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Merge branch 'develop' of github.com:CartoDB/crankshaft into add-PIA

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abelvm 2017-01-18 17:28:14 +01:00
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.brackets.json Normal file
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{
"sbruchmann.staticpreview.basepath": "/home/carto/Projects/crankshaft/"
}

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.github/PULL_REQUEST_TEMPLATE.md vendored
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- [ ] 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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@ -35,14 +35,18 @@ before_install:
- sudo apt-get -y remove --purge postgresql-9.2
- sudo apt-get -y remove --purge postgresql-9.3
- sudo apt-get -y remove --purge postgresql-9.4
- sudo apt-get -y remove --purge postgis
- sudo apt-get -y remove --purge postgresql-9.5
- sudo rm -rf /var/lib/postgresql/
- sudo rm -rf /var/log/postgresql/
- sudo rm -rf /etc/postgresql/
- sudo apt-get -y remove --purge postgis-2.2
- sudo apt-get -y autoremove
- sudo apt-get -y install postgresql-9.5=9.5.2-2ubuntu1
- sudo apt-get -y install postgresql-server-dev-9.5=9.5.2-2ubuntu1
- sudo apt-get -y install postgresql-plpython-9.5=9.5.2-2ubuntu1
- sudo apt-get -y install postgresql-9.5-postgis-2.2=2.2.2.0-cdb2
- sudo apt-get -y install postgresql-9.5=9.5.2-3cdb2
- sudo apt-get -y install postgresql-server-dev-9.5=9.5.2-3cdb2
- sudo apt-get -y install postgresql-plpython-9.5=9.5.2-3cdb2
- sudo apt-get -y install postgresql-9.5-postgis-scripts=2.2.2.0-cdb2
- sudo apt-get -y install postgresql-9.5-postgis-2.2=2.2.2.0-cdb2
# configure it to accept local connections from postgres
- echo -e "# TYPE DATABASE USER ADDRESS METHOD \nlocal all postgres trust\nlocal all all trust\nhost all all 127.0.0.1/32 trust" \

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NEWS.md
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0.5.0 (2016-12-15)
------------------
* Updated PULL_REQUEST_TEMPLATE
* Fixed a bug that flips the order of the numerator in denominator for calculating using Moran Local Rate because previously the code sorted the keys alphabetically.
* Add new CDB_GetisOrdsG functions. Getis-Ord's G\* is a geo-statistical measurement of the intensity of clustering of high or low values
* Add new outlier detection functions: CDB_StaticOutlier, CDB_PercentOutlier and CDB_StdDevOutlier
* Updates in the framework for accessing the Python functions.
0.4.2 (2016-09-22)
------------------
* Bugfix for cdb_areasofinterestglobal: import correct modules
0.4.1 (2016-09-21)
------------------
* Let the user set the resolution in CDB_Contour function
* Add Nearest Neighbors method to CDB_SpatialInterpolation
* Improve error reporting for moran and markov functions
0.4.0 (2016-08-30)
------------------
* Add CDB_Contour
* Add CDB_PIA
* Add CDB_Densify
* Add CDB_TINmap
0.3.1 (2016-08-18)
------------------
* Fix Voronoi projection issue

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RELEASE.md
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@ -15,11 +15,12 @@ shall be performed by the designated *Release Manager*.
1. Generate an upgrade path from the previous to the next release by copying the generated release file. E.g:
```shell
cp release/cranckshaft--X.Y.Z.sql release/cranckshaft--A.B.C--X.Y.Z.sql
cp release/crankshaft--X.Y.Z.sql release/crankshaft--A.B.C--X.Y.Z.sql
```
NOTE: you can rely on this thanks to the compatibility checks.
TODO: automate this step [#94](https://github.com/CartoDB/crankshaft/issues/94)
2. Update the [NEWS.md](https://github.com/CartoDB/crankshaft/blob/master/NEWS.md) file
1. Commit and push the generated files.
1. Tag the release:
@ -29,7 +30,6 @@ shall be performed by the designated *Release Manager*.
```
1. Deploy and test in staging
1. Deploy and test in production
2. Update the [NEWS.md](https://github.com/CartoDB/crankshaft/blob/master/NEWS.md) file
1. Merge back into develop

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@ -37,7 +37,7 @@ SELECT
aoi.quads,
aoi.significance,
c.num_cyclists_per_total_population
FROM CDB_GetAreasOfInterestLocal('SELECT * FROM commute_data'
FROM CDB_AreasOfInterestLocal('SELECT * FROM commute_data'
'num_cyclists_per_total_population') As aoi
JOIN commute_data As c
ON c.cartodb_id = aoi.rowid;
@ -113,7 +113,7 @@ SELECT
aoi.quads,
aoi.significance,
c.cyclists_per_total_population
FROM CDB_GetAreasOfInterestLocalRate('SELECT * FROM commute_data'
FROM CDB_AreasOfInterestLocalRate('SELECT * FROM commute_data'
'num_cyclists',
'total_population') As aoi
JOIN commute_data As c

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Function to interpolate a numeric attribute of a point in a scatter dataset of points, using one of three methos:
* [Nearest neighbor](https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
* [Nearest neighbor(s)](https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
* [Barycentric](https://en.wikipedia.org/wiki/Barycentric_coordinate_system)
* [IDW](https://en.wikipedia.org/wiki/Inverse_distance_weighting)
@ -15,7 +15,7 @@ Function to interpolate a numeric attribute of a point in a scatter dataset of p
| query | text | query that returns at least `the_geom` and a numeric value as `attrib` |
| point | geometry | The target point to calc the value |
| method | integer | 0:nearest neighbor, 1: barycentric, 2: IDW|
| p1 | integer | IDW: limit the number of neighbors, 0->no limit|
| p1 | integer | limit the number of neighbors, IDW: 0->no limit, NN: 0-> closest one|
| p2 | integer | IDW: order of distance decay, 0-> order 1|
### CDB_SpatialInterpolation (geom geometry[], values numeric[], point geometry, method integer DEFAULT 1, p1 integer DEFAULT 0, ps integer DEFAULT 0)
@ -28,7 +28,7 @@ Function to interpolate a numeric attribute of a point in a scatter dataset of p
| values | numeric[] | Array of points' values for the param under study|
| point | geometry | The target point to calc the value |
| method | integer | 0:nearest neighbor, 1: barycentric, 2: IDW|
| p1 | integer | IDW: limit the number of neighbors, 0->no limit|
| p1 | integer | limit the number of neighbors, IDW: 0->no limit, NN: 0-> closest one|
| p2 | integer | IDW: order of distance decay, 0-> order 1|
### Returns
@ -37,6 +37,9 @@ Function to interpolate a numeric attribute of a point in a scatter dataset of p
|-------------|------|-------------|
| value | numeric | Interpolated value at the given point, `-888.888` if the given point is out of the boundaries of the source points set |
Default values:
* -888.888: when using Barycentric, the target point is out of the realm of the input points
* -777.777: asking for a method not available
#### Example Usage

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## Densify function
Iterative densification of a set of scattered points using Delaunay triangulation. The new points are located at the centroids of the grid cells and have as assigned value the barycentric average value of the cell's vertex.
### CDB_Densify(geomin geometry[], colin numeric[], iterations integer)
#### Arguments
| Name | Type | Description |
|------|------|-------------|
| geomin | geometry[] | Array of points geometries |
| colin | numeric[] | Array of points' values |
| iterations | integer | Number of iterations |
### Returns
Returns a table object
| Name | Type | Description |
|------|------|-------------|
| geomout | geometry | Geometries of new dataset of points|
| colout | numeric | Values of points|
#### Example Usage
```sql
with data as (
select
ARRAY[7.0,8.0,1.0,2.0,3.0,5.0,6.0,4.0] as colin,
ARRAY[ST_GeomFromText('POINT(2.1744 41.4036)'),ST_GeomFromText('POINT(2.1228 41.3809)'),ST_GeomFromText('POINT(2.1511 41.3742)'),ST_GeomFromText('POINT(2.1528 41.4136)'),ST_GeomFromText('POINT(2.165 41.3917)'),ST_GeomFromText('POINT(2.1498 41.3713)'),ST_GeomFromText('POINT(2.1533 41.3683)'),ST_GeomFromText('POINT(2.131386 41.413998)')] as geomin
)
select CDB_Densify(geomin, colin, 2) from data;
```

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## TINMAP function
Generates a fake contour map, in the form of a TIN map, from a set of scattered points.Depends on **CDB_Densify**.
Its iterative nature lets the user smooth the final result as much as desired, but with a exponential time cost increase.
### CDB_TINmap(geomin geometry[], colin numeric[], iterations integer)
#### Arguments
| Name | Type | Description |
|------|------|-------------|
| geomin | geometry[] | Array of points geometries |
| colin | numeric[] | Array of points' values |
| iterations | integer | Number of iterations |
### Returns
Returns a table object
| Name | Type | Description |
|------|------|-------------|
| geomout | geometry | Geometries of new dataset of polygons|
| colout | numeric | Values of each cell|
#### Example Usage
```sql
with data as (
select
ARRAY[7.0,8.0,1.0,2.0,3.0,5.0,6.0,4.0] as colin,
ARRAY[ST_GeomFromText('POINT(2.1744 41.4036)'),ST_GeomFromText('POINT(2.1228 41.3809)'),ST_GeomFromText('POINT(2.1511 41.3742)'),ST_GeomFromText('POINT(2.1528 41.4136)'),ST_GeomFromText('POINT(2.165 41.3917)'),ST_GeomFromText('POINT(2.1498 41.3713)'),ST_GeomFromText('POINT(2.1533 41.3683)'),ST_GeomFromText('POINT(2.131386 41.413998)')] as geomin
)
select CDB_TINmap(geomin, colin, 2) from data;
```

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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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## 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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## Contour maps
Function to generate a contour map from an scatter dataset of points, using one of three methos:
Function to generate a contour map from an scatter dataset of points, using one of these three methods:
* [Nearest neighbor](https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
* [Barycentric](https://en.wikipedia.org/wiki/Barycentric_coordinate_system)
@ -18,7 +18,7 @@ Function to generate a contour map from an scatter dataset of points, using one
| method | integer | 0:nearest neighbor, 1: barycentric, 2: IDW|
| classmethod | integer | 0:equals, 1: heads&tails, 2:jenks, 3:quantiles |
| steps | integer | Number of steps in the classification|
| max_time | integer | Max time in millisecons for processing time
| max_time | integer | if <= 0: max processing time in seconds (smart resolution) , if >0: resolution in meters
### Returns
Returns a table object

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comment = 'CartoDB Spatial Analysis extension'
default_version = '0.3.1'
default_version = '0.5.1'
requires = 'plpythonu, postgis'
superuser = true
schema = cdb_crankshaft

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"""Import all modules"""
import crankshaft.random_seeds
import crankshaft.clustering
import crankshaft.space_time_dynamics
import crankshaft.segmentation

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"""Import all functions from for clustering"""
from moran import *
from kmeans import *

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from sklearn.cluster import KMeans
import plpy
def kmeans(query, no_clusters, no_init=20):
data = plpy.execute('''select array_agg(cartodb_id order by cartodb_id) as ids,
array_agg(ST_X(the_geom) order by cartodb_id) xs,
array_agg(ST_Y(the_geom) order by cartodb_id) ys from ({query}) a
where the_geom is not null
'''.format(query=query))
xs = data[0]['xs']
ys = data[0]['ys']
ids = data[0]['ids']
km = KMeans(n_clusters= no_clusters, n_init=no_init)
labels = km.fit_predict(zip(xs,ys))
return zip(ids,labels)

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"""
Moran's I geostatistics (global clustering & outliers presence)
"""
# TODO: Fill in local neighbors which have null/NoneType values with the
# average of the their neighborhood
import pysal as ps
import plpy
from collections import OrderedDict
# crankshaft module
import crankshaft.pysal_utils as pu
# High level interface ---------------------------------------
def moran(subquery, attr_name,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Moran's I (global)
Implementation building neighbors with a PostGIS database and Moran's I
core clusters with PySAL.
Andy Eschbacher
"""
qvals = OrderedDict([("id_col", id_col),
("attr1", attr_name),
("geom_col", geom_col),
("subquery", subquery),
("num_ngbrs", num_ngbrs)])
query = pu.construct_neighbor_query(w_type, qvals)
plpy.notice('** Query: %s' % query)
try:
result = plpy.execute(query)
# if there are no neighbors, exit
if len(result) == 0:
return pu.empty_zipped_array(2)
plpy.notice('** Query returned with %d rows' % len(result))
except plpy.SPIError:
plpy.error('Error: areas of interest query failed, check input parameters')
plpy.notice('** Query failed: "%s"' % query)
plpy.notice('** Error: %s' % plpy.SPIError)
return pu.empty_zipped_array(2)
## collect attributes
attr_vals = pu.get_attributes(result)
## calculate weights
weight = pu.get_weight(result, w_type, num_ngbrs)
## calculate moran global
moran_global = ps.esda.moran.Moran(attr_vals, weight,
permutations=permutations)
return zip([moran_global.I], [moran_global.EI])
def moran_local(subquery, attr,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Moran's I implementation for PL/Python
Andy Eschbacher
"""
# geometries with attributes that are null are ignored
# resulting in a collection of not as near neighbors
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(5)
except plpy.SPIError:
plpy.error('Error: areas of interest query failed, check input parameters')
plpy.notice('** Query failed: "%s"' % query)
return pu.empty_zipped_array(5)
attr_vals = pu.get_attributes(result)
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate LISA values
lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
permutations=permutations)
# find quadrants for each geometry
quads = quad_position(lisa.q)
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
def moran_rate(subquery, numerator, denominator,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Moran's I Rate (global)
Andy Eschbacher
"""
qvals = OrderedDict([("id_col", id_col),
("attr1", numerator),
("attr2", denominator)
("geom_col", geom_col),
("subquery", subquery),
("num_ngbrs", num_ngbrs)])
query = pu.construct_neighbor_query(w_type, qvals)
plpy.notice('** Query: %s' % query)
try:
result = plpy.execute(query)
# if there are no neighbors, exit
if len(result) == 0:
return pu.empty_zipped_array(2)
plpy.notice('** Query returned with %d rows' % len(result))
except plpy.SPIError:
plpy.error('Error: areas of interest query failed, check input parameters')
plpy.notice('** Query failed: "%s"' % query)
plpy.notice('** Error: %s' % plpy.SPIError)
return pu.empty_zipped_array(2)
## collect attributes
numer = pu.get_attributes(result, 1)
denom = pu.get_attributes(result, 2)
weight = pu.get_weight(result, w_type, num_ngbrs)
## calculate moran global rate
lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
permutations=permutations)
return zip([lisa_rate.I], [lisa_rate.EI])
def moran_local_rate(subquery, numerator, denominator,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Moran's I Local Rate
Andy Eschbacher
"""
# geometries with values that are null are ignored
# resulting in a collection of not as near neighbors
qvals = OrderedDict([("id_col", id_col),
("numerator", numerator),
("denominator", denominator),
("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(5)
except plpy.SPIError:
plpy.error('Error: areas of interest query failed, check input parameters')
plpy.notice('** Query failed: "%s"' % query)
plpy.notice('** Error: %s' % plpy.SPIError)
return pu.empty_zipped_array(5)
## collect attributes
numer = pu.get_attributes(result, 1)
denom = pu.get_attributes(result, 2)
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate LISA values
lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
permutations=permutations)
# find quadrants for each geometry
quads = quad_position(lisa.q)
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
def moran_local_bv(subquery, attr1, attr2,
permutations, geom_col, id_col, w_type, num_ngbrs):
"""
Moran's I (local) Bivariate (untested)
"""
plpy.notice('** Constructing query')
qvals = OrderedDict([("id_col", id_col),
("attr1", attr1),
("attr2", attr2),
("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:
plpy.error("Error: areas of interest query failed, " \
"check input parameters")
plpy.notice('** Query failed: "%s"' % query)
return pu.empty_zipped_array(4)
## collect attributes
attr1_vals = pu.get_attributes(result, 1)
attr2_vals = pu.get_attributes(result, 2)
# create weights
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate LISA values
lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
permutations=permutations)
plpy.notice("len of Is: %d" % len(lisa.Is))
# find clustering of significance
lisa_sig = quad_position(lisa.q)
plpy.notice('** Finished calculations')
return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
# Low level functions ----------------------------------------
def map_quads(coord):
"""
Map a quadrant number to Moran's I designation
HH=1, LH=2, LL=3, HL=4
Input:
@param coord (int): quadrant of a specific measurement
Output:
classification (one of 'HH', 'LH', 'LL', or 'HL')
"""
if coord == 1:
return 'HH'
elif coord == 2:
return 'LH'
elif coord == 3:
return 'LL'
elif coord == 4:
return 'HL'
else:
return None
def quad_position(quads):
"""
Produce Moran's I classification based of n
Input:
@param quads ndarray: an array of quads classified by
1-4 (PySAL default)
Output:
@param list: an array of quads classied by 'HH', 'LL', etc.
"""
return [map_quads(q) for q in quads]

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"""Import all functions for pysal_utils"""
from crankshaft.pysal_utils.pysal_utils import *

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"""
Utilities module for generic PySAL functionality, mainly centered on
translating queries into numpy arrays or PySAL weights objects
"""
import numpy as np
import pysal as ps
def construct_neighbor_query(w_type, query_vals):
"""Return query (a string) used for finding neighbors
@param w_type text: type of neighbors to calculate ('knn' or 'queen')
@param query_vals dict: values used to construct the query
"""
if w_type.lower() == 'knn':
return knn(query_vals)
else:
return queen(query_vals)
## Build weight object
def get_weight(query_res, w_type='knn', num_ngbrs=5):
"""
Construct PySAL weight from return value of query
@param query_res dict-like: query results with attributes and neighbors
"""
# if w_type.lower() == 'knn':
# row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
# weights = {x['id']: row_normed_weights for x in query_res}
# else:
# weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
# if len(x['neighbors']) > 0
# else [] for x in query_res}
neighbors = {x['id']: x['neighbors'] for x in query_res}
print 'len of neighbors: %d' % len(neighbors)
built_weight = ps.W(neighbors)
built_weight.transform = 'r'
return built_weight
def query_attr_select(params):
"""
Create portion of SELECT statement for attributes inolved in query.
@param params: dict of information used in query (column names,
table name, etc.)
"""
attr_string = ""
template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, "
if 'time_cols' in params:
## if markov analysis
attrs = params['time_cols']
for idx, val in enumerate(attrs):
attr_string += template % {"col": val, "alias_num": idx + 1}
else:
## if moran's analysis
attrs = [k for k in params
if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')]
for idx, val in enumerate(sorted(attrs)):
attr_string += template % {"col": params[val], "alias_num": idx + 1}
return attr_string
def query_attr_where(params):
"""
Construct where conditions when building neighbors query
Create portion of WHERE clauses for weeding out NULL-valued geometries
Input: dict of params:
{'subquery': ...,
'numerator': 'data1',
'denominator': 'data2',
'': ...}
Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL'
Input:
{'subquery': ...,
'time_cols': ['time1', 'time2', 'time3'],
'etc': ...}
Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT
NULL AND idx_replace."time3" IS NOT NULL'
"""
attr_string = []
template = "idx_replace.\"%s\" IS NOT NULL"
if 'time_cols' in params:
## markov where clauses
attrs = params['time_cols']
# add values to template
for attr in attrs:
attr_string.append(template % attr)
else:
## moran where clauses
# get keys
attrs = sorted([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]])
out = " AND ".join(attr_string)
return out
def knn(params):
"""SQL query for k-nearest neighbors.
