We will train an XGBoost model on the Adult's Income dataset and deploy it on Hugging Face spaces.

In [1]:
!pip install xgboost==1.6.2

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: xgboost==1.6.2 in /home/bom/.local/lib/python3.10/site-packages (1.6.2)
Requirement already satisfied: numpy in /home/bom/.local/lib/python3.10/site-packages (from xgboost==1.6.2) (1.23.4)
Requirement already satisfied: scipy in /home/bom/.local/lib/python3.10/site-packages (from xgboost==1.6.2) (1.9.2)
In [2]:
!wget http://www.donlapark.cmustat.com/Income.csv

--2022-10-25 14:47:43--  http://www.donlapark.cmustat.com/Income.csv
Resolving www.donlapark.cmustat.com (www.donlapark.cmustat.com)... 150.107.31.67
Connecting to www.donlapark.cmustat.com (www.donlapark.cmustat.com)|150.107.31.67|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 3069744 (2.9M) [text/csv]
Saving to: ‘Income.csv’

Income.csv          100%[===================>]   2.93M  4.91MB/s    in 0.6s    

2022-10-25 14:47:43 (4.91 MB/s) - ‘Income.csv’ saved [3069744/3069744]
In [3]:
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler

from xgboost import XGBClassifier


EDU_DICT = {'Preschool': 1,
            '1st-4th': 2,
            '5th-6th': 3,
            '7th-8th': 4,
            '9th': 5,
            '10th': 6,
            '11th': 7,
            '12th': 8,
            'HS-grad': 9, 
            'Some-college': 10,
            'Assoc-voc': 11,
            'Assoc-acdm': 12,
            'Bachelors': 13,
            'Masters': 14,
            'Prof-school': 15,
            'Doctorate': 16
            }


X_train = pd.read_csv('Income.csv')

X_train
Out[3]:
age workclass education marital.status occupation relationship race sex hours.per.week native.country income
0 90 ? HS-grad Widowed ? Not-in-family White Female 40 United-States <=50K
1 82 Private HS-grad Widowed Exec-managerial Not-in-family White Female 18 United-States <=50K
2 66 ? Some-college Widowed ? Unmarried Black Female 40 United-States <=50K
3 54 Private 7th-8th Divorced Machine-op-inspct Unmarried White Female 40 United-States <=50K
4 41 Private Some-college Separated Prof-specialty Own-child White Female 40 United-States <=50K
... ... ... ... ... ... ... ... ... ... ... ...
32556 22 Private Some-college Never-married Protective-serv Not-in-family White Male 40 United-States <=50K
32557 27 Private Assoc-acdm Married-civ-spouse Tech-support Wife White Female 38 United-States <=50K
32558 40 Private HS-grad Married-civ-spouse Machine-op-inspct Husband White Male 40 United-States >50K
32559 58 Private HS-grad Widowed Adm-clerical Unmarried White Female 40 United-States <=50K
32560 22 Private HS-grad Never-married Adm-clerical Own-child White Male 20 United-States <=50K

32561 rows × 11 columns

In [4]:
y_train = X_train.pop("income")
y_train = (y_train == ">50K").astype(int)
X_train['education'].replace(EDU_DICT, inplace=True)

# Names of numerical features
num_col = X_train.select_dtypes(include=['int64', 'float64']).columns
# Names of categorical features
cat_col = X_train.select_dtypes(include=['object', 'bool']).columns

print(num_col)
print(cat_col)

Index(['age', 'education', 'hours.per.week'], dtype='object')
Index(['workclass', 'marital.status', 'occupation', 'relationship', 'race',
       'sex', 'native.country'],
      dtype='object')
In [5]:
preprocessor = ColumnTransformer([("scaler", StandardScaler(), num_col), 
                                  ("onehot", OneHotEncoder(sparse=False), cat_col)])

model = Pipeline(steps=[('preprocessor', preprocessor), 
                        ('classifier', XGBClassifier())])  

