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Update README.md

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  1. README.md +34 -9
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@@ -1,9 +1,6 @@
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  ---
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  language:
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  - zxx
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- ---
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- language:
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- - zxx
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  license: cc-by-4.0
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  tags:
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  - chemistry
@@ -19,6 +16,12 @@ source_datasets:
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  task_categories:
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  - tabular-classification
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  configs:
 
 
 
 
 
 
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  - config_name: csv
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  data_files:
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  - split: train
@@ -41,7 +44,7 @@ dataset_info:
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  dtype: float32
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  - name: has_cl
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  dtype: int8
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- config_name: csv
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  ---
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  # Dataset Summary
@@ -55,6 +58,25 @@ Binary classification of chlorine presence using simulated MS1 isotopic patterns
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  - Splits: 80% train (618,272), 20% test (154,568).
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  - Files: 968442_non_cl_filter_S10.rds, 386420_cl_data.rds, 80%_618272_train_binary.rds, 20%_154568_test_binary.rds.
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  ## Features
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  - mz0: m/z of M
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  - int2_o_int0: intensity ratio M+2/M
@@ -68,11 +90,13 @@ Binary classification of chlorine presence using simulated MS1 isotopic patterns
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  ```python
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  from datasets import load_dataset
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- # If you convert to CSV (recommended for Hub viewers)
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  ds = load_dataset(
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- "chen1028/Cl-Containing-Compound",
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- name="csv",
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- data_files={"train":"train.csv","test":"test.csv"}
 
 
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  )
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  ```
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@@ -88,4 +112,5 @@ ds = load_dataset(
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  year = {2024},
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  doi = {10.1021/acs.analchem.3c05124},
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  note = {PMID: 38294426},
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- url = {https://doi.org/10.1021/acs.analchem.3c05124},
 
 
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  ---
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  language:
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  - zxx
 
 
 
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  license: cc-by-4.0
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  tags:
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  - chemistry
 
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  task_categories:
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  - tabular-classification
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  configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: 80%_618272_train_binary.rds
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+ - split: test
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+ path: 20%_154568_test_binary.rds
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  - config_name: csv
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  data_files:
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  - split: train
 
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  dtype: float32
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  - name: has_cl
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  dtype: int8
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+ config_name: default
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  ---
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  # Dataset Summary
 
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  - Splits: 80% train (618,272), 20% test (154,568).
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  - Files: 968442_non_cl_filter_S10.rds, 386420_cl_data.rds, 80%_618272_train_binary.rds, 20%_154568_test_binary.rds.
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+ ## How train.csv and test.csv were created
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+ - Source splits come from the RDS files above: 80%_618272_train_binary.rds (train) and 20%_154568_test_binary.rds (test).
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+ - Conversion was done locally using Python with pyreadr and pandas (see `data/converter.ipynb`).
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+ - Steps:
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+ 1. Read each .rds table using pyreadr.read_r(...)
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+ 2. Optionally cast numeric columns to float32/int32 for compact CSVs
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+ 3. Save to CSV with index=False as `train.csv` and `test.csv`
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+
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+ Example code used:
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+ ```python
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+ import pyreadr, pandas as pd
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+
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+ train_df = next(iter(pyreadr.read_r("80%_618272_train_binary.rds").values()))
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+ test_df = next(iter(pyreadr.read_r("20%_154568_test_binary.rds").values()))
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+
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+ train_df.to_csv("train.csv", index=False)
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+ test_df.to_csv("test.csv", index=False)
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+ ```
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+
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  ## Features
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  - mz0: m/z of M
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  - int2_o_int0: intensity ratio M+2/M
 
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  ```python
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  from datasets import load_dataset
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+ # Load CSVs from the Hub using the CSV builder
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  ds = load_dataset(
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+ "csv",
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+ data_files={
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+ "train": "hf://datasets/chen1028/Cl-Containing-Compound/train.csv",
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+ "test": "hf://datasets/chen1028/Cl-Containing-Compound/test.csv",
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+ }
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  )
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  ```
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  year = {2024},
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  doi = {10.1021/acs.analchem.3c05124},
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  note = {PMID: 38294426},
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+ url = {https://doi.org/10.1021/acs.analchem.3c05124},
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+ ```