask[~self.tfilter] = False
mask = update_mask(mask, df, window_str, 'lower', direction)
self._append_test_results(mask, error_msg, min_failures)
# Upper Bound
if bound[1] not in none_list:
mask = (diff_df > bound[1])
error_msg = error_prefix+' > upper bound, '+str(bound[1])
if not self.tfilter.empty:
mask[~self.tfilter] = False
mask = update_mask(mask, df, window_str, 'upper', direction)
self._append_test_results(mask, error_msg, min_failures)
def check_outlier(self, bound, key=None, window=3600, absolute_value=True,
min_failures=1):
"""
Check for outliers using normalized data within a rolling window
The upper and lower bounds are specified in standard deviations.
Data normalized using (data-mean)/std.
Parameters
----------
bound : list of floats
[lower bound, upper bound], None can be used in place of a lower
or upper bound
key : string (optional)
Data column name or translation dictionary key. If not specified,
all columns are used in the test.
window : int or float (optional)
Size of the rolling window (in seconds) used to normalize data,
default = 3600. If window is set to None, data is normalized using
the entire data sets mean and standard deviation (column by column).
absolute_value : boolean (optional)
Use the absolute value the normalized data, default = True
min_failures : int (optional)
Minimum number of consecutive failures required for reporting,
default = 1
"""
assert isinstance(bound, list), 'bound must be of type list'
assert isinstance(key, (NoneType, str)), 'key must be None or of type string'
assert isinstance(window, (NoneType, int, float)), 'window must be None or of type int or float'
assert isinstance(absolute_value, bool), 'absolute_value must be of type bool'
assert isinstance(min_failures, int), 'min_failures must be type int'
assert self.df.index.is_monotonic, 'index must be monotonic'
logger.info("Check for outliers")
df = self._setup_data(key)
if df is None:
return
# Compute normalized data
if window is not None:
window_str = str(int(window*1e3)) + 'ms' # milliseconds
df_mean = df.rolling(window_str, min_periods=2, closed='both').mean()
df_std = df.rolling(window_str, min_periods=2, closed='both').std()
df = (df - df_mean)/df_std
else:
df = (df - df.mean())/df.std()
if absolute_value:
df = np.abs(df)
df.replace([np.inf, -np.inf], np.nan, inplace=True)
if absolute_value:
error_prefix = '|Outlier|'
else:
error_prefix = 'Outlier'
#df[df.index[0]:df.index[0]+datetime.timedelta(seconds=window)] = np.nan
self._generate_test_results(df, bound, min_failures, error_prefix)
def check_missing(self, key=None, min_failures=1):
"""
Check for missing data
Parameters
----------
key : string (optional)
Data column name or translation dictionary key. If not specified,
all columns are used in the test.
min_failures : int (optional)
Minimum number of consecutive failures required for reporting,
default = 1
"""
assert isinstance(key, (NoneType, str)), 'key must be None or of type string'
assert isinstance(min_failures, int), 'min_failures must be type int'
logger.info("Check for missing data")
df = self._setup_data(key)
if df is None:
return
# Extract missing data
mask = pd.isnull(df) # checks for np.nan, np.inf
missing_timestamps = self.test_results[
self.test_results['Error Flag'] == 'Missing timestamp']
for index, row in missing_timestamps.iterrows():
mask.loc[row['Start Time']:row['End Time']] = False
self._append_test_results(mask, 'Missing data', min_failures=min_failures)
def check_corrupt(self, corrupt_values, key=None, min_failures=1):
"""
Check for corrupt data
Parameters
----------
corrupt_values : list of int or floats
List of corrupt data values
key : string (optional)
Data column name or translation dictionary key. If not specified,
all columns are used in the test.
min_failures : int (optional)
Minimum number of consecutive failures required for reporting,
default = 1
"""
assert isinstance(corrupt_values, list), 'corrupt_values must be of type list'
assert isinstance(key, (NoneType, str)), 'key must be None or of type string'
assert isinstance(min_failures, int), 'min_failures must be type int'
logger.info("Check for corrupt data")
df = self._setup_data(key)
if df is None:
return
# Extract corrupt data
mask = pd.DataFrame(data = np.zeros(df.shape), index = df.index, columns = df.columns, dtype = bool) # all False
for i in corrupt_values:
mask = mask | (df == i)
self.df[mask] = np.nan
self._append_test_results(mask, 'Corrupt data', min_failures=min_failures)
### Functional approach
@_documented_by(PerformanceMonitoring.check_timestamp)
def check_timestamp(data, frequency, expected_start_time=None,
expected_end_time=None, min_failures=1, exact_times=True):
pm = PerformanceMonitoring()
pm.add_dataframe(data)
pm.check_timestamp(frequency, expected_start_time, expected_end_time,
min_failures, exact_times)
mask = pm.mask
return {'cleaned_data': pm.df, 'mask': mask, 'test_results': pm.test_results}
@_documented_by(PerformanceMonitoring.check_range)
def check_range(data, bound, key=None, min_failures=1):
pm = PerformanceMonitoring()
pm.add_dataframe(data)
pm.check_range(bound, key, min_failures)
mask = pm.mask
return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results}
@_documented_by(PerformanceMonitoring.check_increment)
def check_increment(data, bound, key=None, increment=1, absolute_value=True,
min_failures=1):
pm = PerformanceMonitoring()
pm.add_dataframe(data)
pm.check_increment(bound, key, increment, absolute_value, min_failures)
mask = pm.mask
return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results}
@_documented_by(PerformanceMonitoring.check_delta)
def check_delta(data, bound, key=None, window=3600, direction=None, min_failures=1):
pm = PerformanceMonitoring()
pm.add_dataframe(data)
pm.check_delta(bound, key, window, direction, min_failures)
mask = pm.mask
return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results}
@_documented_by(PerformanceMonitoring.check_outlier)
def check_outlier(data, bound, key=None, window=3600, absolute_value=True,
min_failures=1):
pm = PerformanceMonitoring()
pm.add_dataframe(data)
pm.check_outlier(bound, key, window, absolute_value, min_failures)
mask = pm.mask
return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results}
@_documented_by(PerformanceMonitoring.check_missing)
def check_missing(data, key=None, min_failures=1):
pm = PerformanceMonitoring()
pm.add_dataframe(data)
pm.check_missing(key, min_failures)
mask = pm.mask
return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results}
@_documented_by(PerformanceMonitoring.check_corrupt)
def check_corrupt(data, corrupt_values, key=None, min_failures=1):
pm = PerformanceMonitoring()
pm.add_dataframe(data)
pm.check_corrupt(corrupt_values, key, min_failures)
mask = pm.mask
return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results}