cartodb/crankshaft
1
0
Fork 0
mirror of https://github.com/CartoDB/crankshaft.git synced 2024-11-01 10:20:48 +08:00
Code Issues Packages Projects Releases Wiki Activity

changing form of function to use query

Browse Source
This commit is contained in:
Stuart Lynn 2016-03-16 15:46:59 -04:00
parent f134a54c24
commit d3e1fca2b3
2 changed files with 19 additions and 13 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
pg/sql/0.0.1/05_segmentation.sql
View File

@ -30,8 +30,7 @@ $$ LANGUAGE plpythonu;
CREATE OR REPLACE FUNCTION
cdb_create_and_predict_segment (
segment_name TEXT,
table_name TEXT,
column_name TEXT,
query TEXT,
target_table TEXT,
geoid_column TEXT DEFAULT 'geoid',
census_table TEXT DEFAULT 'block_groups'
@ -40,5 +39,5 @@ RETURNS TABLE (the_geom geometry, geoid text, prediction Numeric )
AS $$
from crankshaft import segmentation
# TODO: use named parameters or a dictionary
return segmentation.create_and_predict_segment(segment_name,table_name,column_name,geoid_column,census_table,target_table,'random_forest')
return segmentation.create_and_predict_segment(segment_name,query,geoid_column,census_table,target_table,'random_forest')
$$ LANGUAGE plpythonu;

27
python/crankshaft/crankshaft/segmentation/segmentation.py
View File

@ -30,14 +30,17 @@ def create_segment(segment_name,table_name,column_name,geoid_column,census_table
# predict_segment
return accuracy
def create_and_predict_segment(segment_name,table_name,column_name,geoid_column,census_table,target_table,method):
def create_and_predict_segment(segment_name,query,geoid_column,census_table,target_table,method):
"""
generate a segment with machine learning
Stuart Lynn
"""
data = pd.DataFrame(join_with_census(table_name, column_name,geoid_column, census_table))
features = data[data.columns.difference([column_name, 'the_geom_webmercator', 'geoid','the_geom'])]
target, mean, std = normalize(data[column_name])
data = pd.DataFrame(join_with_census(query,geoid_column, census_table))
features = data[data.columns.difference(['target', 'the_geom_webmercator', 'geoid','the_geom'])]
target, mean, std = normalize(data['target'])
normed_target,target_mean, target_std = normalize(target)
model, accuracy, used_features = train_model(target,features, test_split=0.2)
# save_model(segment_name, model, accuracy, table_name, column_name, census_table, geoid_column, method)
result = predict_segment(model,used_features,geoid_column,target_table)
@ -71,17 +74,20 @@ def calculate_model_accuracy(model,features,target):
prediction = model.predict(features)
return metrics.mean_squared_error(prediction,target)/np.std(target)
def join_with_census(table_name, column_name, geoid_column, census_table):
columns = plpy.execute('select * from {census_table} limit 1 '.format(**locals()))
combined_columns = [ a for a in columns[0].keys() if a not in ['the_geom','cartodb_id','geoid','the_geom_webmercator']]
def join_with_census(query, geoid_column, census_table):
columns = plpy.execute('select * from {census_table} limit 1 '.format(**locals()))
combined_columns = [ a for a in columns[0].keys() if a not in ['target','the_geom','cartodb_id','geoid','the_geom_webmercator']]
plpy.notice(combined_columns)
feature_names = ",".join([ " {census_table}.\"{a}\"::Numeric as \"{a}\" ".format(**locals()) for a in combined_columns])
plpy.notice(feature_names)
plpy.notice('joining with census data')
join_data = plpy.execute('''
SELECT {feature_names}, {table_name}.{column_name}
FROM {table_name}
SELECT {feature_names}, a.target
FROM ({query}) a
JOIN {census_table}
ON {table_name}.{geoid_column}::numeric = {census_table}.geoid::numeric
ON a.{geoid_column}::numeric = {census_table}.geoid::numeric
'''.format(**locals()))
if len(join_data) == 0:
@ -113,6 +119,7 @@ def predict_segment(model,features,geoid_column,census_table):
plpy.notice('predicting: predicting data')
prediction = model.predict(targets)
de_norm_prediciton = []
plpy.notice('predicting: predicted')
return zip( [a['the_geom'] for a in geoms], [a['geoid'] for a in geo_ids],prediction)