Removing unused code (#400)

* clean-ups

* tiny fix

* format

* format c++

src/bindings/PyDP/algorithms/bounded_functions.cpp

@@ -12,7 +12,6 @@
 #include "algorithms/bounded-variance.h"
 
 #include "../pydp_lib/algorithm_builder.hpp"
-#include "../pydp_lib/casting.hpp"  // our caster helper library
 
 using namespace std;
 

src/bindings/PyDP/algorithms/rand.cpp

@@ -1,6 +1,5 @@
 // Provides bindings for rand
 #include "algorithms/rand.h"
-#include "../pydp_lib/casting.hpp"
 #include "pybind11/pybind11.h"
 
 namespace py = pybind11;

src/bindings/PyDP/base/logging.cpp

@@ -5,8 +5,7 @@
 
 #include "pybind11/pybind11.h"
 
-#include "../pydp_lib/casting.hpp"  // our caster helper library
-#include "base/logging.h"           // the header file associated with logging.cc
+#include "base/logging.h"  // the header file associated with logging.cc
 
 namespace py = pybind11;
 namespace dpb = differential_privacy::base;

src/bindings/PyDP/base/status.cpp

@@ -2,14 +2,12 @@
 
 #include <string>
 
-#include "../pydp_lib/casting.hpp"  // our caster helper library
-#include "pybind11/operators.h"     // for overloading the operators
+#include "pybind11/operators.h"  // for overloading the operators
 #include "pybind11/pybind11.h"
 #include "pybind11/stl.h"
 
 #include "base/status.h"    // the header file associated with status.cc
 #include "base/statusor.h"  //header file associated with statusor.cc
-// #include "differential_privacy/proto/data.pb.h"  // for Output type
 
 using namespace std;
 

src/bindings/PyDP/mechanisms/mechanism.cpp

@@ -3,11 +3,9 @@
 #include "pybind11/functional.h"
 #include "pybind11/stl.h"
 
-#include "algorithms/distributions.h"
 #include "algorithms/numerical-mechanisms.h"
 
 #include "../pydp_lib/algorithm_builder.hpp"
-#include "../pydp_lib/casting.hpp"
 
 using namespace std;
 
@@ -107,11 +105,6 @@ class LaplaceMechanismBinder {
         builder.Build().value());
   }
 
-  static std::unique_ptr<dp::NumericalMechanismBuilder> clone() {
-    dp::LaplaceMechanism::Builder cloner;
-    return std::move(cloner.Clone());
-  }
-
   static void DeclareIn(py::module& m) {
     py::class_<dp::LaplaceMechanism, dp::NumericalMechanism> lap_mech(
         m, "LaplaceMechanism");
@@ -140,11 +133,6 @@ class GaussianMechanismBinder {
         builder.Build().value());
   };
 
-  static std::unique_ptr<dp::NumericalMechanismBuilder> clone() {
-    dp::GaussianMechanism::Builder cloner;
-    return std::move(cloner.Clone());
-  };
-
   static void DeclareIn(py::module& m) {
     py::class_<dp::GaussianMechanism, dp::NumericalMechanism> gaus_mech(
         m, "GaussianMechanism");

src/bindings/PyDP/proto/proto.cpp

@@ -1,11 +1,9 @@
 #include <fstream>
 #include <string>
 
-#include "../pydp_lib/casting.hpp"  // our caster helper library
 #include "pybind11/pybind11.h"
 
 #include "proto/summary.pb.h"
-#include "proto/util.h"  // the header file associated with status.cc
 
 using namespace std;
 

tests/algorithms/test_order_statistics.py

@@ -1,12 +1,7 @@
-# stdlib
-import math
-
 # verify with actual value
 import statistics
 
 # third party
-from conftest import expect_near
-from conftest import percentile
 import pytest
 
 # pydp absolute
@@ -32,8 +27,8 @@ def test_min(data, dtype):
     minn = dp.algorithms.laplacian.Min(
         dtype=dtype, epsilon=1.0, lower_bound=0, upper_bound=200
     )
-    assert expect_near(min(data), minn.quick_result(data), 10)
-    assert expect_near(0, minn.quick_result(data), 10)
+    assert minn.quick_result(data) == pytest.approx(min(data), abs=10)
+    assert minn.quick_result(data) == pytest.approx(0, abs=10)
 
 
 @pytest.mark.parametrize("dtype, data", [("int", data_ints), ("float", data_floats)])
@@ -43,7 +38,7 @@ def test_max(data, dtype):
     )
     assert 190 < maxx.quick_result(data) < 210
 
-    assert expect_near(max(data), maxx.quick_result(data), 10)
+    assert maxx.quick_result(data) == pytest.approx(max(data), abs=10)
 
 
 @pytest.mark.parametrize("dtype, data", [("int", data_ints), ("float", data_floats)])
@@ -53,7 +48,7 @@ def test_median(data, dtype):
         dtype=dtype, epsilon=1.0, lower_bound=0, upper_bound=200
     )
 
-    assert expect_near(statistics.median(data), median.quick_result(data), 20)
+    assert median.quick_result(data) == pytest.approx(statistics.median(data), abs=20)
 
 
 @pytest.mark.parametrize("dtype", ["int", "float"])

tests/algorithms/test_partition_selection.py

@@ -1,6 +1,5 @@
 import numpy as np
 import pytest
-import pydp as dp
 from pydp.algorithms.partition_selection import create_partition_strategy
 
 # TODO - wait for NumericalMechanism implementation to use those for testing Laplace/Gaussian Partition Selection.