From 57b3be2f6d34a20847ce4227be43be6a2b743cd5 Mon Sep 17 00:00:00 2001
From: Umut <umutsahin@protonmail.com>
Date: Thu, 7 Oct 2021 13:31:58 +0300
Subject: [PATCH] fix(benchmarks): treat warnings as errors in benchmarks

---
 benchmarks/124_minus_x.py                     |   3 +
 benchmarks/124_minus_x_tensor.py              |  15 +--
 benchmarks/common.py                          |   8 ++
 benchmarks/linear_regression.py               |   8 +-
 benchmarks/logistic_regression.py             |  32 +++++-
 benchmarks/single_table_lookup.py             |   3 +
 benchmarks/x_minus_1_2_3.py                   |  15 +--
 benchmarks/x_minus_1_2_3_broadcasted.py       |  13 ++-
 benchmarks/x_minus_24.py                      |   5 +-
 benchmarks/x_minus_24_tensor.py               |  15 +--
 benchmarks/x_minus_y.py                       |   9 +-
 benchmarks/x_minus_y_broadcasted_tensors.py   |  16 +--
 benchmarks/x_minus_y_tensor_and_scalar.py     |  17 +--
 benchmarks/x_minus_y_tensors.py               |  15 +--
 benchmarks/x_plus_1_2_3.py                    |  15 +--
 benchmarks/x_plus_1_2_3_broadcasted.py        |  15 +--
 benchmarks/x_plus_42.py                       |   5 +-
 benchmarks/x_plus_42_tensor.py                |  15 +--
 benchmarks/x_plus_y.py                        |   5 +-
 benchmarks/x_plus_y_broadcasted_tensors.py    |  14 ++-
 benchmarks/x_plus_y_tensor_and_scalar.py      |  15 +--
 benchmarks/x_plus_y_tensors.py                |  14 ++-
 benchmarks/x_times_1_2_3.py                   |  15 +--
 benchmarks/x_times_1_2_3_broadcasted.py       |  15 +--
 benchmarks/x_times_7.py                       |   3 +
 benchmarks/x_times_7_tensor.py                |  15 +--
 benchmarks/x_times_y.py                       |   9 +-
 benchmarks/x_times_y_broadcasted_tensors.py   |  14 ++-
 benchmarks/x_times_y_tensor_and_scalar.py     |  15 +--
 benchmarks/x_times_y_tensors.py               |  14 ++-
 benchmarks/x_to_the_power_of_2.py             |   5 +-
 .../QuantizedLinearRegression.ipynb           |  18 ++--
 .../QuantizedLogisticRegression.ipynb         | 102 +++++++++++++-----
 script/progress_tracker_utils/measure.py      |   3 +-
 34 files changed, 330 insertions(+), 170 deletions(-)
 create mode 100644 benchmarks/common.py

diff --git a/benchmarks/124_minus_x.py b/benchmarks/124_minus_x.py
index 8a574e254..1b8e4f13f 100644
--- a/benchmarks/124_minus_x.py
+++ b/benchmarks/124_minus_x.py
@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -16,6 +18,7 @@ def main():
         function_to_compile,
         {"x": x},
         [(i,) for i in range(2 ** 3)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 
diff --git a/benchmarks/124_minus_x_tensor.py b/benchmarks/124_minus_x_tensor.py
index 5b5d035f9..ed91bc310 100644
--- a/benchmarks/124_minus_x_tensor.py
+++ b/benchmarks/124_minus_x_tensor.py
@@ -1,6 +1,7 @@
 # Target: 124 - x (Tensor)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(6), shape=(3,))
 
-    inputset = [
-        (np.array([36, 50, 24]),),
-        (np.array([41, 60, 51]),),
-        (np.array([25, 31, 24]),),
-        (np.array([34, 47, 27]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 6, size=(3,)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/common.py b/benchmarks/common.py
new file mode 100644
index 000000000..35acb1934
--- /dev/null
+++ b/benchmarks/common.py
@@ -0,0 +1,8 @@
+import concrete.numpy as hnp
+
+BENCHMARK_CONFIGURATION = hnp.CompilationConfiguration(
+    dump_artifacts_on_unexpected_failures=True,
+    enable_topological_optimizations=True,
+    check_every_input_in_inputset=True,
+    treat_warnings_as_errors=True,
+)
diff --git a/benchmarks/linear_regression.py b/benchmarks/linear_regression.py
index f94413e76..c74b2d0b6 100644
--- a/benchmarks/linear_regression.py
+++ b/benchmarks/linear_regression.py
@@ -5,13 +5,16 @@
 # pylint: disable=C0301
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
 
 def main():
-    x = np.array([[130], [110], [100], [145], [160], [185], [200], [80], [50]], dtype=np.float32)
-    y = np.array([325, 295, 268, 400, 420, 500, 520, 220, 120], dtype=np.float32)
+    x = np.array(
+        [[69], [130], [110], [100], [145], [160], [185], [200], [80], [50]], dtype=np.float32
+    )
+    y = np.array([181, 325, 295, 268, 400, 420, 500, 520, 220, 120], dtype=np.float32)
 
     class Model:
         w = None
@@ -150,6 +153,7 @@ def main():
         function_to_compile,
         {"x_0": hnp.EncryptedScalar(hnp.UnsignedInteger(input_bits))},
         inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 
diff --git a/benchmarks/logistic_regression.py b/benchmarks/logistic_regression.py
index dc8bc9bb9..664ac700b 100644
--- a/benchmarks/logistic_regression.py
+++ b/benchmarks/logistic_regression.py
@@ -2,13 +2,40 @@
 
 import numpy as np
 import torch
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
 
 def main():
-    x = torch.tensor([[1, 1], [1, 2], [2, 1], [4, 1], [3, 2], [4, 2]]).float()
-    y = torch.tensor([[0], [0], [0], [1], [1], [1]]).float()
+    x = torch.tensor(
+        [
+            [1, 1],
+            [1, 1.5],
+            [1.5, 1.2],
+            [1, 2],
+            [2, 1],
+            [4, 1],
+            [4, 1.5],
+            [3.5, 1.8],
+            [3, 2],
+            [4, 2],
+        ]
+    ).float()
+    y = torch.tensor(
+        [
+            [0],
+            [0],
+            [0],
+            [0],
+            [0],
+            [1],
+            [1],
+            [1],
+            [1],
+            [1],
+        ]
+    ).float()
 