@param vars: dict of values to fill template
"""
attr_select = query_attr_select(params)
attr_where = query_attr_where(params)
replacements = {"attr_select": attr_select,
"attr_where_i": attr_where.replace("idx_replace", "i"),
"attr_where_j": attr_where.replace("idx_replace", "j")}
query = "SELECT " \
"i.\"{id_col}\" As id, " \
"%(attr_select)s" \
"(SELECT ARRAY(SELECT j.\"{id_col}\" " \
"FROM ({subquery}) As j " \
"WHERE " \
"i.\"{id_col}\" <> j.\"{id_col}\" AND " \
"%(attr_where_j)s " \
"ORDER BY " \
"j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
"LIMIT {num_ngbrs})" \
") As neighbors " \
"FROM ({subquery}) As i " \
"WHERE " \
"%(attr_where_i)s " \
"ORDER BY i.\"{id_col}\" ASC;" % replacements
return query.format(**params)
## SQL query for finding queens neighbors (all contiguous polygons)
def queen(params):
"""SQL query for queen neighbors.
@param params dict: information to fill query
"""
attr_select = query_attr_select(params)
attr_where = query_attr_where(params)
replacements = {"attr_select": attr_select,
"attr_where_i": attr_where.replace("idx_replace", "i"),
"attr_where_j": attr_where.replace("idx_replace", "j")}
query = "SELECT " \
"i.\"{id_col}\" As id, " \
"%(attr_select)s" \
"(SELECT ARRAY(SELECT j.\"{id_col}\" " \
"FROM ({subquery}) As j " \
"WHERE i.\"{id_col}\" <> j.\"{id_col}\" AND " \
"ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
"%(attr_where_j)s)" \
") As neighbors " \
"FROM ({subquery}) As i " \
"WHERE " \
"%(attr_where_i)s " \
"ORDER BY i.\"{id_col}\" ASC;" % replacements
return query.format(**params)
## to add more weight methods open a ticket or pull request
def get_attributes(query_res, attr_num=1):
"""
@param query_res: query results with attributes and neighbors
@param attr_num: attribute number (1, 2, ...)
"""
return np.array([x['attr' + str(attr_num)] for x in query_res], dtype=np.float)
def empty_zipped_array(num_nones):
"""
prepare return values for cases of empty weights objects (no neighbors)
Input:
@param num_nones int: number of columns (e.g., 4)
Output:
[(None, None, None, None)]
"""
return [tuple([None] * num_nones)]

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"""Random seed generator used for non-deterministic functions in crankshaft"""
import random
import numpy
def set_random_seeds(value):
"""
Set the seeds of the RNGs (Random Number Generators)
used internally.
"""
random.seed(value)
numpy.random.seed(value)

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from segmentation import *

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"""
Segmentation creation and prediction
"""
import sklearn
import numpy as np
import plpy
from sklearn.ensemble import GradientBoostingRegressor
from sklearn import metrics
from sklearn.cross_validation import train_test_split
# Lower level functions
#----------------------
def replace_nan_with_mean(array):
"""
Input:
@param array: an array of floats which may have null-valued entries
Output:
array with nans filled in with the mean of the dataset
"""
# returns an array of rows and column indices
indices = np.where(np.isnan(array))
# iterate through entries which have nan values
for row, col in zip(*indices):
array[row, col] = np.mean(array[~np.isnan(array[:, col]), col])
return array
def get_data(variable, feature_columns, query):
"""
Fetch data from the database, clean, and package into
numpy arrays
Input:
@param variable: name of the target variable
@param feature_columns: list of column names
@param query: subquery that data is pulled from for the packaging
Output:
prepared data, packaged into NumPy arrays
"""
columns = ','.join(['array_agg("{col}") As "{col}"'.format(col=col) for col in feature_columns])
try:
data = plpy.execute('''SELECT array_agg("{variable}") As target, {columns} FROM ({query}) As a'''.format(
variable=variable,
columns=columns,
query=query))
except Exception, e:
plpy.error('Failed to access data to build segmentation model: %s' % e)
# extract target data from plpy object
target = np.array(data[0]['target'])
# put n feature data arrays into an n x m array of arrays
features = np.column_stack([np.array(data[0][col], dtype=float) for col in feature_columns])
return replace_nan_with_mean(target), replace_nan_with_mean(features)
# High level interface
# --------------------
def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters):
"""
Version of create_and_predict_segment that works on arrays that come stright form the SQL calling
the function.
Input:
@param target: The 1D array of lenth NSamples containing the target variable we want the model to predict
@param features: Thw 2D array of size NSamples * NFeatures that form the imput to the model
@param target_ids: A 1D array of target_ids that will be used to associate the results of the prediction with the rows which they come from
@param model_parameters: A dictionary containing parameters for the model.
"""
clean_target = replace_nan_with_mean(target)
clean_features = replace_nan_with_mean(features)
target_features = replace_nan_with_mean(target_features)
model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2)
prediction = model.predict(target_features)
accuracy_array = [accuracy]*prediction.shape[0]
return zip(target_ids, prediction, np.full(prediction.shape, accuracy_array))
def create_and_predict_segment(query, variable, target_query, model_params):
"""
generate a segment with machine learning
Stuart Lynn
"""
## fetch column names
try:
columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1 '.format(query=query))[0].keys()
except Exception, e:
plpy.error('Failed to build segmentation model: %s' % e)
## extract column names to be used in building the segmentation model
feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator'])
## get data from database
target, features = get_data(variable, feature_columns, query)
model, accuracy = train_model(target, features, model_params, 0.2)
cartodb_ids, result = predict_segment(model, feature_columns, target_query)
accuracy_array = [accuracy]*result.shape[0]
return zip(cartodb_ids, result, accuracy_array)
def train_model(target, features, model_params, test_split):
"""
Train the Gradient Boosting model on the provided data and calculate the accuracy of the model
Input:
@param target: 1D Array of the variable that the model is to be trianed to predict
@param features: 2D Array NSamples * NFeatures to use in trining the model
@param model_params: A dictionary of model parameters, the full specification can be found on the
scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)
@parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray
"""
features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
model = GradientBoostingRegressor(**model_params)
model.fit(features_train, target_train)
accuracy = calculate_model_accuracy(model, features, target)
return model, accuracy
def calculate_model_accuracy(model, features, target):
"""
Calculate the mean squared error of the model prediction
Input:
@param model: model trained from input features
@param features: features to make a prediction from
@param target: target to compare prediction to
Output:
mean squared error of the model prection compared to the target
"""
prediction = model.predict(features)
return metrics.mean_squared_error(prediction, target)
def predict_segment(model, features, target_query):
"""
Use the provided model to predict the values for the new feature set
Input:
@param model: The pretrained model
@features: A list of features to use in the model prediction (list of column names)
@target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it.
"""
batch_size = 1000
joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features])
try:
cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format(
joined_features=joined_features,
target_query=target_query))
except Exception, e:
plpy.error('Failed to build segmentation model: %s' % e)
results = []
while True:
rows = cursor.fetch(batch_size)
if not rows:
break
batch = np.row_stack([np.array(row['features'], dtype=float) for row in rows])
#Need to fix this. Should be global mean. This will cause weird effects
batch = replace_nan_with_mean(batch)
prediction = model.predict(batch)
results.append(prediction)
try:
cartodb_ids = plpy.execute('''SELECT array_agg(cartodb_id ORDER BY cartodb_id) As cartodb_ids FROM ({0}) As a'''.format(target_query))[0]['cartodb_ids']
except Exception, e:
plpy.error('Failed to build segmentation model: %s' % e)
return cartodb_ids, np.concatenate(results)

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"""Import all functions from clustering libraries."""
from markov import *

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"""
Spatial dynamics measurements using Spatial Markov
"""
import numpy as np
import pysal as ps
import plpy
import crankshaft.pysal_utils as pu
def spatial_markov_trend(subquery, time_cols, num_classes=7,
w_type='knn', num_ngbrs=5, permutations=0,
geom_col='the_geom', id_col='cartodb_id'):
"""
Predict the trends of a unit based on:
1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
2. average class of its neighbors
Inputs:
@param subquery string: e.g., SELECT the_geom, cartodb_id,
interesting_time_column FROM table_name
@param time_cols list of strings: list of strings of column names
@param num_classes (optional): number of classes to break distribution
of values into. Currently uses quantile bins.
@param w_type string (optional): weight type ('knn' or 'queen')
@param num_ngbrs int (optional): number of neighbors (if knn type)
@param permutations int (optional): number of permutations for test
stats
@param geom_col string (optional): name of column which contains the
geometries
@param id_col string (optional): name of column which has the ids of
the table
Outputs:
@param trend_up float: probablity that a geom will move to a higher
class
@param trend_down float: probablity that a geom will move to a lower
class
@param trend float: (trend_up - trend_down) / trend_static
@param volatility float: a measure of the volatility based on
probability stddev(prob array)
"""
if len(time_cols) < 2:
plpy.error('More than one time column needs to be passed')
qvals = {"id_col": id_col,
"time_cols": time_cols,
"geom_col": geom_col,
"subquery": subquery,
"num_ngbrs": num_ngbrs}
try:
query_result = plpy.execute(
pu.construct_neighbor_query(w_type, qvals)
)
if len(query_result) == 0:
return zip([None], [None], [None], [None], [None])
except plpy.SPIError, err:
plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
plpy.error('Query failed, check the input parameters')
return zip([None], [None], [None], [None], [None])
## build weight
weights = pu.get_weight(query_result, w_type)
weights.transform = 'r'
## prep time data
t_data = get_time_data(query_result, time_cols)
plpy.debug('shape of t_data %d, %d' % t_data.shape)
plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape)
plpy.debug('first num elements: %f' % t_data[0, 0])
sp_markov_result = ps.Spatial_Markov(t_data,
weights,
k=num_classes,
fixed=False,
permutations=permutations)
## get lag classes
lag_classes = ps.Quantiles(
ps.lag_spatial(weights, t_data[:, -1]),
k=num_classes).yb
## look up probablity distribution for each unit according to class and lag class
prob_dist = get_prob_dist(sp_markov_result.P,
lag_classes,
sp_markov_result.classes[:, -1])
## find the ups and down and overall distribution of each cell
trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist,
sp_markov_result.classes[:, -1])
## output the results
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
"""
num_attrs = len(time_cols)
return np.array([[x['attr' + str(i)] for x in markov_data]
for i in range(1, num_attrs+1)], dtype=float).transpose()
## not currently used
def rebin_data(time_data, num_time_per_bin):
"""
Convert an n x l matrix into an (n/m) x l matrix where the values are
reduced (averaged) for the intervening states:
1 2 3 4 1.5 3.5
5 6 7 8 -> 5.5 7.5
9 8 7 6 8.5 6.5
5 4 3 2 4.5 2.5
if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix.
This process effectively resamples the data at a longer time span n
units longer than the input data.
For cases when there is a remainder (remainder(5/3) = 2), the remaining
two columns are binned together as the last time period, while the
first three are binned together for the first period.
Input:
@param time_data n x l ndarray: measurements of an attribute at
different time intervals
@param num_time_per_bin int: number of columns to average into a new
column
Output:
ceil(n / m) x l ndarray of resampled time series
"""
if time_data.shape[1] % num_time_per_bin == 0:
## if fit is perfect, then use it
n_max = time_data.shape[1] / num_time_per_bin
else:
## fit remainders into an additional column
n_max = time_data.shape[1] / num_time_per_bin + 1
return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
for i in range(n_max)]).T
def get_prob_dist(transition_matrix, lag_indices, unit_indices):
"""
Given an array of transition matrices, look up the probability
associated with the arrangements passed
Input:
@param transition_matrix ndarray[k,k,k]:
@param lag_indices ndarray:
@param unit_indices ndarray:
Output:
Array of probability distributions
"""
return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
for i in range(len(lag_indices))])
def get_prob_stats(prob_dist, unit_indices):
"""
get the statistics of the probability distributions
Outputs:
@param trend_up ndarray(float): sum of probabilities for upward
movement (relative to the unit index of that prob)
@param trend_down ndarray(float): sum of probabilities for downward
movement (relative to the unit index of that prob)
@param trend ndarray(float): difference of upward and downward
movements
"""
num_elements = len(unit_indices)
trend_up = np.empty(num_elements, dtype=float)
trend_down = np.empty(num_elements, dtype=float)
trend = np.empty(num_elements, dtype=float)
for i in range(num_elements):
trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
if prob_dist[i, unit_indices[i]] > 0.0:
trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
else:
trend[i] = None
## calculate volatility of distribution
volatility = prob_dist.std(axis=1)
return trend_up, trend_down, trend, volatility

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"""
CartoDB Spatial Analysis Python Library
See:
https://github.com/CartoDB/crankshaft
"""
from setuptools import setup, find_packages
setup(
name='crankshaft',
version='0.0.0',
description='CartoDB Spatial Analysis Python Library',
url='https://github.com/CartoDB/crankshaft',
author='Data Services Team - CartoDB',
author_email='dataservices@cartodb.com',
license='MIT',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Mapping comunity',
'Topic :: Maps :: Mapping Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2.7',
],
keywords='maps mapping tools spatial analysis geostatistics',
packages=find_packages(exclude=['contrib', 'docs', 'tests']),
extras_require={
'dev': ['unittest'],
'test': ['unittest', 'nose', 'mock'],
},
# The choice of component versions is dictated by what's
# provisioned in the production servers.
# IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
requires=['pysal', 'numpy', 'sklearn'],
test_suite='test'
)

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"""
CartoDB Spatial Analysis Python Library
See:
https://github.com/CartoDB/crankshaft
"""
from setuptools import setup, find_packages
setup(
name='crankshaft',
version='0.0.0',
description='CartoDB Spatial Analysis Python Library',
url='https://github.com/CartoDB/crankshaft',
author='Data Services Team - CartoDB',
author_email='dataservices@cartodb.com',
license='MIT',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Mapping comunity',
'Topic :: Maps :: Mapping Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2.7',
],
keywords='maps mapping tools spatial analysis geostatistics',
packages=find_packages(exclude=['contrib', 'docs', 'tests']),
extras_require={
'dev': ['unittest'],
'test': ['unittest', 'nose', 'mock'],
},
# The choice of component versions is dictated by what's
# provisioned in the production servers.
# IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
requires=['pysal', 'numpy', 'sklearn'],
test_suite='test'
)

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[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 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]}]

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@ -0,0 +1 @@
[[0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 0], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 1], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 2], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 3], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 4], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 5], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 6], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 7], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 8], [0.19047619047619049, 0.16, 0.0, 0.32594478059941379, 9], [-0.23529411764705882, 0.0, 0.19047619047619047, 0.31356338348865387, 10], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 11], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 12], [0.027777777777777783, 0.11111111111111112, 0.088888888888888892, 0.30339641183779581, 13], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 14], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 15], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 16], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 17], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 18], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 19], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 20], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 21], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 22], [-0.16666666666666663, 0.18181818181818182, 0.27272727272727271, 0.20246415864836445, 23], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 24], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 25], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 26], [-0.043478260869565216, 0.0, 0.041666666666666664, 0.37950991789118999, 27], [0.22222222222222221, 0.18181818181818182, 0.0, 0.31701083225750354, 28], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 29], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 30], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 31], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 32], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 33], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 34], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 35], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 36], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 37], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 38], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 39], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 40], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 41], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 42], [0.0, 0.0, 0.0, 0.40000000000000002, 43], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 44], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 45], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 46], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 47]]

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@ -0,0 +1,52 @@
[[0.9319096128346788, "HH"],
[-1.135787401862846, "HL"],
[0.11732030672508517, "LL"],
[0.6152779669180425, "LL"],
[-0.14657336660125297, "LH"],
[0.6967858120189607, "LL"],
[0.07949310115714454, "HH"],
[0.4703198759258987, "HH"],
[0.4421125200498064, "HH"],
[0.5724288737143592, "LL"],
[0.8970743435692062, "LL"],
[0.18327334401918674, "LL"],
[-0.01466729201304962, "HL"],
[0.3481559372544409, "LL"],
[0.06547094736902978, "LL"],
[0.15482141569329988, "HH"],
[0.4373841193538136, "HH"],
[0.15971286468915544, "LL"],
[1.0543588860308968, "HH"],
[1.7372866900020818, "HH"],
[1.091998586053999, "LL"],
[0.1171572584252222, "HH"],
[0.08438455015300014, "LL"],
[0.06547094736902978, "LL"],
[0.15482141569329985, "HH"],
[1.1627044812890683, "HH"],
[0.06547094736902978, "LL"],
[0.795275137550483, "HH"],
[0.18562939195219, "LL"],
[0.3010757406693439, "LL"],
[2.8205795942839376, "HH"],
[0.11259190602909264, "LL"],
[-0.07116352791516614, "HL"],
[-0.09945240794119009, "LH"],
[0.18562939195219, "LL"],
[0.1832733440191868, "LL"],
[-0.39054253768447705, "HL"],
[-0.1672071289487642, "HL"],
[0.3337669247916343, "HH"],
[0.2584386102554792, "HH"],
[-0.19733845476322634, "HL"],
[-0.9379282899805409, "LH"],
[-0.028770969951095866, "LH"],
[0.051367269430983485, "LL"],
[-0.2172548045913472, "LH"],
[0.05136726943098351, "LL"],
[0.04191046803899837, "LL"],
[0.7482357030403517, "HH"],
[-0.014585767863118111, "LH"],
[0.5410013139159929, "HH"],
[1.0223932668429925, "LL"],
[1.4179402898927476, "LL"]]

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[
{"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5},
{"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7},
{"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2},
{"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1},
{"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3},
{"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05},
{"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4},
{"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7},
{"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5},
{"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04},
{"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08},
{"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2},
{"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4},
{"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2},
{"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3},
{"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4},
{"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6},
{"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3},
{"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7},
{"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8},
{"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1},
{"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4},
{"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1},
{"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3},
{"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4},
{"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6},
{"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3},
{"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8},
{"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3},
{"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1},
{"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9},
{"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3},
{"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4},
{"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3},
{"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3},
{"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2},
{"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5},
{"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4},
{"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6},
{"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5},
{"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4},
{"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2},
{"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3},
{"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2},
{"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3},
{"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2},
{"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3},
{"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5},
{"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2},
{"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6},
{"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01},
{"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01}
]

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import unittest
from mock_plpy import MockPlPy
plpy = MockPlPy()
import sys
sys.modules['plpy'] = plpy
import os
def fixture_file(name):
dir = os.path.dirname(os.path.realpath(__file__))
return os.path.join(dir, 'fixtures', name)

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import re
class MockCursor:
def __init__(self, data):
self.cursor_pos = 0
self.data = data
def fetch(self, batch_size):
batch = self.data[self.cursor_pos : self.cursor_pos + batch_size]
self.cursor_pos += batch_size
return batch
class MockPlPy:
def __init__(self):
self._reset()
def _reset(self):
self.infos = []
self.notices = []
self.debugs = []
self.logs = []
self.warnings = []
self.errors = []
self.fatals = []
self.executes = []
self.results = []
self.prepares = []
self.results = []
def _define_result(self, query, result):
pattern = re.compile(query, re.IGNORECASE | re.MULTILINE)
self.results.append([pattern, result])
def notice(self, msg):
self.notices.append(msg)
def debug(self, msg):
self.notices.append(msg)
def info(self, msg):
self.infos.append(msg)
def cursor(self, query):
data = self.execute(query)
return MockCursor(data)
def execute(self, query): # TODO: additional arguments
for result in self.results:
if result[0].match(query):
return result[1]
return []

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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
class MoranTest(unittest.TestCase):
"""Testing class for Moran's I functions"""
def setUp(self):
plpy._reset()
self.params = {"id_col": "cartodb_id",
"attr1": "andy",
"attr2": "jay_z",
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.params_markov = {"id_col": "cartodb_id",
"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read())
self.moran_data = json.loads(open(fixture_file('moran.json')).read())
def test_map_quads(self):
"""Test map_quads"""
self.assertEqual(cc.map_quads(1), 'HH')
self.assertEqual(cc.map_quads(2), 'LH')
self.assertEqual(cc.map_quads(3), 'LL')
self.assertEqual(cc.map_quads(4), 'HL')
self.assertEqual(cc.map_quads(33), None)
self.assertEqual(cc.map_quads('andy'), None)
def test_quad_position(self):
"""Test lisa_sig_vals"""
quads = np.array([1, 2, 3, 4], np.int)
ans = np.array(['HH', 'LH', 'LL', 'HL'])
test_ans = cc.quad_position(quads)
self.assertTrue((test_ans == ans).all())
def test_moran_local(self):
"""Test Moran's I local"""
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.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
result = [(row[0], row[1]) for row in result]
expected = self.moran_data
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
self.assertAlmostEqual(res_val, exp_val)
self.assertEqual(res_quad, exp_quad)
def test_moran_local_rate(self):
"""Test Moran's I rate"""
data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1234)
result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id')
print 'result == None? ', result == None
result = [(row[0], row[1]) for row in result]
expected = self.moran_data
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
self.assertAlmostEqual(res_val, exp_val)
def test_moran(self):
"""Test Moran's I global"""
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(1235)
result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
print 'result == None?', result == None
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)

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import unittest
import crankshaft.pysal_utils as pu
from crankshaft import random_seeds
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.params_array = {"id_col": "cartodb_id",
"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
def test_query_attr_select(self):
"""Test query_attr_select"""
ans = "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, "
self.assertEqual(pu.query_attr_select(self.params), ans)
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"
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.params_array), ans_array)
def test_knn(self):
"""Test knn neighbors constructor"""
ans = "SELECT i.\"cartodb_id\" As id, " \
"i.\"andy\"::numeric As attr1, " \
"i.\"jay_z\"::numeric As attr2, " \
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
"FROM (SELECT * FROM a_list) As j " \
"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 " \
"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 " \
"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, " \
"i.\"_2014_feb\"::numeric As attr3, " \
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
"FROM (SELECT * FROM a_list) As j " \
"WHERE i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
"j.\"_2013_dec\" IS NOT NULL AND " \
"j.\"_2014_jan\" IS NOT NULL AND " \
"j.\"_2014_feb\" 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.\"_2013_dec\" IS NOT NULL AND " \
"i.\"_2014_jan\" IS NOT NULL AND " \
"i.\"_2014_feb\" IS NOT NULL "\
"ORDER BY i.\"cartodb_id\" ASC;"
self.assertEqual(pu.knn(self.params), ans)
self.assertEqual(pu.knn(self.params_array), ans_array)
def test_queen(self):
"""Test queen neighbors constructor"""
ans = "SELECT i.\"cartodb_id\" As id, " \
"i.\"andy\"::numeric As attr1, " \
"i.\"jay_z\"::numeric As attr2, " \
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
"FROM (SELECT * FROM a_list) As j " \
"WHERE " \
"i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
"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)" \
") 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 " \
"ORDER BY i.\"cartodb_id\" ASC;"
self.assertEqual(pu.queen(self.params), ans)
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))
def test_get_attributes(self):
"""Test get_attributes"""
## need to add tests
self.assertEqual(True, True)
def test_get_weight(self):
"""Test get_weight"""
self.assertEqual(True, True)
def test_empty_zipped_array(self):
"""Test empty_zipped_array"""
ans2 = [(None, None)]
ans4 = [(None, None, None, None)]
self.assertEqual(pu.empty_zipped_array(2), ans2)
self.assertEqual(pu.empty_zipped_array(4), ans4)

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import unittest
import numpy as np
from helper import plpy, fixture_file
import crankshaft.segmentation as segmentation
import json
class SegmentationTest(unittest.TestCase):
"""Testing class for Moran's I functions"""
def setUp(self):
plpy._reset()
def generate_random_data(self,n_samples,random_state, row_type=False):
x1 = random_state.uniform(size=n_samples)
x2 = random_state.uniform(size=n_samples)
x3 = random_state.randint(0, 4, size=n_samples)
y = x1+x2*x2+x3
cartodb_id = range(len(x1))
if row_type:
return [ {'features': vals} for vals in zip(x1,x2,x3)], y
else:
return [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))]
def test_replace_nan_with_mean(self):
test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
def test_create_and_predict_segment(self):
n_samples = 1000
random_state_train = np.random.RandomState(13)
random_state_test = np.random.RandomState(134)
training_data = self.generate_random_data(n_samples, random_state_train)
test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True)
ids = [{'cartodb_ids': range(len(test_data))}]
rows = [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
plpy._define_result('select \* from \(select \* from training\) a limit 1',rows)
plpy._define_result('.*from \(select \* from training\) as a' ,training_data)
plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
plpy._define_result('.*select \* from test.*' ,test_data)
model_parameters = {'n_estimators': 1200,
'max_depth': 3,
'subsample' : 0.5,
'learning_rate': 0.01,
'min_samples_leaf': 1}
result = segmentation.create_and_predict_segment(
'select * from training',
'target',
'select * from test',
model_parameters)
prediction = [r[1] for r in result]
accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y))))
self.assertEqual(len(result),len(test_data))
self.assertTrue( result[0][2] < 0.01)
self.assertTrue( accuracy < 0.5*np.mean(test_y) )

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import unittest
import numpy as np
import unittest
# from mock_plpy import MockPlPy
# plpy = MockPlPy()
#
# import sys
# sys.modules['plpy'] = plpy
from helper import plpy, fixture_file
import crankshaft.space_time_dynamics as std
from crankshaft import random_seeds
import json
class SpaceTimeTests(unittest.TestCase):
"""Testing class for Markov Functions."""
def setUp(self):
plpy._reset()
self.params = {"id_col": "cartodb_id",
"time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read())
self.markov_data = json.loads(open(fixture_file('markov.json')).read())
self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T
self.transition_matrix = np.array([
[[ 0.96341463, 0.0304878 , 0.00609756, 0. , 0. ],
[ 0.06040268, 0.83221477, 0.10738255, 0. , 0. ],
[ 0. , 0.14 , 0.74 , 0.12 , 0. ],
[ 0. , 0.03571429, 0.32142857, 0.57142857, 0.07142857],
[ 0. , 0. , 0. , 0.16666667, 0.83333333]],
[[ 0.79831933, 0.16806723, 0.03361345, 0. , 0. ],
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
[ 0.00537634, 0.06989247, 0.8655914 , 0.05913978, 0. ],
[ 0. , 0. , 0.06372549, 0.90196078, 0.03431373],
[ 0. , 0. , 0. , 0.19444444, 0.80555556]],
[[ 0.84693878, 0.15306122, 0. , 0. , 0. ],
[ 0.08133971, 0.78947368, 0.1291866 , 0. , 0. ],
[ 0.00518135, 0.0984456 , 0.79274611, 0.0984456 , 0.00518135],
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
[ 0. , 0. , 0. , 0.10204082, 0.89795918]],
[[ 0.8852459 , 0.09836066, 0. , 0.01639344, 0. ],
[ 0.03875969, 0.81395349, 0.13953488, 0. , 0.00775194],
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
[ 0. , 0.02339181, 0.12865497, 0.75438596, 0.09356725],
[ 0. , 0. , 0. , 0.09661836, 0.90338164]],
[[ 0.33333333, 0.66666667, 0. , 0. , 0. ],
[ 0.0483871 , 0.77419355, 0.16129032, 0.01612903, 0. ],
[ 0.01149425, 0.16091954, 0.74712644, 0.08045977, 0. ],
[ 0. , 0.01036269, 0.06217617, 0.89637306, 0.03108808],
[ 0. , 0. , 0. , 0.02352941, 0.97647059]]]
)
def test_spatial_markov(self):
"""Test Spatial Markov."""
data = [ { 'id': d['id'],
'attr1': d['y1995'],
'attr2': d['y1996'],
'attr3': d['y1997'],
'attr4': d['y1998'],
'attr5': d['y1999'],
'attr6': d['y2000'],
'attr7': d['y2001'],
'attr8': d['y2002'],
'attr9': d['y2003'],
'attr10': d['y2004'],
'attr11': d['y2005'],
'attr12': d['y2006'],
'attr13': d['y2007'],
'attr14': d['y2008'],
'attr15': d['y2009'],
'neighbors': d['neighbors'] } for d in self.neighbors_data]
print(str(data[0]))
plpy._define_result('select', data)
random_seeds.set_random_seeds(1234)
result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id')
self.assertTrue(result != None)
result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
print result[0]
expected = self.markov_data
for ([res_trend, res_up, res_down, res_vol, res_id],
[exp_trend, exp_up, exp_down, exp_vol, exp_id]
) in zip(result, expected):
self.assertAlmostEqual(res_trend, exp_trend)
def test_get_time_data(self):
"""Test get_time_data"""
data = [ { 'attr1': d['y1995'],
'attr2': d['y1996'],
'attr3': d['y1997'],
'attr4': d['y1998'],
'attr5': d['y1999'],
'attr6': d['y2000'],
'attr7': d['y2001'],
'attr8': d['y2002'],
'attr9': d['y2003'],
'attr10': d['y2004'],
'attr11': d['y2005'],
'attr12': d['y2006'],
'attr13': d['y2007'],
'attr14': d['y2008'],
'attr15': d['y2009'] } for d in self.neighbors_data]
result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'])
## expected was prepared from PySAL example:
### f = ps.open(ps.examples.get_path("usjoin.csv"))
### pci = np.array([f.by_col[str(y)] for y in range(1995, 2010)]).transpose()
### rpci = pci / (pci.mean(axis = 0))
expected = np.array([[ 0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154, 0.83271652
, 0.83786314, 0.85012593, 0.85509656, 0.86416612, 0.87119375, 0.86302631
, 0.86148267, 0.86252252, 0.86746356],
[ 0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388, 0.90746978
, 0.89830489, 0.89431991, 0.88924794, 0.89815176, 0.91832091, 0.91706054
, 0.90139505, 0.87897455, 0.86216858],
[ 0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, 0.78964559
, 0.80584442, 0.8084998, 0.82258551, 0.82668196, 0.82373724, 0.81814804
, 0.83675961, 0.83574199, 0.84647177],
[ 1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, 1.14506948
, 1.12151133, 1.11160697, 1.10888621, 1.11399806, 1.12168029, 1.13164797
, 1.12958508, 1.11371818, 1.09936775],
[ 1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, 1.16898201
, 1.17212488, 1.14752303, 1.11843284, 1.11024964, 1.11943471, 1.11736468
, 1.10863242, 1.09642516, 1.07762337],
[ 1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, 1.44184737
, 1.44782832, 1.41978227, 1.39092208, 1.4059372, 1.40788646, 1.44052766
, 1.45241216, 1.43306098, 1.4174431 ],
[ 1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, 1.10888138
, 1.11856629, 1.13062931, 1.11944984, 1.12446239, 1.11671008, 1.10880034
, 1.08401709, 1.06959206, 1.07875225],
[ 1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, 0.99854316
, 0.9880258, 0.99669587, 0.99327676, 1.01400905, 1.03176742, 1.040511
, 1.01749645, 0.9936394, 0.98279746],
[ 0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845, 0.99127006
, 0.97925917, 0.9683482, 0.95335147, 0.93694787, 0.94308213, 0.92232874
, 0.91284091, 0.89689833, 0.88928858],
[ 0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044, 0.8578708
, 0.86036185, 0.86107306, 0.8500772, 0.86981998, 0.86837929, 0.87204141
, 0.86633032, 0.84946077, 0.83287146],
[ 1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, 1.14450183
, 1.12349752, 1.12596664, 1.12213996, 1.1119989, 1.10257792, 1.10491258
, 1.11059842, 1.10509795, 1.10020097],
[ 0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687, 0.95831051
, 0.94480909, 0.94804195, 0.95430286, 0.94103989, 0.92122519, 0.91010201
, 0.89280392, 0.89298243, 0.89165385],
[ 0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, 0.9480927
, 0.93539182, 0.95388718, 0.94597005, 0.96918424, 0.94781281, 0.93466815
, 0.94281559, 0.96520315, 0.96715441],
[ 0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, 0.98687073
, 0.99237486, 0.98209969, 0.9877653, 0.97399471, 0.96910087, 0.98416665
, 0.98423613, 0.99823861, 0.99545704],
[ 0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012, 0.86191535
, 0.84981451, 0.85472102, 0.84564835, 0.83998883, 0.83478547, 0.82803648
, 0.8198736, 0.82265395, 0.8399404 ],
[ 0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, 0.82785597
, 0.86008789, 0.86776298, 0.86720209, 0.8676334, 0.89179317, 0.94202108
, 0.9422231, 0.93902708, 0.94479184],
[ 0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, 0.90906632
, 0.92693339, 0.93695966, 0.94242697, 0.94338265, 0.91981796, 0.91108804
, 0.90543476, 0.91737138, 0.94793657],
[ 1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723, 1.20172869
, 1.21328691, 1.22624778, 1.22397075, 1.23857042, 1.24419893, 1.23929384
, 1.23418676, 1.23626739, 1.26754398],
[ 1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, 1.34790023
, 1.34399863, 1.32575181, 1.30795492, 1.30544841, 1.30303302, 1.32107766
, 1.32936244, 1.33001241, 1.33288462],
[ 1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, 1.05059016
, 1.03405057, 1.02747623, 1.03162734, 0.9961416, 0.97356208, 0.94241549
, 0.92754547, 0.92549227, 0.92138102],
[ 1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, 1.13889622
, 1.12442212, 1.13367018, 1.13982256, 1.14029944, 1.11979401, 1.10905389
, 1.10577769, 1.11166825, 1.09985155],
[ 0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, 0.74480073
, 0.76098396, 0.76156903, 0.76651952, 0.76533288, 0.78205934, 0.76842416
, 0.77487118, 0.77768683, 0.78801192],
[ 0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, 0.97370819
, 0.96419154, 0.97209861, 0.97441313, 0.96356162, 0.94745352, 0.93965462
, 0.93069645, 0.94020973, 0.94358232],
[ 0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, 0.80071489
, 0.83358256, 0.83451613, 0.85175032, 0.85954307, 0.86790024, 0.87170334
, 0.87863799, 0.87497981, 0.87888675],
[ 0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, 0.98733195
, 0.99644997, 0.99669587, 1.02559097, 1.01116651, 0.99988024, 0.97906749
, 0.99323123, 1.00204939, 0.99602148],
[ 1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, 1.08312397
, 1.05192626, 1.04230892, 1.05577278, 1.08569751, 1.12443486, 1.08891079
, 1.08603695, 1.05997314, 1.02160943],
[ 1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272, 1.18257029
, 1.16226243, 1.16009196, 1.14467789, 1.14820235, 1.12386598, 1.12680236
, 1.12357937, 1.1159258, 1.12570828],
[ 1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, 1.31210239
, 1.29989156, 1.29203193, 1.27183516, 1.26830786, 1.2617743, 1.28656675
, 1.29734097, 1.29390205, 1.29345446],
[ 0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, 0.78772975
, 0.82848011, 0.8259679, 0.82435705, 0.83108634, 0.84373784, 0.83891093
, 0.84349247, 0.85637272, 0.86539395],
[ 1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626, 1.2256767
, 1.21126648, 1.19377804, 1.18355337, 1.19674434, 1.21536573, 1.23653297
, 1.27962009, 1.27968392, 1.25907738],
[ 0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282, 0.96480308
, 0.94686376, 0.93679073, 0.92540049, 0.92988835, 0.93442917, 0.92100464
, 0.91475304, 0.90249622, 0.9021363 ],
[ 0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368, 0.88937573
, 0.894401, 0.90448993, 0.95495898, 0.92698333, 0.94745352, 0.92562488
, 0.96635366, 1.02520312, 1.0394296 ],
[ 1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, 1.00759019
, 0.99192968, 0.99747298, 0.99550759, 0.97583768, 0.9610168, 0.94779638
, 0.93759089, 0.93353431, 0.94121705],
[ 0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, 0.83434854
, 0.85813595, 0.84667961, 0.84374558, 0.85951183, 0.87194227, 0.89455097
, 0.88283929, 0.90349491, 0.90600675],
[ 1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, 1.00581626
, 0.98850522, 0.99291168, 0.98983209, 0.97511924, 0.96134615, 0.96382634
, 0.95011401, 0.9434686, 0.94637765],
[ 1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, 1.04800023
, 1.03024941, 1.04200483, 1.0402554, 1.03296979, 1.02191682, 1.02476275
, 1.02347523, 1.02517684, 1.04359571],
[ 1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043, 1.0531801
, 1.07452771, 1.09383478, 1.1052447, 1.10322136, 1.09167939, 1.08772756
, 1.08859544, 1.09177338, 1.1096083 ],
[ 0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, 0.86287327
, 0.85169796, 0.85411285, 0.84886336, 0.84517414, 0.84843858, 0.84488343
, 0.83374329, 0.82812044, 0.82878599],
[ 0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, 0.92652175
, 0.94278865, 0.93682452, 0.98655146, 0.992237, 0.9798497, 0.93869677
, 0.96947771, 1.00362626, 0.98102351],
[ 0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967, 0.93092109
, 0.92662519, 0.93412152, 0.93501274, 0.92879506, 0.92110542, 0.91035556
, 0.90430364, 0.89994694, 0.90073864],
[ 0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, 0.98882205
, 0.97662234, 0.95601578, 0.94905385, 0.94934888, 0.97152609, 0.97163004
, 0.9700702, 0.97158948, 0.95884908],
[ 0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751, 0.84818516
, 0.85265681, 0.84502402, 0.82645665, 0.81743586, 0.83550406, 0.83338919
, 0.83511679, 0.82136617, 0.80921874],
[ 0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441, 0.95440787
, 0.96364363, 0.96804412, 0.97136214, 0.97583768, 0.95571724, 0.96895368
, 0.97001634, 0.97082733, 0.98782366],
[ 1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, 1.10558188
, 1.1214086, 1.12292577, 1.13021031, 1.13342735, 1.14686068, 1.14502975
, 1.14474747, 1.14084037, 1.16142926],
[ 1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, 1.11856702
, 1.09764283, 1.08815849, 1.08044313, 1.09278827, 1.07003204, 1.08398066
, 1.09831768, 1.09298232, 1.09176125],
[ 0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, 0.77751194
, 0.79902974, 0.81437881, 0.80788828, 0.79603865, 0.78966436, 0.79949807
, 0.80172182, 0.82168155, 0.85587911],
[ 1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561, 1.00162979
, 0.99860739, 1.00814981, 1.00574316, 0.99030032, 0.97682565, 0.97292596
, 0.96519561, 0.96173403, 0.95890284],
[ 0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289, 0.96608031
, 0.99727185, 1.00781194, 1.03484236, 1.05333619, 1.0983263, 1.1704974
, 1.17025154, 1.18730553, 1.14242645]])
self.assertTrue(np.allclose(result, expected))
self.assertTrue(type(result) == type(expected))
self.assertTrue(result.shape == expected.shape)
def test_rebin_data(self):
"""Test rebin_data"""
## sample in double the time (even case since 10 % 2 = 0):
## (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
## = 0.5, 2.5, 4.5, 6.5, 8.5
ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float)
for i in range(0, 10, 2)]).T
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
## sample in triple the time (uneven since 10 % 3 = 1):
## (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
## = 1, 4, 7, 9
ans_odd = np.array([i * np.ones(10, dtype=float)
for i in (1, 4, 7, 9)]).T
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
def test_get_prob_dist(self):
"""Test get_prob_dist"""
lag_indices = np.array([1, 2, 3, 4])
unit_indices = np.array([1, 3, 2, 4])
answer = np.array([
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
])
result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices)
self.assertTrue(np.array_equal(result, answer))
def test_get_prob_stats(self):
"""Test get_prob_stats"""
probs = np.array([
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
])
unit_indices = np.array([1, 3, 2, 4])
answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824, 0.02475248 / 0.77722772, -0.02352941 / 0.97647059])
answer_volatility = np.array([ 0.34221495, 0.33705421, 0.29226542, 0.38834223])
result = std.get_prob_stats(probs, unit_indices)
result_up = result[0]
result_down = result[1]
result_trend = result[2]
result_volatility = result[3]
self.assertTrue(np.allclose(result_up, answer_up))
self.assertTrue(np.allclose(result_down, answer_down))
self.assertTrue(np.allclose(result_trend, answer_trend))
self.assertTrue(np.allclose(result_volatility, answer_volatility))

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"""Import all modules"""
import crankshaft.random_seeds
import crankshaft.clustering
import crankshaft.space_time_dynamics
import crankshaft.segmentation

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"""Import all functions from for clustering"""
from moran import *
from kmeans import *

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from sklearn.cluster import KMeans
import plpy
def kmeans(query, no_clusters, no_init=20):
data = plpy.execute('''select array_agg(cartodb_id order by cartodb_id) as ids,
array_agg(ST_X(the_geom) order by cartodb_id) xs,
array_agg(ST_Y(the_geom) order by cartodb_id) ys from ({query}) a
where the_geom is not null
'''.format(query=query))
xs = data[0]['xs']
ys = data[0]['ys']
ids = data[0]['ids']
km = KMeans(n_clusters= no_clusters, n_init=no_init)
labels = km.fit_predict(zip(xs,ys))
return zip(ids,labels)

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"""
Moran's I geostatistics (global clustering & outliers presence)
"""
# TODO: Fill in local neighbors which have null/NoneType values with the
# average of the their neighborhood
import pysal as ps
import plpy
from collections import OrderedDict
# crankshaft module
import crankshaft.pysal_utils as pu
# High level interface ---------------------------------------
def moran(subquery, attr_name,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Moran's I (global)
Implementation building neighbors with a PostGIS database and Moran's I
core clusters with PySAL.
