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1 change: 1 addition & 0 deletions .gitignore
Original file line number Diff line number Diff line change
Expand Up @@ -155,3 +155,4 @@ docs/_build

# License copied to conda build_dir
pkg/conda/LICENSE
env/*
18 changes: 15 additions & 3 deletions gramex/handlers/mlhandler.py
Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@
from tornado.gen import coroutine
from tornado.web import HTTPError
from sklearn.metrics import get_scorer
from sklearn.model_selection import cross_val_predict, cross_val_score

op = os.path
MLCLASS_MODULES = [
Expand All @@ -40,7 +41,8 @@
'pipeline': True,
'nums': [],
'cats': [],
'target_col': None
'target_col': None,
'cv': True,
}
ACTIONS = ['predict', 'score', 'append', 'train', 'retrain']
DEFAULT_TEMPLATE = op.join(op.dirname(__file__), '..', 'apps', 'mlhandler', 'template.html')
Expand Down Expand Up @@ -112,14 +114,23 @@ def setup(cls, data=None, model={}, config_dir='', **kwargs):
data = cls._filtercols(data)
data = cls._filterrows(data)
cls.model = cls._assemble_pipeline(data, mclass=mclass, params=params)

# train the model
target = data[target_col]
train = data[[c for c in data if c != target_col]]
# cross validation
cls.cross_validation(train,target)
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Not required here.

gramex.service.threadpool.submit(
_fit, cls.model, train, target, cls.model_path, cls.name)
cls.config_store.flush()


@classmethod
def cross_validation(cls,train,target):
cv = cls.get_opt('cv',True)
if cv:
CVscore = cross_val_score(cls.model.steps[-1][1], X=train, y=target, cv=cv)
CVavg = sum(CVscore)/len(CVscore)
print('Cross Validation Score : ',CVavg)
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CV should take place within the train method only.

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if cv:
    cvscore = cross_val_score(mod, X=train, y=target, cv=cv)
else:
   # Do the usual .fit


@classmethod
def load_data(cls, default=pd.DataFrame()):
try:
Expand Down Expand Up @@ -351,6 +362,7 @@ def _train(self, data=None):
target = data[target_col]
train = data[[c for c in data if c != target_col]]
self.model = self._assemble_pipeline(data, force=True)
self.cross_validation(train,target)
_fit(self.model, train, target, self.model_path)
return {'score': self.model.score(train, target)}

Expand Down