Update README.md

README.md

@@ -176,6 +176,53 @@ Data from:
 
 > Dulce Rubio et al. "[RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization](https://arxiv.org/abs/2311.03115)." *ACM Journal on Computing and Sustainable Societies* 2.2 (2024): 1–29.
 
+## Usage
+
+### Loading with 🤗 Datasets
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("cmpatino/landmine-detection")
+df = ds["train"].to_pandas()
+
+# Features and target
+X = df.drop(columns=["LATITUD_Y", "LONGITUD_X", "Municipio", "mines_outcome"])
+y = df["mines_outcome"]
+
+print(f"Samples: {len(df)}, Features: {X.shape[1]}, Positive rate: {y.mean():.3%}")
+# Samples: 26761, Features: 63, Positive rate: 1.607%
+```
+
+### Loading directly with pandas
+
+```python
+import pandas as pd
+
+df = pd.read_parquet(
+    "hf://datasets/cmpatino/landmine-detection/data/train-00000-of-00001.parquet"
+)
+
+# Quick overview
+print(df.shape)            # (26761, 67)
+print(df.dtypes.value_counts())
+# float64    30
+# int64      37
+
+# Target distribution
+print(df["mines_outcome"].value_counts())
+# 0    26331
+# 1      430
+
+# Contamination rate per municipality
+print(
+    df.groupby("Municipio")["mines_outcome"]
+    .mean()
+    .sort_values(ascending=False)
+    .head()
+)
+```
+
 ## Dataset Summary
 
 | Property | Value |
@@ -302,53 +349,6 @@ These are binary (0/1) indicator columns derived from three categorical variable
 | La Unión | 678 | 2.5% |
 | Alejandría | 519 | 1.9% |
 
-## Usage
-
-### Loading with 🤗 Datasets
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("cmpatino/landmine-detection")
-df = ds["train"].to_pandas()
-
-# Features and target
-X = df.drop(columns=["LATITUD_Y", "LONGITUD_X", "Municipio", "mines_outcome"])
-y = df["mines_outcome"]
-
-print(f"Samples: {len(df)}, Features: {X.shape[1]}, Positive rate: {y.mean():.3%}")
-# Samples: 26761, Features: 63, Positive rate: 1.607%
-```
-
-### Loading directly with pandas
-
-```python
-import pandas as pd
-
-df = pd.read_parquet(
-    "hf://datasets/cmpatino/landmine-detection/data/train-00000-of-00001.parquet"
-)
-
-# Quick overview
-print(df.shape)            # (26761, 67)
-print(df.dtypes.value_counts())
-# float64    30
-# int64      37
-
-# Target distribution
-print(df["mines_outcome"].value_counts())
-# 0    26331
-# 1      430
-
-# Contamination rate per municipality
-print(
-    df.groupby("Municipio")["mines_outcome"]
-    .mean()
-    .sort_values(ascending=False)
-    .head()
-)
-```
-
 ## Validation Protocols
 
 The RELand paper proposes three validation protocols that account for the spatial structure of the data and simulate real demining operations: