def subject_regularize(rfcs,
X_int,
X_other,
Y,
oob=False,
regularize=[0.75, 0.3, 0.65]):
if len(regularize) == 1:
regularize = regularize * 3
observed_ = []
predicted_ = []
for subject in range(1, 50):
observed = Y['subject'][subject]
rfc = rfcs[1][subject]
if oob:
predicted = rfc.oob_prediction_
else:
predicted = rfc.predict(X_other)
predicted_int = rfc.predict(X_int)
predicted[:, 0] = predicted_int[:, 0]
observed_.append(observed)
predicted_.append(predicted)
predicted = np.dstack(predicted_)
observed = np.ma.dstack(observed_)
predicted_mean = np.mean(predicted, axis=2, keepdims=True)
predicted_std = np.std(predicted, axis=2, keepdims=True)
predicted_mean_std = np.hstack((predicted_mean, predicted_std)).squeeze()
predicted_int = regularize[0] * (predicted_mean) + (
1 - regularize[0]) * predicted
predicted_ple = regularize[1] * (predicted_mean) + (
1 - regularize[1]) * predicted
predicted_dec = regularize[2] * (predicted_mean) + (
1 - regularize[2]) * predicted
predicted = regularize[0] * (predicted_mean) + (1 -
regularize[0]) * predicted
r_int = scoring.r('int', predicted_int, observed)
r_ple = scoring.r('ple', predicted_ple, observed)
r_dec = scoring.r('dec', predicted_dec, observed)
score1_ = scoring.score(predicted, observed, n_subjects=49)
score1 = scoring.rs2score(r_int, r_ple, r_dec)
#print(score1_,score1)
print("For subchallenge %d, score = %.3f (%.3f,%.3f,%.3f)" %
(1, score1, r_int, r_ple, r_dec))
score2 = scoring.score2(predicted_mean_std, Y['mean_std'])
r_int_mean = scoring.r2('int', 'mean', predicted_mean_std, Y['mean_std'])
r_ple_mean = scoring.r2('ple', 'mean', predicted_mean_std, Y['mean_std'])
r_dec_mean = scoring.r2('dec', 'mean', predicted_mean_std, Y['mean_std'])
r_int_sigma = scoring.r2('int', 'sigma', predicted_mean_std, Y['mean_std'])
r_ple_sigma = scoring.r2('ple', 'sigma', predicted_mean_std, Y['mean_std'])
r_dec_sigma = scoring.r2('dec', 'sigma', predicted_mean_std, Y['mean_std'])
print("For subchallenge %d, score = %.2f (%.2f,%.2f,%.2f,%.2f,%.2f,%.2f)" % \
(2,score2,r_int_mean,r_ple_mean,r_dec_mean,r_int_sigma,r_ple_sigma,r_dec_sigma))
return (r_int, r_ple, r_dec, r_int_mean, r_ple_mean, r_dec_mean,
r_int_sigma, r_ple_sigma, r_dec_sigma)
def lasso_(X_train,
Y_train,
X_test,
Y_test,
alpha=0.1,
regularize=[0.7, 0.7, 0.7]):
if len(regularize) == 1:
regularize = regularize * 3
def lasso_maker():
return Lasso(alpha=alpha)
n_subjects = 49
predicted_train = []
observed_train = []
predicted_test = []
observed_test = []
lassos = {subject: lasso_maker() for subject in range(1, n_subjects + 1)}
for subject in range(1, n_subjects + 1):
observed = Y_train[subject][:, 1:2]
lasso = lassos[subject]
lasso.fit(X_train, observed)
predicted = lasso.predict(X_train)[:, np.newaxis]
observed_train.append(observed)
predicted_train.append(predicted)
observed = Y_test[subject][:, 1:2]
predicted = lasso.predict(X_test)[:, np.newaxis]
observed_test.append(observed)
predicted_test.append(predicted)
scores = {}
for phase, predicted_, observed_ in [
('train', predicted_train, observed_train),
('test', predicted_test, observed_test)
]:
predicted = np.dstack(predicted_)
observed = np.ma.dstack(observed_)
predicted_mean = np.mean(predicted, axis=2, keepdims=True)
#predicted_int = regularize[0]*(predicted_mean) + (1-regularize[0])*predicted
predicted_ple = regularize[1] * (predicted_mean) + (
1 - regularize[1]) * predicted
#predicted_dec = regularize[2]*(predicted_mean) + (1-regularize[2])*predicted
#score1_ = scoring.score(predicted_int,observed,n_subjects=n_subjects)
