python - scikit learn logistic regression precision calculation weird warning -

using scikit-learn python 2.7 on windows. here code , code has no warning if change precision precision_weighted scoring parameter. not know warning mean , reason behind scene explicitly specify average 1 of (none, 'micro', 'macro', 'weighted', 'samples')? in case, want treat samples of equal weight, seems there no such option in 5 choices?

from sklearn import linear_model, datasets sklearn.cross_validation import cross_val_score  # import data play iris = datasets.load_iris() x = iris.data[:, :2]  # take first 2 features. y = iris.target  h = .02  # step size in mesh  logreg = linear_model.logisticregression(c=1e5)  # create instance of neighbours classifier , fit data. logreg.fit(x, y)  print cross_val_score(logreg, x, y, cv=10, scoring="precision") #print cross_val_score(logreg, x, y, cv=10, scoring="precision_weighted") 

warning message,

c:\python27\lib\site-packages\sklearn\metrics\classification.py:1203: deprecationwarning: default `weighted` averaging deprecated, , version 0.18, use of precision, recall or f-score multiclass or multilabel data or pos_label=none result in exception. please set explicit value `average`, 1 of (none, 'micro', 'macro', 'weighted', 'samples'). in cross validation use, instance, scoring="f1_weighted" instead of scoring="f1".   sample_weight=sample_weight) c:\python27\lib\site-packages\sklearn\metrics\classification.py:1203: deprecationwarning: default `weighted` averaging deprecated, , version 0.18, use of precision, recall or f-score multiclass or multilabel data or pos_label=none result in exception. please set explicit value `average`, 1 of (none, 'micro', 'macro', 'weighted', 'samples'). in cross validation use, instance, scoring="f1_weighted" instead of scoring="f1".   sample_weight=sample_weight) c:\python27\lib\site-packages\sklearn\metrics\classification.py:1203: deprecationwarning: default `weighted` averaging deprecated, , version 0.18, use of precision, recall or f-score multiclass or multilabel data or pos_label=none result in exception. please set explicit value `average`, 1 of (none, 'micro', 'macro', 'weighted', 'samples'). in cross validation use, instance, scoring="f1_weighted" instead of scoring="f1".   sample_weight=sample_weight) c:\python27\lib\site-packages\sklearn\metrics\classification.py:1203: deprecationwarning: default `weighted` averaging deprecated, , version 0.18, use of precision, recall or f-score multiclass or multilabel data or pos_label=none result in exception. please set explicit value `average`, 1 of (none, 'micro', 'macro', 'weighted', 'samples'). in cross validation use, instance, scoring="f1_weighted" instead of scoring="f1".   sample_weight=sample_weight) c:\python27\lib\site-packages\sklearn\metrics\classification.py:1203: deprecationwarning: default `weighted` averaging deprecated, , version 0.18, use of precision, recall or f-score multiclass or multilabel data or pos_label=none result in exception. please set explicit value `average`, 1 of (none, 'micro', 'macro', 'weighted', 'samples'). in cross validation use, instance, scoring="f1_weighted" instead of scoring="f1".   sample_weight=sample_weight) c:\python27\lib\site-packages\sklearn\metrics\classification.py:1203: deprecationwarning: default `weighted` averaging deprecated, , version 0.18, use of precision, recall or f-score multiclass or multilabel data or pos_label=none result in exception. please set explicit value `average`, 1 of (none, 'micro', 'macro', 'weighted', 'samples'). in cross validation use, instance, scoring="f1_weighted" instead of scoring="f1".   sample_weight=sample_weight) c:\python27\lib\site-packages\sklearn\metrics\classification.py:1203: deprecationwarning: default `weighted` averaging deprecated, , version 0.18, use of precision, recall or f-score multiclass or multilabel data or pos_label=none result in exception. please set explicit value `average`, 1 of (none, 'micro', 'macro', 'weighted', 'samples'). in cross validation use, instance, scoring="f1_weighted" instead of scoring="f1".   sample_weight=sample_weight) c:\python27\lib\site-packages\sklearn\metrics\classification.py:1203: deprecationwarning: default `weighted` averaging deprecated, , version 0.18, use of precision, recall or f-score multiclass or multilabel data or pos_label=none result in exception. please set explicit value `average`, 1 of (none, 'micro', 'macro', 'weighted', 'samples'). in cross validation use, instance, scoring="f1_weighted" instead of scoring="f1".   sample_weight=sample_weight) c:\python27\lib\site-packages\sklearn\metrics\classification.py:1203: deprecationwarning: default `weighted` averaging deprecated, , version 0.18, use of precision, recall or f-score multiclass or multilabel data or pos_label=none result in exception. please set explicit value `average`, 1 of (none, 'micro', 'macro', 'weighted', 'samples'). in cross validation use, instance, scoring="f1_weighted" instead of scoring="f1".   sample_weight=sample_weight) c:\python27\lib\site-packages\sklearn\metrics\classification.py:1203: deprecationwarning: default `weighted` averaging deprecated, , version 0.18, use of precision, recall or f-score multiclass or multilabel data or pos_label=none result in exception. please set explicit value `average`, 1 of (none, 'micro', 'macro', 'weighted', 'samples'). in cross validation use, instance, scoring="f1_weighted" instead of scoring="f1".   sample_weight=sample_weight) [ 0.66666667  0.80555556  0.9047619   0.86666667  0.80555556  0.875   0.94444444  0.80555556  0.82222222  0.80555556]