     class Model(torch.nn.Module):
         def __init__(self, n):
@@ -218,6 +245,7 @@ def main():
             "x_1": hnp.EncryptedScalar(hnp.UnsignedInteger(input_bits)),
         },
         inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 
diff --git a/benchmarks/single_table_lookup.py b/benchmarks/single_table_lookup.py
index d52523d67..98f3018a6 100644
--- a/benchmarks/single_table_lookup.py
+++ b/benchmarks/single_table_lookup.py
@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -21,6 +23,7 @@ def main():
         function_to_compile,
         {"x": x},
         [(i,) for i in range(2 ** input_bits)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 
diff --git a/benchmarks/x_minus_1_2_3.py b/benchmarks/x_minus_1_2_3.py
index 7e692b735..746f69d39 100644
--- a/benchmarks/x_minus_1_2_3.py
+++ b/benchmarks/x_minus_1_2_3.py
@@ -1,6 +1,7 @@
 # Target: x - [1, 2, 3]
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [
-        (np.array([6, 2, 4]),),
-        (np.array([1, 3, 5]),),
-        (np.array([5, 7, 2]),),
-        (np.array([1, 7, 7]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 2, size=(3,)) + np.array([1, 2, 3]),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_minus_1_2_3_broadcasted.py b/benchmarks/x_minus_1_2_3_broadcasted.py
index b67e1d138..b78e4a2f4 100644
--- a/benchmarks/x_minus_1_2_3_broadcasted.py
+++ b/benchmarks/x_minus_1_2_3_broadcasted.py
@@ -1,6 +1,7 @@
 # Target: x - [1, 2, 3] (Broadcasted)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -12,14 +13,16 @@ def main():
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
     inputset = [
-        (np.array([[4, 7, 7], [6, 2, 4]]),),
-        (np.array([[6, 2, 4], [1, 3, 1]]),),
-        (np.array([[6, 2, 4], [5, 7, 5]]),),
-        (np.array([[5, 7, 5], [4, 7, 7]]),),
+        (np.random.randint(0, 2 ** 2, size=(2, 3)) + np.array([1, 2, 3]),) for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_minus_24.py b/benchmarks/x_minus_24.py
index 16e711f8a..9a39d0b22 100644
--- a/benchmarks/x_minus_24.py
+++ b/benchmarks/x_minus_24.py
@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -15,7 +17,8 @@ def main():
     engine = hnp.compile_numpy_function(
         function_to_compile,
         {"x": x},
-        [(i,) for i in range(2 ** 6)],
+        [(i,) for i in range(24, 2 ** 6)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 
diff --git a/benchmarks/x_minus_24_tensor.py b/benchmarks/x_minus_24_tensor.py
index f7ec198cb..b88be1f8d 100644
--- a/benchmarks/x_minus_24_tensor.py
+++ b/benchmarks/x_minus_24_tensor.py
@@ -1,6 +1,7 @@
 # Target: x - 24 (Tensor)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(6), shape=(3,))
 
-    inputset = [
-        (np.array([36, 50, 24]),),
-        (np.array([41, 60, 51]),),
-        (np.array([25, 31, 24]),),
-        (np.array([34, 47, 27]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 5, size=(3,)) + 24,) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_minus_y.py b/benchmarks/x_minus_y.py
index ca04b3117..47c24a09e 100644
--- a/benchmarks/x_minus_y.py
+++ b/benchmarks/x_minus_y.py
@@ -3,6 +3,8 @@
 import itertools
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -16,7 +18,12 @@ def main():
     inputset = itertools.product(range(4, 8), range(0, 4))
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_minus_y_broadcasted_tensors.py b/benchmarks/x_minus_y_broadcasted_tensors.py
index ea6408350..d586a8ab2 100644
--- a/benchmarks/x_minus_y_broadcasted_tensors.py
+++ b/benchmarks/x_minus_y_broadcasted_tensors.py
@@ -1,6 +1,7 @@
 # Target: x - y (Broadcasted Tensors)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -10,17 +11,20 @@ def main():
         return x - y
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
-    y = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
+    y = hnp.EncryptedTensor(hnp.UnsignedInteger(2), shape=(2, 3))
 
     inputset = [
-        (np.array([6, 2, 4]), np.array([[5, 1, 3], [0, 0, 4]])),
-        (np.array([1, 3, 1]), np.array([[0, 3, 1], [1, 2, 1]])),
-        (np.array([5, 1, 2]), np.array([[5, 0, 2], [2, 1, 1]])),
-        (np.array([0, 7, 7]), np.array([[0, 5, 1], [0, 7, 2]])),
+        (np.random.randint(4, 8, size=(3,)), np.random.randint(0, 4, size=(2, 3)))
+        for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_minus_y_tensor_and_scalar.py b/benchmarks/x_minus_y_tensor_and_scalar.py
index 3ab299c65..27e825ca4 100644
--- a/benchmarks/x_minus_y_tensor_and_scalar.py
+++ b/benchmarks/x_minus_y_tensor_and_scalar.py
@@ -3,6 +3,7 @@
 import random
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -12,17 +13,17 @@ def main():
         return x - y
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
-    y = hnp.EncryptedScalar(hnp.UnsignedInteger(3))
+    y = hnp.EncryptedScalar(hnp.UnsignedInteger(2))
 
-    inputset = [
-        (np.array([6, 2, 4]), 2),
-        (np.array([1, 3, 1]), 1),
-        (np.array([5, 4, 7]), 4),
-        (np.array([5, 7, 6]), 5),
-    ]
+    inputset = [(np.random.randint(4, 8, size=(3,)), random.randint(0, 3)) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_minus_y_tensors.py b/benchmarks/x_minus_y_tensors.py
index 6c681e0bc..5932e97c2 100644
--- a/benchmarks/x_minus_y_tensors.py
+++ b/benchmarks/x_minus_y_tensors.py
@@ -1,6 +1,7 @@
 # Target: x - y (Tensors)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -10,17 +11,19 @@ def main():
         return x - y
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
-    y = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
+    y = hnp.EncryptedTensor(hnp.UnsignedInteger(2), shape=(3,))
 
