Added instructions on running a small subset of NAB

README.md

@@ -137,11 +137,13 @@ PYTHONPATH setup:
 
 There are several different use cases for NAB:
 
-1. If you just want to look at all
-the results we reported in the paper, there is no need to run anything.
-All the data files are in the data subdirectory and all individual detections
-for reported algorithms are checked in to the results subdirectory. Please see
-the README files in those locations.
+1. If you just want to look at all the results we reported in the paper, there
+is no need to run anything. All the data files are in the data subdirectory and
+all individual detections for reported algorithms are checked in to the results
+subdirectory. Please see the README files in those locations.
+
+1. If you want to plot some of the results, please see the README in the
+`scripts` directory for `scripts/plot.py`
 
 1. If you have your own algorithm and want to run the NAB benchmark, please see
 the [NAB Entry Points](https://github.com/numenta/NAB/wiki#nab-entry-diagram)
@@ -157,6 +159,9 @@ the directions below to "Run HTM with NAB".
 the directions below to "Run full NAB". Note that this will take hours as the
 Skyline code is quite slow.
 
+1. If you just want to run NAB on one or more data files (e.g. for debugging)
+follow the directions below to "Run a subset of NAB".
+
 
 ##### Run HTM with NAB
 
@@ -190,16 +195,38 @@ the specific version of NuPIC (and associated nupic.core) that is noted in the
     cd /path/to/nab
     python run.py
 
-This will run everything and produce results files for the anomaly detection
-methods. Included in the repo are the Numenta anomaly detection method, as well
-as methods from the [Etsy Skyline](https://github.com/etsy/skyline) anomaly
-detection library, a random detector, and a null detector. This will also pass
-those results files to the scoring script to generate final NAB scores.
-**Note**: this option will take many many hours to run.
+This will run everything and produce results files for all anomaly detection
+methods. Several algorithms are included in the repo, such as the Numenta
+HTM anomaly detection method, as well as methods from the [Etsy
+Skyline](https://github.com/etsy/skyline) anomaly detection library, a sliding
+window detector, Bayes Changepoint, and so on. This will also pass those results
+files to the scoring script to generate final NAB scores. *Note**: this option
+will take many many hours to run.
 
 The run.py command has a number of useful options. To view a description of the
 command line options please enter
 
 	python run.py --help 
 
 
+##### Run subset of NAB data files
+
+For debugging it is sometimes useful to be able to run your algorithm on a
+subset of the NAB data files or on your own set of data files. You can do that
+by creating a custom `combined_windows.json` file that only contains labels for
+the files you want to run. This new file should be in exactly the same format as
+`combined_windows.json` except it would only contain windows for the files you
+are interested in. 
+
+**Example**: an example file containing two files is in
+`labels/combined_windows_tiny.json`.  The following command shows you how to run
+NAB on a subset of labels:
+
+    cd /path/to/nab
+    python run.py -d numenta --detect --windowsFile labels/combined_windows_tiny.json
+
+This will run the `detect` phase of NAB on the data files specified in the above
+JSON file. Scoring and normalization are not supported with this option. Note
+that you may see warning messages regarding the lack of labels for other files.
+You can ignore these warnings.
+

labels/combined_windows_tiny.json

@@ -0,0 +1,34 @@
+{
+    "realKnownCause/nyc_taxi.csv": [
+        [
+            "2014-10-30 15:30:00.000000",
+            "2014-11-03 22:30:00.000000"
+        ],
+        [
+            "2014-11-25 12:00:00.000000",
+            "2014-11-29 19:00:00.000000"
+        ],
+        [
+            "2014-12-23 11:30:00.000000",
+            "2014-12-27 18:30:00.000000"
+        ],
+        [
+            "2014-12-29 21:30:00.000000",
+            "2015-01-03 04:30:00.000000"
+        ],
+        [
+            "2015-01-24 20:30:00.000000",
+            "2015-01-29 03:30:00.000000"
+        ]
+    ],
+    "realKnownCause/rogue_agent_key_hold.csv": [
+        [
+            "2014-07-15 04:35:00.000000",
+            "2014-07-15 13:25:00.000000"
+        ],
+        [
+            "2014-07-17 05:50:00.000000",
+            "2014-07-18 06:45:00.000000"
+        ]
+    ]
+}