def calibration():
steps = []
for n_estimators, k_folds in product(range(50,300,100), [2,5]):
d = data.ClassificationData(target=True, n_samples=1000, n_features=100)
est = step.Construct('sklearn.ensemble.RandomForestClassifier',
n_estimators=n_estimators, name='estimator')
fit = model.Fit(inputs=[est, d], return_estimator=True, target=True, name='uncalibrated')
predict = model.Predict(inputs=[fit,d], target=True, name='y')
cal = step.Construct('sklearn.calibration.CalibratedClassifierCV', cv=k_folds,
inputs=[predict], inputs_mapping={'y':None}, name='calibrator')
cal_est = model.FitPredict(inputs=[cal, d], target=True, name='calibrated')
metrics = model.PrintMetrics([
{'metric':'baseline'},
{'metric':'precision', 'k':100},
{'metric':'precision', 'k':200},
{'metric':'precision', 'k':300},
], inputs=[cal_est])
steps.append(metrics)
return steps
def forest():
return [
step.Construct('sklearn.ensemble.RandomForestClassifier',
n_estimators=500,
n_jobs=-1,
criterion='entropy',
balanced=True,
max_features='sqrt')
]
def product_model():
d = data.ClassificationData(target=True, n_samples=1000, n_features=100)
est = step.Construct('sklearn.ensemble.RandomForestClassifier',
n_estimators=10, name='estimator')
m1 = model.FitPredict(inputs=[est, d], target=True, name='m1')
m2 = model.FitPredict(inputs=[est, d], target=True, name='m2')
p = model.PredictProduct(inputs=[m1,m2], target=True, inputs_mapping=['m1', 'm2'], name='p')
return p
def n_estimators_search():
d = data.ClassificationData(n_samples=1000, n_features=100)
d.target = True
predict = []
for n_estimators in range(1, 4):
e = step.Construct(_class='sklearn.ensemble.RandomForestClassifier',
n_estimators=n_estimators)
f = model.Fit(inputs=[e, d], return_estimator=True, return_feature_importances=True)
p = model.Predict(inputs=[f, d])
p.target = True
predict.append(p)
return predict
def forest(**update_kwargs):
"""
Returns a step constructing a scikit-learn RandomForestClassifier
"""
kwargs = dict(_class='sklearn.ensemble.RandomForestClassifier',
n_estimators=1000,
n_jobs=-1,
criterion='entropy',
class_weight='balanced_bootstrap',
max_features='sqrt',
random_state=0)
kwargs.update(**update_kwargs)
return [step.Construct(**kwargs)]
def prediction():
# generate the data including a training and test split
d = data.ClassificationData(n_samples=1000, n_features=100)
d.target = True
# construct a random forest estimator
e = step.Construct(_class='sklearn.ensemble.RandomForestClassifier', n_estimators=1)
e.target = False
# fit the estimator
f = model.Fit(inputs=[e, d], return_estimator=True, return_feature_importances=True)
# make predictions
p = model.Predict(inputs=[f, d])
p.target = True
return p
def product_model():
d = data.ClassificationData(n_samples=1000, n_features=100)
d.target = True
est = step.Construct(_class='sklearn.ensemble.RandomForestClassifier',
n_estimators=10)
est.name = 'estimator'
m1 = model.FitPredict(inputs=[est, d])
m1.target = True
m1.name = 'm1'
m2 = model.FitPredict(inputs=[est, d])
m2.target = True
m2.name = 'm2'
p = model.PredictProduct(inputs=[m1,m2], inputs_mapping=['m1', 'm2'])
p.target = True
p.name = 'p'
return p
