add UnoMT implemented in pytorch

ECP-CANDLE · Sep 13, 2018 · 7c4a1f2 · 7c4a1f2
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diff --git a/Pilot1/SCRATCH/UnoMT/README.md b/Pilot1/SCRATCH/UnoMT/README.md
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+# UnoMT in Pytorch
+Multi-tasking (drug response, cell line classification, etc.) Uno Implemented in PyTorch.
+
+## Todos
+* More labels for the network like drug labels;
+* Dataloader hanging problem when num_workers set to more than 0;
+* Better pre-processing for drug descriptor integer features;
+* Network regularization with weight decay and/or dropout;
+* Hyper-parameter searching; 
+
+## Prerequisites
+```
+Python          3.6.4
+PyTorch         0.4.1
+SciPy           1.1.0
+pandas          0.23.4
+Scikit-Learn    0.19.1
+urllib3         1.23
+joblib          0.12.2
+```
+
+
+The default network structure is shown below: 
+<img src="https://github.com/xduan7/UnoPytorch/blob/master/images/default_network.jpg" width="100%">
+
+An example of the program output for training on NCI60 and valdiation on all other data sources is shown below:
+```
+python3.6 ./launcher.py
+Training Arguments:
+{
+    "trn_src": "NCI60",
+    "val_srcs": [
+        "NCI60",
+        "CTRP",
+        "GDSC",
+        "CCLE",
+        "gCSI"
+    ],
+    "grth_scaling": "none",
+    "dscptr_scaling": "std",
+    "rnaseq_scaling": "std",
+    "dscptr_nan_threshold": 0.0,
+    "qed_scaling": "none",
+    "rnaseq_feature_usage": "source_scale",
+    "drug_feature_usage": "both",
+    "validation_ratio": 0.2,
+    "disjoint_drugs": false,
+    "disjoint_cells": true,
+    "gene_layer_dim": 1024,
+    "gene_latent_dim": 512,
+    "gene_num_layers": 2,
+    "drug_layer_dim": 4096,
+    "drug_latent_dim": 2048,
+    "drug_num_layers": 2,
+    "autoencoder_init": true,
+    "resp_layer_dim": 2048,
+    "resp_num_layers_per_block": 2,
+    "resp_num_blocks": 4,
+    "resp_num_layers": 2,
+    "resp_dropout": 0.0,
+    "resp_activation": "none",
+    "cl_clf_layer_dim": 256,
+    "cl_clf_num_layers": 2,
+    "drug_target_layer_dim": 512,
+    "drug_target_num_layers": 2,
+    "drug_qed_layer_dim": 512,
+    "drug_qed_num_layers": 2,
+    "drug_qed_activation": "sigmoid",
+    "resp_loss_func": "mse",
+    "resp_opt": "SGD",
+    "resp_lr": 1e-05,
+    "cl_clf_opt": "SGD",
+    "cl_clf_lr": 0.01,
+    "drug_target_opt": "SGD",
+    "drug_target_lr": 0.01,
+    "drug_qed_loss_func": "mse",
+    "drug_qed_opt": "SGD",
+    "drug_qed_lr": 0.01,
+    "resp_val_start_epoch": 0,
+    "early_stop_patience": 20,
+    "lr_decay_factor": 0.98,
+    "trn_batch_size": 32,
+    "val_batch_size": 256,
+    "max_num_batches": 1000,
+    "max_num_epochs": 1000,
+    "multi_gpu": false,
+    "no_cuda": false,
+    "rand_state": 0
+}
+RespNet(
+  (_RespNet__gene_encoder): Sequential(
+    (dense_0): Linear(in_features=942, out_features=1024, bias=True)
+    (relu_0): ReLU()
+    (dense_1): Linear(in_features=1024, out_features=1024, bias=True)
+    (relu_1): ReLU()
+    (dense_2): Linear(in_features=1024, out_features=512, bias=True)
+  )
+  (_RespNet__drug_encoder): Sequential(
+    (dense_0): Linear(in_features=4688, out_features=4096, bias=True)
+    (relu_0): ReLU()
+    (dense_1): Linear(in_features=4096, out_features=4096, bias=True)
+    (relu_1): ReLU()
+    (dense_2): Linear(in_features=4096, out_features=2048, bias=True)
+  )
+  (_RespNet__resp_net): Sequential(
+    (dense_0): Linear(in_features=2561, out_features=2048, bias=True)
+    (activation_0): ReLU()
+    (residual_block_0): ResBlock(
+      (block): Sequential(
+        (res_dense_0): Linear(in_features=2048, out_features=2048, bias=True)
+        (res_relu_0): ReLU()
+        (res_dense_1): Linear(in_features=2048, out_features=2048, bias=True)
+      )
+      (activation): ReLU()
+    )
+    (residual_block_1): ResBlock(
+      (block): Sequential(
