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catching errors

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This commit is contained in:
Andy Eschbacher 2016-06-28 10:06:54 -04:00
parent 99dc363c7d
commit 2bb2e60af8
1 changed files with 27 additions and 14 deletions
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41
src/py/crankshaft/crankshaft/segmentation/segmentation.py
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@ -48,11 +48,12 @@ def get_data(variable, feature_columns, query):
columns=columns, columns=columns,
query=query)) query=query))
except Exception, e: except Exception, e:
plpy.error('failed to fetch data to construct model') plpy.error('Failed to access data to build segmentation model: %s' % e)
# extract target data from plpy object
target = np.array(data[0]['target']) target = np.array(data[0]['target'])
# put arrays into an n x m array of arrays # 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]) 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) return replace_nan_with_mean(target), replace_nan_with_mean(features)
@ -69,7 +70,7 @@ def create_and_predict_segment_agg(target, features, target_features, target_ids
clean_features = replace_nan_with_mean(features) clean_features = replace_nan_with_mean(features)
target_features = replace_nan_with_mean(target_features) target_features = replace_nan_with_mean(target_features)
model, accuracy = train_model(clean_target,clean_features, model_parameters, 0.2) model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2)
prediction = model.predict(target_features) prediction = model.predict(target_features)
return zip(target_ids, prediction, np.full(prediction.shape, accuracy)) return zip(target_ids, prediction, np.full(prediction.shape, accuracy))
@ -81,14 +82,21 @@ def create_and_predict_segment(query, variable, target_query, model_params):
Stuart Lynn Stuart Lynn
""" """
columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1 '.format(query=query))[0].keys() ## 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']) feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator'])
target,features = get_data(variable, feature_columns, query)
model, accuracy = train_model(target,features, model_params, 0.2) ## get data from database
cartodb_ids, result = predict_segment(model,feature_columns,target_query) target, features = get_data(variable, feature_columns, query)
return zip(cartodb_ids, result, np.full(result.shape, accuracy ))
model, accuracy = train_model(target, features, model_params, 0.2)
cartodb_ids, result = predict_segment(model, feature_columns, target_query)
return zip(cartodb_ids, result, np.full(result.shape, accuracy))
def train_model(target, features, model_params, test_split): def train_model(target, features, model_params, test_split):
@ -98,7 +106,7 @@ def train_model(target, features, model_params, test_split):
features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split) features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
model = GradientBoostingRegressor(**model_params) model = GradientBoostingRegressor(**model_params)
model.fit(features_train, target_train) model.fit(features_train, target_train)
accuracy = calculate_model_accuracy(model,features,target) accuracy = calculate_model_accuracy(model, features, target)
return model, accuracy return model, accuracy
def calculate_model_accuracy(model, features, target): def calculate_model_accuracy(model, features, target):
@ -125,9 +133,12 @@ def predict_segment(model, features, target_query):
batch_size = 1000 batch_size = 1000
joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features]) joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features])
cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format( try:
joined_features=joined_features, cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format(
target_query= target_query)) joined_features=joined_features,
target_query=target_query))
except Exception, e:
plpy.error('Failed to build segmentation model: %s' % e)
results = [] results = []
@ -142,7 +153,9 @@ def predict_segment(model, features, target_query):
prediction = model.predict(batch) prediction = model.predict(batch)
results.append(prediction) 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'] 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) return cartodb_ids, np.concatenate(results)