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adding one shot train and predict function

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Stuart Lynn
2016-03-15 12:44:33 -04:00
parent 803781e08d
commit f134a54c24
2 changed files with 11 additions and 10 deletions
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14
python/crankshaft/crankshaft/segmentation/segmentation.py
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@@ -23,24 +23,24 @@ def create_segment(segment_name,table_name,column_name,geoid_column,census_table
Stuart Lynn
"""
data = pd.DataFrame(join_with_census(table_name, column_name,geoid_column, census_table))
features = data[data.columns.difference([column_name, 'geoid','the_geom'])]
features = data[data.columns.difference([column_name, 'geoid','the_geom', 'the_geom_webmercator'])]
target, mean, std = normalize(data[column_name])
model, accuracy = train_model(target,features, test_split=0.2)
save_model(segment_name, model, accuracy, table_name, column_name, census_table, geoid_column, method)
# predict_segment
return accuracy
def create_and_predict_segment(segment_name,table_name,column_name,geoid_column,census_table,method):
def create_and_predict_segment(segment_name,table_name,column_name,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, 'geoid','the_geom'])]
features = data[data.columns.difference([column_name, 'the_geom_webmercator', 'geoid','the_geom'])]
target, mean, std = normalize(data[column_name])
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,census_table)
result = predict_segment(model,used_features,geoid_column,target_table)
return result
@@ -73,8 +73,8 @@ def calculate_model_accuracy(model,features,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']]
feature_names = ",".join([ " {census_table}.\"{a}\" as \"{a}\" ".format(**locals()) for a in combined_columns])
combined_columns = [ a for a in columns[0].keys() if a not in ['the_geom','cartodb_id','geoid','the_geom_webmercator']]
feature_names = ",".join([ " {census_table}.\"{a}\"::Numeric as \"{a}\" ".format(**locals()) for a in combined_columns])
plpy.notice('joining with census data')
join_data = plpy.execute('''
@@ -101,7 +101,7 @@ def predict_segment(model,features,geoid_column,census_table):
# model = data['model']
# features = ",".join(features)
joined_features = ','.join(['\"'+a+'\"' for a in features])
joined_features = ','.join(['\"'+a+'\"::numeric' for a in features])
targets = pd.DataFrame(query_to_dictionary(plpy.execute('select {joined_features} from {census_table}'.format(**locals()))))
plpy.notice('predicting:' + str(len(features)) + ' '+str(np.shape(targets)))
plpy.notice(joined_features)