def check_classifiers(n_samples=10000, output_name_pattern=None):
"""
This function is not tested by default, it should be called manually
"""
testX, testY = generate_sample(n_samples, 10, 0.6)
trainX, trainY = generate_sample(n_samples, 10, 0.6)
uniform_variables = ['column0']
ada = AdaBoostClassifier(n_estimators=50)
ideal_bayes = HidingClassifier(train_variables=trainX.columns[1:],
base_estimator=GaussianNB())
uBoost_SAMME = uBoostClassifier(
uniform_variables=uniform_variables,
n_neighbors=50,
efficiency_steps=5,
n_estimators=50,
algorithm="SAMME")
uBoost_SAMME_R = uBoostClassifier(
uniform_variables=uniform_variables,
n_neighbors=50,
efficiency_steps=5,
n_estimators=50,
algorithm="SAMME.R")
clf_dict = ClassifiersDict({
"Ada": ada,
"Ideal": ideal_bayes,
"uBOOST": uBoost_SAMME,
"uBOOST.R": uBoost_SAMME_R
})
clf_dict.fit(trainX, trainY)
predictions = Predictions(clf_dict, testX, testY)
# predictions.print_mse(uniform_variables, in_html=False)
print(predictions.compute_metrics())
predictions.sde_curves(uniform_variables)
if output_name_pattern is not None:
pl.savefig(output_name_pattern % "mse_curves", bbox="tight")
_ = pl.figure()
predictions.learning_curves()
if output_name_pattern is not None:
pl.savefig(output_name_pattern % "learning_curves", bbox="tight")
predictions.efficiency(uniform_variables)
if output_name_pattern is not None:
pl.savefig(output_name_pattern % "efficiency_curves", bbox="tight")
def check_classifiers(n_samples=10000, output_name_pattern=None):
"""
This function is not tested by default, it should be called manually
"""
testX, testY = generate_sample(n_samples, 10, 0.6)
trainX, trainY = generate_sample(n_samples, 10, 0.6)
uniform_variables = ['column0']
ada = AdaBoostClassifier(n_estimators=50)
ideal_bayes = HidingClassifier(train_variables=trainX.columns[1:],
base_estimator=GaussianNB())
uBoost_SAMME = uBoostClassifier(uniform_variables=uniform_variables,
n_neighbors=50,
efficiency_steps=5,
n_estimators=50,
algorithm="SAMME")
uBoost_SAMME_R = uBoostClassifier(uniform_variables=uniform_variables,
n_neighbors=50,
efficiency_steps=5,
n_estimators=50,
algorithm="SAMME.R")
clf_dict = ClassifiersDict({
"Ada": ada,
"Ideal": ideal_bayes,
"uBOOST": uBoost_SAMME,
"uBOOST.R": uBoost_SAMME_R
})
clf_dict.fit(trainX, trainY)
predictions = Predictions(clf_dict, testX, testY)
# predictions.print_mse(uniform_variables, in_html=False)
print(predictions.compute_metrics())
predictions.sde_curves(uniform_variables)
if output_name_pattern is not None:
pl.savefig(output_name_pattern % "mse_curves", bbox="tight")
_ = pl.figure()
predictions.learning_curves()
if output_name_pattern is not None:
pl.savefig(output_name_pattern % "learning_curves", bbox="tight")
predictions.efficiency(uniform_variables)
if output_name_pattern is not None:
pl.savefig(output_name_pattern % "efficiency_curves", bbox="tight")
from sklearn.metrics import roc_auc_score
from hep_ml import reports
from hep_ml.reports import ClassifiersDict
from hep_ml.commonutils import generate_sample
class MyNull(Null):
def ylim(self, *args):
return [0, 1]
trainX, trainY = generate_sample(1000, 10)
testX, testY = generate_sample(1000, 10)
classifiers = ClassifiersDict()
classifiers['ada'] = AdaBoostClassifier(n_estimators=20)
classifiers['forest'] = RandomForestClassifier(n_estimators=20)
predictions = classifiers.fit(trainX, trainY).test_on(testX, testY)
def test_reports(null_pylab=True):
if null_pylab:
reports.pylab = MyNull()
predictions.sde_curves(['column0'])
predictions.correlation_curves('column1', ).show()
predictions.learning_curves()
predictions.show()
predictions.hist(['column0']).show()
from sklearn.metrics import roc_auc_score
from hep_ml import reports
from hep_ml.reports import ClassifiersDict
from hep_ml.commonutils import generate_sample
class MyNull(Null):
def ylim(self, *args):
return [0, 1]
trainX, trainY = generate_sample(1000, 10)
testX, testY = generate_sample(1000, 10)
classifiers = ClassifiersDict()
classifiers['ada'] = AdaBoostClassifier(n_estimators=20)
classifiers['forest'] = RandomForestClassifier(n_estimators=20)
predictions = classifiers.fit(trainX, trainY).test_on(testX, testY)
def test_reports(null_pylab=True):
if null_pylab:
reports.pylab = MyNull()
predictions.sde_curves(['column0'])
predictions.correlation_curves('column1', ).show()
predictions.learning_curves()
predictions.show()
predictions.hist(['column0']).show()
