h2oai/ pystacknet
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About
pystacknetis a light python version of StackNet which was originally made in Java.
It supports many of the original features, with some new elements.
Installation
git clone https://github.com/h2oai/pystacknet cd pystacknet python setup.py install
New features
pystacknet's main object is a 2-dimensional list of sklearn type of models. This list defines the StackNet structure. This is the equivalent of parameters in the Java version. A representative example could be:
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier from sklearn.linear_model import LogisticRegressionmodels=[ ######## First level ######## [RandomForestClassifier (n_estimators=100, criterion="entropy", max_depth=5, max_features=0.5, random_state=1), ExtraTreesClassifier (n_estimators=100, criterion="entropy", max_depth=5, max_features=0.5, random_state=1), GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=5, max_features=0.5, random_state=1), LogisticRegression(random_state=1) ], ######## Second level ######## [RandomForestClassifier (n_estimators=200, criterion="entropy", max_depth=5, max_features=0.5, random_state=1)] ]
pystacknetis not as strict as in the
Javaversion and can allow
Regressors,
Classifiersor even
Transformersat any level of StackNet. In other words the following could work just fine:
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, ExtraTreesClassifier, ExtraTreesRegressor, GradientBoostingClassifier,GradientBoostingRegressor
from sklearn.linear_model import LogisticRegression, Ridge
from sklearn.decomposition import PCA
models=[
[RandomForestClassifier (n_estimators=100, criterion="entropy", max_depth=5, max_features=0.5, random_state=1),
ExtraTreesRegressor (n_estimators=100, max_depth=5, max_features=0.5, random_state=1),
GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=5, max_features=0.5, random_state=1),
LogisticRegression(random_state=1),
PCA(n_components=4,random_state=1)
],
[RandomForestClassifier (n_estimators=200, criterion="entropy", max_depth=5, max_features=0.5, random_state=1)]
]
Note that not all transformers are meaningful in this context and you should use it at your own risk.
Parameters
A typical usage for classification could be :
from pystacknet.pystacknet import StackNetClassifiermodel=StackNetClassifier(models, metric="auc", folds=4, restacking=False,use_retraining=True, use_proba=True, random_state=12345,n_jobs=1, verbose=1)
model.fit(x,y) preds=model.predict_proba(x_test)
Where :
Command |
Explanation |
|---|---|
| models | List of models. This should be a 2-dimensional list . The first level hould defice the stacking level and each entry is the model. |
| metric | Can be "auc","logloss","accuracy","f1","matthews" or your own custom metric as long as it implements (ytrue,ypred,sampleweight=) |
| folds | This can be either integer to define the number of folds used in StackNetor an iterable yielding train/test splits. |
| restacking | True for restacking else False |
| useproba | When evaluating the metric, it will use probabilities instead of class predictions if use_proba==True |
| useretraining | If Trueit does one model based on the whole training data in order to score the test data. Otherwise it takes the average of all models used in the folds ( however this takes more memory and there is no guarantee that it will work better.) |
| randomstate | Integer for randomised procedures |
| njobs | Number of models to run in parallel. This is independent of any extra threads allocated |
| njobs | Number of models to run in parallel. This is independent of any extra threads allocated from the selected algorithms. e.g. it is possible to run 4 models in parallel where one is a randomforest that runs on 10 threads (it selected). |
| verbose | Integer value higher than zero to allow printing at the console. |