     inputset = [
-        (np.array([6, 2, 4]), np.array([4, 1, 2])),
-        (np.array([1, 3, 1]), np.array([1, 1, 0])),
-        (np.array([5, 1, 2]), np.array([4, 1, 1])),
-        (np.array([0, 7, 7]), np.array([0, 7, 0])),
+        (np.random.randint(4, 8, size=(3,)), np.random.randint(0, 4, size=(3,))) for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_plus_1_2_3.py b/benchmarks/x_plus_1_2_3.py
index de73fda65..e9339d97f 100644
--- a/benchmarks/x_plus_1_2_3.py
+++ b/benchmarks/x_plus_1_2_3.py
@@ -1,6 +1,7 @@
 # Target: x + [1, 2, 3]
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [
-        (np.array([6, 2, 4]),),
-        (np.array([1, 3, 1]),),
-        (np.array([5, 1, 2]),),
-        (np.array([0, 7, 7]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_plus_1_2_3_broadcasted.py b/benchmarks/x_plus_1_2_3_broadcasted.py
index 9826714f4..0095cb8c1 100644
--- a/benchmarks/x_plus_1_2_3_broadcasted.py
+++ b/benchmarks/x_plus_1_2_3_broadcasted.py
@@ -1,6 +1,7 @@
 # Target: x + [1, 2, 3] (Broadcasted)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
-    inputset = [
-        (np.array([[0, 7, 7], [6, 2, 4]]),),
-        (np.array([[6, 2, 4], [1, 3, 1]]),),
-        (np.array([[6, 2, 4], [5, 1, 2]]),),
-        (np.array([[5, 1, 2], [0, 7, 7]]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 3, size=(2, 3)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_plus_42.py b/benchmarks/x_plus_42.py
index 98a5c1c0a..95f7be9ed 100644
--- a/benchmarks/x_plus_42.py
+++ b/benchmarks/x_plus_42.py
@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -15,7 +17,8 @@ def main():
     engine = hnp.compile_numpy_function(
         function_to_compile,
         {"x": x},
-        [(6,), (1,), (5,), (2,)],
+        [(i,) for i in range(2 ** 3)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 
diff --git a/benchmarks/x_plus_42_tensor.py b/benchmarks/x_plus_42_tensor.py
index 1829304e8..6ca272380 100644
--- a/benchmarks/x_plus_42_tensor.py
+++ b/benchmarks/x_plus_42_tensor.py
@@ -1,6 +1,7 @@
 # Target: x + 42 (Tensor)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [
-        (np.array([6, 2, 4]),),
-        (np.array([1, 3, 1]),),
-        (np.array([5, 1, 2]),),
-        (np.array([0, 7, 7]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_plus_y.py b/benchmarks/x_plus_y.py
index 829d1f9dc..a4c809b9c 100644
--- a/benchmarks/x_plus_y.py
+++ b/benchmarks/x_plus_y.py
@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -16,7 +18,8 @@ def main():
     engine = hnp.compile_numpy_function(
         function_to_compile,
         {"x": x, "y": y},
-        [(6, 1), (1, 4), (5, 3), (2, 0), (7, 7)],
+        [(random.randint(0, 7), random.randint(0, 7)) for _ in range(32)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 
diff --git a/benchmarks/x_plus_y_broadcasted_tensors.py b/benchmarks/x_plus_y_broadcasted_tensors.py
index c13aea795..91756ec8b 100644
--- a/benchmarks/x_plus_y_broadcasted_tensors.py
+++ b/benchmarks/x_plus_y_broadcasted_tensors.py
@@ -1,6 +1,7 @@
 # Target: x + y (Broadcasted Tensors)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -13,14 +14,17 @@ def main():
     y = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
     inputset = [
-        (np.array([6, 2, 4]), np.array([[5, 1, 2], [0, 7, 7]])),
-        (np.array([1, 3, 1]), np.array([[0, 7, 7], [6, 2, 4]])),
-        (np.array([5, 1, 2]), np.array([[6, 2, 4], [1, 3, 1]])),
-        (np.array([0, 7, 7]), np.array([[1, 3, 1], [5, 1, 2]])),
+        (np.random.randint(0, 2 ** 3, size=(3,)), np.random.randint(0, 2 ** 3, size=(2, 3)))
+        for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_plus_y_tensor_and_scalar.py b/benchmarks/x_plus_y_tensor_and_scalar.py
index e314a99e9..c44dd9e81 100644
--- a/benchmarks/x_plus_y_tensor_and_scalar.py
+++ b/benchmarks/x_plus_y_tensor_and_scalar.py
@@ -3,6 +3,7 @@
 import random
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -14,15 +15,15 @@ def main():
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
     y = hnp.EncryptedScalar(hnp.UnsignedInteger(3))
 
-    inputset = [
-        (np.array([6, 2, 4]), 4),
-        (np.array([1, 3, 1]), 1),
-        (np.array([5, 1, 2]), 2),
-        (np.array([0, 7, 7]), 5),
-    ]
+    inputset = [(np.random.randint(0, 8, size=(3,)), random.randint(0, 7)) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_plus_y_tensors.py b/benchmarks/x_plus_y_tensors.py
index 16718cc5e..4508d2ecc 100644
--- a/benchmarks/x_plus_y_tensors.py
+++ b/benchmarks/x_plus_y_tensors.py
@@ -1,6 +1,7 @@
 # Target: x + y (Tensors)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -13,14 +14,17 @@ def main():
     y = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
     inputset = [
-        (np.array([6, 2, 4]), np.array([0, 7, 7])),
-        (np.array([1, 3, 1]), np.array([6, 2, 4])),
-        (np.array([5, 1, 2]), np.array([1, 3, 1])),
-        (np.array([0, 7, 7]), np.array([5, 1, 2])),
+        (np.random.randint(0, 2 ** 3, size=(3,)), np.random.randint(0, 2 ** 3, size=(3,)))
+        for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_times_1_2_3.py b/benchmarks/x_times_1_2_3.py
index 90812272d..1f3b4bc85 100644
--- a/benchmarks/x_times_1_2_3.py
+++ b/benchmarks/x_times_1_2_3.py
@@ -1,6 +1,7 @@
 # Target: x * [1, 2, 3]
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [
-        (np.array([6, 2, 4]),),
-        (np.array([1, 3, 1]),),
-        (np.array([5, 1, 2]),),
-        (np.array([0, 7, 7]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_times_1_2_3_broadcasted.py b/benchmarks/x_times_1_2_3_broadcasted.py
index f6b8cb665..cefe9f8ec 100644
--- a/benchmarks/x_times_1_2_3_broadcasted.py
+++ b/benchmarks/x_times_1_2_3_broadcasted.py
@@ -1,6 +1,7 @@
 # Target: x * [1, 2, 3] (Broadcasted)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
-    inputset = [
-        (np.array([[0, 7, 7], [6, 2, 4]]),),
-        (np.array([[6, 2, 4], [1, 3, 1]]),),
-        (np.array([[6, 2, 4], [5, 1, 2]]),),
-        (np.array([[5, 1, 2], [0, 7, 7]]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 3, size=(2, 3)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_times_7.py b/benchmarks/x_times_7.py
index 1faa58112..f5338bfb5 100644
--- a/benchmarks/x_times_7.py
+++ b/benchmarks/x_times_7.py
@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -16,6 +18,7 @@ def main():
         function_to_compile,
         {"x": x},
         [(i,) for i in range(2 ** 4)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 
diff --git a/benchmarks/x_times_7_tensor.py b/benchmarks/x_times_7_tensor.py
index c1ad87b73..7798ad568 100644
--- a/benchmarks/x_times_7_tensor.py
+++ b/benchmarks/x_times_7_tensor.py
@@ -1,6 +1,7 @@
 # Target: x * 7 (Tensor)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [
-        (np.array([6, 2, 4]),),
-        (np.array([1, 3, 1]),),
-        (np.array([5, 1, 2]),),
-        (np.array([0, 7, 7]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_times_y.py b/benchmarks/x_times_y.py
index b29c9440c..562289db3 100644
--- a/benchmarks/x_times_y.py
+++ b/benchmarks/x_times_y.py
@@ -3,6 +3,8 @@
 import itertools
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -16,7 +18,12 @@ def main():
     inputset = itertools.product(range(4, 8), range(0, 4))
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_times_y_broadcasted_tensors.py b/benchmarks/x_times_y_broadcasted_tensors.py
index 89a6bf8df..a804ad773 100644
--- a/benchmarks/x_times_y_broadcasted_tensors.py
+++ b/benchmarks/x_times_y_broadcasted_tensors.py
@@ -1,6 +1,7 @@
 # Target: x * y (Broadcasted Tensors)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -13,14 +14,17 @@ def main():
     y = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
     inputset = [
-        (np.array([6, 2, 4]), np.array([[5, 1, 2], [0, 7, 7]])),
-        (np.array([1, 3, 1]), np.array([[0, 7, 7], [6, 2, 4]])),
-        (np.array([5, 1, 2]), np.array([[6, 2, 4], [1, 3, 1]])),
-        (np.array([0, 7, 7]), np.array([[1, 3, 1], [5, 1, 2]])),
+        (np.random.randint(0, 2 ** 3, size=(3,)), np.random.randint(0, 2 ** 3, size=(2, 3)))
+        for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_times_y_tensor_and_scalar.py b/benchmarks/x_times_y_tensor_and_scalar.py
index 4a13b9947..906c31fa1 100644
--- a/benchmarks/x_times_y_tensor_and_scalar.py
+++ b/benchmarks/x_times_y_tensor_and_scalar.py
@@ -3,6 +3,7 @@
 import random
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -14,15 +15,15 @@ def main():
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
     y = hnp.EncryptedScalar(hnp.UnsignedInteger(3))
 