Andy Eschbacher
"""
qvals = OrderedDict([("id_col", id_col),
("attr1", attr_name),
("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(2)
except plpy.SPIError, e:
plpy.error('Analysis failed: %s' % e)
return pu.empty_zipped_array(2)
## collect attributes
attr_vals = pu.get_attributes(result)
## calculate weights
weight = pu.get_weight(result, w_type, num_ngbrs)
## calculate moran global
moran_global = ps.esda.moran.Moran(attr_vals, weight,
permutations=permutations)
return zip([moran_global.I], [moran_global.EI])
def moran_local(subquery, attr,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Moran's I implementation for PL/Python
Andy Eschbacher
"""
# geometries with attributes that are null are ignored
# resulting in a collection of not as near neighbors
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(5)
except plpy.SPIError, e:
plpy.error('Analysis failed: %s' % e)
return pu.empty_zipped_array(5)
attr_vals = pu.get_attributes(result)
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate LISA values
lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
permutations=permutations)
# find quadrants for each geometry
quads = quad_position(lisa.q)
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
def moran_rate(subquery, numerator, denominator,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Moran's I Rate (global)
Andy Eschbacher
"""
qvals = OrderedDict([("id_col", id_col),
("attr1", numerator),
("attr2", denominator)
("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(2)
except plpy.SPIError, e:
plpy.error('Analysis failed: %s' % e)
return pu.empty_zipped_array(2)
## collect attributes
numer = pu.get_attributes(result, 1)
denom = pu.get_attributes(result, 2)
weight = pu.get_weight(result, w_type, num_ngbrs)
## calculate moran global rate
lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
permutations=permutations)
return zip([lisa_rate.I], [lisa_rate.EI])
def moran_local_rate(subquery, numerator, denominator,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Moran's I Local Rate
Andy Eschbacher
"""
# geometries with values that are null are ignored
# resulting in a collection of not as near neighbors
qvals = OrderedDict([("id_col", id_col),
("numerator", numerator),
("denominator", denominator),
("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(5)
except plpy.SPIError, e:
plpy.error('Analysis failed: %s' % e)
return pu.empty_zipped_array(5)
## collect attributes
numer = pu.get_attributes(result, 1)
denom = pu.get_attributes(result, 2)
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate LISA values
lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
permutations=permutations)
# find quadrants for each geometry
quads = quad_position(lisa.q)
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
def moran_local_bv(subquery, attr1, attr2,
permutations, geom_col, id_col, w_type, num_ngbrs):
"""
Moran's I (local) Bivariate (untested)
"""
qvals = OrderedDict([("id_col", id_col),
("attr1", attr1),
("attr2", attr2),
("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:
plpy.error("Error: areas of interest query failed, " \
"check input parameters")
return pu.empty_zipped_array(4)
## collect attributes
attr1_vals = pu.get_attributes(result, 1)
attr2_vals = pu.get_attributes(result, 2)
# create weights
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate LISA values
lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
permutations=permutations)
# find clustering of significance
lisa_sig = quad_position(lisa.q)
return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
# Low level functions ----------------------------------------
def map_quads(coord):
"""
Map a quadrant number to Moran's I designation
HH=1, LH=2, LL=3, HL=4
Input:
@param coord (int): quadrant of a specific measurement
Output:
classification (one of 'HH', 'LH', 'LL', or 'HL')
"""
if coord == 1:
return 'HH'
elif coord == 2:
return 'LH'
elif coord == 3:
return 'LL'
elif coord == 4:
return 'HL'
else:
return None
def quad_position(quads):
"""
Produce Moran's I classification based of n
Input:
@param quads ndarray: an array of quads classified by
1-4 (PySAL default)
Output:
@param list: an array of quads classied by 'HH', 'LL', etc.
"""
return [map_quads(q) for q in quads]

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"""Import all functions for pysal_utils"""
from crankshaft.pysal_utils.pysal_utils import *

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"""
Utilities module for generic PySAL functionality, mainly centered on
translating queries into numpy arrays or PySAL weights objects
"""
import numpy as np
import pysal as ps
def construct_neighbor_query(w_type, query_vals):
"""Return query (a string) used for finding neighbors
@param w_type text: type of neighbors to calculate ('knn' or 'queen')
@param query_vals dict: values used to construct the query
"""
if w_type.lower() == 'knn':
return knn(query_vals)
else:
return queen(query_vals)
## Build weight object
def get_weight(query_res, w_type='knn', num_ngbrs=5):
"""
Construct PySAL weight from return value of query
@param query_res dict-like: query results with attributes and neighbors
"""
# if w_type.lower() == 'knn':
# row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
# weights = {x['id']: row_normed_weights for x in query_res}
# else:
# weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
# if len(x['neighbors']) > 0
# else [] for x in query_res}
neighbors = {x['id']: x['neighbors'] for x in query_res}
print 'len of neighbors: %d' % len(neighbors)
built_weight = ps.W(neighbors)
built_weight.transform = 'r'
return built_weight
def query_attr_select(params):
"""
Create portion of SELECT statement for attributes inolved in query.
@param params: dict of information used in query (column names,
table name, etc.)
"""
attr_string = ""
template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, "
if 'time_cols' in params:
## if markov analysis
attrs = params['time_cols']
for idx, val in enumerate(attrs):
attr_string += template % {"col": val, "alias_num": idx + 1}
else:
## if moran's analysis
attrs = [k for k in params
if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')]
for idx, val in enumerate(sorted(attrs)):
attr_string += template % {"col": params[val], "alias_num": idx + 1}
return attr_string
def query_attr_where(params):
"""
Construct where conditions when building neighbors query
Create portion of WHERE clauses for weeding out NULL-valued geometries
Input: dict of params:
{'subquery': ...,
'numerator': 'data1',
'denominator': 'data2',
'': ...}
Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL'
Input:
{'subquery': ...,
'time_cols': ['time1', 'time2', 'time3'],
'etc': ...}
Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT
NULL AND idx_replace."time3" IS NOT NULL'
"""
attr_string = []
template = "idx_replace.\"%s\" IS NOT NULL"
if 'time_cols' in params:
## markov where clauses
attrs = params['time_cols']
# add values to template
for attr in attrs:
attr_string.append(template % attr)
else:
## moran where clauses
# get keys
attrs = sorted([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]])
out = " AND ".join(attr_string)
return out
def knn(params):
"""SQL query for k-nearest neighbors.
@param vars: dict of values to fill template
"""
attr_select = query_attr_select(params)
attr_where = query_attr_where(params)
replacements = {"attr_select": attr_select,
"attr_where_i": attr_where.replace("idx_replace", "i"),
"attr_where_j": attr_where.replace("idx_replace", "j")}
query = "SELECT " \
"i.\"{id_col}\" As id, " \
"%(attr_select)s" \
"(SELECT ARRAY(SELECT j.\"{id_col}\" " \
"FROM ({subquery}) As j " \
"WHERE " \
"i.\"{id_col}\" <> j.\"{id_col}\" AND " \
"%(attr_where_j)s " \
"ORDER BY " \
"j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
"LIMIT {num_ngbrs})" \
") As neighbors " \
"FROM ({subquery}) As i " \
"WHERE " \
"%(attr_where_i)s " \
"ORDER BY i.\"{id_col}\" ASC;" % replacements
return query.format(**params)
## SQL query for finding queens neighbors (all contiguous polygons)
def queen(params):
"""SQL query for queen neighbors.
@param params dict: information to fill query
"""
attr_select = query_attr_select(params)
attr_where = query_attr_where(params)
replacements = {"attr_select": attr_select,
"attr_where_i": attr_where.replace("idx_replace", "i"),
"attr_where_j": attr_where.replace("idx_replace", "j")}
query = "SELECT " \
"i.\"{id_col}\" As id, " \
"%(attr_select)s" \
"(SELECT ARRAY(SELECT j.\"{id_col}\" " \
"FROM ({subquery}) As j " \
"WHERE i.\"{id_col}\" <> j.\"{id_col}\" AND " \
"ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
"%(attr_where_j)s)" \
") As neighbors " \
"FROM ({subquery}) As i " \
"WHERE " \
"%(attr_where_i)s " \
"ORDER BY i.\"{id_col}\" ASC;" % replacements
return query.format(**params)
## to add more weight methods open a ticket or pull request
def get_attributes(query_res, attr_num=1):
"""
@param query_res: query results with attributes and neighbors
@param attr_num: attribute number (1, 2, ...)
"""
return np.array([x['attr' + str(attr_num)] for x in query_res], dtype=np.float)
def empty_zipped_array(num_nones):
"""
prepare return values for cases of empty weights objects (no neighbors)
Input:
@param num_nones int: number of columns (e.g., 4)
Output:
[(None, None, None, None)]
"""
return [tuple([None] * num_nones)]

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"""Random seed generator used for non-deterministic functions in crankshaft"""
import random
import numpy
def set_random_seeds(value):
"""
Set the seeds of the RNGs (Random Number Generators)
used internally.
"""
random.seed(value)
numpy.random.seed(value)

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from segmentation import *

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"""
Segmentation creation and prediction
"""
import sklearn
import numpy as np
import plpy
from sklearn.ensemble import GradientBoostingRegressor
from sklearn import metrics
from sklearn.cross_validation import train_test_split
# Lower level functions
#----------------------
def replace_nan_with_mean(array):
"""
Input:
@param array: an array of floats which may have null-valued entries
Output:
array with nans filled in with the mean of the dataset
"""
# returns an array of rows and column indices
indices = np.where(np.isnan(array))
# iterate through entries which have nan values
for row, col in zip(*indices):
array[row, col] = np.mean(array[~np.isnan(array[:, col]), col])
return array
def get_data(variable, feature_columns, query):
"""
Fetch data from the database, clean, and package into
numpy arrays
Input:
@param variable: name of the target variable
@param feature_columns: list of column names
@param query: subquery that data is pulled from for the packaging
Output:
prepared data, packaged into NumPy arrays
"""
columns = ','.join(['array_agg("{col}") As "{col}"'.format(col=col) for col in feature_columns])
try:
data = plpy.execute('''SELECT array_agg("{variable}") As target, {columns} FROM ({query}) As a'''.format(
variable=variable,
columns=columns,
query=query))
except Exception, e:
plpy.error('Failed to access data to build segmentation model: %s' % e)
# extract target data from plpy object
target = np.array(data[0]['target'])
# put n feature data arrays into an n x m array of arrays
features = np.column_stack([np.array(data[0][col], dtype=float) for col in feature_columns])
return replace_nan_with_mean(target), replace_nan_with_mean(features)
# High level interface
# --------------------
def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters):
"""
Version of create_and_predict_segment that works on arrays that come stright form the SQL calling
the function.
Input:
@param target: The 1D array of lenth NSamples containing the target variable we want the model to predict
@param features: Thw 2D array of size NSamples * NFeatures that form the imput to the model
@param target_ids: A 1D array of target_ids that will be used to associate the results of the prediction with the rows which they come from
@param model_parameters: A dictionary containing parameters for the model.
"""
clean_target = replace_nan_with_mean(target)
clean_features = replace_nan_with_mean(features)
target_features = replace_nan_with_mean(target_features)
model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2)
prediction = model.predict(target_features)
accuracy_array = [accuracy]*prediction.shape[0]
return zip(target_ids, prediction, np.full(prediction.shape, accuracy_array))
def create_and_predict_segment(query, variable, target_query, model_params):
"""
generate a segment with machine learning
Stuart Lynn
"""
## fetch column names
try:
columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1 '.format(query=query))[0].keys()
except Exception, e:
plpy.error('Failed to build segmentation model: %s' % e)
## extract column names to be used in building the segmentation model
feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator'])
## get data from database
target, features = get_data(variable, feature_columns, query)
model, accuracy = train_model(target, features, model_params, 0.2)
cartodb_ids, result = predict_segment(model, feature_columns, target_query)
accuracy_array = [accuracy]*result.shape[0]
return zip(cartodb_ids, result, accuracy_array)
def train_model(target, features, model_params, test_split):
"""
Train the Gradient Boosting model on the provided data and calculate the accuracy of the model
Input:
@param target: 1D Array of the variable that the model is to be trianed to predict
@param features: 2D Array NSamples * NFeatures to use in trining the model
@param model_params: A dictionary of model parameters, the full specification can be found on the
scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)
@parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray
"""
features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
model = GradientBoostingRegressor(**model_params)
model.fit(features_train, target_train)
accuracy = calculate_model_accuracy(model, features, target)
return model, accuracy
def calculate_model_accuracy(model, features, target):
"""
Calculate the mean squared error of the model prediction
Input:
@param model: model trained from input features
@param features: features to make a prediction from
@param target: target to compare prediction to
Output:
mean squared error of the model prection compared to the target
"""
prediction = model.predict(features)
return metrics.mean_squared_error(prediction, target)
def predict_segment(model, features, target_query):
"""
Use the provided model to predict the values for the new feature set
Input:
@param model: The pretrained model
@features: A list of features to use in the model prediction (list of column names)
@target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it.
"""
batch_size = 1000
joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features])
try:
cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format(
joined_features=joined_features,
target_query=target_query))
except Exception, e:
plpy.error('Failed to build segmentation model: %s' % e)
results = []
while True:
rows = cursor.fetch(batch_size)
if not rows:
break
batch = np.row_stack([np.array(row['features'], dtype=float) for row in rows])
#Need to fix this. Should be global mean. This will cause weird effects
batch = replace_nan_with_mean(batch)
prediction = model.predict(batch)
results.append(prediction)
try:
cartodb_ids = plpy.execute('''SELECT array_agg(cartodb_id ORDER BY cartodb_id) As cartodb_ids FROM ({0}) As a'''.format(target_query))[0]['cartodb_ids']
except Exception, e:
plpy.error('Failed to build segmentation model: %s' % e)
return cartodb_ids, np.concatenate(results)

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"""Import all functions from clustering libraries."""
from markov import *

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"""
Spatial dynamics measurements using Spatial Markov
"""
import numpy as np
import pysal as ps
import plpy
import crankshaft.pysal_utils as pu
def spatial_markov_trend(subquery, time_cols, num_classes=7,
w_type='knn', num_ngbrs=5, permutations=0,
geom_col='the_geom', id_col='cartodb_id'):
"""
Predict the trends of a unit based on:
1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
2. average class of its neighbors
Inputs:
@param subquery string: e.g., SELECT the_geom, cartodb_id,
interesting_time_column FROM table_name
@param time_cols list of strings: list of strings of column names
@param num_classes (optional): number of classes to break distribution
of values into. Currently uses quantile bins.
@param w_type string (optional): weight type ('knn' or 'queen')
@param num_ngbrs int (optional): number of neighbors (if knn type)
@param permutations int (optional): number of permutations for test
stats
@param geom_col string (optional): name of column which contains the
geometries
@param id_col string (optional): name of column which has the ids of
the table
Outputs:
@param trend_up float: probablity that a geom will move to a higher
class
@param trend_down float: probablity that a geom will move to a lower
class
@param trend float: (trend_up - trend_down) / trend_static
@param volatility float: a measure of the volatility based on
probability stddev(prob array)
"""
if len(time_cols) < 2:
plpy.error('More than one time column needs to be passed')
qvals = {"id_col": id_col,
"time_cols": time_cols,
"geom_col": geom_col,
"subquery": subquery,
"num_ngbrs": num_ngbrs}
try:
query_result = plpy.execute(
pu.construct_neighbor_query(w_type, qvals)
)
if len(query_result) == 0:
return zip([None], [None], [None], [None], [None])
except plpy.SPIError, e:
plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
plpy.error('Analysis failed: %s' % e)
return zip([None], [None], [None], [None], [None])
## build weight
weights = pu.get_weight(query_result, w_type)
weights.transform = 'r'
## prep time data
t_data = get_time_data(query_result, time_cols)
plpy.debug('shape of t_data %d, %d' % t_data.shape)
plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape)
plpy.debug('first num elements: %f' % t_data[0, 0])
sp_markov_result = ps.Spatial_Markov(t_data,
weights,
k=num_classes,
fixed=False,
permutations=permutations)
## get lag classes
lag_classes = ps.Quantiles(
ps.lag_spatial(weights, t_data[:, -1]),
k=num_classes).yb
## look up probablity distribution for each unit according to class and lag class
prob_dist = get_prob_dist(sp_markov_result.P,
lag_classes,
sp_markov_result.classes[:, -1])
## find the ups and down and overall distribution of each cell
trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist,
sp_markov_result.classes[:, -1])
## output the results
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
"""
num_attrs = len(time_cols)
return np.array([[x['attr' + str(i)] for x in markov_data]
for i in range(1, num_attrs+1)], dtype=float).transpose()
## not currently used
def rebin_data(time_data, num_time_per_bin):
"""
Convert an n x l matrix into an (n/m) x l matrix where the values are
reduced (averaged) for the intervening states:
1 2 3 4 1.5 3.5
5 6 7 8 -> 5.5 7.5
9 8 7 6 8.5 6.5
5 4 3 2 4.5 2.5
if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix.
This process effectively resamples the data at a longer time span n
units longer than the input data.
For cases when there is a remainder (remainder(5/3) = 2), the remaining
two columns are binned together as the last time period, while the
first three are binned together for the first period.
Input:
@param time_data n x l ndarray: measurements of an attribute at
different time intervals
@param num_time_per_bin int: number of columns to average into a new
column
Output:
ceil(n / m) x l ndarray of resampled time series
"""
if time_data.shape[1] % num_time_per_bin == 0:
## if fit is perfect, then use it
n_max = time_data.shape[1] / num_time_per_bin
else:
## fit remainders into an additional column
n_max = time_data.shape[1] / num_time_per_bin + 1
return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
for i in range(n_max)]).T
def get_prob_dist(transition_matrix, lag_indices, unit_indices):
"""
Given an array of transition matrices, look up the probability
associated with the arrangements passed
Input:
@param transition_matrix ndarray[k,k,k]:
@param lag_indices ndarray:
@param unit_indices ndarray:
Output:
Array of probability distributions
"""
return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
for i in range(len(lag_indices))])
def get_prob_stats(prob_dist, unit_indices):
"""
get the statistics of the probability distributions
Outputs:
@param trend_up ndarray(float): sum of probabilities for upward
movement (relative to the unit index of that prob)
@param trend_down ndarray(float): sum of probabilities for downward
movement (relative to the unit index of that prob)
@param trend ndarray(float): difference of upward and downward
movements
"""
num_elements = len(unit_indices)
trend_up = np.empty(num_elements, dtype=float)
trend_down = np.empty(num_elements, dtype=float)
trend = np.empty(num_elements, dtype=float)
for i in range(num_elements):
trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
if prob_dist[i, unit_indices[i]] > 0.0:
trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
else:
trend[i] = None
## calculate volatility of distribution
volatility = prob_dist.std(axis=1)
return trend_up, trend_down, trend, volatility

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"""
CartoDB Spatial Analysis Python Library
See:
https://github.com/CartoDB/crankshaft
"""
from setuptools import setup, find_packages
setup(
name='crankshaft',
version='0.0.0',
description='CartoDB Spatial Analysis Python Library',
url='https://github.com/CartoDB/crankshaft',
author='Data Services Team - CartoDB',
author_email='dataservices@cartodb.com',
license='MIT',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Mapping comunity',
'Topic :: Maps :: Mapping Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2.7',
],
keywords='maps mapping tools spatial analysis geostatistics',
packages=find_packages(exclude=['contrib', 'docs', 'tests']),
extras_require={
'dev': ['unittest'],
'test': ['unittest', 'nose', 'mock'],
},
# The choice of component versions is dictated by what's
# provisioned in the production servers.
# IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
requires=['pysal', 'numpy', 'sklearn'],
test_suite='test'
)

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"""
CartoDB Spatial Analysis Python Library
See:
https://github.com/CartoDB/crankshaft
"""
from setuptools import setup, find_packages
setup(
name='crankshaft',
version='0.0.0',
description='CartoDB Spatial Analysis Python Library',
url='https://github.com/CartoDB/crankshaft',
author='Data Services Team - CartoDB',
author_email='dataservices@cartodb.com',
license='MIT',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Mapping comunity',
'Topic :: Maps :: Mapping Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2.7',
],
keywords='maps mapping tools spatial analysis geostatistics',
packages=find_packages(exclude=['contrib', 'docs', 'tests']),
extras_require={
'dev': ['unittest'],
'test': ['unittest', 'nose', 'mock'],
},
# The choice of component versions is dictated by what's
# provisioned in the production servers.
# IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
requires=['pysal', 'numpy', 'sklearn'],
test_suite='test'
)

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[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 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]}]

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[[0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 0], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 1], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 2], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 3], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 4], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 5], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 6], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 7], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 8], [0.19047619047619049, 0.16, 0.0, 0.32594478059941379, 9], [-0.23529411764705882, 0.0, 0.19047619047619047, 0.31356338348865387, 10], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 11], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 12], [0.027777777777777783, 0.11111111111111112, 0.088888888888888892, 0.30339641183779581, 13], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 14], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 15], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 16], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 17], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 18], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 19], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 20], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 21], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 22], [-0.16666666666666663, 0.18181818181818182, 0.27272727272727271, 0.20246415864836445, 23], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 24], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 25], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 26], [-0.043478260869565216, 0.0, 0.041666666666666664, 0.37950991789118999, 27], [0.22222222222222221, 0.18181818181818182, 0.0, 0.31701083225750354, 28], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 29], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 30], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 31], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 32], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 33], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 34], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 35], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 36], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 37], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 38], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 39], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 40], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 41], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 42], [0.0, 0.0, 0.0, 0.40000000000000002, 43], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 44], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 45], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 46], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 47]]

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[[0.9319096128346788, "HH"],
[-1.135787401862846, "HL"],
[0.11732030672508517, "LL"],
[0.6152779669180425, "LL"],
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[-0.014585767863118111, "LH"],
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[1.0223932668429925, "LL"],
[1.4179402898927476, "LL"]]

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[
{"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5},
{"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7},
{"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2},
{"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1},
{"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3},
{"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05},
{"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4},
{"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7},
{"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5},
{"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04},
{"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08},
{"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2},
{"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4},
{"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2},
{"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3},
{"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4},
{"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6},
{"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3},
{"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7},
{"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8},
{"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1},
{"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4},
{"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1},
{"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3},
{"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4},
{"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6},
{"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3},
{"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8},
{"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3},
{"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1},
{"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9},
{"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3},
{"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4},
{"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3},
{"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3},
{"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2},
{"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5},
{"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4},
{"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6},
{"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5},
{"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4},
{"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2},
{"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3},
{"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2},
{"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3},
{"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2},
{"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3},
{"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5},
{"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2},
{"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6},
{"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01},
{"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01}
]

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import unittest
from mock_plpy import MockPlPy
plpy = MockPlPy()
import sys
sys.modules['plpy'] = plpy
import os
def fixture_file(name):
dir = os.path.dirname(os.path.realpath(__file__))
return os.path.join(dir, 'fixtures', name)

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import re
class MockCursor:
def __init__(self, data):
self.cursor_pos = 0
self.data = data
def fetch(self, batch_size):
batch = self.data[self.cursor_pos : self.cursor_pos + batch_size]
self.cursor_pos += batch_size
return batch
class MockPlPy:
def __init__(self):
self._reset()
def _reset(self):
self.infos = []
self.notices = []
self.debugs = []
self.logs = []
self.warnings = []
self.errors = []
self.fatals = []
self.executes = []
self.results = []
self.prepares = []
self.results = []
def _define_result(self, query, result):
pattern = re.compile(query, re.IGNORECASE | re.MULTILINE)
self.results.append([pattern, result])
def notice(self, msg):
self.notices.append(msg)
def debug(self, msg):
self.notices.append(msg)
def info(self, msg):
self.infos.append(msg)
def cursor(self, query):
data = self.execute(query)
return MockCursor(data)
def execute(self, query): # TODO: additional arguments
for result in self.results:
if result[0].match(query):
return result[1]
return []

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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 numpy as np
import crankshaft.clustering as cc
import crankshaft.pysal_utils as pu
from crankshaft import random_seeds
import json
class KMeansTest(unittest.TestCase):
"""Testing class for Moran's I functions"""
def setUp(self):
plpy._reset()
self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read())
self.params = {"subquery": "select * from table",
"no_clusters": "10"
}
def test_kmeans(self):
data = self.cluster_data
plpy._define_result('select' ,data)
clusters = cc.kmeans('subquery', 2)
labels = [a[1] for a in clusters]
c1 = [a for a in clusters if a[1]==0]
c2 = [a for a in clusters if a[1]==1]
self.assertEqual(len(np.unique(labels)),2)
self.assertEqual(len(c1),20)
self.assertEqual(len(c2),20)

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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
class MoranTest(unittest.TestCase):
"""Testing class for Moran's I functions"""
def setUp(self):
plpy._reset()
self.params = {"id_col": "cartodb_id",
"attr1": "andy",
"attr2": "jay_z",
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.params_markov = {"id_col": "cartodb_id",
"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read())
self.moran_data = json.loads(open(fixture_file('moran.json')).read())
def test_map_quads(self):
"""Test map_quads"""
self.assertEqual(cc.map_quads(1), 'HH')
self.assertEqual(cc.map_quads(2), 'LH')
self.assertEqual(cc.map_quads(3), 'LL')
self.assertEqual(cc.map_quads(4), 'HL')
self.assertEqual(cc.map_quads(33), None)
self.assertEqual(cc.map_quads('andy'), None)
def test_quad_position(self):
"""Test lisa_sig_vals"""
quads = np.array([1, 2, 3, 4], np.int)
ans = np.array(['HH', 'LH', 'LL', 'HL'])
test_ans = cc.quad_position(quads)
self.assertTrue((test_ans == ans).all())
def test_moran_local(self):
"""Test Moran's I local"""
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.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
result = [(row[0], row[1]) for row in result]
expected = self.moran_data
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
self.assertAlmostEqual(res_val, exp_val)
self.assertEqual(res_quad, exp_quad)
def test_moran_local_rate(self):
"""Test Moran's I rate"""
data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1234)
result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id')
print 'result == None? ', result == None
result = [(row[0], row[1]) for row in result]
expected = self.moran_data
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
self.assertAlmostEqual(res_val, exp_val)
def test_moran(self):
"""Test Moran's I global"""
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(1235)
result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
print 'result == None?', result == None
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)

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import unittest
import crankshaft.pysal_utils as pu
from crankshaft import random_seeds
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.params_array = {"id_col": "cartodb_id",
"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
def test_query_attr_select(self):
"""Test query_attr_select"""
ans = "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, "
self.assertEqual(pu.query_attr_select(self.params), ans)
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"
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.params_array), ans_array)
def test_knn(self):
"""Test knn neighbors constructor"""
ans = "SELECT i.\"cartodb_id\" As id, " \
"i.\"andy\"::numeric As attr1, " \
"i.\"jay_z\"::numeric As attr2, " \
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
"FROM (SELECT * FROM a_list) As j " \
"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 " \
"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 " \
"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, " \
"i.\"_2014_feb\"::numeric As attr3, " \
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
"FROM (SELECT * FROM a_list) As j " \
"WHERE i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
"j.\"_2013_dec\" IS NOT NULL AND " \
"j.\"_2014_jan\" IS NOT NULL AND " \
"j.\"_2014_feb\" 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.\"_2013_dec\" IS NOT NULL AND " \
"i.\"_2014_jan\" IS NOT NULL AND " \
"i.\"_2014_feb\" IS NOT NULL "\
"ORDER BY i.\"cartodb_id\" ASC;"
self.assertEqual(pu.knn(self.params), ans)
self.assertEqual(pu.knn(self.params_array), ans_array)
def test_queen(self):
"""Test queen neighbors constructor"""
ans = "SELECT i.\"cartodb_id\" As id, " \
"i.\"andy\"::numeric As attr1, " \
"i.\"jay_z\"::numeric As attr2, " \
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
"FROM (SELECT * FROM a_list) As j " \
"WHERE " \
"i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
"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)" \
") 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 " \
"ORDER BY i.\"cartodb_id\" ASC;"
self.assertEqual(pu.queen(self.params), ans)
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))
def test_get_attributes(self):
"""Test get_attributes"""
## need to add tests
self.assertEqual(True, True)
def test_get_weight(self):
"""Test get_weight"""
self.assertEqual(True, True)
def test_empty_zipped_array(self):
"""Test empty_zipped_array"""
ans2 = [(None, None)]
ans4 = [(None, None, None, None)]
self.assertEqual(pu.empty_zipped_array(2), ans2)
self.assertEqual(pu.empty_zipped_array(4), ans4)

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import unittest
import numpy as np
from helper import plpy, fixture_file
import crankshaft.segmentation as segmentation
import json
class SegmentationTest(unittest.TestCase):
"""Testing class for Moran's I functions"""
def setUp(self):
plpy._reset()
def generate_random_data(self,n_samples,random_state, row_type=False):
x1 = random_state.uniform(size=n_samples)
x2 = random_state.uniform(size=n_samples)
x3 = random_state.randint(0, 4, size=n_samples)
y = x1+x2*x2+x3
cartodb_id = range(len(x1))
if row_type:
return [ {'features': vals} for vals in zip(x1,x2,x3)], y
else:
return [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))]
def test_replace_nan_with_mean(self):
test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
def test_create_and_predict_segment(self):
n_samples = 1000
random_state_train = np.random.RandomState(13)
random_state_test = np.random.RandomState(134)
training_data = self.generate_random_data(n_samples, random_state_train)
test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True)
ids = [{'cartodb_ids': range(len(test_data))}]
rows = [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
plpy._define_result('select \* from \(select \* from training\) a limit 1',rows)
plpy._define_result('.*from \(select \* from training\) as a' ,training_data)
plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
plpy._define_result('.*select \* from test.*' ,test_data)
model_parameters = {'n_estimators': 1200,
'max_depth': 3,
'subsample' : 0.5,
'learning_rate': 0.01,
'min_samples_leaf': 1}
result = segmentation.create_and_predict_segment(
'select * from training',
'target',
'select * from test',
model_parameters)
prediction = [r[1] for r in result]
accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y))))
self.assertEqual(len(result),len(test_data))
self.assertTrue( result[0][2] < 0.01)
self.assertTrue( accuracy < 0.5*np.mean(test_y) )

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import unittest
import numpy as np
import unittest
# from mock_plpy import MockPlPy
# plpy = MockPlPy()
#
# import sys
# sys.modules['plpy'] = plpy
from helper import plpy, fixture_file
import crankshaft.space_time_dynamics as std
from crankshaft import random_seeds
import json
class SpaceTimeTests(unittest.TestCase):
"""Testing class for Markov Functions."""
def setUp(self):
plpy._reset()
self.params = {"id_col": "cartodb_id",
"time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read())
self.markov_data = json.loads(open(fixture_file('markov.json')).read())
self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T
self.transition_matrix = np.array([
[[ 0.96341463, 0.0304878 , 0.00609756, 0. , 0. ],
[ 0.06040268, 0.83221477, 0.10738255, 0. , 0. ],
[ 0. , 0.14 , 0.74 , 0.12 , 0. ],
[ 0. , 0.03571429, 0.32142857, 0.57142857, 0.07142857],
[ 0. , 0. , 0. , 0.16666667, 0.83333333]],
[[ 0.79831933, 0.16806723, 0.03361345, 0. , 0. ],
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
[ 0.00537634, 0.06989247, 0.8655914 , 0.05913978, 0. ],
[ 0. , 0. , 0.06372549, 0.90196078, 0.03431373],
[ 0. , 0. , 0. , 0.19444444, 0.80555556]],
[[ 0.84693878, 0.15306122, 0. , 0. , 0. ],
[ 0.08133971, 0.78947368, 0.1291866 , 0. , 0. ],
[ 0.00518135, 0.0984456 , 0.79274611, 0.0984456 , 0.00518135],
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
[ 0. , 0. , 0. , 0.10204082, 0.89795918]],
[[ 0.8852459 , 0.09836066, 0. , 0.01639344, 0. ],
[ 0.03875969, 0.81395349, 0.13953488, 0. , 0.00775194],
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
[ 0. , 0.02339181, 0.12865497, 0.75438596, 0.09356725],
[ 0. , 0. , 0. , 0.09661836, 0.90338164]],
[[ 0.33333333, 0.66666667, 0. , 0. , 0. ],
[ 0.0483871 , 0.77419355, 0.16129032, 0.01612903, 0. ],
[ 0.01149425, 0.16091954, 0.74712644, 0.08045977, 0. ],
[ 0. , 0.01036269, 0.06217617, 0.89637306, 0.03108808],
[ 0. , 0. , 0. , 0.02352941, 0.97647059]]]
)
def test_spatial_markov(self):
"""Test Spatial Markov."""
