Commit b8db1a9d authored by Noric Couderc's avatar Noric Couderc

Formatting

parent 8728290d
......@@ -105,15 +105,20 @@ def load_jmh_data(filename):
worst_case_times = jmh_data["Score"] - jmh_data["Score Error (99.9%)"]
jmh_data["Lowest score"] = worst_case_times
# We filter out the jmh rows where the lowest score is equal to zero
# This is potentially why some values were dropped in the resulting table,
# even if their data structure type was not a WeakHashMap or an
# IdentityHashMap
jmh_data_filtered = jmh_data[jmh_data["Lowest score"] > 0]
# Grouping the applications, to compare similar ones.
selected_jmh_columns = ["Param: seed",
"Param: applicationSize",
"Param: baseStructureSize",
"Param: baseStructureSize"
"Benchmark"]
# Best data structures
jmh_best_structures = jmh_data_filtered.sort_values(by="Lowest score",
ascending=False).drop_duplicates(selected_jmh_columns)
jmh_best_structures = jmh_data_filtered\
.sort_values(by="Lowest score",
ascending=False)\
.drop_duplicates(selected_jmh_columns)
jmh_best_structures = jmh_best_structures.reset_index(drop=True)
# # Best data structures, computing the improvement
......@@ -344,7 +349,8 @@ if __name__ == "__main__":
axis=1)
poly_transformer = PolynomialFeatures(degree=2)
features_extended_poly = poly_transformer.fit_transform(features_extended)
features_extended_poly = poly_transformer.fit_transform(features_extended,
features_extended.columns.values)
features_extended_norm = StandardScaler().fit_transform(features_extended_poly)
labels = sw_hw_cleaned["Param: datastructureName_best"]
......
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