Hi, Piotr,
> These preprocessing steps have some parameters too, which I would like to
> tune.
> I know that it is possible to tune the parameters of the preprocessing steps,
> if they are part pf my pipeline.
> E.g. if I am using PCA, I could tune the parameter n_components, right?
>
> But what if I have some "custom" preprocessing code with some parameters?
> Is it possible to create a scikit-compatible "object" of my custom code in
> order to tune the
> parameters in the pipeline with grid search?
Yeah, you could use the Pipeline class or the `make_pipeline` function, then
you can create a custom estimator using the BaseEstimator class like so:
class CustomEstimator(BaseEstimator):
def __init__(self, my_param=None):
pass
def fit_transform(self, X, y=None):
return self.fit(X).transform(X)
def transform(self, X, y=None):
return X
def fit(self, X, y=None):
return self
pipe = make_pipeline(CustomEstimator(),
LogisticRegression())
grid = {'customestimator__my_param': [3],
'logisticregression__C': [0.1, 1.0, 10.0]}
gsearch1 = GridSearchCV(estimator=pipe, param_grid=grid)
gsearch1.fit(X, y)
Then, you can put in your desired preprocessing stuff into fit and transform.
Best,
Sebastian
> On Sep 7, 2016, at 2:03 PM, Piotr Bialecki <[email protected]> wrote:
>
> Hi all,
>
> I am currently tuning some parameters of my xgboost model using scikit's
> grid_search, e.g.:
>
> param_test1 = {'max_depth':range(3,10,2),
> 'min_child_weight':range(1,6,2)
> }
> gsearch1 = GridSearchCV(estimator = XGBClassifier(learning_rate =0.1,
> n_estimators=762,
>
> max_depth=5, min_child_weight=1, gamma=0,
>
> subsample=0.8, colsample_bytree=0.8,
>
> objective= 'binary:logistic', nthread=4,
>
> scale_pos_weight=1, seed=2809),
> param_grid = param_test1,
> scoring='roc_auc',
> n_jobs=6,
> iid=False, cv=5)
>
> Before that I preprocessed my dataset X with some different methods.
> These preprocessing steps have some parameters too, which I would like to
> tune.
> I know that it is possible to tune the parameters of the preprocessing steps,
> if they are part pf my pipeline.
> E.g. if I am using PCA, I could tune the parameter n_components, right?
>
> But what if I have some "custom" preprocessing code with some parameters?
> Is it possible to create a scikit-compatible "object" of my custom code in
> order to tune the
> parameters in the pipeline with grid search?
> Imagine I would like to write a custom method FeatureMultiplier() with a
> parameter multiplier_value.
> Is it possible to create a scikit-compatible class out of this method and
> tune it with grid search?
>
> I thought I saw a talk about exactly this topic at some PyData in 2016 or
> 2015,
> but unfortunately I cannot find the video of it.
> Maybe I misunderstood the presentation at that time.
>
>
> Best regards,
> Piotr
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