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make private functions more explictly private

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This commit is contained in:
Andy Eschbacher 2016-10-13 12:50:46 -04:00
parent a95423174c
commit b255fd3e06
1 changed files with 11 additions and 7 deletions
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18
src/py/crankshaft/crankshaft/clustering/kmeans.py
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@ -60,23 +60,27 @@ def kmeans_nonspatial(query, colnames, num_clusters=5,
# fill array with values for kmeans clustering
if standarize:
cluster_columns = scale_data(
extract_columns(db_resp, id_col='cartodb_id'))
cluster_columns = _scale_data(
_extract_columns(db_resp, id_col='cartodb_id'))
else:
cluster_columns = extract_columns(db_resp)
cluster_columns = _extract_columns(db_resp)
# TODO: decide on optimal parameters for most cases
# Are there ways of deciding parameters based on inputs?
kmeans = KMeans(n_clusters=num_clusters,
random_state=0).fit(cluster_columns)
return zip(kmeans.labels_, map(str, kmeans.cluster_centers_),
return zip(kmeans.predict(X),
map(str, kmeans.cluster_centers_[kmeans.labels_]),
db_resp[0][out_id_colname])
def extract_columns(db_resp, id_col):
def _extract_columns(db_resp, id_col):
"""
Extract the features from the query and pack them into a NumPy array
db_resp (plpy data object): result of the kmeans request
id_col (string): name of column which has the row id (not a feature of
the analysis)
"""
return np.array([db_resp[0][c] for c in db_resp.colnames()
if c != id_col],
@ -85,10 +89,10 @@ def extract_columns(db_resp, id_col):
# -- Preprocessing steps
def scale_data(features):
def _scale_data(features):
"""
Scale all input columns to center on 0 with a standard devation of 1
input_data (numpy array): an array of dimension (n_features, n_samples)
features (numpy array): an array of dimension (n_features, n_samples)
"""
from sklearn.preprocessing import StandardScaler
return StandardScaler().fit_transform(features)