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+# Introduction to Datasets
+
+Presolved Data is stored in `.\instance`. The folder structure after the datasets are set up looks as follows
+
+```bash
+instances/
+  MIPLIB/                   -> 1065 instances
+  set_cover/                -> 3994 instances
+  independent_set/          -> 1604 instances
+  nn_verification/          -> 3104 instances
+  load_balancing/           -> 2286 instances
+```
+
+### Dataset Description
+
+#### MIPLIB
+
+Heterogeneous dataset from [MIPLIB 2017](https://miplib.zib.de/), a well-established benchmark for evaluating MILP solvers. The dataset includes a diverse set of particularly challenging mixed-integer programming (MIP) instances, each known for its computational difficulty. 
+
+#### Set Covering
+
+This dataset consists of instances of the classic Set Covering Problem, which can be found [here](https://github.com/ds4dm/learn2branch/tree/master). Each instance requires finding the minimum number of sets that cover all elements in a universe. The problem is formulated as a MIP problem. 
+
+#### Maximum Independent Set
+
+This dataset addresses the Maximum Independent Set Problem, which can be found [here](https://github.com/ds4dm/learn2branch/tree/master). Each instance is modeled as a MIP, with the objective of maximizing the size of the independent set. 
+
+#### NN Verification
+
+This “Neural Network Verification” dataset is to verify whether a neural network is robust to input perturbations can be posed as a MIP. The MIP formulation is described in the paper [On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models (Gowal et al., 2018)](https://arxiv.org/abs/1810.12715). Each input on which to verify the network gives rise to a different MIP. 
+
+#### Load Balancing
+
+This dataset is from [NeurIPS 2021 Competition](https://github.com/ds4dm/ml4co-competition). This problem deals with apportioning workloads. The apportionment is required to be robust to any worker’s failure. Each instance problem is modeled as a MILP, using a bin-packing with an apportionment formulation.
+
+### Dataset Spliting
+
+Each dataset was split into a training set  $D_{\text{train}}$ and a testing set $D_{\text{test}}$, following an approximate 80-20 split. Moreover, we split the dataset by time and "optimality", which means according to the proportion of optimality for each parameter is similar in training and testing sets. This ensures a balanced representation of both temporal variations and the highest levels of parameter efficiency in our data partitions.
+
+To split the datasets and create different folds for cross validation, run
+
+```bash
+   python extract_feature/split_fold.py \
+       --dataset_name "your_dataset_name" \
+       --time_path "/your/path/to/soving times" \
+       --feat_path "/your/path/to/features" \
+   ``` 
+
+## Handcraft Feature Extraction
+
+**Folder:** ```extract_feature```
+
+**Problem format:** ```your_file.mps.gz``` or ```your_file.lp```
+
+**Log format:** ```your_file.log```
+
+1. Static feature extraction for MIP problems: run
+
+   ```bash
+   python extract_feature/extract_problem.py \
+       --problem_folder "/your/path/to/instances" \
+       --dataset_name "your_dataset_name" \
+   ```
+
+2. Extract other features from COPT solution log: run
+
+   ```bash
+   python extract_feature/extract_log_feature.py \
+       --log_folder "/your/path/to/solving logs" \
+       --dataset_name "your_dataset_name" \
+   ```
+
+3. Feature combination and preprocessing: run
+
+   ```bash
+   python extract_feature/combine.py \
+       --dataset_name "your_dataset_name" \
+   ```
+4. Label extraction from COPT solution log: run
+
+   ```bash
+   python extract_feature/extract_time.py \
+       --log_folder "/your/path/to/solving logs" \
+       --dataset_name "your_dataset_name" \