KIDNEY CASE STUDY

DataLink : https://archive.ics.uci.edu/ml/datasets/chronic_kidney_disease

# Import necessary modules
from sklearn_pandas import DataFrameMapper
from sklearn_pandas import CategoricalImputer

# Check number of nulls in each feature column
nulls_per_column = X.isnull().sum()
print(nulls_per_column)

# Create a boolean mask for categorical columns
categorical_feature_mask = X.dtypes == object

# Get list of categorical column names
categorical_columns = X.columns[categorical_feature_mask].tolist()

# Get list of non-categorical column names
non_categorical_columns = X.columns[~categorical_feature_mask].tolist()

# Apply numeric imputer
numeric_imputation_mapper = DataFrameMapper(
                                            [([numeric_feature],Imputer(strategy="median")) for numeric_feature in non_categorical_columns],
                                            input_df=True,
                                            df_out=True
                                           )

# Apply categorical imputer
categorical_imputation_mapper = DataFrameMapper(
                                                [(category_feature, CategoricalImputer()) for category_feature in categorical_columns],
                                                input_df=True,
                                                df_out=True
                                               )
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Kidney disease case study II: Feature Union
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# Import FeatureUnion
from sklearn.pipeline import FeatureUnion

# Combine the numeric and categorical transformations
numeric_categorical_union = FeatureUnion([
                                          ("num_mapper", numeric_imputation_mapper),
                                          ("cat_mapper", categorical_imputation_mapper)
                                         ])
                       
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Kidney disease case study III: Full pipeline
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# Create full pipeline
pipeline = Pipeline([
                     ("featureunion", numeric_categorical_union),
                     ("dictifier", Dictifier()),
                     ("vectorizer", DictVectorizer(sort=False)),
                     ("clf", xgb.XGBClassifier(max_depth=3))
                    ])

# Perform cross-validation
cross_val_scores = cross_val_score(pipeline, kidney_data, y, scoring="roc_auc", cv=3)

# Print avg. AUC
print("3-fold AUC: ", np.mean(cross_val_scores))
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Bringing it all together
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# Create the parameter grid
gbm_param_grid = {
    'clf__learning_rate': np.arange(.05, 1, .05),
    'clf__max_depth': np.arange(3,10, 1),
    'clf__n_estimators': np.arange(50, 200, 50)
}

# Perform RandomizedSearchCV
randomized_roc_auc = RandomizedSearchCV(estimator=pipeline,
                                        param_distributions=gbm_param_grid,
                                        n_iter=2, scoring='roc_auc', cv=2, verbose=1)

# Fit the estimator
randomized_roc_auc.fit(X, y)

# Compute metrics
print(randomized_roc_auc.best_score_)
print(randomized_roc_auc.best_estimator_)
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