-    inputset = [
-        (np.array([6, 2, 4]), 4),
-        (np.array([1, 3, 1]), 1),
-        (np.array([5, 1, 2]), 2),
-        (np.array([0, 7, 7]), 5),
-    ]
+    inputset = [(np.random.randint(0, 8, size=(3,)), random.randint(0, 7)) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_times_y_tensors.py b/benchmarks/x_times_y_tensors.py
index 118a5d3d9..e4e5976c0 100644
--- a/benchmarks/x_times_y_tensors.py
+++ b/benchmarks/x_times_y_tensors.py
@@ -1,6 +1,7 @@
 # Target: x * y (Tensors)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -13,14 +14,17 @@ def main():
     y = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
     inputset = [
-        (np.array([6, 2, 4]), np.array([0, 7, 7])),
-        (np.array([1, 3, 1]), np.array([6, 2, 4])),
-        (np.array([5, 1, 2]), np.array([1, 3, 1])),
-        (np.array([0, 7, 7]), np.array([5, 1, 2])),
+        (np.random.randint(0, 2 ** 3, size=(3,)), np.random.randint(0, 2 ** 3, size=(3,)))
+        for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []
diff --git a/benchmarks/x_to_the_power_of_2.py b/benchmarks/x_to_the_power_of_2.py
index 5e003304a..da3505f64 100644
--- a/benchmarks/x_to_the_power_of_2.py
+++ b/benchmarks/x_to_the_power_of_2.py
@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -15,7 +17,8 @@ def main():
     engine = hnp.compile_numpy_function(
         function_to_compile,
         {"x": x},
-        [(6,), (1,), (5,), (2,)],
+        [(i,) for i in range(2 ** 3)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 
diff --git a/docs/user/advanced_examples/QuantizedLinearRegression.ipynb b/docs/user/advanced_examples/QuantizedLinearRegression.ipynb
index db1b74634..e31e9eb3d 100644
--- a/docs/user/advanced_examples/QuantizedLinearRegression.ipynb
+++ b/docs/user/advanced_examples/QuantizedLinearRegression.ipynb
@@ -64,8 +64,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "x = np.array([[130], [110], [100], [145], [160], [185], [200], [80], [50]], dtype=np.float32)\n",
-    "y = np.array([325, 295, 268, 400, 420, 500, 520, 220, 120], dtype=np.float32)"
+    "x = np.array([[69], [130], [110], [100], [145], [160], [185], [200], [80], [50]], dtype=np.float32)\n",
+    "y = np.array([181, 325, 295, 268, 400, 420, 500, 520, 220, 120], dtype=np.float32)"
    ]
   },
   {
@@ -95,7 +95,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -201,7 +201,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -233,8 +233,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[2.669915]]\n",
-      "-3.2335143\n"
+      "[[2.6698928]]\n",
+      "-3.2299957\n"
      ]
     }
    ],
@@ -517,7 +517,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -694,7 +694,7 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=227x423 at 0x7FFBB7AAF1F0>"
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=227x423 at 0x7FF17C8E64F0>"
       ]
      },
      "metadata": {},
@@ -746,7 +746,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/docs/user/advanced_examples/QuantizedLogisticRegression.ipynb b/docs/user/advanced_examples/QuantizedLogisticRegression.ipynb
index 61fa0b204..ee48a14e4 100644
--- a/docs/user/advanced_examples/QuantizedLogisticRegression.ipynb
+++ b/docs/user/advanced_examples/QuantizedLogisticRegression.ipynb
@@ -65,8 +65,34 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "x = torch.tensor([[1, 1], [1, 2], [2, 1], [4, 1], [3, 2], [4, 2]]).float()\n",
-    "y = torch.tensor([[0], [0], [0], [1], [1], [1]]).float()"
+    "x = torch.tensor(\n",
+    "    [\n",
+    "        [1, 1],\n",
+    "        [1, 1.5],\n",
+    "        [1.5, 1.2],\n",
+    "        [1, 2],\n",
+    "        [2, 1],\n",
+    "        [4, 1],\n",
+    "        [4, 1.5],\n",
