Update README.md

README.md

@@ -644,26 +644,39 @@ We used the following python script to create this Hugging Face dataset.
 
 ```python
 # Load the dataset into a pandas DataFrame
-
 import pandas as pd
 df_compas = pd.read_csv("https://github.com/propublica/compas-analysis/raw/master/compas-scores-two-years.csv")
 
 from datasets import Dataset, DatasetDict, Features, Value, ClassLabel
 
 # Define continuous (numerical) and categorical columns
-categorical_columns = [
+categorical_columns = {
     "sex", "age_cat", "race", "c_charge_degree", "c_charge_desc", "is_recid",
     "r_charge_degree", "violent_recid", "is_violent_recid", "vr_charge_degree",
     "type_of_assessment", "score_text", "v_type_of_assessment", "v_score_text",
     "event", "two_year_recid"
-]
+}
+
+string_columns =  {'c_case_number',
+                    'first',
+                    'last',
+                    'name',
+                    'r_case_number',
+                    'r_charge_desc',
+                    'violent_recid',
+                    'vr_case_number',
+                    'vr_charge_desc',
+                   'c_jail_in', 'c_jail_out'
+}
+
+date_columns = {'compas_screening_date',
+    'c_offense_date',
+    'c_arrest_date',
+    'r_offense_date',
+    'vr_offense_date',
+    'screening_date',
+    'v_screening_date','dob', 'r_jail_in',  'r_jail_out', 'in_custody', 'out_custody'}
 
-string_columns = [
-    "name", "first", "last",
-    "compas_screening_date", "dob", "c_jail_in", "c_jail_out", "c_offense_date",
-    "c_arrest_date", "r_offense_date", "r_jail_in", "r_jail_out", "vr_offense_date",
-    "screening_date", "v_screening_date", "start", "end", 'c_case_number', 'r_case_number', 'r_charge_desc'
-]
 
 # Convert categorical columns to category type and store mappings
 category_mappings = {}
@@ -672,8 +685,7 @@ for col in categorical_columns:
 
 # Define Hugging Face dataset schema
 hf_features = Features({
-    **{col: ClassLabel(names=category_mappings[col]) for col in categorical_columns},
-    **{col: Value("string") for col in list(df_compas.columns) if col not in categorical_columns}
+    col: Value("date32") if col in date_columns else Value("string") if col in string_columns else ClassLabel(names=category_mappings[col]) if col in categorical_columns else Value("int64") for col in df_compas.columns
 })
 
 # Create a dataset dictionary
@@ -690,7 +702,7 @@ The printed output could look like
 ```
 DatasetDict({
     train: Dataset({
-        features: ['sex', 'age_cat', 'race', 'c_charge_degree', 'c_charge_desc', 'is_recid', 'r_charge_degree', 'violent_recid', 'is_violent_recid', 'vr_charge_degree', 'type_of_assessment', 'score_text', 'v_type_of_assessment', 'v_score_text', 'event', 'two_year_recid', 'id', 'name', 'first', 'last', 'compas_screening_date', 'dob', 'age', 'juv_fel_count', 'decile_score', 'juv_misd_count', 'juv_other_count', 'priors_count', 'days_b_screening_arrest', 'c_jail_in', 'c_jail_out', 'c_case_number', 'c_offense_date', 'c_arrest_date', 'c_days_from_compas', 'r_case_number', 'r_days_from_arrest', 'r_offense_date', 'r_charge_desc', 'r_jail_in', 'r_jail_out', 'vr_case_number', 'vr_offense_date', 'vr_charge_desc', 'decile_score.1', 'screening_date', 'v_decile_score', 'v_screening_date', 'in_custody', 'out_custody', 'priors_count.1', 'start', 'end'],
+        features: ['id', 'name', 'first', 'last', 'compas_screening_date', 'sex', 'dob', 'age', 'age_cat', 'race', 'juv_fel_count', 'decile_score', 'juv_misd_count', 'juv_other_count', 'priors_count', 'days_b_screening_arrest', 'c_jail_in', 'c_jail_out', 'c_case_number', 'c_offense_date', 'c_arrest_date', 'c_days_from_compas', 'c_charge_degree', 'c_charge_desc', 'is_recid', 'r_case_number', 'r_charge_degree', 'r_days_from_arrest', 'r_offense_date', 'r_charge_desc', 'r_jail_in', 'r_jail_out', 'violent_recid', 'is_violent_recid', 'vr_case_number', 'vr_charge_degree', 'vr_offense_date', 'vr_charge_desc', 'type_of_assessment', 'decile_score.1', 'score_text', 'screening_date', 'v_type_of_assessment', 'v_decile_score', 'v_score_text', 'v_screening_date', 'in_custody', 'out_custody', 'priors_count.1', 'start', 'end', 'event', 'two_year_recid'],
         num_rows: 7214
     })
 })