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Merge branch 'develop' into adds-nonspatial-kmeans

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Mario de Frutos
2016-12-01 16:26:52 +01:00
parent 6ab1c285d9 15b460eeb9
commit 2c6fcfc294
19 changed files with 854 additions and 155 deletions
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9
.github/PULL_REQUEST_TEMPLATE.md vendored
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@@ -2,6 +2,9 @@
- [ ] All declared geometries are `geometry(Geometry, 4326)` for general geoms, or `geometry(Point, 4326)`
- [ ] Existing functions in crankshaft python library called from the extension are kept at least from version N to version N+1 (to avoid breakage during upgrades).
- [ ] Docs for public-facing functions are written
- [ ] New functions follow the naming conventions: `CDB_NameOfFunction`. Where internal functions begin with an underscore `_`.
- [ ] If appropriate, new functions accepts an arbitrary query as an input (see [Crankshaft Issue #6](https://github.com/CartoDB/crankshaft/issues/6) for more information)
- [ ] New functions follow the naming conventions: `CDB_NameOfFunction`. Where internal functions begin with an underscore
- [ ] Video explaining the analysis and showing examples
- [ ] Analysis Documentation written [template](https://docs.google.com/a/cartodb.com/document/d/1X2KOtaiEBKWNMp8UjwcLB-kE9aIOw09aOjX3oaCjeME/edit?usp=sharing)
- [ ] Smoke test written
- [ ] Hand-off document for camshaft node written
- [ ] If function is in Python, code conforms to [PEP8 Style Guide](https://www.python.org/dev/peps/pep-0008/)

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doc/16_getis_ord_gstar.md Normal file
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## Getis-Ord's G\*
Getis-Ord's G\* is a geo-statistical measurement of the intensity of clustering of high or low values. The clustering of high values can be referred to as "hotspots" because these are areas of high activity or large (relative to the global mean) measurement values. Coldspots are clustered areas with low activity or small measurement values.
### CDB_GetisOrdsG(subquery text, column_name text)
#### Arguments
| Name | Type | Description |
|------|------|-------------|
| subquery | text | A query of the data you want to pass to the function. It must include `column_name`, a geometry column (usually `the_geom`) and an id column (usually `cartodb_id`) |
| column_name | text | This is the column of interest for performing this analysis on. This column should be a numeric type. |
| w_type (optional) | text | Type of weight to use when finding neighbors. Currently available options are 'knn' (default) and 'queen'. Read more about weight types in [PySAL's weights documentation.](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html) |
| num_ngbrs (optional) | integer | Default: 5. If `knn` is chosen, this will set the number of neighbors. If `knn` is not chosen, any entered value will be ignored. Use `NULL` if not choosing `knn`. |
| permutations (optional) | integer | The number of permutations for calculating p-values. Default: 999 |
| geom_col (optional) | text | The column where the geometry information is stored. The format must be PostGIS Geometry type (SRID 4326). Default: `the_geom`. |
| id_col (optional) | text | The column that has the unique row identifier. |
### Returns
Returns a table with the following columns.
| Name | Type | Description |
|------|------|-------------|
| z_score | numeric | z-score, a measure of the intensity of clustering of high values (hotspots) or low values (coldspots). Positive values represent 'hotspots', while negative values represent 'coldspots'. |
| p_value | numeric | p-value, a measure of the significance of the intensity of clustering |
| p_z_sim | numeric | p-value based on standard normal approximation from permutations |
| rowid | integer | The original `id_col` that can be used to associate the outputs with the original geometry and inputs |
#### Example Usage
The following query returns the original table augmented with the values calculated from the Getis-Ord's G\* analysis.
```sql
SELECT i.*, m.z_score, m.p_value
FROM cdb_crankshaft.CDB_GetisOrdsG('SELECT * FROM incident_reports_clustered',
'num_incidents') As m
JOIN incident_reports_clustered As i
ON i.cartodb_id = m.rowid;
```