data = [ { 'id': d['id'],
'attr1': d['y1995'],
'attr2': d['y1996'],
'attr3': d['y1997'],
'attr4': d['y1998'],
'attr5': d['y1999'],
'attr6': d['y2000'],
'attr7': d['y2001'],
'attr8': d['y2002'],
'attr9': d['y2003'],
'attr10': d['y2004'],
'attr11': d['y2005'],
'attr12': d['y2006'],
'attr13': d['y2007'],
'attr14': d['y2008'],
'attr15': d['y2009'],
'neighbors': d['neighbors'] } for d in self.neighbors_data]
print(str(data[0]))
plpy._define_result('select', data)
random_seeds.set_random_seeds(1234)
result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id')
self.assertTrue(result != None)
result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
print result[0]
expected = self.markov_data
for ([res_trend, res_up, res_down, res_vol, res_id],
[exp_trend, exp_up, exp_down, exp_vol, exp_id]
) in zip(result, expected):
self.assertAlmostEqual(res_trend, exp_trend)
def test_get_time_data(self):
"""Test get_time_data"""
data = [ { 'attr1': d['y1995'],
'attr2': d['y1996'],
'attr3': d['y1997'],
'attr4': d['y1998'],
'attr5': d['y1999'],
'attr6': d['y2000'],
'attr7': d['y2001'],
'attr8': d['y2002'],
'attr9': d['y2003'],
'attr10': d['y2004'],
'attr11': d['y2005'],
'attr12': d['y2006'],
'attr13': d['y2007'],
'attr14': d['y2008'],
'attr15': d['y2009'] } for d in self.neighbors_data]
result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'])
## expected was prepared from PySAL example:
### f = ps.open(ps.examples.get_path("usjoin.csv"))
### pci = np.array([f.by_col[str(y)] for y in range(1995, 2010)]).transpose()
### rpci = pci / (pci.mean(axis = 0))
expected = np.array([[ 0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154, 0.83271652
, 0.83786314, 0.85012593, 0.85509656, 0.86416612, 0.87119375, 0.86302631
, 0.86148267, 0.86252252, 0.86746356],
[ 0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388, 0.90746978
, 0.89830489, 0.89431991, 0.88924794, 0.89815176, 0.91832091, 0.91706054
, 0.90139505, 0.87897455, 0.86216858],
[ 0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, 0.78964559
, 0.80584442, 0.8084998, 0.82258551, 0.82668196, 0.82373724, 0.81814804
, 0.83675961, 0.83574199, 0.84647177],
[ 1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, 1.14506948
, 1.12151133, 1.11160697, 1.10888621, 1.11399806, 1.12168029, 1.13164797
, 1.12958508, 1.11371818, 1.09936775],
[ 1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, 1.16898201
, 1.17212488, 1.14752303, 1.11843284, 1.11024964, 1.11943471, 1.11736468
, 1.10863242, 1.09642516, 1.07762337],
[ 1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, 1.44184737
, 1.44782832, 1.41978227, 1.39092208, 1.4059372, 1.40788646, 1.44052766
, 1.45241216, 1.43306098, 1.4174431 ],
[ 1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, 1.10888138
, 1.11856629, 1.13062931, 1.11944984, 1.12446239, 1.11671008, 1.10880034
, 1.08401709, 1.06959206, 1.07875225],
[ 1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, 0.99854316
, 0.9880258, 0.99669587, 0.99327676, 1.01400905, 1.03176742, 1.040511
, 1.01749645, 0.9936394, 0.98279746],
[ 0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845, 0.99127006
, 0.97925917, 0.9683482, 0.95335147, 0.93694787, 0.94308213, 0.92232874
, 0.91284091, 0.89689833, 0.88928858],
[ 0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044, 0.8578708
, 0.86036185, 0.86107306, 0.8500772, 0.86981998, 0.86837929, 0.87204141
, 0.86633032, 0.84946077, 0.83287146],
[ 1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, 1.14450183
, 1.12349752, 1.12596664, 1.12213996, 1.1119989, 1.10257792, 1.10491258
, 1.11059842, 1.10509795, 1.10020097],
[ 0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687, 0.95831051
, 0.94480909, 0.94804195, 0.95430286, 0.94103989, 0.92122519, 0.91010201
, 0.89280392, 0.89298243, 0.89165385],
[ 0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, 0.9480927
, 0.93539182, 0.95388718, 0.94597005, 0.96918424, 0.94781281, 0.93466815
, 0.94281559, 0.96520315, 0.96715441],
[ 0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, 0.98687073
, 0.99237486, 0.98209969, 0.9877653, 0.97399471, 0.96910087, 0.98416665
, 0.98423613, 0.99823861, 0.99545704],
[ 0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012, 0.86191535
, 0.84981451, 0.85472102, 0.84564835, 0.83998883, 0.83478547, 0.82803648
, 0.8198736, 0.82265395, 0.8399404 ],
[ 0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, 0.82785597
, 0.86008789, 0.86776298, 0.86720209, 0.8676334, 0.89179317, 0.94202108
, 0.9422231, 0.93902708, 0.94479184],
[ 0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, 0.90906632
, 0.92693339, 0.93695966, 0.94242697, 0.94338265, 0.91981796, 0.91108804
, 0.90543476, 0.91737138, 0.94793657],
[ 1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723, 1.20172869
, 1.21328691, 1.22624778, 1.22397075, 1.23857042, 1.24419893, 1.23929384
, 1.23418676, 1.23626739, 1.26754398],
[ 1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, 1.34790023
, 1.34399863, 1.32575181, 1.30795492, 1.30544841, 1.30303302, 1.32107766
, 1.32936244, 1.33001241, 1.33288462],
[ 1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, 1.05059016
, 1.03405057, 1.02747623, 1.03162734, 0.9961416, 0.97356208, 0.94241549
, 0.92754547, 0.92549227, 0.92138102],
[ 1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, 1.13889622
, 1.12442212, 1.13367018, 1.13982256, 1.14029944, 1.11979401, 1.10905389
, 1.10577769, 1.11166825, 1.09985155],
[ 0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, 0.74480073
, 0.76098396, 0.76156903, 0.76651952, 0.76533288, 0.78205934, 0.76842416
, 0.77487118, 0.77768683, 0.78801192],
[ 0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, 0.97370819
, 0.96419154, 0.97209861, 0.97441313, 0.96356162, 0.94745352, 0.93965462
, 0.93069645, 0.94020973, 0.94358232],
[ 0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, 0.80071489
, 0.83358256, 0.83451613, 0.85175032, 0.85954307, 0.86790024, 0.87170334
, 0.87863799, 0.87497981, 0.87888675],
[ 0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, 0.98733195
, 0.99644997, 0.99669587, 1.02559097, 1.01116651, 0.99988024, 0.97906749
, 0.99323123, 1.00204939, 0.99602148],
[ 1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, 1.08312397
, 1.05192626, 1.04230892, 1.05577278, 1.08569751, 1.12443486, 1.08891079
, 1.08603695, 1.05997314, 1.02160943],
[ 1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272, 1.18257029
, 1.16226243, 1.16009196, 1.14467789, 1.14820235, 1.12386598, 1.12680236
, 1.12357937, 1.1159258, 1.12570828],
[ 1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, 1.31210239
, 1.29989156, 1.29203193, 1.27183516, 1.26830786, 1.2617743, 1.28656675
, 1.29734097, 1.29390205, 1.29345446],
[ 0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, 0.78772975
, 0.82848011, 0.8259679, 0.82435705, 0.83108634, 0.84373784, 0.83891093
, 0.84349247, 0.85637272, 0.86539395],
[ 1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626, 1.2256767
, 1.21126648, 1.19377804, 1.18355337, 1.19674434, 1.21536573, 1.23653297
, 1.27962009, 1.27968392, 1.25907738],
[ 0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282, 0.96480308
, 0.94686376, 0.93679073, 0.92540049, 0.92988835, 0.93442917, 0.92100464
, 0.91475304, 0.90249622, 0.9021363 ],
[ 0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368, 0.88937573
, 0.894401, 0.90448993, 0.95495898, 0.92698333, 0.94745352, 0.92562488
, 0.96635366, 1.02520312, 1.0394296 ],
[ 1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, 1.00759019
, 0.99192968, 0.99747298, 0.99550759, 0.97583768, 0.9610168, 0.94779638
, 0.93759089, 0.93353431, 0.94121705],
[ 0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, 0.83434854
, 0.85813595, 0.84667961, 0.84374558, 0.85951183, 0.87194227, 0.89455097
, 0.88283929, 0.90349491, 0.90600675],
[ 1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, 1.00581626
, 0.98850522, 0.99291168, 0.98983209, 0.97511924, 0.96134615, 0.96382634
, 0.95011401, 0.9434686, 0.94637765],
[ 1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, 1.04800023
, 1.03024941, 1.04200483, 1.0402554, 1.03296979, 1.02191682, 1.02476275
, 1.02347523, 1.02517684, 1.04359571],
[ 1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043, 1.0531801
, 1.07452771, 1.09383478, 1.1052447, 1.10322136, 1.09167939, 1.08772756
, 1.08859544, 1.09177338, 1.1096083 ],
[ 0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, 0.86287327
, 0.85169796, 0.85411285, 0.84886336, 0.84517414, 0.84843858, 0.84488343
, 0.83374329, 0.82812044, 0.82878599],
[ 0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, 0.92652175
, 0.94278865, 0.93682452, 0.98655146, 0.992237, 0.9798497, 0.93869677
, 0.96947771, 1.00362626, 0.98102351],
[ 0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967, 0.93092109
, 0.92662519, 0.93412152, 0.93501274, 0.92879506, 0.92110542, 0.91035556
, 0.90430364, 0.89994694, 0.90073864],
[ 0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, 0.98882205
, 0.97662234, 0.95601578, 0.94905385, 0.94934888, 0.97152609, 0.97163004
, 0.9700702, 0.97158948, 0.95884908],
[ 0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751, 0.84818516
, 0.85265681, 0.84502402, 0.82645665, 0.81743586, 0.83550406, 0.83338919
, 0.83511679, 0.82136617, 0.80921874],
[ 0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441, 0.95440787
, 0.96364363, 0.96804412, 0.97136214, 0.97583768, 0.95571724, 0.96895368
, 0.97001634, 0.97082733, 0.98782366],
[ 1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, 1.10558188
, 1.1214086, 1.12292577, 1.13021031, 1.13342735, 1.14686068, 1.14502975
, 1.14474747, 1.14084037, 1.16142926],
[ 1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, 1.11856702
, 1.09764283, 1.08815849, 1.08044313, 1.09278827, 1.07003204, 1.08398066
, 1.09831768, 1.09298232, 1.09176125],
[ 0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, 0.77751194
, 0.79902974, 0.81437881, 0.80788828, 0.79603865, 0.78966436, 0.79949807
, 0.80172182, 0.82168155, 0.85587911],
[ 1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561, 1.00162979
, 0.99860739, 1.00814981, 1.00574316, 0.99030032, 0.97682565, 0.97292596
, 0.96519561, 0.96173403, 0.95890284],
[ 0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289, 0.96608031
, 0.99727185, 1.00781194, 1.03484236, 1.05333619, 1.0983263, 1.1704974
, 1.17025154, 1.18730553, 1.14242645]])
self.assertTrue(np.allclose(result, expected))
self.assertTrue(type(result) == type(expected))
self.assertTrue(result.shape == expected.shape)
def test_rebin_data(self):
"""Test rebin_data"""
## sample in double the time (even case since 10 % 2 = 0):
## (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
## = 0.5, 2.5, 4.5, 6.5, 8.5
ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float)
for i in range(0, 10, 2)]).T
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
## sample in triple the time (uneven since 10 % 3 = 1):
## (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
## = 1, 4, 7, 9
ans_odd = np.array([i * np.ones(10, dtype=float)
for i in (1, 4, 7, 9)]).T
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
def test_get_prob_dist(self):
"""Test get_prob_dist"""
lag_indices = np.array([1, 2, 3, 4])
unit_indices = np.array([1, 3, 2, 4])
answer = np.array([
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
])
result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices)
self.assertTrue(np.array_equal(result, answer))
def test_get_prob_stats(self):
"""Test get_prob_stats"""
probs = np.array([
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
])
unit_indices = np.array([1, 3, 2, 4])
answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824, 0.02475248 / 0.77722772, -0.02352941 / 0.97647059])
answer_volatility = np.array([ 0.34221495, 0.33705421, 0.29226542, 0.38834223])
result = std.get_prob_stats(probs, unit_indices)
result_up = result[0]
result_down = result[1]
result_trend = result[2]
result_volatility = result[3]
self.assertTrue(np.allclose(result_up, answer_up))
self.assertTrue(np.allclose(result_down, answer_down))
self.assertTrue(np.allclose(result_trend, answer_trend))
self.assertTrue(np.allclose(result_volatility, answer_volatility))

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"""Import all modules"""
import crankshaft.random_seeds
import crankshaft.clustering
import crankshaft.space_time_dynamics
import crankshaft.segmentation

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"""Import all functions from for clustering"""
from moran import *
from kmeans import *

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from sklearn.cluster import KMeans
import plpy
def kmeans(query, no_clusters, no_init=20):
data = plpy.execute('''select array_agg(cartodb_id order by cartodb_id) as ids,
array_agg(ST_X(the_geom) order by cartodb_id) xs,
array_agg(ST_Y(the_geom) order by cartodb_id) ys from ({query}) a
where the_geom is not null
'''.format(query=query))
xs = data[0]['xs']
ys = data[0]['ys']
ids = data[0]['ids']
km = KMeans(n_clusters= no_clusters, n_init=no_init)
labels = km.fit_predict(zip(xs,ys))
return zip(ids,labels)

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"""
Moran's I geostatistics (global clustering & outliers presence)
"""
# TODO: Fill in local neighbors which have null/NoneType values with the
# average of the their neighborhood
import pysal as ps
import plpy
from collections import OrderedDict
# crankshaft module
import crankshaft.pysal_utils as pu
# High level interface ---------------------------------------
def moran(subquery, attr_name,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Moran's I (global)
Implementation building neighbors with a PostGIS database and Moran's I
core clusters with PySAL.
Andy Eschbacher
"""
qvals = OrderedDict([("id_col", id_col),
("attr1", attr_name),
("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(2)
except plpy.SPIError, e:
plpy.error('Analysis failed: %s' % e)
return pu.empty_zipped_array(2)
# collect attributes
attr_vals = pu.get_attributes(result)
# calculate weights
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate moran global
moran_global = ps.esda.moran.Moran(attr_vals, weight,
permutations=permutations)
return zip([moran_global.I], [moran_global.EI])
def moran_local(subquery, attr,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Moran's I implementation for PL/Python
Andy Eschbacher
"""
# geometries with attributes that are null are ignored
# resulting in a collection of not as near neighbors
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(5)
except plpy.SPIError, e:
plpy.error('Analysis failed: %s' % e)
return pu.empty_zipped_array(5)
attr_vals = pu.get_attributes(result)
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate LISA values
lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
permutations=permutations)
# find quadrants for each geometry
quads = quad_position(lisa.q)
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
def moran_rate(subquery, numerator, denominator,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Moran's I Rate (global)
Andy Eschbacher
"""
qvals = OrderedDict([("id_col", id_col),
("attr1", numerator),
("attr2", denominator)
("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(2)
except plpy.SPIError, e:
plpy.error('Analysis failed: %s' % e)
return pu.empty_zipped_array(2)
# collect attributes
numer = pu.get_attributes(result, 1)
denom = pu.get_attributes(result, 2)
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate moran global rate
lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
permutations=permutations)
return zip([lisa_rate.I], [lisa_rate.EI])
def moran_local_rate(subquery, numerator, denominator,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Moran's I Local Rate
Andy Eschbacher
"""
# geometries with values that are null are ignored
# resulting in a collection of not as near neighbors
qvals = OrderedDict([("id_col", id_col),
("numerator", numerator),
("denominator", denominator),
("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(5)
except plpy.SPIError, e:
plpy.error('Analysis failed: %s' % e)
return pu.empty_zipped_array(5)
# collect attributes
numer = pu.get_attributes(result, 1)
denom = pu.get_attributes(result, 2)
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate LISA values
lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
permutations=permutations)
# find quadrants for each geometry
quads = quad_position(lisa.q)
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
def moran_local_bv(subquery, attr1, attr2,
permutations, geom_col, id_col, w_type, num_ngbrs):
"""
Moran's I (local) Bivariate (untested)
"""
qvals = OrderedDict([("id_col", id_col),
("attr1", attr1),
("attr2", attr2),
("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:
plpy.error("Error: areas of interest query failed, "
"check input parameters")
return pu.empty_zipped_array(4)
# collect attributes
attr1_vals = pu.get_attributes(result, 1)
attr2_vals = pu.get_attributes(result, 2)
# create weights
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate LISA values
lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
permutations=permutations)
# find clustering of significance
lisa_sig = quad_position(lisa.q)
return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
# Low level functions ----------------------------------------
def map_quads(coord):
"""
Map a quadrant number to Moran's I designation
HH=1, LH=2, LL=3, HL=4
Input:
@param coord (int): quadrant of a specific measurement
Output:
classification (one of 'HH', 'LH', 'LL', or 'HL')
"""
if coord == 1:
return 'HH'
elif coord == 2:
return 'LH'
elif coord == 3:
return 'LL'
elif coord == 4:
return 'HL'
else:
return None
def quad_position(quads):
"""
Produce Moran's I classification based of n
Input:
@param quads ndarray: an array of quads classified by
1-4 (PySAL default)
Output:
@param list: an array of quads classied by 'HH', 'LL', etc.
"""
return [map_quads(q) for q in quads]

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"""Import all functions for pysal_utils"""
from crankshaft.pysal_utils.pysal_utils import *

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"""
Utilities module for generic PySAL functionality, mainly centered on
translating queries into numpy arrays or PySAL weights objects
"""
import numpy as np
import pysal as ps
def construct_neighbor_query(w_type, query_vals):
"""Return query (a string) used for finding neighbors
@param w_type text: type of neighbors to calculate ('knn' or 'queen')
@param query_vals dict: values used to construct the query
"""
if w_type.lower() == 'knn':
return knn(query_vals)
else:
return queen(query_vals)
# Build weight object
def get_weight(query_res, w_type='knn', num_ngbrs=5):
"""
Construct PySAL weight from return value of query
@param query_res dict-like: query results with attributes and neighbors
"""
# if w_type.lower() == 'knn':
# row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
# weights = {x['id']: row_normed_weights for x in query_res}
# else:
# weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
# if len(x['neighbors']) > 0
# else [] for x in query_res}
neighbors = {x['id']: x['neighbors'] for x in query_res}
print 'len of neighbors: %d' % len(neighbors)
built_weight = ps.W(neighbors)
built_weight.transform = 'r'
return built_weight
def query_attr_select(params):
"""
Create portion of SELECT statement for attributes inolved in query.
@param params: dict of information used in query (column names,
table name, etc.)
"""
attr_string = ""
template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, "
if 'time_cols' in params:
# if markov analysis
attrs = params['time_cols']
for idx, val in enumerate(attrs):
attr_string += template % {"col": val, "alias_num": idx + 1}
else:
# if moran's analysis
attrs = [k for k in params
if k not in ('id_col', 'geom_col', 'subquery',
'num_ngbrs', 'subquery')]
for idx, val in enumerate(sorted(attrs)):
attr_string += template % {"col": params[val],
"alias_num": idx + 1}
return attr_string
def query_attr_where(params):
"""
Construct where conditions when building neighbors query
Create portion of WHERE clauses for weeding out NULL-valued geometries
Input: dict of params:
{'subquery': ...,
'numerator': 'data1',
'denominator': 'data2',
'': ...}
Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2"
IS NOT NULL'
Input:
{'subquery': ...,
'time_cols': ['time1', 'time2', 'time3'],
'etc': ...}
Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT
NULL AND idx_replace."time3" IS NOT NULL'
"""
attr_string = []
template = "idx_replace.\"%s\" IS NOT NULL"
if 'time_cols' in params:
# markov where clauses
attrs = params['time_cols']
# add values to template
for attr in attrs:
attr_string.append(template % attr)
else:
# moran where clauses
# get keys
attrs = sorted([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]])
out = " AND ".join(attr_string)
return out
def knn(params):
"""SQL query for k-nearest neighbors.
@param vars: dict of values to fill template
"""
attr_select = query_attr_select(params)
attr_where = query_attr_where(params)
replacements = {"attr_select": attr_select,
"attr_where_i": attr_where.replace("idx_replace", "i"),
"attr_where_j": attr_where.replace("idx_replace", "j")}
query = "SELECT " \
"i.\"{id_col}\" As id, " \
"%(attr_select)s" \
"(SELECT ARRAY(SELECT j.\"{id_col}\" " \
"FROM ({subquery}) As j " \
"WHERE " \
"i.\"{id_col}\" <> j.\"{id_col}\" AND " \
"%(attr_where_j)s " \
"ORDER BY " \
"j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
"LIMIT {num_ngbrs})" \
") As neighbors " \
"FROM ({subquery}) As i " \
"WHERE " \
"%(attr_where_i)s " \
"ORDER BY i.\"{id_col}\" ASC;" % replacements
return query.format(**params)
# SQL query for finding queens neighbors (all contiguous polygons)
def queen(params):
"""SQL query for queen neighbors.
@param params dict: information to fill query
"""
attr_select = query_attr_select(params)
attr_where = query_attr_where(params)
replacements = {"attr_select": attr_select,
"attr_where_i": attr_where.replace("idx_replace", "i"),
"attr_where_j": attr_where.replace("idx_replace", "j")}
query = "SELECT " \
"i.\"{id_col}\" As id, " \
"%(attr_select)s" \
"(SELECT ARRAY(SELECT j.\"{id_col}\" " \
"FROM ({subquery}) As j " \
"WHERE i.\"{id_col}\" <> j.\"{id_col}\" AND " \
"ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
"%(attr_where_j)s)" \
") As neighbors " \
"FROM ({subquery}) As i " \
"WHERE " \
"%(attr_where_i)s " \
"ORDER BY i.\"{id_col}\" ASC;" % replacements
return query.format(**params)
# to add more weight methods open a ticket or pull request
def get_attributes(query_res, attr_num=1):
"""
@param query_res: query results with attributes and neighbors
@param attr_num: attribute number (1, 2, ...)
"""
return np.array([x['attr' + str(attr_num)] for x in query_res],
dtype=np.float)
def empty_zipped_array(num_nones):
"""
prepare return values for cases of empty weights objects (no neighbors)
Input:
@param num_nones int: number of columns (e.g., 4)
Output:
[(None, None, None, None)]
"""
return [tuple([None] * num_nones)]

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"""Random seed generator used for non-deterministic functions in crankshaft"""
import random
import numpy
def set_random_seeds(value):
"""
Set the seeds of the RNGs (Random Number Generators)
used internally.
