GritBot: An Informal Tutorial

"Grit" is a general term for small contaminants -- sand in food at the beach, foreign particles in gears, dust in one's eyes -- that can cause problems or discomfort. GritBot is a program that scans data to find implausible values or anomalies, a kind of "data grit". The presence of such anomalies compromises data quality and can also reduce the effectiveness of tools such as See5/C5.0 and Cubist that construct models from the data.

This short tutorial covers preparation of data for GritBot and the simple controls that influence its behavior.

In this tutorial, file names and GritBot input appear in blue fixed-width font while file extensions and other general forms are shown highlighted in green.

Preparing Data for GritBot

This section can be skimmed if you are familiar with preparing data for See5/C5.0 or Cubist, since GritBot accepts files for either system. Otherwise, read on ....

We will illustrate GritBot using a medical application -- an experimental database of thyroid assays obtained from the Garvan Institute of Medical Research in the early 1980s. Each case concerns a single referral and contains information on the source of the referral, assays requested, patient data, referring physician's comments, and one aspect of diagnosis (whether or not the patient suffers from hypothyroidism). Here are two examples:

Attribute                 Case 1    Case 2    .....

age                       41        23
sex                       F         F    
on thyroxine              f         f    
query on thyroxine        f         f
on antithyroid medication f         f
sick                      f         f    
pregnant                  f         f    
thyroid surgery           f         f    
I131 treatment            f         f    
query hypothyroid         f         f    
query hyperthyroid        f         f    
lithium                   f         f    
tumor                     f         f    
goitre                    f         f    
hypopituitary             f         f    
psych                     f         f    
TSH                       1.3       4.1  
T3                        2.5       2
TT4                       125       102
T4U                       1.14      unknown
FTI                       109       unknown
referral source           SVHC      other
diagnosis                 negative  negative
ID                        3733      1442

GritBot exploits interdependencies among the attributes in order to identify possible anomalies in the data. The interdependencies are discovered by GritBot itself, so its operation is largely automatic.

Application files

Every GritBot application has a short name called a filestem; we will use the filestem hypothyroid for this illustration. All files read or written by GritBot for an application have names of the form filestem.extension, where filestem identifies the application and extension describes the contents of the file.

Here is a summary table of the extensions used by GritBot (to be described in later sections):

Names file

Two files are essential for all GritBot applications and there is one optional file. The first essential file is the names file (e.g. hypothyroid.names) that describes the attributes. There are two important subgroups of attributes:

The file hypothyroid.names looks like this:

diagnosis.                     | the target attribute for See5/C5.0

age:                           continuous.
sex:                           M, F.
on thyroxine:                  f, t.
query on thyroxine:            f, t.
on antithyroid medication:     f, t.
sick:                          f, t.
pregnant:                      f, t.
thyroid surgery:               f, t.
I131 treatment:                f, t.
query hypothyroid:             f, t.
query hyperthyroid:            f, t.
lithium:                       f, t.
tumor:                         f, t.
goitre:                        f, t.
hypopituitary:                 f, t.
psych:                         f, t.
TSH:                           continuous.
T3:                            continuous.
TT4:                           continuous.
T4U:                           continuous.
FTI:=                          TT4 / T4U.
referral source:               WEST, STMW, SVHC, SVI, SVHD, other.

diagnosis:                     primary, compensated, secondary, negative.

ID:                            label.

What's in a name?

Names, labels, and discrete values are represented by arbitrary strings of characters, with some fine print: Whitespace (blank lines, spaces, and tab characters) is ignored except inside a name or value and can be used to improve legibility. Unless it is escaped as above, the vertical bar `|' causes the remainder of the line to be ignored and is handy for including comments. This use of `|' should not occur inside a name or value.

The first line of the names file is not used by GritBot but is included for compatibility with See5/C5.0 and Cubist. It specifies a target or dependent attribute for modeling, either by naming an attribute (See5/C5.0 or Cubist) or by listing two or more discrete class names (See5/C5.0 only). If you are preparing data for GritBot, just put here the name of any one of the attributes.

The attributes are then defined in the order that they will be given for each case.

