From ce8f6922bcf20414e883d0ba69cf0321ad754e69 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Mon, 11 Dec 2023 07:20:47 +0530
Subject: [PATCH 01/38] changes done during setup

---
 .gitignore       | 3 ++-
 recover/train.py | 2 +-
 requirements.txt | 2 +-
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/.gitignore b/.gitignore
index 1e0728c..983550d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
+Reservoir
 RayLogs
 __pycache__
 *pyc
 *egg-info
-.DS_Store
\ No newline at end of file
+.DS_Store
diff --git a/recover/train.py b/recover/train.py
index 6b398ad..3da20e0 100644
--- a/recover/train.py
+++ b/recover/train.py
@@ -213,7 +213,7 @@ def step(self):
     def save_checkpoint(self, checkpoint_dir):
         checkpoint_path = os.path.join(checkpoint_dir, "model.pth")
         torch.save(self.model.state_dict(), checkpoint_path)
-        return checkpoint_path
+        return None
 
     def load_checkpoint(self, checkpoint_path):
         self.model.load_state_dict(torch.load(checkpoint_path))
diff --git a/requirements.txt b/requirements.txt
index 88814f8..ac3619b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,6 +5,6 @@ seaborn==0.11.0
 tqdm
 scikit-learn==0.23.2
 umap-learn
-numpy
+numpy==1.22.4
 pandas
 scipy

From 6ec9c0f74073c6aca6ad55970759df5b3b9839f7 Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Mon, 11 Dec 2023 14:52:12 +0100
Subject: [PATCH 02/38] Adding project infographics

---
 docs/images/Project Infographics.jpg | Bin 0 -> 216113 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 docs/images/Project Infographics.jpg

diff --git a/docs/images/Project Infographics.jpg b/docs/images/Project Infographics.jpg
new file mode 100644
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From 6eee1db28a92cf76d5df435b47c6bb5d4521c8df Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Mon, 11 Dec 2023 14:55:29 +0100
Subject: [PATCH 03/38] Update README.md

---
 README.md | 26 +++++++-------------------
 1 file changed, 7 insertions(+), 19 deletions(-)

diff --git a/README.md b/README.md
index d42df10..d670b60 100644
--- a/README.md
+++ b/README.md
@@ -1,23 +1,17 @@
-# RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds *in vitro*
+# Machine Learning Driven Candidate Compound Generation for Drug Repurposing 
+Based on RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds *in vitro*
 [![DOI](https://zenodo.org/badge/320327566.svg)](https://zenodo.org/badge/latestdoi/320327566)
 
-RECOVER is a platform that can guide wet lab experiments to quickly discover synergistic drug combinations active 
-against a cancer cell line, requiring substantially less screening than an exhaustive evaluation 
-([preprint](https://arxiv.org/abs/2202.04202)).
-
+This repository is an implementation of RECOVER, a platform that can guide wet lab experiments to quickly discover synergistic drug combinations,
+([preprint](https://arxiv.org/abs/2202.04202)), howerver instead of using an ensemble model to get Synergy predictions with uncertainty, we used multiple realization of a Bayesian Neural Network model. 
+Since the weights are drawn from a distribution, they differ for every run of a trained model and hence give different results. The goal was to get a more precise uncertainty and achieve i quicker since the model doesn't have to be trained multiple times. 
 
 ![Overview](docs/images/overview.png "Overview")
 
 ## Environment setup
 
-**Requirements**: Anaconda (https://www.anaconda.com/) and Git LFS (https://git-lfs.github.com/). Please make sure 
-both are installed on the system prior to running installation.
-
-**Installation**: enter the command `source install.sh` and follow the instructions. This will create a conda 
-environment named **recover** and install all the required packages including the 
-[reservoir](https://github.com/RECOVERcoalition/Reservoir) package that stores the primary data acquisition scripts.
-
-In case you have any issue with the installation procedure of the *reservoir*, you can access and download all files directly from this [google drive](https://drive.google.com/drive/folders/1MYeDoAi0-qnhSJTvs68r861iMOdoqYki?usp=share_link).
+**Requirements and Installation**: 
+For all the requirements and installation steps check th orginal RECOVER repository (https://github.com/RECOVERcoalition/Recover.git). 
 
 ## Running the pipeline
 
@@ -31,9 +25,3 @@ For example, to run the pipeline with configuration from
 the file `model_evaluation.py`, run `python train.py --config model_evaluation`.
 
 Log files will automatically be created to save the results of the experiments.
-
-## Note
-
-This Recover repository is based on research funded by (or in part by) the Bill & Melinda Gates Foundation. The 
-findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies 
-of the Bill & Melinda Gates Foundation.

From 6c7fe27ad88fa33895e02005d8a5b51fefd2cefc Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Mon, 11 Dec 2023 14:56:12 +0100
Subject: [PATCH 04/38] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index d670b60..4d26092 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@ This repository is an implementation of RECOVER, a platform that can guide wet l
 ([preprint](https://arxiv.org/abs/2202.04202)), howerver instead of using an ensemble model to get Synergy predictions with uncertainty, we used multiple realization of a Bayesian Neural Network model. 
 Since the weights are drawn from a distribution, they differ for every run of a trained model and hence give different results. The goal was to get a more precise uncertainty and achieve i quicker since the model doesn't have to be trained multiple times. 
 
-![Overview](docs/images/overview.png "Overview")
+![Overview](docs/images/Project Infographics.jpg "Overview")
 
 ## Environment setup
 

From 03351ae28aadf365e037322229a89583defdb52e Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Mon, 11 Dec 2023 14:57:31 +0100
Subject: [PATCH 05/38] Delete docs/images/Project Infographics.jpg

---
 docs/images/Project Infographics.jpg | Bin 216113 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 docs/images/Project Infographics.jpg

diff --git a/docs/images/Project Infographics.jpg b/docs/images/Project Infographics.jpg
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From 80364c7ae3c2ef092adb5e576dd6fefb951c193d Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Mon, 11 Dec 2023 14:57:55 +0100
Subject: [PATCH 06/38] Adding infographics

---
 docs/images/Project Infographics.png | Bin 0 -> 335997 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 docs/images/Project Infographics.png

diff --git a/docs/images/Project Infographics.png b/docs/images/Project Infographics.png
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From 0ed3b5468ffaa656161eb89a300d4bdd69dfb9dd Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Mon, 11 Dec 2023 14:58:56 +0100
Subject: [PATCH 07/38] Rename Project Infographics.png to
 ProjectInfographics.png

---
 ...ect Infographics.png => ProjectInfographics.png} | Bin
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename docs/images/{Project Infographics.png => ProjectInfographics.png} (100%)

diff --git a/docs/images/Project Infographics.png b/docs/images/ProjectInfographics.png
similarity index 100%
rename from docs/images/Project Infographics.png
rename to docs/images/ProjectInfographics.png

From 897e12c63167a250dcf5fb889e03db9bc69ae9e7 Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Mon, 11 Dec 2023 14:59:24 +0100
Subject: [PATCH 08/38] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 4d26092..e314f2a 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@ This repository is an implementation of RECOVER, a platform that can guide wet l
 ([preprint](https://arxiv.org/abs/2202.04202)), howerver instead of using an ensemble model to get Synergy predictions with uncertainty, we used multiple realization of a Bayesian Neural Network model. 
 Since the weights are drawn from a distribution, they differ for every run of a trained model and hence give different results. The goal was to get a more precise uncertainty and achieve i quicker since the model doesn't have to be trained multiple times. 
 
-![Overview](docs/images/Project Infographics.jpg "Overview")
+![Overview](docs/images/ProjectInfographics.png "Overview")
 
 ## Environment setup
 

From 050cec6c11127ee0c838022f42f590ee4d46ece0 Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Mon, 11 Dec 2023 15:23:29 +0100
Subject: [PATCH 09/38] Update README.md

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index e314f2a..f923cf7 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# Machine Learning Driven Candidate Compound Generation for Drug Repurposing 
+# Machine Learning Driven Candidate Compound Generation for Drug Repurposing
 Based on RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds *in vitro*
 [![DOI](https://zenodo.org/badge/320327566.svg)](https://zenodo.org/badge/latestdoi/320327566)
 
@@ -6,7 +6,9 @@ This repository is an implementation of RECOVER, a platform that can guide wet l
 ([preprint](https://arxiv.org/abs/2202.04202)), howerver instead of using an ensemble model to get Synergy predictions with uncertainty, we used multiple realization of a Bayesian Neural Network model. 
 Since the weights are drawn from a distribution, they differ for every run of a trained model and hence give different results. The goal was to get a more precise uncertainty and achieve i quicker since the model doesn't have to be trained multiple times. 
 
-![Overview](docs/images/ProjectInfographics.png "Overview")
+<div>
+  <img src="docs/images/ProjectInfographics.png" alt="Overview" width="500"/>
+</div>
 
 ## Environment setup
 

From 0ed04bd10863a67e7f6b40178bb9ba06ef18f8a8 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Tue, 12 Dec 2023 19:46:24 +0530
Subject: [PATCH 10/38] Bayesian methods implemented

---
 recover/models/predictors.py | 129 ++++++++++++++++
 recover/train.py             | 282 ++++++++++++++++++++++++++++++++++-
 2 files changed, 410 insertions(+), 1 deletion(-)

diff --git a/recover/models/predictors.py b/recover/models/predictors.py
index db62b44..a0234f2 100644
--- a/recover/models/predictors.py
+++ b/recover/models/predictors.py
@@ -1,6 +1,7 @@
 import torch
 import numpy as np
 import torch.nn as nn
+import torchbnn as bnn
 from torch.nn import Parameter
 
 ########################################################################################################################
@@ -17,6 +18,15 @@ def forward(self, input):
         return [super().forward(x), cell_line]
 
 
+class BayesianLinearModule(bnn.BayesLinear):
+    def __init__(self, in_features, out_features, prior_mu=0, prior_sigma=0.01):
+        super(BayesianLinearModule, self).__init__(prior_mu=prior_mu, prior_sigma=prior_sigma, in_features=in_features, out_features=out_features)
+
+    def forward(self, input):
+        x, cell_line = input[0], input[1]
+        return [super().forward(x), cell_line]
+
+
 class ReLUModule(nn.ReLU):
     def __init__(self):
         super(ReLUModule, self).__init__()
@@ -211,6 +221,125 @@ def add_layer(self, layers, i, dim_i, dim_i_plus_1):
     def linear_layer(self, i, dim_i, dim_i_plus_1):
         return [LinearModule(dim_i, dim_i_plus_1)]
 
+########################################################################################################################
+# Bilinear MLP with Bayesian
+########################################################################################################################
+
+
+class BayesianBilinearMLPPredictor(torch.nn.Module):
+    def __init__(self, data, config, predictor_layers):
+
+        super(BayesianBilinearMLPPredictor, self).__init__()
+
+        self.num_cell_lines = len(data.cell_line_to_idx_dict.keys())
+        device_type = "cuda" if torch.cuda.is_available() else "cpu"
+        self.device = torch.device(device_type)
+
+        self.layer_dims = predictor_layers
+
+        self.merge_n_layers_before_the_end = config["merge_n_layers_before_the_end"]
+        self.merge_dim = self.layer_dims[-self.merge_n_layers_before_the_end - 1]
+
+        assert 0 < self.merge_n_layers_before_the_end < len(predictor_layers)
+
+        layers_before_merge = []
+        layers_after_merge = []
+
+        # Build early layers (before addition of the two embeddings)
+        for i in range(len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end):
+            layers_before_merge = self.add_layer(
+                layers_before_merge,
+                i,
+                self.layer_dims[i],
+                self.layer_dims[i + 1]
+            )
+
+        # Build last layers (after addition of the two embeddings)
+        for i in range(
+            len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end,
+            len(self.layer_dims) - 1,
+        ):
+
+            layers_after_merge = self.add_bayes_layer(
+                layers_after_merge,
+                i,
+                self.layer_dims[i],
+                self.layer_dims[i + 1]
+            )
+
+        self.before_merge_mlp = nn.Sequential(*layers_before_merge)
+        self.after_merge_mlp = nn.Sequential(*layers_after_merge)
+
+        # Number of bilinear transformations == the dimension of the layer at which the merge is performed
+        # Initialize weights close to identity
+        self.bilinear_weights = Parameter(
+            1 / 100 * torch.randn((self.merge_dim, self.merge_dim, self.merge_dim))
+            + torch.cat([torch.eye(self.merge_dim)[None, :, :]] * self.merge_dim, dim=0)
+        )
+        self.bilinear_offsets = Parameter(1 / 100 * torch.randn((self.merge_dim)))
+
+        self.allow_neg_eigval = config["allow_neg_eigval"]
+        if self.allow_neg_eigval:
+            self.bilinear_diag = Parameter(1 / 100 * torch.randn((self.merge_dim, self.merge_dim)) + 1)
+
+    def forward(self, data, drug_drug_batch):
+        h_drug_1, h_drug_2, cell_lines = self.get_batch(data, drug_drug_batch)
+
+        # Apply before merge MLP
+        h_1 = self.before_merge_mlp([h_drug_1, cell_lines])[0]
+        h_2 = self.before_merge_mlp([h_drug_2, cell_lines])[0]
+
+        # compute <W.h_1, W.h_2> = h_1.T . W.T.W . h_2
+        h_1 = self.bilinear_weights.matmul(h_1.T).T
+        h_2 = self.bilinear_weights.matmul(h_2.T).T
+
+        if self.allow_neg_eigval:
+            # Multiply by diagonal matrix to allow for negative eigenvalues
+            h_2 *= self.bilinear_diag
+
+        # "Transpose" h_1
+        h_1 = h_1.permute(0, 2, 1)
+
+        # Multiplication
+        h_1_scal_h_2 = (h_1 * h_2).sum(1)
+
+        # Add offset
+        h_1_scal_h_2 += self.bilinear_offsets
+
+        comb = self.after_merge_mlp([h_1_scal_h_2, cell_lines])[0]
+
+        return comb
+
+    def get_batch(self, data, drug_drug_batch):
+
+        drug_1s = drug_drug_batch[0][:, 0]  # Edge-tail drugs in the batch
+        drug_2s = drug_drug_batch[0][:, 1]  # Edge-head drugs in the batch
+        cell_lines = drug_drug_batch[1]  # Cell line of all examples in the batch
+
+        h_drug_1 = data.x_drugs[drug_1s]
+        h_drug_2 = data.x_drugs[drug_2s]
+
+        return h_drug_1, h_drug_2, cell_lines
+
+    def add_layer(self, layers, i, dim_i, dim_i_plus_1):
+        layers.extend(self.linear_layer(i, dim_i, dim_i_plus_1))
+        if i != len(self.layer_dims) - 2:
+            layers.append(ReLUModule())
+
+        return layers
+
+    def add_bayes_layer(self, layers, i, dim_i, dim_i_plus_1):
+        layers.extend(self.bayes_linear_layer(i, dim_i, dim_i_plus_1))
+        if i != len(self.layer_dims) - 2:
+            layers.append(ReLUModule())
+
+        return layers
+
+    def linear_layer(self, i, dim_i, dim_i_plus_1):
+        return [LinearModule(dim_i, dim_i_plus_1)]
+        
+    def bayes_linear_layer(self, i, dim_i, dim_i_plus_1):
+        return [BayesianLinearModule(dim_i, dim_i_plus_1)]
 
 ########################################################################################################################
 # Bilinear MLP with Film conditioning
diff --git a/recover/train.py b/recover/train.py
index 3da20e0..c5142c7 100644
--- a/recover/train.py
+++ b/recover/train.py
@@ -1,4 +1,5 @@
 import torch
+import torchbnn as bnn
 import os
 from torch.utils.data import DataLoader
 from recover.utils.utils import get_tensor_dataset, trial_dirname_creator
@@ -8,6 +9,7 @@
 import time
 import argparse
 import numpy as np
+import pandas as pd
 import importlib
 from scipy import stats
 from scipy.stats import spearmanr
@@ -55,6 +57,55 @@ def train_epoch(data, loader, model, optim):
 
     return summary_dict
 
+"""
+The cost function is updated to consider both MSE loss and KL loss
+KL weight is set to 0.01
+Default model loss is unchanged - MSE (For comparision with Rec
+"""
+def bayesian_train_epoch(data, loader, model, optim):
+
+    model.train()
+
+    epoch_loss = 0
+    num_batches = len(loader)
+
+    all_mean_preds = []
+    all_targets = []
+    
+    kl_loss = bnn.BKLLoss(reduction='mean', last_layer_only=False)
+    kl_weight = 0.01
+
+    for _, drug_drug_batch in enumerate(loader):
+        optim.zero_grad()
+
+        out = model.forward(data, drug_drug_batch)
+
+        # Save all predictions and targets
+        all_mean_preds.extend(out.mean(dim=1).tolist())
+        all_targets.extend(drug_drug_batch[2].tolist())
+
+        loss = model.loss(out, drug_drug_batch) #MSE
+        kl = kl_loss(model)
+        
+        cost = loss + kl_weight*kl
+
+        cost.backward()
+        optim.step()
+
+        epoch_loss += loss.item()
+
+    epoch_comb_r_squared = stats.linregress(all_mean_preds, all_targets).rvalue**2
+
+    summary_dict = {
+        "loss_mean": epoch_loss / num_batches,
+        "comb_r_squared": epoch_comb_r_squared
+        #"cost": cost.item()
+    }
+
+    print("Training", summary_dict)
+
+    return summary_dict
+
 
 def eval_epoch(data, loader, model):
     model.eval()
@@ -94,6 +145,56 @@ def eval_epoch(data, loader, model):
     return summary_dict, all_out
 
 
+def bayesian_eval_epoch(data, loader, model):
+    model.eval()
+
+    epoch_loss = 0
+    num_batches = len(loader)
+
+    all_out = []
+    all_mean_preds = []
+    all_targets = []
+    all_combs = []
+
+    with torch.no_grad():
+        for _, drug_drug_batch in enumerate(loader):
+            out = model.forward(data, drug_drug_batch)
+
+            # Save all predictions and targets - rec_id_to_idx_dict
+            all_out.append(out)
+            all_mean_preds.extend(out.mean(dim=1).tolist())
+            all_targets.extend(drug_drug_batch[2].tolist())
+            all_combs.extend(drug_drug_batch[0].tolist())
+
+            loss = model.loss(out, drug_drug_batch)
+            epoch_loss += loss.item()
+        epoch_comb_r_squared = stats.linregress(all_mean_preds, all_targets).rvalue**2
+        epoch_spear = spearmanr(all_targets, all_mean_preds).correlation
+
+    summary_dict = {
+        "loss_mean": epoch_loss / num_batches,
+        "comb_r_squared": epoch_comb_r_squared,
+        "spearman": epoch_spear
+    }
+    
+    print("Testing", summary_dict, '\n')
+
+    all_out = torch.cat(all_out)
+
+    return summary_dict, all_out, all_combs
+
+"""
+Custom Aggregration function for obtaining results when training with multi cell-lines 
+
+Functionality: group data by the combination (permute invarient) and get maximum mean synergy score and the corresponding std
+"""
+def custom_agg(group):
+    max_row = group.loc[group['mean'].idxmax()]
+    return pd.Series({'combination': max_row['combination'],
+                  'mean': max_row['mean'],
+                  'std': max_row['std']})
+
+
 ########################################################################################################################
 # Basic trainer
 ########################################################################################################################
@@ -213,12 +314,191 @@ def step(self):
     def save_checkpoint(self, checkpoint_dir):
         checkpoint_path = os.path.join(checkpoint_dir, "model.pth")
         torch.save(self.model.state_dict(), checkpoint_path)
-        return None
+        return checkpoint_dir
+
+    def load_checkpoint(self, checkpoint_path):
+        self.model.load_state_dict(torch.load(checkpoint_path))
+
+
+########################################################################################################################
+# Bayesian Basic trainer
+########################################################################################################################
+
+
+class BayesianBasicTrainer(tune.Trainable):
+    def setup(self, config):
+        print("Initializing regular training pipeline")
+
+        self.batch_size = config["batch_size"]
+        device_type = "cuda" if torch.cuda.is_available() else "cpu"
+        self.device = torch.device(device_type)
+        self.training_it = 0
+
+        # Initialize dataset
+        dataset = config["dataset"](
+            fp_bits=config["fp_bits"],
+            fp_radius=config["fp_radius"],
+            cell_line=config["cell_line"],
+            study_name=config["study_name"],
+            in_house_data=config["in_house_data"],
+            rounds_to_include=config["rounds_to_include"],
+        )
+        
+        self.patience_stop = config["stop"]["patience"]
+        self.max_iter = config["stop"]["training_iteration"]
+
+        self.data = dataset.data.to(self.device)
+
+        # If a score is the target, we store it in the ddi_edge_response attribute of the data object
+        if "target" in config.keys():
+            possible_target_dicts = {
+                "bliss_max": self.data.ddi_edge_bliss_max,
+                "bliss_av": self.data.ddi_edge_bliss_av,
+                "css_av": self.data.ddi_edge_css_av,
+            }
+            self.data.ddi_edge_response = possible_target_dicts[config["target"]]
+
+        torch.manual_seed(config["seed"])
+        np.random.seed(config["seed"])
+
+        # Perform train/valid/test split. Test split is fixed regardless of the user defined seed
+        self.train_idxs, self.val_idxs, self.test_idxs = dataset.random_split(config)
+
+        # Train loader
+        train_ddi_dataset = get_tensor_dataset(self.data, self.train_idxs)
+
+        self.train_loader = DataLoader(
+            train_ddi_dataset,
+            batch_size=config["batch_size"]
+        )
+
+        # Valid loader
+        valid_ddi_dataset = get_tensor_dataset(self.data, self.val_idxs)
+
+        self.valid_loader = DataLoader(
+            valid_ddi_dataset,
+            batch_size=config["batch_size"]
+        )
+        
+        # Test loader
+        test_ddi_dataset = get_tensor_dataset(self.data, self.test_idxs)
+
+        self.test_loader = DataLoader(
+            test_ddi_dataset,
+            batch_size=config["batch_size"]
+        )
+
+
+        # Initialize model
+        self.model = config["model"](self.data, config)
+
+        # Initialize model with weights from file
+        load_model_weights = config.get("load_model_weights", False)
+        if load_model_weights:
+            model_weights_file = config.get("model_weights_file")
+            model_weights = torch.load(model_weights_file, map_location="cpu")
+            self.model.load_state_dict(model_weights)
+            print("pretrained weights loaded")
+        else:
+            print("model initialized randomly")
+
+        self.model = self.model.to(self.device)
+        print(self.model)
+
+        # Initialize optimizer
+        self.optim = torch.optim.Adam(
+            self.model.parameters(),
+            lr=config["lr"],
+            weight_decay=config["weight_decay"],
+        )
+
+        self.train_epoch = config["train_epoch"]
+        self.eval_epoch = config["eval_epoch"]
+
+        self.patience = 0
+        self.max_eval_r_squared = -1
+
+    def step(self):
+    
+        num_realizations = 10
+
+        train_metrics = self.train_epoch(
+            self.data,
+            self.train_loader,
+            self.model,
+            self.optim,
+        )
+
+        eval_metrics, _, _ = self.eval_epoch(self.data, self.valid_loader, self.model)
+
+        train_metrics = [("train/" + k, v) for k, v in train_metrics.items()]
+        eval_metrics = [("eval/" + k, v) for k, v in eval_metrics.items()]
+
+        metrics = dict(train_metrics + eval_metrics)
+        
+        metrics["training_iteration"] = self.training_it
+        self.training_it += 1
+                
+        # Compute patience
+        if metrics['eval/comb_r_squared'] > self.max_eval_r_squared:
+            self.patience = 0
+            self.max_eval_r_squared = metrics['eval/comb_r_squared']
+        else:
+            self.patience += 1
+            
+        metrics['patience'] = self.patience
+        metrics['all_space_explored'] = 0                        
+
+
+        if ((self.patience >= self.patience_stop) | (self.training_it > self.max_iter)):
+            test_result = {}
+            realization_results, result_synergy, drug_combs = self.eval_epoch(self.data, self.test_loader, self.model)
+            drug_combinations_synergy = {'data': self.test_idxs}
+            test_metrics = dict([("test/" + k, [v]) for k, v in realization_results.items()])
+            for i in range(num_realizations-1):
+                realization_results, new_synergy, _ = self.eval_epoch(self.data, self.test_loader, self.model)
+                result_synergy = torch.cat((result_synergy, new_synergy), dim=1)
+                for k, v in realization_results.items():
+                    test_metrics["test/" + k].append(v)
+                    
+            for key in test_metrics:
+                test_result[str(key)+ "/mean"] = np.mean(test_metrics[key])
+                test_result[str(key)+ "/std"] = np.std(test_metrics[key])
+                
+            
+            synergy_mean = list(torch.mean(result_synergy, dim=1).numpy())
+            synergy_std = list(torch.std(result_synergy, dim=1).numpy())
+            
+            result_tuples = list(map(lambda inner_list: tuple(sorted(inner_list)), drug_combs))
+            
+            dataset = pd.DataFrame({'combination': result_tuples, 'mean': synergy_mean, 'std': synergy_std }, columns=['combination', 'mean', 'std'])
+            
+            result_df = dataset.groupby('combination').apply(custom_agg) # Do custom aggregration  - Not needed when working on one cell-line but no difference in results
+            
+            result_df.reset_index(drop=True, inplace=True)
+            
+            metrics.update(dict(test_result)) # Add test results to output files
+            
+            metrics['synergy_combs'] = list(result_df['combination'])
+            metrics['synergy_mean'] = list(result_df['mean'])
+            metrics['synergy_std'] = list(result_df['std'])
+            
+            #metrics.update(dict(drug_combinations_synergy))
+        
+        return metrics
+    
+    
+    def save_checkpoint(self, checkpoint_dir):
+        checkpoint_path = os.path.join(checkpoint_dir, "model.pth")
+        torch.save(self.model.state_dict(), checkpoint_path)
+        
+        return checkpoint_dir
 
     def load_checkpoint(self, checkpoint_path):
         self.model.load_state_dict(torch.load(checkpoint_path))
 
 
+
 ########################################################################################################################
 # Active learning Trainer
 ########################################################################################################################

From 946cf64125580a1cb9513b6fdacdcc6d383db02e Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Tue, 12 Dec 2023 19:47:17 +0530
Subject: [PATCH 11/38] Bayesian config files added

---
 recover/config/bayesian_active_learning.py  |  0
 recover/config/bayesian_model_evaluation.py | 84 +++++++++++++++++++++
 2 files changed, 84 insertions(+)
 create mode 100644 recover/config/bayesian_active_learning.py
 create mode 100644 recover/config/bayesian_model_evaluation.py

diff --git a/recover/config/bayesian_active_learning.py b/recover/config/bayesian_active_learning.py
new file mode 100644
index 0000000..e69de29
diff --git a/recover/config/bayesian_model_evaluation.py b/recover/config/bayesian_model_evaluation.py
new file mode 100644
index 0000000..4d886cb
--- /dev/null
+++ b/recover/config/bayesian_model_evaluation.py
@@ -0,0 +1,84 @@
+from recover.datasets.drugcomb_matrix_data import DrugCombMatrix
+from recover.models.models import Baseline
+from recover.models.predictors import BilinearFilmMLPPredictor, BayesianBilinearMLPPredictor
+from recover.utils.utils import get_project_root
+from recover.train import train_epoch, bayesian_train_epoch, eval_epoch, bayesian_eval_epoch, BasicTrainer, BayesianBasicTrainer
+import os
+from ray import tune
+from importlib import import_module
+
+########################################################################################################################
+# Configuration
+########################################################################################################################
+
+
+pipeline_config = {
+    "use_tune": True,
+    "num_epoch_without_tune": 500,  # Used only if "use_tune" == False
+    "seed": tune.grid_search([2, 3, 42]),
+    # Optimizer config
+    "lr": 1e-4,
+    "weight_decay": 1e-2,
+    "batch_size": 128,
+    # Train epoch and eval_epoch to use
+    "train_epoch": bayesian_train_epoch,
+    "eval_epoch": bayesian_eval_epoch,
+}
+
+predictor_config = {
+    "predictor": BayesianBilinearMLPPredictor,
+    "predictor_layers":
+        [
+            2048,
+            128,
+            64,
+            1,
+        ],
+    "merge_n_layers_before_the_end": 2,  # Computation on the sum of the two drug embeddings for the last n layers
+    "allow_neg_eigval": True,
+    "stop": {"training_iteration": 1000, 'patience': 10}
+}
+
+model_config = {
+    "model": Baseline,
+    "load_model_weights": False,
+}
+
+dataset_config = {
+    "dataset": DrugCombMatrix,
+    "study_name": 'ALMANAC',
+    "in_house_data": 'without',
+    "rounds_to_include": [],
+    "val_set_prop": 0.2,
+    "test_set_prop": 0.1,
+    "test_on_unseen_cell_line": False,
+    "split_valid_train": "pair_level",
+    "cell_line": 'MCF7',  # 'PC-3',
+    "target": "bliss_max",  # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]),
+    "fp_bits": 1024,
+    "fp_radius": 2
+}
+
+########################################################################################################################
+# Configuration that will be loaded
+########################################################################################################################
+
+configuration = {
+    "trainer": BayesianBasicTrainer,  # PUT NUM GPU BACK TO 1
+    "trainer_config": {
+        **pipeline_config,
+        **predictor_config,
+        **model_config,
+        **dataset_config,
+    },
+    "summaries_dir": os.path.join(get_project_root(), "RayLogs"),
+    "memory": 1800,
+    "stop": {"training_iteration": 1000, 'patience': 10},
+    "checkpoint_score_attr": 'eval/comb_r_squared',
+    "keep_checkpoints_num": 1,
+    "checkpoint_at_end": True,
+    "checkpoint_freq": 0,
+    "resources_per_trial": {"cpu": 16, "gpu": 0},
+    "scheduler": None,
+    "search_alg": None,
+}
\ No newline at end of file

From d5f64829131447b5bba695b5c2a3889837ae05e2 Mon Sep 17 00:00:00 2001
From: Dhanushki Mapitigama <dhanumapitigama@gmail.com>
Date: Tue, 12 Dec 2023 17:41:56 +0100
Subject: [PATCH 12/38] Bayesian config files

For Bayesian model evaluation
For active learning with Bayesian model
---
 recover/config/bayesian_active_learning.py  |  0
 recover/config/bayesian_model_evaluation.py | 84 +++++++++++++++++++++
 2 files changed, 84 insertions(+)
 create mode 100644 recover/config/bayesian_active_learning.py
 create mode 100644 recover/config/bayesian_model_evaluation.py

diff --git a/recover/config/bayesian_active_learning.py b/recover/config/bayesian_active_learning.py
new file mode 100644
index 0000000..e69de29
diff --git a/recover/config/bayesian_model_evaluation.py b/recover/config/bayesian_model_evaluation.py
new file mode 100644
index 0000000..4d886cb
--- /dev/null
+++ b/recover/config/bayesian_model_evaluation.py
@@ -0,0 +1,84 @@
+from recover.datasets.drugcomb_matrix_data import DrugCombMatrix
+from recover.models.models import Baseline
+from recover.models.predictors import BilinearFilmMLPPredictor, BayesianBilinearMLPPredictor
+from recover.utils.utils import get_project_root
+from recover.train import train_epoch, bayesian_train_epoch, eval_epoch, bayesian_eval_epoch, BasicTrainer, BayesianBasicTrainer
+import os
+from ray import tune
+from importlib import import_module
+
+########################################################################################################################
+# Configuration
+########################################################################################################################
+
+
+pipeline_config = {
+    "use_tune": True,
+    "num_epoch_without_tune": 500,  # Used only if "use_tune" == False
+    "seed": tune.grid_search([2, 3, 42]),
+    # Optimizer config
+    "lr": 1e-4,
+    "weight_decay": 1e-2,
+    "batch_size": 128,
+    # Train epoch and eval_epoch to use
+    "train_epoch": bayesian_train_epoch,
+    "eval_epoch": bayesian_eval_epoch,
+}
+
+predictor_config = {
+    "predictor": BayesianBilinearMLPPredictor,
+    "predictor_layers":
+        [
+            2048,
+            128,
+            64,
+            1,
+        ],
+    "merge_n_layers_before_the_end": 2,  # Computation on the sum of the two drug embeddings for the last n layers
+    "allow_neg_eigval": True,
+    "stop": {"training_iteration": 1000, 'patience': 10}
+}
+
+model_config = {
+    "model": Baseline,
+    "load_model_weights": False,
+}
+
+dataset_config = {
+    "dataset": DrugCombMatrix,
+    "study_name": 'ALMANAC',
+    "in_house_data": 'without',
+    "rounds_to_include": [],
+    "val_set_prop": 0.2,
+    "test_set_prop": 0.1,
+    "test_on_unseen_cell_line": False,
+    "split_valid_train": "pair_level",
+    "cell_line": 'MCF7',  # 'PC-3',
+    "target": "bliss_max",  # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]),
+    "fp_bits": 1024,
+    "fp_radius": 2
+}
+
+########################################################################################################################
+# Configuration that will be loaded
+########################################################################################################################
+
+configuration = {
+    "trainer": BayesianBasicTrainer,  # PUT NUM GPU BACK TO 1
+    "trainer_config": {
+        **pipeline_config,
+        **predictor_config,
+        **model_config,
+        **dataset_config,
+    },
+    "summaries_dir": os.path.join(get_project_root(), "RayLogs"),
+    "memory": 1800,
+    "stop": {"training_iteration": 1000, 'patience': 10},
+    "checkpoint_score_attr": 'eval/comb_r_squared',
+    "keep_checkpoints_num": 1,
+    "checkpoint_at_end": True,
+    "checkpoint_freq": 0,
+    "resources_per_trial": {"cpu": 16, "gpu": 0},
+    "scheduler": None,
+    "search_alg": None,
+}
\ No newline at end of file

From c2f6eb5a488f1863c80292ac09de7d33c8896b5d Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Thu, 14 Dec 2023 12:48:35 +0100
Subject: [PATCH 13/38] Added optimized kl weight

---
 recover/train.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/recover/train.py b/recover/train.py
index c5142c7..38e81d6 100644
--- a/recover/train.py
+++ b/recover/train.py
@@ -68,12 +68,12 @@ def bayesian_train_epoch(data, loader, model, optim):
 
     epoch_loss = 0
     num_batches = len(loader)
+    batch = 1
 
     all_mean_preds = []
     all_targets = []
     
     kl_loss = bnn.BKLLoss(reduction='mean', last_layer_only=False)
-    kl_weight = 0.01
 
     for _, drug_drug_batch in enumerate(loader):
         optim.zero_grad()
@@ -86,6 +86,9 @@ def bayesian_train_epoch(data, loader, model, optim):
 
         loss = model.loss(out, drug_drug_batch) #MSE
         kl = kl_loss(model)
+
+        kl_weight = pow(2, num_batches-batch)/(pow(2, num_batches)-1) #kl weight based on the paper "Weight Uncertainty in Neural Networks"
+        batch += 1
         
         cost = loss + kl_weight*kl
 

From 47f857f222cacb195712e71f1be3552d573f0366 Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Thu, 14 Dec 2023 19:41:03 +0100
Subject: [PATCH 14/38] Added Expected Improvement

---
 recover/acquisition/acquisition.py | 25 ++++++++++++++++++++++++-
 1 file changed, 24 insertions(+), 1 deletion(-)

diff --git a/recover/acquisition/acquisition.py b/recover/acquisition/acquisition.py
index f19076e..a933c50 100644
--- a/recover/acquisition/acquisition.py
+++ b/recover/acquisition/acquisition.py
@@ -1,4 +1,6 @@
 import torch
+import numpy as np
+from scipy.special import erf
 
 ########################################################################################################################
 # Abstract Acquisition
@@ -18,16 +20,37 @@ def get_scores(self, output):
         raise NotImplementedError
 
     def get_mean_and_std(self, output):
+        output = torch.tensor(output)
         mean = output.mean(dim=1)
         std = output.std(dim=1)
-
         return mean, std
 
+    def get_best(self, output):
+        best, _ = output.max(dim=1)
+        
+        return best
+
 
 ########################################################################################################################
 # Acquisition functions
 ########################################################################################################################
 
+class ExpectedImprovementAcquisition(AbstractAcquisition):
+    def __init__(self, config):
+        super().__init__(config)
+        
+    def get_scores(self, output):
+        mean, std = self.get_mean_and_std(output)
+        best = self.get_best(output)
+        epsilon = 1e-6
+        
+        z = (mean-best-epsilon)/(std+epsilon)
+        phi = np.exp(-0.5*(z**2))/np.sqrt(2*np.pi)
+        Phi = 0.5*(1+erf(z/np.sqrt(2)))
+        scores = (mean-best)*Phi+std*phi
+
+        return scores.to("cpu")
+
 
 class RandomAcquisition(AbstractAcquisition):
     def __init__(self, config):

From 58d38bee04b056ede0fba40978f0a363e7245510 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Fri, 15 Dec 2023 03:21:14 +0530
Subject: [PATCH 15/38] Automated the switch between default and bayesian
 modelling through configs

---
 recover/config/bayesian_model_evaluation.py |   7 +-
 recover/models/predictors.py                | 334 ++++++++++----------
 recover/train.py                            |  31 +-
 3 files changed, 185 insertions(+), 187 deletions(-)

diff --git a/recover/config/bayesian_model_evaluation.py b/recover/config/bayesian_model_evaluation.py
index 4d886cb..8728a43 100644
--- a/recover/config/bayesian_model_evaluation.py
+++ b/recover/config/bayesian_model_evaluation.py
@@ -1,6 +1,6 @@
 from recover.datasets.drugcomb_matrix_data import DrugCombMatrix
 from recover.models.models import Baseline
-from recover.models.predictors import BilinearFilmMLPPredictor, BayesianBilinearMLPPredictor
+from recover.models.predictors import BilinearFilmMLPPredictor, BilinearMLPPredictor, MLPPredictor
 from recover.utils.utils import get_project_root
 from recover.train import train_epoch, bayesian_train_epoch, eval_epoch, bayesian_eval_epoch, BasicTrainer, BayesianBasicTrainer
 import os
@@ -26,7 +26,9 @@
 }
 
 predictor_config = {
-    "predictor": BayesianBilinearMLPPredictor,
+    "predictor": MLPPredictor,
+    "bayesian_predictor": True,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
     "predictor_layers":
         [
             2048,
@@ -35,6 +37,7 @@
             1,
         ],
     "merge_n_layers_before_the_end": 2,  # Computation on the sum of the two drug embeddings for the last n layers
+    "num_realizations": 10, # For bayesian uncertainty
     "allow_neg_eigval": True,
     "stop": {"training_iteration": 1000, 'patience': 10}
 }
diff --git a/recover/models/predictors.py b/recover/models/predictors.py
index a0234f2..ede761f 100644
--- a/recover/models/predictors.py
+++ b/recover/models/predictors.py
@@ -112,7 +112,7 @@ def forward(self, input):
 
 
 ########################################################################################################################
-# Bilinear MLP
+# Bilinear MLP for default and baysian versions 
 ########################################################################################################################
 