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doc/18_outliers.md Normal file
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## Outlier Detection
This set of functions detects the presence of outliers. There are three functions for finding outliers from non-spatial data:
1. Static Outliers
1. Percentage Outliers
1. Standard Deviation Outliers
### CDB_StaticOutlier(column_value numeric, threshold numeric)
#### Arguments
| Name | Type | Description |
|------|------|-------------|
| column_value | numeric | The column of values on which to apply the threshold |
| threshold | numeric | The static threshold which is used to indicate whether a `column_value` is an outlier or not |
### Returns
Returns a boolean (true/false) depending on whether a value is above or below (or equal to) the threshold
| Name | Type | Description |
|------|------|-------------|
| outlier | boolean | classification of whether a row is an outlier or not |
#### Example Usage
With a table `website_visits` and a column of the number of website visits in units of 10,000 visits:
```
| id | visits_10k |
|----|------------|
| 1 | 1 |
| 2 | 3 |
| 3 | 5 |
| 4 | 1 |
| 5 | 32 |
| 6 | 3 |
| 7 | 57 |
| 8 | 2 |
```
```sql
SELECT
id,
CDB_StaticOutlier(visits_10k, 11.0) As outlier,
visits_10k
FROM website_visits
```
```
| id | outlier | visits_10k |
|----|---------|------------|
| 1 | f | 1 |
| 2 | f | 3 |
| 3 | f | 5 |
| 4 | f | 1 |
| 5 | t | 32 |
| 6 | f | 3 |
| 7 | t | 57 |
| 8 | f | 2 |
```
### CDB_PercentOutlier(column_values numeric[], outlier_fraction numeric, ids int[])
`CDB_PercentOutlier` calculates whether or not a value falls above a given threshold based on a percentage above the mean value of the input values.
#### Arguments
| Name | Type | Description |
|------|------|-------------|
| column_values | numeric[] | An array of the values to calculate the outlier classification on |
| outlier_fraction | numeric | The threshold above which a column value divided by the mean of all values is considered an outlier |
| ids | int[] | An array of the unique row ids of the input data (usually `cartodb_id`) |
### Returns
Returns a table of the outlier classification with the following columns
| Name | Type | Description |
|------|------|-------------|
| is_outlier | boolean | classification of whether a row is an outlier or not |
| rowid | int | original row id (e.g., input `cartodb_id`) of the row which has the outlier classification |
#### Example Usage
This example find outliers which are more than 100% larger than the average (that is, more than 2.0 times larger).
```sql
WITH cte As (
SELECT
unnest(Array[1,2,3,4,5,6,7,8]) As id,
unnest(Array[1,3,5,1,32,3,57,2]) As visits_10k
)
SELECT
(CDB_PercentOutlier(array_agg(visits_10k), 2.0, array_agg(id))).*
FROM cte;
```
Output
```
| outlier | rowid |
|---------+-------|
| f | 1 |
| f | 2 |
| f | 3 |
| f | 4 |
| t | 5 |
| f | 6 |
| t | 7 |
| f | 8 |
```
### CDB_StdDevOutlier(column_values numeric[], num_deviations numeric, ids int[], is_symmetric boolean DEFAULT true)
`CDB_StdDevOutlier` calculates whether or not a value falls above or below a given threshold based on the number of standard deviations from the mean.
#### Arguments
| Name | Type | Description |
|------|------|-------------|
| column_values | numeric[] | An array of the values to calculate the outlier classification on |
| num_deviations | numeric | The threshold in units of standard deviation |
| ids | int[] | An array of the unique row ids of the input data (usually `cartodb_id`) |
| is_symmetric (optional) | boolean | Consider outliers that are symmetric about the mean (default: true) |
### Returns
Returns a table of the outlier classification with the following columns
| Name | Type | Description |
|------|------|-------------|
| is_outlier | boolean | classification of whether a row is an outlier or not |
| rowid | int | original row id (e.g., input `cartodb_id`) of the row which has the outlier classification |
#### Example Usage
This example find outliers which are more than 100% larger than the average (that is, more than 2.0 times larger).
```sql
WITH cte As (
SELECT
unnest(Array[1,2,3,4,5,6,7,8]) As id,
unnest(Array[1,3,5,1,32,3,57,2]) As visits_10k
)
SELECT
(CDB_StdDevOutlier(array_agg(visits_10k), 2.0, array_agg(id))).*
FROM cte;
```
Output
```
| outlier | rowid |
|---------+-------|
| f | 1 |
| f | 2 |
| f | 3 |
| f | 4 |
| f | 5 |
| f | 6 |
| t | 7 |
| f | 8 |
```

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src/pg/sql/16_getis.sql Normal file
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-- Getis-Ord's G
-- Hotspot/Coldspot Analysis tool
CREATE OR REPLACE FUNCTION
CDB_GetisOrdsG(
subquery TEXT,
column_name TEXT,
w_type TEXT DEFAULT 'knn',
num_ngbrs INT DEFAULT 5,
permutations INT DEFAULT 999,
geom_col TEXT DEFAULT 'the_geom',
id_col TEXT DEFAULT 'cartodb_id')
RETURNS TABLE (z_score NUMERIC, p_value NUMERIC, p_z_sim NUMERIC, rowid BIGINT)
AS $$
from crankshaft.clustering import getis_ord
return getis_ord(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
$$ LANGUAGE plpythonu;
-- TODO: make a version that accepts the values as arrays

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src/pg/sql/18_outliers.sql Normal file
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-- Find outliers using a static threshold
--
CREATE OR REPLACE FUNCTION CDB_StaticOutlier(column_value numeric, threshold numeric)
RETURNS boolean
AS $$
BEGIN
RETURN column_value > threshold;
END;
$$ LANGUAGE plpgsql;
-- Find outliers by a percentage above the threshold
-- TODO: add symmetric option? `is_symmetric boolean DEFAULT false`
CREATE OR REPLACE FUNCTION CDB_PercentOutlier(column_values numeric[], outlier_fraction numeric, ids int[])
RETURNS TABLE(is_outlier boolean, rowid int)
AS $$
DECLARE
avg_val numeric;
out_vals boolean[];
BEGIN
SELECT avg(i) INTO avg_val
FROM unnest(column_values) As x(i);
IF avg_val = 0 THEN
RAISE EXCEPTION 'Mean value is zero. Try another outlier method.';
END IF;
SELECT array_agg(
outlier_fraction < i / avg_val) INTO out_vals
FROM unnest(column_values) As x(i);
RETURN QUERY
SELECT unnest(out_vals) As is_outlier,
unnest(ids) As rowid;
END;
$$ LANGUAGE plpgsql;
-- Find outliers above a given number of standard deviations from the mean
CREATE OR REPLACE FUNCTION CDB_StdDevOutlier(column_values numeric[], num_deviations numeric, ids int[], is_symmetric boolean DEFAULT true)
RETURNS TABLE(is_outlier boolean, rowid int)
AS $$
DECLARE
stddev_val numeric;
avg_val numeric;
out_vals boolean[];
BEGIN
SELECT stddev(i), avg(i) INTO stddev_val, avg_val
FROM unnest(column_values) As x(i);
IF stddev_val = 0 THEN
RAISE EXCEPTION 'Standard deviation of input data is zero';
END IF;
IF is_symmetric THEN
SELECT array_agg(
abs(i - avg_val) / stddev_val > num_deviations) INTO out_vals
FROM unnest(column_values) As x(i);
ELSE
SELECT array_agg(
(i - avg_val) / stddev_val > num_deviations) INTO out_vals
FROM unnest(column_values) As x(i);
END IF;
RETURN QUERY
SELECT unnest(out_vals) As is_outlier,
unnest(ids) As rowid;
END;
$$ LANGUAGE plpgsql;