+    "        [3.5, 1.8],\n",
+    "        [3, 2],\n",
+    "        [4, 2],\n",
+    "    ]\n",
+    ").float()\n",
+    "y = torch.tensor(\n",
+    "    [\n",
+    "        [0],\n",
+    "        [0],\n",
+    "        [0],\n",
+    "        [0],\n",
+    "        [0],\n",
+    "        [1],\n",
+    "        [1],\n",
+    "        [1],\n",
+    "        [1],\n",
+    "        [1],\n",
+    "    ]\n",
+    ").float()"
    ]
   },
   {
@@ -96,7 +122,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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A/HySv1k0ZuRzvtkK/R7gDb13XVwNPFVVj4861CCS/PD8elySl9D9vxv5X9BepvcBj1TVO5YZNnbzPkjuMZ7ziSQ7eve/H/gF4N8WDbsH+I3e/WuBT1bv1bpRGST3otdWrqF7bWPkquqPqmpXVU3SveD5yar69UXDRj7nW9fzZGstyTG6dybsTHIKuIXuhReq6jDwMbp3XDwKfAe4cTRJ/78Bsl8L/FaSs8B/A68b9V/QnpcBrwce6q2NArwZ2A1jPe+D5B7XOb8UeH+SZ9D9kPm7qro3yZ8Cs1V1D90Pq79O8ijdi+2vG13c7xok9+8luQY4S5f7hpGlHcC4zbkf/ZekRmy2JRdJapaFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhrxf09l6LOTuZAtAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -175,22 +201,42 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 1 | Loss: 0.9555807113647461\n",
-      "Epoch: 101 | Loss: 0.12865914404392242\n",
-      "Epoch: 201 | Loss: 0.0773993730545044\n",
-      "Epoch: 301 | Loss: 0.05493553355336189\n",
-      "Epoch: 401 | Loss: 0.042454302310943604\n",
-      "Epoch: 501 | Loss: 0.034546975046396255\n",
-      "Epoch: 601 | Loss: 0.02910044603049755\n",
-      "Epoch: 701 | Loss: 0.02512541227042675\n",
-      "Epoch: 801 | Loss: 0.02209884487092495\n",
-      "Epoch: 901 | Loss: 0.019718701019883156\n",
-      "Epoch: 1001 | Loss: 0.017798559740185738\n",
-      "Epoch: 1101 | Loss: 0.016217226162552834\n",
-      "Epoch: 1201 | Loss: 0.014892562292516232\n",
-      "Epoch: 1301 | Loss: 0.013766967691481113\n",
-      "Epoch: 1401 | Loss: 0.012798796407878399\n",
-      "Epoch: 1501 | Loss: 0.011957273818552494\n"
+      "Epoch: 1 | Loss: 0.693336546421051\n",
+      "Epoch: 101 | Loss: 0.1125209778547287\n",
+      "Epoch: 201 | Loss: 0.07049673795700073\n",
+      "Epoch: 301 | Loss: 0.050856731832027435\n",
+      "Epoch: 401 | Loss: 0.039525073021650314\n",
+      "Epoch: 501 | Loss: 0.0322115495800972\n",
+      "Epoch: 601 | Loss: 0.027129750698804855\n",
+      "Epoch: 701 | Loss: 0.023406751453876495\n",
+      "Epoch: 801 | Loss: 0.02056846395134926\n",
+      "Epoch: 901 | Loss: 0.018336370587348938\n",
+      "Epoch: 1001 | Loss: 0.01653693988919258\n",
+      "Epoch: 1101 | Loss: 0.015056520700454712\n",
+      "Epoch: 1201 | Loss: 0.013817812316119671\n",
+      "Epoch: 1301 | Loss: 0.012766523286700249\n",
+      "Epoch: 1401 | Loss: 0.01186333317309618\n",
+      "Epoch: 1501 | Loss: 0.011079175397753716\n",
+      "Epoch: 1601 | Loss: 0.010392050258815289\n",
+      "Epoch: 1701 | Loss: 0.009785104542970657\n",
+      "Epoch: 1801 | Loss: 0.009245104156434536\n",
+      "Epoch: 1901 | Loss: 0.008761593140661716\n",
+      "Epoch: 2001 | Loss: 0.00832616537809372\n",
+      "Epoch: 2101 | Loss: 0.007932038977742195\n",
+      "Epoch: 2201 | Loss: 0.007573576178401709\n",
+      "Epoch: 2301 | Loss: 0.007246167398989201\n",
+      "Epoch: 2401 | Loss: 0.006945951841771603\n",
+      "Epoch: 2501 | Loss: 0.006669704802334309\n",
+      "Epoch: 2601 | Loss: 0.006414605770260096\n",
+      "Epoch: 2701 | Loss: 0.006178391166031361\n",
+      "Epoch: 2801 | Loss: 0.00595900509506464\n",
+      "Epoch: 2901 | Loss: 0.005754708778113127\n",
+      "Epoch: 3001 | Loss: 0.005564006045460701\n",
+      "Epoch: 3101 | Loss: 0.0053855921141803265\n",
+      "Epoch: 3201 | Loss: 0.005218283273279667\n",
+      "Epoch: 3301 | Loss: 0.005061114672571421\n",