 
@@ -126,117 +126,9 @@ def __init__(self, data, config, predictor_layers):
         self.device = torch.device(device_type)
 
         self.layer_dims = predictor_layers
-
-        self.merge_n_layers_before_the_end = config["merge_n_layers_before_the_end"]
-        self.merge_dim = self.layer_dims[-self.merge_n_layers_before_the_end - 1]
-
-        assert 0 < self.merge_n_layers_before_the_end < len(predictor_layers)
-
-        layers_before_merge = []
-        layers_after_merge = []
-
-        # Build early layers (before addition of the two embeddings)
-        for i in range(len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end):
-            layers_before_merge = self.add_layer(
-                layers_before_merge,
-                i,
-                self.layer_dims[i],
-                self.layer_dims[i + 1]
-            )
-
-        # Build last layers (after addition of the two embeddings)
-        for i in range(
-            len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end,
-            len(self.layer_dims) - 1,
-        ):
-
-            layers_after_merge = self.add_layer(
-                layers_after_merge,
-                i,
-                self.layer_dims[i],
-                self.layer_dims[i + 1]
-            )
-
-        self.before_merge_mlp = nn.Sequential(*layers_before_merge)
-        self.after_merge_mlp = nn.Sequential(*layers_after_merge)
-
-        # Number of bilinear transformations == the dimension of the layer at which the merge is performed
-        # Initialize weights close to identity
-        self.bilinear_weights = Parameter(
-            1 / 100 * torch.randn((self.merge_dim, self.merge_dim, self.merge_dim))
-            + torch.cat([torch.eye(self.merge_dim)[None, :, :]] * self.merge_dim, dim=0)
-        )
-        self.bilinear_offsets = Parameter(1 / 100 * torch.randn((self.merge_dim)))
-
-        self.allow_neg_eigval = config["allow_neg_eigval"]
-        if self.allow_neg_eigval:
-            self.bilinear_diag = Parameter(1 / 100 * torch.randn((self.merge_dim, self.merge_dim)) + 1)
-
-    def forward(self, data, drug_drug_batch):
-        h_drug_1, h_drug_2, cell_lines = self.get_batch(data, drug_drug_batch)
-
-        # Apply before merge MLP
-        h_1 = self.before_merge_mlp([h_drug_1, cell_lines])[0]
-        h_2 = self.before_merge_mlp([h_drug_2, cell_lines])[0]
-
-        # compute <W.h_1, W.h_2> = h_1.T . W.T.W . h_2
-        h_1 = self.bilinear_weights.matmul(h_1.T).T
-        h_2 = self.bilinear_weights.matmul(h_2.T).T
-
-        if self.allow_neg_eigval:
-            # Multiply by diagonal matrix to allow for negative eigenvalues
-            h_2 *= self.bilinear_diag
-
-        # "Transpose" h_1
-        h_1 = h_1.permute(0, 2, 1)
-
-        # Multiplication
-        h_1_scal_h_2 = (h_1 * h_2).sum(1)
-
-        # Add offset
-        h_1_scal_h_2 += self.bilinear_offsets
-
-        comb = self.after_merge_mlp([h_1_scal_h_2, cell_lines])[0]
-
-        return comb
-
-    def get_batch(self, data, drug_drug_batch):
-
-        drug_1s = drug_drug_batch[0][:, 0]  # Edge-tail drugs in the batch
-        drug_2s = drug_drug_batch[0][:, 1]  # Edge-head drugs in the batch
-        cell_lines = drug_drug_batch[1]  # Cell line of all examples in the batch
-
-        h_drug_1 = data.x_drugs[drug_1s]
-        h_drug_2 = data.x_drugs[drug_2s]
-
-        return h_drug_1, h_drug_2, cell_lines
-
-    def add_layer(self, layers, i, dim_i, dim_i_plus_1):
-        layers.extend(self.linear_layer(i, dim_i, dim_i_plus_1))
-        if i != len(self.layer_dims) - 2:
-            layers.append(ReLUModule())
-
-        return layers
-
-    def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1)]
-
-########################################################################################################################
-# Bilinear MLP with Bayesian
-########################################################################################################################
-
-
-class BayesianBilinearMLPPredictor(torch.nn.Module):
-    def __init__(self, data, config, predictor_layers):
-
-        super(BayesianBilinearMLPPredictor, self).__init__()
-
-        self.num_cell_lines = len(data.cell_line_to_idx_dict.keys())
-        device_type = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = torch.device(device_type)
-
-        self.layer_dims = predictor_layers
-
+        
+        self.bayesian_predictor = config["bayesian_predictor"]
+        self.bayesian_before_merge = config["bayesian_before_merge"]
         self.merge_n_layers_before_the_end = config["merge_n_layers_before_the_end"]
         self.merge_dim = self.layer_dims[-self.merge_n_layers_before_the_end - 1]
 
@@ -246,27 +138,47 @@ def __init__(self, data, config, predictor_layers):
         layers_after_merge = []
 
         # Build early layers (before addition of the two embeddings)
-        for i in range(len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end):
-            layers_before_merge = self.add_layer(
-                layers_before_merge,
-                i,
-                self.layer_dims[i],
-                self.layer_dims[i + 1]
-            )
+        if (self.bayesian_predictor & self.bayesian_before_merge) :
+            for i in range(len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end):
+                layers_before_merge = self.add_bayes_layer(
+                    layers_before_merge,
+                    i,
+                    self.layer_dims[i],
+                    self.layer_dims[i + 1]
+                )
+        else :
+            for i in range(len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end):
+                layers_before_merge = self.add_layer(
+                    layers_before_merge,
+                    i,
+                    self.layer_dims[i],
+                    self.layer_dims[i + 1]
+                )
 
         # Build last layers (after addition of the two embeddings)
-        for i in range(
-            len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end,
-            len(self.layer_dims) - 1,
-        ):
-
-            layers_after_merge = self.add_bayes_layer(
-                layers_after_merge,
-                i,
-                self.layer_dims[i],
-                self.layer_dims[i + 1]
-            )
-
+        if (self.bayesian_predictor) :
+            for i in range(
+                len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end,
+                len(self.layer_dims) - 1,
+            ):
+                layers_after_merge = self.add_bayes_layer(
+                    layers_after_merge,
+                    i,
+                    self.layer_dims[i],
+                    self.layer_dims[i + 1]
+                )
+        else :
+            for i in range(
+                len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end,
+                len(self.layer_dims) - 1,
+            ):
+                layers_after_merge = self.add_layer(
+                    layers_after_merge,
+                    i,
+                    self.layer_dims[i],
+                    self.layer_dims[i + 1]
+                )
+                
         self.before_merge_mlp = nn.Sequential(*layers_before_merge)
         self.after_merge_mlp = nn.Sequential(*layers_after_merge)
 
@@ -341,6 +253,7 @@ def linear_layer(self, i, dim_i, dim_i_plus_1):
     def bayes_linear_layer(self, i, dim_i, dim_i_plus_1):
         return [BayesianLinearModule(dim_i, dim_i_plus_1)]
 
+
 ########################################################################################################################
 # Bilinear MLP with Film conditioning
 ########################################################################################################################
@@ -349,18 +262,26 @@ def bayes_linear_layer(self, i, dim_i, dim_i_plus_1):
 class BilinearFilmMLPPredictor(BilinearMLPPredictor):
     def __init__(self, data, config, predictor_layers):
         super(BilinearFilmMLPPredictor, self).__init__(data, config, predictor_layers)
+        self.bayesian_predictor = config["bayesian_predictor"]
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])]
+        if self.bayesian_predictor :
+            return [BayesianLinearModule(dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])]
+        else :
+            return [LinearModule(dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])]
 
 
 class BilinearFilmWithFeatMLPPredictor(BilinearMLPPredictor):
     def __init__(self, data, config, predictor_layers):
-        self. cl_features_dim = data.cell_line_features.shape[1]
         super(BilinearFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers)
+        self. cl_features_dim = data.cell_line_features.shape[1]
+        self.bayesian_predictor = config["bayesian_predictor"]
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+        if self.bayesian_predictor :
+            return [BayesianLinearModule(dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+        else:
+            return [LinearModule(dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
 
     def get_batch(self, data, drug_drug_batch):
 
@@ -378,10 +299,13 @@ def get_batch(self, data, drug_drug_batch):
 class BilinearLinFilmWithFeatMLPPredictor(BilinearFilmWithFeatMLPPredictor):
     def __init__(self, data, config, predictor_layers):
         super(BilinearLinFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers)
+        self.bayesian_predictor = config["bayesian_predictor"]
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim,
-                                                                               self.layer_dims[i + 1])]
+        if self.bayesian_predictor :
+            return [BayesianLinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+        else:
+            return [LinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
 
 
 # ########################################################################################################################
@@ -413,7 +337,7 @@ def linear_layer(self, i, dim_i, dim_i_plus_1):
 
 
 ########################################################################################################################
-# No permutation invariance MLP
+# No permutation invariance MLP for default and baysian versions 
 ########################################################################################################################
 
 
@@ -427,7 +351,9 @@ def __init__(self, data, config, predictor_layers):
         self.device = torch.device(device_type)
 
         self.layer_dims = predictor_layers
-
+        
+        self.bayesian_predictor = config["bayesian_predictor"]
+        self.bayesian_before_merge = config["bayesian_before_merge"]
         self.merge_n_layers_before_the_end = config["merge_n_layers_before_the_end"]
         self.merge_dim = self.layer_dims[-self.merge_n_layers_before_the_end - 1]
 
@@ -435,31 +361,51 @@ def __init__(self, data, config, predictor_layers):
 
         layers_before_merge = []
         layers_after_merge = []
-
-        # Build early layers (before addition of the two embeddings)
-        for i in range(len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end):
-            layers_before_merge = self.add_layer(
-                layers_before_merge,
-                i,
-                self.layer_dims[i],
-                self.layer_dims[i + 1]
-            )
-
+  
+            
+        if (self.bayesian_predictor & self.bayesian_before_merge) :
+            for i in range(len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end):
+                layers_before_merge = self.add_bayes_layer(
+                    layers_before_merge,
+                    i,
+                    self.layer_dims[i],
+                    self.layer_dims[i + 1]
+                )
+        else :
+            for i in range(len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end):
+                layers_before_merge = self.add_layer(
+                    layers_before_merge,
+                    i,
+                    self.layer_dims[i],
+                    self.layer_dims[i + 1]
+                )
+                
         # We will concatenate the two single drug embeddings so the input of the after_merge_mlp is twice its usual dim
         self.layer_dims[- 1 - self.merge_n_layers_before_the_end] *= 2
 
         # Build last layers (after addition of the two embeddings)
-        for i in range(
-            len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end,
-            len(self.layer_dims) - 1,
-        ):
-
-            layers_after_merge = self.add_layer(
-                layers_after_merge,
-                i,
-                self.layer_dims[i],
-                self.layer_dims[i + 1]
-            )
+        if (self.bayesian_predictor) :
+            for i in range(
+                len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end,
+                len(self.layer_dims) - 1,
+            ):
+                layers_after_merge = self.add_bayes_layer(
+                    layers_after_merge,
+                    i,
+                    self.layer_dims[i],
+                    self.layer_dims[i + 1]
+                )
+        else :
+            for i in range(
+                len(self.layer_dims) - 1 - self.merge_n_layers_before_the_end,
+                len(self.layer_dims) - 1,
+            ):
+                layers_after_merge = self.add_layer(
+                    layers_after_merge,
+                    i,
+                    self.layer_dims[i],
+                    self.layer_dims[i + 1]
+                )
 
         self.before_merge_mlp = nn.Sequential(*layers_before_merge)
         self.after_merge_mlp = nn.Sequential(*layers_after_merge)
@@ -495,6 +441,16 @@ def add_layer(self, layers, i, dim_i, dim_i_plus_1):
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
         return [LinearModule(dim_i, dim_i_plus_1)]
+        
+    def add_bayes_layer(self, layers, i, dim_i, dim_i_plus_1):
+        layers.extend(self.bayes_linear_layer(i, dim_i, dim_i_plus_1))
+        if i != len(self.layer_dims) - 2:
+            layers.append(ReLUModule())
+
+        return layers
+        
+    def bayes_linear_layer(self, i, dim_i, dim_i_plus_1):
+        return [BayesianLinearModule(dim_i, dim_i_plus_1)]
 
 
 ########################################################################################################################
@@ -505,19 +461,27 @@ def linear_layer(self, i, dim_i, dim_i_plus_1):
 class FilmMLPPredictor(MLPPredictor):
     def __init__(self, data, config, predictor_layers):
         super(FilmMLPPredictor, self).__init__(data, config, predictor_layers)
+        self.bayesian_predictor = config["bayesian_predictor"]
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])]
+        if self.bayesian_predictor :
+            return [BayesianLinearModule(dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])]
+        else :
+            return [LinearModule(dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])]
 
 
 class FilmWithFeatMLPPredictor(MLPPredictor):
     def __init__(self, data, config, predictor_layers):
         self. cl_features_dim = data.cell_line_features.shape[1]
+        self.bayesian_predictor = config["bayesian_predictor"]
         super(FilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers)
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
-
+        if self.bayesian_predictor :
+            return [BayesianLinearModule(dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+        else :
+            return [LinearModule(dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+            
     def get_batch(self, data, drug_drug_batch):
 
         drug_1s = drug_drug_batch[0][:, 0]  # Edge-tail drugs in the batch
@@ -534,10 +498,13 @@ def get_batch(self, data, drug_drug_batch):
 class LinFilmWithFeatMLPPredictor(FilmWithFeatMLPPredictor):
     def __init__(self, data, config, predictor_layers):
         super(LinFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers)
+        self.bayesian_predictor = config["bayesian_predictor"]
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim,
-                                                                               self.layer_dims[i + 1])]
+        if self.bayesian_predictor :
+            return [BayesianLinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+        else :
+            return [LinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
 
 
 ########################################################################################################################
@@ -646,18 +613,26 @@ def __init__(self, data, config, predictor_layers):
 class ShuffledBilinearFilmMLPPredictor(ShuffledBilinearMLPPredictor):
     def __init__(self, data, config, predictor_layers):
         super(ShuffledBilinearFilmMLPPredictor, self).__init__(data, config, predictor_layers)
+        self.bayesian_predictor = config["bayesian_predictor"]
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])]
+        if self.bayesian_predictor :
+            return [BayesianLinearModule(dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])]
+        else :
+            return [LinearModule(dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])]
 
 
 class ShuffledBilinearFilmWithFeatMLPPredictor(ShuffledBilinearMLPPredictor):
     def __init__(self, data, config, predictor_layers):
         self. cl_features_dim = data.cell_line_features.shape[1]
+        self.bayesian_predictor = config["bayesian_predictor"]
         super(ShuffledBilinearFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers)
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+        if self.bayesian_predictor :
+            return [BayesianLinearModule(dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+        else :
+            return [LinearModule(dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
 
     def get_batch(self, data, drug_drug_batch):
 
@@ -675,10 +650,13 @@ def get_batch(self, data, drug_drug_batch):
 class ShuffledBilinearLinFilmWithFeatMLPPredictor(ShuffledBilinearFilmWithFeatMLPPredictor):
     def __init__(self, data, config, predictor_layers):
         super(ShuffledBilinearLinFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers)
+        self.bayesian_predictor = config["bayesian_predictor"]
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim,
-                                                                               self.layer_dims[i + 1])]
+        if self.bayesian_predictor :
+            return [BayesianLinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+        else :
+            return [LinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
 
 
 ########################################################################################################################
@@ -708,18 +686,26 @@ def __init__(self, data, config, predictor_layers):
 class PartiallyShuffledBilinearFilmMLPPredictor(PartiallyShuffledBilinearMLPPredictor):
     def __init__(self, data, config, predictor_layers):
         super(PartiallyShuffledBilinearFilmMLPPredictor, self).__init__(data, config, predictor_layers)
+        self.bayesian_predictor = config["bayesian_predictor"]
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])]
+        if self.bayesian_predictor :
+            return [BayesianLinearModule(dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])]
+        else :
+            return [LinearModule(dim_i, dim_i_plus_1), FilmModule(self.num_cell_lines, self.layer_dims[i + 1])]
 
 
 class PartiallyShuffledBilinearFilmWithFeatMLPPredictor(PartiallyShuffledBilinearMLPPredictor):
     def __init__(self, data, config, predictor_layers):
         self. cl_features_dim = data.cell_line_features.shape[1]
+        self.bayesian_predictor = config["bayesian_predictor"]
         super(PartiallyShuffledBilinearFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers)
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+        if self.bayesian_predictor :
+            return [BayesianLinearModule(dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+        else :
+            return [LinearModule(dim_i, dim_i_plus_1), FilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
 
     def get_batch(self, data, drug_drug_batch):
 
@@ -737,8 +723,12 @@ def get_batch(self, data, drug_drug_batch):
 class PartiallyShuffledBilinearLinFilmWithFeatMLPPredictor(PartiallyShuffledBilinearFilmWithFeatMLPPredictor):
     def __init__(self, data, config, predictor_layers):
         super(PartiallyShuffledBilinearLinFilmWithFeatMLPPredictor, self).__init__(data, config, predictor_layers)
+        self.bayesian_predictor = config["bayesian_predictor"]
 
     def linear_layer(self, i, dim_i, dim_i_plus_1):
-        return [LinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim,
-                                                                               self.layer_dims[i + 1])]
+        if self.bayesian_predictor :
+            return [BayesianLinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+        else:
+            return [LinearModule(dim_i, dim_i_plus_1), LinearFilmWithFeatureModule(self. cl_features_dim, self.layer_dims[i + 1])]
+                                                                               
 
diff --git a/recover/train.py b/recover/train.py
index 38e81d6..c149688 100644
--- a/recover/train.py
+++ b/recover/train.py
@@ -59,7 +59,7 @@ def train_epoch(data, loader, model, optim):
 
 """
 The cost function is updated to consider both MSE loss and KL loss
-KL weight is set to 0.01
+KL weight is set according to batchsize 
 Default model loss is unchanged - MSE (For comparision with Rec
 """
 def bayesian_train_epoch(data, loader, model, optim):
@@ -88,21 +88,20 @@ def bayesian_train_epoch(data, loader, model, optim):
         kl = kl_loss(model)
 
         kl_weight = pow(2, num_batches-batch)/(pow(2, num_batches)-1) #kl weight based on the paper "Weight Uncertainty in Neural Networks"
-        batch += 1
-        
+
         cost = loss + kl_weight*kl
 
         cost.backward()
         optim.step()
 
         epoch_loss += loss.item()
+        batch += 1
 
     epoch_comb_r_squared = stats.linregress(all_mean_preds, all_targets).rvalue**2
 
     summary_dict = {
         "loss_mean": epoch_loss / num_batches,
         "comb_r_squared": epoch_comb_r_squared
-        #"cost": cost.item()
     }
 
     print("Training", summary_dict)
@@ -148,6 +147,10 @@ def eval_epoch(data, loader, model):
     return summary_dict, all_out
 
 
+"""
+Eval epoch is the same as the non-bayesian method, 
+minor changes were done to return drug combinations for ease of analysis
+"""
 def bayesian_eval_epoch(data, loader, model):
     model.eval()
 
@@ -186,6 +189,7 @@ def bayesian_eval_epoch(data, loader, model):
 
     return summary_dict, all_out, all_combs
 
+
 """
 Custom Aggregration function for obtaining results when training with multi cell-lines 
 
@@ -333,6 +337,7 @@ def setup(self, config):
         print("Initializing regular training pipeline")
 
         self.batch_size = config["batch_size"]
+        self.num_realizations = config["num_realizations"]
         device_type = "cuda" if torch.cuda.is_available() else "cpu"
         self.device = torch.device(device_type)
         self.training_it = 0
@@ -423,7 +428,7 @@ def setup(self, config):
 
     def step(self):
     
-        num_realizations = 10
+        num_realizations = self.num_realizations
 
         train_metrics = self.train_epoch(
             self.data,
@@ -472,21 +477,21 @@ def step(self):
             synergy_mean = list(torch.mean(result_synergy, dim=1).numpy())
             synergy_std = list(torch.std(result_synergy, dim=1).numpy())
             
+            """
+            # Uncomment this to get the synergy prediction performance when working with multi-cell lines 
+            # Aggregate result for duplicate sets regardless of cell-line
             result_tuples = list(map(lambda inner_list: tuple(sorted(inner_list)), drug_combs))
-            
             dataset = pd.DataFrame({'combination': result_tuples, 'mean': synergy_mean, 'std': synergy_std }, columns=['combination', 'mean', 'std'])
-            
             result_df = dataset.groupby('combination').apply(custom_agg) # Do custom aggregration  - Not needed when working on one cell-line but no difference in results
-            
             result_df.reset_index(drop=True, inplace=True)
+            """
             
-            metrics.update(dict(test_result)) # Add test results to output files
+            metrics.update(dict(test_result))
             
-            metrics['synergy_combs'] = list(result_df['combination'])
-            metrics['synergy_mean'] = list(result_df['mean'])
-            metrics['synergy_std'] = list(result_df['std'])
+            metrics['synergy_combs'] = list(drug_combs)
+            metrics['synergy_mean'] = list(synergy_mean)
+            metrics['synergy_std'] = list(synergy_std)
             
-            #metrics.update(dict(drug_combinations_synergy))
         
         return metrics
     

From 234d907a9103621eef6270e4507513b43ca69c3f Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Fri, 15 Dec 2023 03:34:42 +0530
Subject: [PATCH 16/38] Added bayesian parameters to configs

---
 recover/config/active_learning_UCB.py                          | 3 +++
 recover/config/active_learning_bliss_av.py                     | 3 +++
 recover/config/active_learning_pretraining_Feb.py              | 3 +++
 recover/config/active_learning_pretraining_Feb_ens.py          | 3 +++
 recover/config/bayesian_model_evaluation.py                    | 2 +-
 recover/config/cell_line_transfer.py                           | 3 +++
 recover/config/cell_line_transfer_deep_syn.py                  | 3 +++
 recover/config/cell_line_transfer_no_permut_invariance.py      | 3 +++
 recover/config/cell_line_transfer_shuffled.py                  | 3 +++
 recover/config/highlight_smo_deep_syn.py                       | 3 +++
 recover/config/highlight_smo_deep_syn_no_adapt.py              | 3 +++
 .../config/highlight_smo_deep_syn_no_adapt_single_cell_line.py | 3 +++
 recover/config/highlight_smo_deep_syn_single_cell_line.py      | 3 +++
 recover/config/highlight_smo_recover.py                        | 3 +++
 recover/config/highlight_smo_recover_no_adapt.py               | 3 +++
 .../config/highlight_smo_recover_no_adapt_single_cell_line.py  | 3 +++
 recover/config/highlight_smo_recover_single_cell_line.py       | 3 +++
 .../config/highlight_smo_recover_single_cell_line_others.py    | 3 +++
 recover/config/model_drug_level_split.py                       | 3 +++
 recover/config/model_drug_level_split_deep_syn.py              | 3 +++
 recover/config/model_drug_level_split_no_permut_invariance.py  | 3 +++
 recover/config/model_drug_level_split_partially_shuffled.py    | 3 +++
 recover/config/model_drug_level_split_shuffled.py              | 3 +++
 recover/config/model_evaluation.py                             | 3 +++
 recover/config/model_evaluation_deep_syn.py                    | 3 +++
 recover/config/model_evaluation_multi_cell_line.py             | 3 +++
 recover/config/model_evaluation_multi_cell_line_deep_syn.py    | 3 +++
 .../model_evaluation_multi_cell_line_no_permut_invariance.py   | 3 +++
 recover/config/model_evaluation_multi_cell_line_shuffled.py    | 3 +++
 recover/config/model_evaluation_no_fp.py                       | 3 +++
 recover/config/model_evaluation_no_one_hot.py                  | 3 +++
 recover/config/model_evaluation_no_permut_invariance.py        | 3 +++
 recover/config/one_hidden_drug_split.py                        | 3 +++
 recover/config/one_hidden_drug_split_deep_syn.py               | 3 +++
 recover/config/one_hidden_drug_split_no_permut_invariance.py   | 3 +++
 recover/config/one_hidden_drug_split_partially_shuffled.py     | 3 +++
 recover/config/one_hidden_drug_split_shuffled.py               | 3 +++
 recover/config/pretrain_ONEIL.py                               | 3 +++
 recover/config/pretrain_ONEIL_Feb.py                           | 3 +++
 recover/config/pretrain_ONEIL_Feb_ens.py                       | 3 +++
 recover/config/pretrain_ONEIL_deep_syn.py                      | 3 +++
 recover/config/pretrain_ONEIL_deep_syn_ens.py                  | 3 +++
 recover/config/pretrain_ONEIL_no_permut_invariance.py          | 3 +++
 recover/config/pretrain_ONEIL_shuffled.py                      | 3 +++
 recover/config/training_after_round_5.py                       | 3 +++
 recover/config/training_after_round_5_no_in_house.py           | 3 +++
 recover/config/training_after_round_5_successive_rounds.py     | 3 +++
 recover/config/uncertainty_estimate_analysis_deup.py           | 3 +++
 recover/config/uncertainty_estimate_analysis_ens.py            | 3 +++
 recover/config/uncertainty_estimate_analysis_ens_20.py         | 3 +++
 50 files changed, 148 insertions(+), 1 deletion(-)

diff --git a/recover/config/active_learning_UCB.py b/recover/config/active_learning_UCB.py
index 7329a00..84f35ba 100644
--- a/recover/config/active_learning_UCB.py
+++ b/recover/config/active_learning_UCB.py
@@ -28,6 +28,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/active_learning_bliss_av.py b/recover/config/active_learning_bliss_av.py
index 2ce9d20..eba0f4a 100644
--- a/recover/config/active_learning_bliss_av.py
+++ b/recover/config/active_learning_bliss_av.py
@@ -28,6 +28,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/active_learning_pretraining_Feb.py b/recover/config/active_learning_pretraining_Feb.py
index a8ae2a6..d8e9bf2 100644
--- a/recover/config/active_learning_pretraining_Feb.py
+++ b/recover/config/active_learning_pretraining_Feb.py
@@ -29,6 +29,9 @@
 
 predictor_config = {
     "predictor": BilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/active_learning_pretraining_Feb_ens.py b/recover/config/active_learning_pretraining_Feb_ens.py
index 34d4b4d..a746dbe 100644
--- a/recover/config/active_learning_pretraining_Feb_ens.py
+++ b/recover/config/active_learning_pretraining_Feb_ens.py
@@ -29,6 +29,9 @@
 
 predictor_config = {
     "predictor": BilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/bayesian_model_evaluation.py b/recover/config/bayesian_model_evaluation.py
index 8728a43..e2f14c9 100644
--- a/recover/config/bayesian_model_evaluation.py
+++ b/recover/config/bayesian_model_evaluation.py
@@ -29,6 +29,7 @@
     "predictor": MLPPredictor,
     "bayesian_predictor": True,
     "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 10, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
@@ -37,7 +38,6 @@
             1,
         ],
     "merge_n_layers_before_the_end": 2,  # Computation on the sum of the two drug embeddings for the last n layers
-    "num_realizations": 10, # For bayesian uncertainty
     "allow_neg_eigval": True,
     "stop": {"training_iteration": 1000, 'patience': 10}
 }
diff --git a/recover/config/cell_line_transfer.py b/recover/config/cell_line_transfer.py
index aa51be8..8466157 100644
--- a/recover/config/cell_line_transfer.py
+++ b/recover/config/cell_line_transfer.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearLinFilmWithFeatMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/cell_line_transfer_deep_syn.py b/recover/config/cell_line_transfer_deep_syn.py
index 53087b1..f8c6e2c 100644
--- a/recover/config/cell_line_transfer_deep_syn.py
+++ b/recover/config/cell_line_transfer_deep_syn.py
@@ -28,6 +28,9 @@
 
 predictor_config = {
     "predictor": DeepSynergyPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             8182,
diff --git a/recover/config/cell_line_transfer_no_permut_invariance.py b/recover/config/cell_line_transfer_no_permut_invariance.py
index 8e1aa24..be18d67 100644
--- a/recover/config/cell_line_transfer_no_permut_invariance.py
+++ b/recover/config/cell_line_transfer_no_permut_invariance.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": LinFilmWithFeatMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/cell_line_transfer_shuffled.py b/recover/config/cell_line_transfer_shuffled.py
index b72b875..31797ba 100644
--- a/recover/config/cell_line_transfer_shuffled.py
+++ b/recover/config/cell_line_transfer_shuffled.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": ShuffledBilinearLinFilmWithFeatMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/highlight_smo_deep_syn.py b/recover/config/highlight_smo_deep_syn.py
index adbfda4..42589f8 100644
--- a/recover/config/highlight_smo_deep_syn.py
+++ b/recover/config/highlight_smo_deep_syn.py
@@ -29,6 +29,9 @@
 
 predictor_config = {
     "predictor": DeepSynergyPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             8182,
diff --git a/recover/config/highlight_smo_deep_syn_no_adapt.py b/recover/config/highlight_smo_deep_syn_no_adapt.py
index e820c0c..1c5038f 100644
--- a/recover/config/highlight_smo_deep_syn_no_adapt.py
+++ b/recover/config/highlight_smo_deep_syn_no_adapt.py
@@ -29,6 +29,9 @@
 
 predictor_config = {
     "predictor": DeepSynergyPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             8182,
diff --git a/recover/config/highlight_smo_deep_syn_no_adapt_single_cell_line.py b/recover/config/highlight_smo_deep_syn_no_adapt_single_cell_line.py
index 43a5603..cfcb28a 100644
--- a/recover/config/highlight_smo_deep_syn_no_adapt_single_cell_line.py
+++ b/recover/config/highlight_smo_deep_syn_no_adapt_single_cell_line.py
@@ -29,6 +29,9 @@
 
 predictor_config = {
     "predictor": DeepSynergyPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             8182,
diff --git a/recover/config/highlight_smo_deep_syn_single_cell_line.py b/recover/config/highlight_smo_deep_syn_single_cell_line.py
index db8b549..def9265 100644
--- a/recover/config/highlight_smo_deep_syn_single_cell_line.py
+++ b/recover/config/highlight_smo_deep_syn_single_cell_line.py
@@ -29,6 +29,9 @@
 
 predictor_config = {
     "predictor": DeepSynergyPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             8182,
diff --git a/recover/config/highlight_smo_recover.py b/recover/config/highlight_smo_recover.py
index 0d9dfd3..cba19e3 100644
--- a/recover/config/highlight_smo_recover.py
+++ b/recover/config/highlight_smo_recover.py
@@ -29,6 +29,9 @@
 
 predictor_config = {
     "predictor": BilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/highlight_smo_recover_no_adapt.py b/recover/config/highlight_smo_recover_no_adapt.py
index a12dd27..1f8290c 100644
--- a/recover/config/highlight_smo_recover_no_adapt.py
+++ b/recover/config/highlight_smo_recover_no_adapt.py
@@ -29,6 +29,9 @@
 
 predictor_config = {
     "predictor": BilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/highlight_smo_recover_no_adapt_single_cell_line.py b/recover/config/highlight_smo_recover_no_adapt_single_cell_line.py
index 541e268..7cbf263 100644
--- a/recover/config/highlight_smo_recover_no_adapt_single_cell_line.py
+++ b/recover/config/highlight_smo_recover_no_adapt_single_cell_line.py
@@ -29,6 +29,9 @@
 
 predictor_config = {
     "predictor": BilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/highlight_smo_recover_single_cell_line.py b/recover/config/highlight_smo_recover_single_cell_line.py
index 2d0b715..c8547bf 100644
--- a/recover/config/highlight_smo_recover_single_cell_line.py
+++ b/recover/config/highlight_smo_recover_single_cell_line.py
@@ -29,6 +29,9 @@
 
 predictor_config = {
     "predictor": BilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/highlight_smo_recover_single_cell_line_others.py b/recover/config/highlight_smo_recover_single_cell_line_others.py
index e2ab9e9..aaf472c 100644
--- a/recover/config/highlight_smo_recover_single_cell_line_others.py
+++ b/recover/config/highlight_smo_recover_single_cell_line_others.py
@@ -29,6 +29,9 @@
 
 predictor_config = {
     "predictor": BilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/model_drug_level_split.py b/recover/config/model_drug_level_split.py
index 0ff3a2a..06d3eee 100644
--- a/recover/config/model_drug_level_split.py
+++ b/recover/config/model_drug_level_split.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/model_drug_level_split_deep_syn.py b/recover/config/model_drug_level_split_deep_syn.py
index 79a867c..e4df7cd 100644
--- a/recover/config/model_drug_level_split_deep_syn.py
+++ b/recover/config/model_drug_level_split_deep_syn.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": DeepSynergyPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             8182,
diff --git a/recover/config/model_drug_level_split_no_permut_invariance.py b/recover/config/model_drug_level_split_no_permut_invariance.py
index 1ef67ba..b224e11 100644
--- a/recover/config/model_drug_level_split_no_permut_invariance.py
+++ b/recover/config/model_drug_level_split_no_permut_invariance.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": MLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/model_drug_level_split_partially_shuffled.py b/recover/config/model_drug_level_split_partially_shuffled.py
index 990520f..e95d915 100644
--- a/recover/config/model_drug_level_split_partially_shuffled.py
+++ b/recover/config/model_drug_level_split_partially_shuffled.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": PartiallyShuffledBilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "prop_of_shuffled_drugs": tune.grid_search([0., 0.25, 0.5, 0.75, 1.]),
     "predictor_layers":
         [
diff --git a/recover/config/model_drug_level_split_shuffled.py b/recover/config/model_drug_level_split_shuffled.py
index 60c2fe6..5d0f080 100644
--- a/recover/config/model_drug_level_split_shuffled.py
+++ b/recover/config/model_drug_level_split_shuffled.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": ShuffledBilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/model_evaluation.py b/recover/config/model_evaluation.py
index 391b907..207b133 100644
--- a/recover/config/model_evaluation.py
+++ b/recover/config/model_evaluation.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/model_evaluation_deep_syn.py b/recover/config/model_evaluation_deep_syn.py
index 8697ba6..ab9cced 100644
--- a/recover/config/model_evaluation_deep_syn.py
+++ b/recover/config/model_evaluation_deep_syn.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": DeepSynergyPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             8182,
diff --git a/recover/config/model_evaluation_multi_cell_line.py b/recover/config/model_evaluation_multi_cell_line.py
index 6f0398d..7fa0779 100644
--- a/recover/config/model_evaluation_multi_cell_line.py
+++ b/recover/config/model_evaluation_multi_cell_line.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/model_evaluation_multi_cell_line_deep_syn.py b/recover/config/model_evaluation_multi_cell_line_deep_syn.py
index fc4bc04..486555a 100644
--- a/recover/config/model_evaluation_multi_cell_line_deep_syn.py
+++ b/recover/config/model_evaluation_multi_cell_line_deep_syn.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": DeepSynergyPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             8182,
diff --git a/recover/config/model_evaluation_multi_cell_line_no_permut_invariance.py b/recover/config/model_evaluation_multi_cell_line_no_permut_invariance.py
index 008fbc2..5e6c9fa 100644
--- a/recover/config/model_evaluation_multi_cell_line_no_permut_invariance.py
+++ b/recover/config/model_evaluation_multi_cell_line_no_permut_invariance.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": FilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/model_evaluation_multi_cell_line_shuffled.py b/recover/config/model_evaluation_multi_cell_line_shuffled.py
index 2139d85..923db11 100644
--- a/recover/config/model_evaluation_multi_cell_line_shuffled.py
+++ b/recover/config/model_evaluation_multi_cell_line_shuffled.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": ShuffledBilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/model_evaluation_no_fp.py b/recover/config/model_evaluation_no_fp.py
index 01b0ca1..e682269 100644
--- a/recover/config/model_evaluation_no_fp.py
+++ b/recover/config/model_evaluation_no_fp.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/model_evaluation_no_one_hot.py b/recover/config/model_evaluation_no_one_hot.py
index f04e210..0d94d2b 100644
--- a/recover/config/model_evaluation_no_one_hot.py
+++ b/recover/config/model_evaluation_no_one_hot.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/model_evaluation_no_permut_invariance.py b/recover/config/model_evaluation_no_permut_invariance.py
index c5f23e6..35e217e 100644
--- a/recover/config/model_evaluation_no_permut_invariance.py
+++ b/recover/config/model_evaluation_no_permut_invariance.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": MLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/one_hidden_drug_split.py b/recover/config/one_hidden_drug_split.py
index 47eacdc..0722cfb 100644
--- a/recover/config/one_hidden_drug_split.py
+++ b/recover/config/one_hidden_drug_split.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/one_hidden_drug_split_deep_syn.py b/recover/config/one_hidden_drug_split_deep_syn.py
index 9d21900..bf5dbe8 100644
--- a/recover/config/one_hidden_drug_split_deep_syn.py
+++ b/recover/config/one_hidden_drug_split_deep_syn.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": DeepSynergyPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             8182,
diff --git a/recover/config/one_hidden_drug_split_no_permut_invariance.py b/recover/config/one_hidden_drug_split_no_permut_invariance.py
index 167c712..b515526 100644
--- a/recover/config/one_hidden_drug_split_no_permut_invariance.py
+++ b/recover/config/one_hidden_drug_split_no_permut_invariance.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": MLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/one_hidden_drug_split_partially_shuffled.py b/recover/config/one_hidden_drug_split_partially_shuffled.py
index 6411829..4758f71 100644
--- a/recover/config/one_hidden_drug_split_partially_shuffled.py
+++ b/recover/config/one_hidden_drug_split_partially_shuffled.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": PartiallyShuffledBilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "prop_of_shuffled_drugs": tune.grid_search([0., 0.25, 0.5, 0.75, 1.]),
     "predictor_layers":
         [
diff --git a/recover/config/one_hidden_drug_split_shuffled.py b/recover/config/one_hidden_drug_split_shuffled.py
index d2acc27..81cd128 100644
--- a/recover/config/one_hidden_drug_split_shuffled.py
+++ b/recover/config/one_hidden_drug_split_shuffled.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": ShuffledBilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/pretrain_ONEIL.py b/recover/config/pretrain_ONEIL.py
index fb6e594..c2c3ecb 100644
--- a/recover/config/pretrain_ONEIL.py
+++ b/recover/config/pretrain_ONEIL.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/pretrain_ONEIL_Feb.py b/recover/config/pretrain_ONEIL_Feb.py
index 07e5649..466b4bc 100644
--- a/recover/config/pretrain_ONEIL_Feb.py
+++ b/recover/config/pretrain_ONEIL_Feb.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/pretrain_ONEIL_Feb_ens.py b/recover/config/pretrain_ONEIL_Feb_ens.py
index fc1065a..43624d1 100644
--- a/recover/config/pretrain_ONEIL_Feb_ens.py
+++ b/recover/config/pretrain_ONEIL_Feb_ens.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/pretrain_ONEIL_deep_syn.py b/recover/config/pretrain_ONEIL_deep_syn.py
index 43cf51d..6ac0e0a 100644
--- a/recover/config/pretrain_ONEIL_deep_syn.py
+++ b/recover/config/pretrain_ONEIL_deep_syn.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": DeepSynergyPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             8182,
diff --git a/recover/config/pretrain_ONEIL_deep_syn_ens.py b/recover/config/pretrain_ONEIL_deep_syn_ens.py
index 7af243d..aa05d6c 100644
--- a/recover/config/pretrain_ONEIL_deep_syn_ens.py
+++ b/recover/config/pretrain_ONEIL_deep_syn_ens.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": DeepSynergyPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             8182,
diff --git a/recover/config/pretrain_ONEIL_no_permut_invariance.py b/recover/config/pretrain_ONEIL_no_permut_invariance.py
index 99aeedf..2e8b986 100644
--- a/recover/config/pretrain_ONEIL_no_permut_invariance.py
+++ b/recover/config/pretrain_ONEIL_no_permut_invariance.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": FilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/pretrain_ONEIL_shuffled.py b/recover/config/pretrain_ONEIL_shuffled.py
index da729e5..6cb3d96 100644
--- a/recover/config/pretrain_ONEIL_shuffled.py
+++ b/recover/config/pretrain_ONEIL_shuffled.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": ShuffledBilinearFilmMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/training_after_round_5.py b/recover/config/training_after_round_5.py
index 228b2ef..7b40925 100644
--- a/recover/config/training_after_round_5.py
+++ b/recover/config/training_after_round_5.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/training_after_round_5_no_in_house.py b/recover/config/training_after_round_5_no_in_house.py
index 534d14c..abe8906 100644
--- a/recover/config/training_after_round_5_no_in_house.py
+++ b/recover/config/training_after_round_5_no_in_house.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/training_after_round_5_successive_rounds.py b/recover/config/training_after_round_5_successive_rounds.py
index b5a2ceb..2f30a13 100644
--- a/recover/config/training_after_round_5_successive_rounds.py
+++ b/recover/config/training_after_round_5_successive_rounds.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/uncertainty_estimate_analysis_deup.py b/recover/config/uncertainty_estimate_analysis_deup.py
index 6836fb3..5b8782b 100644
--- a/recover/config/uncertainty_estimate_analysis_deup.py
+++ b/recover/config/uncertainty_estimate_analysis_deup.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/uncertainty_estimate_analysis_ens.py b/recover/config/uncertainty_estimate_analysis_ens.py
index 097b554..b66b91d 100644
--- a/recover/config/uncertainty_estimate_analysis_ens.py
+++ b/recover/config/uncertainty_estimate_analysis_ens.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
diff --git a/recover/config/uncertainty_estimate_analysis_ens_20.py b/recover/config/uncertainty_estimate_analysis_ens_20.py
index eb28368..514202b 100644
--- a/recover/config/uncertainty_estimate_analysis_ens_20.py
+++ b/recover/config/uncertainty_estimate_analysis_ens_20.py
@@ -27,6 +27,9 @@
 
 predictor_config = {
     "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": False,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,

From 242bddc4e1b6663f3a260624555325697cbaf0d6 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Fri, 15 Dec 2023 04:57:33 +0530
Subject: [PATCH 17/38] Probability of improvement aquisition added

---
 recover/acquisition/acquisition.py | 27 +++++++++++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/recover/acquisition/acquisition.py b/recover/acquisition/acquisition.py
index a933c50..b25df38 100644
--- a/recover/acquisition/acquisition.py
+++ b/recover/acquisition/acquisition.py
@@ -1,6 +1,7 @@
 import torch
 import numpy as np
 from scipy.special import erf
+from scipy.stats import norm
 
 ########################################################################################################################
 # Abstract Acquisition
@@ -25,9 +26,13 @@ def get_mean_and_std(self, output):
         std = output.std(dim=1)
         return mean, std
 
-    def get_best(self, output):
+    """
+    The max synergy is considered as current best since we don't have access to ground truth
+    """
+    def get_current_best(self, output):
         best, _ = output.max(dim=1)
         