"""
random.seed(value)
numpy.random.seed(value)

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from segmentation import *

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"""
Segmentation creation and prediction
"""
import sklearn
import numpy as np
import plpy
from sklearn.ensemble import GradientBoostingRegressor
from sklearn import metrics
from sklearn.cross_validation import train_test_split
# Lower level functions
#----------------------
def replace_nan_with_mean(array):
"""
Input:
@param array: an array of floats which may have null-valued entries
Output:
array with nans filled in with the mean of the dataset
"""
# returns an array of rows and column indices
indices = np.where(np.isnan(array))
# iterate through entries which have nan values
for row, col in zip(*indices):
array[row, col] = np.mean(array[~np.isnan(array[:, col]), col])
return array
def get_data(variable, feature_columns, query):
"""
Fetch data from the database, clean, and package into
numpy arrays
Input:
@param variable: name of the target variable
@param feature_columns: list of column names
@param query: subquery that data is pulled from for the packaging
Output:
prepared data, packaged into NumPy arrays
"""
columns = ','.join(['array_agg("{col}") As "{col}"'.format(col=col) for col in feature_columns])
try:
data = plpy.execute('''SELECT array_agg("{variable}") As target, {columns} FROM ({query}) As a'''.format(
variable=variable,
columns=columns,
query=query))
except Exception, e:
plpy.error('Failed to access data to build segmentation model: %s' % e)
# extract target data from plpy object
target = np.array(data[0]['target'])
# put n feature data arrays into an n x m array of arrays
features = np.column_stack([np.array(data[0][col], dtype=float) for col in feature_columns])
return replace_nan_with_mean(target), replace_nan_with_mean(features)
# High level interface
# --------------------
def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters):
"""
Version of create_and_predict_segment that works on arrays that come stright form the SQL calling
the function.
Input:
@param target: The 1D array of lenth NSamples containing the target variable we want the model to predict
@param features: Thw 2D array of size NSamples * NFeatures that form the imput to the model
@param target_ids: A 1D array of target_ids that will be used to associate the results of the prediction with the rows which they come from
@param model_parameters: A dictionary containing parameters for the model.
"""
clean_target = replace_nan_with_mean(target)
clean_features = replace_nan_with_mean(features)
target_features = replace_nan_with_mean(target_features)
model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2)
prediction = model.predict(target_features)
accuracy_array = [accuracy]*prediction.shape[0]
return zip(target_ids, prediction, np.full(prediction.shape, accuracy_array))
def create_and_predict_segment(query, variable, target_query, model_params):
"""
generate a segment with machine learning
Stuart Lynn
"""
## fetch column names
try:
columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1 '.format(query=query))[0].keys()
except Exception, e:
plpy.error('Failed to build segmentation model: %s' % e)
## extract column names to be used in building the segmentation model
feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator'])
## get data from database
target, features = get_data(variable, feature_columns, query)
model, accuracy = train_model(target, features, model_params, 0.2)
cartodb_ids, result = predict_segment(model, feature_columns, target_query)
accuracy_array = [accuracy]*result.shape[0]
return zip(cartodb_ids, result, accuracy_array)
def train_model(target, features, model_params, test_split):
"""
Train the Gradient Boosting model on the provided data and calculate the accuracy of the model
Input:
@param target: 1D Array of the variable that the model is to be trianed to predict
@param features: 2D Array NSamples * NFeatures to use in trining the model
@param model_params: A dictionary of model parameters, the full specification can be found on the
scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)
@parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray
"""
features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
model = GradientBoostingRegressor(**model_params)
model.fit(features_train, target_train)
accuracy = calculate_model_accuracy(model, features, target)
return model, accuracy
def calculate_model_accuracy(model, features, target):
"""
Calculate the mean squared error of the model prediction
Input:
@param model: model trained from input features
@param features: features to make a prediction from
@param target: target to compare prediction to
Output:
mean squared error of the model prection compared to the target
"""
prediction = model.predict(features)
return metrics.mean_squared_error(prediction, target)
def predict_segment(model, features, target_query):
"""
Use the provided model to predict the values for the new feature set
Input:
@param model: The pretrained model
@features: A list of features to use in the model prediction (list of column names)
@target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it.
"""
batch_size = 1000
joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features])
try:
cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format(
joined_features=joined_features,
target_query=target_query))
except Exception, e:
plpy.error('Failed to build segmentation model: %s' % e)
results = []
while True:
rows = cursor.fetch(batch_size)
if not rows:
break
batch = np.row_stack([np.array(row['features'], dtype=float) for row in rows])
#Need to fix this. Should be global mean. This will cause weird effects
batch = replace_nan_with_mean(batch)
prediction = model.predict(batch)
results.append(prediction)
try:
cartodb_ids = plpy.execute('''SELECT array_agg(cartodb_id ORDER BY cartodb_id) As cartodb_ids FROM ({0}) As a'''.format(target_query))[0]['cartodb_ids']
except Exception, e:
plpy.error('Failed to build segmentation model: %s' % e)
return cartodb_ids, np.concatenate(results)

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"""Import all functions from clustering libraries."""
from markov import *

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"""
Spatial dynamics measurements using Spatial Markov
"""
import numpy as np
import pysal as ps
import plpy
import crankshaft.pysal_utils as pu
def spatial_markov_trend(subquery, time_cols, num_classes=7,
w_type='knn', num_ngbrs=5, permutations=0,
geom_col='the_geom', id_col='cartodb_id'):
"""
Predict the trends of a unit based on:
1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
2. average class of its neighbors
Inputs:
@param subquery string: e.g., SELECT the_geom, cartodb_id,
interesting_time_column FROM table_name
@param time_cols list of strings: list of strings of column names
@param num_classes (optional): number of classes to break distribution
of values into. Currently uses quantile bins.
@param w_type string (optional): weight type ('knn' or 'queen')
@param num_ngbrs int (optional): number of neighbors (if knn type)
@param permutations int (optional): number of permutations for test
stats
@param geom_col string (optional): name of column which contains the
geometries
@param id_col string (optional): name of column which has the ids of
the table
Outputs:
@param trend_up float: probablity that a geom will move to a higher
class
@param trend_down float: probablity that a geom will move to a lower
class
@param trend float: (trend_up - trend_down) / trend_static
@param volatility float: a measure of the volatility based on
probability stddev(prob array)
"""
if len(time_cols) < 2:
plpy.error('More than one time column needs to be passed')
qvals = {"id_col": id_col,
"time_cols": time_cols,
"geom_col": geom_col,
"subquery": subquery,
"num_ngbrs": num_ngbrs}
try:
query_result = plpy.execute(
pu.construct_neighbor_query(w_type, qvals)
)
if len(query_result) == 0:
return zip([None], [None], [None], [None], [None])
except plpy.SPIError, e:
plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
plpy.error('Analysis failed: %s' % e)
return zip([None], [None], [None], [None], [None])
## build weight
weights = pu.get_weight(query_result, w_type)
weights.transform = 'r'
## prep time data
t_data = get_time_data(query_result, time_cols)
plpy.debug('shape of t_data %d, %d' % t_data.shape)
plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape)
plpy.debug('first num elements: %f' % t_data[0, 0])
sp_markov_result = ps.Spatial_Markov(t_data,
weights,
k=num_classes,
fixed=False,
permutations=permutations)
## get lag classes
lag_classes = ps.Quantiles(
ps.lag_spatial(weights, t_data[:, -1]),
k=num_classes).yb
## look up probablity distribution for each unit according to class and lag class
prob_dist = get_prob_dist(sp_markov_result.P,
lag_classes,
sp_markov_result.classes[:, -1])
## find the ups and down and overall distribution of each cell
trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist,
sp_markov_result.classes[:, -1])
## output the results
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
"""
num_attrs = len(time_cols)
return np.array([[x['attr' + str(i)] for x in markov_data]
for i in range(1, num_attrs+1)], dtype=float).transpose()
## not currently used
def rebin_data(time_data, num_time_per_bin):
"""
Convert an n x l matrix into an (n/m) x l matrix where the values are
reduced (averaged) for the intervening states:
1 2 3 4 1.5 3.5
5 6 7 8 -> 5.5 7.5
9 8 7 6 8.5 6.5
5 4 3 2 4.5 2.5
if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix.
This process effectively resamples the data at a longer time span n
units longer than the input data.
For cases when there is a remainder (remainder(5/3) = 2), the remaining
two columns are binned together as the last time period, while the
first three are binned together for the first period.
Input:
@param time_data n x l ndarray: measurements of an attribute at
different time intervals
@param num_time_per_bin int: number of columns to average into a new
column
Output:
ceil(n / m) x l ndarray of resampled time series
"""
if time_data.shape[1] % num_time_per_bin == 0:
## if fit is perfect, then use it
n_max = time_data.shape[1] / num_time_per_bin
else:
## fit remainders into an additional column
n_max = time_data.shape[1] / num_time_per_bin + 1
return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
for i in range(n_max)]).T
def get_prob_dist(transition_matrix, lag_indices, unit_indices):
"""
Given an array of transition matrices, look up the probability
associated with the arrangements passed
Input:
@param transition_matrix ndarray[k,k,k]:
@param lag_indices ndarray:
@param unit_indices ndarray:
Output:
Array of probability distributions
"""
return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
for i in range(len(lag_indices))])
def get_prob_stats(prob_dist, unit_indices):
"""
get the statistics of the probability distributions
Outputs:
@param trend_up ndarray(float): sum of probabilities for upward
movement (relative to the unit index of that prob)
@param trend_down ndarray(float): sum of probabilities for downward
movement (relative to the unit index of that prob)
@param trend ndarray(float): difference of upward and downward
movements
"""
num_elements = len(unit_indices)
trend_up = np.empty(num_elements, dtype=float)
trend_down = np.empty(num_elements, dtype=float)
trend = np.empty(num_elements, dtype=float)
for i in range(num_elements):
trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
if prob_dist[i, unit_indices[i]] > 0.0:
trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
else:
trend[i] = None
## calculate volatility of distribution
volatility = prob_dist.std(axis=1)
return trend_up, trend_down, trend, volatility

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joblib==0.8.3
numpy==1.6.1
scipy==0.14.0
pysal==1.11.2
scikit-learn==0.14.1

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"""
CartoDB Spatial Analysis Python Library
See:
https://github.com/CartoDB/crankshaft
"""
from setuptools import setup, find_packages
setup(
name='crankshaft',
version='0.0.0',
description='CartoDB Spatial Analysis Python Library',
url='https://github.com/CartoDB/crankshaft',
author='Data Services Team - CartoDB',
author_email='dataservices@cartodb.com',
license='MIT',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Mapping comunity',
'Topic :: Maps :: Mapping Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2.7',
],
keywords='maps mapping tools spatial analysis geostatistics',
packages=find_packages(exclude=['contrib', 'docs', 'tests']),
extras_require={
'dev': ['unittest'],
'test': ['unittest', 'nose', 'mock'],
},
# The choice of component versions is dictated by what's
# provisioned in the production servers.
# IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
requires=['pysal', 'numpy', 'sklearn'],
test_suite='test'
)

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"""
CartoDB Spatial Analysis Python Library
See:
https://github.com/CartoDB/crankshaft
"""
from setuptools import setup, find_packages
setup(
name='crankshaft',
version='0.0.0',
description='CartoDB Spatial Analysis Python Library',
url='https://github.com/CartoDB/crankshaft',
author='Data Services Team - CartoDB',
author_email='dataservices@cartodb.com',
license='MIT',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Mapping comunity',
'Topic :: Maps :: Mapping Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2.7',
],
keywords='maps mapping tools spatial analysis geostatistics',
packages=find_packages(exclude=['contrib', 'docs', 'tests']),
extras_require={
'dev': ['unittest'],
'test': ['unittest', 'nose', 'mock'],
},
# The choice of component versions is dictated by what's
# provisioned in the production servers.
# IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
requires=['pysal', 'numpy', 'sklearn'],
test_suite='test'
)

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[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 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]}]

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[[0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 0], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 1], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 2], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 3], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 4], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 5], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 6], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 7], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 8], [0.19047619047619049, 0.16, 0.0, 0.32594478059941379, 9], [-0.23529411764705882, 0.0, 0.19047619047619047, 0.31356338348865387, 10], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 11], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 12], [0.027777777777777783, 0.11111111111111112, 0.088888888888888892, 0.30339641183779581, 13], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 14], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 15], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 16], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 17], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 18], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 19], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 20], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 21], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 22], [-0.16666666666666663, 0.18181818181818182, 0.27272727272727271, 0.20246415864836445, 23], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 24], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 25], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 26], [-0.043478260869565216, 0.0, 0.041666666666666664, 0.37950991789118999, 27], [0.22222222222222221, 0.18181818181818182, 0.0, 0.31701083225750354, 28], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 29], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 30], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 31], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 32], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 33], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 34], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 35], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 36], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 37], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 38], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 39], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 40], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 41], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 42], [0.0, 0.0, 0.0, 0.40000000000000002, 43], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 44], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 45], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 46], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 47]]

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[[0.9319096128346788, "HH"],
[-1.135787401862846, "HL"],
[0.11732030672508517, "LL"],
[0.6152779669180425, "LL"],
[-0.14657336660125297, "LH"],
[0.6967858120189607, "LL"],
[0.07949310115714454, "HH"],
[0.4703198759258987, "HH"],
[0.4421125200498064, "HH"],
[0.5724288737143592, "LL"],
[0.8970743435692062, "LL"],
[0.18327334401918674, "LL"],
[-0.01466729201304962, "HL"],
[0.3481559372544409, "LL"],
[0.06547094736902978, "LL"],
[0.15482141569329988, "HH"],
[0.4373841193538136, "HH"],
[0.15971286468915544, "LL"],
[1.0543588860308968, "HH"],
[1.7372866900020818, "HH"],
[1.091998586053999, "LL"],
[0.1171572584252222, "HH"],
[0.08438455015300014, "LL"],
[0.06547094736902978, "LL"],
[0.15482141569329985, "HH"],
[1.1627044812890683, "HH"],
[0.06547094736902978, "LL"],
[0.795275137550483, "HH"],
[0.18562939195219, "LL"],
[0.3010757406693439, "LL"],
[2.8205795942839376, "HH"],
[0.11259190602909264, "LL"],
[-0.07116352791516614, "HL"],
[-0.09945240794119009, "LH"],
[0.18562939195219, "LL"],
[0.1832733440191868, "LL"],
[-0.39054253768447705, "HL"],
[-0.1672071289487642, "HL"],
[0.3337669247916343, "HH"],
[0.2584386102554792, "HH"],
[-0.19733845476322634, "HL"],
[-0.9379282899805409, "LH"],
[-0.028770969951095866, "LH"],
[0.051367269430983485, "LL"],
[-0.2172548045913472, "LH"],
[0.05136726943098351, "LL"],
[0.04191046803899837, "LL"],
[0.7482357030403517, "HH"],
[-0.014585767863118111, "LH"],
[0.5410013139159929, "HH"],
[1.0223932668429925, "LL"],
[1.4179402898927476, "LL"]]

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[
{"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5},
{"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7},
{"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2},
{"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1},
{"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3},
{"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05},
{"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4},
{"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7},
{"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5},
{"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04},
{"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08},
{"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2},
{"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4},
{"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2},
{"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3},
{"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4},
{"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6},
{"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3},
{"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7},
{"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8},
{"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1},
{"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4},
{"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1},
{"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3},
{"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4},
{"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6},
{"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3},
{"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8},
{"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3},
{"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1},
{"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9},
{"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3},
{"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4},
{"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3},
{"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3},
{"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2},
{"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5},
{"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4},
{"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6},
{"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5},
{"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4},
{"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2},
{"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3},
{"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2},
{"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3},
{"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2},
{"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3},
{"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5},
{"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2},
{"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6},
{"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01},
{"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01}
]

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import unittest
from mock_plpy import MockPlPy
plpy = MockPlPy()
import sys
sys.modules['plpy'] = plpy
import os
def fixture_file(name):
dir = os.path.dirname(os.path.realpath(__file__))
return os.path.join(dir, 'fixtures', name)

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import re
class MockCursor:
def __init__(self, data):
self.cursor_pos = 0
self.data = data
def fetch(self, batch_size):
batch = self.data[self.cursor_pos : self.cursor_pos + batch_size]
self.cursor_pos += batch_size
return batch
class MockPlPy:
def __init__(self):
self._reset()
def _reset(self):
self.infos = []
self.notices = []
self.debugs = []
self.logs = []
self.warnings = []
self.errors = []
self.fatals = []
self.executes = []
self.results = []
self.prepares = []
self.results = []
def _define_result(self, query, result):
pattern = re.compile(query, re.IGNORECASE | re.MULTILINE)
self.results.append([pattern, result])
def notice(self, msg):
self.notices.append(msg)
def debug(self, msg):
self.notices.append(msg)
def info(self, msg):
self.infos.append(msg)
def cursor(self, query):
data = self.execute(query)
return MockCursor(data)
def execute(self, query): # TODO: additional arguments
for result in self.results:
if result[0].match(query):
return result[1]
return []

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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 numpy as np
import crankshaft.clustering as cc
import crankshaft.pysal_utils as pu
from crankshaft import random_seeds
import json
class KMeansTest(unittest.TestCase):
"""Testing class for Moran's I functions"""
def setUp(self):
plpy._reset()
self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read())
self.params = {"subquery": "select * from table",
"no_clusters": "10"
}
def test_kmeans(self):
data = self.cluster_data
plpy._define_result('select' ,data)
clusters = cc.kmeans('subquery', 2)
labels = [a[1] for a in clusters]
c1 = [a for a in clusters if a[1]==0]
c2 = [a for a in clusters if a[1]==1]
self.assertEqual(len(np.unique(labels)),2)
self.assertEqual(len(c1),20)
self.assertEqual(len(c2),20)

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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
class MoranTest(unittest.TestCase):
"""Testing class for Moran's I functions"""
def setUp(self):
plpy._reset()
self.params = {"id_col": "cartodb_id",
"attr1": "andy",
"attr2": "jay_z",
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.params_markov = {"id_col": "cartodb_id",
"time_cols": ["_2013_dec", "_2014_jan",
"_2014_feb"],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.neighbors_data = json.loads(
open(fixture_file('neighbors.json')).read())
self.moran_data = json.loads(
open(fixture_file('moran.json')).read())
def test_map_quads(self):
"""Test map_quads"""
self.assertEqual(cc.map_quads(1), 'HH')
self.assertEqual(cc.map_quads(2), 'LH')
self.assertEqual(cc.map_quads(3), 'LL')
self.assertEqual(cc.map_quads(4), 'HL')
self.assertEqual(cc.map_quads(33), None)
self.assertEqual(cc.map_quads('andy'), None)
def test_quad_position(self):
"""Test lisa_sig_vals"""
quads = np.array([1, 2, 3, 4], np.int)
ans = np.array(['HH', 'LH', 'LL', 'HL'])
test_ans = cc.quad_position(quads)
self.assertTrue((test_ans == ans).all())
def test_moran_local(self):
"""Test Moran's I local"""
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.moran_local('subquery', 'value',
'knn', 5, 99, 'the_geom', 'cartodb_id')
result = [(row[0], row[1]) for row in result]
zipped_values = zip(result, self.moran_data)
for ([res_val, res_quad], [exp_val, exp_quad]) in zipped_values:
self.assertAlmostEqual(res_val, exp_val)
self.assertEqual(res_quad, exp_quad)
def test_moran_local_rate(self):
"""Test Moran's I rate"""
data = [{'id': d['id'],
'attr1': d['value'],
'attr2': 1,
'neighbors': d['neighbors']} for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1234)
result = cc.moran_local_rate('subquery', 'numerator', 'denominator',
'knn', 5, 99, 'the_geom', 'cartodb_id')
result = [(row[0], row[1]) for row in result]
zipped_values = zip(result, self.moran_data)
for ([res_val, res_quad], [exp_val, exp_quad]) in zipped_values:
self.assertAlmostEqual(res_val, exp_val)
def test_moran(self):
"""Test Moran's I global"""
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(1235)
result = cc.moran('table', 'value',
'knn', 5, 99, 'the_geom', 'cartodb_id')
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)

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import unittest
import crankshaft.pysal_utils as pu
from crankshaft import random_seeds
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.params_array = {"id_col": "cartodb_id",
"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
def test_query_attr_select(self):
"""Test query_attr_select"""
ans = "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, "
self.assertEqual(pu.query_attr_select(self.params), ans)
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"
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.params_array), ans_array)
def test_knn(self):
"""Test knn neighbors constructor"""
ans = "SELECT i.\"cartodb_id\" As id, " \
"i.\"andy\"::numeric As attr1, " \
"i.\"jay_z\"::numeric As attr2, " \
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
"FROM (SELECT * FROM a_list) As j " \
"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 " \
"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 " \
"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, " \
"i.\"_2014_feb\"::numeric As attr3, " \
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
"FROM (SELECT * FROM a_list) As j " \
"WHERE i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
"j.\"_2013_dec\" IS NOT NULL AND " \
"j.\"_2014_jan\" IS NOT NULL AND " \
"j.\"_2014_feb\" 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.\"_2013_dec\" IS NOT NULL AND " \
"i.\"_2014_jan\" IS NOT NULL AND " \
"i.\"_2014_feb\" IS NOT NULL "\
"ORDER BY i.\"cartodb_id\" ASC;"
self.assertEqual(pu.knn(self.params), ans)
self.assertEqual(pu.knn(self.params_array), ans_array)
def test_queen(self):
"""Test queen neighbors constructor"""
ans = "SELECT i.\"cartodb_id\" As id, " \
"i.\"andy\"::numeric As attr1, " \
"i.\"jay_z\"::numeric As attr2, " \
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
"FROM (SELECT * FROM a_list) As j " \
"WHERE " \
"i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
"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)" \
") 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 " \
"ORDER BY i.\"cartodb_id\" ASC;"
self.assertEqual(pu.queen(self.params), ans)
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))
def test_get_attributes(self):
"""Test get_attributes"""
## need to add tests
self.assertEqual(True, True)
def test_get_weight(self):
"""Test get_weight"""
self.assertEqual(True, True)
def test_empty_zipped_array(self):
"""Test empty_zipped_array"""
ans2 = [(None, None)]
ans4 = [(None, None, None, None)]
self.assertEqual(pu.empty_zipped_array(2), ans2)
self.assertEqual(pu.empty_zipped_array(4), ans4)

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import unittest
import numpy as np
from helper import plpy, fixture_file
import crankshaft.segmentation as segmentation
import json
class SegmentationTest(unittest.TestCase):
"""Testing class for Moran's I functions"""
def setUp(self):
plpy._reset()
def generate_random_data(self,n_samples,random_state, row_type=False):
x1 = random_state.uniform(size=n_samples)
x2 = random_state.uniform(size=n_samples)
x3 = random_state.randint(0, 4, size=n_samples)
y = x1+x2*x2+x3
cartodb_id = range(len(x1))
if row_type:
return [ {'features': vals} for vals in zip(x1,x2,x3)], y
else:
return [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))]
def test_replace_nan_with_mean(self):
test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
def test_create_and_predict_segment(self):
n_samples = 1000
random_state_train = np.random.RandomState(13)
random_state_test = np.random.RandomState(134)
training_data = self.generate_random_data(n_samples, random_state_train)
test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True)
ids = [{'cartodb_ids': range(len(test_data))}]
rows = [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
plpy._define_result('select \* from \(select \* from training\) a limit 1',rows)
plpy._define_result('.*from \(select \* from training\) as a' ,training_data)
plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
plpy._define_result('.*select \* from test.*' ,test_data)
model_parameters = {'n_estimators': 1200,
'max_depth': 3,
'subsample' : 0.5,
'learning_rate': 0.01,
'min_samples_leaf': 1}
result = segmentation.create_and_predict_segment(
'select * from training',
'target',
'select * from test',
model_parameters)
prediction = [r[1] for r in result]
accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y))))
self.assertEqual(len(result),len(test_data))
self.assertTrue( result[0][2] < 0.01)
self.assertTrue( accuracy < 0.5*np.mean(test_y) )

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import unittest
import numpy as np
import unittest
# from mock_plpy import MockPlPy
# plpy = MockPlPy()
#
# import sys
# sys.modules['plpy'] = plpy
from helper import plpy, fixture_file
import crankshaft.space_time_dynamics as std
from crankshaft import random_seeds
import json
class SpaceTimeTests(unittest.TestCase):
"""Testing class for Markov Functions."""
