From b8222a2f25b0e0d48a4ef121e0de1ac90c148c2b Mon Sep 17 00:00:00 2001 From: ksanu Date: Tue, 3 Dec 2019 23:08:49 +0100 Subject: [PATCH] sane --- .idea/workspace.xml | 8 +- dev-0/out.tsv | 14680 +++++++++++++-------------- dev-0/out_float.tsv | 22050 ++++++++++++++++++++-------------------- s.py | 35 +- test-A/out.tsv | 14598 +++++++++++++-------------- test-A/out_float.tsv | 22118 ++++++++++++++++++++--------------------- 6 files changed, 36749 insertions(+), 36740 deletions(-) diff --git a/.idea/workspace.xml b/.idea/workspace.xml index cc7e201..38619d2 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -146,14 +146,20 @@ file://$PROJECT_DIR$/s.py - 139 + 141 + + file://$PROJECT_DIR$/s.py + 193 + + diff --git a/dev-0/out.tsv b/dev-0/out.tsv index ea6e500..f1d4787 100644 --- a/dev-0/out.tsv +++ b/dev-0/out.tsv @@ -1,405 +1,948 @@ 0 +1 +1 +1 0 0 0 +1 +1 0 +1 +1 0 0 0 +1 +1 0 +1 +1 0 0 0 +1 +1 +1 0 0 +1 0 +1 +1 0 0 +1 0 0 +1 0 +1 +1 +1 +1 +1 0 0 0 0 +1 +1 +1 0 0 0 0 +1 +1 0 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+1.0 +1.0 +1.0 +1.0 +1.0 +0.999997615814209 +1.0 +1.0 +0.9999998807907104 +1.0 +1.0 +1.0 +0.00010593909973977134 +0.0006079493323341012 +1.0 +0.9999992847442627 diff --git a/s.py b/s.py index eb0474a..3e4be6a 100644 --- a/s.py +++ b/s.py @@ -11,16 +11,18 @@ from torch.utils.data import Dataset, DataLoader #10 features: 4 normal + 6 from domain_onehot + 38 char labels model = nn.Sequential( - nn.Linear(48, 16), + nn.Linear(48, 96, bias=True), nn.ReLU(), - nn.Linear(16,1), + nn.Linear(96,48,bias=True), + nn.ReLU(), + nn.Linear(48, 1, bias=True), nn.Sigmoid()) -criterion = nn.MSELoss() -optimizer = optim.SGD(model.parameters(), lr=0.000001, momentum=0.9) -#optimizer = optim.Adam(model.parameters()) +criterion = nn.BCELoss() +#optimizer = optim.SGD(model.parameters(), lr=0.00001, momentum=0.9) +optimizer = optim.Adam(model.parameters()) -minibatch_size = 1000 +minibatch_size = 200 def count_polish_diacritics(x): @@ -105,7 +107,7 @@ class TrainDataset(Dataset): self.y = y def __len__(self): - return len(self.X) + return self.X.shape[0] def __getitem__(self, idx): return self.X[idx], self.y[idx] @@ -173,34 +175,35 @@ testA_x = torch.cat([testA_x_temp2, testA_x_words_onehot], 1) dataset_train = TrainDataset(x, y) -trainloader=DataLoader(dataset=dataset_train,batch_size=5) +trainloader=DataLoader(dataset=dataset_train, batch_size=minibatch_size, shuffle=True) -def train_loop(i = 3): +def train_loop(i = 100): for i in range(i): - for xb, yb_expected in trainloader: # for each iteration a bach of samples is taken from loader(currently batch_size=5) + for xb, yb_expected in trainloader: + optimizer.zero_grad() yp = model(xb) # debug """ debug_xb = pandas.DataFrame(xb.numpy()) debug_yb_expected = pandas.DataFrame(yb_expected.numpy()) - debug_yp = pandas.DataFrame(yp.detach().numpy()) """ + debug_yp = pandas.DataFrame(yp.detach().numpy()) - loss = criterion(yp, yb_expected) - optimizer.zero_grad() + + loss = criterion(torch.squeeze(yp), yb_expected) dev_y_pred_float_tensor = model(dev_x) dev_y_pred_float_df = pandas.DataFrame(dev_y_pred_float_tensor.detach().numpy()) auc_score = roc_auc_score(dev_y_test, dev_y_pred_float_df) - print("auc: ", auc_score, "loss: ", loss.item()) - if(auc_score > 0.9): + print("auc:\t", auc_score, "\tloss:\t", loss.item()) + if ((auc_score > 0.80)): break loss.backward() optimizer.step() - if (auc_score > 0.9): + if ((auc_score > 0.80)): break #print(loss) diff --git a/test-A/out.tsv b/test-A/out.tsv index 475f276..db71dd4 100644 --- a/test-A/out.tsv +++ b/test-A/out.tsv @@ -1,317 +1,741 @@ 0 0 0 +1 +1 0 +1 +1 0 +1 +1 +1 +1 0 0 0 -0 -0 -0 +1 0 0 0 0 1 0 +1 +1 +1 0 0 0 +1 0 +1 +1 0 0 +1 +1 +1 +1 +1 +1 0 0 +1 0 1 1 0 +1 +1 0 +1 +1 0 +1 +1 +1 +1 +1 +1 +1 0 +1 0 0 +1 +1 +1 +1 0 +1 0 +1 +1 +1 0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 0 0 0 +1 0 0 +1 0 0 0 +1 +1 0 0 +1 +1 +1 0 +1 +1 +1 +1 0 0 +1 +1 0 0 +1 0 +1 +1 +1 0 +1 +1 +1 0 0 0 +1 +1 0 +1 +1 +1 +1 +1 +1 0 +1 +1 +1 0 0 0 +1 +1 +1 0 0 +1 0 +1 0 +1 +1 +1 +1 +1 +1 0 +1 0 0 0 +1 0 +1 +1 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