+        
         return best
 
 
@@ -41,7 +46,7 @@ def __init__(self, config):
         
     def get_scores(self, output):
         mean, std = self.get_mean_and_std(output)
-        best = self.get_best(output)
+        best = self.get_current_best(output)
         epsilon = 1e-6
         
         z = (mean-best-epsilon)/(std+epsilon)
@@ -58,6 +63,24 @@ def __init__(self, config):
 
     def get_scores(self, output):
         return torch.randn(output.shape[0])
+        
+
+class ProbabilityOfImprovementAcquisition(AbstractAcquisition):
+    """
+    Probability of Improvement Aquisition Function
+    
+    """
+    def __init__(self, config):
+        super().__init__(config)
+        
+    def get_scores(self, output):
+        mean, std = self.get_mean_and_std(output)
+        current_best = self.get_current_best(output)
+        
+        z = (mean - current_best) / std
+        prob_of_improvement_scores = norm.cdf(z)
+
+        return torch.tensor(prob_of_improvement_scores).to("cpu")
 
 
 class UCB(AbstractAcquisition):

From 46a65d17d6129f5053cd0547fe0b52011753e863 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Fri, 15 Dec 2023 04:59:11 +0530
Subject: [PATCH 18/38] Active learning trainer and configs modified

---
 recover/config/bayesian_active_learning.py | 113 +++++++++++
 recover/train.py                           | 217 +++++++++++++++++++++
 2 files changed, 330 insertions(+)

diff --git a/recover/config/bayesian_active_learning.py b/recover/config/bayesian_active_learning.py
index e69de29..d8e949d 100644
--- a/recover/config/bayesian_active_learning.py
+++ b/recover/config/bayesian_active_learning.py
@@ -0,0 +1,113 @@
+from recover.datasets.drugcomb_matrix_data import DrugCombMatrix
+from recover.models.models import Baseline, EnsembleModel
+from recover.models.predictors import BilinearFilmMLPPredictor, \
+    BilinearMLPPredictor, BilinearFilmWithFeatMLPPredictor
+from recover.utils.utils import get_project_root
+from recover.acquisition.acquisition import RandomAcquisition, GreedyAcquisition, UCB, ProbabilityOfImprovementAcquisition
+from recover.train import train_epoch, eval_epoch, bayesian_train_epoch, bayesian_eval_epoch, BasicTrainer, ActiveTrainer, ActiveTrainerBayesian
+import os
+from ray import tune
+
+########################################################################################################################
+# Configuration
+########################################################################################################################
+
+
+pipeline_config = {
+    "use_tune": True,
+    "num_epoch_without_tune": 500,  # Used only if "use_tune" == False
+    "seed": tune.grid_search([1, 2, 3]),
+    # Optimizer config
+    "lr": 1e-4,
+    "weight_decay": 1e-2,
+    "batch_size": 128,
+    # Train epoch and eval_epoch to use
+    "train_epoch": bayesian_train_epoch,
+    "eval_epoch": bayesian_eval_epoch,
+}
+
+predictor_config = {
+    "predictor": BilinearMLPPredictor,
+    "bayesian_predictor": True,
+    "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "num_realizations": 10, # For bayesian uncertainty
+    "predictor_layers":
+        [
+            2048,
+            128,
+            64,
+            1,
+        ],
+    "merge_n_layers_before_the_end": 2,  # Computation on the sum of the two drug embeddings for the last n layers
+    "allow_neg_eigval": True,
+    "stop": {"training_iteration": 1000, 'patience': 4}
+}
+
+model_config = {
+    "model": Baseline,
+    # Loading pretrained model
+    "load_model_weights": False,  # tune.grid_search([True, False]),
+    "model_weights_file": "",
+}
+
+"""
+List of cell line names:
+
+['786-0', 'A498', 'A549', 'ACHN', 'BT-549', 'CAKI-1', 'EKVX', 'HCT-15', 'HCT116', 'HOP-62', 'HOP-92', 'HS 578T', 'HT29',
+ 'IGROV1', 'K-562', 'KM12', 'LOX IMVI', 'MALME-3M', 'MCF7', 'MDA-MB-231', 'MDA-MB-468', 'NCI-H226', 'NCI-H460', 
+ 'NCI-H522', 'NCIH23', 'OVCAR-4', 'OVCAR-5', 'OVCAR-8', 'OVCAR3', 'PC-3', 'RPMI-8226', 'SF-268', 'SF-295', 'SF-539', 
+ 'SK-MEL-2', 'SK-MEL-28', 'SK-MEL-5', 'SK-OV-3', 'SNB-75', 'SR', 'SW-620', 'T-47D', 'U251', 'UACC-257', 'UACC62', 
+ 'UO-31']
+"""
+
+dataset_config = {
+    "dataset": DrugCombMatrix,
+    "study_name": 'ALMANAC',
+    "in_house_data": 'without',
+    "rounds_to_include": [],
+    "cell_line": 'MCF7',  # Restrict to a specific cell line
+    "val_set_prop": 0.1,
+    "test_set_prop": 0.,
+    "test_on_unseen_cell_line": False,
+    "split_valid_train": "pair_level",  # either "cell_line_level" or "pair_level"
+    "cell_lines_in_test": None,  # ['MCF7', 'PC-3'],
+    "target": "bliss_max",
+    "fp_bits": 1024,
+    "fp_radius": 2
+}
+
+active_learning_config = {
+    "ensemble_size": 5,
+    "acquisition": tune.grid_search([ProbabilityOfImprovementAcquisition, UCB, RandomAcquisition]),
+    "patience_max": 4,
+    "kappa": 1,
+    "kappa_decrease_factor": 1,
+    "n_epoch_between_queries": 500,
+    "acquire_n_at_a_time": 30,
+    "n_initial": 30,
+}
+
+########################################################################################################################
+# Configuration that will be loaded
+########################################################################################################################
+
+configuration = {
+    "trainer": ActiveTrainerBayesian,  # PUT NUM GPU BACK TO 1
+    "trainer_config": {
+        **pipeline_config,
+        **predictor_config,
+        **model_config,
+        **dataset_config,
+        **active_learning_config
+    },
+    "summaries_dir": os.path.join(get_project_root(), "RayLogs"),
+    "memory": 1800,
+    "stop": {"training_iteration": 1000, 'all_space_explored': 1},
+    "checkpoint_score_attr": 'eval/comb_r_squared',
+    "keep_checkpoints_num": 1,
+    "checkpoint_at_end": False,
+    "checkpoint_freq": 1,
+    "resources_per_trial": {"cpu": 16, "gpu": 0},
+    "scheduler": None,
+    "search_alg": None,
+}
\ No newline at end of file
diff --git a/recover/train.py b/recover/train.py
index c149688..db25146 100644
--- a/recover/train.py
+++ b/recover/train.py
@@ -711,6 +711,223 @@ def update_loaders(self, seen_idxs, unseen_idxs):
         return seen_loader, unseen_loader
 
 
+########################################################################################################################
+# Bayesian Active learning Trainer 
+########################################################################################################################
+
+
+class ActiveTrainerBayesian(BasicTrainer):
+    """
+    Trainer class to perform active learning with Bayesian Model. Retrains models from scratch after each query. Uses early stopping
+    """
+
+    def setup(self, config):
+        print("Initializing active training pipeline")
+        super(ActiveTrainerBayesian, self).setup(config)
+
+        self.acquire_n_at_a_time = config["acquire_n_at_a_time"]
+        self.acquisition = config["acquisition"](config)
+        self.n_epoch_between_queries = config["n_epoch_between_queries"]
+
+        # randomly acquire data at the beginning
+        self.seen_idxs = self.train_idxs[:config["n_initial"]]
+        self.unseen_idxs = self.train_idxs[config["n_initial"]:]
+        self.immediate_regrets = torch.empty(0)
+
+        # Initialize variable that saves the last query
+        self.last_query_idxs = self.seen_idxs
+
+        # Initialize dataloaders
+        self.seen_loader, self.unseen_loader = self.update_loaders(self.seen_idxs, self.unseen_idxs)
+
+        # Get the set of top 1% most synergistic combinations
+        one_perc = int(0.01 * len(self.unseen_idxs))
+        scores = self.data.ddi_edge_response[self.unseen_idxs]
+        self.best_score = scores.max()
+        self.top_one_perc = set(self.unseen_idxs[torch.argsort(scores, descending=True)[:one_perc]].numpy())
+        self.count = 0
+
+    def step(self):
+        # Check whether we have explored everything
+        if len(self.unseen_loader) == 0:
+            print("All space has been explored")
+            return {"all_space_explored": 1, "training_iteration": self.training_it}
+
+        # Train on seen examples
+        if len(self.seen_idxs) > 0:
+            seen_metrics = self.train_between_queries()
+        else:
+            seen_metrics = {}
+
+        # Evaluate on valid set
+        eval_metrics, _, _ = self.eval_epoch(self.data, self.valid_loader, self.model)
+        
+        unseen_metrics = {}
+        realization_results, unseen_preds, drug_combs = self.eval_epoch(self.data, self.unseen_loader, self.model)
+        unseen_result = dict([("unseen/" + k, [v]) for k, v in realization_results.items()])
+        for i in range(9):
+            realization_results, result_tensor, _ = self.eval_epoch(self.data, self.unseen_loader, self.model)
+            unseen_preds = torch.cat((unseen_preds, result_tensor), dim=1)
+            for k, v in realization_results.items():
+                unseen_result["unseen/" + k].append(v)
+                
+        for key in unseen_result:
+            unseen_metrics[str(key)+ "/mean"] = np.mean(unseen_result[key])
+            unseen_metrics[str(key)+ "/std"] = np.var(unseen_result[key])
+            
+
+        active_scores = self.acquisition.get_scores(unseen_preds)
+
+        # Build summary
+        seen_metrics = [("seen/" + k, v) for k, v in seen_metrics.items()]
+        eval_metrics = [("eval/" + k, v) for k, v in eval_metrics.items()]
+
+        metrics = dict(
+            seen_metrics
+            + eval_metrics
+        )
+        
+        metrics.update(dict(unseen_metrics))
+        
+        
+
+        # Acquire new data
+        print("query data")
+        query = self.unseen_idxs[torch.argsort(active_scores, descending=True)[:self.acquire_n_at_a_time]]
+
+        # Get the best score among unseen examples
+        self.best_score = self.data.ddi_edge_response[self.unseen_idxs].max().detach().cpu()
+        # remove the query from the unseen examples
+        self.unseen_idxs = self.unseen_idxs[torch.argsort(active_scores, descending=True)[self.acquire_n_at_a_time:]]
+
+        # Add the query to the seen examples
+        self.seen_idxs = torch.cat((self.seen_idxs, query))
+        metrics["seen_idxs"] = self.data.ddi_edge_idx[:, self.seen_idxs].detach().cpu().tolist()
+        metrics["seen_idxs_in_dataset"] = self.seen_idxs.detach().cpu().tolist()
+
+        # Compute proportion of top 1% synergistic drugs which have been discovered
+        query_set = set(query.detach().numpy())
+        self.count += len(query_set & self.top_one_perc)
+        metrics["top"] = self.count / len(self.top_one_perc)
+
+        query_ground_truth = self.data.ddi_edge_response[query].detach().cpu()
+
+        query_pred_syn = unseen_preds[torch.argsort(active_scores, descending=True)[:self.acquire_n_at_a_time]]
+        query_pred_syn = query_pred_syn.detach().cpu()
+
+        metrics["query_pred_syn_mean"] = query_pred_syn.mean().item()
+        metrics["query_true_syn_mean"] = query_ground_truth.mean().item()
+
+        # Diversity metric
+        metrics["n_unique_drugs_in_query"] = len(self.data.ddi_edge_idx[:, query].unique())
+
+        # Get the quantiles of the distribution of true synergy in the query
+        for q in np.arange(0, 1.1, 0.1):
+            metrics["query_pred_syn_quantile_" + str(q)] = np.quantile(query_pred_syn, q)
+            metrics["query_true_syn_quantile_" + str(q)] = np.quantile(query_ground_truth, q)
+
+        query_immediate_regret = torch.abs(self.best_score - query_ground_truth)
+        self.immediate_regrets = torch.cat((self.immediate_regrets, query_immediate_regret))
+
+        metrics["med_immediate_regret"] = self.immediate_regrets.median().item()
+        metrics["log10_med_immediate_regret"] = np.log10(metrics["med_immediate_regret"])
+        metrics["min_immediate_regret"] = self.immediate_regrets.min().item()
+        metrics["log10_min_immediate_regret"] = np.log10(metrics["min_immediate_regret"])
+
+        # Update the dataloaders
+        self.seen_loader, self.unseen_loader = self.update_loaders(self.seen_idxs, self.unseen_idxs)
+
+        metrics["training_iteration"] = self.training_it
+        metrics["all_space_explored"] = 0
+        self.training_it += 1
+
+        return metrics
+
+    def train_between_queries(self):
+        # Create the train and early_stop loaders for this iteration
+        iter_dataset = self.seen_loader.dataset
+        train_length = int(0.8 * len(iter_dataset))
+        early_stop_length = len(iter_dataset) - train_length
+
+        train_dataset, early_stop_dataset = random_split(iter_dataset, [train_length, early_stop_length])
+
+        train_loader = DataLoader(
+            train_dataset,
+            batch_size=self.batch_size,
+            pin_memory=(self.device == "cpu"),
+            shuffle=len(train_dataset) > 0,
+        )
+
+        early_stop_loader = DataLoader(
+            early_stop_dataset,
+            batch_size=self.batch_size,
+            pin_memory=(self.device == "cpu"),
+            shuffle=len(early_stop_dataset) > 0,
+        )
+
+        # Reinitialize model before training
+        self.model = self.config["model"](self.data, self.config).to(self.device)
+
+        # Initialize model with weights from file
+        load_model_weights = self.config.get("load_model_weights", False)
+        if load_model_weights:
+            model_weights_file = self.config.get("model_weights_file")
+            model_weights = torch.load(model_weights_file, map_location="cpu")
+            self.model.load_state_dict(model_weights)
+            print("pretrained weights loaded")
+        else:
+            print("model initialized randomly")
+
+        # Reinitialize optimizer
+        self.optim = torch.optim.Adam(self.model.parameters(), lr=self.config["lr"],
+                                      weight_decay=self.config["weight_decay"])
+
+        best_eval_r2 = float("-inf")
+        patience_max = self.config["patience_max"]
+        patience = 0
+
+        for _ in range(self.n_epoch_between_queries):
+            # Perform several training epochs. Save only metrics from the last epoch
+            train_metrics = self.train_epoch(self.data, train_loader, self.model, self.optim)
+
+            early_stop_metrics, _results, _ = self.eval_epoch(self.data, early_stop_loader, self.model)
+
+            if early_stop_metrics["comb_r_squared"] > best_eval_r2:
+                best_eval_r2 = early_stop_metrics["comb_r_squared"]
+                print("best early stop r2", best_eval_r2)
+                patience = 0
+            else:
+                patience += 1
+
+            if patience > patience_max:
+                break
+
+        return train_metrics
+
+    def update_loaders(self, seen_idxs, unseen_idxs):
+        # Seen loader
+        seen_ddi_dataset = get_tensor_dataset(self.data, seen_idxs)
+
+        seen_loader = DataLoader(
+            seen_ddi_dataset,
+            batch_size=self.batch_size,
+            pin_memory=(self.device == "cpu"),
+            shuffle=len(seen_idxs) > 0,
+        )
+
+        # Unseen loader
+        unseen_ddi_dataset = get_tensor_dataset(self.data, unseen_idxs)
+
+        unseen_loader = DataLoader(
+            unseen_ddi_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            pin_memory=(self.device == "cpu"),
+        )
+
+        return seen_loader, unseen_loader
+
+
 ########################################################################################################################
 # Main train method
 ########################################################################################################################

From 8671f897580fd0d4d44a7e3843298fb115cda609 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Fri, 15 Dec 2023 05:11:34 +0530
Subject: [PATCH 19/38] sigmoid added

---
 recover/config/bayesian_model_evaluation.py |  2 +-
 recover/models/predictors.py                | 10 ++++++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/recover/config/bayesian_model_evaluation.py b/recover/config/bayesian_model_evaluation.py
index e2f14c9..8f68aab 100644
--- a/recover/config/bayesian_model_evaluation.py
+++ b/recover/config/bayesian_model_evaluation.py
@@ -26,7 +26,7 @@
 }
 
 predictor_config = {
-    "predictor": MLPPredictor,
+    "predictor": BilinearMLPPredictor,
     "bayesian_predictor": True,
     "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
     "num_realizations": 10, # For bayesian uncertainty
diff --git a/recover/models/predictors.py b/recover/models/predictors.py
index ede761f..0e2fbdd 100644
--- a/recover/models/predictors.py
+++ b/recover/models/predictors.py
@@ -43,7 +43,15 @@ def __init__(self, p):
     def forward(self, input):
         x, cell_line = input[0], input[1]
         return [super().forward(x), cell_line]
+        
+class ScaledSigmoid(nn.Sigmoid):
+    def __init__(self):
+        super(ScaledSigmoid, self).__init__()
 
+    def forward(self, input):
+        x, cell_line = input[0], input[1]
+        output_tensor = super().forward(x) * 100.0
+        return [output_tensor, cell_line]
 
 class FilmModule(torch.nn.Module):
     def __init__(self, num_cell_lines, out_dim):
@@ -244,6 +252,8 @@ def add_bayes_layer(self, layers, i, dim_i, dim_i_plus_1):
         layers.extend(self.bayes_linear_layer(i, dim_i, dim_i_plus_1))
         if i != len(self.layer_dims) - 2:
             layers.append(ReLUModule())
+        else:
+            layers.append(ScaledSigmoid())
 
         return layers
 

From 707a18664330fdc4edbdc09a80ec505bf6abfd9a Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Fri, 15 Dec 2023 12:01:28 +0100
Subject: [PATCH 20/38] Model Overview

---
 docs/images/ModelOverview.png | Bin 0 -> 48437 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 docs/images/ModelOverview.png

diff --git a/docs/images/ModelOverview.png b/docs/images/ModelOverview.png
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From 974cf3f38a13d143ef2267e5014b1529ff3abc9e Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Fri, 15 Dec 2023 12:23:15 +0100
Subject: [PATCH 21/38] Update README.md

---
 README.md | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index f923cf7..9da3ba6 100644
--- a/README.md
+++ b/README.md
@@ -6,9 +6,20 @@ This repository is an implementation of RECOVER, a platform that can guide wet l
 ([preprint](https://arxiv.org/abs/2202.04202)), howerver instead of using an ensemble model to get Synergy predictions with uncertainty, we used multiple realization of a Bayesian Neural Network model. 
 Since the weights are drawn from a distribution, they differ for every run of a trained model and hence give different results. The goal was to get a more precise uncertainty and achieve i quicker since the model doesn't have to be trained multiple times. 
 
-<div>
-  <img src="docs/images/ProjectInfographics.png" alt="Overview" width="500"/>
-</div>
+<p float="left">
+  <img src="docs/images/ProjectInfographics.png" alt="Overview" width="400"/>
+  <img src="docs/images/ModelOverview.png" alt="Model Overview" width="500"/>
+</p>
+
+## Repository overview
+There are 6 branches in this Git Repository.
+
+**Master**, where the original RECOVER is stored with Bayesian setup but original config files, so the initial pipeline can be easily recreated.   
+**Bayesian_after_merge**, with a Bayesian model used only in the layers after the bilinear merge.   
+**Bayesian_before_and_after_merge**, with a Bayesian model used in the layers before and after the bilinear merge.  
+**Weight_uncertainty**, a Bayesian Neural Network library implemented from scratch and two priors introduced in order to optimize the model. (https://doi.org/10.48550/arXiv.1505.05424)  
+
+Every branch will be further explaned in its own README.
 
 ## Environment setup
 

From e7e8429a363cf2def1ee74a05a75cf26cf3efb91 Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Fri, 15 Dec 2023 13:53:52 +0100
Subject: [PATCH 22/38] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 9da3ba6..1cd25c1 100644
--- a/README.md
+++ b/README.md
@@ -7,8 +7,8 @@ This repository is an implementation of RECOVER, a platform that can guide wet l
 Since the weights are drawn from a distribution, they differ for every run of a trained model and hence give different results. The goal was to get a more precise uncertainty and achieve i quicker since the model doesn't have to be trained multiple times. 
 
 <p float="left">
-  <img src="docs/images/ProjectInfographics.png" alt="Overview" width="400"/>
-  <img src="docs/images/ModelOverview.png" alt="Model Overview" width="500"/>
+  <img src="docs/images/ProjectInfographics.png" alt="Overview" width="300"/>
+  <img src="docs/images/ModelOverview.png" alt="Model Overview" width="400"/>
 </p>
 
 ## Repository overview

From 3d07745752f89d2628f450f8851793802fa969a9 Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Fri, 15 Dec 2023 15:30:07 +0100
Subject: [PATCH 23/38] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 1cd25c1..8367df6 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,7 @@ Since the weights are drawn from a distribution, they differ for every run of a
 </p>
 
 ## Repository overview
-There are 6 branches in this Git Repository.
+There are 4 branches in this Git Repository.
 
 **Master**, where the original RECOVER is stored with Bayesian setup but original config files, so the initial pipeline can be easily recreated.   
 **Bayesian_after_merge**, with a Bayesian model used only in the layers after the bilinear merge.   

From ea2d99407caef85df0735af26b773f395bb74ac8 Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Fri, 15 Dec 2023 15:37:59 +0100
Subject: [PATCH 24/38] Create b

---
 experiments/b | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 experiments/b

diff --git a/experiments/b b/experiments/b
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/experiments/b
@@ -0,0 +1 @@
+

From ad960086270838d1b5262be4e0f5dcc2aecc37d0 Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Fri, 15 Dec 2023 15:44:28 +0100
Subject: [PATCH 25/38] Added experiments

---
 experiments/experiments.ipynb | 582 ++++++++++++++++++++++++++++++++++
 1 file changed, 582 insertions(+)
 create mode 100644 experiments/experiments.ipynb

