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ins_model/README.md

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+---
+library_name: transformers
+tags:
+- generated_from_trainer
+datasets:
+- crispr_data
+model-index:
+- name: SX_ispymac_Lindel_ins
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# SX_ispymac_Lindel_ins
+
+This model is a fine-tuned version of [](https://huggingface.co/) on the crispr_data dataset.
+It achieves the following results on the evaluation set:
+- Loss: 114.3085
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.001
+- train_batch_size: 100
+- eval_batch_size: 100
+- seed: 63036
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.05
+- num_epochs: 30.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 2557.6189     | 1.0   | 326  | 2286.4143       |
+| 1853.5564     | 2.0   | 652  | 1416.5610       |
+| 1095.4547     | 3.0   | 978  | 815.7089        |
+| 629.2861      | 4.0   | 1304 | 467.8559        |
+| 368.1106      | 5.0   | 1630 | 281.6502        |
+| 233.841       | 6.0   | 1956 | 190.0098        |
+| 169.8489      | 7.0   | 2282 | 148.1783        |
+| 141.378       | 8.0   | 2608 | 129.9077        |
+| 128.9001      | 9.0   | 2934 | 121.7621        |
+| 123.3282      | 10.0  | 3260 | 118.0483        |
+| 120.763       | 11.0  | 3586 | 116.2743        |
+| 119.5329      | 12.0  | 3912 | 115.3635        |
+| 118.924       | 13.0  | 4238 | 114.9560        |
+| 118.6128      | 14.0  | 4564 | 114.7279        |
+| 118.4493      | 15.0  | 4890 | 114.6119        |
+| 118.358       | 16.0  | 5216 | 114.4937        |
+| 118.299       | 17.0  | 5542 | 114.4606        |
+| 118.2543      | 18.0  | 5868 | 114.4432        |
+| 118.2231      | 19.0  | 6194 | 114.4022        |
+| 118.1948      | 20.0  | 6520 | 114.3616        |
+| 118.1767      | 21.0  | 6846 | 114.3577        |
+| 118.154       | 22.0  | 7172 | 114.3569        |
+| 118.1371      | 23.0  | 7498 | 114.3513        |
+| 118.1204      | 24.0  | 7824 | 114.3301        |
+| 118.1073      | 25.0  | 8150 | 114.3264        |
+| 118.0942      | 26.0  | 8476 | 114.3197        |
+| 118.0838      | 27.0  | 8802 | 114.3144        |
+| 118.072       | 28.0  | 9128 | 114.3112        |
+| 118.0618      | 29.0  | 9454 | 114.3085        |
+| 118.0521      | 30.0  | 9780 | 114.3085        |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu124
+- Datasets 2.21.0
+- Tokenizers 0.19.1

ins_model/config.json

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 {
-  "_name_or_path": "/home/ljw/sdc1/CRISPR_results/Lindel/SX_ispymac_Lindel_ins",
   "architectures": [
     "LindelModel"
   ],

ins_model/model.py

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+from transformers import PretrainedConfig, PreTrainedModel
+import torch.nn as nn
+import torch
+import torch.nn.functional as F
+
+class LindelConfig(PretrainedConfig):
+    model_type = "Lindel"
+    label_names = ["count"]
+
+    def __init__(
+        self,
+        dlen = 30, # the upper limit of deletion length (strictly less than dlen)
+        mh_len = 4, # the upper limit of micro-homology length
+        model = "indel", # the actual model, should be "indel", "del", or "ins"
+        reg_mode = "l2", # regularization method, should be "l2" or "l1"
+        reg_const = 0.01, # regularization coefficient
+        seed = 63036, # random seed for intialization
+        **kwargs,
+    ):
+        self.dlen = dlen
+        self.mh_len = mh_len
+        self.model = model
+        self.reg_mode = reg_mode
+        self.reg_const = reg_const
+        self.seed = seed
+        super().__init__(**kwargs)
+
+class LindelModel(PreTrainedModel):
+    config_class = LindelConfig
+
+    def __init__(self, config) -> None:
+        super().__init__(config)
+        # In more recent versions of PyTorch, you no longer need to explicitly register_parameter, it's enough to set a member of your nn.Module with nn.Parameter to "notify" pytorch that this variable should be treated as a trainable parameter (https://stackoverflow.com/questions/59234238/how-to-add-parameters-in-module-class-in-pytorch-custom-model).
+        self.generator = torch.Generator().manual_seed(config.seed)
+        self.reg_mode = config.reg_mode
+        self.reg_const = config.reg_const
+        if config.model == "indel":
+            # onehotencoder(ref[cut-17:cut+3])
+            feature_dim = 20 * 4 + 19 * 16
+            class_dim = 2
+        elif config.model == "ins":
+            # onehotencoder(ref[cut-3:cut+3])
+            feature_dim = 6 * 4 + 5 * 16
+            class_dim = 21
+        elif config.model == "del":
+            class_dim = (4 + 1 + 4 + config.dlen - 1) * (config.dlen - 1) // 2
+            # concatenate get_feature and onehotencoder(ref[cut-17:cut+3])
+            feature_dim = class_dim * (config.mh_len + 1) + 20 * 4 + 19 * 16
+        self.linear = nn.Linear(in_features=feature_dim, out_features=class_dim)
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, mean=0, std=1, generator=self.generator)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, input, count=None) -> torch.Tensor:
+        logit = self.linear(input)
+        if count is not None:
+            return {
+                "logit": logit,
+                "loss": self.cross_entropy_reg(logit, count)
+            }
+        return {"logit": logit}
+
+    def cross_entropy_reg(self, logit, count):
+        if self.reg_mode == "l2":
+            reg_term = (self.linear.weight ** 2).sum()
+        elif self.reg_mode == "l1":
+            reg_term = abs(self.linear.weight).sum()
+        return -(F.log_softmax(logit, dim=1) * F.normalize(count.to(torch.float32), p=1.0, dim=1)).sum() + logit.shape[0] * self.reg_const * reg_term

ins_model/runs/Nov20_11-56-17_ljw-System-Product-Name/events.out.tfevents.1732075920.ljw-System-Product-Name.1244212.1

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ins_model/training_args.bin

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