add usage of e and 10^ for exponent format as input for yaml file + n…

…ew tests for optional type argument

src/neps/search_spaces/hyperparameters/categorical.py

@@ -2,11 +2,10 @@
 
 import random
 from copy import copy, deepcopy
-from typing import Iterable
+from typing import Iterable, Literal
 
 import numpy as np
 import numpy.typing as npt
-from typing_extensions import Literal
 
 from ..parameter import Parameter
 
@@ -32,9 +31,7 @@ def __init__(
         self.upper = default
         self.default_confidence_score = CATEGORICAL_CONFIDENCE_SCORES[default_confidence]
         self.has_prior = self.default is not None
-
         self.is_fidelity = is_fidelity
-
         self.choices = list(choices)
         self.num_choices = len(self.choices)
         self.probabilities: list[npt.NDArray] = list(

src/neps/search_spaces/hyperparameters/float.py

@@ -2,10 +2,10 @@
 
 import math
 from copy import deepcopy
+from typing import Literal
 
 import numpy as np
 import scipy.stats
-from typing_extensions import Literal
 
 from .numerical import NumericalParameter
 
@@ -37,7 +37,6 @@ def __init__(
 
         if self.lower >= self.upper:
             raise ValueError("Float parameter: bounds error (lower >= upper).")
-
         self.log = log
 
         if self.log:

tests/test_yaml_search_space/config_including_unknown_types.yaml

@@ -0,0 +1,20 @@
+search_space:
+  learning_rate:
+    type: numerical
+    lower: 0.00001
+    upper: 0.1
+    log: true
+
+  num_epochs:
+    type: numerical
+    lower: 3
+    upper: 30
+    is_fidelity: True
+
+  optimizer:
+    type: numerical
+    choices: ["adam", "sgd", "rmsprop"]
+
+  dropout_rate:
+    type: numerical
+    value: 0.5

tests/test_yaml_search_space/config_including_wrong_types.yaml

@@ -0,0 +1,20 @@
+search_space:
+  learning_rate:
+    type: int
+    lower: 0.00001
+    upper: 0.1
+    log: true
+
+  num_epochs:
+    type: float
+    lower: 3
+    upper: 30
+    is_fidelity: True
+
+  optimizer:
+    type: cat
+    choices: ["adam", "sgd", "rmsprop"]
+
+  dropout_rate:
+    type: const
+    value: 0.5

tests/test_yaml_search_space/correct_config.yaml

@@ -7,6 +7,7 @@ search_space:
   num_epochs:
     lower: 3
     upper: 30
+    log: false
     is_fidelity: True
 
   optimizer:

tests/test_yaml_search_space/correct_config_including_types.yaml

@@ -0,0 +1,20 @@
+search_space:
+  learning_rate:
+    type: float
+    lower: 0.00001
+    upper: 0.1
+    log: true
+
+  num_epochs:
+    type: int
+    lower: 3
+    upper: 30
+    is_fidelity: True
+
+  optimizer:
+    type: cat
+    choices: ["adam", "sgd", "rmsprop"]
+
+  dropout_rate:
+    type: const
+    value: 0.5

tests/test_yaml_search_space/test_search_space.py

@@ -1,38 +1,45 @@
 import pytest
 
 from neps import CategoricalParameter, ConstantParameter, FloatParameter, IntegerParameter
-from neps.search_spaces.search_space import pipeline_space_from_yaml
+from neps.search_spaces.search_space import (
+    SearchSpaceFromYamlFileError,
+    pipeline_space_from_yaml,
+)
 