diff --git a/experiments/experiments.ipynb b/experiments/experiments.ipynb
new file mode 100644
index 0000000..f7f8caf
--- /dev/null
+++ b/experiments/experiments.ipynb
@@ -0,0 +1,582 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "caf9b4ea",
+      "metadata": {
+        "id": "caf9b4ea"
+      },
+      "outputs": [],
+      "source": [
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "070d713f",
+      "metadata": {
+        "id": "070d713f"
+      },
+      "outputs": [],
+      "source": [
+        "d_data = [[30, 28], [51, 94], [43, 67], [15, 1], [54, 36], [50, 21], [21, 93], [47, 36], [36, 42], [51, 9], [11, 93], [75, 68], [45, 15], [35, 2], [47, 65], [83, 67], [10, 6], [7, 41], [55, 94], [60, 94], [64, 17], [6, 82], [14, 61], [27, 26], [1, 82], [0, 4], [45, 7], [85, 93], [23, 79], [20, 76], [75, 67], [35, 85], [45, 73], [15, 13], [0, 14], [67, 41], [74, 78], [74, 85], [0, 81], [2, 42], [91, 42], [82, 40], [2, 91], [27, 8], [74, 43], [25, 38], [81, 92], [40, 41], [46, 60], [65, 29], [51, 45], [45, 18], [1, 88], [16, 3], [74, 79], [69, 19], [14, 41], [40, 91], [7, 38], [77, 79], [32, 90], [0, 87], [1, 5], [56, 14], [86, 77], [83, 38], [6, 27], [21, 14], [27, 74], [48, 93], [48, 52], [16, 57], [12, 44], [9, 25], [39, 8], [75, 33], [15, 8], [62, 20], [85, 25], [86, 71], [90, 82], [10, 43], [28, 36], [14, 84], [74, 41], [83, 55], [55, 66], [76, 74], [84, 42], [47, 24], [77, 57], [37, 68], [62, 22], [11, 38], [31, 34], [82, 94], [39, 65], [11, 61], [37, 92], [67, 7], [73, 24], [64, 28], [6, 73], [67, 82], [35, 29], [28, 62], [51, 33], [46, 83], [17, 34], [65, 13], [12, 55], [47, 21], [51, 93], [29, 36], [35, 59], [16, 86], [77, 26], [1, 82], [48, 60], [82, 86], [10, 9], [89, 34], [22, 57], [43, 3], [21, 81], [28, 79], [21, 65], [56, 7], [8, 3], [51, 91], [7, 57], [17, 55], [10, 81], [32, 15], [63, 80], [82, 82], [3, 91], [72, 36], [15, 5], [72, 13], [19, 68], [54, 44], [67, 20], [6, 94], [35, 67], [45, 54], [59, 42], [2, 4], [40, 16], [68, 34], [15, 91], [2, 28], [64, 65], [16, 21], [27, 87], [83, 36], [18, 53], [75, 43], [39, 2], [48, 76], [80, 59], [56, 82], [75, 80], [67, 82], [80, 65], [52, 35], [48, 58], [69, 29], [6, 34], [75, 61], [5, 92], [54, 72], [48, 80], [3, 85], [16, 18], [46, 52], [47, 79], [64, 85], [45, 19], [29, 8], [0, 90], [40, 62], [0, 69], [70, 8], [81, 20], [70, 34], [26, 41], [3, 4], [9, 91], [80, 94], [45, 70], [42, 41], [83, 85], [37, 79], [70, 12], [43, 24], [48, 82], [40, 87], [22, 74], [39, 81], [45, 17], [56, 61], [19, 34], [69, 68], [2, 20], [88, 78], [71, 6], [50, 88], [63, 61], [48, 27], [37, 89], [82, 4], [19, 4], [24, 91], [14, 78], [73, 41], [47, 71], [8, 59], [40, 66], [33, 41], [68, 8], [49, 42], [13, 76], [61, 26], [13, 42], [58, 93], [47, 85], [61, 94], [91, 79], [90, 79], [32, 61], [68, 58], [76, 85], [30, 80], [7, 36], [84, 89], [47, 41], [32, 5], [4, 41], [19, 36], [72, 87], [36, 34], [46, 70], [10, 56], [39, 5], [7, 74], [33, 85], [77, 85], [39, 11], [82, 16], [70, 84], [16, 29], [81, 9], [62, 66], [10, 90], [82, 68], [14, 89], [85, 79], [8, 25], [32, 80], [23, 50], [62, 87], [21, 80], [73, 8], [48, 43], [70, 4], [43, 60], [12, 36], [40, 2], [69, 13], [21, 57], [31, 28], [20, 41], [56, 82], [19, 88], [14, 87], [81, 42], [12, 68], [70, 15], [66, 4], [83, 61], [61, 7], [82, 85], [17, 53], [50, 26], [86, 4], [15, 59], [14, 2], [32, 86], [31, 25], [4, 59], [30, 94], [69, 61], [16, 93], [18, 1], [52, 22], [16, 53], [4, 29], [28, 78], [72, 66], [16, 4], [51, 27], [54, 7], [0, 30], [40, 29], [18, 66], [14, 8], [51, 0], [13, 5], [48, 55], [23, 67], [69, 66], [30, 87], [86, 54], [51, 22], [71, 31], [86, 26], [70, 53], [46, 47], [67, 79], [75, 60], [3, 84], [71, 22], [49, 82], [51, 21], [43, 79], [65, 38], [48, 64], [0, 88], [49, 3], [51, 89], [16, 78], [18, 60], [56, 78], [69, 77], [54, 93], [61, 27], [82, 32], [23, 90], [23, 4], [16, 85], [30, 73], [16, 2], [56, 9], [47, 1], [63, 44], [26, 62], [65, 7], [23, 17], [47, 5], [40, 43], [54, 14], [39, 60], [26, 92], [19, 58], [6, 67], [55, 27], [40, 63], [76, 22], [66, 27], [46, 38], [55, 87], [7, 8], [64, 31], [4, 62], [69, 72], [81, 85], [82, 94], [15, 90], [64, 81], [17, 84], [71, 72], [15, 31], [14, 91], [62, 13], [17, 93], [15, 9], [3, 82], [86, 90], [0, 57], [76, 73], [23, 28], [37, 49], [86, 49], [51, 20], [17, 42], [77, 58], [39, 89], [0, 59], [23, 18], [33, 76], [46, 39], [92, 25], [17, 36], [13, 57], [39, 53], [39, 49], [50, 80], [52, 93], [76, 72], [31, 60], [9, 79], [21, 61], [37, 21], [4, 94], [26, 59], [6, 57], [69, 1], [45, 2], [15, 80], [3, 82], [6, 22], [50, 35], [84, 38], [50, 28], [76, 60], [0, 66], [50, 91], [48, 71], [51, 7], [12, 27], [8, 41], [10, 60], [49, 10], [8, 20], [50, 77], [37, 78], [67, 53], [46, 33], [35, 65], [64, 53], [6, 82], [82, 41], [50, 33], [64, 73], [68, 29], [35, 88], [86, 12], [63, 18], [32, 87], [31, 91], [73, 61], [11, 44], [89, 41], [23, 16], [22, 94], [73, 93], [19, 91]]\n",
+        "\n",
+        "columns = ['Drug_1', 'Drug_2']\n",
+        "df = pd.DataFrame(d_data, columns=columns)\n",
+        "df['mean'] =[49.29517288208008, 6.172921371459961, 5.574721813201904, 10.0439884185791, 4.05626277923584, 18.13949661254883, 15.953001594543457, 16.54880256652832, 7.745485305786133, 15.95972557067871, 9.506382942199707, 11.126901817321777, 15.092543983459473, 26.13888931274414, 20.092384338378906, 5.085054397583008, 15.57064266204834, 8.349395179748536, 11.39694766998291, 5.977288150787354, 6.6163506507873535, 13.473756217956543, 12.239928436279296, 4.548686790466308, 30.61661720275879, 11.503194046020507, 12.146053504943847, 8.830685424804688, 6.790773963928222, 16.647670364379884, 8.160617256164551, 10.927204895019532, 26.619203567504883, 9.403142547607422, 7.292382621765137, 6.888001918792725, 6.012288284301758, 4.948505020141601, 8.796662902832031, 12.134679222106934, 8.204130363464355, 9.700010681152344, 10.644589614868163, 4.901075267791748, 2.190503191947937, 4.349216413497925, 5.682965469360352, 8.010100746154786, 9.12760944366455, 10.215876960754395, 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+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "d3865e1d",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "d3865e1d",
+        "outputId": "2c272ac8-3f4f-46e3-f692-69616c14f18f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Drug_1  Drug_2       mean       std\n",
+              "0        30      28  49.295173  0.726790\n",
+              "1        51      94   6.172921  0.491095\n",
+              "2        43      67   5.574722  0.141868\n",
+              "3        15       1  10.043988  0.831564\n",
+              "4        54      36   4.056263  0.283467\n",
+              "..      ...     ...        ...       ...\n",
+              "443      89      41   6.650350  0.182780\n",
+              "444      23      16  18.775818  0.722541\n",
+              "445      22      94  16.383420  0.896908\n",
+              "446      73      93   7.660311  0.466947\n",
+              "447      19      91   8.157634  0.419394\n",
+              "\n",
+              "[448 rows x 4 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a4b22b03-17c7-445b-b3e5-f07a99c5e0dc\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "        vertical-align: top;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Drug_1</th>\n",
+              "      <th>Drug_2</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>30</td>\n",
+              "      <td>28</td>\n",
+              "      <td>49.295173</td>\n",
+              "      <td>0.726790</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>51</td>\n",
+              "      <td>94</td>\n",
+              "      <td>6.172921</td>\n",
+              "      <td>0.491095</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>43</td>\n",
+              "      <td>67</td>\n",
+              "      <td>5.574722</td>\n",
+              "      <td>0.141868</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>15</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10.043988</td>\n",
+              "      <td>0.831564</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>54</td>\n",
+              "      <td>36</td>\n",
+              "      <td>4.056263</td>\n",
+              "      <td>0.283467</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>443</th>\n",
+              "      <td>89</td>\n",
+              "      <td>41</td>\n",
+              "      <td>6.650350</td>\n",
+              "      <td>0.182780</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>444</th>\n",
+              "      <td>23</td>\n",
+              "      <td>16</td>\n",
+              "      <td>18.775818</td>\n",
+              "      <td>0.722541</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>445</th>\n",
+              "      <td>22</td>\n",
+              "      <td>94</td>\n",
+              "      <td>16.383420</td>\n",
+              "      <td>0.896908</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>446</th>\n",
+              "      <td>73</td>\n",
+              "      <td>93</td>\n",
+              "      <td>7.660311</td>\n",
+              "      <td>0.466947</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>447</th>\n",
+              "      <td>19</td>\n",
+              "      <td>91</td>\n",
+              "      <td>8.157634</td>\n",
+              "      <td>0.419394</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>448 rows × 4 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4b22b03-17c7-445b-b3e5-f07a99c5e0dc')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a4b22b03-17c7-445b-b3e5-f07a99c5e0dc button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a4b22b03-17c7-445b-b3e5-f07a99c5e0dc');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-9cdd8328-13da-4f64-b6bf-855eb2424969\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9cdd8328-13da-4f64-b6bf-855eb2424969')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-9cdd8328-13da-4f64-b6bf-855eb2424969 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = df.sort_values(by=\"mean\", ascending=False)\n",
+        "\n",
+        "# Drop duplicates keeping the first occurrence (highest mean value)\n",
+        "df = df.drop_duplicates(subset= [\"Drug_1\", \"Drug_2\"], keep=\"first\")\n",
+        "\n",
+        "def swap_columns(row):\n",
+        "    if row['Drug_1'] > row['Drug_2']:\n",
+        "        return pd.Series({'Drug_1': row['Drug_2'], 'Drug_2': row['Drug_1']})\n",
+        "    else:\n",
+        "        return row\n",
+        "\n",
+        "# Apply the function to each row\n",
+        "df = df.apply(swap_columns, axis=1)"
+      ],
+      "metadata": {
+        "id": "bG6bcNFAroJy"
+      },
+      "id": "bG6bcNFAroJy",
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "06c9d31d",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 811
+        },
+        "id": "06c9d31d",
+        "outputId": "7b75df64-ab05-40ac-ace6-a2133c80fb38"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 2 Axes>"
+            ],
+            "image/png": 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FC/V3fl05OTnpY2Nj69zj9rkvvviiID5q1Ci9u7t7vflt27ZND0CfkpIiiNfW1uovX74sOG7evGkoHzhwoOD9/PDDD/UA9CdPnhTUExQUJHg/Fy9erHdyctKfPn1acN78+fP1tra2+gsXLuj1er2+tLRUD0Dv7u6uv3LlSp18v/3223qfq1WrVvqQkJB6z7mtrKxMb29vrx8yZIi+pqbGEF+9erUegP6TTz4RPDcAfVpamiF26tQpPQC9jY2N/siRI4b47t279QD069evN8Ru/1uNGDFCkMOrr76qB6D/97//bYi1b99e8O99+zMfGRkp+MzPnj1bb2trq6+oqDDEjP18TZ8+Xf/II4/oq6urDbHo6Gh9+/bt65x7+/3/a+6hoaH6Nm3a6H///XdD7N///rfexsZGP2nSpDrPeLfP48qVK/UA9JcvX65z/7sx9/ujffv2egD6AwcOGGJlZWV6qVSqnzNnjiGWkJCgB6DPy8sTnCeTyfQA9KWlpfXmc+XKFb1UKtVPnDhREJ8/f74egL64uNhk3rm5uXoA+s8++8wQ27dvnx6Aft++fYJnMfbzf+fPornfw/fz/hOZcnb4BKs8rBGbbvRQOHXqFOLi4qBQKBAbG9uga2//FfTq1at3PdfBwQFZWVlGjztt2bIFnTt3RmBgIMrLyw3H4MGDAQD79u0znPvXvzxqtVqUl5dj4MCB+Pnnnxs0lGzq1KmCoU3h4eHQ6/WYOnWqIWZra4sePXrg559/Flz71xz++OMPaLVa9O/fHwUFBXXuo1AoDJOiAcDHxwcjR47E7t27BcNg7tS/f3/U1NTg8OHDAG71hPTv3x/9+/fHwYMHAQBFRUWoqKhA//79zX5uY2bMmFHn3r///jsqKytNXnO77M6/jGu1WrRu3VpwFBYWmqxn9OjRsLOzE/yVvqioCD/99BPGjx9viG3ZsgX9+/dHq1atBJ+RyMhI1NTU1BkGN378eLRq1UrwTADq/Fsae647eyVM2bNnD27cuIGEhATBJPKXX34ZLi4u2Llzp+D8li1bYsKECYbXAQEBcHV1RefOnREeHm6I3/5vY7ne+Vf8mTNnAgB27dp113ynTZsm+Mzf/oydP3/eEPvrZ/vq1asoLy9H//79cf36dZw6dequ97jTpUuXUFhYiMmTJ8PNzc0QDw4OxpNPPmk077t9Hl1dXQHcGl5mztCov2rI90dQUJDgZ6t169YICAgQ/Lvs2rULvXv3Rq9evQTnmTvvo1WrVnjqqaewfft2VFVVAbjVg71x40b06NEDjz32WJ28b968id9//x3+/v5wdXU1+r1zL8z9Hr6f95+I7h8bJPTAU6vViI6Ohkwmw9atW2Fra9ug62/PUzDnFzZbW1tERkYaPe505swZ/Pjjj3V+kb39P+OysjLDud9//z0iIyMNY9Fbt25tGLvckAaJj4+P4LVMJgNwa1z3nfE//vhDENuxYwd69+4NBwcHuLm5oXXr1khNTTV6/06dOtWJPfbYY7h+/Xqd8ft/1b17dzzyyCOGxsftBsmAAQNw/PhxVFdXG8r69etnxhObdud7cfsX+Tuf+69ufwZufyZua9mypaHh+frrr9/13h4eHoiIiMDmzZsNsU2bNsHOzg6jR482xM6cOYPMzMw6n5Hbn6e/fkbu9ZkAwMXFxawGNwDDL/IBAQGCuL29PTp06CD4RR+4tYLdnXvEyGQyo585U7ne+Xnq2LEjbGxszJpLYM578uOPP2LUqFGQyWRwcXFB69atDYtc3MvcMVPvEQB07twZ5eXlhl/Ezc1z/Pjx6Nu3L1566SXI5XJMmDABmzdvNuuX44Z8f9yZx+1c/vp+nT9/3ujPuLHnNeW5555DVVUVtm3bBgA4fPgwzp07J2jU/Pnnn0hKSjLMn/Lw8EDr1q1RUVFhsTl95n4P38/7T2SKxEZilYc14hwSeqBptVoMGzYMFRUVOHjwILy9vRtcR1FREQDA39/fornV1taiW7duWLFihdHy27+wlZSUICIiAoGBgVixYgXatWsHe3t77Nq1CytXrmzQ/xBNNcaMxfV/mdR+8OBBjBgxAgMGDMDatWvh5eWFFi1aYP369UhLSzP7/nfTokULhIeH48CBAzh79izUajX69+8PuVyOmzdvIi8vDwcPHkRgYCBat259X/cy9V7o69kf4faE79ufidvs7OwMjYRff/3VrPtPmDABU6ZMQWFhIUJDQ7F582ZERETAw8PDcE5tbS2efPJJzJs3z2gdt39puu1engm49VyFhYW4ceOGxVcEa8hnDrh7rgDM3gTTnPtUVFRg4MCBcHFxwd/+9jd07NgRDg4OKCgowBtvvNFkv3DeLU9HR0ccOHAA+/btw86dO5GZmYlNmzZh8ODB+Ne//mXy+oZ+f9zPv0tDPP3005DJZEhLS8Ozzz6LtLQ02NraCnrTZs6cifXr1yMhIQEKhQIymQwSiQQTJky467+Lqc9ITU2N4BnN/R6+1/efiCyDDRJ6YFVXV2P48OE4ffo09uzZg6CgoAbXUVNTg7S0NDzyyCP3/Rf5O3Xs2BH//ve/ERERUe8vWN9++y10Oh22b98u+OvlX4d03daQX9Qa4uuvv4aDgwN2794tWF51/fr1Rs8/c+ZMndjp06fxyCOP3LUh0b9/f7z33nvYs2cPPDw8EBgYCIlEgi5duuDgwYM4ePCgYCKsKY3xXgQEBKBTp07IyMhASkoKnJyc7rmumJgYTJ8+3TBs6/Tp01AqlYJzOnbsiGvXrhntYbOk4cOHIzc3F19//TUmTpxY77nt27cHcGtFsA4dOhjiN27cQGlpaaPkeubMGfj5+Rlenz17FrW1tXUmht+L/fv34/fff8c333wjWIa2tLS0zrnmfqb++h7d6dSpU/Dw8Linz46NjQ0iIiIQERGBFStW4N1338X//d//Yd++fSbf94Z8f5irffv2Rn/GjT2vKVKpFGPHjsVnn30GjUaDLVu2YPDgwfD09DScs3XrVsTGxuL99983xKqrq83apLNVq1ZGzzt//rzgc2vu9zBwb+8/EVkGh2zRA6mmpgbjx49Hbm4utmzZAoVCcU91vPbaa/jPf/6D1157DS4uLhbNcdy4cfjtt9+M7u/w559/GoZ03P7L21//QqnVao02BpycnBplR21bW1tIJBLB/I9z586Z3Ak9NzdXMMb7l19+wbZt2zBkyJC7/iWxf//+0Ol0SElJQb9+/Qy/JPTv3x+ff/45Ll68aNb8kcZ6LxYtWoTy8nK8/PLLRpctNfcvya6uroiKisLmzZuxceNG2Nvb19nEcdy4ccjNzcXu3bvrXF9RUWGxnaxnzJgBLy8vzJkzB6dPn65TXlZWhuTkZABAZGQk7O3tsWrVKsGzfvzxx9BqtWatRNdQa9asEbz++9//DgAYNmzYfddt7Ofrxo0bWLt2bZ1znZyczBoq5OXlhdDQUHz66aeCz2BRURH+9a9/4amnnmpwnleuXKkTCw0NBYA6y9P+VUO+P8z11FNP4ciRIzh69KghdvnyZXz55ZcNque5557DzZs3MX36dFy+fLnOHBRbW9s6P09///vf652HdlvHjh1x5MgR3LhxwxDbsWMHfvnlF8F55n4P3+v7T1QvicQ6DyvEHhJ6IM2ZMwfbt2/H8OHDceXKlTobId65CaJWqzWcc/36dcNO7SUlJZgwYQIWL15s8RxfeOEFbN68GTNmzMC+ffvQt29f1NTU4NSpU9i8eTN2796NHj16YMiQIbC3t8fw4cMxffp0XLt2Df/4xz/Qpk0bXLp0SVBnWFgYUlNTkZycDH9/f7Rp08YwOfN+REdHY8WKFRg6dCieffZZlJWVYc2aNfD398cPP/xQ5/yuXbsiKipKsOwvAKN7b9xJoVDAzs4OxcXFhiV7AWDAgAGGvRPMaZCEhYVhz549WLFiBby9veHn5yeYRH2vnn32WRQVFUGlUuHo0aOYMGEC/Pz8UFVVhaKiInz11VdwdnYWTC43Zfz48Xj++eexdu1aREVFGSbO3vb6669j+/btePrppw1Lr1ZVVeHkyZPYunUrzp07Jxjida9atWqF9PR0PPXUUwgNDRXs1F5QUICvvvrK0Khv3bo1lEol3n77bQwdOhQjRoxAcXEx1q5di549e9b52bKE0tJSjBgxAkOHDkVubi6++OILPPvsswgJCbnvuvv06YNWrVohNjYWr732GiQSCT7//HOjDcuwsDBs2rQJiYmJ6NmzJ1q2bInhw4cbrXfZsmUYNmwYFAoFpk6dalj219g+Neb429/+hgMHDiA6Ohrt27dHWVkZ1q5di7Zt29bbe9uQ7w9zzZs3D59//jmGDh2KWbNmGZb9bd++vdHvA1MGDhyItm3bYtu2bXB0dBTMnwJuDev6/PPPIZPJEBQUhNzcXOzZswfu7u53rfull17C1q1bMXToUIwbNw4lJSX44osv6iz/be738L2+/0RkGWyQ0APp9gpH3377Lb799ts65Xf+0vTrr7/ihRdeAHBrgrKXlxcUCgVSU1Px5JNPNkqONjY2yMjIwMqVK/HZZ58hPT0djzzyCDp06IBZs2YZ5gcEBARg69ateOuttzB37lx4enrilVdeQevWrfHiiy8K6kxKSsL58+exdOlSXL16FQMHDrRIg2Tw4MH4+OOPsWTJEiQkJMDPzw/vvfcezp07Z/QXkIEDB0KhUODtt9/GhQsXEBQUhA0bNiA4OPiu93JycsLjjz+OY8eOCf5Hf7sR0q5dO8OQmPqsWLEC06ZNw1tvvYU///wTsbGxFmmQAMC7776LqKgorF69Gp988gnKy8vh6OiIxx57DHPmzMGMGTMEQ09MGTFiBBwdHXH16lXB6lq3PfLII8jJycG7776LLVu24LPPPoOLiwsee+wxvP3224aJ4JYQHh6OoqIiLFu2DDt37sTnn38OGxsbdO7cGfPnz0d8fLzh3EWLFqF169ZYvXo1Zs+eDTc3N0ybNg3vvvtuvfud3KtNmzYhKSkJ8+fPh52dHeLj4w37g9wvd3d37NixA3PmzMFbb72FVq1a4fnnn0dERASioqIE57766qsoLCzE+vXrsXLlSrRv395kgyQyMhKZmZlYuHAhkpKS0KJFCwwcOBDvvfeeYPiZuUaMGIFz584ZPm8eHh4YOHDgXT8HDfn+MJeXlxf27duHmTNnYsmSJXB3d8eMGTPg7e0tWLHvbmxsbDBx4kQsW7YMw4cPr7NwyAcffABbW1t8+eWXqK6uRt++fbFnz546/y7GREVF4f3338eKFSuQkJCAHj16GP6d78zBnO/he33/icgyJHpLz2QjooeaRCJBXFycYLdxIiIiEvp59Atip2BUh28+FzuFOjiHhIiIiIiIRMMGCRERERERiYZzSIiIiIiILM1KNyG0RmyQEFGDcNoZERERWRKHbBERERERkWgeiAbJmjVr4OvrCwcHB4SHhws2azJmy5YtCAwMhIODA7p164Zdu3Y1UaZERERERBB/A8QHaGNEq2+Q3N6kauHChSgoKEBISAiioqJQVlZm9PzDhw9j4sSJmDp1Kk6cOIGYmBjExMSgqKioiTMnIiIiIqK7sfp9SMLDw9GzZ0/Dnge1tbVo164dZs6cifnz59c5f/z48aiqqsKOHTsMsd69eyM0NBTr1q1rsryJiIiIqPn6+ZlYsVMwqsOWT8VOoQ6rntR+48YN5OfnQ6lUGmI2NjaIjIxEbm6u0Wtyc3ORmJgoiEVFRSEjI6PB9z/zq/FeGADo1LZNg+uzlPTD/zFZNqpP5ybMpHmr+NH0v4NrF/47UPPzVY7xnuiJA7ve9dr1WYUmy6Y8GXqPGYnL1He1Jb6nP/yuwGh8+rDu9103/c/+f/9ssmxQSIcmzKRhNuz5t9H45MgQHC/+1eR1PQLa1lvv7vwzJsuiwjqZl1wzIrGx+oFIVsOq36ny8nLU1NRALpcL4nK5HGq12ug1arW6QecTEREREZF4rLqHpKnodDrodDpBTCqVipQNEREREVHzYdU9JB4eHrC1tYVGoxHENRoNPD09jV7j6enZoPMBQKVSQSaTCQ6VSnX/D0BEREREzZPYq2lxlS3LsLe3R1hYGLKzsw2x2tpaZGdnQ6FQGL1GoVAIzgeArKwsk+cDgFKphFarFRx/nbdCRERERESNw+qHbCUmJiI2NhY9evRAr169kJKSgqqqKkyZMgUAMGnSJDz66KOGHo1Zs2Zh4MCBeP/99xEdHY2NGzfi+PHj+Oijj0zeQyqVcogWEREREZEIrL5BMn78eFy+fBlJSUlQq9UIDQ1FZmamYeL6hQsXYPOXVQz69OmDtLQ0vPXWW3jzzTfRqVMnZGRkoGvXu6/yQkRERERkEVxly2xWvw8JEREREdGDpvTZl8ROwSi/tH+KnUIdVt9DYs02HTC+5v74AeyNISIietid6RdlNN7p0O4mzsR8qi2HTZYpn+nThJkQ/Q8bJERERERElmalK1pZIw5uIyIiIiIi0bBBQkREREREorH6BolKpULPnj3h7OyMNm3aICYmBsXFxXe9bsuWLQgMDISDgwO6deuGXbt2NUG2REREREQQfwNEboxoOTk5OYiLi8ORI0eQlZWFmzdvYsiQIaiqqjJ5zeHDhzFx4kRMnToVJ06cQExMDGJiYlBUZHwSOhERERERieOBW/b38uXLaNOmDXJycjBgwACj54wfPx5VVVXYsWOHIda7d2+EhoZi3bp1TZUqERERETVTpc9PEzsFo/y+ML1ZuFgeuFW2tFotAMDNzc3kObm5uUhMTBTEoqKikJGR0aB7/Z5zyGSZ+8B+DaqrOUg//B+j8VF9OjdxJuar1JSZLHORt2nCTJq3C+rfTZb5eLo3YSZERHSvTv58yWi8WwevJs7EOki4MaLZHqh3qra2FgkJCejbt2+9O6+r1WrDTu63yeVyqNXqxk6RiIiIiIga4IHqIYmLi0NRUREOHTLdc3EvdDoddDqdICaVSi16DyIiIiIiquuB6SGJj4/Hjh07sG/fPrRt27becz09PaHRaAQxjUYDT09Po+erVCrIZDLBoVKpLJY7ERERETUzYq+mxVW2LEev1yM+Ph7p6enYu3cv/Pz87nqNQqFAdna2IJaVlQWFQmH0fKVSCa1WKziUSqVF8iciIiIiItOsfshWXFwc0tLSsG3bNjg7OxvmgchkMjg6OgIAJk2ahEcffdTQqzFr1iwMHDgQ77//PqKjo7Fx40YcP34cH31kfFUBqVRqdIjWtUZ6JiIiIiIiusXqe0hSU1Oh1WoxaNAgeHl5GY5NmzYZzrlw4QIuXfrfyg59+vRBWloaPvroI4SEhGDr1q3IyMiodyI8EREREZHF2Eis87BCD9w+JERERERE1u7clFfFTsEo3/VrxU6hDqsfsiWmolLj62kDQFe/5rmmNhEREd3ywfajRuOzRvS667X/93mOybJ3XhiILQd/Mlr2TP8g85ITwaGic0bj/br6Nmke9OBhg4SIiIiIyNIkVj8zwmrwnSIiIiIiItGwQUJERERERKJ5oBokS5YsgUQiQUJCQr3nbdmyBYGBgXBwcEC3bt2wa9eupkmQiIiIiAgQfzWtB2iVrQemQXLs2DF8+OGHCA4Orve8w4cPY+LEiZg6dSpOnDiBmJgYxMTEoKioqIkyJSIiIiIicz0Qy/5eu3YN3bt3x9q1a5GcnIzQ0FCkpKQYPXf8+PGoqqrCjh07DLHevXsjNDQU69ata6KMiYiIiKg5O/fSTLFTMMr3n38XO4U6HohVtuLi4hAdHY3IyEgkJyfXe25ubi4SExMFsaioKGRkZDRihvSw+v7H80bjfbu0b+JMHnxfHzK+hOWYfta7hCU1nSVbc02WzR+ruK+6T/9SZjT+WLs291UvETWdK0ePG4279erRxJk0gMQ6h0dZI6tvkGzcuBEFBQU4duyYWeer1WrI5XJBTC6XQ61WN0Z6RERERER0H6y6QfLLL79g1qxZyMrKgoODQ6PdR6fTQafTCWJSqRRSqbTR7klERERERFY+qT0/Px9lZWXo3r077OzsYGdnh5ycHKxatQp2dnaoqampc42npyc0Go0gptFo4OnpafI+KpUKMplMcKhUKos/DxERERE1DxKJjVUe1siqe0giIiJw8uRJQWzKlCkIDAzEG2+8AVtb2zrXKBQKZGdnC5YGzsrKgkJhegyyUqmsM++EvSNERERERI3Pqhskzs7O6Nq1qyDm5OQEd3d3Q3zSpEl49NFHDT0as2bNwsCBA/H+++8jOjoaGzduxPHjx/HRRx+ZvA+HZxERERERicOqGyTmuHDhAmxs/tf91KdPH6SlpeGtt97Cm2++iU6dOiEjI6NOw4aIiIiIqNFY6SaE1uiB2IeEiIiIiOhBcn7GbLFTMKr9upVip1DHA99DQkRERETWbeW2PJNls0eGN2EmZI3YICEiIiIisjRujGg261z7i4iIiIiImgU2SIiIiIiISDRW3yD57bff8Pzzz8Pd3R2Ojo7o1q0bjh8/Xu81+/fvR/fu3SGVSuHv748NGzY0TbJERERERABgY2OdhxWyzqz+vz/++AN9+/ZFixYt8N133+Gnn37C+++/j1atWpm8prS0FNHR0XjiiSdQWFiIhIQEvPTSS9i9e3cTZk5EREREROaw6mV/58+fj++//x4HDx40+5o33ngDO3fuRFFRkSE2YcIEVFRUIDMzszHSJCIiIiISOB83V+wUjGq/ZrnYKdRh1atsbd++HVFRUXjmmWeQk5ODRx99FK+++ipefvllk9fk5uYiMjJSEIuKikJCQkIjZ0v3o/iCxmg8wEd+33UXlV4yGu/q53XfdTeWf2QWGI2/PLR7E2dCZL5tuaeMxkcqAps4E+u3O/+MybKosE5NmEnDfH3oJ6PxMf2CmjgT61ChvWo07ipzbuJMHn7lew8YjXsMHtDEmTQAV9kym1UP2fr555+RmpqKTp06Yffu3XjllVfw2muv4dNPPzV5jVqthlwu/CVWLpejsrISf/75Z2OnTEREREREDWDVPSS1tbXo0aMH3n33XQDA448/jqKiIqxbtw6xsbEWu49Op4NOpxPEpFIppFKpxe5BRERERER1WXUPiZeXF4KChN3AnTt3xoULF0xe4+npCY1GOPxHo9HAxcUFjo6ORq9RqVSQyWSCQ6VS3f8DEBEREVGzJLGRWOVhjay6h6Rv374oLi4WxE6fPo327dubvEahUGDXrl2CWFZWFhQKhclrlEolEhMTBTH2jhARERERNT6r7iGZPXs2jhw5gnfffRdnz55FWloaPvroI8TFxRnOUSqVmDRpkuH1jBkz8PPPP2PevHk4deoU1q5di82bN2P27Nkm7yOVSuHi4iI42CAhIiIiImp8Vt0g6dmzJ9LT0/HVV1+ha9euWLx4MVJSUvDcc88Zzrl06ZJgCJefnx927tyJrKwshISE4P3338c///lPREVFifEIRERERNQcSWys87BCVr0PCRERERHRg+jCrPlip2CUzwdLxE6hDqueQ/Ig+3zvDybLXhgc3ISZUHOzavsxk2WvjehZ77VbDhrfYwAAnul/f/sM/Cv/rNH4kDD/+6qXrMeOvGKj8afDA5o4ExLDzqOnTZZF93qsCTMhogcNGyRERERERJbGjRHNZp0DyYiIiIiIqFlgg4SIiIiIiERj1Q2SmpoaLFiwAH5+fnB0dETHjh2xePFi3G0e/v79+9G9e3dIpVL4+/tjw4YNTZMwEREREREA2Eis87BCVj2H5L333kNqaio+/fRTdOnSBcePH8eUKVMgk8nw2muvGb2mtLQU0dHRmDFjBr788ktkZ2fjpZdegpeXF5f+JSIiIiKyMlbdIDl8+DBGjhyJ6OhoAICvry+++uorHD161OQ169atg5+fH95//30AQOfOnXHo0CGsXLmSDRIiIiIiIitj1Q2SPn364KOPPsLp06fx2GOP4d///jcOHTqEFStWmLwmNzcXkZGRglhUVBQSEhIaOVshLu1LYrnb0r71ud+lfevD5X0fflzet3nj0r5Ed7DSTQitkVU3SObPn4/KykoEBgbC1tYWNTU1eOeddwQ7td9JrVZDLpcLYnK5HJWVlfjzzz/h6OjY2Gk3W8u+OWI0/vro3o1637T9J02WPTuoW6Pem8z3zff/MRof3bdzE2dC1mjJ1lyTZfPHKpowEyIiampW3SDZvHkzvvzyS6SlpaFLly4oLCxEQkICvL29ERsba7H76HQ66HQ6QUwqlUIqlVrsHkREREREVJdV9yW9/vrrmD9/PiZMmIBu3brhhRdewOzZs6FSqUxe4+npCY1GI4hpNBq4uLiY7B1RqVSQyWSCo757EBERERHVR2IjscrDGll1D8n169dhYyNsM9na2qK2ttbkNQqFArt27RLEsrKyoFCY7vJXKpVITEwUxNg7QkRERETU+Ky6QTJ8+HC888478PHxQZcuXXDixAmsWLECL774ouEcpVKJ3377DZ999hkAYMaMGVi9ejXmzZuHF198EXv37sXmzZuxc+dOk/fh8CwiIiIiInFY9ZCtv//97xg7dixeffVVdO7cGXPnzsX06dOxePFiwzmXLl3ChQsXDK/9/Pywc+dOZGVlISQkBO+//z7++c9/cslfIiIiImo6Eol1Hg2gUqnQs2dPODs7o02bNoiJiUFxcbHgnEGDBkEikQiOGTNmNOyt0t9t23MiIiIiImqQX+YliZ2CUe2W/s3sc4cOHYoJEyagZ8+e+O9//4s333wTRUVF+Omnn+Dk5ATgVoPksccew9/+9r96H3nkEbi4uJh9H6seskVkjvVZhSbLpjwZ2mR5EBGR9UndlW+y7JWnwpowk4ffjrxio3HuUfTgyszMFLzesGED2rRpg/z8fAwYMMAQf+SRR+Dp6XnP97HqIVtERERERA8kGxvrPO6DVqsFALi5uQniX375JTw8PNC1a1colUpcv369QfWyh4SIiIiIqJm41/33amtrkZCQgL59+6Jr166G+LPPPov27dvD29sbP/zwA9544w0UFxfjm2++MTsnNkiIiIiIiJoJlUqFt99+WxBbuHAhFi1aVO91cXFxKCoqwqFDhwTxadOmGf67W7du8PLyQkREBEpKStCxY0ezchJ1yNaBAwcwfPhweHt7QyKRICMjQ1Cu1+uRlJQELy8vODo6IjIyEmfOnLlrvWvWrIGvry8cHBwQHh6Oo0ePNtITEBEREREZIfZqWiYOpVIJrVYrOJRKZb2PEh8fjx07dmDfvn1o27ZtveeGh4cDAM6