def setUp(self):
plpy._reset()
self.params = {"id_col": "cartodb_id",
"time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read())
self.markov_data = json.loads(open(fixture_file('markov.json')).read())
self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T
self.transition_matrix = np.array([
[[ 0.96341463, 0.0304878 , 0.00609756, 0. , 0. ],
[ 0.06040268, 0.83221477, 0.10738255, 0. , 0. ],
[ 0. , 0.14 , 0.74 , 0.12 , 0. ],
[ 0. , 0.03571429, 0.32142857, 0.57142857, 0.07142857],
[ 0. , 0. , 0. , 0.16666667, 0.83333333]],
[[ 0.79831933, 0.16806723, 0.03361345, 0. , 0. ],
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
[ 0.00537634, 0.06989247, 0.8655914 , 0.05913978, 0. ],
[ 0. , 0. , 0.06372549, 0.90196078, 0.03431373],
[ 0. , 0. , 0. , 0.19444444, 0.80555556]],
[[ 0.84693878, 0.15306122, 0. , 0. , 0. ],
[ 0.08133971, 0.78947368, 0.1291866 , 0. , 0. ],
[ 0.00518135, 0.0984456 , 0.79274611, 0.0984456 , 0.00518135],
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
[ 0. , 0. , 0. , 0.10204082, 0.89795918]],
[[ 0.8852459 , 0.09836066, 0. , 0.01639344, 0. ],
[ 0.03875969, 0.81395349, 0.13953488, 0. , 0.00775194],
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
[ 0. , 0.02339181, 0.12865497, 0.75438596, 0.09356725],
[ 0. , 0. , 0. , 0.09661836, 0.90338164]],
[[ 0.33333333, 0.66666667, 0. , 0. , 0. ],
[ 0.0483871 , 0.77419355, 0.16129032, 0.01612903, 0. ],
[ 0.01149425, 0.16091954, 0.74712644, 0.08045977, 0. ],
[ 0. , 0.01036269, 0.06217617, 0.89637306, 0.03108808],
[ 0. , 0. , 0. , 0.02352941, 0.97647059]]]
)
def test_spatial_markov(self):
"""Test Spatial Markov."""
data = [ { 'id': d['id'],
'attr1': d['y1995'],
'attr2': d['y1996'],
'attr3': d['y1997'],
'attr4': d['y1998'],
'attr5': d['y1999'],
'attr6': d['y2000'],
'attr7': d['y2001'],
'attr8': d['y2002'],
'attr9': d['y2003'],
'attr10': d['y2004'],
'attr11': d['y2005'],
'attr12': d['y2006'],
'attr13': d['y2007'],
'attr14': d['y2008'],
'attr15': d['y2009'],
'neighbors': d['neighbors'] } for d in self.neighbors_data]
print(str(data[0]))
plpy._define_result('select', data)
random_seeds.set_random_seeds(1234)
result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id')
self.assertTrue(result != None)
result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
print result[0]
expected = self.markov_data
for ([res_trend, res_up, res_down, res_vol, res_id],
[exp_trend, exp_up, exp_down, exp_vol, exp_id]
) in zip(result, expected):
self.assertAlmostEqual(res_trend, exp_trend)
def test_get_time_data(self):
"""Test get_time_data"""
data = [ { 'attr1': d['y1995'],
'attr2': d['y1996'],
'attr3': d['y1997'],
'attr4': d['y1998'],
'attr5': d['y1999'],
'attr6': d['y2000'],
'attr7': d['y2001'],
'attr8': d['y2002'],
'attr9': d['y2003'],
'attr10': d['y2004'],
'attr11': d['y2005'],
'attr12': d['y2006'],
'attr13': d['y2007'],
'attr14': d['y2008'],
'attr15': d['y2009'] } for d in self.neighbors_data]
result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'])
## expected was prepared from PySAL example:
### f = ps.open(ps.examples.get_path("usjoin.csv"))
### pci = np.array([f.by_col[str(y)] for y in range(1995, 2010)]).transpose()
### rpci = pci / (pci.mean(axis = 0))
expected = np.array([[ 0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154, 0.83271652
, 0.83786314, 0.85012593, 0.85509656, 0.86416612, 0.87119375, 0.86302631
, 0.86148267, 0.86252252, 0.86746356],
[ 0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388, 0.90746978
, 0.89830489, 0.89431991, 0.88924794, 0.89815176, 0.91832091, 0.91706054
, 0.90139505, 0.87897455, 0.86216858],
[ 0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, 0.78964559
, 0.80584442, 0.8084998, 0.82258551, 0.82668196, 0.82373724, 0.81814804
, 0.83675961, 0.83574199, 0.84647177],
[ 1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, 1.14506948
, 1.12151133, 1.11160697, 1.10888621, 1.11399806, 1.12168029, 1.13164797
, 1.12958508, 1.11371818, 1.09936775],
[ 1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, 1.16898201
, 1.17212488, 1.14752303, 1.11843284, 1.11024964, 1.11943471, 1.11736468
, 1.10863242, 1.09642516, 1.07762337],
[ 1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, 1.44184737
, 1.44782832, 1.41978227, 1.39092208, 1.4059372, 1.40788646, 1.44052766
, 1.45241216, 1.43306098, 1.4174431 ],
[ 1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, 1.10888138
, 1.11856629, 1.13062931, 1.11944984, 1.12446239, 1.11671008, 1.10880034
, 1.08401709, 1.06959206, 1.07875225],
[ 1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, 0.99854316
, 0.9880258, 0.99669587, 0.99327676, 1.01400905, 1.03176742, 1.040511
, 1.01749645, 0.9936394, 0.98279746],
[ 0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845, 0.99127006
, 0.97925917, 0.9683482, 0.95335147, 0.93694787, 0.94308213, 0.92232874
, 0.91284091, 0.89689833, 0.88928858],
[ 0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044, 0.8578708
, 0.86036185, 0.86107306, 0.8500772, 0.86981998, 0.86837929, 0.87204141
, 0.86633032, 0.84946077, 0.83287146],
[ 1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, 1.14450183
, 1.12349752, 1.12596664, 1.12213996, 1.1119989, 1.10257792, 1.10491258
, 1.11059842, 1.10509795, 1.10020097],
[ 0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687, 0.95831051
, 0.94480909, 0.94804195, 0.95430286, 0.94103989, 0.92122519, 0.91010201
, 0.89280392, 0.89298243, 0.89165385],
[ 0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, 0.9480927
, 0.93539182, 0.95388718, 0.94597005, 0.96918424, 0.94781281, 0.93466815
, 0.94281559, 0.96520315, 0.96715441],
[ 0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, 0.98687073
, 0.99237486, 0.98209969, 0.9877653, 0.97399471, 0.96910087, 0.98416665
, 0.98423613, 0.99823861, 0.99545704],
[ 0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012, 0.86191535
, 0.84981451, 0.85472102, 0.84564835, 0.83998883, 0.83478547, 0.82803648
, 0.8198736, 0.82265395, 0.8399404 ],
[ 0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, 0.82785597
, 0.86008789, 0.86776298, 0.86720209, 0.8676334, 0.89179317, 0.94202108
, 0.9422231, 0.93902708, 0.94479184],
[ 0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, 0.90906632
, 0.92693339, 0.93695966, 0.94242697, 0.94338265, 0.91981796, 0.91108804
, 0.90543476, 0.91737138, 0.94793657],
[ 1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723, 1.20172869
, 1.21328691, 1.22624778, 1.22397075, 1.23857042, 1.24419893, 1.23929384
, 1.23418676, 1.23626739, 1.26754398],
[ 1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, 1.34790023
, 1.34399863, 1.32575181, 1.30795492, 1.30544841, 1.30303302, 1.32107766
, 1.32936244, 1.33001241, 1.33288462],
[ 1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, 1.05059016
, 1.03405057, 1.02747623, 1.03162734, 0.9961416, 0.97356208, 0.94241549
, 0.92754547, 0.92549227, 0.92138102],
[ 1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, 1.13889622
, 1.12442212, 1.13367018, 1.13982256, 1.14029944, 1.11979401, 1.10905389
, 1.10577769, 1.11166825, 1.09985155],
[ 0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, 0.74480073
, 0.76098396, 0.76156903, 0.76651952, 0.76533288, 0.78205934, 0.76842416
, 0.77487118, 0.77768683, 0.78801192],
[ 0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, 0.97370819
, 0.96419154, 0.97209861, 0.97441313, 0.96356162, 0.94745352, 0.93965462
, 0.93069645, 0.94020973, 0.94358232],
[ 0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, 0.80071489
, 0.83358256, 0.83451613, 0.85175032, 0.85954307, 0.86790024, 0.87170334
, 0.87863799, 0.87497981, 0.87888675],
[ 0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, 0.98733195
, 0.99644997, 0.99669587, 1.02559097, 1.01116651, 0.99988024, 0.97906749
, 0.99323123, 1.00204939, 0.99602148],
[ 1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, 1.08312397
, 1.05192626, 1.04230892, 1.05577278, 1.08569751, 1.12443486, 1.08891079
, 1.08603695, 1.05997314, 1.02160943],
[ 1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272, 1.18257029
, 1.16226243, 1.16009196, 1.14467789, 1.14820235, 1.12386598, 1.12680236
, 1.12357937, 1.1159258, 1.12570828],
[ 1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, 1.31210239
, 1.29989156, 1.29203193, 1.27183516, 1.26830786, 1.2617743, 1.28656675
, 1.29734097, 1.29390205, 1.29345446],
[ 0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, 0.78772975
, 0.82848011, 0.8259679, 0.82435705, 0.83108634, 0.84373784, 0.83891093
, 0.84349247, 0.85637272, 0.86539395],
[ 1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626, 1.2256767
, 1.21126648, 1.19377804, 1.18355337, 1.19674434, 1.21536573, 1.23653297
, 1.27962009, 1.27968392, 1.25907738],
[ 0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282, 0.96480308
, 0.94686376, 0.93679073, 0.92540049, 0.92988835, 0.93442917, 0.92100464
, 0.91475304, 0.90249622, 0.9021363 ],
[ 0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368, 0.88937573
, 0.894401, 0.90448993, 0.95495898, 0.92698333, 0.94745352, 0.92562488
, 0.96635366, 1.02520312, 1.0394296 ],
[ 1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, 1.00759019
, 0.99192968, 0.99747298, 0.99550759, 0.97583768, 0.9610168, 0.94779638
, 0.93759089, 0.93353431, 0.94121705],
[ 0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, 0.83434854
, 0.85813595, 0.84667961, 0.84374558, 0.85951183, 0.87194227, 0.89455097
, 0.88283929, 0.90349491, 0.90600675],
[ 1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, 1.00581626
, 0.98850522, 0.99291168, 0.98983209, 0.97511924, 0.96134615, 0.96382634
, 0.95011401, 0.9434686, 0.94637765],
[ 1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, 1.04800023
, 1.03024941, 1.04200483, 1.0402554, 1.03296979, 1.02191682, 1.02476275
, 1.02347523, 1.02517684, 1.04359571],
[ 1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043, 1.0531801
, 1.07452771, 1.09383478, 1.1052447, 1.10322136, 1.09167939, 1.08772756
, 1.08859544, 1.09177338, 1.1096083 ],
[ 0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, 0.86287327
, 0.85169796, 0.85411285, 0.84886336, 0.84517414, 0.84843858, 0.84488343
, 0.83374329, 0.82812044, 0.82878599],
[ 0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, 0.92652175
, 0.94278865, 0.93682452, 0.98655146, 0.992237, 0.9798497, 0.93869677
, 0.96947771, 1.00362626, 0.98102351],
[ 0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967, 0.93092109
, 0.92662519, 0.93412152, 0.93501274, 0.92879506, 0.92110542, 0.91035556
, 0.90430364, 0.89994694, 0.90073864],
[ 0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, 0.98882205
, 0.97662234, 0.95601578, 0.94905385, 0.94934888, 0.97152609, 0.97163004
, 0.9700702, 0.97158948, 0.95884908],
[ 0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751, 0.84818516
, 0.85265681, 0.84502402, 0.82645665, 0.81743586, 0.83550406, 0.83338919
, 0.83511679, 0.82136617, 0.80921874],
[ 0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441, 0.95440787
, 0.96364363, 0.96804412, 0.97136214, 0.97583768, 0.95571724, 0.96895368
, 0.97001634, 0.97082733, 0.98782366],
[ 1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, 1.10558188
, 1.1214086, 1.12292577, 1.13021031, 1.13342735, 1.14686068, 1.14502975
, 1.14474747, 1.14084037, 1.16142926],
[ 1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, 1.11856702
, 1.09764283, 1.08815849, 1.08044313, 1.09278827, 1.07003204, 1.08398066
, 1.09831768, 1.09298232, 1.09176125],
[ 0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, 0.77751194
, 0.79902974, 0.81437881, 0.80788828, 0.79603865, 0.78966436, 0.79949807
, 0.80172182, 0.82168155, 0.85587911],
[ 1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561, 1.00162979
, 0.99860739, 1.00814981, 1.00574316, 0.99030032, 0.97682565, 0.97292596
, 0.96519561, 0.96173403, 0.95890284],
[ 0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289, 0.96608031
, 0.99727185, 1.00781194, 1.03484236, 1.05333619, 1.0983263, 1.1704974
, 1.17025154, 1.18730553, 1.14242645]])
self.assertTrue(np.allclose(result, expected))
self.assertTrue(type(result) == type(expected))
self.assertTrue(result.shape == expected.shape)
def test_rebin_data(self):
"""Test rebin_data"""
## sample in double the time (even case since 10 % 2 = 0):
## (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
## = 0.5, 2.5, 4.5, 6.5, 8.5
ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float)
for i in range(0, 10, 2)]).T
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
## sample in triple the time (uneven since 10 % 3 = 1):
## (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
## = 1, 4, 7, 9
ans_odd = np.array([i * np.ones(10, dtype=float)
for i in (1, 4, 7, 9)]).T
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
def test_get_prob_dist(self):
"""Test get_prob_dist"""
lag_indices = np.array([1, 2, 3, 4])
unit_indices = np.array([1, 3, 2, 4])
answer = np.array([
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
])
result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices)
self.assertTrue(np.array_equal(result, answer))
def test_get_prob_stats(self):
"""Test get_prob_stats"""
probs = np.array([
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
])
unit_indices = np.array([1, 3, 2, 4])
answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824, 0.02475248 / 0.77722772, -0.02352941 / 0.97647059])
answer_volatility = np.array([ 0.34221495, 0.33705421, 0.29226542, 0.38834223])
result = std.get_prob_stats(probs, unit_indices)
result_up = result[0]
result_down = result[1]
result_trend = result[2]
result_volatility = result[3]
self.assertTrue(np.allclose(result_up, answer_up))
self.assertTrue(np.allclose(result_down, answer_down))
self.assertTrue(np.allclose(result_trend, answer_trend))
self.assertTrue(np.allclose(result_volatility, answer_volatility))

6
release/python/0.5.0/crankshaft/crankshaft/__init__.py Normal file
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@ -0,0 +1,6 @@
"""Import all modules"""
import crankshaft.random_seeds
import crankshaft.clustering
import crankshaft.space_time_dynamics
import crankshaft.segmentation
import analysis_data_provider

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