add instructions for jet clustering

README.md

@@ -13,3 +13,92 @@ git clone https://github.com/xju2/root_gnn.git
 cd root_gnn
 pip install -e .
 ```
+
+## Input data
+Save data in the folder `data`.
+
+## Commands for the edge classifier
+* Graph Construction
+```bash
+create_tfrecord data/wboson_big.txt tfRec/fully_connected --max-evts 200 --evts-per-record 200 --type WTaggerDataset
+```
+The graphs are saved into the folder `tfRec`. Then create another folder `tfRec_val` and move some TFRecord files to the folder `tfRec_val` for validation purpose.
+Then modify `tfrec_dir_train` and `tfrec_dir_val` in the `configs/train_wtaggers_edges.yaml` so that they point to the training and validation data. 
+Modify `output_dir` so it points to a output directory.
+* Graph Training
+```bash
+train_classifier configs/train_wtaggers_edges.yaml
+```
+
+create a `data/wboson_small.txt` file from the `wboson.txt` using the events that are not used in training.
+* Evaluation
+```bash
+evaluate_wtagger data/wboson_small.txt configs/train_wtaggers_edges.yaml test --nevts 10
+```
+
+* Metrics calculation
+```bash
+calculate_wtagger_metrics test.npz test
+```
+
+## Commands for event classifier
+Traing two event classifiers with different inputs, one from the edge classifier and the other from the anti-$k_t$ algorithm.
+
+### event classifier using outputs from the edge classifier
+* Graph Construction for W boson events
+```bash
+create_tfrecord "tfRec/fully_connected*.tfrec" tfRec_filtered/wboson \
+    --type WTaggerFilteredDataset \
+    --signal --model-config configs/train_wtaggers_edges.yaml \
+    --max-evts 100 --evts-per-record 100
+```
+
+* Graph Construction for q* events
+```bash
+create_tfrecord data/qstar.txt tfrec_qcd/qcd --type WTaggerDataset --max-evts 100 --evts-per-record 100
+```
+```bash
+create_tfrecord "tfrec_qcd/qcd*.tfrec" \
+	tfRec_filtered/qcd \
+	--type WTaggerFilteredDataset  \
+    --model-config configs/train_wtaggers_edges.yaml \
+	--max-evts 100 --evts-per-record 100
+```
+Create `tfRec_filtered_val` and put events there for validation.
+Change the `tfrec_dir_train`, `tfrec_dir_val` and `output_dir` in `configs/train_w_qcd.yaml` accordingly.
+* Graph training
+```bash
+train_classifier configs/train_w_qcd.yaml
+```
+
+### event classifier using outputs from the anti-$k_t$ algorithm
+* Graph construction for W boson events
+```bash
+create_tfrecord data/wboson.txt \
+	tfRec_ljet/wboson \
+	--type WTaggerLeadingJetDataset \
+	--signal \
+	--max-evts 95000 --evts-per-record 1000
+```
+
+* Graphc osntruction for q* events
+```bash
+create_tfrecord data/qstar.txt \
+	tfRec_ljet/qcd \
+	--type WTaggerLeadingJetDataset \
+	--max-evts 95000 --evts-per-record 1000
+```
+Create `tfRec_ljet_val` and put events there for validation.
+* Graph training
+Again, change the `tfrec_dir_train`, `tfrec_dir_val` and `output_dir` in `configs/train_w_qcd_ljet.yaml` accordingly.
+```bash
+train_classifier configs/train_w_qcd_ljet.yaml
+```
+
+### Evaluate both GNNs
+```bash
+evaluate_w_qcd_classifier "tfRec_filtered_val/*.tfrec" configs/train_w_qcd.yaml classifier_gnn
+```
+```bash
+evaluate_w_qcd_classifier "tfRec_ljet_val/*.tfrec" configs/train_w_qcd_ljet.yaml classifier_ljet
+```