ePWv2WyVqg6SqqgohISFYs2aN0fKlS5di1apVWLduHfLy8uDk5ISoqChUV1ebrHPTpk1ITEzEwoULUVBQgJCQEERFRaGsrKyxHoOIiIiI6IEglUrh4uIiOEwN19Lr9YiPj0d6ejr27t0LPz+/u9ZfWFgIAPDy8jI7J1GHbA0bNgzDhg0zWqbX65GSkoK33noLI0eOBAB89tlnkMvlyMjIwIQJE4xet2LFCrz88suYMmUKAGDdunXYuXMnPvnkE8yfP79xHoSIiIiI6CETFxeHtLQ0bNu2Dc7OzlCr1QAAmUwGR0dHlJSUIC0tDU899RTc3d3xww8/YPbs2RgwYACCg4PNvo/VziEpLS2FWq1GZGSkISaTyRAeHo7c3FyjDZIbN24gPz9f0O1kY2ODyMhI5ObmNkne1PS4tC8REZnCpX2bDpf3vUMDNyG0RqmpqQBu7TXyV+vXr8fkyZNhb2+PPXv2ICUlBVVVVWjXrh3GjBmDt956q0H3sdoGye0WmFwuF8Tlcrmh7E7l5eWoqakxes2pU6caJ1EianTLvjlisuz10b2bMJPmTVt6zmSZzM+3Ue+9Ldf4d/hIRWCj3peat8rKqybLXFycmzAT63Bp7T9Nlnm9+lITZkJN5W77p7dr1w45OTn3fR+rbZA0pXtd/oyIiIiIiO6P1W6MeHu3R41GI4hrNBqTO0F6eHjA1ta2QdcAt5Y/k8lkgkOlUt3nExARERFRcyWxsbHKwxpZZ1YA/Pz84OnpiezsbEOssrISeXl5UCgURq+xt7dHWFiY4Jra2lpkZ2ebvAbAPS1/RkRERERE90/UIVvXrl0TrFFcWlqKwsJCuLm5wcfHBwkJCUhOTkanTp3g5+eHBQsWwNvbGzExMYZrIiIiMGrUKMTHxwMAEhMTERsbix49eqBXr16GSTa3V90yhsOziIiIiIjEIWqD5Pjx43jiiScMrxMTEwEAsbGx2LBhA+bNm4eqqipMmzYNFRUV6NevHzIzM+Hg4GC4pqSkBOXl5YbX48ePx+XLl5GUlAS1Wo3Q0FBkZmbWmehORERERNRoHoJVtpqKRH+36fNERERERNQgvy54R+wUjGq7+P/ETqEOrrJFRAabDhSZLBs/oGsTZtIwJ878ZjT+eKdHmzgTEsOeghKTZZHdOzZhJkR0r3J/Om+yTBHUvgkzITGwQUJEREREZGk2HLJlLqtdZYuIiIiIiB5+bJAQEREREZFoOGSLiIiIiMjSJPy7v7lEfacOHDiA4cOHw9vbGxKJBBkZGYaymzdv4o033kC3bt3g5OQEb29vTJo0CRcvXrxrvWvWrIGvry8cHBwQHh6Oo0ePNuJTEBERERHRvRK1QVJVVYWQkBCsWbOmTtn169dRUFCABQsWoKCgAN988w2Ki4sxYsSIeuvctGkTEhMTsXDhQhQUFCAkJARRUVEoKytrrMcgIiIiIqJ7ZDX7kEgkEqSnpwt2Yb/TsWPH0KtXL5w/fx4+Pj5GzwkPD0fPnj2xevVqAEBtbS3atWuHmTNnYv78+Y2ROhERERGRwK9/e0/sFIxqm/SG2CnU8UDNIdFqtZBIJHB1dTVafuPGDeTn50OpVBpiNjY2iIyMRG5ubhNlSdam9GK50bift0cTZ9I0PvlXocmyF4eENlkeTenMr8Z7QDu1bdPEmTz4Ks6cNRp37eTfxJk0b9//aHpPhr5duCcDET1cHpjZNtXV1XjjjTcwceJEuLi4GD2nvLwcNTU1kMvlgrhcLodarW6KNImIiIiIqAEeiB6SmzdvYty4cdDr9UhNTbV4/TqdDjqdThCTSqWQSqUWvxcRERERNQMSboxoLqvvIbndGDl//jyysrJM9o4AgIeHB2xtbaHRaARxjUYDT09Pk9epVCrIZDLBoVKpLPYMRERERERknFU3SG43Rs6cOYM9e/bA3d293vPt7e0RFhaG7OxsQ6y2thbZ2dlQKBQmr1MqldBqtYLjr/NQiIiIiIiocYg6ZOvatWs4e/Z/EyhLS0tRWFgINzc3eHl5YezYsSgoKMCOHTtQU1NjmAfi5uYGe3t7AEBERARGjRqF+Ph4AEBiYiJiY2PRo0cP9OrVCykpKaiqqsKUKVNM5sHhWURERERkSRJujGg2URskx48fxxNPPGF4nZiYCACIjY3FokWLsH37dgBAaGio4Lp9+/Zh0KBBAICSkhKUl/9vFaXx48fj8uXLSEpKglqtRmhoKDIzM+tMdKfm42FdTcuUh3UlrfpwNS3L4Wpa1oEraRFRc2I1+5AQUfN18OQ5k2X9u/k2WR50f775/j9G46P7dm7iTOhBY2p5dqD5/VGJHh6/JS8XOwWjHn1rrtgp1PFArLJFRERERPRAseEqW+bi4DYiIiIiIhINGyRERERERCQaDtkiIiIiIrI0boxoNlF7SA4cOIDhw4fD29sbEokEGRkZJs+dMWMGJBIJUlJS7lrvmjVr4OvrCwcHB4SHh+Po0aOWS5qIiIiIiCxG1AZJVVUVQkJCsGbNmnrPS09Px5EjR+Dt7X3XOjdt2oTExEQsXLgQBQUFCAkJQVRUFMrKyiyVNhERERERWYjVLPsrkUiQnp6OmJgYQfy3335DeHg4du/ejejoaCQkJCAhIcFkPeHh4ejZsydWr14N4NZO7e3atcPMmTMxf/78RnwCIiIiIqJbfnsvRewUjHr0jQSxU6jDqueQ1NbW4oUXXsDrr7+OLl263PX8GzduID8/H0ql0hCzsbFBZGQkcnNzGzNVIrqLnB9KjcYHBvs1cSb0sPlFc8VkWTu5WxNmQmIxtZeRNe9j9PPYSSbLOmz97L7q3n7klNH4iN6Bd732fr6rT/9ifDTKY+24eS3Vz6pX2XrvvfdgZ2eH1157zazzy8vLUVNTU2dXdrlcDrVa3RgpEhERERHRfbDaHpL8/Hx88MEHKCgogKSRVynQ6XTQ6XSCmFQqhVQqbdT7EhEREdFDiqtsmc1qe0gOHjyIsrIy+Pj4wM7ODnZ2djh//jzmzJkDX19fo9d4eHjA1tYWGo1GENdoNPD09DR5L5VKBZlMJjhUKpUlH4eIiIiIiIyw2h6SF154AZGRkYJYVFQUXnjhBUyZMsXoNfb29ggLC0N2drZhcnxtbS2ys7MRHx9v8l5KpRKJiYmCGHtHiIiIiIgan6gNkmvXruHs2bOG16WlpSgsLISbmxt8fHzg7u4uOL9Fixbw9PREQECAIRYREYFRo0YZGhyJiYmIjY1Fjx490KtXL6SkpKCqqspkIwbg8CwiIiIisiyJDYdsmUvUBsnx48fxxBNPGF7f7qWIjY3Fhg0bzKqjpKQE5eXlhtfjx4/H5cuXkZSUBLVajdDQUGRmZtaZ6E5ETYuraVFj4UpaZM2raZlyvytp1cec1bRMuZ/vaq6mRffKavYhIXrY7Dx62mg8utdjTZwJkfVb+rXppdnnjVE0YSbU3NzPErnNzYffFZgsmz6sO74+9JPRsjH9ghorJat2cfkqsVMwynuueavXNiWrnUNCRERERPTAkljt2lFWh+8UERERERGJhg0SIiIiIiISDYdsERERERFZGjdGNJuoPSQHDhzA8OHD4e3tDYlEgoyMjDrn/Oc//8GIESMgk8ng5OSEnj174sKFC/XWu2XLFgQGBsLBwQHdunXDrl27GukJiIiIiIjofojaIKmqqkJISAjWrFljtLykpAT9+vVDYGAg9u/fjx9++AELFiyAg4ODyToPHz6MiRMnYurUqThx4gRiYmIQExODoqKixnoMIiIiIiK6R1az7K9EIkF6erphh3UAmDBhAlq0aIHPP//c7HrGjx+Pqqoq7NixwxDr3bs3QkNDsW7dOkumTERERERk1MWUtWKnYJR3wqtip1CH1c4hqa2txc6dOzFv3jxERUXhxIkT8PPzg1KpFDRa7pSbm2vYYPG2qKgoo8PBqK69hT8bjQ8O7XBf9f7xg+keqlbBXe+r7sZ0rPgXo/GeAe2aOBMi852/VG403t7Lo1Hvu+mA8Z/z8QOs92ecHg4HT54zGn8QN0y8be3O40bjr0b3wIqMPJPXJcaEN1ZKjepQ0TmTZf26+jZZHiQOq11lq6ysDNeuXcOSJUswdOhQ/Otf/8KoUaMwevRo5OTkmLxOrVbX2ZVdLpdDrVY3dspERERERNRAVt1DAgAjR47E7NmzAQChoaE4fPgw1q1bh4EDB1rsXjqdDjqdThCTSqWQSqUWuwcRERERNSPcGNFsVvtOeXh4wM7ODkFBQYJ4586d611ly9PTExqNRhDTaDTw9PQ0eY1KpYJMJhMcKpXq/h6AiIiIiIjuymobJPb29ujZsyeKi4sF8dOnT6N9+/Ymr1MoFMjOzhbEsrKyoFAoTF6jVCqh1WoFh1KpvL8HICIiIiKiuxJ1yNa1a9dw9uxZw+vS0lIUFhbCzc0NPj4+eP311zF+/HgMGDAATzzxBDIzM/Htt99i//79hmsmTZqERx991NCjMWvWLAwcOBDvv/8+oqOjsXHjRhw/fhwfffSRyTw4PIuIiIiILEliw40RzSVqD8nx48fx+OOP4/HHHwcAJCYm4vHHH0dSUhIAYNSoUVi3bh2WLl2Kbt264Z///Ce+/vpr9OvXz1DHhQsXcOnSJcPrPn36IC0tDR999BFCQkKwdetWZGRkoGtXrvJCRERERGRtrGYfEiIiIiKih8Wlv38odgpGec2cLnYKdVjtKltEzVl968/fzZKtuSbL5o81PZdKTMeLfzVZ1iOgbRNm8nBYnn7EaHzuqN53vbbi1GmjcdfAx+4rJyISGrxgk8myvYvHN2Em1iH7RInJsojHOzZhJhYk4ZAtc1ntpHYiIiIiInr4sUFCRERERESi4ZAtIiIiIiJLs+Hf/c0l6jt14MABDB8+HN7e3pBIJMjIyBCUX7t2DfHx8Wjbti0cHR0RFBSEdevW3bXeLVu2IDAwEA4ODujWrRt27drVSE9ARERERET3Q9QGSVVVFUJCQrBmzRqj5YmJicjMzMQXX3yB//znP0hISEB8fDy2b99uss7Dhw9j4sSJmDp1Kk6cOIGYmBjExMSgqKiosR6DiIiIiIjukdUs+yuRSJCeno6YmBhDrGvXrhg/fjwWLFhgiIWFhWHYsGFITk42Ws/48eNRVVWFHTt2GGK9e/dGaGioWb0rRERERET369K6T8ROwSivGS+KnUIdVj2HpE+fPti+fTtefPFFeHt7Y//+/Th9+jRWrlxp8prc3FwkJiYKYlFRUXWGgxERPaze+iLHaDz5+YFNnInlfPP9f4zGR/ft3MSZmG/LwZ+Mxp/pH9TEmRARWTerbpD8/e9/x7Rp09C2bVvY2dnBxsYG//jHPzBgwACT16jVasjlckFMLpdDrVY3drpERERERNRAVt8gOXLkCLZv34727dvjwIEDiIuLg7e3NyIjIy12H51OB51OJ4hJpVJIpVKL3YOIiIiImhFujGg2q12P7M8//8Sbb76JFStWYPjw4QgODkZ8fDzGjx+P5cuXm7zO09MTGo1GENNoNPD09DR5jUqlgkwmExwqlcpiz0JERERERMZZbYPk5s2buHnzJmzuWMPZ1tYWtbW1Jq9TKBTIzs4WxLKysqBQKExeo1QqodVqBYdSqby/ByAiIiIiorsSdcjWtWvXcPbsWcPr0tJSFBYWws3NDT4+Phg4cCBef/11ODo6on379sjJycFnn32GFStWGK6ZNGkSHn30UUOPxqxZszBw4EC8//77iI6OxsaNG3H8+HF89NFHJvPg8CwiIiIisiSJDYdsmUvUBsnx48fxxBNPGF7fXh0rNjYWGzZswMaNG6FUKvHcc8/hypUraN++Pd555x3MmDHDcM2FCxcEvSh9+vRBWloa3nrrLbz55pvo1KkTMjIy0LVr16Z7MCIiIiIiMovV7ENCRERERPSwUP9jg9gpGOX58mSxU6jDqlfZImquvth30mj8+Se63fXaNTuOmyyLe7rHPedEdD92558xWRYV1qkJM3kwZJ8oMVkW8XjHJsyEHjbbck+ZLBupCLyvuk39nDfbn3GJ1U7Vtjp8p4iIiIiISDRskBARERERkWg4ZIuIiIiIyNK4MaLZRO0hUalU6NmzJ5ydndGmTRvExMSguLhYcE51dTXi4uLg7u6Oli1bYsyYMXU2PryTXq9HUlISvLy84OjoiMjISJw5Y3r8MhERERERiUPUBklOTg7i4uJw5MgRZGVl4ebNmxgyZAiqqqoM58yePRvffvsttmzZgpycHFy8eBGjR4+ut96lS5di1apVWLduHfLy8uDk5ISoqChUV1c39iMREREREVEDWNWyv5cvX0abNm2Qk5ODAQMGQKvVonXr1khLS8PYsWMBAKdOnULnzp2Rm5uL3r1716lDr9fD29sbc+bMwdy5cwEAWq0WcrkcGzZswIQJE5r0mYiIiIio+VGv/0LsFIzynPK82CnUYVVzSLRaLQDAzc0NAJCfn4+bN28iMjLScE5gYCB8fHxMNkhKS0uhVqsF18hkMoSHhyM3N5cNEiKRfJVTZDQ+cSA3LSVq7v74wfj3AwC0CuZ3BNHDzmpW2aqtrUVCQgL69u1r2FVdrVbD3t4erq6ugnPlcjnUarXRem7H5XK52dcQEREREZE4rKaHJC4uDkVFRTh06FCT31un00Gn0wliUqkUUqm0yXMhIiIioocAN0Y0m1W8U/Hx8dixYwf27duHtm3bGuKenp64ceMGKioqBOdrNBp4enoaret2/M6VuOq7RqVSQSaTCQ6VSnUfT0REREREROYQtUGi1+sRHx+P9PR07N27F35+foLysLAwtGjRAtnZ2YZYcXExLly4AIVCYbROPz8/eHp6Cq6prKxEXl6eyWuUSiW0Wq3gUCqVFnhCIiIiIiKqj6hDtuLi4pCWloZt27bB2dnZMMdDJpPB0dERMpkMU6dORWJiItzc3ODi4oKZM2dCoVAIJrQHBgZCpVJh1KhRkEgkSEhIQHJyMjp16gQ/Pz8sWLAA3t7eiImJMZoHh2cRERERkSVJbLgxorlEbZCkpqYCAAYNGiSIr1+/HpMnTwYArFy5EjY2NhgzZgx0Oh2ioqKwdu1awfnFxcWGFboAYN68eaiqqsK0adNQUVGBfv36ITMzEw4ODo36PERERERE1DBWtQ8JEREREdHDQPPZV2KnYJR80kSxU6jDalbZIiKiB9vVOxYguc35jqXbLe2Hkosmy4I7ejfafSt/M31fl0cb775kWdqfS43GZR388PNvl01e1+HR1o2VUrNUcfqMyTLXxzo1YSYkBjZIiIiIiIgsTcI5JOayimV/iYiIiIioeWKDhIiIiIiIRCNqg0SlUqFnz55wdnZGmzZtEBMTg+LiYkP5lStXMHPmTAQEBMDR0RE+Pj547bXXBCtqGaPX65GUlAQvLy84OjoiMjISZ86YHptIRERERGRRNjbWeVghUbPKyclBXFwcjhw5gqysLNy8eRNDhgxBVVUVAODixYu4ePEili9fjqKiImzYsAGZmZmYOnVqvfUuXboUq1atwrp165CXlwcnJydERUWhurq6KR6LiIiIiIjMZFXL/l6+fBlt2rRBTk4OBgwYYPScLVu24Pnnn0dVVRXs7OrOydfr9fD29sacOXMwd+5cAIBWq4VcLseGDRswYcKERn0GIiIiIiLNl5vFTsEo+XPjxE6hDqtaZev2UCw3N7d6z3FxcTHaGAGA0tJSqNVqREZGGmIymQzh4eHIzc1lg4SIqJFsOlBkND5+QNcmzoTEsKegxGRZZPeOTZgJPYwqfvyP0bhrl85NnEkDcJUts1nNQLLa2lokJCSgb9++6NrV+P+8ysvLsXjxYkybNs1kPWq1GgAgl8sFcblcbigjIiIiIiLrYDU9JHFxcSgqKsKhQ4eMlldWViI6OhpBQUFYtGiRRe+t0+mg0+kEMalUCqlUatH7EBERERGRkFX0kMTHx2PHjh3Yt28f2rZtW6f86tWrGDp0KJydnZGeno4WLVqYrMvT0xMAoNFoBHGNRmMou5NKpYJMJhMcKpXqPp6IiIiIiJo1icQ6DyskaoNEr9cjPj4e6enp2Lt3L/z8/OqcU1lZiSFDhsDe3h7bt2+Hg4NDvXX6+fnB09MT2dnZgjry8vKgUCiMXqNUKqHVagWHUqm8v4cjIiIiIqK7ErVBEhcXhy+++AJpaWlwdnaGWq2GWq3Gn3/+CeB/jZGqqip8/PHHqKysNJxTU1NjqCcwMBDp6ekAAIlEgoSEBCQnJ2P79u04efIkJk2aBG9vb8TExBjNQyqVwsXFRXBwuBYRERERUeMTdQ5JamoqAGDQoEGC+Pr16zF58mQUFBQgLy8PAODv7y84p7S0FL6+vgCA4uJiwWaJ8+bNQ1VVFaZNm4aKigr069cPmZmZd+1dISIiIiKyBImVbkJojaxqHxIiIiIioodB2aZvxE7BqDbjR4udQh1Ws8oWkRgqL14yWebi7dWEmVBz8833xtfUH93XitfUJ4s5/UuZybLH2rVpwkwefpsP/GiybNyALk2YyYNvW+4pk2UjFYFNmAk9bNggISIiIiKyNCtd0coacXAbERERERGJhg0SIiIiIiISjagNEpVKhZ49e8LZ2Rlt2rRBTEwMiouLjZ6r1+sxbNgwSCQSZGRk1FuvXq9HUlISvLy84OjoiMjISJw5c6YRnoCIiIiIyAgbiXUeVkjUBklOTg7i4uJw5MgRZGVl4ebNm4Z9R+6UkpICiZlj8ZYuXYpVq1Zh3bp1yMvLg5OTE6KiolBdXW3pRyAiIiIiovsgaoMkMzMTkydPRpcuXRASEoINGzbgwoULyM/PF5xXWFiI999/H5988sld69Tr9UhJScFbb72FkSNHIjg4GJ999hkuXrx4154VIiIiIiK6xZzRTNXV1YiLi4O7uztatmyJMWPGQKPRNOg+VrXK1u3NDd3c3Ayx69ev49lnn8WaNWvg6el51zpKS0uhVqsRGRlpiMlkMoSHhyM3NxcTJkywfOL0wOLSviQWLu/bvHFp36bDpX0th0v7NpDkwZ+qfXs0U8+ePfHf//4Xb775JoYMGYKffvoJTk5OAIDZs2dj586d2LJlC2QyGeLj4zF69Gh8//33Zt/HahoktbW1SEhIQN++fdG1a1dDfPbs2ejTpw9GjhxpVj1qtRoAIJfLBXG5XG4oIyIiIiLLOlR0zmi8X1ffJs2DLCczM1PwesOGDWjTpg3y8/MxYMAAaLVafPzxx0hLS8PgwYMBAOvXr0fnzp1x5MgR9O7d26z7WE2DJC4uDkVFRTh06JAhtn37duzduxcnTpxo1HvrdDrodDpBTCqVQiqVNup9iYiIiIia0v383nvnaKb8/HzcvHlTMDIpMDAQPj4+yM3NNbtBYhV9SfHx8dixYwf27duHtm3bGuJ79+5FSUkJXF1dYWdnBzu7W+2nMWPGYNCgQUbruj2s686xaxqNxuSQL5VKBZlMJjhUKpUFnoyIiIiImiWxV9Mycdzr773GRjOp1WrY29vD1dVVcG5DRyaJ2kOi1+sxc+ZMpKenY//+/fDz8xOUz58/Hy+99JIg1q1bN6xcuRLDhw83Wqefnx88PT2RnZ2N0NBQAEBlZSXy8vLwyiuvGL1GqVQiMTFREGPvCBERERE9bO71915jo5ksRdQGSVxcHNLS0rBt2zY4OzsbWlIymQyOjo7w9PQ02qvh4+MjaLwEBgZCpVJh1KhRkEgkSEhIQHJyMjp16gQ/Pz8sWLAA3t7eiImJMZoHh2cRERERUXNwL7/33h7NdODAAcFoJk9PT9y4cQMVFRWCXpL6RiYZI2qDJDU1FQDqDL9av349Jk+ebHY9xcXFhjFtADBv3jxUVVVh2rRpqKioQL9+/ZCZmQkHBwdLpE1EREREVC9z98+zZncbzRQWFoYWLVogOzsbY8aMAXDr9/ILFy5AoVCYfR+JXq/XWzRzIiIiIqJm7nLGDrFTMKp1zNNmn/vqq68aRjMFBAQY4rdHMwHAK6+8gl27dmHDhg1wcXHBzJkzAQCHDx82+z5skBCRwQfbj5osmzWiVxNmQkRE9GB7GBokpnp5/jqaqbq6GnPmzMFXX30FnU6HqKgorF279sEZskVERERE9FB6CDZGNKffwsHBAWvWrMGaNWvu+T4P/jtFREREREQPLDZIiIiIiIhINKI2SFQqFXr27AlnZ2e0adMGMTExKC4urnNebm4uBg8eDCcnJ7i4uGDAgAH4888/6617zZo18PX1hYODA8LDw3H0qOmx8UREREREFmUFmyAaPayQqA2SnJwcxMXF4ciRI8jKysLNmzcxZMgQVFVVGc7Jzc3F0KFDMWTIEBw9ehTHjh1DfHw8bGxMp75p0yYkJiZi4cKFKCgoQEhICKKiolBWVtYUj0VERERERGayqlW2Ll++jDZt2iAnJwcDBgwAAPTu3RtPPvkkFi9ebHY94eHh6NmzJ1avXg3g1lb37dq1w8yZMzF//vxGyZ3oYcBVtoiIiCzj8rffiZ2CUa2HDxM7hTqsapWt25sburm5AQDKysqQl5eH5557Dn369EFJSQkCAwPxzjvvoF+/fkbruHHjBvLz86FUKg0xGxsbREZGIjc3t/EfgugBxkYHERGRhTwEGyM2FatpkNTW1iIhIQF9+/ZF165dAQA///wzAGDRokVYvnw5QkND8dlnnyEiIgJFRUXo1KlTnXrKy8tRU1MDuVwuiMvlcpw6darxH4SIjNpy8Cej8Wf6BzVxJmSN/pFZYLLs5aHd76vuL/adNBp//olu91UvEZnv798eM1k2c3jPJsyErJHVNEji4uJQVFSEQ4cOGWK1tbUAgOnTp2PKlCkAgMcffxzZ2dn45JNPoFKpLHJvnU4HnU4niEmlUkilUovUT0RERERExlnFsr/x8fHYsWMH9u3bh7Zt2xriXl5eAICgIOFfUDt37owLFy4YrcvDwwO2trbQaDSCuEajMbljpEqlgkwmExyWauwQERERUTNkY2OdhxUSNSu9Xo/4+Hikp6dj79698PPzE5T7+vrC29u7zlLAp0+fRvv27Y3WaW9vj7CwMGRnZxtitbW1yM7OhkKhMHqNUqmEVqsVHH+dg0JERERERI1D1CFbcXFxSEtLw7Zt2+Ds7Ay1Wg0AkMlkcHR0hEQiweuvv46FCxciJCQEoaGh+PTTT3Hq1Cls3brVUE9ERARGjRqF+Ph4AEBiYiJiY2PRo0cP9OrVCykpKaiqqjIM+7oTh2cREREREYlD1AZJamoqAGDQoEGC+Pr16zF58mQAQEJCAqqrqzF79mxcuXIFISEhyMrKQseOHQ3nl5SUoLy83PB6/PjxuHz5MpKSkqBWqxEaGorMzMw6E92JiIiIiBoFV9kym1XtQ0JERERE9DC4/F2W2CkY1XrYk2KnUIfVrLJFRERED66vcopMlk0c2LUJM6FV240vsfvaCC6vS9aJDRIiIiIiIguT2HDIlrmsc+0vIiIiIiJqFtggISIiIiIi0XDIFhERERGRpUn4d39zifpOqVQq9OzZE87OzmjTpg1iYmLqbIKoVqvxwgsvwNPTE05OTujevTu+/vrru9a9Zs0a+Pr6wsHBAeHh4Th69GhjPQYREREREd0jURskOTk5iIuLw5EjR5CVlYWbN29iyJAhqKqqMpwzadIkFBcXY/v27Th58iRGjx6NcePG4cSJEybr3bRpExITE7Fw4UIUFBQgJCQEUVFRKCsra4rHIiIiIiIiM1nVPiSXL19GmzZtkJOTgwEDBgAAWrZsidTUVLzwwguG89zd3fHee+/hpZdeMlpPeHg4evbsidWrVwMAamtr0a5dO8ycORPz589v/AchIiIiomatfM8+sVMwyiPyCbFTqMOq5pBotVoAgJubmyHWp08fbNq0CdHR0XB1dcXmzZtRXV1dZ3f3227cuIH8/HwolUpDzMbGBpGRkcjNzW3U/ImsgWrLYZNlymf6NGEmRNbhePGvRuM9Ato2cSbUWNbuPG40/mp0D7y7+XuT1705rm9jpUREDWA1s21qa2uRkJCAvn37omvX/22gtHnzZty8eRPu7u6QSqWYPn060tPT4e/vb7Se8vJy1NTUQC6XC+JyuRxqtbpRn4GIiIiIiBrGanpI4uLiUFRUhEOHDgniCxYsQEVFBfbs2QMPDw9kZGRg3LhxOHjwILp162aRe+t0Ouh0OkFMKpVCKpVapH4iIiIiamYk3BjRXFbRQxIfH48dO3Zg3759aNv2f13oJSUlWL16NT755BNEREQgJCQECxcuRI8ePbBmzRqjdXl4eMDW1hYajUYQ12g08PT0NHqNSqWCTCYTHCqVynIPSERERERERonaINHr9YiPj0d6ejr27t0LPz8/Qfn169cB3JoD8le2traora01Wqe9vT3CwsKQnZ1tiNXW1iI7OxsKhcLoNUqlElqtVnD8dQ4KERERERE1DlGHbMXFxSEtLQ3btm2Ds7OzYY6HTCaDo6MjAgMD4e/vj+nTp2P58uVwd3dHRkYGsrKysGPHDkM9ERERGDVqFOLj4wEAiYmJiI2NRY8ePdCrVy+kpKSgqqoKU6ZMMZoHh2cRERERkUVxY0Szibrsr8TE2Lr169dj8uTJAIAzZ85g/vz5OHToEK5duwZ/f3/MnTtXsAywr68vJk+ejEWLFhliq1evxrJly6BWqxEaGopVq1YhPDy8MR+HiIiIiAgAUL73gNgpGOUxeIDYKdRhVfuQENH9W7PD+PKXABD3dI8mzISIiKj5YoPEfFazyhYRERER0cNCYsNVtszFwW1ERERERCQaNkiIiIiIiEg0HLJFRERERGRp3BjRbKL2kKSmpiI4OBguLi5wcXGBQqHAd999Zyivrq5GXFwc3N3d0bJlS4wZM6bOhod30uv1SEpKgpeXFxwdHREZGYkzZ8409qMQEREREdE9ELVB0rZtWyxZsgT5+fk4fvw4Bg8ejJEjR+LHH38EAMyePRvffvsttmzZgpycHFy8eBGjR4+ut86lS5di1apVWLduHfLy8uDk5ISoqChUV1c3xSMREREREVEDWN2yv25ubli2bBnGjh2L1q1bIy0tDWPHjgUAnDp1Cp07d0Zubi569+5d51q9Xg9vb2/MmTMHc+fOBQBotVrI5XJs2LABEyZMaNJnISIiIqLm6feDh8VOwSj3/n3ETqEOq5lDUlNTgy1btqCqqgoKhQL5+fm4efMmIiMjDecEBgbCx8fHZIOktLQUarVacI1MJkN4eDhyc3PZICEiIroP5+PmGo23X7O8iTOxnKsVFUbjzq6uTZqHJRWc/tVovPtjbZs4E8v5V/5Zo/EhYf5NnAk1BtEbJCdPnoRCoUB1dTVatmyJ9PR0BAUFobCwEPb29nC94wtBLpdDrVYbret2XC6Xm30NERERERGJR/QGSUBAAAoLC6HVarF161bExsYiJyenSXPQ6XTQ6XSCmFQqhVQqbdI8iIiIiOghwVW2zCb6PiT29vbw9/dHWFgYVCoVQkJC8MEHH8DT0xM3btxAxR1dqRqNBp6enkbruh2/cyWu+q4BAJVKBZlMJjhUKtX9PRgREREREd2V6A2SO9XW1kKn0yEsLAwtWrRAdna2oay4uBgXLlyAQqEweq2fnx88PT0F11RWViIvL8/kNQCgVCqh1WoFh1KptNxDERERERGRUaIO2VIqlRg2bBh8fHxw9epVpKWlYf/+/di9ezdkMhmmTp2KxMREuLm5wcXFBTNnzoRCoRBMaA8MDIRKpcKoUaMgkUiQkJCA5ORkdOrUCX5+fliwYAG8vb0RExNjMg8OzyIiIiIii7LhkC1zidogKSsrw6RJk3Dp0iXIZDIEBwdj9+7dePLJJwEAK1euhI2NDcaMGQOdToeoqCisXbtWUEdxcTG0Wq3h9bx581BVVYVp06ahoqIC/fr1Q2ZmJhwcHJr02YiIiB42D/JqWqY8yKtpmfIgr6ZlClfTerhZ3T4kRAT8I7PAaPzlod2bOBMi6/DFvpNG488/0a2JMyEiU775/j9G46P7dm7iTKzD74ePiJ2CUe596m6dITbRV9kiIiIiInrYSCRWN1XbavGdIiIiIiIi0bBBQkREREREouGQLSIiIiIiS+PGiGYTtYckNTUVwcHBcHFxgYuLCxQKBb777jsAwJUrVzBz5kwEBATA0dERPj4+eO211wQrahmj1+uRlJQELy8vODo6IjIyEmfOnGmKxyEiIiIiogYStUHStm1bLFmyBPn5+Th+/DgGDx6MkSNH4scff8TFixdx8eJFLF++HEVFRdiwYQMyMzMxderUeutcunQpVq1ahXXr1iEvLw9OTk6IiopCdXV1Ez0VERERERGZy+qW/XVzc8OyZcuMNjy2bNmC559/HlVVVbCzqzvaTK/Xw9vbG3PmzMHcuXMBAFqtFnK5HBs2bMCECRMaPX8iIiIioitHj4udglFuvXqInUIdVjOHpKamBlu2bEFVVRUUCoXRc7RaLVxcXIw2RgCgtLQUarUakZGRhphMJkN4eDhyc3PZICEiegiV/HrZZFnHtq2bMBN6EJ2/VG403t7Lo4kzMd/Xh34yWTamXxB2Hj1ttCy612P3dd+8UxdMloUH+txX3dS8id4gOXnyJBQKBaqrq9GyZUukp6cjKCioznnl5eVYvHgxpk2bZrIutVoNAJDL5YK4XC43lBERERERkfUQvUESEBCAwsJCaLVabN26FbGxscjJyRE0SiorKxEdHY2goCAsWrTI4jnodDrodDpBTCqVQiqVWvxeRERERNQMcGNEs4n+Ttnb28Pf3x9hYWFQqVQICQnBBx98YCi/evUqhg4dCmdnZ6Snp6NFixYm6/L09AQAaDQaQVyj0RjKjFGpVJDJZIJDpVLd55MREREREdHdiN4guVNtba2ht6KyshJDhgyBvb09tm/fDgcHh3qv9fPzg6enJ7Kzsw2xyspK5OXlmZyXAgBKpRJarVZwKJVKyzwQERERERGZJOqQLaVSiWHDhsHHxwdXr15FWloa9u/fj927dxsaI9evX8cXX3yByspKVFZWAgBat24NW1tbAEBgYCBUKhVGjRoFiUSChIQEJCcno1OnTvDz88OCBQvg7e2NmJgYk3lweBYRERERWZQNN0Y0l6gNkrKyMkyaNAmXLl2CTCZDcHAwdu/ejSeffBL79+9HXl4eAMDf319wXWlpKXx9fQEAxcXFgs0S582bh6qqKkybNg0VFRXo168fMjMz79q7QkRERERETc/q9iEhIiIiInrQXck/IXYKRrmFPS52CnWIvsoWETUP67MKjcanPBnapHkQkfX5Yt9Jk2XPP9GtCTOxHiu35RmNzx4Z3sSZ0L2SSDhky1xWN6mdiIiIiIiaDzZIiIiIiIhINByyRURERERkaTb8u7+5RH2nUlNTERwcDBcXF7i4uEChUOC7776rc55er8ewYcMgkUiQkZFRb516vR5JSUnw8vKCo6MjIiMjcebMmUZ6AiIiIiIiuh+iNkjatm2LJUuWID8/H8ePH8fgwYMxcuRI/Pjjj4LzUlJSzJ4YtHTpUqxatQrr1q1DXl4enJycEBUVherq6sZ4BCIiIiIiug9Wt+yvm5sbli1bhqlTpwIACgsL8fTTT+P48ePw8vJCenq6yU0O9Xo9vL29MWfOHMydOxcAoNVqIZfLsWHDBkyYMKGpHoOIiIiImrE//m169TgxtQqxvpXrrGZwW01NDTZu3IiqqiooFAoAwPXr1/Hss89izZo18PT0vGsdpaWlUKvViIyMNMRkMhnCw8ORm5vbaLkTEREREdG9EX1S+8mTJ6FQKFBdXY2WLVsiPT0dQUFBAIDZs2ejT58+GDlypFl1qdVqAIBcLhfE5XK5oYyIiIiIiKyH6A2SgIAAFBYWQqvVYuvWrYiNjUVOTg7Onj2LvXv34sSJxt/lUqfTQafTCWJSqRRSqbTR701EREREDyFujGg20Yds2dvbw9/fH2FhYVCpVAgJCcEHH3yAvXv3oqSkBK6urrCzs4Od3a2205gxYzBo0CCjdd0e1qXRaARxjUZT75AvlUoFmUwmOFQqlWUekIiIiIiITBK9h+ROtbW10Ol0ePvtt/HSSy8Jyrp164aVK1di+PDhRq/18/ODp6cnsrOzERoaCgCorKxEXl4eXnnlFZP3VCqVSExMFMTYO0JERERE1PhEbZAolUoMGzYMPj4+uHr1KtLS0rB//37s3r0bnp6eRns1fHx84OfnZ3gdGBgIlUqFUaNGQSKRICEhAcnJyejUqRP8/PywYMECeHt7m1yZC+DwLCIiIiKyMG6MaDZRGyRlZWWYNGkSLl26BJlMhuDgYOzevRtPPvmk2XUUFxdDq9UaXs+bNw9VVVWYNm0aKioq0K9fP2RmZsLBwaExHoGIiIiIiO6D1e1DQkRERET0oPuj6CexUzCqVdcgsVOow+rmkBARUfNz+pcyk2WPtWvThJmY72plpckyZxeXeq8tvqAxWRbgIzdZRs3HT+eMb1cQ5Hv3fdnq8/v+gybL3Af1v6+6SUjCVbbMxsFtREREREQkGjZIiIiIiIhINByyRURERERkaTYcsmUuUXtIUlNTERwcDBcXF7i4uEChUOC7774TnJObm4vBgwfDyckJLi4uGDBgAP788896612zZg18fX3h4OCA8PBwHD16tDEfg4iIiIiI7pGoDZK2bdtiyZIlyM/Px/HjxzF48GCMHDkSP/74I4BbjZGhQ4diyJAhOHr0KI4dO4b4+HjY1LOu86ZNm5CYmIiFCxeioKAAISEhiIqKQlmZ6QmTREREREQkDqtb9tfNzQ3Lli3D1KlT0bt3bzz55JNYvHix2deHh4ejZ8+eWL16NYBbO7+3a9cOM2fOxPz58xsrbSIiIiIig4pTp8VOwSjXwMfETqEOq5lDUlNTgy1btqCqqgoKhQJlZWXIy8vDc889hz59+qCkpASBgYF455130K9fP6N13LhxA/n5+VAqlYaYjY0NIiMjkZub21SPQkQNtPOo6S/t6F7W98VJREREliP6KlsnT55Ey5YtIZVKMWPGDKSnpyMoKAg///wzAGDRokV4+eWXkZmZie7duyMiIgJnzpwxWld5eTlqamoglwvXcJfL5VCrja/nTURERERE4hG9hyQgIACFhYXQarXYunUrYmNjkZOTg9raWgDA9OnTMWXKFADA448/juzsbHzyySdQqVQWy0Gn00Gn0wliUqkUUqnUYvcgIiIiomaEq2yZTfQeEnt7e/j7+yMsLAwqlQohISH44IMP4OXlBQAIChJub9+5c2dcuHDBaF0eHh6wtbWFRiPcAVej0cDT0/TOpiqVCjKZTHBYssFDRERERETGid4guVNtbS10Oh18fX3h7e2N4uJiQfnp06fRvn17o9fa29sjLCwM2dnZgvqys7OhUChM3lOpVEKr1QqOv85DISIiIiKixiHqkC2lUolhw4bBx8cHV69eRVpaGvbv34/du3dDIpHg9ddfx8KFCxESEoLQ0FB8+umnOHXqFLZu3WqoIyIiAqNGjUJ8fDwAIDExEbGxsejRowd69eqFlJQUVFVVGYZ9GcPhWURERERkURIO2TKXqA2SsrIyTJo0CZcuXYJMJkNwcDB2796NJ598EgCQkJCA6upqzJ49G1euXEFISAiysrLQsWNHQx0lJSUoLy83vB4/fjwuX76MpKQkqNVqhIaGIjMzs85EdyIiIiIiEp/V7UNCRERERPSgqzhzVuwUjHLt5C92CnWIvsoWEdHDKHVXvsmyV54Ka8JM6EG0+cCPJsvGDejShJk8GD75V6HR+ItDQvmz2EAffldgND59WPf7qvfdzd+bLHtzXN/7qttqSaxuqrbV4jtFRERERESiYYOEiIiIiIhEwyFbREREREQWJuHGiGYTtYckNTUVwcHBcHFxgYuLCxQKBb777jtDuVqtxgsvvABPT084OTmhe/fu+Prrr+9a75o1a+Dr6wsHBweEh4fj6NGjjfkYRERERER0j0RtkLRt2xZLlixBfn4+jh8/jsGDB2PkyJH48cdbk/kmTZqE4uJibN++HSdPnsTo0aMxbtw4nDhxwmSdmzZtQmJiIhYuXIiCggKEhIQgKioKZWVlTfVYRERERERkJqtb9tfNzQ3Lli3D1KlT0bJlS6SmpuKFF14wlLu7u+O9997DSy+9ZPT68PBw9OzZE6tXrwZwa6f2du3aYebMmZg/f36TPAMRERERNW/a0nNip2CUzM9X7BTqsJpJ7TU1Ndi4cSOqqqqgUCgAAH369MGmTZtw5coV1NbWYuPGjaiursagQYOM1nHjxg3k5+cjMjLSELOxsUFkZCRyc3Ob4jGIiIiIiKgBRJ/UfvLkSSgUClRXV6Nly5ZIT09HUFAQAGDz5s0YP3483N3dYWdnh0ceeQTp6enw9ze+oUt5eTlqamrq7Moul8tx6tSpRn8WIiIiIiJqGNEbJAEBASgsLIRWq8XWrVsRGxuLnJwcBAUFYcGCBaioqMCePXvg4eGBjIwMjBs3DgcPHkS3bt0sloNOp4NOpxPEpFIppFKpxe5BRERERM2IjdUMRLJ6or9T9vb28Pf3R1hYGFQqFUJCQvDBBx+gpKQEq1evxieffIKIiAiEhIRg4cKF6NGjB9asWWO0Lg8PD9ja2kKj0QjiGo0Gnp6eJnNQqVSQyWSCQ6VSWfQ5iYiIiIioLtEbJHeqra2FTqfD9evXAdyaA/JXtra2qK2tNXqtvb09wsLCkJ2dLagvOzvbMC/FGKVSCa1WKziUSqUFnoaIiIiIiOoj6pAtpVKJYcOGwcfHB1evXkVaWhr279+P3bt3IzAwEP7+/pg+fTqWL18Od3d3ZGRkICsrCzt27DDUERERgVGjRiE+Ph4AkJiYiNjYWPTo0QO9evVCSkoKqqqqMGXKFJN5cHgWEREREVmUhBsjmkvUHpKysjJMmjQJAQEBiIiIwLFjx7B79248+eSTaNGiBXbt2oXWrVtj+PDhCA4OxmeffYZPP/0UTz31lKGOkpISlJeXG16PHz8ey5cvR1JSEkJDQ1FYWIjMzMw6E92JiIiIiKh+Bw4cwPDhw+Ht7Q2JRIKMjAxB+eTJkyGRSATH0KFDG3QPq9uHhIiIiIjoQae98IvYKRgl82nXoPO/++47fP/99wgLC8Po0aORnp6OmJgYQ/nkyZOh0Wiwfv16Q0wqlaJVq1Zm30P0VbaIiB5GmcfPmCwb2qNTo97760M/GY2P6RfUqPdtTLvzjb+fUWGN+17W5xfNFZNl7eRuTZgJNZaC07+aLOv+WNsmzKTp7C382Wh8cGiHJs7kIWDzcAzZGjZsGIYNG1bvOVKptN4FpO7G6ia1ExERERHRg2P//v1o06YNAgIC8Morr+D3339v0PXsISEiIiIiaiYsvf/e0KFDMXr0aPj5+aGkpARvvvkmhg0bhtzcXNja2ppVB3tIiIiIiIgsTCKxscrD0vvvTZgwASNGjEC3bt0QExODHTt24NixY9i/f7/ZdVhNg2TJkiWQSCRISEgwxKqrqxEXFwd3d3e0bNkSY8aMqbPp4Z30ej2SkpLg5eUFR0dHREZG4swZ02O5iYiIiIiai8bef69Dhw7w8PDA2bNnzb7GKhokx44dw4cffojg4GBBfPbs2fj222+xZcsW5OTk4OLFixg9enS9dS1duhSrVq3CunXrkJeXBycnJ0RFRaG6uroxH4GIiIiIyOpJpVK4uLgIDkvux/frr7/i999/h5eXl9nXiL7s77Vr19C9e3esXbsWycnJCA0NRUpKCrRaLVq3bo20tDSMHTsWAHDq1Cl07twZubm56N27d5269Ho9vL29MWfOHMydOxcAoNVqIZfLsWHDBkyYMKFJn42IiIiImqfKi5fETsEoF2/zGwrArd/Vb/d2PP7441ixYgWeeOIJuLm5wc3NDW+//TbGjBkDT09PlJSUYN68ebh69SpOnjxpdkNH9EntcXFxiI6ORmRkJJKTkw3x/Px83Lx5E5GRkYZYYGAgfHx8TDZISktLoVarBdfIZDKEh4cjNzeXDRIiEZlaBtecJXAXfXXQdNnE/vecEzUPB0+eMxrv380XfxT+YPK6VqHBJsvMsSOv2Gj86fCA+6qX6G7+ufuE0fhLUY836n3r+1mjB9fx48fxxBNPGF4nJiYCAGJjY5GamooffvgBn376KSoqKuDt7Y0hQ4Zg8eLFDep1EbVBsnHjRhQUFODYsWN1ytRqNezt7eHq6iqIy+VyqNVqo/Xdjt+5K3t91xARERERkXGDBg1CfQOqdu/efd/3EK1B8ssvv2DWrFnIysqCg4ODWGkAsPzyZ0RERETUzD0kGyM2BdEmtefn56OsrAzdu3eHnZ0d7OzskJOTg1WrVsHOzg5yuRw3btxARUWF4DqNRmNyJ8jb8TtX4qrvGgAWX/6MiIiIiIjMI1qDJCIiAidPnkRhYaHh6NGjB5577jnDf7do0QLZ2dmGa4qLi3HhwgUoFAqjdfr5+cHT01NwTWVlJfLy8kxeAzT+8mdERERERGScRYdslZSU4OWXX8bevXvveq6zszO6du0qiDk5OcHd3d0Qnzp1KhITE+Hm5gYXFxfMnDkTCoVCMKE9MDAQKpUKo0aNMuxjkpycjE6dOsHPzw8LFiyAt7c3YmJiTObC4VlEREREZFESq9hd44Fg0QbJtWvXkJOTY7H6Vq5cCRsbG4wZMwY6nQ5RUVFYu3at4Jzi4mJotVrD63nz5qGqqgrTpk1DRUUF+vXrh8zMTNHnqRARERERUV0N2odk1apV9Zb/9ttvWL58OWpqau47MSIiIiKiB1WlpkzsFIxykbcRO4U6GtQgsbGxgZeXF+zt7Y2W37hxA2q1mg0SIiIiImrWKi9fFjsFo1xatxY7hToaNGSrffv2eO+99zBu3Dij5YWFhQgLC7NIYkRERERE9PBr0GybsLAw5OfnmyyXSCT1bpxCRERERET0Vw0asvXTTz/h+vXr6NGjh9Hymzdv4uLFi2jfvr3FEiQiIiIietBcLS8XOwWjnD08xE6hjgb1kAQFBZlsjABAixYtBI2R77//vs4O6KYsWbLEsGwvAFy5cgUzZ85EQEAAHB0d4ePjg9dee02wopYxer0eSUlJ8PLygqOjIyIjI3HmzBmzciAiIiIioqbVqAskDxs2DL/99ttdzzt27Bg+/PBDBAcHG2IXL17ExYsXsXz5chQVFWHDhg3IzMzE1KlT661r6dKlWLVqFdatW4e8vDw4OTkhKioK1dXV9/08RERERERkWQ0astVQzs7O+Pe//40OHTqYPOfatWvo3r071q5di+TkZISGhiIlJcXouVu2bMHzzz+Pqqoq2NnVnY+v1+vh7e2NOXPmYO7cuQAArVYLuVyODRs2YMKECRZ5LiIiIiKi+ly9ckXsFIxydnMTO4U6RN9CMi4uDtHR0YiMjLzruVqtFi4uLkYbIwBQWloKtVotqEsmkyE8PBy5ubkWy5mIiIiIiCzDoju1N9TGjRtRUFCAY8eO3fXc8vJyLF68GNOmTTN5jlqtBgDI5XJBXC6XG8qIiIiIiMh6iNYg+eWXXzBr1ixkZWXBwcGh3nMrKysRHR2NoKAgLFq0yOK56HS6OpPvpVIppFKpxe9FRERERM2ARCJ2Bg+MRh2yJannHyI/Px9lZWXo3r077OzsYGdnh5ycHKxatQp2dnaG3d6vXr2KoUOHwtnZGenp6WjRooXJOj09PQEAGo1GENdoNIYyY1QqFWQymeBQqVQNeVQiIiIiIroHjdpDUt98+YiICJw8eVIQmzJlCgIDA/HGG2/A1tYWlZWViIqKglQqxfbt2+/ak+Ln5wdPT09kZ2cjNDQUwK3elby8PLzyyismr1MqlUhMTBTE2DtCRERERNT4GrVBcvXqVZNlzs7O6Nq1qyDm5OQEd3d3dO3aFZWVlRgyZAiuX7+OL774ApWVlaisrAQAtG7dGra2tgCAwMBAqFQqjBo1yrCPSXJyMjp16gQ/Pz8sWLAA3t7eiImJMZkLh2cRERERkUVxyJbZ7qlB8vjjjxsdjiWRSODg4AB/f39MnjwZTzzxxD0nVlBQgLy8PACAv7+/oKy0tBS+vr4AgOLiYsFmifPmzUNVVRWmTZuGiooK9OvXD5mZmXftXSEiIiIioqZ3T/uQKJVKpKamolu3bujVqxeAW5sb/vDDD5g8eTJ++uknZGdn45tvvsHIkSMtnjQRERERkTW7WlEhdgpGObu6ip1CHffUIHn55Zfh4+ODBQsWCOLJyck4f/48/vGPf2DhwoXYuXMnjh8/brFkiYioeVq70/j/S16N7tHEmVjGJ/8qNFn24pDQJsujOfhHZoHJspeHdm/CTKi5ufr/pxpYG2cXF7FTqOOeVtnavHkzJk6cWCc+YcIEbN68GQAwceJEFBcX3192RERERET0ULunBomDgwMOHz5cJ3748GHDXI3a2lrO2yAiIiIionrd06T2mTNnYsaMGcjPz0fPnj0B3JpD8s9//hNvvvkmAGD37t2GpXeJiIiIiJoTPbjKlrnuqUHy1ltvwc/PD6tXr8bnn38OAAgICMA//vEPPPvsswCAGTNm1Lv3BxERERER0T3v1P7cc88hNzcXV65cwZUrV5Cbm2tojACAo6Njg4ZsLVmyxLCPyJ30ej2GDRsGiUSCjIyMeuvR6/VISkqCl5cXHB0dERkZiTNnzpidBxERERERNZ17bpBY0rFjx/Dhhx8iODjYaHlKSorRfU+MWbp0KVatWoV169YhLy8PTk5OiIqKQnV1tSVTJiIiIiIyqVZvnYc1uqdlf21sbOptINTU1Jhd17Vr19C9e3esXbsWycnJCA0NRUpKiqG8sLAQTz/9NI4fPw4vLy+kp6eb3HVdr9fD29sbc+bMwdy5cwEAWq0WcrkcGzZswIQJE8zOi4iIiIjoXlVor4qdglGuMmexU6jjnuaQpKenC17fvHkTJ06cwKeffoq33367QXXFxcUhOjoakZGRSE5OFpRdv34dzz77LNasWQNPT8+71lVaWgq1Wo3IyEhDTCaTITw8HLm5uWyQEBERERFZmXtqkBjbfX3s2LHo0qULNm3ahKlTp5pVz8aNG1FQUIBjx44ZLZ89ezb69Olj9m7varUaACCXywVxuVxuKCMiIiIiamy1DR+E1GzdU4PElN69e2PatGlmnfvLL79g1qxZyMrKMjr5ffv27di7dy9OnDhhyRSN0ul00Ol0gphUKoVUKm30exMRERERNWcWm9T+559/YtWqVXj00UfNOj8/Px9lZWXo3r077OzsYGdnh5ycHKxatQp2dnbIyspCSUkJXF1dDeUAMGbMGAwaNMhonbeHdWk0GkFco9HUO+RLpVJBJpMJDpVKZdZzEBERERHRvbunSe2tWrUSTGrX6/W4evUqHnnkEXzxxRcYMWLEXeu4evUqzp8/L4hNmTIFgYGBeOONN+Dh4YHy8nJBebdu3fDBBx9g+PDh8PPzq1Pn7Untc+fOxZw5cwAAlZWVaNOmTb2T2tlDQkRERESW9PsflWKnYJR7KxexU6jjnoZs/XUVLODWqlutW7dGeHg4WrVqZVYdzs7O6Nq1qyDm5OQEd3d3Q9xYr4aPj4+gMRIYGAiVSoVRo0YZ9jFJTk5Gp06d4OfnhwULFsDb29vkylwAGx9ERERERGJpcIPkv//9L86fP48XX3wRbdu2bYycGqS4uBhardbwet68eaiqqsK0adNQUVGBfv36ITMzs0GbNBIRERERUdO4pyFbzs7OOHnyJHx9fRshJSIiaix/FP1ksqxV16AmzIQai7b0nMkymZ/vfdV9flqC0Xj7j1Luq16ih1H5H9q7nyQCj1YysVOo454mtQ8ePBg5OTmWzoWIiIiIiJqZe5pDMmzYMMyfPx8nT55EWFgYnJycBOXmTGonIiIiIiK6pwbJq6++CgBYsWJFnTKJRIKampr7y4qIiIiI6AFWy30RzXZPDZLa2lpL50FERERERM1Qg+eQ1NbW4pNPPsHTTz+Nrl27olu3bhg5ciQ+++wz3MP8eIMlS5YYlu39q9zcXAwePBhOTk5wcXHBgAED8Oeff9Zb15o1a+Dr6wsHBweEh4fj6NGj95wXERERERE1ngY1SPR6PUaMGIGXXnoJv/32G7p164YuXbrg3LlzmDx5MkaNGnVPSRw7dgwffvghgoODBfHc3FwMHToUQ4YMwdGjR3Hs2DHEx8fDxsZ02ps2bUJiYiIWLlyIgoIChISEICoqCmVlZfeUGxERERFRQ+n1eqs8rFGDlv1dv349Zs2ahW3btuGJJ54QlO3duxcxMTFYvXo1Jk2aZHYC165dQ/fu3bF27VokJycjNDTUsPFi79698eSTT2Lx4sVm1xceHo6ePXti9erVAG716LRr1w4zZ87E/Pnzza6HiIiIiOheaX6vEDsFo+TurmKnUEeD5pB89dVXePPNN+s0RoBbSwHPnz8fX375ZYMaJHFxcYiOjkZkZCSSk5MN8bKyMuTl5eG5555Dnz59UFJSgsDAQLzzzjvo16+f0bpu3LiB/Px8KJVKQ8zGxgaRkZHIzc1twJMSEQElw8ebLOv47aYmzESo4PSvJsu6Pyb+hrWN4eDJc0bj/bv5NmkeD4JKtdpkmYunZxNm0rxlHj9jsmxoj05NmAmR9WvQkK0ffvgBQ4cONVk+bNgw/Pvf/za7vo0bN6KgoAAqlapO2c8//wwAWLRoEV5++WVkZmaie/fuiIiIwJkzxn/Iy8vLUVNTA7lcLojL5XKo6/mCJiIiIiKyJLGHZj1IQ7Ya1ENy5cqVOr/s/5VcLscff/xhVl2//PILZs2ahaysLDg4ONQpv72S1/Tp0zFlyhQAwOOPP47s7Gx88sknRhsx90qn00Gn0wliUqkUUqnUYvcgIiIiIqK6GtRDUlNTAzs7020YW1tb/Pe//zWrrvz8fJSVlaF79+6ws7ODnZ0dcnJysGrVKtjZ2RkaPkFBQYLrOnfujAsXLhit08PDA7a2ttBoNIK4RqOBZz3d1CqVCjKZTHBYssFDRERERETGNaiHRK/XY/LkySZ7Du7sZahPREQETp48KYhNmTIFgYGBeOONN9ChQwd4e3ujuLhYcM7p06cxbNgwo3Xa29sjLCwM2dnZiImJAXCrpyU7Oxvx8fEmc1EqlUhMTBTE2DtCRERERPeKGyOar0ENktjY2LueY+6EdmdnZ3Tt2lUQc3Jygru7uyH++uuvY+HChQgJCUFoaCg+/fRTnDp1Clu3bjVcExERgVGjRhkaHImJiYiNjUWPHj3Qq1cvpKSkoKqqyjDsyxgOzyIiIiIiEkeDGiTr169vrDyMSkhIQHV1NWbPno0rV64gJCQEWVlZ6Nixo+GckpISlJeXG16PHz8ely9fRlJSEtRqNUJDQ5GZmVnv3BciImPEXEmrPg/rSlr14Wpa5uNKWtaBK2kRma9B+5AQERHRLWVfbzdZ1mbMiCbMpGEqThtfqdL1sYfzF+j69oKwxv0YbvvPeeOrg3Zuzwbng+K3sitip2DUo23cxE6hjgZNaiciIiIiIrIkNkiIiIiIiEg0DZpDQkREREREd8dVtszHHhIiIiIiIhKN1TRIlixZAolEgoSEBENMrVbjhRdegKenJ5ycnNC9e3d8/fXXd61rzZo18PX1hYODA8LDw3H06NFGzJyIiIiIiO6VVTRIjh07hg8//BDBwcGC+KRJk1BcXIzt27fj5MmTGD16NMaNG4cTJ06YrGvTpk1ITEzEwoULUVBQgJCQEERFRaGsrKyxH4OIiIiICMCtDcWt8bBGoi/7e+3aNXTv3h1r165FcnIyQkNDkZKSAgBo2bIlUlNT8cILLxjOd3d3x3vvvYeXXnrJaH3h4eHo2bMnVq9eDeDWTu3t2rXDzJkzMX/+/EZ/HiIiIiKiC+rfxU7BKB9Pd7FTqEP0Se1xcXGIjo5GZGQkkpOTBWV9+vTBpk2bEB0dDVdXV2zevBnV1dUYNGiQ0bpu3LiB/Px8KJVKQ8zGxgaRkZHIzc1tzMcgImoy3/943mRZ3y7t76vuCu1Vo3FXmfN91fuv/LMmy4aE+d/1+uwTJUbjEY93NBq3lKuVlUbjzi4ujXrfxpR36oLReHigTxNn8j+m9twAGn/fjbIrWqPxNm6yu15r6mfxfn8Om6P69uywxn0zyLJEbZBs3LgRBQUFOHbsmNHyzZs3Y/z48XB3d4ednR0eeeQRpKenw9/f+P+8ysvLUVNTU2dXdrlcjlOnTlk8fyIiIiIiY6x1eJQ1Eq1B8ssvv2DWrFnIysqCg4OD0XMWLFiAiooK7NmzBx4eHsjIyMC4ceNw8OBBdOvWzWK56HQ66HQ6QUwqlUIqlVrsHkREREREVJdok9rz8/NRVlaG7t27w87ODnZ2dsjJycGqVatgZ2eHkpISrF69Gp988gkiIiIQEhKChQsXokePHlizZo3ROj08PGBrawuNRiOIazQaeHqa7vJVqVSQyWSCQ6VSWfR5iYiIiIioLtF6SCIiInDy5ElBbMqUKQgMDMQbb7yB69evA7g1B+SvbG1tUVtba7ROe3t7hIWFITs7GzExMQBuTWrPzs5GfHy8yVyUSiUSExMFMfaOEBEREdG94saI5hOtQeLs7IyuXbsKYk5OTnB3d0fXrl1x8+ZN+Pv7Y/r06Vi+fDnc3d2RkZGBrKws7Nixw3BNREQERo0aZWhwJCYmIjY2Fj169ECvXr2QkpKCqqoqTJkyxWQuHJ5FRERERCQO0VfZMqVFixbYtWsX5s+fj+HDh+PatWvw9/fHp59+iqeeespwXklJCcrLyw2vx48fj8uXLyMpKQlqtRqhoaHIzMysM9GdiIiIiIjEJ/o+JERERERED5uff7ssdgpGdXi0tdgp1GG1PSRERERE1ux48a9G4z0C2jZxJkQPNtFW2SIiIiIiImIPCRERERGRhXFShPnYQ0JERERERKIRtUGyaNEiSCQSwREYGGgor66uRlxcHNzd3dGyZUuMGTOmzqaHd9Lr9UhKSoKXlxccHR0RGRmJM2fONPajEBERERHRPRC9h6RLly64dOmS4Th06JChbPbs2fj222+xZcsW5OTk4OLFixg9enS99S1duhSrVq3CunXrkJeXBycnJ0RFRaG6urqxH4WIiIiICABQC71VHtZI9DkkdnZ28PT0rBPXarX4+OOPkZaWhsGDBwMA1q9fj86dO+PIkSPo3bt3nWv0ej1SUlLw1ltvYeTIkQCAzz77DHK5HBkZGZgwYULjPgwRERE1G1xNi8gyRG+QnDlzBt7e3nBwcIBCoYBKpYKPjw/y8/Nx8+ZNREZGGs4NDAyEj48PcnNzjTZISktLoVarBdfIZDKEh4cjNzeXDRIiIqIHUOVl0/s5uLS+vz0VTv9SZjT+WLs291Wvtaq8eMlkmYu31/3VrVYbr9fIH56J/krUBkl4eDg2bNiAgIAAXLp0CW+//Tb69++PoqIiqNVq2Nvbw9XVVXCNXC6H2sQH/nb8zl3Z67uGiIiIiMjSuMqW+URtkAwbNszw38HBwQgPD0f79u2xefNmODo6NlkeOp0OOp1OEJNKpZBKpU2WAxERERFRcyT6pPa/cnV1xWOPPYazZ8/C09MTN27cQEVFheAcjUZjdM4JAEP8zpW46rsGAFQqFWQymeBQqVT39zBERERERHRXVtUguXbtGkpKSuDl5YWwsDC0aNEC2dnZhvLi4mJcuHABCoXC6PV+fn7w9PQUXFNZWYm8vDyT1wCAUqmEVqsVHEql0nIPRkRERETNil6vt8rDGok6ZGvu3LkYPnw42rdvj4sXL2LhwoWwtbXFxIkTIZPJMHXqVCQmJsLNzQ0uLi6YOXMmFAqFYEJ7YGAgVCoVRo0aBYlEgoSEBCQnJ6NTp07w8/PDggUL4O3tjZiYGJN5cHgWEREREZE4RG2Q/Prrr5g4cSJ+//13tG7dGv369cORI0fQ+v+vmLFy5UrY2NhgzJgx0Ol0iIqKwtq1awV1FBcXQ6vVGl7PmzcPVVVVmDZtGioqKtCvXz9kZmbCwcGhSZ+NiIiIiIjuTqK31r4bIiIiIqIH1H/OW+cKr53bW98yzKLvQ0JERERE1qH4gsZoPMBHbjT+V1eOFRiNu/Xsftdrf/+j0mSZeysXfLHvpNGy55/odte6yfpZ1aR2IiIiIiJqXthDQkRERERkYZwUYT72kBARERERkWhEbZAsWrQIEolEcAQGBgIArly5gpkzZyIgIACOjo7w8fHBa6+9JlhRyxi9Xo+kpCR4eXnB0dERkZGROHPmTFM8DhERERERNZDoQ7a6dOmCPXv2GF7b2d1K6eLFi7h48SKWL1+OoKAgnD9/HjNmzMDFixexdetWk/UtXboUq1atwqeffmrYhyQqKgo//fQTl/4lIiIioiZRyzFbZhN12d9FixYhIyMDhYWFZp2/ZcsWPP/886iqqjI0XP5Kr9fD29sbc+bMwdy5cwEAWq0WcrkcGzZswIQJEyyZPhERERGRUUWll8ROwaiufl5ip1CH6D0kZ86cgbe3NxwcHKBQKKBSqeDj42P0XK1WCxcXF6ONEQAoLS2FWq1GZGSkISaTyRAeHo7c3Fw2SIiIiIis0E/nTO/ZEeRrfftmkGWJOockPDwcGzZsQGZmJlJTU1FaWor+/fvj6tWrdc4tLy/H4sWLMW3aNJP1qdW3PsxyuXCtbLlcbigjIiIiImpser11HtZI1B6SYcOGGf47ODgY4eHhaN++PTZv3oypU6cayiorKxEdHY2goCAsWrTI4nnodDrodDpBTCqVQiqVWvxeRERERET0P1a17K+rqysee+wxnD171hC7evUqhg4dCmdnZ6Snp6NFixYmr/f0vNWlp9EIdxnVaDSGMmNUKhVkMpngUKlU9/k0RERERER0N1bVILl27RpKSkrg5XVrsk1lZSWGDBkCe3t7bN++/a6rZPn5+cHT0xPZ2dmGWGVlJfLy8qBQKExep1QqodVqBYdSqbTMQxERERFRs6PX663ysEaiNkjmzp2LnJwcnDt3DocPH8aoUaNga2uLiRMnGhojVVVV+Pjjj1FZWQm1Wg21Wo2amhpDHYGBgUhPTwcASCQSJCQkIDk5Gdu3b8fJkycxadIkeHt7IyYmxmQeUqkULi4ugoPDtYiIiIiIGp+oc0h+/fVXTJw4Eb///jtat26Nfv364ciRI2jdujX279+PvLw8AIC/v7/gutLSUvj6+gIAiouLBZslzps3D1VVVZg2bRoqKirQr18/ZGZmcg8SIiIiIiIrJOo+JERERPT/2rv3uKiqvX/gn+E2IAoqKBeTi1fUo6CgE2hpgZKaipmimaLmJUsNSQs6AZaXUZ+8lHrg0cdbmYldNLNEBdPjBUXBSx4VES+UOoNUQoIMxqzfH/6c48gMIgPsQT7vXvv1atbe37XXmlnCLNbaaxHR0+jUpetSF8EgvzYtpC5COWb1DAkREREREdUv7JAQEREREZFkJN+pnYiIiIjoacOHIiqPIyRERERERCQZSTskc+bMgUwm0zt8fHzKXSeEQP/+/SGTybB9+/YK8xRCIC4uDm5ubrCzs0NISAiys7NrqAZERERERGQKyUdIOnXqhJs3b+qOQ4cOlbtm+fLlkMlklcpv8eLF+Oyzz5CYmIhjx47B3t4eoaGhKCkpqe6iExEREREZJPUGiNwY8QlYWVnB1dVVdzg7O+udP3XqFJYsWYJ169Y9Ni8hBJYvX44PP/wQQ4YMQZcuXfD555/jxo0bjx1ZISIiIiIiff/+978xaNAguLu7G5ytVB2zkyR/qD07Oxvu7u6wtbVFYGAglEolPDw8AADFxcV47bXXsGrVKri6uj42rytXrkClUiEkJESX5ujoCIVCgbS0NIwcObLG6kFERFTX7cm4ZPRcP/82Rs8RmWrfqctGz73o16oWS0KPKioqgq+vLyZMmIBXXnml3PkHs5M2btwIb29vxMbGIjQ0FOfOnav0xuSSdkgUCgU2bNiA9u3b4+bNm/joo4/w3HPP4ezZs2jUqBFmzpyJoKAgDBkypFL5qVQqAICLi4teuouLi+4cEREREVFN05rn7Kgn1r9/f/Tv39/guUdnJwHA559/DhcXF2zfvr3SgwGSdkgerlyXLl2gUCjg6emJrVu3olmzZti3bx9OnjxZ4+XQaDTQaDR6aXK5HHK5vMbvTURERERUF1XX7CTJnyF5WOPGjdGuXTtcunQJ+/btQ05ODho3bgwrKytYWd3vOw0bNgx9+vQxGP9gWpdardZLV6vVFU75UiqVcHR01DuUSmX1VIqIiIiIyExoNBoUFhbqHY/+Yb6yqmt2kll1SO7cuYOcnBy4ubkhOjoaZ86cwalTp3QHACxbtgzr1683GO/t7Q1XV1ekpqbq0goLC3Hs2DEEBgYavW9MTAwKCgr0jpiYmGqtGxERERHVH8JM/zPHP8RLOmVr1qxZGDRoEDw9PXHjxg3Ex8fD0tISo0aNQrNmzQyOanh4eMDb21v32sfHB0qlEkOHDoVMJkNkZCTmzZuHtm3b6h6scXd3R1hYmNFycHoWEREREdUHMTExiIqK0kur6vfgh2cnubm56dLVajX8/PwqnY+kHZLffvsNo0aNwu+//45mzZqhV69eOHr0KJo1a1bpPLKyslBQUKB7/d5776GoqAiTJ0/G7du30atXLyQnJ1f6KX8iIiIioqdVdf4h/uHZSQ86IA9mJ02dOrXS+ciEue6QQkRERERURx27kCt1EQxS+Hg80fV37tzBpUv3lwTv2rUrli5dihdeeAFNmzaFh4cHFi1ahIULF+ot+3vmzJm6s+wvERHR02rH0QsG0wc/61PLJaH6ZtXOEwbT3345oJZLUn0u/ppnML1dy+a1XJL658SJE3jhhRd0rx9M94qIiMCGDRuqZXYSOyRERERERGRQnz59UNGEKplMho8//hgff/xxle/BDgkRERERUTXjQxGVZ1bL/hIRERERUf0iaYdkzpw5kMlkeoePj/7c2rS0NLz44ouwt7eHg4MDnn/+edy9e7fCfFetWgUvLy/Y2tpCoVAgPT29JqtBRERERERVJPmUrU6dOiElJUX3+sGO7MD9zshLL72EmJgYrFixAlZWVjh9+jQsLIz3o5KSkhAVFYXExEQoFAosX74coaGhyMrKQvPmfPCJiIiIiGoeF7KtPEmX/Z0zZw62b9+u24X9Uc8++yz69u2LuXPnVjpPhUKB7t27Y+XKlQAArVaLli1bYvr06YiOjq6OYhMRERERVSjt3DWpi2BQYEdPqYtQjuQjJNnZ2XB3d4etrS0CAwOhVCrh4eGBvLw8HDt2DKNHj0ZQUBBycnLg4+OD+fPno1evXgbzKi0tRUZGBmJiYnRpFhYWCAkJQVpaWm1ViYiIatGnO4xPy31ncI9aLAlJRaplbjeknDaYPi7Et0bvS/S0kfQZEoVCgQ0bNiA5ORkJCQm4cuUKnnvuOfz111+4fPkygPujKJMmTUJycjK6deuG4OBgZGdnG8wvPz8fZWVlcHFx0Ut3cXGBSqWq8foQEREREQGAVpjnYY4kHSHp37+/7v+7dOkChUIBT09PbN26FR06dAAATJkyBePHjwdwf3fI1NRUrFu3DkqlstrKodFooNFo9NLkcjnkcnm13YOIiIiIiMozq2V/GzdujHbt2uHSpUtwc3MDAHTs2FHvmg4dOiA3N9dgvLOzMywtLaFWq/XS1Wo1XF1djd5XqVTC0dFR76jODg8RERERERlmVh2SO3fuICcnB25ubvDy8oK7uzuysrL0rrl48SI8PQ0/jGNjYwN/f3+kpqbq0rRaLVJTUxEYGGj0vjExMSgoKNA7Hn4OhYiIiIjoSQghzPIwR5JO2Zo1axYGDRoET09P3LhxA/Hx8bC0tMSoUaMgk8kwe/ZsxMfHw9fXF35+fti4cSMuXLiAb775RpdHcHAwhg4dimnTpgEAoqKiEBERgYCAAPTo0QPLly9HUVGRbtqXIZyeRUREREQkDUk7JL/99htGjRqF33//Hc2aNUOvXr1w9OhRNGvWDAAQGRmJkpISzJw5E3/88Qd8fX2xd+9etG7dWpdHTk4O8vPzda/Dw8Nx69YtxMXFQaVSwc/PD8nJyeUedCciIiIiIulJug8JEREREdHT6OAvV6UugkHPdfaSugjlmNUzJEREREREVL+wQ0JERERERJKRfKd2IiIiIqKnjZZPRVQaR0iIiIiIiEgyknZI5syZA5lMpnf4+PjozqtUKowZMwaurq6wt7dHt27d8O233z4231WrVsHLywu2trZQKBRIT0+vyWoQEREREVEVST5lq1OnTkhJSdG9trL6b5HGjh2L27dvY8eOHXB2dsbmzZsxYsQInDhxAl27djWYX1JSEqKiopCYmAiFQoHly5cjNDQUWVlZaN68eY3Xh4iIiIiIC9lWnuRTtqysrODq6qo7nJ2ddeeOHDmC6dOno0ePHmjVqhU+/PBDNG7cGBkZGUbzW7p0KSZNmoTx48ejY8eOSExMRIMGDbBu3braqA4RERERET0ByTsk2dnZcHd3R6tWrTB69Gjk5ubqzgUFBSEpKQl//PEHtFottmzZgpKSEvTp08dgXqWlpcjIyEBISIguzcLCAiEhIUhLS6vpqhARERER0ROSdMqWQqHAhg0b0L59e9y8eRMfffQRnnvuOZw9exaNGjXC1q1bER4eDicnJ1hZWaFBgwbYtm0b2rRpYzC//Px8lJWVlduV3cXFBRcuXKiNKhERERHRE9q8/xej517r07kWS1J9tJyxVWmSdkj69++v+/8uXbpAoVDA09MTW7duxRtvvIHY2Fjcvn0bKSkpcHZ2xvbt2zFixAgcPHgQnTtXX+PUaDTQaDR6aXK5HHK5vNruQURERERE5Uk+ZethjRs3Rrt27XDp0iXk5ORg5cqVWLduHYKDg+Hr64v4+HgEBARg1apVBuOdnZ1haWkJtVqtl65Wq+Hq6mr0vkqlEo6OjnqHUqms1roREREREVF5ZtUhuXPnDnJycuDm5obi4mIA958BeZilpSW0Wq3BeBsbG/j7+yM1NVWXptVqkZqaisDAQKP3jYmJQUFBgd4RExNTDTUiIiIiovpICGGWhzmStEMya9YsHDhwAFevXsWRI0cwdOhQWFpaYtSoUfDx8UGbNm0wZcoUpKenIycnB0uWLMHevXsRFhamyyM4OBgrV67UvY6KisKaNWuwceNGnD9/HlOnTkVRURHGjx9vtBxyuRwODg56B6drERERERHVPEmfIfntt98watQo/P7772jWrBl69eqFo0ePolmzZgCAn376CdHR0Rg0aBDu3LmDNm3aYOPGjRgwYIAuj5ycHOTn5+teh4eH49atW4iLi4NKpYKfnx+Sk5PLPehORERERETSkwlzHbshIiIiIqqjUjJzpC6CQSHdWktdhHLM6hkSIiIiIiKqX9ghISIiIiIiyUj6DAkRERER0dNIy6ciKo0jJEREREREJBl2SIiIiIiISDKSd0iuX7+O119/HU5OTrCzs0Pnzp1x4sQJ3XkhBOLi4uDm5gY7OzuEhIQgOzv7sfmuWrUKXl5esLW1hUKhQHp6ek1Wg4iIiIhIR5jpf+ZI0g7Jn3/+iZ49e8La2hq7du3CuXPnsGTJEjRp0kR3zeLFi/HZZ58hMTERx44dg729PUJDQ1FSUmI036SkJERFRSE+Ph6ZmZnw9fVFaGgo8vLyaqNaRERERERUSZLuQxIdHY3Dhw/j4MGDBs8LIeDu7o53330Xs2bNAgAUFBTAxcUFGzZswMiRIw3GKRQKdO/eXbeDu1arRcuWLTF9+nRER0fXTGWIiIiIiP6/3RmPn9EjhVD/tlIXoRxJV9nasWMHQkNDMXz4cBw4cAAtWrTAW2+9hUmTJgEArly5ApVKhZCQEF2Mo6MjFAoF0tLSDHZISktLkZGRgZiYGF2ahYUFQkJCkJaWVvOVIiIiIqJq9XnqaYPpY4N9a7kklcdFtipP0ilbly9fRkJCAtq2bYvdu3dj6tSpmDFjBjZu3AgAUKlUAAAXFxe9OBcXF925R+Xn56OsrOyJYjQaDQoLC/UOjUZjavWIiIiIiOgxJO2QaLVadOvWDQsWLEDXrl0xefJkTJo0CYmJibVaDqVSCUdHR71DqVTWahmIiIiIiOojSadsubm5oWPHjnppHTp0wLfffgsAcHV1BQCo1Wq4ubnprlGr1fDz8zOYp7OzMywtLaFWq/XS1Wq1Lr9HxcTEICoqSi9NLpc/UV2IiIiIiB7gxoiVJ+kISc+ePZGVlaWXdvHiRXh6egIAvL294erqitTUVN35wsJCHDt2DIGBgQbztLGxgb+/v16MVqtFamqq0Ri5XA4HBwe9gx0SIiIiIqKaJ2mHZObMmTh69CgWLFiAS5cuYfPmzVi9ejXefvttAIBMJkNkZCTmzZuHHTt24JdffsHYsWPh7u6OsLAwXT7BwcG6FbUAICoqCmvWrMHGjRtx/vx5TJ06FUVFRRg/fnxtV5GIiIiIiCog6ZSt7t27Y9u2bYiJicHHH38Mb29vLF++HKNHj9Zd895776GoqAiTJ0/G7du30atXLyQnJ8PW1lZ3TU5ODvLz83Wvw8PDcevWLcTFxUGlUsHPzw/JycnlHnQnIiIiIvNnzqtpGcMZW5Un6T4kRERERESPk/vuhwbTPZbMq+WSVN6P6RelLoJBA3u0k7oI5Ug6ZYuIiIiIiOo3SadsERERERE9jTgJqfI4QkJERERERJJhh4SIiIiIiCQjeYfk+vXreP311+Hk5AQ7Ozt07twZJ06cAADcu3cP77//Pjp37gx7e3u4u7tj7NixuHHjxmPzXbVqFby8vGBrawuFQoH09PSargoREREREYD7GyOa42GOJO2Q/Pnnn+jZsyesra2xa9cunDt3DkuWLEGTJk0AAMXFxcjMzERsbCwyMzPx3XffISsrC4MHD64w36SkJERFRSE+Ph6ZmZnw9fVFaGgo8vLyaqNaRERERERUSZIu+xsdHY3Dhw/j4MGDlY45fvw4evTogWvXrsHDw8PgNQqFAt27d9dtlqjVatGyZUtMnz4d0dHR1VJ2IiIiIiJjdhy9IHURDBr8rI/URShH0lW2duzYgdDQUAwfPhwHDhxAixYt8NZbb2HSpElGYwoKCiCTydC4cWOD50tLS5GRkYGYmBhdmoWFBUJCQpCWllbdVSAiIjLoqwNnDaaP6v2PWi4J1Tef7jA8Tf2dwT1quST1m5nOjjJLkk7Zunz5MhISEtC2bVvs3r0bU6dOxYwZM7Bx40aD15eUlOD999/HqFGj4ODgYPCa/Px8lJWVlduV3cXFBSqVqtrrQEREREREVSfpCIlWq0VAQAAWLFgAAOjatSvOnj2LxMRERERE6F177949jBgxAkIIJCQkVGs5NBoNNBqNXppcLodcLq/W+xARERERkT5JR0jc3NzQsWNHvbQOHTogNzdXL+1BZ+TatWvYu3ev0dERAHB2doalpSXUarVeulqthqurq8EYpVIJR0dHvUOpVFaxVkRERERU30m9mhZX2aqknj17IisrSy/t4sWL8PT01L1+0BnJzs5GSkoKnJycKszTxsYG/v7+SE1N1aVptVqkpqYiMDDQYExMTAwKCgr0joefQSEiIiIiopoh6ZStmTNnIigoCAsWLMCIESOQnp6O1atXY/Xq1QDud0ZeffVVZGZmYufOnSgrK9M9B9K0aVPY2NgAAIKDgzF06FBMmzYNABAVFYWIiAgEBASgR48eWL58OYqKijB+/HiD5eD0LCIiIiIiaUjaIenevTu2bduGmJgYfPzxx/D29sby5csxevRoAPc3TdyxYwcAwM/PTy/2559/Rp8+fQAAOTk5yM/P150LDw/HrVu3EBcXB5VKBT8/PyQnJ5d70J2IiKimcDUtkgpX0zIPZjo7yixJug8JERERkZT+eugPmg9r5OxcyyWhp813h89LXQSDXunZQeoilCPpMyRERERERFS/STpli4iIiIjoacRJSJXHERIiIiIiIpIMOyRERERERCQZyTsk169fx+uvvw4nJyfY2dmhc+fOOHHihMFr33zzTchkMixfvvyx+a5atQpeXl6wtbWFQqFAenp6NZeciIiIiMgwrTDPwxxJ2iH5888/0bNnT1hbW2PXrl04d+4clixZgiZNmpS7dtu2bTh69Cjc3d0fm29SUhKioqIQHx+PzMxM+Pr6IjQ0FHl5eTVRDSIiIiIiqiJJH2pftGgRWrZsifXr1+vSvL29y113/fp1TJ8+Hbt378bAgQMfm+/SpUsxadIk3UaIiYmJ+PHHH7Fu3TpER0dXXwWIiIioTuPyvkTSk7RDsmPHDoSGhmL48OE4cOAAWrRogbfeeguTJk3SXaPVajFmzBjMnj0bnTp1emyepaWlyMjIQExMjC7NwsICISEhSEtLq5F6EBFR/XP0fK7Rc8928KjFktSe9XtPGUwf39evVstRW85dVRk919HLtUbvve/UZYPpL/q1qtH7UvXhKluVJ+mUrcuXLyMhIQFt27bF7t27MXXqVMyYMQMbN27UXbNo0SJYWVlhxowZlcozPz8fZWVl5XZld3FxgUpl/AcLERERERHVPklHSLRaLQICArBgwQIAQNeuXXH27FkkJiYiIiICGRkZ+PTTT5GZmQmZTFZj5dBoNNBoNHppcrkccrm8xu5JREREREQSj5C4ubmhY8eOemkdOnRAbu79YfCDBw8iLy8PHh4esLKygpWVFa5du4Z3330XXl5eBvN0dnaGpaUl1Gq1XrparYarq+HhVaVSCUdHR71DqVSaXkEiIiIiqpeEmf5njiQdIenZsyeysrL00i5evAhPT08AwJgxYxASEqJ3PjQ0FGPGjNE9sP4oGxsb+Pv7IzU1FWFhYQDuj8SkpqZi2rRpBmNiYmIQFRWll8bRESIiIiKimidph2TmzJkICgrCggULMGLECKSnp2P16tVYvXo1AMDJyQlOTk56MdbW1nB1dUX79u11acHBwRg6dKiuwxEVFYWIiAgEBASgR48eWL58OYqKiox2Yjg9i4iIiIhIGpJ2SLp3745t27YhJiYGH3/8Mby9vbF8+XKMHj36ifLJyclBfn6+7nV4eDhu3bqFuLg4qFQq+Pn5ITk5udyD7kRERERENcFcNyE0RzLBNcmIiIiIiKrVVwfOSl0Eg0b1/ofURShH0ofaiYiIiIiofpN0yhYRERER0dOIk5AqjyMkREREREQkGXZIiIiIiIhIMpJ3SK5fv47XX38dTk5OsLOzQ+fOnXHixAm9a86fP4/BgwfD0dER9vb26N69u27zRGO+/vpr+Pj4wNbWFp07d8ZPP/1Uk9UgIiIiItLRCvM8zJGkHZI///wTPXv2hLW1NXbt2oVz585hyZIlaNKkie6anJwc9OrVCz4+Pti/fz/OnDmD2NhY2NraGs33yJEjGDVqFN544w2cPHkSYWFhCAsLw9mz5rnaARERERFRfSXpsr/R0dE4fPgwDh48aPSakSNHwtraGl988UWl8w0PD0dRURF27typS3v22Wfh5+eHxMREk8pMRERERPQ4m37+ReoiGPT6C52lLkI5ko6Q7NixAwEBARg+fDiaN2+Orl27Ys2aNbrzWq0WP/74I9q1a4fQ0FA0b94cCoUC27dvrzDftLQ0hISE6KWFhoYiLS2tJqpBRERERKRHCGGWhzmStENy+fJlJCQkoG3btti9ezemTp2KGTNmYOPGjQCAvLw83LlzBwsXLsRLL72EPXv2YOjQoXjllVdw4MABo/mqVKpyu7K7uLhApVLVaH2IiIiIiOjJSLoPiVarRUBAABYsWAAA6Nq1K86ePYvExERERERAq9UCAIYMGYKZM2cCAPz8/HDkyBEkJiaid+/e1VIOjUYDjUajlyaXyyGXy6slfyIiIiIiMkzSERI3Nzd07NhRL61Dhw66FbScnZ1hZWVV4TWGuLq6Qq1W66Wp1Wq4uroavF6pVMLR0VHvUCqVVakSEREREZHkU7M4ZauSevbsiaysLL20ixcvwtPTEwBgY2OD7t27V3iNIYGBgUhNTdVL27t3LwIDAw1eHxMTg4KCAr0jJiamKlUiIiIiIqInIOmUrZkzZyIoKAgLFizAiBEjkJ6ejtWrV2P16tW6a2bPno3w8HA8//zzeOGFF5CcnIwffvgB+/fv110zduxYtGjRQjeq8c4776B3795YsmQJBg4ciC1btuDEiRN6+T6M07OIiIiIiKQh6bK/ALBz507ExMQgOzsb3t7eiIqKwqRJk/SuWbduHZRKJX777Te0b98eH330EYYMGaI736dPH3h5eWHDhg26tK+//hoffvghrl69irZt22Lx4sUYMGBAbVWLiIiIiOqxDSmnpS6CQeNCfKUuQjmSd0iIiIiIiJ427JBUnqRTtoiIiIhq0vq9p4yeG9/Xz6S8bxf8ZTC9sWMjk/Ilqm/YISEiIiIiqmachFR5kq6yRURERERE9Rs7JEREREREJBnJOyTXr1/H66+/DicnJ9jZ2aFz5844ceKE7vydO3cwbdo0PPPMM7Czs0PHjh2RmJj42Hy//vpr+Pj4wNbWFp07d8ZPP/1Uk9UgIiIiItIRwjwPcyRph+TPP/9Ez549YW1tjV27duHcuXNYsmQJmjRporsmKioKycnJ2LRpE86fP4/IyEhMmzYNO3bsMJrvkSNHMGrUKLzxxhs4efIkwsLCEBYWhrNnz9ZGtYiIiIiIqJIkXfY3Ojoahw8fxsGDB41e849//APh4eGIjY3Vpfn7+6N///6YN2+ewZjw8HAUFRVh586durRnn30Wfn5+lRpdISIiIiIyxbo9p6QugkET+vlJXYRyJB0h2bFjBwICAjB8+HA0b94cXbt2xZo1a/SuCQoKwo4dO3D9+nUIIfDzzz/j4sWL6Nevn9F809LSEBISopcWGhqKtLS0GqkHEREREdHDtEKY5WGOJO2QXL58GQkJCWjbti12796NqVOnYsaMGdi4caPumhUrVqBjx4545plnYGNjg5deegmrVq3C888/bzRflUoFFxcXvTQXFxeoVKoaqwsRERERET05Sfch0Wq1CAgIwIIFCwAAXbt2xdmzZ5GYmIiIiAgA9zskR48exY4dO+Dp6Yl///vfePvtt+Hu7l5uFKSqNBoNNBqNXppcLodcLq+W/ImIiIiIyDBJR0jc3NzQsWNHvbQOHTogNzcXAHD37l188MEHWLp0KQYNGoQuXbpg2rRpCA8PxyeffGI0X1dXV6jVar00tVoNV1dXg9crlUo4OjrqHUql0sTaEREREVF9JYQwy8McSdoh6dmzJ7KysvTSLl68CE9PTwDAvXv3cO/ePVhY6BfT0tISWq3WaL6BgYFITU3VS9u7dy8CAwMNXh8TE4OCggK9IyYmpipVIiIiIiKiJyDplK2ZM2ciKCgICxYswIgRI5Ceno7Vq1dj9erVAAAHBwf07t0bs2fPhp2dHTw9PXHgwAF8/vnnWLp0qS6fsWPHokWLFrpRjXfeeQe9e/fGkiVLMHDgQGzZsgUnTpzQ5fsoTs8iIiIiIpKGpMv+AsDOnTsRExOD7OxseHt7IyoqCpMmTdKdV6lUiImJwZ49e/DHH3/A09MTkydPxsyZMyGTyQAAffr0gZeXFzZs2KCL+/rrr/Hhhx/i6tWraNu2LRYvXowBAwbUdvWIiIiIqB76312ZUhfBoCn9u0ldhHIk75AQERERET1tnoYOyZw5c/DRRx/ppbVv3x4XLlyo1jJJOmWLiIiIzEfmxd+MnuvW7plaLIm+89eML9vfwdPwgjVEVD06deqElJQU3Wsrq+rvPrBDQkRERERUzcx1E8InZWVlZXSl2uoi6SpbRERERERUezQaDQoLC/WOR/fje1h2djbc3d3RqlUrjB49Wrc9R3Vih4SIiIiIqJ54kv33FAoFNmzYgOTkZCQkJODKlSt47rnn8Ndff1VrmSTtkHh5eUEmk5U73n77bQBASUkJ3n77bTg5OaFhw4YYNmxYuQ0PHyWEQFxcHNzc3GBnZ4eQkBBkZ2fXRnWIiIiIiACY78aIT7L/Xv/+/TF8+HB06dIFoaGh+Omnn3D79m1s3bq1Wt8rSTskx48fx82bN3XH3r17AQDDhw8HcH+fkh9++AFff/01Dhw4gBs3buCVV16pMM/Fixfjs88+Q2JiIo4dOwZ7e3uEhoaipKSkxutDRERERGTO5HI5HBwc9I7K7sfXuHFjtGvXDpcuXarWMpnVsr+RkZHYuXMnsrOzUVhYiGbNmmHz5s149dVXAQAXLlxAhw4dkJaWhmeffbZcvBAC7u7uePfddzFr1iwAQEFBAVxcXLBhwwaMHDmyVutDRERERPXTv348IXURDHprYECVY+/cuQMPDw/MmTMHM2bMqLYymc0qW6Wlpdi0aROioqIgk8mQkZGBe/fuISQkRHeNj48PPDw8jHZIrly5ApVKpRfj6OgIhUKBtLQ0dkiIiIjILPyRlm70XNPAHrVYEqop5vMn/6qbNWsWBg0aBE9PT9y4cQPx8fGwtLTEqFGjqvU+ZtMh2b59O27fvo1x48YBuL9Du42NDRo3bqx3nYuLC1Qqw+uRP0h3cXGpdAwREREREZX322+/YdSoUfj999/RrFkz9OrVC0ePHkWzZs2q9T5m0yFZu3Yt+vfvD3d391q/t0ajKbfcmVwur/R8OiIiIiKip82WLVtq5T5msezvtWvXkJKSgokTJ+rSXF1dUVpaitu3b+tdq1arjW7O8iD90ZW4KooBnmz5MyIiIiKix9EKYZaHOTKLDsn69evRvHlzDBw4UJfm7+8Pa2trpKam6tKysrKQm5uLwMBAg/l4e3vD1dVVL6awsBDHjh0zGgPgiZY/IyIiIiKi6iP5lC2tVov169cjIiICVlb/LY6joyPeeOMNREVFoWnTpnBwcMD06dMRGBio90C7j48PlEolhg4dCplMhsjISMybNw9t27aFt7c3YmNj4e7ujrCwMKNl4PQsIiIiIiJpSN4hSUlJQW5uLiZMmFDu3LJly2BhYYFhw4ZBo9EgNDQU//rXv/SuycrKQkFBge71e++9h6KiIkyePBm3b99Gr169kJycDFtb2xqvCxERERER8HSsslVbzGofEiIiIiKip8FnO45LXQSDZgzuLnURypF8hISIiIjMQ9q5a0bPBXb0rMWSEFF9wg4JEREREVE1E+AkpMoyi1W2iIiIiIiofmKHhIiIiIiIJCNph8TLywsymazc8fbbb+OPP/7A9OnT0b59e9jZ2cHDwwMzZszQW1HLECEE4uLi4ObmBjs7O4SEhCA7O7uWakRERERExI0Rn4SkHZLjx4/j5s2bumPv3r0AgOHDh+PGjRu4ceMGPvnkE5w9exYbNmxAcnIy3njjjQrzXLx4MT777DMkJibi2LFjsLe3R2hoKEpKSmqjSkRERERE9ATMatnfyMhI7Ny5E9nZ2ZDJZOXOf/3113j99ddRVFSkt4niA0IIuLu7491338WsWbMAAAUFBXBxccGGDRswcuTIGq8DEREREdGy749JXQSDZg5RSF2Ecsxmla3S0lJs2rQJUVFRBjsjwP3OhYODg8HOCABcuXIFKpUKISEhujRHR0coFAqkpaWxQ0JERERPvc9TTxtMHxvsW8slIaocs+mQbN++Hbdv38a4ceMMns/Pz8fcuXMxefJko3moVCoAgIuLi166i4uL7hwRERERUU0znzlI5s9sOiRr165F//794e7uXu5cYWEhBg4ciI4dO2LOnDnVfm+NRgONRqOXJpfLIZfLq/1eRERERET0X2ax7O+1a9eQkpKCiRMnljv3119/4aWXXkKjRo2wbds2WFtbG83H1dUVAKBWq/XS1Wq17pwhSqUSjo6OeodSqaxibYiIiIiIqLLMYoRk/fr1aN68OQYOHKiXXlhYiNDQUMjlcuzYsQO2trYV5uPt7Q1XV1ekpqbCz89Pl8exY8cwdepUo3ExMTGIiorSS+PoCBERERFVlbkusWuOJB8h0Wq1WL9+PSIiIvQeVi8sLES/fv1QVFSEtWvXorCwECqVCiqVCmVlZbrrfHx8sG3bNgCATCZDZGQk5s2bhx07duCXX37B2LFj4e7ujrCwMKNlkMvlcHBw0DvYISEiIiIiqnmSj5CkpKQgNzcXEyZM0EvPzMzEsWP3l0tr06aN3rkrV67Ay8sLAJCVlaW3WeJ7772HoqIiTJ48Gbdv30avXr2QnJz82NEVIiIiIiKqfWa1DwkRERER0dPgf747KnURDJr9yrNSF6EcyUdIiIiIiKh+23ksy+i5lxXta7EkJAXJnyEhIiIiIqL6iyMkRERERETVjE9FVB5HSIiIiIiISDKSdki8vLwgk8nKHW+//bbedUII9O/fHzKZDNu3b68wTyEE4uLi4ObmBjs7O4SEhCA7O7sGa0FERERERFUlaYfk+PHjuHnzpu7Yu3cvAGD48OF61y1fvhwymaxSeS5evBifffYZEhMTcezYMdjb2yM0NBQlJSXVXn4iIiIiIkO0wjwPcyTpMyTNmjXTe71w4UK0bt0avXv31qWdOnUKS5YswYkTJ+Dm5lZhfkIILF++HB9++CGGDBkCAPj888/h4uKC7du3Y+TIkdVfCSIiIiIiqjKzeai9tLQUmzZtQlRUlG40pLi4GK+99hpWrVoFV1fXx+Zx5coVqFQqhISE6NIcHR2hUCiQlpbGDgkRERGRGeLSvvWb2XRItm/fjtu3b2PcuHG6tJkzZyIoKEg32vE4KpUKAODi4qKX7uLiojtHRERET5cv9p0xmD7mxS5I+CnDaNzUAf41VSSTGSu3OZeZ9HGVrcozmw7J2rVr0b9/f7i7uwMAduzYgX379uHkyZM1fm+NRgONRqOXJpfLIZfLa/zeRERERET1mVks+3vt2jWkpKRg4sSJurR9+/YhJycHjRs3hpWVFays7vedhg0bhj59+hjM58G0LrVarZeuVqsrnPKlVCrh6OiodyiVShNrRUREREREj2MWIyTr169H8+bNMXDgQF1adHS0XgcFADp37oxly5Zh0KBBBvPx9vaGq6srUlNT4efnBwAoLCzEsWPHMHXqVKP3j4mJQVRUlF4aR0eIiIiIqKo4ZavyJO+QaLVarF+/HhEREbpREOD+aIehUQ0PDw94e3vrXvv4+ECpVGLo0KGQyWSIjIzEvHnz0LZtW3h7eyM2Nhbu7u4ICwszWgZOzyIiIiIikobkHZKUlBTk5uZiwoQJVYrPyspCQUGB7vV7772HoqIiTJ48Gbdv30avXr2QnJwMW1vb6ioyERERERFVE5ngeBIRERERUbWal3RY6iIY9GF4T6mLUI7kIyREREREUsm8+JvB9G7tnqnlkhDVX2axyhYREREREdVPHCEhIiIiIqpmfCqi8jhCQkREREREkmGHhIiIiIiIJCNph8TLywsymazc8fbbb+uuSUtLw4svvgh7e3s4ODjg+eefx927dyvMd9WqVfDy8oKtrS0UCgXS09NruipERERERDpaYZ6HOZK0Q3L8+HHcvHlTd+zduxcAMHz4cAD3OyMvvfQS+vXrh/T0dBw/fhzTpk2DhYXxYiclJSEqKgrx8fHIzMyEr68vQkNDkZeXVyt1IiIiIiKiyjOrfUgiIyOxc+dOZGdnQyaT4dlnn0Xfvn0xd+7cSuehUCjQvXt3rFy5EsD9neBbtmyJ6dOnIzo6uqaKTkRERESk89FXh6QugkHxo3pJXYRyzOYZktLSUmzatAkTJkyATCZDXl4ejh07hubNmyMoKAguLi7o3bs3Dh0y/uGWlpYiIyMDISEhujQLCwuEhIQgLS2tNqpBREREZLJf1X8YPKjuEGb6nzkymw7J9u3bcfv2bYwbNw4AcPnyZQDAnDlzMGnSJCQnJ6Nbt24IDg5Gdna2wTzy8/NRVlYGFxcXvXQXFxeoVCqj99ZoNCgsLNQ7NBpN9VSMiIiIiIiMMpsOydq1a9G/f3+4u7sDuD/VCgCmTJmC8ePHo2vXrli2bBnat2+PdevWVeu9lUolHB0d9Q6lUlmt9yAiIiIiovLMYmPEa9euISUlBd99950uzc3NDQDQsWNHvWs7dOiA3Nxcg/k4OzvD0tISarVaL12tVsPV1dXo/WNiYhAVFaWXJpfLn6gOREREREQPmNFj2mbPLEZI1q9fj+bNm2PgwIG6NC8vL7i7uyMrK0vv2osXL8LT09NgPjY2NvD390dqaqouTavVIjU1FYGBgUbvL5fL4eDgoHewQ0JEREREVPMkHyHRarVYv349IiIiYGX13+LIZDLMnj0b8fHx8PX1hZ+fHzZu3IgLFy7gm2++0V0XHByMoUOHYtq0aQCAqKgoREREICAgAD169MDy5ctRVFSE8ePH13rdiIiIiIioYpJ3SFJSUpCbm4sJEyaUOxcZGYmSkhLMnDkTf/zxB3x9fbF37160bt1ad01OTg7y8/N1r8PDw3Hr1i3ExcVBpVLBz88PycnJ5R50JyIiIjJXLV2aSl0EMpG5bkJojsxqHxIiIiKi6rRq5wmj595+OaAWS/Jk/vWj4XK/NdB8y0z6Yr/8t9RFMGju6OelLkI5ZvEMCRERERER1U+ST9kiIiIiInracBJS5XGEhIiIiIiIJMMOCRERERERSUbSDomXlxdkMlm54+233wYAqFQqjBkzBq6urrC3t0e3bt3w7bffPjbfVatWwcvLC7a2tlAoFEhPT6/pqhARERER6Qhhnoc5krRDcvz4cdy8eVN37N27FwAwfPhwAMDYsWORlZWFHTt24JdffsErr7yCESNG4OTJk0bzTEpKQlRUFOLj45GZmQlfX1+EhoYiLy+vVupERERERESVZ1bL/kZGRmLnzp3Izs6GTCZDw4YNkZCQgDFjxuiucXJywqJFizBx4kSDeSgUCnTv3h0rV64EcH/jxZYtW2L69OmIjo6ulXoQERERUf32zy8OSF0Eg+aP6S11Ecoxm2dISktLsWnTJkyYMAEymQwAEBQUhKSkJPzxxx/QarXYsmULSkpK0KdPH6N5ZGRkICQkRJdmYWGBkJAQpKWl1UY1iIiIiIigFcIsD3NkNh2S7du34/bt2xg3bpwubevWrbh37x6cnJwgl8sxZcoUbNu2DW3atDGYR35+PsrKysrtyu7i4gKVSlWTxSciIiIioiowm31I1q5di/79+8Pd3V2XFhsbi9u3byMlJQXOzs7Yvn07RowYgYMHD6Jz587Vdm+NRgONRqOXJpfLIZfLq+0eRERERERUnlmMkFy7dg0pKSl6z4Xk5ORg5cqVWLduHYKDg+Hr64v4+HgEBARg1apVBvNxdnaGpaUl1Gq1XrparYarq6vR+yuVSjg6OuodSqWyeipHRERERPWOEMIsD3NkFh2S9evXo3nz5hg4cKAurbi4GMD9Z0AeZmlpCa1WazAfGxsb+Pv7IzU1VZem1WqRmpqKwMBAo/ePiYlBQUGB3hETE2NKlYiIiIiIqBIk75BotVqsX78eERERsLL67wwyHx8ftGnTBlOmTEF6ejpycnKwZMkS7N27F2FhYbrrgoODdStqAUBUVBTWrFmDjRs34vz585g6dSqKioowfvx4o2WQy+VwcHDQOzhdi4iIiIio5kn+DElKSgpyc3MxYcIEvXRra2v89NNPiI6OxqBBg3Dnzh20adMGGzduxIABA3TX5eTkID8/X/c6PDwct27dQlxcHFQqFfz8/JCcnFzuQXciIiIiopqiNc/ZUWbJrPYhISIiIiJ6Gry34Wepi2DQ4nEvSF2EciSfskVERERERPWX5FO2iIiIiIieNpyEVHkcISEiIiIiIsmwQ0JERERERJKRtENSVlaG2NhYeHt7w87ODq1bt8bcuXP1hriEEIiLi4Obmxvs7OwQEhKC7Ozsx+a9atUqeHl5wdbWFgqFAunp6TVZFSIiIiIiHSHM8zBHknZIFi1ahISEBKxcuRLnz5/HokWLsHjxYqxYsUJ3zeLFi/HZZ58hMTERx44dg729PUJDQ1FSUmI036SkJERFRSE+Ph6ZmZnw9fVFaGgo8vLyaqNaRERERERUSZIu+/vyyy/DxcUFa9eu1aUNGzYMdnZ22LRpE4QQcHd3x7vvvotZs2YBAAoKCuDi4oINGzZg5MiRBvNVKBTo3r27bsNErVaLli1bYvr06YiOjq75ihERERFRvTZr3T6pi2DQJxNelLoI5Ug6QhIUFITU1FRcvHgRAHD69GkcOnQI/fv3BwBcuXIFKpUKISEhuhhHR0coFAqkpaUZzLO0tBQZGRl6MRYWFggJCTEaQ0RERETmq1ClMniYM60QZnmYI0mX/Y2OjkZhYSF8fHxgaWmJsrIyzJ8/H6NHjwYAqP5/Q3t0l3UXFxfduUfl5+ejrKzMYMyFCxdqoBZERERERFRVknZItm7dii+//BKbN29Gp06dcOrUKURGRsLd3R0RERG1Vg6NRgONRqOXJpfLIZfLa60MRERERET1kaRTtmbPno3o6GiMHDkSnTt3xpgxYzBz5kwolUoAgKurKwBArVbrxanVat25Rzk7O8PS0vKJYpRKJRwdHfWOB2UgIiIiInpSUq+mxVW2Kqm4uBgWFvpFsLS0hFarBQB4e3vD1dUVqampuvOFhYU4duwYAgMDDeZpY2MDf39/vRitVovU1FSjMTExMSgoKNA7YmJiTK0eERERERE9hqRTtgYNGoT58+fDw8MDnTp1wsmTJ7F06VJMmDABACCTyRAZGYl58+ahbdu28Pb2RmxsLNzd3REWFqbLJzg4GEOHDsW0adMAAFFRUYiIiEBAQAB69OiB5cuXo6ioCOPHjzdYDk7PIiIiIiKShqQdkhUrViA2NhZvvfUW8vLy4O7ujilTpiAuLk53zXvvvYeioiJMnjwZt2/fRq9evZCcnAxbW1vdNTk5OcjPz9e9Dg8Px61btxAXFweVSgU/Pz8kJyeXe9CdiIiIiMyfg5Fp9+ZMwEznR5khSfchISIiIqLy9mRcMpjez79NLZekeqzfe8roufF9/WqtHLUp8v9SpC6CQcsnhjz+olom6TMkRERERERUv0k6ZYuIiIiI6GlkrpsQmiOOkBARERERkWTYISEiIiIiIslI2iEpKytDbGwsvL29YWdnh9atW2Pu3Ll48Jz9vXv38P7776Nz586wt7eHu7s7xo4dixs3bjw271WrVsHLywu2trZQKBRIT0+v6eoQEREREQGQfgNEboxYSYsWLUJCQgJWrlyJ8+fPY9GiRVi8eDFWrFgB4P7GiZmZmYiNjUVmZia+++47ZGVlYfDgwRXmm5SUhKioKMTHxyMzMxO+vr4IDQ1FXl5ebVSLiIiIiIgqSdJlf19++WW4uLhg7dq1urRhw4bBzs4OmzZtMhhz/Phx9OjRA9euXYOHh4fBaxQKBbp3746VK1cCuL9Te8uWLTF9+nRER0dXf0WIiIiIiB4yffVeqYtg0IrJfaUuQjmSrrIVFBSE1atX4+LFi2jXrh1Onz6NQ4cOYenSpUZjCgoKIJPJ0LhxY4PnS0tLkZGRgZiYGF2ahYUFQkJCkJaWVt1VICIionpKtWGzwXTXca+ZnPcvl28aTO/cyg3fHT5vNO6Vnh1Mvrc5yu4z0GB62/0/1nJJKo9b/VWepB2S6OhoFBYWwsfHB5aWligrK8P8+fMxevRog9eXlJTg/fffx6hRo+Dg4GDwmvz8fJSVlZXbld3FxQUXLlyo9joQEREREVHVSdoh2bp1K7788kts3rwZnTp1wqlTpxAZGQl3d3dEREToXXvv3j2MGDECQggkJCRUazk0Gg00Go1emlwuh1wur9b7EBERERGRPkkfap89ezaio6MxcuRIdO7cGWPGjMHMmTOhVCr1rnvQGbl27Rr27t1rdHQEAJydnWFpaQm1Wq2Xrlar4erqajBGqVTC0dFR73i0DERERERElaUV5nmYI0k7JMXFxbCw0C+CpaUltFqt7vWDzkh2djZSUlLg5ORUYZ42Njbw9/dHamqqLk2r1SI1NRWBgYEGY2JiYlBQUKB3PPwMChERERER1QxJp2wNGjQI8+fPh4eHBzp16oSTJ09i6dKlmDBhAoD7nZFXX30VmZmZ2LlzJ8rKyqBSqQAATZs2hY2NDQAgODgYQ4cOxbRp0wAAUVFRiIiIQEBAAHr06IHly5ejqKgI48ePN1gOTs8iIiIiIpKGpB2SFStWIDY2Fm+99Rby8vLg7u6OKVOmIC4uDgBw/fp17NixAwDg5+enF/vzzz+jT58+AICcnBzk5+frzoWHh+PWrVuIi4uDSqWCn58fkpOTyz3oTkRERERUE7jKVuVJug8JEREREdHTaGrCbqmLYFDC1FCpi1COpCMkRERERFTe0u3HDKZHhSlquSRENY8dEiIiIiKiaqblJKRKk3SVLSIiIiIiqt/YISEiIiIiIslI2iEpKytDbGwsvL29YWdnh9atW2Pu3LlGVyV48803IZPJsHz58sfmvWrVKnh5ecHW1hYKhQLp6enVXHoiIiIiIsOEMM/DHEnaIVm0aBESEhKwcuVKnD9/HosWLcLixYuxYsWKctdu27YNR48ehbu7+2PzTUpKQlRUFOLj45GZmQlfX1+EhoYiLy+vJqpBRERERERVJOlD7UeOHMGQIUMwcOBAAICXlxe++uqrcqMZ169fx/Tp07F7927dtRVZunQpJk2apNsIMTExET/++CPWrVuH6Ojo6q8IERERUTXialpkTlatWoX/+Z//gUqlgq+vL1asWIEePXpUW/6SdkiCgoKwevVqXLx4Ee3atcPp06dx6NAhLF26VHeNVqvFmDFjMHv2bHTq1OmxeZaWliIjIwMxMTG6NAsLC4SEhCAtLa1G6kFERET0NPjfXZkG06f071bLJan7npat/h7MPEpMTIRCocDy5csRGhqKrKwsNG/evFruIemUrejoaIwcORI+Pj6wtrZG165dERkZidGjR+uuWbRoEaysrDBjxoxK5Zmfn4+ysrJyu7K7uLhApVJVa/mJiIiIiJ5mD8886tixIxITE9GgQQOsW7eu2u4h6QjJ1q1b8eWXX2Lz5s3o1KkTTp06hcjISLi7uyMiIgIZGRn49NNPkZmZCZlMVmPl0Gg00Gg0emlyuRxyubzG7klEREREZM5qa+aRpCMks2fP1o2SdO7cGWPGjMHMmTOhVCoBAAcPHkReXh48PDxgZWUFKysrXLt2De+++y68vLwM5uns7AxLS0uo1Wq9dLVaDVdXV4MxSqUSjo6OeseDMhARERERPSmtMM9Do9GgsLBQ73j0D/MP1NbMI0k7JMXFxbCw0C+CpaUltFotAGDMmDE4c+YMTp06pTvc3d0xe/Zs7N6922CeNjY28Pf3R2pqqi5Nq9UiNTUVgYGBBmNiYmJQUFCgdzzcEyQiIiIiehqY4x/iJZ2yNWjQIMyfPx8eHh7o1KkTTp48iaVLl2LChAkAACcnJzg5OenFWFtbw9XVFe3bt9elBQcHY+jQoZg2bRoAICoqChEREQgICECPHj2wfPlyFBUV6VbdehSnZxERERFRfRATE4OoqCi9NGPfg6sy86gqJO2QrFixArGxsXjrrbeQl5cHd3d3TJkyBXFxcU+UT05ODvLz83Wvw8PDcevWLcTFxUGlUsHPzw/JycnlhpuIiIiIiGqCua6y9SR/iH945lFYWBiA/848ejAQUB1kwlzfLSIiIiKiOmrCZz9JXQSD1s0Y8ETXJyUlISIiAv/7v/+rm3m0detWXLhwodr+2C/pCAkRERER0f7Tl42e6+PbqhZLQo+qjZlH7JAQEREREVWzp2kS0rRp06p1itajJF1li4iIiIiI6jd2SIiIiIiISDKSdkjKysoQGxsLb29v2NnZoXXr1pg7d265Ia7z589j8ODBcHR0hL29Pbp3747c3NwK8/7666/h4+MDW1tbdO7cGT/9ZJ4PFhERERHR00fqDRCNHeZI0g7JokWLkJCQgJUrV+L8+fNYtGgRFi9ejBUrVuiuycnJQa9eveDj44P9+/fjzJkziI2Nha2trdF8jxw5glGjRuGNN97AyZMnERYWhrCwMJw9e7Y2qkVERERERJUk6bK/L7/8MlxcXLB27Vpd2rBhw2BnZ4dNmzYBAEaOHAlra2t88cUXlc43PDwcRUVF2Llzpy7t2WefhZ+fHxITE6uvAkREREREBkQs/1HqIhi0MXKg1EUoR9IRkqCgIKSmpuLixYsAgNOnT+PQoUPo378/gPsbr/z4449o164dQkND0bx5cygUCmzfvr3CfNPS0hASEqKXFhoairS0tBqpBxEREdHTbv3eU0YPKk+Y6X/mSNIOSXR0NEaOHAkfHx9YW1uja9euiIyMxOjRowEAeXl5uHPnDhYuXIiXXnoJe/bswdChQ/HKK6/gwIEDRvNVqVTl1kZ2cXGBSqWq0foQEREREdGTkXQfkq1bt+LLL7/E5s2b0alTJ5w6dQqRkZFwd3dHREQEtFotAGDIkCGYOXMmAMDPzw9HjhxBYmIievfuXS3l0Gg00Gg0emlyuRxyubxa8iciIiIiIsMkHSGZPXu2bpSkc+fOGDNmDGbOnAmlUgkAcHZ2hpWVFTp27KgX16FDhwpX2XJ1dYVardZLU6vVcHV1NXi9UqmEo6Oj3vGgDERERERET0rq1bS4ylYlFRcXw8JCvwiWlpa6kREbGxt0794dWVlZetdcvHgRnp6eRvMNDAxEamqqXtrevXsRGBho8PqYmBgUFBToHTExMVWpEhERERERPQFJp2wNGjQI8+fPh4eHBzp16oSTJ09i6dKlmDBhgu6a2bNnIzw8HM8//zxeeOEFJCcn44cffsD+/ft114wdOxYtWrTQjWq888476N27N5YsWYKBAwdiy5YtOHHiBFavXm2wHJyeRUREREQkDUk7JCtWrEBsbCzeeust5OXlwd3dHVOmTEFcXJzumqFDhyIxMRFKpRIzZsxA+/bt8e2336JXr166a3Jzc/VGWoKCgrB582Z8+OGH+OCDD9C2bVts374d//jHP2q1fkRERERUP0m4s0adI+mUrUaNGmH58uW4du0a7t69i5ycHMybNw82NjZ6102YMAHZ2dm4e/cuTp06hSFDhuid379/PzZs2KCXNnz4cGRlZUGj0eDs2bMYMGBAlcup0WgwZ86ccg++M9a87s3Y2omV8t6MNf9YKe/N2NqJlfLejK2dWGPx4/v6GT3MpdxUN0m6MWJdUVhYCEdHRxQUFMDBwYGxZnpvxtZOrJT3Zqz5x0p5b8bWTqyU92Zs7cRKeW9Ty21ORi/9QeoiGPRl1CCpi1COpFO2iIiIiIieRvyTf+VJOmWLiIiIiIjqN3ZIiIiIiIhIMpyyVQlyuRzx8fFVWhq4vsVKeW/G1k6slPdmrPnHSnlvxtZOrJT3ZmztxEp5b1PLbU60nLNVaXyonYiIiIiomo38ZIfURTBoy6zBUhehHE7ZIiIiIiIiyXDKFhERERFRNeMkpMrjCAkREREREUmGHRIiIiIiIpIMp2wREREREVUzztiqPHZIDDh37hxWrlyJtLQ0qFQqAICrqysCAwMxbdo0dOzYsVL5aDQaAHgqlq6rSGlpKbZv317u/QoKCsKQIUNgY2NjdrH5+flYt26dwdhx48ahWbNmT/w+UHmmvM+mfL6mxrNt1Q2m/Kw25XOSKra6fjfR49W3tmVqnaV6v+jpwWV/H7Fr1y6EhYWhW7duCA0NhYuLCwBArVZj7969yMjIwPfff4/Q0FCD8Xv37sWyZcuQlpaGwsJCAICDgwMCAwMRFRWFkJCQCu9f137RXbp0CaGhobhx4wYUCoXe+3Xs2DE888wz2LVrF9q0aWM2scePH0doaCgaNGiAkJAQvdjU1FQUFxdj9+7dCAgIMFjnutgBM/XLfVXalinvsymfr6nxUrYtduAqX2dTflab8jlJFWvq7yZT3mugbv7sYtuq/M8eU+os1ftVF4xY/L3URTBo63tDpC5COeyQPMLX1xdDhgzBxx9/bPD8nDlz8N133+HMmTPlzm3cuBETJ07Eq6++Wu4f5Z49e/DNN99g7dq1GDNmjMG86+Ivur59+8Le3h6ff/45HBwc9M4VFhZi7NixuHv3Lnbv3m02sc8++yx8fX2RmJgImUymd04IgTfffBNnzpxBWlpaudi62AEz9ct9VduWKe+zKZ+vqfFStS124J6szqb8rDblc5Iq1pT6AtK1L7at/zLXtmVqnaV6v+qCVxdtl7oIBn3zfpjURSiHHZJH2NnZ4dSpU2jfvr3B81lZWfDz88Pdu3fLnWvXrh3eeecdvP322wZj//Wvf2HZsmXIzs42eL4u/qJr0KAB0tPT8Y9//MNg7C+//AKFQoHi4mKzibWzs8PJkyfh4+NjMPbChQvo2rWrwc+4LnbATP1yX9W2Zcr7bMrna2q8VG2LHbgnq7MpP6tN+ZykjK1qfQHp2hfblj5zbFsP4k2psxTvV13ADknlcZWtR3h5eeHHH380ev7HH3+Ep6enwXO5ubkVTskKDg7Gb7/9ZvT86dOnMXPmzHI/PAFAJpNh5syZOHXqVLXHXrx4EaNHjzZarlGjRhntRDVu3BhXr141Gnv16lU0btzYrGJdXV2Rnp5uNDY9PV3317RHHT58GPPmzSv3yxG4PzVv7ty5OHjw4FMTC1S9bZnyPpvy+ZoaL1XbMuXfsKmfsVTty5Q6m/Kz2pTPSapYU+oLSNe+2Lb0mWPbAkyrs1TvFz1d+FD7Iz7++GO89tpr2L9/v8Fh3uTkZGzevNlgbKdOnbB27VosXrzY4Pl169ZV+GDXg3+Yxv5SUJkfRlWJffDDxNhfNyr6YTJx4kSMHTsWsbGxCA4OLvd+zZs3D9OnTzer2FmzZmHy5MnIyMgwGLtmzRp88sknBmMffFk19tfzynzRrUuxQNXblinvsymfr6nxUrUtU/4Nm/oZS9W+TKmzKT+rTfmcpIo1pb6AdO2Lbcv825apdZbq/aoLOAfpCQgq5/DhwyI8PFx4eHgIGxsbYWNjIzw8PER4eLg4cuSI0biff/5Z2Nvbi86dO4uZM2eKhQsXioULF4qZM2eKLl26iIYNG4oDBw4YjV+5cqWQy+VixowZ4vvvvxdHjx4VR48eFd9//72YMWOGsLOzE6tWrar22K1btworKysxaNAg8emnn4otW7aILVu2iE8//VQMHjxY2NjYiG+++cZouRcuXCjc3NyETCYTFhYWwsLCQshkMuHm5iYWLVpkNE7K2C1btgiFQiGsrKyETCYTMplMWFlZCYVCIZKSkozGxcbGiiZNmoilS5eK06dPC5VKJVQqlTh9+rRYunSpaNq0qYiPj39qYoUwrW1V9X0WwrTP19R4KdqWKe+zqZ+xVO3LlDoLUfWf1UKY1jalijWlvlK1L7atutG2TK2zVO+XuXtFuc0sD3PEZ0iq2dWrV5GQkICjR4+WW63qzTffhJeXV4XxSUlJWLZsGTIyMlBWVgYAsLS0hL+/P6KiojBixIgaiT1y5Ag+++wzg6tsvfPOOwgMDHxs3a9cuaIX6+3t/dgYqWPv3buH/Px8AICzszOsra0fG7No0SJ8+umnUKlUuukAQgi4uroiMjIS77333lMVC5jWtoCqvc8PmPL5mhpf223LlPfZ1M9YqvZlatsylSltU6rYqpKqfbFtPf1tqzrU1XJXZNjC7VIXwaBvo8OkLkI57JCYqfr4w6iuqYsdMFO/3LNt1Q524Ni2apJU7Ytti+qbocptUhfBoG0xQ6UuQjnskDyhDz74ACqVCuvWrZO6KHXC999/j4KCAowdO7bOxP7rX/9Cfn4+4uLinjiWKs+U99mUz9fUeLatusGUn9WmfE5SxfJ3U+2pb20LMK3OUr1f5oAdksrjKltP6Pr16xWuwFORiIgIvPjii1W+97/+9S+jS/PWZOwHH3yACRMmVCn2/fffx/jx4+tU7LfffosNGzZUKfb777/H559/Xm9igaq3LVPeZ1M+X1PjpWpbpvwbNvUzlqp9mVJnU35Wm/I5SRVrSn0B6doX25b5xwKm1Vmq94vqFo6QVJIQwuDygU/igw8+wM2bN7F+/foqxQcHB+PKlSu4fPlyrcZGRETg119/xb59+544tr7x8fFBdna2br7y0x4LmNa2qPJMeZ9N/Yylal9sW7VHqvbFtkVPs7AF30ldBIO2f/CK1EUohx2SSrKxscHp06fRoUMHqYtCRERERGaOHZLK4z4kj4iKijKYXlZWhoULF8LJyQkAsHTp0tosltlLT083uEJXjx49Hhur1WphYVF+9qBWq8Vvv/0GDw+PSpXhypUruHTpEtzc3IyuW//A6dOnkZGRgT59+qBVq1b4z3/+g1WrVkGr1WLo0KEIDQ2t1D2p6v7880/88MMPRp/FEELg6tWraNmyJaysrFBaWopt27ZBo9FgwIABcHZ2fqL7VbZ9fPvtt+jfvz8aNGjwRPk/bN++fTh06BBu3rwJCwsLtGrVCoMHD0bbtm2rnCc9GbVajf/93/81Ovf8999/x5kzZ+Dr64umTZsiPz8fa9euhUajwfDhwyv9xychBPbv369rW6GhoUYfml6yZAleffXVCjcwrMjdu3fx1VdflWtbYWFhCA4OrlKe9OSexrYF1Fz7qq33i+o2jpA8wsLCAr6+vuU2Wzpw4AACAgJgb28PmUxmdPrSypUrkZ6ejgEDBmDkyJH44osvoFQqodVq8corr+Djjz+GlZXxfqAUP8gAYOfOnUhPT0doaCh69uyJffv24ZNPPtGVe/LkyQbj8vLyMGzYMBw+fBgeHh56mxrl5uaiZ8+e+Pbbb9G8efNysYWFhZg4cSJ++OEHODg4YMqUKYiPj4elpaUuD3d3d4PD8W+99RYWL16Mhg0b4u7duxgzZgy2bdumm1rXu3dv7NixAw0bNiwX+91332HEiBFo3LgxNBoNtm3bhuHDhyMgIACWlpZISUnB559/jtdee83o+1WXOmDV8QUbqP4v2adPn0a3bt0Mfr5ZWVkIDQ3Fr7/+ilatWmHPnj0YPnw4Lly4ACEEGjRogCNHjhi9tyntw8LCAo0aNUJ4eDjeeOMNKBSKStcpLy8PgwYNwokTJ2BhYQGtVouuXbvi+vXruHXrFqKiooxunPo47MA9mYraV3p6Ovr164fCwkI0btwYe/fuxfDhw2FlZQWtVosbN27g0KFD6NatW7nYAQMG4KuvvoKjoyP++OMPDBgwAOnp6XB2dsbvv/+Odu3a4d///jeaNWtWLtbCwgIWFhZ44YUXMHHiRAwdOhQ2NjaVqs+lS5cQEhKCu3fvQi6X47fffsOAAQOQn5+PEydO4JVXXsHmzZsr/P1SkdpsX2xb5tW2gJptXzX1ftUFQ+ab5wjJ9/80vxESboz4CKVSKby9vUVqaqpeupWVlfjPf/5TYezcuXNFo0aNxLBhw4Srq6tYuHChcHJyEvPmzRMLFiwQzZo1E3FxcUbjjx07JhwdHYVMJhNNmjQRJ06cEN7e3qJt27aidevWws7OTmRkZBiM7d+/v7h9+7YQQojff/9dKBQKIZPJRLNmzYSFhYXw8fEReXl5BmMTExOFlZWV8Pf3Fw4ODuKLL74QjRo1EhMnThRTpkwRdnZ2Yvny5QZjhw0bJgIDA8WFCxfKnbtw4YIICgoSr776qsHYGTNmiHbt2omvv/5arFmzRnh6eoqBAwcKjUYjhBBCpVIJmUxmMNbCwkKo1WohhBAxMTHimWeeEfv27RNFRUXi0KFDonXr1iI6OtpgbLdu3cS8efOEEEJ89dVXonHjxuLjjz/Wnf/kk0+En5+fwVi1Wi169eolZDKZ8PT0FD169BA9evQQnp6eQiaTiV69eunK9aiCggIxfPhwYWtrK5o3by5iY2PF33//rTuvUqmEhYWFwdipU6eKv/76SwghRHFxsRg2bJhusz4LCwvxwgsv6M4/SiaTCQcHBzFp0iRx9OhRg9dURK1Wix49eggLCwthZWUlLCwshL+/v3B1dRWWlpZi9uzZRutb0XHw4EGj9R0yZIgYPHiwOHPmjIiMjBQdOnQQQ4YMEaWlpaKkpEQMGjRIvP7660bLbEr7kMlk4uOPPxZdu3YVMplMdOrUSSxbtkzk5+c/9r0KDw8XYWFhoqCgQJSUlIhp06aJsWPHCiGESE1NFU5OTkb/LT3OqVOnjL5fFy5cEJ6ensLCwkK0adNGXL58Wfj7+wt7e3vRoEED4ezsLC5evGg0b6naV1XblhBCnD59usIjKSnJ6PsVEhIiJk6cKAoLC8X//M//iGeeeUZMnDhRd378+PEiLCzMaH0ftK2pU6eKjh07isuXLwshhPj111+Fv7+/ePPNN43Grl+/XgwZMkRYW1sLJycn8c4774hffvnlse9V//79xZQpU4RWqxVC3N+8s3///kIIIS5evCi8vLwq3PzycWqqfbFtmX/bEsK09iXV+1UXDJ73rVke5ogdEgPS09NFu3btxLvvvitKS0uFEJXrkLRu3Vp8++39D/rUqVPC0tJSbNq0SXf+u+++E23atDEaL9UPso4dO4rVq1cLIYTYt2+fsLW11dvBdv369aJDhw4GYxs2bCgyMzON1unEiROiYcOGBs95eHiIn3/+Wff61q1bokePHqJfv36ipKSkwi/oD9f3H//4h9i8ebPe+e+//160a9fOYKy9vb24cuWKEEIIrVYrrK2txZkzZ3Tnc3JyjJa5LnbATPmCLUTVv2Q/vMu5oePBeUOaNWsmTp48KYQQ4s6dO0Imk4mDBw/qzh8+fFh4eHgYLbMp7ePh2BMnToipU6eKxo0bC7lcLoYPHy727Nlj9L4ODg7i7Nmzutd37twR1tbWoqCgQAghxBdffCHat29vMJYduCfrwD1oPw92dn74eFz7atKkiTh37pwQQojS0lJhYWEhjh07pjufkZEhWrRoYfS+D96r9u3bi++//17vfEpKivD29n5srFqtFosWLRI+Pj7CwsJCdO/eXaxevVoUFhYajG3QoIHel36NRiOsra117/P27duFl5eXwVghpGtfbFvm37aEMK19SfV+1QVSdzzYIXkK/PXXX2Ls2LGiS5cu4pdffhHW1taP7ZDY2dmJa9eu6V5bW1vrfTm5evWqaNCggdF4qX6QGSr3w39VuXLlitFyOzk5if379xut088//yycnJyM3vdBp+mBwsJCERgYKF588UVx+fLlCjskD0Z8nJ2d9d5nIe6/13Z2dgZjXV1dxYkTJ4QQQvzxxx9CJpPpdYzS09OFq6urwdi62AEz5Qu2EFX/ku3g4CAWLVok9u/fb/BYs2aN0fo+2iYbNmwoLl26pHudm5sr5HK50TKb0j4efr8euHv3rvj8889Fnz59hIWFhdFfzM2aNdP7OVFcXCwsLCzE77//LoS439k1Vm524J6sA+fk5CTWrl0rrl69avD48ccfjb5fD/9RQoj77SsnJ0f3+tq1a8LW1tZofR+0rebNmxtsWxV9xoZGUP/973+LiIgIYW9vL+zt7Q3Guru7642Q//nnn0Imk+m+ZF6+fPmx/yakaF9sW+bftoQwrX1J9X7VBS/P+8YsD3PEh9qNaNiwITZu3IgtW7YgJCSkUssKurq64ty5c/Dw8NAtRXju3Dl06tQJAPCf//zH4LMUD5SWlsLOzg4AYG1tjQYNGujNy30wj9SYB8sS//nnn2jdurXeuTZt2uDGjRsG45ycnHDt2jV4eHjgxo0b+Pvvv5Gbm6ub23vt2jU0bdrUYGx4eDgiIiKwbNkyBAcHw8HBAcD950NSU1MRFRWFUaNGGYz18PDA+fPn9XbcbdSoEfbs2YN+/fph6NCKN+6JjY1FgwYNYGFhgRs3bujeZ+D+szj29vYG40JCQvD2229j+vTpSEpKQr9+/RATE4P169dDJpNh9uzZ6NWrl8FYuVyOwsJCo2X666+/IJfLDZ67deuW3gOHzs7OSElJQWhoKAYMGID/+7//q7C+Dz5flUqFLl266J3z9fXFr7/+WmE8APj7+8Pf3x9Lly7F119/jXXr1uGll16Ch4cHrly5YjBGLpfrLXltYWGBsrIy/P333wCAoKAgg2vMP5jz27t3b4P5Nm7cGMLII2zu7u7Izc3VPU+zePFivX87t27dQpMmTSqsa1Xbh6HlvW1tbTFmzBiMGTMGly5dMrp0d69evRAXF4eNGzfCxsYGH3zwAVq1aqX791NRuRs1aoR//vOfRp9Zyc7OxpQpUwyeu3Pnju4e9vb2sLe3h5ubm+58y5YtoVarDcY+IEX7qmrbenCvGzduGH2I9/bt20bbV8uWLXH58mV4eXkBALZs2aL3ft28ebPCZyLGjRsHuVyOe/fu4cqVK3ptS6VSlXsG8QFjS8c/99xzeO655/DZZ58hKSnJ4DV9+/ZFVFQUEhMTIZfLERMTAz8/PzRq1AgAkJubW+HvFynbF9uWebctwLT2JeX7RU8PdkgeY+TIkejVqxcyMjIeu3rF6NGjMXbsWAwZMgSpqal47733MGvWLPz++++QyWSYP38+Xn31VaPxUv0gGzJkCN544w1ERERgx44dGDt2LN59911YWFjovqD369fPYOzSpUuh1WoxcuRI/P3337qH6EpLS2FlZYU33ngDn3zyicHYfv36Yf369RgwYIBeesOGDbF792707dvXaF2ff/55ZGVlAQA6duyIa9eu6Z3/6aef9Or/sE8++QRjxozBm2++iZ49eyIpKQkffvghOnbsCJlMhtatW2Pt2rUGY+tiB8yUL9hA1b9kv/baa7h7967RfF1dXREfH2/wXEhICC5cuKDrGE6dOlXv/J49eyp8yNGU9mHsF+cDbdq0wfz58w2e++STT9CvXz80btwYMpkM9vb2+Prrr3Xnz58/j3HjxhmMZQfuyTpwb775JoqKiozWx8PDw+h9R44ciby8PN3rgQMH6p3fsWOH0QUqIiIidP8/ZMgQFBcX653/9ttv4efnZzD2cW3LwcEBkyZNMnhu8eLFGDJkiO7nVMuWLbFt2393gb516xZmz55tNG8p2xfb1n+ZY9sCTGtfUr1f9HThKlvVSKvVYuHChUhLS0NQUBCio6ORlJSE9957D8XFxRg0aBBWrlxp9IfvRx99hPbt22PkyJEGz//zn//EhQsX8O2335Y79+jO0f3798eIESN0r9977z2cOXMGycnJ5WKLioowc+ZMXblXrFiBzz77DP/85z9x79499O7dG0lJSRX+9a2wsBAZGRl6q075+/vrvrAb8ueff5b75fSwv/76C5mZmUZ/gVbk8uXLsLGxwTPPPPNEMcXFxfDx8TG6kohGo0FkZCTWrVtntAO2bNkyg6MkM2bMwM2bN/W+oD7w119/oW/fvjh+/LjB0bg+ffro/XIePXo0Jk6cqHs9b948pKSkYP/+/eViLSwsoFKpKvz8KnL58mX069cP165d0/uSHRISAgDYsGEDsrKyoFQqK5WfMGGT0QexV65cga2trV6H/UniK2of165dQ8uWLcuthFbZchcXF+PQoUMoLS3Fs88+C2dn50rFrlmzBnfv3sWMGTMMnler1UhMTDTYiXvzzTcREBCg1yYetnDhQhw8eBA//vijwfNStS9DbWvr1q26P0Y8aduqLsXFxbC0tDQ62lmRoqIiWFpawtbWtgZKdn8kQ6PRVPhzypA1a9aguLgY77zzjsHzNdW+zKltmfJzq7qYc9sCqt6+aoop75c5eHle+e9r5mDnh8OkLkI57JDUIbX9g6ykpAT37t3TDdnSf9WlDtiD6XhV7QQA99ve4cOHodFodF+yq8qUTUZN3aBUqntLvbFqVTtwD9Rk+6rOtkXSMKV91XTbevSPA1Qzbt68iYSEBIN7mIwbN063nH91x5o7dkgqT/ruL1Xa77//jvj4eKxbt+6JY//4448njrW1tYWtrS1+/fXXCmPv3r2LjIwMNG3aFB07dtQ7V1JSgq1btxpd274uxp4/fx5Hjx5FYGAgXnjhBVy4cAGffvopvvjiC7z++ut48cUXDcY1adIEKpUK69evR2BgIHx8fHSxGo2mwtiH7xsUFIT27dtXOtbT01OvzE96X+D+F4PffvsNgYGBcHZ2rlS8KZuMmrpBqVT3rs6NVYuKirB161bdfg2jRo3Sxddk7JPGPzqV9Unv3aBBA92ISFFREdavX1+p2MzMTDRp0kQ3BfKLL75AYmIicnNz4enpiWnTphkdba5vsQAwffp0jBgxAs8995zRa6oa+/A01CeNbdWqldHYTz75xOQyG5tu/Dim7ClWF2NNiT9x4gRCQkLQpk0b2NnZITs7G6+99hpKS0sxa9YsrFu3DsnJyQb/uGlKLD1lavspeqq6itaJlyo2KytLtweHhYWFeP7558X169d15ytaOcpQ7I0bN8w6dteuXcLGxkY0bdpU2Nrail27dolmzZqJkJAQ8eKLLwpLS8tye9jU5VhT4mUymfDz8xN9+vTRO2Qymejevbvo06ePeOGFFwze05RYKe9tSmyHDh10q3Hl5uYKLy8v4ejoKLp37y6aNm0qmjdvXm5VuuqINRTv6elZK/c25b5dunQRe/fuFUIIsWbNGmFnZydmzJghEhISRGRkpGjYsKFYu3YtY/+/Bz/v2rZtKxYuXChu3rxp9Nr6HmvKnmJ1MdbU+J49e4o5c+boXn/xxRdCoVAIIe6vZOnn5ydmzJhR7bF1wYCPvzbLwxyxQ2JGvv/++wqPZcuWGf2iLFVsWFiYGDhwoLh165bIzs4WAwcOFN7e3rolWyv6cl8XYwMDA8U///lPIcT9TRWbNGkiPvjgA9356Oho0bdv36cm1pR4UzYZNSVWynubEvvwsp2jR48WQUFBus1O//rrLxESEiJGjRpV7bFS3tuUWDs7O3H16lUhhBBdu3bV7aX0wJdffik6duzI2P9PJpOJlJQU8c477whnZ2dhbW0tBg8eLH744QdRVlZmNK4+xpqyp1hdjDU13s7OTm+p3rKyMmFtbS1UKpUQQog9e/YId3f3ao+tC6TueLBDQlVS0eZCD28yZE6xzZs319tUUKvVijfffFN4eHiInJycCr/c18VYBwcHkZ2dLYS4/4PTyspKb1+SX375Rbi4uDw1sabGV3WTUVNjpbx3VWMf/nLeqlWrcnssHD58WLRs2bLaY6W8tymxTk5Ouv2EmjdvLk6dOqV3/tKlS0b3mqlvsULov9elpaUiKSlJhIaGCktLS+Hu7i4++OAD3b/z+h5ryp5idTHW1HhPT09x6NAh3esbN24ImUwmiouLhRD39zIztpeIKbF1gdQdj7rUIbF4/KQuqi1ubm747rvvoNVqDR6ZmZlmF3v37l29OaUymQwJCQkYNGgQevfujYsXLz5VsQ+uB+6vAGNrawtHR0fduUaNGqGgoOCpijUlvnv37sjIyMCtW7cQEBCAs2fPVvoBVVNipby3KbEPrispKSn3gHCLFi1w69atGomV8t5Vje3fvz8SEhIA3F/K9ptvvtE7v3XrVrRp04axBlhbW2PEiBFITk7G5cuXMWnSJHz55Zdo3749Y/HfPcUA6O0p9kBFe4rVxVhT48PCwvDmm28iOTkZP//8M0aPHo3evXvr9lXLyspCixYtqj22LtAKYZaHOeJD7WbE398fGRkZGDJkiMHzMpnM6FrjUsX6+PjgxIkT5VYPWrlyJQBg8ODBBuPqaqyXlxeys7N1G0+mpaXp1uYH7m8eZWylmboYWx3xVdlktDpipbx3VWODg4NhZWWFwsJCZGVl6TYnBe4vLFDRw+GmxEp576rGLlq0CD179kTv3r0REBCAJUuWYP/+/ejQoQOysrJw9OhRvX0U6nNsRTw8PDBnzhzEx8cjJSWFsTBtT7G6GGtq/Lx583Dz5k0MGjQIZWVlCAwMxKZNm3TnZTKZ0eWVTYmlpws7JGZk9uzZFW4u1KZNG/z8889mFTt06FB89dVXGDNmTLlzK1euhFarRWJi4lMTO3XqVL0vlw9/eQKAXbt2GV2xqi7GVkf8A0+yyWh1xkp57yeJfXT/h4YNG+q9/uGHH4yuNmRKrJT3NiXW3d0dJ0+exMKFC/HDDz9ACIH09HT8+uuv6NmzJw4fPoyAgADG/n+enp4VLp8qk8mMbkZb32I/+ugj2NnZIS0tDZMmTUJ0dDR8fX319hSbO3fuUxNranzDhg2RlJSEkpIS/P333+X+HVe00pkpsfR04T4kRERERETV7KWPym+EbA6S44dLXYRy+AwJERERERFJhh0SIiIiIiKSDJ8hISIiIiKqZnwqovI4QkJERERERJJhh4SIiIiIiCTDDgkRkRkZN24cZDIZZDIZrK2t4eLigr59+2LdunXQarVSFw9r1qzBc889hyZNmqBJkyYICQlBenq61MUiIjI7WmGehzlih4SIyMy89NJLuHnzJq5evYpdu3bhhRdewDvvvIOXX34Zf//9t8GYe/fu1UrZ9u/fj1GjRuHnn39GWloaWrZsiX79+uH69eu1cn8iInr6sENCRGRm5HI5XF1d0aJFC3Tr1g0ffPABvv/+e+zatQsbNmwAcH9jt4SEBAwePBj29vaYP38+NmzYgMaNG+vltX37dshkMr20efPmoXnz5mjUqBEmTpyI6Oho+Pn5VapsX375Jd566y34+fnBx8cH//d//wetVovU1NRqqDkREdVH7JAQEdUBL774Inx9ffHdd9/p0ubMmYOhQ4fil19+wYQJEyqVz5dffon58+dj0aJFyMjIgIeHBxISEqpcruLiYty7dw9Nmzatch5ERE8jIYRZHuaIy/4SEdURPj4+OHPmjO71a6+9hvHjxz9RHitWrMAbb7yhi4uLi8OePXtw586dKpXp/fffh7u7O0JCQqoUT0RExBESIqI6QgihN/0qICDgifPIyspCjx499NIefV1ZCxcuxJYtW7Bt2zbY2tpWKQ8iIiKOkBAR1RHnz5+Ht7e37rW9vb3eeQsLi3LD8TX1sPsnn3yChQsXIiUlBV26dKmRexAR1WXmuqKVOeIICRFRHbBv3z788ssvGDZsmNFrmjVrhr/++gtFRUW6tFOnTuld0759exw/flwv7dHXj7N48WLMnTsXycnJVRqlISIiehhHSIiIzIxGo4FKpUJZWRnUajWSk5OhVCrx8ssvY+zYsUbjFAoFGjRogA8++AAzZszAsWPHdKtyPTB9+nRMmjQJAQEBCAoKQlJSEs6cOYNWrVpVqmyLFi1CXFwcNm/eDC8vL6hUKgBAw4YN0bBhwyrXmYiI6i+ZMNfH7YmI6qFx48Zh48aNAAArKys0adIEvr6+eO211xAREQELi/sD2zKZDNu2bUNYWJhe/Pbt2zF79mxcv34dwcHBGDx4MCZPnqw3lWvu3Ln47LPPUFJSghEjRqBhw4ZIT09HWlraY8vn5eWFa9eulUuPj4/HnDlzql5xIiKqt9ghISKq5/r27QtXV1d88cUXUheFiIjqIU7ZIiKqR4qLi5GYmIjQ0FBYWlriq6++QkpKCvbu3St10YiIqJ7iCAkRUT1y9+5dDBo0CCdPnkRJSQnat2+PDz/8EK+88goAVPgcyK5du/Dcc8/VVlGJiKieYIeEiIh0Ll26ZPRcixYtYGdnV4ulISKi+oAdEiIiIiIikgz3ISEiIiIiIsmwQ0JERERERJJhh4SIiIiIiCTDDgkREREREUmGHRIiIiIiIpIMOyRERERERCQZdkiIiIiIiEgy7JAQEREREZFk/h8u5UiHvOnqjAAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Pivot the DataFrame to create a matrix\n",
+        "heatmap_matrix = df.pivot(index='Drug_1', columns='Drug_2', values='mean')\n",
+        "\n",
+        "# Create a custom color map for better visualization\n",
+        "cmap = sns.diverging_palette(240, 10, as_cmap=True)\n",
+        "\n",
+        "# Plot the heatmap\n",
+        "plt.figure(figsize=(10, 10))\n",
+        "sns.heatmap(heatmap_matrix, annot=False, cmap=cmap, linewidths=.5, fmt='.1f', square=True)\n",
+        "\n",
+        "plt.title('2D Heatmap with Given Combinations and Values')\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "3832ff70",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 811
+        },
+        "id": "3832ff70",
+        "outputId": "0ca22f4e-09c7-4add-ad11-d47d683e948d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Pivot the DataFrame to create a matrix\n",
+        "heatmap_matrix = df.pivot(index='Drug_1', columns='Drug_2', values='std')\n",
+        "\n",
+        "# Create a custom color map for better visualization\n",
+        "cmap = sns.diverging_palette(240, 10, as_cmap=True)\n",
+        "\n",
+        "# Plot the heatmap\n",
+        "plt.figure(figsize=(10, 10))\n",
+        "sns.heatmap(heatmap_matrix, annot=False, cmap=cmap, linewidths=.5, fmt='.1f', square=True)\n",
+        "\n",
+        "plt.title('2D Heatmap with Given Combinations and Values')\n",
+        "plt.show()\n",
+        "\n",
+        "#rescale logarithmic or something\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "a42b23f1",
+      "metadata": {
+        "id": "a42b23f1"
+      },
+      "outputs": [],
+      "source": [
+        "top_bayesian = [0.0, 0.05128205128205128, 0.07692307692307693, 0.10256410256410256, 0.1282051282051282, 0.23076923076923078, 0.3076923076923077, 0.5128205128205128, 0.6153846153846154, 0.6666666666666666, 0.7692307692307693, 0.8205128205128205, 0.8205128205128205, 0.8461538461538461, 0.8461538461538461, 0.8461538461538461, 0.8461538461538461, 0.8461538461538461, 0.8461538461538461] #, 0.8461538461538461, 0.8461538461538461]\n",
+        "top_nonbayesian = [0.0, 0.05128205128205128, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.1794871794871795, 0.23076923076923078, 0.28205128205128205, 0.3076923076923077, 0.46153846153846156, 0.6153846153846154, 0.6410256410256411, 0.717948717948718, 0.7692307692307693, 0.7692307692307693, 0.8205128205128205, 0.8461538461538461, 0.8461538461538461, 0.8461538461538461]\n",
+        "n = len(top_bayesian)\n",
+        "iterations = list(range(1, n + 1))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Line plot\n",
+        "plt.plot(iterations, top_bayesian, label= \"Bayesian+Recover\")\n",
+        "plt.plot(iterations, top_nonbayesian, label= \"Recover\")\n",
+        "\n",
+        "# Add axes\n",
+        "plt.axhline(0, color='black', linewidth=0.5)  # Add horizontal line at y=0\n",
+        "plt.axvline(0, color='black', linewidth=0.5)  # Add vertical line at x=0\n",
+        "\n",
+        "# Set labels and title\n",
+        "plt.xlabel('Itrations')\n",
+        "plt.ylabel('Top 1%')\n",
+        "plt.legend()\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "1OUczrGHsWan",
+        "outputId": "e10b4a07-99c9-486a-a903-e5d24f1b0ab8"
+      },
+      "id": "1OUczrGHsWan",
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.12"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file

