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methods_list.py
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methods_list.py
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"""
Create list of delayed classifiers to be used by `crossvalidate.py` and
`aggregate.py`.
"""
from __future__ import division
import sklearn as sl
import sklearn.svm
import sklearn.naive_bayes
import sklearn.ensemble
import sklearn.neighbors
import sklearn.tree
import sklearn.linear_model
from delayed import delayed
def make_methods_list():
methods = []
"""
# SVMs.
for C in [1e2, 1e3, 1e4]:
for gamma in [0.01, 0.05, 0.1]:
methods.append(delayed(sl.svm.SVC)(kernel='rbf', C=C, gamma=gamma))
methods.append(delayed(sl.svm.SVC)(kernel='poly', degree=2, C=C,
gamma=gamma))
methods.append(delayed(sl.svm.SVC)(kernel='poly', degree=3, C=C,
gamma=gamma))
methods.append(delayed(sl.svm.SVC)(kernel='linear', C=C))
# Misc.
methods.append(delayed(sl.tree.DecisionTreeClassifier)())
methods.append(delayed(sl.naive_bayes.GaussianNB)())
"""
# Random Forests.
for n in [10, 50, 100, 200, 500]:
methods.append(delayed(sl.ensemble.RandomForestClassifier)
(n_estimators=n))
for n in [10, 50, 100, 200, 500]:
methods.append(delayed(sl.ensemble.RandomForestClassifier)
(n_estimators=n, criterion='entropy'))
# Gradient boosting.
for n in [10, 50, 100, 200, 500]:
for a in [.0001, .001, .01, .1, 1., 10.]:
methods.append(delayed(sl.ensemble.GradientBoostingClassifier)
(n_estimators=n, learn_rate=a))
# Nearest neighbors.
for n in [1, 5, 10, 15, 20]:
methods.append(delayed(sl.neighbors.KNeighborsClassifier)
(n_neighbors=n))
for n in [1, 5, 10, 15, 20]:
methods.append(delayed(sl.neighbors.KNeighborsClassifier)
(n_neighbors=n, weights='distance'))
# l1-penalized logistic regression
for c in [0.0001, 0.001, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100]:
methods.append(delayed(sl.linear_model.LogisticRegression)
(C=c, penalty='l1', tol=0.01))
# l2-penalized logistic regression
for c in [0.0001, 0.001, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100]:
methods.append(delayed(sl.linear_model.LogisticRegression)
(C=c, penalty='l2', tol=0.01))
return methods