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small fix to the doc

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This commit is contained in:
tqchen 2014-09-04 09:18:52 -07:00
parent da9c856701
commit 6c6d00261c
2 changed files with 22 additions and 13 deletions
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3
demo/guide-python/runall.sh
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@ -2,4 +2,5 @@
python basic_walkthrough.py
python custom_objective.py
python boost_from_prediction.py
rm *~ *.model *.buffer
python cross_validation.py
rm -rf *~ *.model *.buffer

30
wrapper/xgboost.py
View File

@ -296,6 +296,7 @@ class Booster:
evals: list of tuple (DMatrix, string)
lists of items to be evaluated
it: int
current iteration
feval: function
custom evaluation function
Returns:
@ -326,7 +327,8 @@ class Booster:
output_margin: bool
whether output raw margin value that is untransformed
ntree_limit: limit number of trees in prediction, default to 0, 0 means using all the trees
ntree_limit: int
limit number of trees in prediction, default to 0, 0 means using all the trees
Returns:
numpy array of prediction
"""
@ -408,12 +410,13 @@ def train(params, dtrain, num_boost_round = 10, evals = [], obj=None, feval=None
data to be trained
num_boost_round: int
num of round to be boosted
evals: list
list of items to be evaluated
watchlist: list of pairs (DMatrix, string)
list of items to be evaluated during training, this allows user to watch performance on validation set
obj: function
cutomized objective function
feval: function
cutomized evaluation function
Returns: Booster model trained
"""
bst = Booster(params, [dtrain]+[ d[0] for d in evals ] )
for i in range(num_boost_round):
@ -487,15 +490,20 @@ def cv(params, dtrain, num_boost_round = 10, nfold=3, metrics=[], \
num_boost_round: int
num of round to be boosted
nfold: int
folds to do cv
evals: list or
list of items to be evaluated
obj: custom objective function
feval: custom evaluation function
fpreproc: preprocessing function that takes dtrain, dtest,
number of folds to do cv
metrics: list of strings
evaluation metrics to be watched in cv
obj: function
custom objective function
feval: function
custom evaluation function
fpreproc: function
preprocessing function that takes dtrain, dtest,
param and return transformed version of dtrain, dtest, param
show_stdv: whether display standard deviation
seed: seed used to generate the folds
show_stdv: bool
whether display standard deviation
seed: int
seed used to generate the folds, this is passed to numpy.random.seed
Returns: list(string) of evaluation history
"""