Explicitly-defined attributes

The name of each explicitly-defined attribute is followed by a colon `:' and a description of the values taken by the attribute. There are six possibilities:

The attribute takes numeric values.
The attribute's values are dates in the form YYYY/MM/DD or YYYY-MM-DD, e.g. 1999/09/30 or 1999-09-30. Valid dates range from the year 1601 to the year 4000.
The attribute's values are times in the form HH:MM:SS with values between 00:00:00 and 23:59:59.
The attribute's values are times in the form YYYY/MM/DD HH:MM:SS or YYYY-MM-DD HH:MM:SS, e.g. 1999-09-30 15:04:00. (Note that there is a space separating the date and time.)
a comma-separated list of names
The attribute takes discrete values, and these are the allowable values. The values may be prefaced by [ordered] to indicate that they are given in a meaningful ordering, otherwise they will be taken as unordered. For instance, the values low, medium, high are ordered, while meat, poultry, fish, vegetables are not. The former might be declared as
      grade: [ordered] low, medium, high.
If the attribute values have a natural order, it is better to declare them as ordered so that this information can be exploited by GritBot.
discrete N for some integer N
The attribute has discrete, unordered values, but the values are assembled from the data itself; N is the maximum number of such values.
The values of the attribute should be ignored.
This attribute contains an identifying label for each case, such as an account number or an order code. The value of the attribute is ignored when the data is analyzed but is used when referring to individual cases. A label attribute can make it easier to locate format errors in the data and to identify possible anomalies. If there are two or more label attributes, only the last is used.

Attributes defined by formulas

The name of each implicitly-defined attribute is followed by `:=' and then a formula defining the attribute value. The formula is written in the usual way, using parentheses where needed, and may refer to any attribute defined before this one. Constants in the formula can be numbers (written in decimal notation), dates, times, and discrete attribute values (enclosed in string quotes `"'). The operators and functions that can be used in the formula are

The value of such an attribute is either continuous or true/false depending on the formula. For example, the attribute FTI above is continuous, since its value is obtained by dividing one number by another. The value of a hypothetical attribute such as
	strange := referral source = "WEST" or age > 40.
would be either t or f since the value given by the formula is either true or false.

If the value of the formula cannot be determined for a particular case because one or more of the attributes appearing in the formula have unknown values, the value of the implicitly-defined attribute is also unknown.

Dates, times, and timestamps

Dates are stored by GritBot as the number of days since a particular starting point so some operations on dates make sense. Thus, if we have attributes

	d1: date.
        d2: date.
we could define
	interval := d2 - d1.
	gap := d1 <= d2 - 7.
	d1-day-of-week := (d1 + 1) % 7 + 1.
interval then represents the number of days from d1 to d2 (non-inclusive) and gap would have a true/false value signaling whether d1 is at least a week before d2. The last definition is a slightly non-obvious way of determining the day of the week on which d1 falls, with values ranging from 1 (Monday) to 7 (Sunday).

Similarly, times are stored as the number of seconds since midnight. If the names file includes

        start: time.
        finish: time.
        elapsed := finish - start.
the value of elapsed is the number of seconds from start to finish.

Timestamps are a little more complex. A timestamp is rounded to the nearest minute, but limitations on the precision of floating-point numbers mean that the values stored for timestamps from more than thirty years ago are approximate. If the names file includes

	departure: timestamp.
	arrival: timestamp.
	flight time := arrival - departure.
the value of flight time is the number of minutes from departure to arrival.

Selecting the attributes to be checked

An optional final entry in the names file affects the way that GritBot checks the data. This entry takes one of the forms

	attributes included:
	attributes excluded:
followed by a comma-separated list of attribute names. In the first form, the values of only the named attributes will be checked for anomalies, while the second form checks all attributes other than those named. The selection of attributes to be checked does not restrict the attributes used to describe the subsets of cases containing an anomalous value (see below). For example, the entry
	attributes excluded: sex, referral source.
would not check for anomalous values of these two attributes. However, sex and referral source might still be used to describe a subset of cases containing an anomalous value of another attribute.

Data file

The second essential file, the application's data file (e.g. provides information on the cases that GritBot will check. The entry for each case consists of one or more lines that give the values for all explicitly-defined attributes. Values are separated by commas and the entry is optionally terminated by a period. Once again, anything on a line after a vertical bar `|' is ignored. (If the information for a case occupies more than one line, make sure that the line breaks occur after commas.)

The first two cases from file are:

Don't forget the commas between values! If you leave them out, GritBot will not be able to process your data. Notice that the cases do not contain values for the attribute FTI whose values are computed from other attribute values.

Notice that `?' is used to denote a value that is missing or unknown. Similarly, `N/A' denotes a value that is not applicable for a particular case. Also note that the cases do not contain values for the attribute FTI since its values are computed from other attribute values.

Test and cases files (optional)

The test file is optional and has exactly the same format as the data file. If it appears, GritBot will read cases from both the data and test files and analyze them together. A cases (e.g. hypothyroid.cases) is also optional and has the same format. The cases file is discussed in the section on inspecting new data below.

Scanning Data For Anomalies

Once the names, data, and optional test files have been set up, everything is ready to use GritBot.

