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import torch, json, math, os |
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d = { |
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'debug': True, |
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'dataset_path': 'data/path_to_your_dataset.json', |
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'fptype': 'morgan', |
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'valid_ratio': 0.1, |
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'batch_size': 128, |
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'lr': 1e-3, |
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'weight_decay': 1e-3, |
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'patience': 2, |
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'factor': 0.5, |
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'add_nl': True, |
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'binary_intn': False, |
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'max_mz': 2000, |
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'min_mz': 20, |
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'energy': 'Energy1', |
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'epochs': 50, |
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'bin_size': 0.05, |
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'ms_embedding_dim': 300, |
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'projection_dim': 256, |
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'ms_projection_layers': 1, |
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'mol_embedding_dim': 2048, |
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'mol_projection_layers': 1, |
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'tsfm_in_ms': True, |
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'tsfm_in_mol': False, |
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'tsfm_layers': 6, |
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'tsfm_heads': 8, |
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'lstm_layers': 2, |
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'lstm_in_ms': False, |
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'lstm_in_mol': False, |
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'dropout': 0.1, |
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'nmodels': 1, |
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'mol_encoder': 'fp', |
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'molgnn_n_filters_list': [256, 256, 256], |
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'molgnn_nhead': 4, |
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'molgnn_readout_layers': 2, |
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'seed': 1234, |
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'dev_name': 'cuda', |
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'keep_best_models_num': 3 |
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} |
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class ConfigDict(dict): |
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''' |
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Makes a dictionary behave like an object,with attribute-style access. |
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''' |
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def __getattr__(self, name): |
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try: |
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return self[name] |
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except: |
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raise AttributeError(name) |
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def __setattr__(self, name, value): |
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self[name] = value |
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def save(self, fn, onlyprint=False): |
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if onlyprint: |
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print(self) |
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else: |
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json.dump(self, open(fn, 'w'), indent=2) |
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def load_dict(self, dic): |
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for k, v in dic.items(): |
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self[k] = v |
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self.calc_ms_embedding_dim() |
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def load(self, fn): |
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try: |
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if type(fn) is dict: |
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d = fn |
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elif type(fn) is str: |
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if os.path.exists(fn): |
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d = json.load(open(fn, 'r')) |
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else: |
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d = json.loads(fn) |
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self.load_dict(d) |
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except Exception as e: |
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print(e) |
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def calc_ms_embedding_dim(self): |
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if 'bin_size' in self: |
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self['ms_embedding_dim'] = math.ceil((self['max_mz'] - self['min_mz']) / self['bin_size']) |
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if 'ms_embedding_dim' in self and 'add_nl' in self and self['add_nl']: |
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self['ms_embedding_dim'] += math.ceil((200) / self['bin_size']) |
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@property |
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def device(self): |
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try: |
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return torch.device(self['dev_name']) |
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except: |
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return torch.device('cpu') |
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CFG = ConfigDict() |
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CFG.load_dict(d) |
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