<jupyter_start><jupyter_text>XGB vs RF  Single table Classification + Regression<jupyter_code>st_classification = piex.get_test_results(data_modality='single_table', task_type='classification')
st_regression = piex.get_test_results(data_modality='single_table', task_type='regression')
def is_xgb(df):
    return df.pipeline.str.contains('xgb').fillna(False)

def is_rf(df):
    return df.pipeline.str.contains('random_forest').fillna(False)
dfs = {
    'single_table/classification': st_classification,
    'single_table/regression': st_regression,
}
xgb_vs_rf = {
    'XGB': is_xgb,
    'RF': is_rf,
}
get_wins(dfs, xgb_vs_rf)<jupyter_output><empty_output><jupyter_text>Graph<jupyter_code>graph = piex.get_test_results(
    data_modality='graph',
    task_type={
        '$in': [
            'link_prediction',
            'vertex_nomination',
            'graph_matching'
        ]
    }
).dropna(subset=['cv_score'])
def is_xgb(df):
    return df.pipeline.str.contains('default').fillna(True)

def is_rf(df):
    return df.pipeline.str.contains('random_forest').fillna(False)

xgb_vs_rf = {
    'XGB': is_xgb,
    'RF': is_rf,
}
get_wins({'graph': graph}, xgb_vs_rf)<jupyter_output><empty_output><jupyter_text>DFS vs No DFS<jupyter_code>def is_dfs(df):
    return df.pipeline.str.startswith('dfs').fillna(False)

def is_no_dfs(df):
    return ~df.pipeline.str.startswith('dfs').fillna(True)
dfs_vs_no_dfs = {
    'DFS': is_dfs,
    'No DFS': is_no_dfs
}
get_wins(dfs, dfs_vs_no_dfs)<jupyter_output><empty_output>
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