The general form of the Unix command is

        gritbot -f filestem [options]
This invokes GritBot with the -f option that identifies the application name (here hypothyroid). If no filestem is specified using this option, GritBot uses a default filestem that is probably incorrect. (Moral: always use the -f option!)

Default mode

When GritBot is invoked with no additional options, as
	gritbot -f hypothyroid
it produces output like this:
GritBot [Release 2.02]  	Tue Sep 22 19:09:46 2015

	Application `hypothyroid'

Read 2772 cases (24 attributes) from
Read 1000 cases from hypothyroid.test

    while checking age:
	excluding 1 missing value

    while checking sex:
	excluding 150 missing values

    while checking TSH:
	excluding 369 missing values
	excluding high tail (236 cases above 9.4)

    while checking T3:
	excluding 769 missing values

    while checking TT4:
	excluding 231 missing values

    while checking T4U:
	excluding 387 missing values

    while checking FTI:
	excluding 389 missing values
	excluding low tail (49 cases below 36)

5 possible anomalies identified

data case 1365: (label 861)  [0.002]
	age = 455  (3771 cases, mean 52, 99.97% <= 94)

test case 373: (label 769)  [0.006]
	T3 = 7.6  (602 cases, mean 2.08, 99.8% <= 4)
	    TT4 > 83 and <= 155 [120]
	    T4U > 0.99 and <= 1.12 [1.04]

data case 2215: (label 2676)  [0.008]
	TSH = 8.5  (35 cases, mean 1.061, 34 <= 2.9)
	    FTI > 120.75 and <= 121.8 [121]
	    diagnosis in {secondary, negative} [negative]

data case 2224: (label 1562)  [0.014]
	age = 75  (53 cases, mean 32, 51 <= 42)
	    pregnant = t

data case 1610: (label 3023)  [0.016]
	age = 73  (53 cases, mean 32, 51 <= 42)
	    pregnant = t

Time: 0.4 secs
The first part identifies the version of GritBot, the run date, and the filestem option with which the system was invoked.

GritBot reads 2772 cases from file and a further 1000 from hypothyroid.test. Some cases may have missing values or misleadingly high/low values for some attributes, and GritBot excludes these from the analysis with a warning message. For example, TSH has 369 missing values and 236 very high values that might confuse the search for anomalies; all 605 cases are excluded when GritBot searches for anomalous TSH values.

When the analysis is complete, GritBot identifies the possible anomalies that it has discovered; there are five for these data. A possible anomaly exists when a case's value for one attribute is surprising when compared with corresponding values for a subset of cases. Such an anomaly is reported in the following pattern:

case identification:  [significance]
	anomalous value  (N cases, reason)
	    condition 1
	    condition 2
	    . . .
	    condition K

The reason that a value appears anomalous takes two forms:

This does not necessarily mean that this value itself is incorrect -- the case's value for one of the attributes defining the subset may be faulty.

Conditions can take several forms:

The first possible anomaly for the hypothyroid application concerns case number 1365 in this application's data file, and whose label is "861". There are no conditions, so the subset consists of all 3771 cases with known values of the attribute age. This case has a patient age of 455, whereas 99.97% of the cases -- all cases except this one -- have age values no greater than 94. This is clearly anomalous!

The second possible anomaly illustrates subsets of cases characterized by more complex sets of conditions. There are 602 cases whose TT4 value lies between 83 and 155 and whose T4U value lies between 0.99 and 1.12. The average T3 value for these 602 cases is 2.08, and all cases except this one have values less than or equal to 4; this case's value of 7.6 is quite a bit larger. Of course, only an endocrinologist could judge whether this case is truly anomalous.

The last two possible anomalies pick out a couple of unlikely expectant mothers aged in their seventies. Once again, these are pretty clearly "data grit".

The final line shows the time taken for the analysis. This depends on the total number N of cases and the number A of attributes used to describe them; as a rule of thumb, the time to run GritBot varies roughly as N log(N) A2. Progress on long runs can be monitored by examining the last few lines of the temporary file filestem.tmp (e.g. hypothyroid.tmp). This file displays the stage that GritBot has reached and, for most stages, gives an indication of the fraction of the stage that has been completed.

Filtering anomalies

The first parameter affecting GritBot's behavior is the filter level that governs the screening of possible anomalies. Lower values allow more possible anomalies to be found, while higher values filter them more actively and so reduce their number.

The option -l x instructs GritBot to use a filter level of x%. (The default value is 50%.) When the hypothyroid application is run with the lowest possible filter level (0%), 185 possible anomalies are reported. When the highest level (100%) is set, a single anomaly is reported (the patient aged 455).

Restricting the number of conditions

As we noted earlier, the subset of cases that forms the context of a possibly anomalous case is defined by zero or more conditions. GritBot incorporates a maximum conditions parameter to limit their number and so simplify the definition of subsets.