 
 @pytest.mark.yaml_api
-def test_correct_yaml_file():
-    """Test the function with a correctly formatted YAML file."""
-    pipeline_space = pipeline_space_from_yaml(
-        "tests/test_yaml_search_space/correct_config.yaml"
+def test_correct_yaml_files():
+    def test_correct_yaml_file(path):
+        """Test the function with a correctly formatted YAML file."""
+        pipeline_space = pipeline_space_from_yaml(path)
+        assert isinstance(pipeline_space, dict)
+        assert isinstance(pipeline_space["learning_rate"], FloatParameter)
+        assert pipeline_space["learning_rate"].lower == 0.00001
+        assert pipeline_space["learning_rate"].upper == 0.1
+        assert pipeline_space["learning_rate"].log is True
+        assert pipeline_space["optimizer"].is_fidelity is False
+        assert pipeline_space["learning_rate"].default is None
+        assert pipeline_space["learning_rate"].default_confidence_score == 0.5
+        assert isinstance(pipeline_space["num_epochs"], IntegerParameter)
+        assert pipeline_space["num_epochs"].lower == 3
+        assert pipeline_space["num_epochs"].upper == 30
+        assert pipeline_space["num_epochs"].log is False
+        assert pipeline_space["num_epochs"].is_fidelity is True
+        assert pipeline_space["num_epochs"].default is None
+        assert pipeline_space["num_epochs"].default_confidence_score == 0.5
+        assert isinstance(pipeline_space["optimizer"], CategoricalParameter)
+        assert pipeline_space["optimizer"].choices == ["adam", "sgd", "rmsprop"]
+        assert pipeline_space["optimizer"].is_fidelity is False
+        assert pipeline_space["optimizer"].default is None
+        assert pipeline_space["optimizer"].default_confidence_score == 2
+        assert isinstance(pipeline_space["dropout_rate"], ConstantParameter)
+        assert pipeline_space["dropout_rate"].value == 0.5
+        assert pipeline_space["dropout_rate"].is_fidelity is False
+
+    test_correct_yaml_file("tests/test_yaml_search_space/correct_config.yaml")
+    test_correct_yaml_file(
+        "tests/test_yaml_search_space/correct_config_including_types" ".yaml"
     )
-    assert isinstance(pipeline_space, dict)
-    assert isinstance(pipeline_space["learning_rate"], FloatParameter)
-    assert pipeline_space["learning_rate"].lower == 0.00001
-    assert pipeline_space["learning_rate"].upper == 0.1
-    assert pipeline_space["learning_rate"].log is True
-    assert pipeline_space["optimizer"].is_fidelity is False
-    assert pipeline_space["learning_rate"].default is None
-    assert pipeline_space["learning_rate"].default_confidence_score == 0.5
-    assert isinstance(pipeline_space["num_epochs"], IntegerParameter)
-    assert pipeline_space["num_epochs"].lower == 3
-    assert pipeline_space["num_epochs"].upper == 30
-    assert pipeline_space["num_epochs"].log is False
-    assert pipeline_space["num_epochs"].is_fidelity is True
-    assert pipeline_space["num_epochs"].default is None
-    assert pipeline_space["num_epochs"].default_confidence_score == 0.5
-    assert isinstance(pipeline_space["optimizer"], CategoricalParameter)
-    assert pipeline_space["optimizer"].choices == ["adam", "sgd", "rmsprop"]
-    assert pipeline_space["optimizer"].is_fidelity is False
-    assert pipeline_space["optimizer"].default is None
-    assert pipeline_space["optimizer"].default_confidence_score == 2
-    assert isinstance(pipeline_space["dropout_rate"], ConstantParameter)
-    assert pipeline_space["dropout_rate"].value == 0.5
-    assert pipeline_space["dropout_rate"].is_fidelity is False
 
 
 @pytest.mark.yaml_api
@@ -69,22 +76,47 @@ def test_correct_including_priors_yaml_file():
 @pytest.mark.yaml_api
 def test_incorrect_yaml_file():
     """Test the function with an incorrectly formatted YAML file."""
-    with pytest.raises(ValueError):
+    with pytest.raises(SearchSpaceFromYamlFileError) as excinfo:
         pipeline_space_from_yaml("tests/test_yaml_search_space/incorrect_config.txt")
+    assert str(excinfo.value.exception_type == "ValueError")
 
 
 @pytest.mark.yaml_api
 def test_yaml_file_with_missing_key():
     """Test the function with a YAML file missing a required key."""
-    with pytest.raises(KeyError):
+    with pytest.raises(SearchSpaceFromYamlFileError) as excinfo:
         pipeline_space_from_yaml("tests/test_yaml_search_space/missing_key_config.yml")
+    assert str(excinfo.value.exception_type == "KeyError")
 
 
 @pytest.mark.yaml_api
 def test_yaml_file_with_inconsistent_types():
     """Test the function with a YAML file having inconsistent types for
     'lower' and 'upper'."""
-    with pytest.raises(TypeError):
+    with pytest.raises(SearchSpaceFromYamlFileError) as excinfo:
         pipeline_space_from_yaml(
             "tests/test_yaml_search_space/inconsistent_types_config.yml"
         )
+    assert str(excinfo.value.exception_type == "TypeError")
+
+
+@pytest.mark.yaml_api
+def test_yaml_file_including_wrong_types():
+    """Test the function with a YAML file that defines the wrong but existing type
+    int to float as an optional argument"""
+    with pytest.raises(SearchSpaceFromYamlFileError) as excinfo:
+        pipeline_space_from_yaml(
+            "tests/test_yaml_search_space/config_including_wrong_types.yaml"
+        )
+    assert str(excinfo.value.exception_type == "TypeError")
+
+
+@pytest.mark.yaml_api
+def test_yaml_file_including_unkown_types():
+    """Test the function with a YAML file that defines an unknown type as an optional
+    argument"""
+    with pytest.raises(SearchSpaceFromYamlFileError) as excinfo:
+        pipeline_space_from_yaml(
+            "tests/test_yaml_search_space/config_including_unknown_types.yaml"
+        )
+    assert str(excinfo.value.exception_type == "TypeError")