+      "Epoch: 3401 | Loss: 0.004913175944238901\n",
+      "Epoch: 3501 | Loss: 0.004773670341819525\n"
      ]
     }
    ],
@@ -200,7 +246,7 @@
     "optimizer = torch.optim.SGD(model.parameters(), lr=1)\n",
     "criterion = torch.nn.BCELoss()\n",
     "\n",
-    "epochs = 1501\n",
+    "epochs = 3501\n",
     "for e in range(1, epochs + 1):\n",
     "    optimizer.zero_grad()\n",
     "\n",
@@ -253,7 +299,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVhklEQVR4nO3dcWycd33H8ffXqVdnducUBzGnadcJ0Y3DpgEc3KlTenPUpHSo1TSm0W2wVkORttINbdLQ+KPVxl8TG4INQRSVNhRYywSVV1tlKZXJsonVk9M2OTueUFuCiTnL4PYO37G4jvvdH89dYru272w/9nP3u89LsnL3PE98n/6afPLz7x7/bO6OiIjUv6akA4iISDxU6CIigVChi4gEQoUuIhIIFbqISCCuSuqFW1tb/dprr03q5aVGXbx4kR07dtDW1sbOnTvZsWMHO3bsSDqWSM144YUXfurub13pXGKFfu211/LAAw8k9fJSw86dO8cbb7xBOp0mnU7T1tbGNddck3QskZrQ2tr6w9XOaclFak4qlbr8OJfLJRdEpM6o0EVEAqFCFxEJhApdRCQQKnQRkUCo0EVEAqFCFxEJhApdRCQQKnQRkUCo0EVEAqFCFxEJRGJ7uYhUMjMzw/nz59m5cyeA9nMRqSD8QncHs9WfS03q6upibGyMyclJFhYWuOmmmygUCnR2diYdTaRmVSx0M7seeAx4G+DAMXf//LJrDPg8cCfwc+Bed38+/rjrdPIkXLwIhw9HJe4OJ05ASwuk00mnkwrKm3QNDAzQ09PDbbfdBqBSX0EmA0NDkM9Dezv09UF3d9KpwlaLY17NGvol4K/cPQXcAtxvZqll13wAeEfp4wjwpVhTboR7VObDw1GJl8t8eDg67p50QqlSU1MT09PTnD59OukoNSmTgYEByOWiP9a5XPQ8k0k6WbhqdcwrztDdPQtkS49nzWwcuA44t+iyu4HH3N2B58xsl5l1ln5vMsyimTlEJT48HD3u7b0yYxcJwNAQzM8vPTY/Hx1PesYYqlod83Xd5WJmNwLvAYaXnboO+NGi5xdKx5b//iNmNmJmI8VicZ1RN2BxqZepzCUw+fz6jsvm1eqYV13oZtYGfAv4hLv/bCMv5u7H3L3H3XtaW1s38inW+4LRMsti5eUXkUC0t6/vuGxerY55VYVuZs1EZf51d39yhUsmgesXPd9bOpacxWvmvb3w4IPRr4vX1EUC0NcHzc1LjzU3R8dla9TqmFdzl4sBXwbG3f2zq1z2FPBxM3sC6AXyia6fQ7Ss0tKydM28vPzS0qJlFwlGec221u64CFmtjnk196HfCnwEyJjZi6VjnwJuAHD3o8DTRLcsvkR02+J9sSfdiHR66X3n5VJXmUtguruTL5NGU4tjXs1dLv8FrNmApbtb7o8rVKyWl7fKXEQCpb1cREQCoUIXEQmECl1EJBAqdKkLExMTFAoF5ubmmJ2dTTqOSE1SoUvNS6VSNDU1MTIywuDgINlslmw22btiRWpR+NvnShDKOy+Wt9Q9cOAAc3NzdHR0aJ90kRLN0KWupFIpcrkcZ86cYWpqKuk4IjVFhS4iEggVuohIIFToIiKBUKGLiARChS4iEggVuohIIFToIiKBUKGLiARChS4iEggVuohIIFToUreKxSKFQiHpGCI1Q5tzSd1JpVKMjo7S1tbGnj17tEmXSIkKXepSV1fX5Z0Xb731Vm666SYKhQKdnZ1JRxNJjJZcpG6lUin27NnDwMAAp0+fBtAPv5CGpkKXYORyuaQjiCRKhS4iEggVuohIIFToIiKBUKGLiARChS4iEggVuohIICp+Y5GZPQJ8EJh2964VzrcDXwNuKH2+f3D3R+MOKiJhymRgaAjyeWhvh74+6O5OOlV9qmaGfhy4Y43z9wPn3P1mIA38o5n9wuajiUjoMhkYGIBcDtyjXwcGouOyfhUL3d1PAa+udQlwjZkZ0Fa69lI88UQkZENDMD+/9Nj8fHRc1i+OvVy+ADwF/Bi4Bvh9d39jpQvN7AhwBGDXrl0xvLSI1LN8fn3HZW1xvCl6GHgR2APsA75gZr+00oXufszde9y9p7W1NYaXFomcPXuW8+fPMzMzo/1c6kh7+/qOy9riKPT7gCc98hLwA+DXY/i8IlXp6uoil8vR39/P4OAg2WyWbDarYq8DfX3Q3Lz0WHNzdFzWL44llwngIPCfZvY24NeAV2L4vCJVS6VSAIyNjVEoFNi/fz+pVEp7pNe48t0susslHtXctvg40d0ru83sAvAQ0Azg7keBTwPHzSwDGPBJd//pliUWqWBhYYHp6enLJS+1rbtbBR6XioXu7vdUOP9j4FBsiUREZEP0naIiIoFQoYuIBEKFLiISCBW6iEggVOgiIoFQoYuIBEKFLiISCBW6iEggVOgiIoFQoUtwJiYmKBQKzM7Oks1mk44jsm3i2JxLpGaU928ZGRlhbm6OgwcPMjc3R0dHhzbqkuCp0CVIXV1djI2NMTk5yYEDB3jnO98JoFKXoGnJRYKVSqXI5XKcOXOGqamppOOIbDkVuohIIFToIiKBUKGLiARChS4iEggVuohIIFToIiKBUKGLiARChS4iEggVuohIIFToIiKBUKFL8PL5PMVikUKhkHQUkS2lzbkkaKlUitHRUZ599llef/117bwoQdMMXYLX1dVFLpejv7+fwcFBstms9kmXIGmGLg2hvE96JpOho6ODffv20dbWppm6BKXiDN3MHjGzaTMbXeOatJm9aGZjZvYf8UYUEZFqVLPkchy4Y7WTZrYL+CJwl7u/C/i9WJKJiMi6VCx0dz8FvLrGJX8APOnuE6Xrp2PKJiIi6xDHm6I3Adea2UkzO21mH13tQjM7YmYjZjZSLBZjeGkRESmL403Rq4D3AQeBncB/m9lz7v795Re6+zHgGMDevXs9htcWEZGSOAr9AjDj7kWgaGangJuBNxW6iIhsnTiWXP4N+E0zu8rMfhHoBcZj+LwiIrIOFWfoZvY4kAZ2m9kF4CGgGcDdj7r7uJn9O3AWeAN42N1XvcVRRES2RsVCd/d7qrjmM8BnYkkkIiIbom/9FxEJhApdGlI+n086gkjsVOjSUJqamjh79iyvvfaaNumS4GhzLmko5U26+vv76e7u5uDBg9pSV4KhQpeG1NXVxdjYGIVCgf3793P11Ver0KXuaclFGtrCwgLT09p+SMKgQhcRCYQKXUQkEOEXuvvaz0VEAhH2m6InT8LFi3D4MJhFZX7iBLS0QDqddDoRqWOZDAwNQT4P7e3Q1wfd3clmCneG7h6V+fBwVOLlMh8ejo5rpi4iG5TJwMAA5HJRleRy0fNMJtlc4c7QzaKZOUQlPjwcPe7tvTJjFxHZgKEhmJ9femx+Pjqe5Cw93Bk6LC31MpW5iGzSajtHJL2jRNiFXl5mWay8/CIiskHt7es7vl3CLfTFa+a9vfDgg9Gvi9fURUQ2oK8PmpuXHmtujo4nKew19JaWpWvm5eWXlhYtu4jIhpXXyWvtLpdwCx2iWxPdr5R3udRV5iKySd3dyRf4cuEuuZQtL2+VuYgEKvxCF1nDxMQEhUKBubk5Zmdnk44jsikqdGlYqVSKpqYmXnnlFQYHB/UDL6Tuhb2GLlJB+QdeZDIZJicnOXDggH7ghdQtzdBFiH7gRS6X48yZM0xNTSUdR2RDVOgiIoFQoYuIBEKFLiISCBW6iEggVOgiIoGoWOhm9oiZTZvZaIXr9pvZJTP7UHzxRESkWtXM0I8Dd6x1gZntAP4eeCaGTCIisgEVC93dTwGvVrjsAeBbwHQcoUREZP02vYZuZtcBvwN8qYprj5jZiJmNFIvFzb60iIgsEsebop8DPunub1S60N2PuXuPu/e0trbG8NIiIlIWx14uPcATFm1Luxu408wuuXt/DJ9bJBGFQkF7uUjd2XShu/uvlh+b2XFgUGUu9SiVSjE6OkpbWxtvectbtEmX1J2KhW5mjwNpYLeZXQAeApoB3P3olqYT2WZdXV2MjY1d3nnx7W9/O4VCgc7OzqSjiVRUsdDd/Z5qP5m737upNCI1oLylbn9/P+l0mnQ6zezsrGbqUvP0naIiFeRyuaQjiFRFhS4iEggVuohIIFToIiKBUKGLiARChS4iEggVuohIIFToIiKBUKGLiARChS4iEggVuohIIOLYPlckWDMzM5w/f56dO3cCaD8XqWkqdJFVLN558eWXX+bQoUPaeVFqmgpdZA3lnRczmQyFQoH9+/cDqNSlJmkNXaQKTU1NLCwsMD2tn4MutUuFLiISCBW6iEggVOgiIoFQoYuIBEKFLiISCBW6iEggVOgiIoFQoYuIBEKFLiISCBW6iEggVOgiVZqYmKBQKDA3N0c2m006jsibaHOuWuYOZqs/l21T3qRrZGSEubk5Dh06xNzcHB0dHdpSV2pGxRm6mT1iZtNmNrrK+T80s7NmljGz75nZzfHHbEAnT8KJE1GJQ/TriRPRcUlMeUvdRx99lPHxcWZmZpidnU06lghQ3ZLLceCONc7/ALjN3buBTwPHYsjV2Nzh4kUYHr5S6idORM8vXrxS8pKIVCpFLpfjzJkzTE1NJR1H5LKKSy7ufsrMblzj/PcWPX0O2BtDrsZmBocPR4+Hh6MPgN7e6LiWXURkBXG/KfonwLdXO2lmR8xsxMxGisVizC8dmMWlXqYyF5E1xFboZvZbRIX+ydWucfdj7t7j7j2tra1xvXSYysssiy1eUxcRWSaWu1zM7N3Aw8AH3H0mjs/Z0BavmZeXWcrPQTN1EVnRpgvdzG4AngQ+4u7f33wkwQxaWpaumZeXX1paVOYisqKKhW5mjwNpYLeZXQAeApoB3P0o8CDQAXzRoqK55O49WxW4YaTTS+87L5e6ylxEVlHNXS73VDj/MeBjsSWSK5aXt8pcRNagb/0XEQmECl1EJBAqdBGRQKjQRTYon89TLBYpFApJRxEBtNuiyIakUilGR0cZHx/n9ddf186LUhNU6CIbVN55cXJykpdffplDhw5RKBTo7OxMOpo0KBW6yCaU90nPZDJ0dHSwb98+2traNFOXRGgNXUQkECp0EZFAqNBFRAKhQhcRCYQKXUQkECp0EZFAqNBFRAKhQhcRCYQKXUQkECp0EZFAqNBFYpTP55OOIA1MhS4Sg6amJs6ePctrr71GNpslm80mHUkakApdJAapVIo9e/bQ39/PM888w+zsLOfPn2d2djbpaNJAtNuiSIzKW+oWCgX279/P1VdfrZ0XZdtohi6yBRYWFpienk46hjQYFbqISCBU6CIigVChi4gEQoUuIhIIFbqISCDCL3T3tZ9L/DTmIomoeB+6mT0CfBCYdveuFc4b8HngTuDnwL3u/nzcQTfk5Em4eBEOHwazqFhOnICWFkink04XJo25NIhMBoaGIJ+H9nbo64Pu7mQzVTNDPw7cscb5DwDvKH0cAb60+VgxcI+KZXg4KpRysQwPR8c1a4yfxlwaRCYDAwOQy0V/rHO56Hkmk2yuijN0dz9lZjeuccndwGPu7sBzZrbLzDrdPdnNLMyiWSJEhTI8HD3u7b0ye5R4acylQQwNwfz80mPz89HxJGfpcayhXwf8aNHzC6Vjb2JmR8xsxMxGisViDC9dweKCKVOxbC2NuTSA1TbVTHqzzW19U9Tdj7l7j7v3tLa2bscLRl/yL1ZeCpCtoTEHYGJigpmZGWZnZ7XzYoDa29d3fLvEsTnXJHD9oud7S8eStXj9tvwlf/k5aNa4FTTmQLTzIkAmk2FycpIDBw4wNzdHR0eHNuoKRF9ftGa+eNmluTk6nqQ4Cv0p4ONm9gTQC+QTXz+HqDhaWpau35aXAlpaGqJYtp3GfImuri7OnTvHqVOnuHDhArfffjuASj0A5XXyWrvLpZrbFh8H0sBuM7sAPAQ0A7j7UeBpolsWXyK6bfG+rQq7bul0NGssF0m5YBqsWLaVxnyJVCrFuXPnmJmZYWpqio6OjqQjSUy6u5Mv8OWqucvlngrnHbg/tkRxW14kDVos20pjLpKI8L9TVESkQajQRUQCoUIXEQmECl1EJBAqdBGRQKjQRUQCoUIXEQmECl1EJBAqdBGRQKjQRbZBPp+nWCxe3oFRZCuo0EW2WCqVIpfL8eyzzzI+Pk42m9WWurIlVOgi2yCVSrFnzx76+/t55plntE+6bAnzhH7wgJn9BPjhNr7kbuCn2/h6carX7PWaG+o3e73mhvrNvt25f8Xd37rSicQKfbuZ2Yi79ySdYyPqNXu95ob6zV6vuaF+s9dSbi25iIgEQoUuIhKIRir0Y0kH2IR6zV6vuaF+s9drbqjf7DWTu2HW0EVEQtdIM3QRkaCp0EVEAhFUoZvZI2Y2bWajq5w3M/snM3vJzM6a2Xu3O+NqqsieNrO8mb1Y+nhwuzOuxMyuN7Pvmtk5Mxszs79Y4ZqaG/cqc9fqmLeY2f+Y2ZlS9r9d4ZqrzewbpTEfNrMbE4i6PFM1ue81s58sGvOPJZF1NWa2w8xeMLPBFc4lP+buHswHcAB4LzC6yvk7gW8DBtwCDCedeR3Z08Bg0jlXyNUJvLf0+Brg+0Cq1se9yty1OuYGtJUeNwPDwC3Lrvkz4Gjp8YeBb9RJ7nuBLySddY3/hr8E/mWlPxe1MOZBzdDd/RTw6hqX3A085pHngF1m1rk96dZWRfaa5O5Zd3++9HgWGAeuW3ZZzY17lblrUmkcC6WnzaWP5Xc33A18pfT4m8BBM7NtiriiKnPXLDPbC/w28PAqlyQ+5kEVehWuA3606PkF6uQvcclvlL5c/baZvSvpMMuVvsR8D9HMa7GaHvc1ckONjnnpS/8XgWngO+6+6pi7+yUgD3Rsa8gVVJEb4HdLS3PfNLPrtzfhmj4H/DXwxirnEx/zRiv0evY80R4ONwP/DPQnG2cpM2sDvgV8wt1/lnSealXIXbNj7u4L7r4P2Au838y6Eo5UlSpyDwA3uvu7ge9wZcabKDP7IDDt7qeTzrKWRiv0SWDxv/h7S8dqnrv/rPzlqrs/DTSb2e6EYwFgZs1Epfh1d39yhUtqctwr5a7lMS9z9xzwXeCOZacuj7mZXQW0AzPbGm4Nq+V29xl3nys9fRh43zZHW82twF1mdh54Augzs68tuybxMW+0Qn8K+GjprotbgLy718Uepmb2y+X1ODN7P9H/u8T/gpYyfRkYd/fPrnJZzY17NblreMzfama7So93ArcD/7vssqeAPy49/hAw5KV365JSTe5l763cRfTeRuLc/W/cfa+730j0hueQu//RsssSH/OrtvPFtpqZPU50Z8JuM7sAPET0xgvufhR4muiOi5eAnwP3JZP0zarI/iHgT83sEvB/wIeT/gtacivwESBTWhsF+BRwA9T0uFeTu1bHvBP4ipntIPpH5l/dfdDM/g4YcfeniP6x+qqZvUT0ZvuHk4t7WTW5/9zM7gIuEeW+N7G0Vai1Mde3/ouIBKLRllxERIKlQhcRCYQKXUQkECp0EZFAqNBFRAKhQhcRCYQKXUQkEP8PBM9aszN9mZMAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -291,9 +337,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[4.54124975]\n",
