# tile = np.moveaxis(tile, -1, 0)
# tile = np.expand_dims(tile, axis=0)
#
# # Scale to [0, 1]
# tile = tile / 255
#
# # Embed tile
# tile = torch.from_numpy(tile).float()
# tile = Variable(tile)
#
# if cuda: tile = tile.cuda()
# z = tilenet.encode(tile)
# if cuda: z = z.cpu()
# z = z.data.numpy()
#
# X[idx,:] = z
# t1 = time()
# print('Embedded {} tiles: {:0.3f}s'.format(config.n_tiles, t1-t0))
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
# Splitting data and training RF classifer
X_trn, X_val, y_trn, y_val = train_test_split(X, y, test_size=0.2)
rf = RandomForestClassifier(n_estimators=1000,
max_depth=10,
max_features='sqrt',
random_state=1)
rf.fit(X_trn, y_trn)
print("AND EVAL", rf.eval(X_val, y_val))
def main():
print("Loading train data from {}".format(args.raw_data))
df = pd.read_csv(args.raw_data)
df_train_input_sc, df_train_target, df_test_input_sc, df_test_target = lib.clear_data(df, args)
if args.algo == 'decisiontree':
# min_samples_leaf: 0.05, min_samples_split: 10, class_weight: None, splitter: best, max_features: 10
# criterion: entropy, max_depth: 7 for all feature
# min_samples_leaf: 0.05, min_samples_split: 3, class_weight: None, splitter: best, max_features: 8
# criterion: entropy, max_depth: 6 for discrete feature
model = tree.DecisionTreeClassifier(min_samples_leaf=0.05,
min_samples_split=3,
class_weight=None,
splitter="best",
max_features=8,
criterion="entropy",
max_depth=6)
model.fit(df_train_input_sc, df_train_target)
y_pred = model.predict(df_test_input_sc)
if args.algo == 'randomforest':
# random_state: 42, n_estimators: 1000, criterion: gini, max_depth: 7, bootstrap: True, max_features: 5,
# min_samples_leaf: 7, min_samples_split: 7 for all feature
# random_state: 42, n_estimators: 100, criterion: gini, max_depth: 7, bootstrap: True, max_features: 5,
# min_samples_leaf: 7, min_samples_split: 7 for discrete feature
model = RandomForestClassifier(random_state=42, # pafam for using all feature
n_estimators=1000,
criterion="gini",
max_depth=7,
bootstrap=True,
max_features=5,
min_samples_leaf=7,
min_samples_split=7)
model.fit(df_train_input_sc, df_train_target)
y_pred = model.predict(df_test_input_sc)
if args.algo == 'logisticregression':
# penalty: l1, random_state: 42, C: 0.05, tol: 0.01, intercept_scaling: 3, fit_intercept: True,
# max_iter: 10 for all feature
# penalty: l2, random_state: 42, C: 0.05, tol: 0.1, intercept_scaling: 1, fit_intercept: True,
# max_iter: 10 for discrete feature
model = LogisticRegression(penalty="l1",
random_state=42,
C=.05,
tol=0.01,
intercept_scaling=3,
fit_intercept=True,
max_iter=10)
model.fit(df_train_input_sc, df_train_target)
y_pred = model.predict(df_test_input_sc)
if args.algo == 'ADA':
model = AdaBoostClassifier()
model.fit(df_train_input_sc, df_train_target)
y_pred = model.predict(df_test_input_sc)
if args.algo == 'XGB':
model = XGBClassifier()
model.fit(df_train_input_sc, df_train_target)
y_pred = model.predict(df_test_input_sc)
if args.algo == 'FFN':
model = lib.FFN(df_train_input_sc.shape[1], args.output_dim, args.num_classes)
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
dataloader = lib.DataLoader(df_train_input_sc, df_train_target, args.batchsize)
# training
model.train()
for epoch in range(args.num_epochs):
sum_loss = 0
cnt = 0
for it, (input_data, target_data) in enumerate(dataloader):
cnt += 1
input_data = torch.Tensor(input_data)
target_data = torch.LongTensor(target_data)
optimizer.zero_grad()
logit = model(input_data)
loss = F.nll_loss(logit, target_data)
pred = logit.data.max(1)[1]
sum_loss += loss.item()
loss.backward()
optimizer.step()
print("Epoch: {} - loss: {}".format(epoch, float(sum_loss) / cnt))
# testing
model.eval()
with torch.no_grad():
input_data_test = torch.Tensor(df_test_input_sc)
target_data_test = torch.LongTensor(df_test_target)
logit = model(input_data_test)
loss = F.nll_loss(logit, target_data_test)
y_pred = logit.data.max(1)[1]
print(classification_report(df_test_target, y_pred))