220
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@@ -149,135 +149,135 @@ _cdb_random_seeds
(1 row)
code|quads
01|LL
02|LH
03|HH
04|HH
05|LL
06|HH
07|LL
08|LL
09|LL
10|HH
11|HH
12|HL
13|LL
14|HH
01|HH
02|HL
03|LL
04|LL
05|LH
06|LL
07|HH
08|HH
09|HH
10|LL
11|LL
12|LL
13|HL
14|LL
15|LL
16|LL
17|LL
18|LH
19|LL
20|LL
21|HH
22|LL
23|HL
16|HH
17|HH
18|LL
19|HH
20|HH
21|LL
22|HH
23|LL
24|LL
25|LL
26|LL
25|HH
26|HH
27|LL
28|LL
29|LH
30|HH
31|LL
28|HH
29|LL
30|LL
31|HH
32|LL
33|LL
34|LL
35|LH
36|HL
37|LH
38|LH
39|LL
40|LL
41|LH
42|HL
43|LL
44|HL
45|LL
46|HL
33|HL
34|LH
35|LL
36|LL
37|HL
38|HL
39|HH
40|HH
41|HL
42|LH
43|LH
44|LL
45|LH
46|LL
47|LL
48|LL
49|HL
50|LL
51|HH
(51 rows)
48|HH
49|LH
50|HH
51|LL
52|LL
(52 rows)
_cdb_random_seeds
(1 row)
code|quads
03|HH
04|HH
06|HH
10|HH
11|HH
12|HL
14|HH
21|HH
23|HL
30|HH
36|HL
42|HL
44|HL
46|HL
49|HL
51|HH
(16 rows)
01|HH
02|HL
07|HH
08|HH
09|HH
13|HL
16|HH
17|HH
19|HH
20|HH
22|HH
25|HH
26|HH
28|HH
31|HH
33|HL
37|HL
38|HL
39|HH
40|HH
41|HL
48|HH
50|HH
(23 rows)
_cdb_random_seeds
(1 row)
code|quads
01|LL
02|LH
05|LL
07|LL
08|LL
09|LL
13|LL
03|LL
04|LL
05|LH
06|LL
10|LL
11|LL
12|LL
14|LL
15|LL
16|LL
17|LL
18|LH
19|LL
20|LL
22|LL
18|LL
21|LL
23|LL
24|LL
25|LL
26|LL
27|LL
28|LL
29|LH
31|LL
29|LL
30|LL
32|LL
33|LL
34|LL
35|LH
37|LH
38|LH
39|LL
40|LL
41|LH
43|LL
45|LL
34|LH
35|LL
36|LL
42|LH
43|LH
44|LL
45|LH
46|LL
47|LL
48|LL
50|LL
(35 rows)
49|LH
51|LL
52|LL
(29 rows)
_cdb_random_seeds
(1 row)
code|quads
02|LH
12|HL
18|LH
23|HL
29|LH
35|LH
36|HL
37|LH
38|LH
41|LH
42|HL
44|HL
46|HL
49|HL
(14 rows)
02|HL
05|LH
13|HL
33|HL
34|LH
37|HL
38|HL
41|HL
42|LH
43|LH
45|LH
49|LH
(12 rows)

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\pset format unaligned
\set ECHO all
\i test/fixtures/getis_data.sql
SET client_min_messages TO WARNING;
\set ECHO none
_cdb_random_seeds
(1 row)
rowid|z_score|p_value
9|-0.7862|0.0500
22|-0.3955|0.0330
33|2.7045|0.0050
35|1.9524|0.0130
36|-1.2056|0.0170
37|3.4785|0.0020
38|-1.4622|0.0020
40|5.7098|0.0030
46|3.4704|0.0120
47|-0.9994|0.0320
48|-1.3650|0.0340
(11 rows)

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SET client_min_messages TO WARNING;
\set ECHO none
is_outlier|rowid
t|11
t|16
t|17
(3 rows)
is_outlier|rowid
t|16
t|17
(2 rows)
ERROR: Standard deviation of input data is zero
is_outlier|rowid
t|8
t|11
t|16
(3 rows)
is_outlier|rowid
t|8
t|9
t|11
t|16
(4 rows)