-      " [2.37628269]]\n",
-      "-14.683035850524902\n"
+      "[[5.28773165]\n",
+      " [2.6065383 ]]\n",
+      "-16.89045524597168\n"
      ]
     }
    ],
@@ -603,7 +649,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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uBfT2GizrwTHXFtFcJ++2u1zauW3xfmAM2BERJ4C7gR6AzLwH+CLVLYtPUN22+M71KnbVxsaqWWMzSJoBY7CsH8dcW8ToaP0Bvlg7d7nc0eJ4Au/uWEWdtjhIDJb155hLtSj/laKStEUY6JJUiE5cFJW2hOPHj/PCF76Q06dPt+y7fft2+vv7N6Aq6SIDXWrD8PAwAEeOHOH8+fNs27Zt2b5DQ0PcdNNNnDlzhp07d25UiZKBLq3GyMgI09PTK/Y5cuQIJ06c4NZbbwUw1LVhDHRplZqz9eXMzMwwOzvLI488wpjvMKkN5EVRSSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVIjImj54ICK+DXxzA0+5A/jOBp6vkzZr7Zu1bti8tW/WumHz1r7Rdf9UZr5gqQO1BfpGi4gjmbm77jrWYrPWvlnrhs1b+2atGzZv7d1Ut0suklQIA12SCrGVAn1/3QVchs1a+2atGzZv7Zu1bti8tXdN3VtmDV2SSreVZuiSVDQDXZIKUVSgR8S9ETEbEceWOR4R8bcR8UREPBYRr9zoGpfTRu1jETEfEY82HndtdI1LiYjrIuIrETETEdMR8YdL9Om6cW+z7m4d896I+K+I+O9G7X+2RJ9tEfHZxphPRsT1NZS6uKZ26r4zIr69YMzfVUety4mIKyLiaxHxhSWO1T/mmVnMA3gd8Erg2DLH3wB8CQjgNcBk3TWvovYx4At117lEXTuBVza2+4GvA8PdPu5t1t2tYx7A9sZ2DzAJvGZRn98H7mlsvw347Cap+07gI3XXusJ/wx8B/7TUz0U3jHlRM/TMPAx8d4UubwE+mZWHgcGI6IqPk2mj9q6UmU9l5tHG9mngcWDXom5dN+5t1t2VGuN4prHb03gsvrvhLcAnGtufA26LiNigEpfUZt1dKyKGgF8BPrZMl9rHvKhAb8Mu4FsL9k+wSX6JG36+8efqlyLipXUXs1jjT8xXUM28FurqcV+hbujSMW/86f8oMAt8OTOXHfPMfAaYB67d0CKX0EbdAL/WWJr7XERct7EVruhDwJ8Azy1zvPYx32qBvpkdpXoPh5cDfwc8WG85l4qI7cDngfdl5vfqrqddLeru2jHPzGcz82ZgCHh1RIzUXFJb2qj7AHB9Zr4M+DIXZ7y1iog3ArOZ+UjdtaxkqwX6SWDhv/hDjbaul5nfa/65mplfBHoiYkfNZQEQET1UofjpzHxgiS5dOe6t6u7mMW/KzDngK8Dtiw79cMwj4kpgADi1ocWtYLm6M/NUZp5v7H4MeNUGl7ac1wJvjoj/Az4DjEfEpxb1qX3Mt1qgPwS8o3HXxWuA+cx8qu6i2hERP9Fcj4uIV1P9v6v9F7RR08eBxzPzg8t067pxb6fuLh7zF0TEYGP7ecAvAf+zqNtDwO80tt8KTGTjal1d2ql70bWVN1Nd26hdZv5pZg5l5vVUFzwnMvO3F3Wrfcyv3MiTrbeIuJ/qzoQdEXECuJvqwguZeQ/wRao7Lp4Avg+8s55Kf1Qbtb8V+L2IeAb4AfC2un9BG14LvB2YaqyNAnwAeDF09bi3U3e3jvlO4BMRcQXVPzL/nJlfiIg/B45k5kNU/1j9Y0Q8QXWx/W31lftD7dT9BxHxZuAZqrrvrK3aNnTbmPvSf0kqxFZbcpGkYhnoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRD/D8Rw2iY8jIsvAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -807,7 +853,7 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=443x539 at 0x7FCC7D79E400>"
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=443x539 at 0x7F9864517D00>"
       ]
      },
      "metadata": {},
@@ -838,7 +884,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5385b79125e44bc493c7eaf5f8013b14",
+       "model_id": "7de68790a0584e96aa7e8e30a535275a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -878,7 +924,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/script/progress_tracker_utils/measure.py b/script/progress_tracker_utils/measure.py
index 006713d0b..17d466720 100644
--- a/script/progress_tracker_utils/measure.py
+++ b/script/progress_tracker_utils/measure.py
@@ -261,7 +261,8 @@ def main():
     scripts = list(base.glob("*.py"))
 
     # Create a directory to store temporary scripts
-    os.makedirs(".benchmarks/scripts", exist_ok=True)
+    shutil.rmtree(".benchmarks/scripts", ignore_errors=True)
+    shutil.copytree(base, ".benchmarks/scripts")
 
     # Process each script under the base directory
     for path in scripts: