Merge pull request js-ish#7 from zzzseeu/fix-readme

fix: rewrite readme

README.md

@@ -8,44 +8,92 @@
 
 ```python
 # Regression train
-from moltx import tokenizers
-from dooc import models, datasets, nets
+import random
+import torch
+from torch import nn
+import torch.optim as optim
 
+from moltx import tokenizers as tkz
+from moltx.models import AdaMRTokenizerConfig
 
-tk = tokenizers.MoltxTokenizer.from_pretrain(models.AdaMRTokenizerConfig.Prediction)
-ds = datasets.MutSmiXAttention(tokenizer=tk, device=torch.device('cpu'))
+from dooc import models, datasets
+
+
+# datasets
+tokenizer = tkz.MoltxTokenizer.from_pretrain(
+    conf=AdaMRTokenizerConfig.Prediction
+    )
+ds = datasets.MutSmi(tokenizer)
 smiles = ["c1cccc1c", "CC[N+](C)(C)Cc1ccccc1Br"]
-mutations = [[1, 0, 0, ...], [1, 0, 1, ...]]
-# e.g.
-# import random
-# [random.choice([0, 1]) for _ in range(3008)]
+mutations = [[random.choice([0, 1]) for _ in range(3008)],
+             [random.choice([0, 1]) for _ in range(3008)]]
+# mutations contains 0/1 encoding information of the genome
 values = [0.85, 0.78]
 smiles_src, smiles_tgt, mutations_src, out = ds(smiles, mutations, values)
 
-model = models.MutSmiXAttention()
+# MutSmiFullConnection train
+model = models.MutSmiFullConnection()
 model.load_pretrained_ckpt('/path/to/drugcell.ckpt', '/path/to/moltx.ckpt')
+mse_loss = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(),
+            lr=1e-04,
+            foreach=False
+        )
+
+optimizer.zero_grad()
+pred = model(smiles_src, smiles_tgt, mutations_src)
+loss = mse_loss(pred, out)
+loss.backward()
+optimizer.step()
+
+torch.save(model.state_dict(), '/path/to/mutsmifullconnection.ckpt')
 
-crt = nn.MSELoss()
+# MutSmiXAttention train
+model = models.MutSmiXAttention()
+model.load_pretrained_ckpt('/path/to/drugcell.ckpt', '/path/to/moltx.ckpt')
+mse_loss = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(),
+            lr=1e-04,
+            foreach=False
+        )
 
-optim.zero_grad()
+optimizer.zero_grad()
 pred = model(smiles_src, smiles_tgt, mutations_src)
-loss = crt(pred, out)
+loss = mse_loss(pred, out)
 loss.backward()
-optim.step()
+optimizer.step()
 
 torch.save(model.state_dict(), '/path/to/mutsmixattention.ckpt')
 ```
 
 ### Inference
 
 ```python
+import random
+from moltx import tokenizers as tkz
+from moltx.models import AdaMRTokenizerConfig
 from dooc import pipelines, models
-# dooc
-model = models.MutSmiXAttention()
-model.load_ckpt('/path/to/mutsmixattention.ckpt')
-pipeline = pipelines.MutSmiXAttention()
-pipeline([1, 0, 0, ...], "C=CC=CC=C")
-# 0.85
 
 
+# MutSmiFullConnection
+tokenizer = tkz.MoltxTokenizer.from_pretrain(
+    conf=AdaMRTokenizerConfig.Prediction
+    )
+model = models.MutSmiFullConnection()
+model.load_ckpt('/path/to/mutsmifullconnection.ckpt')
+pipeline = pipelines.MutSmiFullConnection(smi_tokenizer=tokenizer, model=model)
+mutations = [random.choice([0, 1]) for _ in range(3008)]
+smiles = "CC[N+](C)(C)Cc1ccccc1Br"
+predict = pipeline(mutations, smiles) # e.g. 0.85
+
+# MutSmiXAttention
+tokenizer = tkz.MoltxTokenizer.from_pretrain(
+    conf=AdaMRTokenizerConfig.Prediction
+    )
+model = models.MutSmiXAttention()
+model.load_ckpt('/path/to/mutsmixattention.ckpt')
+pipeline = pipelines.MutSmiXAttention(smi_tokenizer=tokenizer, model=model)
+mutations = [random.choice([0, 1]) for _ in range(3008)]
+smiles = "CC[N+](C)(C)Cc1ccccc1Br"
+predict = pipeline(mutations, smiles) # e.g. 0.85
 ```