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src/pg/test/fixtures/getis_data.sql vendored Normal file
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SET client_min_messages TO WARNING;
\set ECHO none
--
-- Getis-Ord's G* test dataset, subsetted from PySAL examples:
-- https://github.com/pysal/pysal/tree/952ea04029165048a774d9a1846cf86ad000c096/pysal/examples/stl
--
CREATE TABLE getis_data (
cartodb_id integer,
the_geom geometry(Geometry,4326),
hr8893 numeric
);
COPY getis_data (cartodb_id, the_geom, hr8893) FROM stdin;
22 0106000020E61000000100000001030000000100000007000000000000E0B10056C0000000C0B8964340FFFFFFFF4C1756C00000002054964340000000A00F1E56C00000004072964340000000C02D1E56C0000000A0439B434000000060381E56C00000000036B04340000000E0E20056C0000000608CB04340000000E0B10056C0000000C0B8964340 10.8557430000000004
32 0106000020E6100000010000000103000000010000000B000000FFFFFF1FC26656C0FFFFFFBFE25E4340000000A0D86656C0000000E0976F4340000000A03A6956C0000000C0966F434000000020526956C0000000E08A7F4340000000E0F26556C000000000C87F4340000000E0066656C0000000209C834340000000407F5056C0000000803C83434000000020635056C0000000E016814340000000A0F45056C0000000A0F980434000000060D25056C000000060FA5E4340FFFFFF1FC26656C0FFFFFFBFE25E4340 9.92424500000000087
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
29 0106000020E610000001000000010300000001000000080000000000002025FD55C0FFFFFF1F7F6D434000000080C61056C0000000A04C6D4340000000A0631756C000000000D56D4340000000C05E1756C0FFFFFF5F24754340FFFFFFFF4C1756C00000002054964340000000E0B10056C0000000C0B89643400000006029FD55C000000080C09643400000002025FD55C0FFFFFF1F7F6D4340 3.12759000000000009
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
28 0106000020E61000000100000001030000000100000008000000000000C05E1756C0FFFFFF5F24754340000000C0DD2C56C0000000407A75434000000080DE3356C0000000406375434000000020D73356C000000060729B4340000000C02D1E56C0000000A0439B4340000000A00F1E56C00000004072964340FFFFFFFF4C1756C00000002054964340000000C05E1756C0FFFFFF5F24754340 1.57115800000000005
36 0106000020E6100000010000000103000000010000000D00000000000000EE2C56C000000060424E434000000040F72C56C000000000486A4340000000C0DD2C56C0000000407A754340000000C05E1756C0FFFFFF5F24754340000000A0631756C000000000D56D434000000080C61056C0000000A04C6D4340000000C0BE1056C0000000A0065F4340000000407C1256C0FFFFFF7FA75E434000000000BD1156C000000020A954434000000040D01256C0000000605C524340000000404F1256C000000040734F434000000040011156C000000000AF4D434000000000EE2C56C000000060424E4340 0
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\.
CREATE INDEX getis_data_gix ON getis_data USING GIST(the_geom);

15
src/pg/test/sql/16_getis_test.sql Normal file
View File

@@ -0,0 +1,15 @@
\pset format unaligned
\set ECHO all
\i test/fixtures/getis_data.sql
-- set random seed
SELECT cdb_crankshaft._cdb_random_seeds(1234);
-- test against PySAL example dataset 'stl_hom'
SELECT rowid, round(z_score, 4) As z_score, round(p_value, 4) As p_value
FROM cdb_crankshaft.CDB_GetisOrdsG(
'select * from getis_data',
'hr8893', 'queen', NULL, 999,
'the_geom', 'cartodb_id') As t(z_score, p_value, p_z_sim, rowid)
WHERE round(p_value, 4) <= 0.05
ORDER BY rowid ASC;