+        (res_dense_0): Linear(in_features=2048, out_features=2048, bias=True)
+        (res_relu_0): ReLU()
+        (res_dense_1): Linear(in_features=2048, out_features=2048, bias=True)
+      )
+      (activation): ReLU()
+    )
+    (residual_block_2): ResBlock(
+      (block): Sequential(
+        (res_dense_0): Linear(in_features=2048, out_features=2048, bias=True)
+        (res_relu_0): ReLU()
+        (res_dense_1): Linear(in_features=2048, out_features=2048, bias=True)
+      )
+      (activation): ReLU()
+    )
+    (residual_block_3): ResBlock(
+      (block): Sequential(
+        (res_dense_0): Linear(in_features=2048, out_features=2048, bias=True)
+        (res_relu_0): ReLU()
+        (res_dense_1): Linear(in_features=2048, out_features=2048, bias=True)
+      )
+      (activation): ReLU()
+    )
+    (dense_1): Linear(in_features=2048, out_features=2048, bias=True)
+    (res_relu_1): ReLU()
+    (dense_2): Linear(in_features=2048, out_features=2048, bias=True)
+    (res_relu_2): ReLU()
+    (dense_out): Linear(in_features=2048, out_features=1, bias=True)
+  )
+)
+================================================================================
+Training Epoch   1:
+	Drug Weighted QED Regression Loss: 0.055694
+	Drug Response Regression Loss:  1871.18
+
+Validation Results:
+	Cell Line Classification: 
+		Category Accuracy: 		98.98%; 
+		Site Accuracy: 			80.95%; 
+		Type Accuracy: 			82.76%
+	Drug Target Family Classification Accuracy:  1.85%
+	Drug Weighted QED Regression
+		MSE: 0.028476 	 MAE: 0.137004 	 R2: +0.17
+	Drug Response Regression:
+		NCI60  	 MSE:  1482.07 	 MAE:    27.89 	 R2: +0.53
+		CTRP   	 MSE:  2554.45 	 MAE:    38.62 	 R2: +0.27
+		GDSC   	 MSE:  2955.78 	 MAE:    42.73 	 R2: +0.11
+		CCLE   	 MSE:  2799.06 	 MAE:    42.44 	 R2: +0.31
+		gCSI   	 MSE:  2601.50 	 MAE:    38.44 	 R2: +0.35
+Epoch Running Time: 110.0 Seconds.
+================================================================================
+Training Epoch   2:
+    ...
+    ...
+
+Program Running Time: 8349.6 Seconds.
+================================================================================
+Overall Validation Results:
+
+	Best Results from Different Models (Epochs):
+		Cell Line Categories     Best Accuracy: 99.474% (Epoch =   5)
+		Cell Line Sites          Best Accuracy: 97.401% (Epoch =  60)
+		Cell Line Types          Best Accuracy: 97.368% (Epoch =  40)
+		Drug Target Family 	 Best Accuracy: 66.667% (Epoch =  23)
+		Drug Weighted QED 	 Best R2 Score: +0.7422 (Epoch =  59, MSE = 0.008837, MAE = 0.069400)
+		NCI60  	 Best R2 Score: +0.8107 (Epoch =  56, MSE =   601.18, MAE =  16.57)
+		CTRP   	 Best R2 Score: +0.3945 (Epoch =  37, MSE =  2127.28, MAE =  31.44)
+		GDSC   	 Best R2 Score: +0.2448 (Epoch =  22, MSE =  2506.03, MAE =  35.55)
+		CCLE   	 Best R2 Score: +0.4729 (Epoch =   4, MSE =  2153.30, MAE =  33.63)
+		gCSI   	 Best R2 Score: +0.4512 (Epoch =  31, MSE =  2203.04, MAE =  32.63)
+
+	Best Results from the Same Model (Epoch =  22):
+		Cell Line Categories     Accuracy: 99.408%
+		Cell Line Sites          Accuracy: 97.138%
+		Cell Line Types          Accuracy: 97.039%
+		Drug Target Family 	 Accuracy: 57.407% 
+		Drug Weighted QED 	 R2 Score: +0.6033 (MSE = 0.013601, MAE = 0.093341)
+		NCI60  	 R2 Score: +0.7885 (MSE =   672.00, MAE =  17.89)
+		CTRP   	 R2 Score: +0.3841 (MSE =  2163.66, MAE =  32.28)
+		GDSC   	 R2 Score: +0.2448 (MSE =  2506.03, MAE =  35.55)
+		CCLE   	 R2 Score: +0.4653 (MSE =  2184.62, MAE =  34.12)
+		gCSI   	 R2 Score: +0.4271 (MSE =  2299.59, MAE =  32.93)
+```
+
+For default hyper parameters, the transfer learning matrix results are shown below:
+<p align="center">
+	<img src="https://github.com/xduan7/UnoPytorch/blob/master/images/default_results.jpg" width="80%">
+</p>
+
+Note that the green cells represents R2 score of higher than 0.1, red cells are R2 scores lower than -0.1 and yellows are for all the values in between.