The option -c x instructs GritBot to use no more than x conditions to describe a subset. (The default value is 4.) Setting this parameter to 0 reports only cases that appear to be anomalous with respect to the entire dataset.

Allowing more conditions enables more complex subsets to be explored, generally at the cost of an increase in the time required to analyze a dataset.

Limiting the number of anomalies reported

After a very large dataset has been checked, the user may not wish to see a report showing thousands of possible anomalies!

The option -n x causes GritBot to give details of up to x possible anomalies. GritBot still reports the total number found, but displays no more that the specified number of them.

Saving the analysis process

Checking a large dataset can take some time. If we receive new data for the same application, it could be checked by appending it to the existing data and re-running GritBot on the enlarged dataset. Another alternative, however, would be to check the new data using the information obtained when the original data was analysed.

By default, GritBot continually writes information to an ASCII file filestem.sift (here hypothyroid.sift). This file summarizes the checks that GritBot makes in a form that can be applied to new data. The sift file can be quite large, however, and it will not be generated if the option -s is appended to the command line.

Saving case numbers of possible anomalies

Having identified possible anomalies, the user might want to take some follow-up action. To facilitate this, GritBot can generate a simple ASCII file filestem.list (here hypothyroid.list) that contains the case numbers of the possible anomalies found in serial order, one per line. If both the data and test files have been checked, a blank line is left between the case numbers for the two files.

The option to generate the list file is -r. For example, if the hypothyroid data is checked with this option enabled, GritBot writes a file hypothyroid.list containing the following information:


(Notice the blank line between the last case number from the data file and the only case number from the test file.)

Inspecting New Data

As we noted above, GritBot can save the process found to check an application's data as a sift file that can be used to check new data for the same application.

The new data should be placed in the optional cases file (here hypothyroid.cases). The command to initiate this inspection process is

        inspect -f filestem [options]
where the options are -n x (to limit the number of possible anomalies reported) and/or -r (to generate a list of case numbers) as before.

To illustrate this process, 500 new cases have been placed in hypothyroid.cases. These cases also come from the Garvan Institute and have exactly the same format as the data and test files. The inspection process yields the following results:

GritBot Inspector [Release 2.02]  	Tue Sep 22 19:32:12 2015

	Application `hypothyroid'

Read saved analysis from hypothyroid.sift
Read 500 cases (24 attributes) from hypothyroid.cases

6 possible anomalies identified

case 295: (label 4294)  [0.002]
	TSH = 1.9  (37 cases, mean 0.077, 36 <= 0.25)
	    sex = F
	    FTI > 172 and <= 189.8 [180]
	    diagnosis in {secondary, negative} [negative]

case 266: (label 4265)  [0.004]
	T3 = 9.5  (813 cases, mean 1.9, 100.0% <= 4.5)
	    TT4 > 88 and <= 105 [90]

case 433: (label 4432)  [0.008]
	TT4 = 4  (225 cases, mean 101.7, 100.0% >= 22)
	    query hypothyroid = t

case 189: (label 4188)  [0.009]
	TT4 = 4.06  (225 cases, mean 101.7, 100.0% >= 22)
	    query hypothyroid = t

case 380: (label 4379)  [0.009]
	FTI = 263  (44 cases, mean 122.55, 44 <= 178)
	    TT4 > 76 and <= 91 [84]
	    T4U <= 0.73 [0.32]

case 234: (label 4233)  [0.016]
	age = 73  (53 cases, mean 32, 51 <= 42)
	    pregnant = t

Time: 0.0 secs
Six possible anomalies are detected in the new cases. It is worth noting that the third and fourth have no counterparts in the possible anomalies detected in the original data -- that is, inspection of new cases can uncover new kinds of anomalies. The sift file includes all patterns found in the analysis, even if these patterns revealed no anomalous cases in the original data.

It is generally much faster to use a sift file to identify possible anomalies than to perform the analysis from scratch.

Concluding Remarks

When it analyzes data, GritBot uses several heuristics or rules of thumb to define interesting subsets and to identify potentially anomalous values. GritBot cannot guarantee to find all anomalies in a dataset, and the cases that it reports are only possible anomalies.

After a real anomaly is found, it is good practice to try to establish its cause. Was the value recorded incorrectly, or were digits transposed when entering the value? Did an instrument give a faulty reading? Are certain numeric values used as codes (a common one being to record an unknown value as zero)? Tracking the source of an anomaly in this way can help to improve future data quality.

Appendix: Summary of Options

Options for gritbot

Options for inspect

© RULEQUEST RESEARCH 2015 Last updated September 2015

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