85
src/pg/test/sql/18_outliers_test.sql Normal file
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@@ -0,0 +1,85 @@
SET client_min_messages TO WARNING;
\set ECHO none
\pset format unaligned
--
-- postgres=# select round(avg(i), 3) as avg,
-- round(stddev(i), 3) as stddev,
-- round(avg(i) + stddev(i), 3) as one_stddev,
-- round(avg(i) + 2 * stddev(i), 3) As two_stddev
-- from unnest(ARRAY[1,3,2,3,5,1,2,32,12,3,57,2,1,4,2,100]) As x(i);
-- avg | stddev | one_stddev | two_stddev
-- --------+--------+------------+------------
-- 14.375 | 27.322 | 41.697 | 69.020
-- With an threshold of 1.0 standard deviation, ids 11, 16, and 17 are outliers
WITH a AS (
SELECT
ARRAY[1,3,2,3,5,1,2,32,12, 3,57, 2, 1, 4, 2,100,-100]::numeric[] As vals, ARRAY[1,2,3,4,5,6,7, 8, 9,10,11,12,13,14,15, 16, 17]::int[] As ids
), b As (
SELECT
(cdb_crankshaft.cdb_StdDevOutlier(vals, 1.0, ids)).*
FROM a
ORDER BY ids)
SELECT *
FROM b
WHERE is_outlier IS TRUE;
-- With a threshold of 2.0 standard deviations, id 16 is the only outlier
WITH a AS (
SELECT
ARRAY[1,3,2,3,5,1,2,32,12, 3,57, 2, 1, 4, 2,100,-100]::numeric[] As vals,
ARRAY[1,2,3,4,5,6,7, 8, 9,10,11,12,13,14,15, 16, 17]::int[] As ids
), b As (
SELECT
(cdb_crankshaft.CDB_StdDevOutlier(vals, 2.0, ids)).*
FROM a
ORDER BY ids)
SELECT *
FROM b
WHERE is_outlier IS TRUE;
-- With a Stddev of zero, should throw back error
-- With a threshold of 2.0 standard deviations, id 16 is the only outlier
WITH a AS (
SELECT
ARRAY[5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5]::numeric[] As vals,
ARRAY[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]::int[] As ids
), b As (
SELECT
(cdb_crankshaft.CDB_StdDevOutlier(vals, 1.0, ids)).*
FROM a
ORDER BY ids)
SELECT *
FROM b
WHERE is_outlier IS TRUE;
-- With a ratio threshold of 2.0 threshold (100% above or below the mean)
-- which is greater than ~21, which are values
WITH a AS (
SELECT
ARRAY[1,3,2,3,5,1,2,32,12, 3,57, 2, 1, 4, 2,100,-100]::numeric[] As vals,
ARRAY[1,2,3,4,5,6,7, 8, 9,10,11,12,13,14,15, 16, 17]::int[] As ids
), b As (
SELECT
(cdb_crankshaft.CDB_PercentOutlier(vals, 2.0, ids)).*
FROM a
ORDER BY ids)
SELECT *
FROM b
WHERE is_outlier IS TRUE;
-- With a static threshold of 11, what are the outliers
WITH a AS (
SELECT
ARRAY[1,3,2,3,5,1,2,32,12, 3,57, 2, 1, 4, 2,100,-100]::numeric[] As vals,
ARRAY[1,2,3,4,5,6,7, 8, 9,10,11,12,13,14,15, 16, 17]::int[] As ids
), b As (
SELECT unnest(vals) As v, unnest(ids) as i
FROM a
)
SELECT cdb_crankshaft.CDB_StaticOutlier(v, 11.0) As is_outlier, i As rowid
FROM b
WHERE cdb_crankshaft.CDB_StaticOutlier(v, 11.0) is True
ORDER BY i;

1
src/py/crankshaft/crankshaft/clustering/__init__.py
View File

@@ -1,3 +1,4 @@
"""Import all functions from for clustering"""
from moran import *
from kmeans import *
from getis import *

52
src/py/crankshaft/crankshaft/clustering/getis.py Normal file
View File

@@ -0,0 +1,52 @@
"""
Getis-Ord's G geostatistics (hotspot/coldspot analysis)
"""
import pysal as ps
import plpy
from collections import OrderedDict
# crankshaft module
import crankshaft.pysal_utils as pu
# High level interface ---------------------------------------
def getis_ord(subquery, attr,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Getis-Ord's G*
Implementation building neighbors with a PostGIS database and PySAL's
Getis-Ord's G* hotspot/coldspot module.
Andy Eschbacher
"""
# geometries with attributes that are null are ignored
# resulting in a collection of not as near neighbors if kNN is chosen
qvals = OrderedDict([("id_col", id_col),
("attr1", attr),
("geom_col", geom_col),
("subquery", subquery),
("num_ngbrs", num_ngbrs)])
query = pu.construct_neighbor_query(w_type, qvals)
try:
result = plpy.execute(query)
# if there are no neighbors, exit
if len(result) == 0:
return pu.empty_zipped_array(4)
except plpy.SPIError, err:
plpy.error('Query failed: %s' % err)
attr_vals = pu.get_attributes(result)
# build PySAL weight object
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate Getis-Ord's G* z- and p-values
getis = ps.esda.getisord.G_Local(attr_vals, weight,
star=True, permutations=permutations)
return zip(getis.z_sim, getis.p_sim, getis.p_z_sim, weight.id_order)

26
src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py
View File

@@ -45,8 +45,16 @@ def get_weight(query_res, w_type='knn', num_ngbrs=5):
def query_attr_select(params):
"""
Create portion of SELECT statement for attributes inolved in query.
Defaults to order in the params
@param params: dict of information used in query (column names,
table name, etc.)
Example:
OrderedDict([('numerator', 'price'),
('denominator', 'sq_meters'),
('subquery', 'SELECT * FROM interesting_data')])
Output:
"i.\"price\"::numeric As attr1, " \
"i.\"sq_meters\"::numeric As attr2, "
"""
attr_string = ""
@@ -64,7 +72,7 @@ def query_attr_select(params):
if k not in ('id_col', 'geom_col', 'subquery',
'num_ngbrs', 'subquery')]
for idx, val in enumerate(sorted(attrs)):
for idx, val in enumerate(attrs):
attr_string += template % {"col": params[val],
"alias_num": idx + 1}
@@ -80,8 +88,8 @@ def query_attr_where(params):
'numerator': 'data1',
'denominator': 'data2',
'': ...}
Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2"
IS NOT NULL'
Output:
'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL'
Input:
{'subquery': ...,
'time_cols': ['time1', 'time2', 'time3'],
@@ -102,15 +110,17 @@ def query_attr_where(params):
# moran where clauses
# get keys
attrs = sorted([k for k in params
if k not in ('id_col', 'geom_col', 'subquery',
'num_ngbrs', 'subquery')])
attrs = [k for k in params
if k not in ('id_col', 'geom_col', 'subquery',
'num_ngbrs', 'subquery')]
# add values to template
for attr in attrs:
attr_string.append(template % params[attr])
if len(attrs) == 2:
attr_string.append("idx_replace.\"%s\" <> 0" % params[attrs[1]])
if 'denominator' in attrs:
attr_string.append(
"idx_replace.\"%s\" <> 0" % params['denominator'])
out = " AND ".join(attr_string)