From e7e15802a9d2580060d9863c24e34941fdab8219 Mon Sep 17 00:00:00 2001
From: Ema Duljkovic <82284665+EmaDulj@users.noreply.github.com>
Date: Fri, 15 Dec 2023 15:45:03 +0100
Subject: [PATCH 26/38] Delete experiments/b

---
 experiments/b | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 experiments/b

diff --git a/experiments/b b/experiments/b
deleted file mode 100644
index 8b13789..0000000
--- a/experiments/b
+++ /dev/null
@@ -1 +0,0 @@
-

From 4569d7c5cdba4a1da99e0a2fa7e7a72c29175bd3 Mon Sep 17 00:00:00 2001
From: Dhanushki Mapitigama <dhanumapitigama@gmail.com>
Date: Fri, 15 Dec 2023 17:13:14 +0100
Subject: [PATCH 27/38] Update requirements.txt

Added torchbnn to requirements
---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index ac3619b..03c4dca 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,3 +8,4 @@ umap-learn
 numpy==1.22.4
 pandas
 scipy
+torchbnn

From dddfac373b8beaadb2f037e444ab2a0d08193757 Mon Sep 17 00:00:00 2001
From: Dhanushki Mapitigama <dhanumapitigama@gmail.com>
Date: Fri, 15 Dec 2023 17:29:54 +0100
Subject: [PATCH 28/38] Update train.py

updated Active Learning Trainer
---
 recover/train.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/recover/train.py b/recover/train.py
index db25146..916444f 100644
--- a/recover/train.py
+++ b/recover/train.py
@@ -728,6 +728,7 @@ def setup(self, config):
         self.acquire_n_at_a_time = config["acquire_n_at_a_time"]
         self.acquisition = config["acquisition"](config)
         self.n_epoch_between_queries = config["n_epoch_between_queries"]
+        self.num_realizations = config["num_realizations"]
 
         # randomly acquire data at the beginning
         self.seen_idxs = self.train_idxs[:config["n_initial"]]
@@ -765,7 +766,7 @@ def step(self):
         unseen_metrics = {}
         realization_results, unseen_preds, drug_combs = self.eval_epoch(self.data, self.unseen_loader, self.model)
         unseen_result = dict([("unseen/" + k, [v]) for k, v in realization_results.items()])
-        for i in range(9):
+        for i in range(self.num_realizations - 1):
             realization_results, result_tensor, _ = self.eval_epoch(self.data, self.unseen_loader, self.model)
             unseen_preds = torch.cat((unseen_preds, result_tensor), dim=1)
             for k, v in realization_results.items():

From ff117dee85568da7e798a2b590e4771038f76c39 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Mon, 18 Dec 2023 00:35:14 +0530
Subject: [PATCH 29/38] Implementation of manual test for permuted drug
 combinations

---
 recover/train.py       | 55 ++++++++++++++++++++++++++++++++++++++++--
 recover/utils/utils.py | 12 +++++++++
 2 files changed, 65 insertions(+), 2 deletions(-)

diff --git a/recover/train.py b/recover/train.py
index 916444f..fefb402 100644
--- a/recover/train.py
+++ b/recover/train.py
@@ -2,7 +2,7 @@
 import torchbnn as bnn
 import os
 from torch.utils.data import DataLoader
-from recover.utils.utils import get_tensor_dataset, trial_dirname_creator
+from recover.utils.utils import get_tensor_dataset, get_tensor_dataset_swapped_combination, trial_dirname_creator
 from torch.utils.data import random_split
 from ray import tune
 import ray
@@ -335,6 +335,8 @@ def load_checkpoint(self, checkpoint_path):
 class BayesianBasicTrainer(tune.Trainable):
     def setup(self, config):
         print("Initializing regular training pipeline")
+        
+        self.test_invariance = True 
 
         self.batch_size = config["batch_size"]
         self.num_realizations = config["num_realizations"]
@@ -458,11 +460,18 @@ def step(self):
         metrics['all_space_explored'] = 0                        
 
 
+        """
+        Start the test on unseen dataset once the training is Done
+        P.S. The combinations are unseen, this is not related to the drugs
+        """
         if ((self.patience >= self.patience_stop) | (self.training_it > self.max_iter)):
+            print("Test performance")
             test_result = {}
+            
             realization_results, result_synergy, drug_combs = self.eval_epoch(self.data, self.test_loader, self.model)
             drug_combinations_synergy = {'data': self.test_idxs}
             test_metrics = dict([("test/" + k, [v]) for k, v in realization_results.items()])
+            
             for i in range(num_realizations-1):
                 realization_results, new_synergy, _ = self.eval_epoch(self.data, self.test_loader, self.model)
                 result_synergy = torch.cat((result_synergy, new_synergy), dim=1)
@@ -492,7 +501,47 @@ def step(self):
             metrics['synergy_mean'] = list(synergy_mean)
             metrics['synergy_std'] = list(synergy_std)
             
-        
+            """
+            # Start the test on permuted unseen dataset - To confirm the invariance
+            # Change the test_invariance boolean in Trainer setup to enable/disable this part of the code
+            """
+            if self.test_invariance:
+            
+                print("Test on swapped combinations")
+                
+                test_ddi_dataset_swapped = get_tensor_dataset_swapped_combination(self.data, self.test_idxs)
+                permuted_test_loader = DataLoader(
+                    test_ddi_dataset_swapped,
+                    batch_size=self.batch_size
+                )
+                
+                swapped_test_result = {}
+                swapped_test_metrics = {}
+                
+                realization_results, result_synergy, drug_combs = self.eval_epoch(self.data, permuted_test_loader, self.model)
+                drug_combinations_synergy = {'data': self.test_idxs}
+                swapped_test_metrics = dict([("test_swapped/" + k, [v]) for k, v in realization_results.items()])
+                
+                for i in range(num_realizations-1):
+                    realization_results, new_synergy, _ = self.eval_epoch(self.data, self.test_loader, self.model)
+                    result_synergy = torch.cat((result_synergy, new_synergy), dim=1)
+                    for k, v in realization_results.items():
+                        swapped_test_metrics["test_swapped/" + k].append(v)
+                        
+                for key in swapped_test_metrics:
+                    swapped_test_result[str(key)+ "/mean"] = np.mean(swapped_test_metrics[key])
+                    swapped_test_result[str(key)+ "/std"] = np.std(swapped_test_metrics[key])
+                    
+                
+                synergy_mean = list(torch.mean(result_synergy, dim=1).numpy())
+                synergy_std = list(torch.std(result_synergy, dim=1).numpy())
+                
+                metrics.update(dict(swapped_test_result))
+                
+                metrics['synergy_combs_permuted'] = list(drug_combs)
+                metrics['synergy_mean_permuted'] = list(synergy_mean)
+                metrics['synergy_std_permuted'] = list(synergy_std)
+            
         return metrics
     
     
@@ -763,9 +812,11 @@ def step(self):
         # Evaluate on valid set
         eval_metrics, _, _ = self.eval_epoch(self.data, self.valid_loader, self.model)
         
+        # Test on test set for given number of realizations
         unseen_metrics = {}
         realization_results, unseen_preds, drug_combs = self.eval_epoch(self.data, self.unseen_loader, self.model)
         unseen_result = dict([("unseen/" + k, [v]) for k, v in realization_results.items()])
+
         for i in range(self.num_realizations - 1):
             realization_results, result_tensor, _ = self.eval_epoch(self.data, self.unseen_loader, self.model)
             unseen_preds = torch.cat((unseen_preds, result_tensor), dim=1)
diff --git a/recover/utils/utils.py b/recover/utils/utils.py
index 2349956..4bf5c44 100644
--- a/recover/utils/utils.py
+++ b/recover/utils/utils.py
@@ -1,3 +1,4 @@
+import torch
 from pathlib import Path
 from rdkit import Chem
 from rdkit.Chem import AllChem
@@ -34,6 +35,17 @@ def get_tensor_dataset(data, idxs):
         data.ddi_edge_classes[idxs],
         data.ddi_edge_response[idxs],
     )
+    
+def get_tensor_dataset_swapped_combination(data, idxs):
+
+    combinations = data.ddi_edge_idx[:, idxs].T
+    reversed_tensor = torch.tensor(np.array([list(reversed(inner_list)) for inner_list in combinations]))
+
+    return TensorDataset(
+        reversed_tensor,
+        data.ddi_edge_classes[idxs],
+        data.ddi_edge_response[idxs],
+    )
 
 ########################################################################################################################
 # Ray Tune

From 8c2844053a935cd37d7de646dfd4aab0730f6a52 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Mon, 18 Dec 2023 00:37:19 +0530
Subject: [PATCH 30/38] Final sigmoid layer enabling added to config

---
 recover/models/predictors.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/recover/models/predictors.py b/recover/models/predictors.py
index 0e2fbdd..f0d69b4 100644
--- a/recover/models/predictors.py
+++ b/recover/models/predictors.py
@@ -137,6 +137,10 @@ def __init__(self, data, config, predictor_layers):
         
         self.bayesian_predictor = config["bayesian_predictor"]
         self.bayesian_before_merge = config["bayesian_before_merge"]
+        self.sigmoid = False
+        if self.bayesian_predictor:
+            self.sigmoid = config["sigmoid"]
+            
         self.merge_n_layers_before_the_end = config["merge_n_layers_before_the_end"]
         self.merge_dim = self.layer_dims[-self.merge_n_layers_before_the_end - 1]
 
@@ -250,10 +254,13 @@ def add_layer(self, layers, i, dim_i, dim_i_plus_1):
 
     def add_bayes_layer(self, layers, i, dim_i, dim_i_plus_1):
         layers.extend(self.bayes_linear_layer(i, dim_i, dim_i_plus_1))
+        
         if i != len(self.layer_dims) - 2:
             layers.append(ReLUModule())
-        else:
-            layers.append(ScaledSigmoid())
+            
+        if self.sigmoid:
+            if i == len(self.layer_dims) - 2:
+                layers.append(ScaledSigmoid())
 
         return layers
 
@@ -454,8 +461,13 @@ def linear_layer(self, i, dim_i, dim_i_plus_1):
         
     def add_bayes_layer(self, layers, i, dim_i, dim_i_plus_1):
         layers.extend(self.bayes_linear_layer(i, dim_i, dim_i_plus_1))
+        
         if i != len(self.layer_dims) - 2:
             layers.append(ReLUModule())
+            
+        if self.sigmoid:
+            if i == len(self.layer_dims) - 2:
+                layers.append(ScaledSigmoid())
 
         return layers
         

From 54c90d612041a02355345dddba303db2458e8324 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Mon, 18 Dec 2023 00:38:30 +0530
Subject: [PATCH 31/38] Config changes for sigmoid and other minor changes

---
 recover/config/active_learning_UCB.py       | 7 ++++---
 recover/config/bayesian_active_learning.py  | 7 ++++---
 recover/config/bayesian_model_evaluation.py | 3 ++-
 recover/config/model_evaluation.py          | 4 ++--
 4 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/recover/config/active_learning_UCB.py b/recover/config/active_learning_UCB.py
index 84f35ba..a7c57aa 100644
--- a/recover/config/active_learning_UCB.py
+++ b/recover/config/active_learning_UCB.py
@@ -1,9 +1,9 @@
 from recover.datasets.drugcomb_matrix_data import DrugCombMatrix
 from recover.models.models import Baseline, EnsembleModel
 from recover.models.predictors import BilinearFilmMLPPredictor, \
-    BilinearMLPPredictor, BilinearFilmWithFeatMLPPredictor, BilinearCellLineInputMLPPredictor
+    BilinearMLPPredictor, BilinearFilmWithFeatMLPPredictor
 from recover.utils.utils import get_project_root
-from recover.acquisition.acquisition import RandomAcquisition, GreedyAcquisition, UCB
+from recover.acquisition.acquisition import RandomAcquisition, GreedyAcquisition, UCB, ProbabilityOfImprovementAcquisition, ExpectedImprovementAcquisition
 from recover.train import train_epoch, eval_epoch, BasicTrainer, ActiveTrainer
 import os
 from ray import tune
@@ -16,7 +16,7 @@
 pipeline_config = {
     "use_tune": True,
     "num_epoch_without_tune": 500,  # Used only if "use_tune" == False
-    "seed": tune.grid_search([1, 2, 3]),
+    "seed": tune.grid_search([1]),
     # Optimizer config
     "lr": 1e-4,
     "weight_decay": 1e-2,
@@ -30,6 +30,7 @@
     "predictor": BilinearMLPPredictor,
     "bayesian_predictor": False,
     "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "sigmoid": False,
     "num_realizations": 0, # For bayesian uncertainty
     "predictor_layers":
         [
diff --git a/recover/config/bayesian_active_learning.py b/recover/config/bayesian_active_learning.py
index d8e949d..840330c 100644
--- a/recover/config/bayesian_active_learning.py
+++ b/recover/config/bayesian_active_learning.py
@@ -3,7 +3,7 @@
 from recover.models.predictors import BilinearFilmMLPPredictor, \
     BilinearMLPPredictor, BilinearFilmWithFeatMLPPredictor
 from recover.utils.utils import get_project_root
-from recover.acquisition.acquisition import RandomAcquisition, GreedyAcquisition, UCB, ProbabilityOfImprovementAcquisition
+from recover.acquisition.acquisition import RandomAcquisition, GreedyAcquisition, UCB, ProbabilityOfImprovementAcquisition, ExpectedImprovementAcquisition
 from recover.train import train_epoch, eval_epoch, bayesian_train_epoch, bayesian_eval_epoch, BasicTrainer, ActiveTrainer, ActiveTrainerBayesian
 import os
 from ray import tune
@@ -30,6 +30,7 @@
     "predictor": BilinearMLPPredictor,
     "bayesian_predictor": True,
     "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "sigmoid": False,
     "num_realizations": 10, # For bayesian uncertainty
     "predictor_layers":
         [
@@ -77,8 +78,8 @@
 }
 
 active_learning_config = {
-    "ensemble_size": 5,
-    "acquisition": tune.grid_search([ProbabilityOfImprovementAcquisition, UCB, RandomAcquisition]),
+    "ensemble_size": 10,
+    "acquisition": tune.grid_search([ProbabilityOfImprovementAcquisition, UCB, ExpectedImprovementAcquisition]),
     "patience_max": 4,
     "kappa": 1,
     "kappa_decrease_factor": 1,
diff --git a/recover/config/bayesian_model_evaluation.py b/recover/config/bayesian_model_evaluation.py
index 8f68aab..30df85f 100644
--- a/recover/config/bayesian_model_evaluation.py
+++ b/recover/config/bayesian_model_evaluation.py
@@ -15,7 +15,7 @@
 pipeline_config = {
     "use_tune": True,
     "num_epoch_without_tune": 500,  # Used only if "use_tune" == False
-    "seed": tune.grid_search([2, 3, 42]),
+    "seed": tune.grid_search([1, 2, 3, 42]),
     # Optimizer config
     "lr": 1e-4,
     "weight_decay": 1e-2,
@@ -29,6 +29,7 @@
     "predictor": BilinearMLPPredictor,
     "bayesian_predictor": True,
     "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
+    "sigmoid": True,
     "num_realizations": 10, # For bayesian uncertainty
     "predictor_layers":
         [
diff --git a/recover/config/model_evaluation.py b/recover/config/model_evaluation.py
index 207b133..9194494 100644
--- a/recover/config/model_evaluation.py
+++ b/recover/config/model_evaluation.py
@@ -29,7 +29,7 @@
     "predictor": BilinearMLPPredictor,
     "bayesian_predictor": False,
     "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
-    "num_realizations": 0, # For bayesian uncertainty
+    "num_realizations": 1, # For bayesian uncertainty
     "predictor_layers":
         [
             2048,
@@ -80,7 +80,7 @@
     "keep_checkpoints_num": 1,
     "checkpoint_at_end": False,
     "checkpoint_freq": 1,
-    "resources_per_trial": {"cpu": 8, "gpu": 0},
+    "resources_per_trial": {"cpu": 16, "gpu": 0},
     "scheduler": None,
     "search_alg": None,
 }
\ No newline at end of file

From 534c8f04f7892a862b800877fe530c299e58ed8f Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Mon, 18 Dec 2023 01:00:41 +0530
Subject: [PATCH 32/38] JSON result files added

---
 experiments/data/EI-result.json  | 134 +++++++++++++++++++++++++++++++
 experiments/data/PI-result.json  | 134 +++++++++++++++++++++++++++++++
 experiments/data/UCB-result.json | 134 +++++++++++++++++++++++++++++++
 3 files changed, 402 insertions(+)
 create mode 100644 experiments/data/EI-result.json
 create mode 100644 experiments/data/PI-result.json
 create mode 100644 experiments/data/UCB-result.json

diff --git a/experiments/data/EI-result.json b/experiments/data/EI-result.json
new file mode 100644
index 0000000..46aac03
--- /dev/null
+++ b/experiments/data/EI-result.json
@@ -0,0 +1,134 @@
+{"seen/loss_mean": 1000.53466796875, "seen/comb_r_squared": 0.024644905057545474, "eval/loss_mean": 1035.287338256836, "eval/comb_r_squared": 0.05187900868068022, "eval/spearman": -0.17845846963586445, "unseen/loss_mean/mean": 1019.05787525177, "unseen/loss_mean/std": 58.8624832887945, "unseen/comb_r_squared/mean": 0.06545184023920644, "unseen/comb_r_squared/std": 1.1346828437174435e-06, "unseen/spearman/mean": -0.18480057253181412, "unseen/spearman/std": 3.5328144486033265e-06, "seen_idxs": [[78, 35, 11, 36, 25, 17, 33, 37, 47, 50, 40, 47, 22, 48, 39, 93, 14, 28, 9, 28, 80, 39, 21, 23, 22, 40, 17, 50, 83, 9, 68, 54, 3, 73, 0, 23, 73, 27, 1, 32, 24, 70, 32, 1, 8, 13, 2, 50, 13, 63, 32, 13, 47, 20, 14, 73, 32, 51, 49, 47], [44, 19, 82, 88, 53, 68, 42, 55, 77, 27, 75, 85, 18, 45, 7, 38, 34, 8, 38, 79, 87, 49, 29, 78, 7, 84, 42, 3, 79, 4, 42, 68, 76, 22, 84, 47, 25, 28, 22, 84, 28, 22, 76, 77, 82, 31, 73, 38, 53, 11, 39, 3, 22, 27, 84, 29, 68, 63, 68, 94]], "seen_idxs_in_dataset": [1226, 2635, 4291, 1674, 2072, 983, 797, 2373, 4013, 3273, 3570, 3636, 413, 4006, 3828, 2149, 113, 1578, 1488, 2355, 2266, 2763, 2517, 261, 388, 3210, 1563, 2499, 3126, 833, 928, 864, 1823, 1955, 3220, 455, 252, 942, 1733, 3267, 504, 3007, 2699, 1729, 4285, 653, 645, 2404, 1444, 1257, 4169, 479, 2812, 2089, 1698, 1981, 524, 3258, 2669, 4377], "top": 0.07692307692307693, "query_pred_syn_mean": 30.311874389648438, "query_true_syn_mean": 15.727011680603027, "n_unique_drugs_in_query": 36, "query_pred_syn_quantile_0.0": 19.137351989746094, "query_true_syn_quantile_0.0": 0.0, "query_pred_syn_quantile_0.1": 25.08961181640625, "query_true_syn_quantile_0.1": 2.3810050964355476, "query_pred_syn_quantile_0.2": 26.914864349365235, "query_true_syn_quantile_0.2": 5.345914840698242, "query_pred_syn_quantile_0.30000000000000004": 28.319203186035157, "query_true_syn_quantile_0.30000000000000004": 7.015758419036866, "query_pred_syn_quantile_0.4": 29.735934829711915, "query_true_syn_quantile_0.4": 7.761604976654053, "query_pred_syn_quantile_0.5": 30.501070022583008, "query_true_syn_quantile_0.5": 8.93263053894043, "query_pred_syn_quantile_0.6000000000000001": 31.606356430053715, "query_true_syn_quantile_0.6000000000000001": 11.522210121154792, "query_pred_syn_quantile_0.7000000000000001": 32.45957298278809, "query_true_syn_quantile_0.7000000000000001": 19.88672466278076, "query_pred_syn_quantile_0.8": 33.070579528808594, "query_true_syn_quantile_0.8": 23.70470237731934, "query_pred_syn_quantile_0.9": 34.747482299804695, "query_true_syn_quantile_0.9": 32.28331871032717, "query_pred_syn_quantile_1.0": 41.67721939086914, "query_true_syn_quantile_1.0": 60.45245361328125, "med_immediate_regret": 81.99049377441406, "log10_med_immediate_regret": 1.9137635018880836, "min_immediate_regret": 30.904251098632812, "log10_min_immediate_regret": 1.490018223816146, "training_iteration": 0, "all_space_explored": 0, "done": false, "trial_id": "09089_00002", "date": "2023-12-15_19-10-16", "timestamp": 1702647616, "time_this_iter_s": 9.65357255935669, "time_total_s": 9.65357255935669, "pid": 518178, "hostname": "ampere", "node_ip": "10.40.18.10", "config": {"use_tune": true, "num_epoch_without_tune": 500, "seed": 1, "lr": 0.0001, "weight_decay": 0.01, "batch_size": 128, "train_epoch": "<function bayesian_train_epoch at 0x7f374a160670>", "eval_epoch": "<function bayesian_eval_epoch at 0x7f374a160790>", "predictor": "<class 'recover.models.predictors.BilinearMLPPredictor'>", "bayesian_predictor": true, "bayesian_before_merge": false, "num_realizations": 10, "predictor_layers": [2048, 128, 64, 1], "merge_n_layers_before_the_end": 2, "allow_neg_eigval": true, "stop": {"training_iteration": 1000, "patience": 4}, "model": "<class 'recover.models.models.Baseline'>", "load_model_weights": false, "model_weights_file": "", "dataset": "<class 'recover.datasets.drugcomb_matrix_data.DrugCombMatrix'>", "study_name": "ALMANAC", "in_house_data": "without", "rounds_to_include": [], "cell_line": "MCF7", "val_set_prop": 0.1, "test_set_prop": 0.0, "test_on_unseen_cell_line": false, "split_valid_train": "pair_level", "cell_lines_in_test": null, "target": "bliss_max", "fp_bits": 1024, "fp_radius": 2, "ensemble_size": 10, "acquisition": "<class 'recover.acquisition.acquisition.ExpectedImprovementAcquisition'>", "patience_max": 4, "kappa": 1, "kappa_decrease_factor": 1, "n_epoch_between_queries": 500, "acquire_n_at_a_time": 30, "n_initial": 30}, "time_since_restore": 9.65357255935669, "iterations_since_restore": 1}
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diff --git a/experiments/data/PI-result.json b/experiments/data/PI-result.json
new file mode 100644
index 0000000..4553a62
--- /dev/null
+++ b/experiments/data/PI-result.json
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diff --git a/experiments/data/UCB-result.json b/experiments/data/UCB-result.json
new file mode 100644
index 0000000..d596339
--- /dev/null
+++ b/experiments/data/UCB-result.json
@@ -0,0 +1,134 @@
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bayesian_eval_epoch at 0x7f374a160790>", "predictor": "<class 'recover.models.predictors.BilinearMLPPredictor'>", "bayesian_predictor": true, "bayesian_before_merge": false, "num_realizations": 10, "predictor_layers": [2048, 128, 64, 1], "merge_n_layers_before_the_end": 2, "allow_neg_eigval": true, "stop": {"training_iteration": 1000, "patience": 4}, "model": "<class 'recover.models.models.Baseline'>", "load_model_weights": false, "model_weights_file": "", "dataset": "<class 'recover.datasets.drugcomb_matrix_data.DrugCombMatrix'>", "study_name": "ALMANAC", "in_house_data": "without", "rounds_to_include": [], "cell_line": "MCF7", "val_set_prop": 0.1, "test_set_prop": 0.0, "test_on_unseen_cell_line": false, "split_valid_train": "pair_level", "cell_lines_in_test": null, "target": "bliss_max", "fp_bits": 1024, "fp_radius": 2, "ensemble_size": 10, "acquisition": "<class 'recover.acquisition.acquisition.UCB'>", "patience_max": 4, "kappa": 1, "kappa_decrease_factor": 1, "n_epoch_between_queries": 500, "acquire_n_at_a_time": 30, "n_initial": 30}, "time_since_restore": 2395.3466217517853, "iterations_since_restore": 133}
+{"all_space_explored": 1, "training_iteration": 133, "done": true, "trial_id": "09089_00001", "date": "2023-12-15_19-09-42", "timestamp": 1702647582, "time_this_iter_s": 6.604194641113281e-05, "time_total_s": 2395.3466877937317, "pid": 3957248, "hostname": "ampere", "node_ip": "10.40.18.10", "config": {"use_tune": true, "num_epoch_without_tune": 500, "seed": 1, "lr": 0.0001, "weight_decay": 0.01, "batch_size": 128, "train_epoch": "<function bayesian_train_epoch at 0x7f374a160670>", "eval_epoch": "<function bayesian_eval_epoch at 0x7f374a160790>", "predictor": "<class 'recover.models.predictors.BilinearMLPPredictor'>", "bayesian_predictor": true, "bayesian_before_merge": false, "num_realizations": 10, "predictor_layers": [2048, 128, 64, 1], "merge_n_layers_before_the_end": 2, "allow_neg_eigval": true, "stop": {"training_iteration": 1000, "patience": 4}, "model": "<class 'recover.models.models.Baseline'>", "load_model_weights": false, "model_weights_file": "", "dataset": "<class 'recover.datasets.drugcomb_matrix_data.DrugCombMatrix'>", "study_name": "ALMANAC", "in_house_data": "without", "rounds_to_include": [], "cell_line": "MCF7", "val_set_prop": 0.1, "test_set_prop": 0.0, "test_on_unseen_cell_line": false, "split_valid_train": "pair_level", "cell_lines_in_test": null, "target": "bliss_max", "fp_bits": 1024, "fp_radius": 2, "ensemble_size": 10, "acquisition": "<class 'recover.acquisition.acquisition.UCB'>", "patience_max": 4, "kappa": 1, "kappa_decrease_factor": 1, "n_epoch_between_queries": 500, "acquire_n_at_a_time": 30, "n_initial": 30}, "time_since_restore": 2395.3466877937317, "iterations_since_restore": 134}

From 398e6eac0beceb894ceeffe6f10a794821169c6b Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Mon, 18 Dec 2023 01:01:41 +0530
Subject: [PATCH 33/38] Result plots and notebook added

---
 experiments/result-plots-acquisitions.ipynb | 130 ++++++++++++++++++++
 1 file changed, 130 insertions(+)
 create mode 100644 experiments/result-plots-acquisitions.ipynb

diff --git a/experiments/result-plots-acquisitions.ipynb b/experiments/result-plots-acquisitions.ipynb
new file mode 100644
index 0000000..fac4722
--- /dev/null
+++ b/experiments/result-plots-acquisitions.ipynb
@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a69db1b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bf047c67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read the JSON file\n",
+    "file_path = 'data/EI-result.json'\n",
+    "with open(file_path, 'r') as file:\n",
+    "    json_data = [json.loads(line) for line in file]\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "df_EI = pd.DataFrame(json_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ffe135c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_path = 'data/PI-result.json'\n",
+    "with open(file_path, 'r') as file:\n",
+    "    json_data = [json.loads(line) for line in file]\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "df_PI = pd.DataFrame(json_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b75f95a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_path = 'data/UCB-result.json'\n",
+    "with open(file_path, 'r') as file:\n",
+    "    json_data = [json.loads(line) for line in file]\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "df_UCB = pd.DataFrame(json_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8cd17703",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = len(df_EI[\"top\"])\n",
+    "iterations = list(range(1, n + 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ca12c923",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Line plot\n",
+    "plt.plot(iterations, df_EI[\"top\"], label= \"Expected Improvement\")\n",
+    "plt.plot(iterations, df_PI[\"top\"], label= \"Probability Improvement\")\n",
+    "plt.plot(iterations, df_UCB[\"top\"], label= \"UCB Aquisition\")\n",
+    "\n",
+    "\n",
+    "# Set labels and title\n",
+    "plt.xlabel('Itrations')\n",
+    "plt.ylabel('Top 1%')\n",
+    "plt.title(\"Performance for different aquisition functions with Bayesian Active Learning\")\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From a87eb0a21842b4fb24feb7c6ceba87d86509d6f3 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Mon, 18 Dec 2023 05:32:49 +0530
Subject: [PATCH 34/38] Changed the bayesian trainer to add noise as defined

---
 recover/train.py | 22 +++++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/recover/train.py b/recover/train.py
index fefb402..24ba4d2 100644
--- a/recover/train.py
+++ b/recover/train.py
@@ -3,6 +3,7 @@
 import os
 from torch.utils.data import DataLoader
 from recover.utils.utils import get_tensor_dataset, get_tensor_dataset_swapped_combination, trial_dirname_creator
+from recover.utils.utils import add_gaussian_noise, add_random_noise, add_salt_and_pepper_noise
 from torch.utils.data import random_split
 from ray import tune
 import ray
@@ -343,6 +344,16 @@ def setup(self, config):
         device_type = "cuda" if torch.cuda.is_available() else "cpu"
         self.device = torch.device(device_type)
         self.training_it = 0
+        self.add_noise = False
+        
+        try:
+            self.add_noise = config["add_noise"]
+            self.noise_type = config["noise_type"]
+            self.noise_prop = config["noise_prop"]
+        except Exception as e:
+            self.noise_type = None
+            self.noise_prop = 0.1
+        
 
         # Initialize dataset
         dataset = config["dataset"](
@@ -376,6 +387,15 @@ def setup(self, config):
 
         # Train loader
         train_ddi_dataset = get_tensor_dataset(self.data, self.train_idxs)
+        
+        if self.add_noise:
+            if self.noise_type == 'gaussian':
+                train_ddi_dataset = add_gaussian_noise(self.data, self.train_idxs, self.noise_prop)
+            elif self.noise_type == 'salt_pepper':
+                train_ddi_dataset = add_salt_and_pepper_noise(self.data, self.train_idxs, self.noise_prop)
+            else :
+                train_ddi_dataset = add_random_noise(self.data, self.train_idxs, self.noise_prop)
+            
 
         self.train_loader = DataLoader(
             train_ddi_dataset,
@@ -816,7 +836,7 @@ def step(self):
         unseen_metrics = {}
         realization_results, unseen_preds, drug_combs = self.eval_epoch(self.data, self.unseen_loader, self.model)
         unseen_result = dict([("unseen/" + k, [v]) for k, v in realization_results.items()])
-
+        
         for i in range(self.num_realizations - 1):
             realization_results, result_tensor, _ = self.eval_epoch(self.data, self.unseen_loader, self.model)
             unseen_preds = torch.cat((unseen_preds, result_tensor), dim=1)

From a08e9104454b854c3874648351efcb27f7e8c037 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Mon, 18 Dec 2023 05:32:09 +0530
Subject: [PATCH 35/38] Config files changed

---
 recover/config/bayesian_active_learning.py  | 4 +++-
 recover/config/bayesian_model_evaluation.py | 7 +++++--
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/recover/config/bayesian_active_learning.py b/recover/config/bayesian_active_learning.py
index 840330c..7bc032c 100644
--- a/recover/config/bayesian_active_learning.py
+++ b/recover/config/bayesian_active_learning.py
@@ -74,7 +74,9 @@
     "cell_lines_in_test": None,  # ['MCF7', 'PC-3'],
     "target": "bliss_max",
     "fp_bits": 1024,
-    "fp_radius": 2
+    "fp_radius": 2,
+    "add_noise": False,
+    "noise_type": None # 'gaussian', 'salt_pepper', 'random'
 }
 
 active_learning_config = {
diff --git a/recover/config/bayesian_model_evaluation.py b/recover/config/bayesian_model_evaluation.py
index 30df85f..1df6347 100644
--- a/recover/config/bayesian_model_evaluation.py
+++ b/recover/config/bayesian_model_evaluation.py
@@ -29,7 +29,7 @@
     "predictor": BilinearMLPPredictor,
     "bayesian_predictor": True,
     "bayesian_before_merge": False, # For bayesian predictor implementation - Layers after merge are bayesian by default
-    "sigmoid": True,
+    "sigmoid": False,
     "num_realizations": 10, # For bayesian uncertainty
     "predictor_layers":
         [
@@ -60,7 +60,10 @@
     "cell_line": 'MCF7',  # 'PC-3',
     "target": "bliss_max",  # tune.grid_search(["css", "bliss", "zip", "loewe", "hsa"]),
     "fp_bits": 1024,
-    "fp_radius": 2
+    "fp_radius": 2,
+    "add_noise": True,
+    "noise_type": 'salt_pepper', # 'gaussian', 'salt_pepper', 'random'
+    "noise_prop": 0.1,
 }
 
 ########################################################################################################################

From 3bfd12a6349d652e717429a482795470d1519121 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Mon, 18 Dec 2023 05:31:32 +0530
Subject: [PATCH 36/38] Noise functions added

---
 recover/utils/utils.py | 73 +++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 72 insertions(+), 1 deletion(-)

diff --git a/recover/utils/utils.py b/recover/utils/utils.py
index 4bf5c44..28f3322 100644
--- a/recover/utils/utils.py
+++ b/recover/utils/utils.py
@@ -5,6 +5,7 @@
 import numpy as np
 from torch.utils.data import TensorDataset
 
+np.random.seed(42)
 
 def get_project_root():
     return Path(__file__).parent.parent.parent
@@ -35,7 +36,11 @@ def get_tensor_dataset(data, idxs):
         data.ddi_edge_classes[idxs],
         data.ddi_edge_response[idxs],
     )
-    
+
+
+"""
+Function to get the dataset with the permutation of the combination
+"""
 def get_tensor_dataset_swapped_combination(data, idxs):
 
     combinations = data.ddi_edge_idx[:, idxs].T
@@ -47,6 +52,72 @@ def get_tensor_dataset_swapped_combination(data, idxs):
         data.ddi_edge_response[idxs],
     )
 
+"""    
+Add Gaussian noise to a given percentage of data
+Inputs: data, idxs, Noice percentage and std for noice
+"""
+def add_gaussian_noise(data, idxs, percentage, std_dev=0.1):
+
+    print("Added gaussian noise to ", percentage*100, " percent of training data with ", std_dev, " std.")
+    
+    target = data.ddi_edge_response[idxs]
+    num_samples = len(target)
+    num_noisy_samples = int(percentage * num_samples)
+    noisy_indices = np.random.choice(num_samples, num_noisy_samples, replace=False)
+    noise = np.random.normal(0, std_dev, size=num_noisy_samples).astype(np.float32)
+    target[noisy_indices] += noise
+    
+    return TensorDataset(
+        data.ddi_edge_idx[:, idxs].T,
+        data.ddi_edge_classes[idxs],
+        torch.tensor(target),
+    )
+
+"""
+Add Salt and Pepper noise to a given percentage of data
+Inputs: data, idxs, Noice percentage and probabilities
+"""
+def add_salt_and_pepper_noise(data, idxs, percentage, salt_prob=0.01, pepper_prob=0.1):
+
+    print("Added salt pepper noise to ", percentage*100, " percent of training data.")
+    
+    target = data.ddi_edge_response[idxs]
+    num_samples = len(target)
+    num_noisy_samples = int(percentage * num_samples)
+    noisy_indices = np.random.choice(num_samples, num_noisy_samples, replace=False)
+    salt_indices = np.random.choice(noisy_indices, int(salt_prob * num_noisy_samples), replace=False)
+    pepper_indices = np.random.choice(noisy_indices, int(pepper_prob * num_noisy_samples), replace=False)
+
+    target[salt_indices] = 100.0
+    target[pepper_indices] = 0.0
+
+    return TensorDataset(
+        data.ddi_edge_idx[:, idxs].T,
+        data.ddi_edge_classes[idxs],
+        torch.tensor(target),
+    )
+
+"""
+Add random noise to a given percentage of data
+Inputs: data, idxs, Noice percentage and Factor at which noice should be introduced
+"""
+def add_random_noise(data, idxs, percentage, noise_factor=0.1):
+
+    print("Added random noise to ", percentage*100, " percent of training data.")
+
+    target = data.ddi_edge_response[idxs]
+    num_samples = len(target)
+    num_noisy_samples = int(percentage * num_samples)
+    noisy_indices = np.random.choice(num_samples, num_noisy_samples, replace=False)
+    noise = np.random.uniform(-noise_factor, noise_factor, size=num_noisy_samples).astype(np.float32)
+    target[noisy_indices] += noise
+    
+    return TensorDataset(
+        data.ddi_edge_idx[:, idxs].T,
+        data.ddi_edge_classes[idxs],
+        torch.tensor(target),
+    )
+
 ########################################################################################################################
 # Ray Tune
 ########################################################################################################################

From f0d8a91467279e938ac47b447a8dda99e5c87eb0 Mon Sep 17 00:00:00 2001
From: Pavithya M B D <dhanumapitigama@gmail.com>
Date: Mon, 18 Dec 2023 05:53:15 +0530
Subject: [PATCH 37/38] Job script for parallel execution on cluster

---
 cluster_job.sh | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 cluster_job.sh

diff --git a/cluster_job.sh b/cluster_job.sh
new file mode 100644
index 0000000..badb517
--- /dev/null
+++ b/cluster_job.sh
@@ -0,0 +1,76 @@
+#!/bin/bash -l
+# shellcheck disable=SC2206
+
+#SBATCH -A ADD_THE_PROJECT_NAME_HERE # project name 
+#SBATCH -M ADD_THE_SYSTEM_NAME_HERE # name of system i.e. snowy, dardel
+
+#SBATCH -p node # request a full node 
+#SBATCH -N 2  # Number of nodes  Change --num-gpus "2" in head command and worker loop as well
+#SBATCH -t 0:15:00 # change time accordingly
+#SBATCH --gpus-per-node=2  # change gpus accordingly
+#SBATCH -J exp-seed-3 # name of the job 
+#SBATCH -D ./ # stay in current working directory 
+
+
+source ~/.bashrc
+conda activate recover
+
+set -x
+
+# __doc_head_address_start__
+
+# Getting the node names
+nodes=$(scontrol show hostnames "$SLURM_JOB_NODELIST")
+nodes_array=($nodes)
+
+head_node=${nodes_array[0]}
+head_node_ip=$(srun --nodes=1 --ntasks=1 -w "$head_node" hostname --ip-address)
+
+# if we detect a space character in the head node IP, we'll
+# convert it to an ipv4 address. This step is optional.
+if [[ "$head_node_ip" == *" "* ]]; then
+IFS=' ' read -ra ADDR <<<"$head_node_ip"
+if [[ ${#ADDR[0]} -gt 16 ]]; then
+  head_node_ip=${ADDR[1]}
+else
+  head_node_ip=${ADDR[0]}
+fi
+echo "IPV6 address detected. We split the IPV4 address as $head_node_ip"
+fi
+# __doc_head_address_end__
+
+
+# __doc_head_ray_start__
+port=6379
+ip_head=$head_node_ip:$port
+export ip_head
+echo "IP Head: $ip_head"
+
+echo "Starting HEAD at $head_node"
+srun --nodes=1 --ntasks=1 -w "$head_node" \
+    ray start --head --node-ip-address="$head_node_ip" --port=$port \
+    --num-cpus "16" --num-gpus "2" --block &
+# __doc_head_ray_end__
+
+
+# __doc_worker_ray_start__
+# optional, though may be useful in certain versions of Ray < 1.0.
+sleep 10
+
+# number of nodes other than the head node
+worker_num=$((SLURM_JOB_NUM_NODES - 1))
+
+for ((i = 1; i <= worker_num; i++)); do
+    node_i=${nodes_array[$i]}
+    echo "Starting WORKER $i at $node_i"
+    srun --nodes=1 --ntasks=1 -w "$node_i" \
+        ray start --address "$ip_head" \
+        --num-cpus "16" --num-gpus "2" --block &
+    sleep 5
+done
+# __doc_worker_ray_end__
+
+
+# __doc_script_start__
+#Active learning with Upper Confidence Bound Aquisition 
+python train.py --config active_learning_UCB

From 68feca6a465e5265254b52f37b204d3300ec8fd4 Mon Sep 17 00:00:00 2001
From: Dhanushki Mapitigama <dhanumapitigama@gmail.com>
Date: Mon, 18 Dec 2023 01:53:29 +0100
Subject: [PATCH 38/38] Update README.md

Added instructions on setting up recover and avoiding potential issues
---
 README.md | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 8367df6..5c2d03f 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,16 @@ Every branch will be further explaned in its own README.
 ## Environment setup
 
 **Requirements and Installation**: 
-For all the requirements and installation steps check th orginal RECOVER repository (https://github.com/RECOVERcoalition/Recover.git). 
+For all the requirements and installation steps dataset and the project check the orginal RECOVER repository (https://github.com/RECOVERcoalition/Recover.git). 
+
+**Potential issues and fixes**
+- Use the requirements file from this Repository
+- Cloning dataset: Makesure to install Git LFS (Since there are some files over 100 MB git-lfs is needed for cloning.)
+- Numpy 1.24 does not support - Even though there was no mention about the numpy version in original RECOVER requirements there are some compatibility issues when using other libraries therefore we have to specifically downgrade numpy to make it working without any issues. Try, pip install numpy==1.22.4
+- Ray tune issue: Use the updated train.py within this repo to avoid ray tune path issues when saving checkpoints
+- Issues with the config files: Check for such unused modules which are imported and remove them to avoid module not fount errors. 
+- rdkit installation path in original Repository is outdated. Try, conda install -c conda-forge rdkit 
+
 
 ## Running the pipeline