def deserialize_random_forest(model_dict):
model = RandomForestClassifier(**model_dict['params'])
estimators = [deserialize_decision_tree(decision_tree) for decision_tree in model_dict['estimators_']]
model.estimators_ = np.array(estimators)
model.classes_ = np.array(model_dict['classes_'])
model.n_features_ = model_dict['n_features_']
model.n_outputs_ = model_dict['n_outputs_']
model.max_depth = model_dict['max_depth']
model.min_samples_split = model_dict['min_samples_split']
model.min_samples_leaf = model_dict['min_samples_leaf']
model.min_weight_fraction_leaf = model_dict['min_weight_fraction_leaf']
model.max_features = model_dict['max_features']
model.max_leaf_nodes = model_dict['max_leaf_nodes']
model.min_impurity_decrease = model_dict['min_impurity_decrease']
model.min_impurity_split = model_dict['min_impurity_split']
if 'oob_score_' in model_dict:
model.oob_score_ = model_dict['oob_score_']
if 'oob_decision_function_' in model_dict:
model.oob_decision_function_ = model_dict['oob_decision_function_']
if isinstance(model_dict['n_classes_'], list):
model.n_classes_ = np.array(model_dict['n_classes_'])
else:
model.n_classes_ = model_dict['n_classes_']
return model
def main():
_n_classes = 2
sigmas = [0.3, 0.7, 1., 3.5]
args = parse_args()
path = args.path
_train_split = args.train_split
if args.test is None:
whole = args.whole
if whole:
rfc = RandomForestClassifier(n_estimators=200, n_jobs=-1)
rfc.n_classes_ = _n_classes
rfc = train_forest_whole(rfc, path)
else:
batch_size = args.batch_size
n_estimators = 100
shape = Brats15NumpyDataset(path, True, 1., -1)[0][0].shape[1:]
#np_transform = [crop, resize]
#np_params = [{'size': np.multiply(shape, 0.8).astype(int)},
# {'output_shape': np.multiply(shape, 0.4).astype(int),
# 'mode': 'constant'}]
#np_transform = None
#np_params = None
np_transform = [crop]
np_params = [{'size': np.multiply(shape, 0.8).astype(int)}]
dset_train = Brats15NumpyDataset(path,
True,
_train_split,
_random_state,
transform=None,
np_transform=np_transform,
np_transform_params=np_params,
tensor_conversion=False)
rfc = RandomForestClassifier(n_estimators=0,
warm_start=True,
n_jobs=-1)
rfc.n_classes_ = _n_classes
rfc = train_forest_sequential(rfc,
dset_train,
sigmas,
batch_size=batch_size,
n_estimators=n_estimators)
joblib.dump(rfc, 'rfc_test_{}.pkl'.format(int(whole)))
else:
dset_test = Brats15NumpyDataset(path,
False,
_train_split,
_random_state,
transform=None,
np_transform=None,
np_transform_params=None,
tensor_conversion=False)
test(dset_test,
sigmas,
load_path=args.test,
sample_imgs=args.sample_imgs)
def ReprodIndividualsFromRF(list_indiv, max_id, options):
list_indiv = list(list_indiv)
rf = RandomForestClassifier(n_estimators=len(list_indiv))
trees = list()
for indiv in list_indiv:
trees.append(indiv.clf)
rf.estimators_ = trees
rf.n_classes_ = trees[0].n_classes_
rf.classes_ = trees[0].classes_
new_dt = eqtree_rec_rf(rf,
0,
max_depth=options['max_depth'],
smallest_tree=False)
new_id = max_id + 1
indiv3 = genetic.individual(new_dt,
new_id,
type_rf=False,
alpha=options['alpha'],
evaluate_on_data=options['on_data'],
X=options['X'],
y=options['y'])
return indiv3
def train_random_forest(X, Y, estimators, classes, features):
train_x = X[0:3600, :]
train_y = Y[0:3600]
validate_x = X[3600:, :]
validate_y = Y[3600:]
clf = RandomForestClassifier(n_estimators=estimators)
clf.n_classes_ = classes
clf.n_features_ = features
clf.fit(train_x, train_y)
predictions = clf.predict(validate_x).reshape(400, 1)
result = sum([1 if predictions[i] == validate_y[i] else 0 for i in range(validate_y.shape[0])]) / \
validate_y.shape[0] * 100
return result
def random_forest(X, Y, n_samples):
clf = RandomForestClassifier(n_estimators=500,
max_depth=10,
random_state=0)
clf.n_classes_ = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
clf.fit(X[:n_samples // 2], Y[:n_samples // 2])
start_time = time.time()
predicted = clf.predict(X[n_samples // 2:])
elapsed_time = time.time() - start_time
expected = Y[n_samples // 2:]
return [elapsed_time, sum(expected == predicted) * 1. / len(expected)]
def learn(train_set):
clf = RandomForestClassifier(n_estimators=150)
clf.n_classes_ = len(object_types)
# x -- 2d array, x[i] -- list of feature values for segment i
# y -- 1d array, y[i] -- true class of segment i
x = []
y = []
for num in train_set:
x += get_features_value(num)
y += get_segments_classes(num)
clf.fit(x, y)
cPickle.dump(clf, open("classifier", "wb+"))
def build_classifier(trees):
def build_decision_tree(t):
dt = DecisionTreeClassifier(random_state=0)
dt.n_features_ = t.n_features
dt.n_outputs_ = t.n_outputs
dt.n_classes_ = t.n_classes[0]
dt.classes_ = np.array([x for x in range(dt.n_classes_)])
dt.tree_ = t
return dt
if len(trees) > 1:
clf = RandomForestClassifier(random_state=0, n_estimators=len(trees))
clf.estimators_ = [build_decision_tree(t) for t in trees]
clf.n_features_ = trees[0].n_features
clf.n_outputs_ = trees[0].n_outputs
clf.n_classes_ = trees[0].n_classes[0]
clf.classes_ = np.array([x for x in range(clf.n_classes_)])
else:
clf = build_decision_tree(trees[0])
return clf
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import sys
sys.path.append('..')
import data_loader
from data_loader import *
classifier = RandomForestClassifier(
n_estimators=20,
min_samples_leaf=200
)
classifier.n_classes_ = 2
classifier.fit(train_data, label_data.ravel())
prediction = classifier.predict(test)
frame = pd.DataFrame(data=prediction)
frame.index += 1
frame.to_csv(
"random_forest.csv", index=True, header=['Solution'], index_label='Id')
if __name__ == "__main__":
pass
X[i,:]=image
#images[i,:,:] = image
#print(X)
#print(y)
clf = RandomForestClassifier(n_estimators=500, max_depth=10, random_state=0)
clf.n_classes_=[0,1,2,3,4,5,6,7,8,9]
#clf.n_classes_=[0,1,2,3,4,5,6,7,8,9]
clf.fit(X[:n_samples //2], Y[:n_samples // 2])
predicted = clf.predict(X[n_samples//2:])
expected = Y[n_samples//2:]
print("Classification report for classifier %s:\n%s\n"
% (clf, metrics.classification_report(expected, predicted)))
print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))
images_and_predictions = list(zip(images[n_samples // 2:], predicted))
for index, (image, prediction) in enumerate(images_and_predictions[:25]):
plt.subplot(5, 5, index+1)
plt.axis('off')
plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
index = 0
for tweet in tweets:
words = tweet.split(' ')
for word in words:
tweetX[index][bagOfWords[word]] += 1
index += 1
#75-25 Train-Test split results
X_train, X_test, y_train, y_test = train_test_split(tweetX,
labels,
test_size=0.25,
random_state=42)
rf = RandomForestClassifier(n_estimators=50)
rf.classes_ = [0, 1, -1]
rf.n_classes_ = 3
rf.fit(X_train, y_train)
a = rf.predict(X_test)
print 'Train-Test Split Results'
print np.sum(a == y_test) * 100.0 / len(y_test)
#Cross Validation Results
'''
rf = RandomForestClassifier(n_estimators=50)
rf.classes_ = [0,1,-1]
rf.n_classes_ = 3
rf.fit(tweetX,labels)
RF = RandomForestClassifier(n_estimators=rfSize)
RF.fit(train_x, train_y)
RF_path = model_path + '/RF.m'
joblib.dump(RF, RF_path)
# BRAF
rf3 = RandomForestClassifier(n_estimators=rf2_size)