model.fit(X_train, y_train)
Out[5]:
Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('scaler', StandardScaler(),
                                                  Index(['age', 'education', 'hours.per.week'], dtype='object')),
                                                 ('onehot',
                                                  OneHotEncoder(sparse=False),
                                                  Index(['workclass', 'marital.status', 'occupation', 'relationship', 'race',
       'sex', 'native.country'],
      dtype='object'))])),
                ('classifier',
                 XGBClassifier(base_score=0.5, boo...
                               gamma=0, gpu_id=-1, grow_policy='depthwise',
                               importance_type=None, interaction_constraints='',
                               learning_rate=0.300000012, max_bin=256,
                               max_cat_to_onehot=4, max_delta_step=0,
                               max_depth=6, max_leaves=0, min_child_weight=1,
                               missing=nan, monotone_constraints='()',
                               n_estimators=100, n_jobs=0, num_parallel_tree=1,
                               predictor='auto', random_state=0, reg_alpha=0,
                               reg_lambda=1, ...))])
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Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('scaler', StandardScaler(),
                                                  Index(['age', 'education', 'hours.per.week'], dtype='object')),
                                                 ('onehot',
                                                  OneHotEncoder(sparse=False),
                                                  Index(['workclass', 'marital.status', 'occupation', 'relationship', 'race',
       'sex', 'native.country'],
      dtype='object'))])),
                ('classifier',
                 XGBClassifier(base_score=0.5, boo...
                               gamma=0, gpu_id=-1, grow_policy='depthwise',
                               importance_type=None, interaction_constraints='',
                               learning_rate=0.300000012, max_bin=256,
                               max_cat_to_onehot=4, max_delta_step=0,
                               max_depth=6, max_leaves=0, min_child_weight=1,
                               missing=nan, monotone_constraints='()',
                               n_estimators=100, n_jobs=0, num_parallel_tree=1,
                               predictor='auto', random_state=0, reg_alpha=0,
                               reg_lambda=1, ...))])
ColumnTransformer(transformers=[('scaler', StandardScaler(),
                                 Index(['age', 'education', 'hours.per.week'], dtype='object')),
                                ('onehot', OneHotEncoder(sparse=False),
                                 Index(['workclass', 'marital.status', 'occupation', 'relationship', 'race',
       'sex', 'native.country'],
      dtype='object'))])
Index(['age', 'education', 'hours.per.week'], dtype='object')
StandardScaler()
Index(['workclass', 'marital.status', 'occupation', 'relationship', 'race',
       'sex', 'native.country'],
      dtype='object')
OneHotEncoder(sparse=False)
XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
              early_stopping_rounds=None, enable_categorical=False,
              eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
              importance_type=None, interaction_constraints='',
              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
              missing=nan, monotone_constraints='()', n_estimators=100,
              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,
              reg_alpha=0, reg_lambda=1, ...)

Saving the model¶

In [6]:
import joblib

joblib.dump(model, 'model.joblib')
Out[6]:
['model.joblib']
In [7]:
unique_values = {col:X_train[col].unique() for col in cat_col}
unique_values['education'] = list(EDU_DICT.keys())

joblib.dump(unique_values, 'unique_values.joblib')
Out[7]:
['unique_values.joblib']
In [8]:
unique_values
Out[8]:
{'workclass': array(['?', 'Private', 'State-gov', 'Federal-gov', 'Self-emp-not-inc',
        'Self-emp-inc', 'Local-gov', 'Without-pay', 'Never-worked'],
       dtype=object),
 'marital.status': array(['Widowed', 'Divorced', 'Separated', 'Never-married',
        'Married-civ-spouse', 'Married-spouse-absent', 'Married-AF-spouse'],
       dtype=object),
 'occupation': array(['?', 'Exec-managerial', 'Machine-op-inspct', 'Prof-specialty',
        'Other-service', 'Adm-clerical', 'Craft-repair',
        'Transport-moving', 'Handlers-cleaners', 'Sales',
        'Farming-fishing', 'Tech-support', 'Protective-serv',
        'Armed-Forces', 'Priv-house-serv'], dtype=object),
 'relationship': array(['Not-in-family', 'Unmarried', 'Own-child', 'Other-relative',
        'Husband', 'Wife'], dtype=object),
 'race': array(['White', 'Black', 'Asian-Pac-Islander', 'Other',
        'Amer-Indian-Eskimo'], dtype=object),
 'sex': array(['Female', 'Male'], dtype=object),
 'native.country': array(['United-States', '?', 'Mexico', 'Greece', 'Vietnam', 'China',
        'Taiwan', 'India', 'Philippines', 'Trinadad&Tobago', 'Canada',
        'South', 'Holand-Netherlands', 'Puerto-Rico', 'Poland', 'Iran',
        'England', 'Germany', 'Italy', 'Japan', 'Hong', 'Honduras', 'Cuba',
        'Ireland', 'Cambodia', 'Peru', 'Nicaragua', 'Dominican-Republic',
        'Haiti', 'El-Salvador', 'Hungary', 'Columbia', 'Guatemala',
        'Jamaica', 'Ecuador', 'France', 'Yugoslavia', 'Scotland',
        'Portugal', 'Laos', 'Thailand', 'Outlying-US(Guam-USVI-etc)'],
       dtype=object),
 'education': ['Preschool',
  '1st-4th',
  '5th-6th',
  '7th-8th',
  '9th',
  '10th',
  '11th',
  '12th',
  'HS-grad',
  'Some-college',
  'Assoc-voc',
  'Assoc-acdm',
  'Bachelors',
  'Masters',
  'Prof-school',
  'Doctorate']}