#r_int = scoring.r('int',predicted,observed)
#r_ple = scoring.r('ple',predicted,observed)
r_ple = scoring.r(None, predicted_ple, observed)
r2_ple = scoring.r2(None, None, predicted_ple.mean(axis=2),
observed.mean(axis=2))
#r_dec = scoring.r('dec',predicted,observed)
#score1 = scoring.rs2score(r_int,r_ple,r_dec)
print("For subchallenge 1, %s phase, score = %.2f" % (phase, r_ple))
print("For subchallenge 2, %s phase, score = %.2f" % (phase, r2_ple))
scores[phase] = (r_ple, r2_ple)
return lassos, scores['train'], scores['test']
def rfc_final(X,
Y,
max_features,
min_samples_leaf,
max_depth,
et,
Y_test=None,
regularize=[0.7, 0.7, 0.7],
n_estimators=100,
seed=0):
if Y_test is None:
Y_test = Y
def rfc_maker(n_estimators=n_estimators,
max_features=max_features,
min_samples_leaf=min_samples_leaf,
max_depth=max_depth,
et=False):
if not et:
return RandomForestRegressor(n_estimators=n_estimators,
max_features=max_features,
min_samples_leaf=min_samples_leaf,
max_depth=max_depth,
oob_score=True,
n_jobs=-1,
random_state=seed)
else:
return ExtraTreesRegressor(n_estimators=n_estimators,
max_features=max_features,
min_samples_leaf=min_samples_leaf,
max_depth=max_depth,
n_jobs=-1,
random_state=seed)
kinds = ['int', 'ple', 'dec']
rfcs = {}
for kind in kinds:
rfcs[kind] = {}
for subject in range(1, 50):
rfcs[kind][subject] = rfc_maker(
n_estimators=n_estimators,
max_features=max_features[kind],
min_samples_leaf=min_samples_leaf[kind],
max_depth=max_depth[kind],
et=et[kind])
for subject in range(1, 50):
for kind in kinds:
rfcs[kind][subject].fit(X, Y[subject])
predictions = {}
for kind in kinds:
predictions[kind] = {}
for subject in range(1, 50):
if et[kind]:
# Check in-sample fit because there isn't any alternative.
predictions[kind][subject] = rfcs[kind][subject].predict(X)
else:
predictions[kind][subject] = rfcs[kind][
subject].oob_prediction_
predicted = predictions['int'].copy()
for subject in range(1, 50):
predicted[subject][:, 0] = predictions['int'][subject][:, 0]
predicted[subject][:, 1] = predictions['ple'][subject][:, 1]
predicted[subject][:, 2:] = predictions['dec'][subject][:, 2:]
# Regularize:
predicted_stack = np.zeros(
(predicted[1].shape[0], predicted[1].shape[1], 49))
for subject in range(1, 50):
predicted_stack[:, :, subject - 1] = predicted[subject]
predicted_mean = predicted_stack.mean(axis=2, keepdims=True)
predicted_reg = {kind: predicted.copy() for kind in kinds}
for i, kind in enumerate(kinds):
predicted_reg[kind] = regularize[i] * predicted_mean + (
1 - regularize[i]) * predicted_stack
predicted_stack[:, 0, :] = predicted_reg['int'][:, 0, :]
predicted_stack[:, 1, :] = predicted_reg['ple'][:, 1, :]
predicted_stack[:, 2:, :] = predicted_reg['dec'][:, 2:, :]
predicted = predicted_stack
observed = predicted.copy()
for subject in range(1, 50):
observed[:, :, subject - 1] = Y_test[subject]
score = scoring.score(predicted, observed)
rs = {}
predictions = {}
for kind in ['int', 'ple', 'dec']:
rs[kind] = scoring.r(kind, predicted, observed)
print("For subchallenge 1:")
print("\tScore = %.2f" % score)
for kind in kinds:
print("\t%s = %.3f" % (kind, rs[kind]))
return (rfcs, score, rs)
def rfc_cv(X,
Y,
n_splits=5,
n_estimators=15,
max_features=1000,
min_samples_leaf=1,
max_depth=None,
regularize=[0.7, 0.35, 0.7]):
test_size = 0.2
n_molecules = X.shape[0]
shuffle_split = ShuffleSplit(n_molecules, n_splits, test_size=test_size)
test_size *= n_molecules
rfcs = {}
n_subjects = 49
for subject in range(1, n_subjects + 1):
rfc = RandomForestRegressor(n_estimators=n_estimators,
max_features=max_features,
min_samples_leaf=min_samples_leaf,
max_depth=max_depth,
oob_score=False,
n_jobs=-1,
random_state=0)
rfcs[subject] = rfc
rs = {'int': [], 'ple': [], 'dec': []}
scores = []
for train_index, test_index in shuffle_split:
predicted_list = []
observed_list = []
for subject in range(1, n_subjects + 1):
rfc = rfcs[subject]
X_train = X[train_index]
Y_train = Y[subject][train_index]
rfc.fit(X_train, Y_train)
X_test = X[test_index]
predicted = rfc.predict(X_test)
observed = Y[subject][test_index]
predicted_list.append(predicted)
observed_list.append(observed)
observed = np.ma.dstack(observed_list)
predicted = np.dstack(predicted_list)
predicted_mean = predicted.mean(axis=2, keepdims=True)
predicted_int = regularize[0] * (predicted_mean) + (
1 - regularize[0]) * predicted
predicted_ple = regularize[1] * (predicted_mean) + (
1 - regularize[1]) * predicted
predicted = regularize[2] * (predicted_mean) + (
1 - regularize[2]) * predicted
predicted[:, 0, :] = predicted_int[:, 0, :]
predicted[:, 1, :] = predicted_ple[:, 1, :]
score = scoring.score(predicted, observed)
scores.append(score)
for kind in ['int', 'ple', 'dec']:
rs[kind].append(scoring.r(kind, predicted, observed))
for kind in ['int', 'ple', 'dec']:
rs[kind] = {
'mean': np.mean(rs[kind]),
'sem': np.std(rs[kind]) / np.sqrt(n_splits)
}
scores = {
'mean': np.mean(scores),
'sem': np.std(scores) / np.sqrt(n_splits)
}
print(
"For subchallenge 1, using cross-validation with at least %d samples_per_leaf:"
% min_samples_leaf)
print("\tscore = %.2f+/- %.2f" % (scores['mean'], scores['sem']))
for kind in ['int', 'ple', 'dec']:
print("\t%s = %.2f+/- %.2f" %
(kind, rs[kind]['mean'], rs[kind]['sem']))
return scores, rs
def rfc_(X_train,
Y_train,
X_test_int,
X_test_other,
Y_test,
max_features=1500,
n_estimators=1000,
max_depth=None,
min_samples_leaf=1):
print(max_features)
def rfc_maker():
return RandomForestRegressor(max_features=max_features,
n_estimators=n_estimators,
max_depth=max_depth,
min_samples_leaf=min_samples_leaf,
n_jobs=-1,
oob_score=True,
random_state=0)
n_subjects = 49
predicted_train = []
observed_train = []
predicted_test = []
observed_test = []
rfcs = {subject: rfc_maker() for subject in range(1, n_subjects + 1)}
for subject in range(1, n_subjects + 1):
print(subject)
observed = Y_train[subject]
rfc = rfcs[subject]
rfc.fit(X_train, observed)
#predicted = rfc.predict(X_train)
predicted = rfc.oob_prediction_
observed_train.append(observed)
predicted_train.append(predicted)
observed = Y_test[subject]
rfc = rfcs[subject]
if Y_train is Y_test: # OOB prediction
predicted = rfc.oob_prediction_
else:
predicted = rfc.predict(X_test_other)
predicted_int = rfc.predict(X_test_int)
predicted[:, 0] = predicted_int[:, 0]
observed_test.append(observed)
predicted_test.append(predicted)
scores = {}
for phase, predicted_, observed_ in [
('train', predicted_train, observed_train),
('test', predicted_test, observed_test)
]:
predicted = np.dstack(predicted_)
observed = np.ma.dstack(observed_)
predicted_mean = np.mean(predicted, axis=2, keepdims=True)
regularize = 0.7
predicted = regularize * (predicted_mean) + (1 -
regularize) * predicted
score = scoring.score(predicted, observed, n_subjects=n_subjects)
r_int = scoring.r('int', predicted, observed)
r_ple = scoring.r('ple', predicted, observed)
r_dec = scoring.r('dec', predicted, observed)
print("For subchallenge 1, %s phase, score = %.2f (%.2f,%.2f,%.2f)" %
(phase, score, r_int, r_ple, r_dec))
scores[phase] = score
return rfcs, scores['train'], scores['test']