rf3.fit(training_c_x, training_c_y)
rf3_path = model_path + '/rf3.m'
joblib.dump(rf3, rf3_path)
RF1 = RandomForestClassifier(n_estimators=rfSize)
Gobaltree = rf1.estimators_ + rf3.estimators_
RF1.estimators_ = Gobaltree
RF1.classes_ = rf1.classes_
RF1.n_classes_ = rf1.n_classes_
RF1.n_outputs_ = rf1.n_outputs_
RF1_path = model_path + '/braf.m'
joblib.dump(RF1, RF1_path)
# DBRF
RF2 = RandomForestClassifier(n_estimators=rfSize)
mod_Gobaltree = rf1.estimators_ + rf2.estimators_
RF2.estimators_ = mod_Gobaltree
RF2.classes_ = rf2.classes_
RF2.n_classes_ = rf2.n_classes_
RF2.n_outputs_ = rf2.n_outputs_
RF2_path = model_path + '/borderlindbscan.m'
joblib.dump(RF2, RF2_path)
from sklearn import metrics
def Train1(X, y): rfc = RandomForestClassifier(n_estimators=10, oob_score=True) rfc.n_classes_ = 3 model = rfc.fit(X, y) return model
def Train_Kfold(X, y, K):
#y = np.array(y)
kf = KFold(X.shape[0], n_folds = K)
record = {}
k = 0
for train_index, test_index in kf:
k = k + 1
rfc = RandomForestClassifier(n_estimators=5, oob_score=True)
rfc.n_classes_ = 3
model = rfc.fit(X[train_index], y[train_index])
pred = model.predict(X[test_index])
count = 0
#AB: predicted as "1" while really is "2"
AA, AB, AC, BA, BB, BC, CA, CB, CC = 0,0,0,0,0,0,0,0,0
TA, TB, TC, PA, PB, PC = 0,0,0,0,0,0
for i in range(len(pred)):
if pred[i] == '1' and y[test_index][i] == '1':
AA = AA + 1
PA = PA + 1
TA = TA + 1
if pred[i] == '1' and y[test_index][i] == '2':
AB = AB + 1
PA = PA + 1
TB = TB + 1
if pred[i] == '1' and y[test_index][i] == '3':
AC = AC + 1
PA = PA + 1
TC = TC + 1
if pred[i] == '2' and y[test_index][i] == '1':
BA = BA + 1
PB = PB + 1
TA = TA + 1
if pred[i] == '2' and y[test_index][i] == '2':
BB = BB + 1
PB = PB + 1
TB = TB + 1
if pred[i] == '2' and y[test_index][i] == '3':
BC = BC + 1
PB = PB + 1
TC = TC + 1
if pred[i] == '3' and y[test_index][i] == '1':
CA = CA + 1
PC = PC + 1
TA = TA + 1
if pred[i] == '3' and y[test_index][i] == '2':
CB = CB + 1
PC = PC + 1
TB = TB + 1
if pred[i] == '3' and y[test_index][i] == '3':
CC = CC + 1
PC = PC + 1
TC = TC + 1
if pred[i] != y[test_index][i]:
count = count + 1
record[str(k)] = [count, AA, AB, AC, BA, BB, BC, CA, CB, CC, TA, TB, TC, PA, PB, PC, len(pred)]
err, Aerr, Berr, Cerr = 0, 0, 0, 0
for key in record:
Aerr = Aerr + (record[key][2]+record[key][3])/float(record[key][13])
Berr = Berr + (record[key][4]+record[key][5])/float(record[key][14])
#Cerr = Cerr + (record[key][7]+record[key][8])/float(record[key][15])
err = err + record[key][0]/float(record[key][16])
err = err/float(K)
Aerr = err/float(K)
Berr = err/float(K)
#Cerr = err/float(K)
#err = float(count)/K
#AA, AB, AC, BA, BB, BC, CA, CB, CC = float(AA)/K, float(AB)/K, float(AC)/K, float(BA)/K, float(BB)/K, float(BC)/K, float(CA)/K, float(CB)/K, float(CC)/K
return (err, Aerr, Berr)
local_dict = sorted(local_dict)
############################# Part 3: Linearity ##########################
# random forest 1
rf1 = RandomForestClassifier(random_state=10)
rf1.fit(train_vectors, y_train)
# random forest 2
rf2 = RandomForestClassifier(random_state=15)
rf2.fit(train_vectors, y_train)
# random forest 3
rf3 = RandomForestClassifier(random_state=22)
rf3.estimators_ = rf1.estimators_ + rf2.estimators_
rf3.n_classes_ = rf1.n_classes_
# model 1
def model_rf1(data):
n_data = len(data)
res = np.zeros((n_data, 2))
tfidf = vectorizer.transform(data)
p = rf1.predict_proba(tfidf)
res[:, 0] = p[:, 1]
res[:, 1] = p[:, 1]
return res
# model 2
def model_rf2(data):
n_data = len(data)
res = np.zeros((n_data, 2))