1
src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
View File

@@ -93,6 +93,7 @@ class Markov:
return zip(trend, trend_up, trend_down, volatility, weights.id_order)
def get_time_data(markov_data, time_cols):
"""
Extract the time columns and bin appropriately

1
src/py/crankshaft/test/fixtures/getis.json vendored Normal file
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@@ -0,0 +1 @@
[[0.004793783909323601, 0.17999999999999999, 0.49808756424021061], [-1.0701189472090842, 0.079000000000000001, 0.14228288580832316], [-0.67867750971877305, 0.42099999999999999, 0.24867110969448558], [-0.67407386707620487, 0.246, 0.25013217644612995], [-0.79495689068870035, 0.33200000000000002, 0.21331928959090596], [-0.49279481022182703, 0.058999999999999997, 0.31107878905057329], [-0.38075627530057132, 0.28399999999999997, 0.35169205342069643], [-0.86710921611314895, 0.23699999999999999, 0.19294108571294855], [-0.78618647240956485, 0.050000000000000003, 0.2158791250244505], [-0.76108527223116984, 0.064000000000000001, 0.22330306830813684], [-0.13340753531942209, 0.247, 0.44693554317763651], [-0.57584545722033043, 0.48999999999999999, 0.28235982246156488], [-0.78882694661192831, 0.433, 0.2151065788731219], [-0.38769767950046219, 0.375, 0.34911988661484239], [-0.56057819488052207, 0.41399999999999998, 0.28754255985169652], [-0.41354017495644935, 0.45500000000000002, 0.339605447117173], [-0.23993577722243081, 0.49099999999999999, 0.40519002230969337], [-0.1389080156677496, 0.40400000000000003, 0.44476141839645233], [-0.25485737510500855, 0.376, 0.39941662953554224], [-0.71218610582902353, 0.17399999999999999, 0.23817476979886087], [-0.54533105995872144, 0.13700000000000001, 0.2927629228714812], [-0.39547917847510977, 0.033000000000000002, 0.34624464252424236], [-0.43052658996257548, 0.35399999999999998, 0.33340631435564982], [-0.37296719193774736, 0.40300000000000002, 0.35458643102865428], [-0.66482612169465694, 0.31900000000000001, 0.25308085650392698], [-0.13772133540823422, 0.34699999999999998, 0.44523032843016275], [-0.6765304487868502, 0.20999999999999999, 0.24935196033890672], [-0.64518763494323472, 0.32200000000000001, 0.25940279912025543], [-0.5078622084312413, 0.41099999999999998, 0.30577498972600159], [-0.12652006733772059, 0.42899999999999999, 0.44966013262301163], [-0.32691133022814595, 0.498, 0.37186747562269029], [0.25533848511500978, 0.42399999999999999, 0.39923083899077472], [2.7045138116476508, 0.0050000000000000001, 0.0034202212972238577], [-0.1551614486076057, 0.44400000000000001, 0.43834701985429037], [1.9524487722567723, 0.012999999999999999, 0.025442473674991528], [-1.2055816465306763, 0.017000000000000001, 0.11398941970467646], [3.478472976017831, 0.002, 0.00025213964072468009], [-1.4621715757903719, 0.002, 0.071847099325659136], [-0.84010307600180256, 0.085000000000000006, 0.20042529779230778], [5.7097646237318243, 0.0030000000000000001, 5.6566262784940591e-09], [1.5082367956567375, 0.065000000000000002, 0.065746966514827365], [-0.58337270103430816, 0.44, 0.27982121546450034], [-0.083271860457022437, 0.45100000000000001, 0.46681768733385554], [-0.46872337815000953, 0.34599999999999997, 0.31963368715684204], [0.18490279849545319, 0.23799999999999999, 0.42665263797981101], [3.470424529947997, 0.012, 0.00025981817437825683], [-0.99942612137154796, 0.032000000000000001, 0.15879415560388499], [-1.3650387953594485, 0.034000000000000002, 0.08612042845912049], [1.8617160516432014, 0.081000000000000003, 0.03132156240215267], [1.1321188945775384, 0.11600000000000001, 0.12879222611766061], [0.064116686050580601, 0.27300000000000002, 0.4744386578180424], [-0.42032194540259099, 0.29999999999999999, 0.33712514016213468], [-0.79581215423980922, 0.123, 0.21307061309098785], [-0.42792753720906046, 0.45600000000000002, 0.33435193892883741], [-1.0629378527428395, 0.051999999999999998, 0.14390506780140866], [-0.54164761752225477, 0.33700000000000002, 0.29403064095211839], [1.0934778886820793, 0.13700000000000001, 0.13709201601893539], [-0.094068785378413719, 0.38200000000000001, 0.46252725802998929], [0.13482026574801856, 0.36799999999999999, 0.44637699118865737], [-0.13976995315653129, 0.34699999999999998, 0.44442087706276601], [-0.051047663924746682, 0.32000000000000001, 0.47964376985626245], [-0.21468297736730158, 0.41699999999999998, 0.41500724761906527], [-0.20873154637330626, 0.38800000000000001, 0.41732890604390893], [-0.32427876152583485, 0.49199999999999999, 0.37286349875557478], [-0.65254842943280977, 0.374, 0.25702372075306734], [-0.48611858196118796, 0.23300000000000001, 0.31344154643990074], [-0.14482354344529477, 0.32600000000000001, 0.44242509660469886], [-0.51052030974200002, 0.439, 0.30484349480873729], [0.56814382285283538, 0.14999999999999999, 0.28496865660103166], [0.58680919931668207, 0.161, 0.27866592887231878], [0.013390357044409013, 0.25800000000000001, 0.49465818005865647], [-0.19050728887961568, 0.41399999999999998, 0.4244558160399462], [-0.60531777422216049, 0.35199999999999998, 0.2724839368239631], [1.0899331115425805, 0.127, 0.13787130480311838], [0.17015055382651084, 0.36899999999999999, 0.43244586845546418], [-0.21738337124409801, 0.40600000000000003, 0.41395479459421991], [1.0329303331079593, 0.079000000000000001, 0.15081825117169467], [1.0218317101096221, 0.104, 0.15343027913308094]]

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src/py/crankshaft/test/fixtures/neighbors_getis.json vendored Normal file
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src/py/crankshaft/test/test_clustering_getis.py Normal file
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@@ -0,0 +1,74 @@
import unittest
import numpy as np
# from mock_plpy import MockPlPy
# plpy = MockPlPy()
#
# import sys
# sys.modules['plpy'] = plpy
from helper import plpy, fixture_file
import crankshaft.clustering as cc
import crankshaft.pysal_utils as pu
from crankshaft import random_seeds
import json
# Fixture files produced as follows
#
# import pysal as ps
# import numpy as np
# import random
#
# # setup variables
# f = ps.open(ps.examples.get_path("stl_hom.dbf"))
# y = np.array(f.by_col['HR8893'])
# w_queen = ps.queen_from_shapefile(ps.examples.get_path("stl_hom.shp"))
#
# out_queen = [{"id": index + 1,
# "neighbors": [x+1 for x in w_queen.neighbors[index]],
# "value": val} for index, val in enumerate(y)]
#
# with open('neighbors_queen_getis.json', 'w') as f:
# f.write(str(out_queen))
#
# random.seed(1234)
# np.random.seed(1234)
# lgstar_queen = ps.esda.getisord.G_Local(y, w_queen, star=True,
# permutations=999)
#
# with open('getis_queen.json', 'w') as f:
# f.write(str(zip(lgstar_queen.z_sim,
# lgstar_queen.p_sim, lgstar_queen.p_z_sim)))
class GetisTest(unittest.TestCase):
"""Testing class for Getis-Ord's G* funtion
This test replicates the work done in PySAL documentation:
https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/autocorrelation.html#local-g-and-g
"""
def setUp(self):
plpy._reset()
# load raw data for analysis
self.neighbors_data = json.loads(
open(fixture_file('neighbors_getis.json')).read())
# load pre-computed/known values
self.getis_data = json.loads(
open(fixture_file('getis.json')).read())
def test_getis_ord(self):
"""Test Getis-Ord's G*"""
data = [{'id': d['id'],
'attr1': d['value'],
'neighbors': d['neighbors']} for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1234)
result = cc.getis_ord('subquery', 'value',
'queen', None, 999, 'the_geom', 'cartodb_id')
result = [(row[0], row[1]) for row in result]
expected = np.array(self.getis_data)[:, 0:2]
for ([res_z, res_p], [exp_z, exp_p]) in zip(result, expected):
self.assertAlmostEqual(res_z, exp_z, delta=1e-2)

86
src/py/crankshaft/test/test_pysal_utils.py
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@@ -2,18 +2,33 @@ import unittest
import crankshaft.pysal_utils as pu
from crankshaft import random_seeds
from collections import OrderedDict
class PysalUtilsTest(unittest.TestCase):
"""Testing class for utility functions related to PySAL integrations"""
def setUp(self):
self.params = {"id_col": "cartodb_id",
"attr1": "andy",
"attr2": "jay_z",
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.params1 = OrderedDict([("id_col", "cartodb_id"),
("attr1", "andy"),
("attr2", "jay_z"),
("subquery", "SELECT * FROM a_list"),
("geom_col", "the_geom"),
("num_ngbrs", 321)])
self.params2 = OrderedDict([("id_col", "cartodb_id"),
("numerator", "price"),
("denominator", "sq_meters"),
("subquery", "SELECT * FROM pecan"),
("geom_col", "the_geom"),
("num_ngbrs", 321)])
self.params3 = OrderedDict([("id_col", "cartodb_id"),
("numerator", "sq_meters"),
("denominator", "price"),
("subquery", "SELECT * FROM pecan"),
("geom_col", "the_geom"),
("num_ngbrs", 321)])
self.params_array = {"id_col": "cartodb_id",
"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
@@ -24,34 +39,41 @@ class PysalUtilsTest(unittest.TestCase):
def test_query_attr_select(self):
"""Test query_attr_select"""
ans = "i.\"andy\"::numeric As attr1, " \
"i.\"jay_z\"::numeric As attr2, "
ans1 = ("i.\"andy\"::numeric As attr1, "
"i.\"jay_z\"::numeric As attr2, ")
ans_array = "i.\"_2013_dec\"::numeric As attr1, " \
"i.\"_2014_jan\"::numeric As attr2, " \
"i.\"_2014_feb\"::numeric As attr3, "
ans2 = ("i.\"price\"::numeric As attr1, "
"i.\"sq_meters\"::numeric As attr2, ")
self.assertEqual(pu.query_attr_select(self.params), ans)
ans3 = ("i.\"sq_meters\"::numeric As attr1, "
"i.\"price\"::numeric As attr2, ")
ans_array = ("i.\"_2013_dec\"::numeric As attr1, "
"i.\"_2014_jan\"::numeric As attr2, "
"i.\"_2014_feb\"::numeric As attr3, ")
self.assertEqual(pu.query_attr_select(self.params1), ans1)
self.assertEqual(pu.query_attr_select(self.params2), ans2)
self.assertEqual(pu.query_attr_select(self.params3), ans3)
self.assertEqual(pu.query_attr_select(self.params_array), ans_array)
def test_query_attr_where(self):
"""Test pu.query_attr_where"""
ans = "idx_replace.\"andy\" IS NOT NULL AND " \
"idx_replace.\"jay_z\" IS NOT NULL AND " \
"idx_replace.\"jay_z\" <> 0"
ans1 = ("idx_replace.\"andy\" IS NOT NULL AND "
"idx_replace.\"jay_z\" IS NOT NULL")
ans_array = "idx_replace.\"_2013_dec\" IS NOT NULL AND " \
"idx_replace.\"_2014_jan\" IS NOT NULL AND " \
"idx_replace.\"_2014_feb\" IS NOT NULL"
ans_array = ("idx_replace.\"_2013_dec\" IS NOT NULL AND "
"idx_replace.\"_2014_jan\" IS NOT NULL AND "
"idx_replace.\"_2014_feb\" IS NOT NULL")
self.assertEqual(pu.query_attr_where(self.params), ans)
self.assertEqual(pu.query_attr_where(self.params1), ans1)
self.assertEqual(pu.query_attr_where(self.params_array), ans_array)
def test_knn(self):
"""Test knn neighbors constructor"""
ans = "SELECT i.\"cartodb_id\" As id, " \
ans1 = "SELECT i.\"cartodb_id\" As id, " \
"i.\"andy\"::numeric As attr1, " \
"i.\"jay_z\"::numeric As attr2, " \
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
@@ -59,17 +81,15 @@ class PysalUtilsTest(unittest.TestCase):
"WHERE " \
"i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
"j.\"andy\" IS NOT NULL AND " \
"j.\"jay_z\" IS NOT NULL AND " \
"j.\"jay_z\" <> 0 " \
"j.\"jay_z\" IS NOT NULL " \
"ORDER BY " \
"j.\"the_geom\" <-> i.\"the_geom\" ASC " \
"LIMIT 321)) As neighbors " \
"FROM (SELECT * FROM a_list) As i " \
"WHERE i.\"andy\" IS NOT NULL AND " \
"i.\"jay_z\" IS NOT NULL AND " \
"i.\"jay_z\" <> 0 " \
"i.\"jay_z\" IS NOT NULL " \
"ORDER BY i.\"cartodb_id\" ASC;"
ans_array = "SELECT i.\"cartodb_id\" As id, " \
"i.\"_2013_dec\"::numeric As attr1, " \
"i.\"_2014_jan\"::numeric As attr2, " \
@@ -88,13 +108,13 @@ class PysalUtilsTest(unittest.TestCase):
"i.\"_2014_feb\" IS NOT NULL "\
"ORDER BY i.\"cartodb_id\" ASC;"
self.assertEqual(pu.knn(self.params), ans)
self.assertEqual(pu.knn(self.params1), ans1)
self.assertEqual(pu.knn(self.params_array), ans_array)
def test_queen(self):
"""Test queen neighbors constructor"""
ans = "SELECT i.\"cartodb_id\" As id, " \
ans1 = "SELECT i.\"cartodb_id\" As id, " \
"i.\"andy\"::numeric As attr1, " \
"i.\"jay_z\"::numeric As attr2, " \
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
@@ -104,23 +124,21 @@ class PysalUtilsTest(unittest.TestCase):
"ST_Touches(i.\"the_geom\", " \
"j.\"the_geom\") AND " \
"j.\"andy\" IS NOT NULL AND " \
"j.\"jay_z\" IS NOT NULL AND " \
"j.\"jay_z\" <> 0)" \
"j.\"jay_z\" IS NOT NULL)" \
") As neighbors " \
"FROM (SELECT * FROM a_list) As i " \
"WHERE i.\"andy\" IS NOT NULL AND " \
"i.\"jay_z\" IS NOT NULL AND " \
"i.\"jay_z\" <> 0 " \
"i.\"jay_z\" IS NOT NULL " \
"ORDER BY i.\"cartodb_id\" ASC;"
self.assertEqual(pu.queen(self.params), ans)
self.assertEqual(pu.queen(self.params1), ans1)
def test_construct_neighbor_query(self):
"""Test construct_neighbor_query"""
# Compare to raw knn query
self.assertEqual(pu.construct_neighbor_query('knn', self.params),
pu.knn(self.params))
self.assertEqual(pu.construct_neighbor_query('knn', self.params1),
pu.knn(self.params1))
def test_get_attributes(self):
"""Test get_attributes"""