add tests for conversion between hyperactive and gradient-free-optimi…

…zers

tests/test_hyper_gradient_trafo.py

@@ -0,0 +1,215 @@
+import time
+import pytest
+import numpy as np
+import pandas as pd
+
+from hyperactive import Hyperactive
+
+
+def objective_function_0(opt):
+    score = -opt["x1"] * opt["x1"]
+    return score
+
+
+search_space_0 = {
+    "x1": list(np.arange(-5, 6, 1)),
+}
+search_space_1 = {
+    "x1": list(np.arange(0, 6, 1)),
+}
+search_space_2 = {
+    "x1": list(np.arange(-5, 1, 1)),
+}
+
+
+search_space_3 = {
+    "x1": list(np.arange(-1, 1, 0.1)),
+}
+search_space_4 = {
+    "x1": list(np.arange(-1, 0, 0.1)),
+}
+search_space_5 = {
+    "x1": list(np.arange(0, 1, 0.1)),
+}
+
+
+search_space_para_0 = [
+    (search_space_0),
+    (search_space_1),
+    (search_space_2),
+    (search_space_3),
+    (search_space_4),
+    (search_space_5),
+]
+
+
+@pytest.mark.parametrize("search_space", search_space_para_0)
+def test_trafo_0(search_space):
+    hyper = Hyperactive()
+    hyper.add_search(objective_function_0, search_space, n_iter=25)
+    hyper.run()
+
+    for value in hyper.results(objective_function_0)["x1"].values:
+        if value not in search_space["x1"]:
+            assert False
+
+
+# ----------------- # Test if wrong memory warm starts do work as intended
+
+
+from sklearn.datasets import load_breast_cancer
+from sklearn.model_selection import cross_val_score
+from sklearn.tree import DecisionTreeClassifier
+
+data = load_breast_cancer()
+X, y = data.data, data.target
+
+
+def objective_function_1(opt):
+    dtc = DecisionTreeClassifier(min_samples_split=opt["min_samples_split"])
+    scores = cross_val_score(dtc, X, y, cv=5)
+
+    return scores.mean()
+
+
+search_space_0 = {
+    "min_samples_split": list(np.arange(2, 12)),
+}
+
+search_space_1 = {
+    "min_samples_split": list(np.arange(12, 22)),
+}
+
+search_space_2 = {
+    "min_samples_split": list(np.arange(22, 32)),
+}
+
+memory_dict = {"min_samples_split": range(2, 12), "score": range(2, 12)}
+memory_warm_start_0 = pd.DataFrame(memory_dict)
+
+memory_dict = {"min_samples_split": range(12, 22), "score": range(12, 22)}
+memory_warm_start_1 = pd.DataFrame(memory_dict)
+
+memory_dict = {"min_samples_split": range(22, 32), "score": range(22, 32)}
+memory_warm_start_2 = pd.DataFrame(memory_dict)
+
+search_space_para_1 = [
+    (search_space_0, memory_warm_start_0),
+    (search_space_1, memory_warm_start_1),
+    (search_space_2, memory_warm_start_2),
+]
+
+random_state_para_0 = [
+    (0),
+    (1),
+    (2),
+    (3),
+    (4),
+]
+
+
+@pytest.mark.parametrize("random_state", random_state_para_0)
+@pytest.mark.parametrize("search_space, memory_warm_start", search_space_para_1)
+def test_trafo_1(random_state, search_space, memory_warm_start):
+    search_space = search_space
+    memory_warm_start = memory_warm_start
+
+    c_time_0 = time.time()
+    hyper = Hyperactive()
+    hyper.add_search(
+        objective_function_1,
+        search_space,
+        n_iter=25,
+        random_state=random_state,
+        initialize={"random": 1},
+    )
+    hyper.run()
+    d_time_0 = time.time() - c_time_0
+
+    c_time_1 = time.time()
+    hyper = Hyperactive()
+    hyper.add_search(
+        objective_function_1,
+        search_space,
+        n_iter=25,
+        random_state=random_state,
+        initialize={"random": 1},
+        memory_warm_start=memory_warm_start,
+    )
+    hyper.run()
+    d_time_1 = time.time() - c_time_1
+
+    assert d_time_1 < d_time_0 * 0.8
+
+
+# ----------------- # Test if wrong memory warm starts do not work as intended
+
+search_space_0 = {
+    "min_samples_split": list(np.arange(2, 12)),
+}
+
+search_space_1 = {
+    "min_samples_split": list(np.arange(12, 22)),
+}
+
+search_space_2 = {
+    "min_samples_split": list(np.arange(22, 32)),
+}
+
+memory_dict = {"min_samples_split": range(12, 22), "score": range(2, 12)}
+memory_warm_start_0 = pd.DataFrame(memory_dict)
+
+memory_dict = {"min_samples_split": range(22, 32), "score": range(12, 22)}
+memory_warm_start_1 = pd.DataFrame(memory_dict)
+
+memory_dict = {"min_samples_split": range(2, 12), "score": range(22, 32)}
+memory_warm_start_2 = pd.DataFrame(memory_dict)
+
+search_space_para_2 = [
+    (search_space_0, memory_warm_start_0),
+    (search_space_1, memory_warm_start_1),
+    (search_space_2, memory_warm_start_2),
+]
+
+random_state_para_0 = [
+    (0),
+    (1),
+    (2),
+    (3),
+    (4),
+]
+
+""" test is possible in future gfo versions
+@pytest.mark.parametrize("random_state", random_state_para_0)
+@pytest.mark.parametrize("search_space, memory_warm_start", search_space_para_2)
+def test_trafo_2(random_state, search_space, memory_warm_start):
+    search_space = search_space
+    memory_warm_start = memory_warm_start
+
+    c_time_0 = time.time()
+    hyper = Hyperactive()
+    hyper.add_search(
+        objective_function_1,
+        search_space,
+        n_iter=25,
+        random_state=random_state,
+        initialize={"random": 1},
+    )
+    hyper.run()
+    d_time_0 = time.time() - c_time_0
+
+    c_time_1 = time.time()
+    hyper = Hyperactive()
+    hyper.add_search(
+        objective_function_1,
+        search_space,
+        n_iter=25,
+        random_state=random_state,
+        initialize={"random": 1},
+        memory_warm_start=memory_warm_start,
+    )
+    hyper.run()
+    d_time_1 = time.time() - c_time_1
+
+    assert not (d_time_1 < d_time_0 * 0.8)
+"""