diff --git a/.github/workflows/pypi_release.yaml b/.github/workflows/pypi_release.yaml
index 6f2cc49..3c175af 100644
--- a/.github/workflows/pypi_release.yaml
+++ b/.github/workflows/pypi_release.yaml
@@ -18,3 +18,4 @@ jobs:
       uses: JRubics/poetry-publish@v2.0
       with:
         pypi_token: ${{ secrets.PYPI_TOKEN }}
+        poetry_install_options: "--without dev"
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
new file mode 100644
index 0000000..a066362
--- /dev/null
+++ b/.github/workflows/test.yaml
@@ -0,0 +1,33 @@
+name: Tests
+
+on:
+  pull_request:
+    types: [opened, reopened, ready_for_review, review_requested]
+
+jobs:
+  run-pytest:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.12'
+
+    - name: Install Poetry
+      uses: snok/install-poetry@v1
+
+    - name: Install dependencies
+      run: poetry install --no-interaction --with dev
+
+    - name: Run tests
+      run: poetry run pytest --junitxml=pytest.xml --cov-report=term-missing:skip-covered --cov=tests/ > pytest-coverage.txt
+
+    - name: Pytest coverage comment
+      uses: MishaKav/pytest-coverage-comment@main
+      with:
+        pytest-coverage-path: ./pytest-coverage.txt
+        junitxml-path: ./pytest.xml
diff --git a/mqboost/constraints.py b/mqboost/constraints.py
index ba4e68a..b9ef056 100644
--- a/mqboost/constraints.py
+++ b/mqboost/constraints.py
@@ -1,13 +1,6 @@
 import pandas as pd
 
-from mqboost.base import (
-    FUNC_TYPE,
-    ModelName,
-    MQStr,
-    ParamsLike,
-    TypeName,
-    ValidationException,
-)
+from mqboost.base import FUNC_TYPE, ModelName, MQStr, ParamsLike, TypeName
 
 
 def set_monotone_constraints(
@@ -27,10 +20,6 @@ def set_monotone_constraints(
         ParamsLike
     """
     constraints_fucs = FUNC_TYPE.get(model_name).get(TypeName.constraints_type)
-    if MQStr.obj.value in params:
-        raise ValidationException(
-            "The parameter named 'objective' must be excluded in params"
-        )
     _params = params.copy()
     if MQStr.mono.value in _params:
         _monotone_constraints = list(_params[MQStr.mono.value])
diff --git a/mqboost/dataset.py b/mqboost/dataset.py
index 7ff34a3..4e85a21 100644
--- a/mqboost/dataset.py
+++ b/mqboost/dataset.py
@@ -74,11 +74,6 @@ def predict_dtype(self) -> Callable:
         """Get the data type function for prediction data."""
         return self._predict_dtype
 
-    @property
-    def model(self) -> ModelName:
-        """Get the model type."""
-        return self._model
-
     @property
     def columns(self) -> pd.Index:
         """Get the column names of the input features."""
diff --git a/mqboost/objective.py b/mqboost/objective.py
index 72fe4f4..2dfb8ab 100644
--- a/mqboost/objective.py
+++ b/mqboost/objective.py
@@ -4,7 +4,7 @@
 import numpy as np
 
 from mqboost.base import DtrainLike, ModelName, ObjectiveName
-from mqboost.utils import delta_validate
+from mqboost.utils import delta_validate, epsilon_validate
 
 CHECK_LOSS: str = "check_loss"
 
@@ -26,19 +26,13 @@ def _hess_rho(error: np.ndarray, alpha: float) -> np.ndarray:
 
 
 # Huber loss
-def _error_delta_compare(
-    error: np.ndarray, delta: float
-) -> tuple[np.ndarray, np.ndarray]:
-    """Rerutn two boolean arrays indicating where the errors are smaller or larger than delta."""
-    _abs_error = np.abs(error)
-    return (_abs_error <= delta).astype(int), (_abs_error > delta).astype(int)
-
-
 def _grad_huber(error: np.ndarray, alpha: float, delta: float) -> np.ndarray:
     """Compute the gradient of the huber loss function."""
-    _smaller_delta, _bigger_delta = _error_delta_compare(error=error, delta=delta)
-    _grad = _grad_rho(error=error, alpha=alpha)
+    _abs_error = np.abs(error)
+    _smaller_delta = (_abs_error <= delta).astype(int)
+    _bigger_delta = (_abs_error > delta).astype(int)
     _r = _rho(error=error, alpha=alpha)
+    _grad = _grad_rho(error=error, alpha=alpha)
     return _r * _smaller_delta + _grad * _bigger_delta
 
 
@@ -174,9 +168,10 @@ def __init__(
         if objective == ObjectiveName.check:
             self._fobj = partial(check_loss_grad_hess, alphas=alphas)
         elif objective == ObjectiveName.huber:
-            _delta = delta_validate(delta=delta)
-            self._fobj = partial(huber_loss_grad_hess, alphas=alphas, delta=_delta)
+            delta_validate(delta=delta)
+            self._fobj = partial(huber_loss_grad_hess, alphas=alphas, delta=delta)
         elif objective == ObjectiveName.approx:
+            epsilon_validate(epsilon=epsilon)
             self._fobj = partial(approx_loss_grad_hess, alphas=alphas, epsilon=epsilon)
 
         if model == ModelName.lightgbm:
diff --git a/mqboost/optimize.py b/mqboost/optimize.py
index 06552e4..e3882d4 100644
--- a/mqboost/optimize.py
+++ b/mqboost/optimize.py
@@ -19,7 +19,7 @@
 from mqboost.constraints import set_monotone_constraints
 from mqboost.dataset import MQDataset
 from mqboost.objective import MQObjective
-from mqboost.utils import delta_validate
+from mqboost.utils import delta_validate, epsilon_validate, params_validate
 
 __all__ = ["MQOptimizer"]
 
@@ -93,9 +93,12 @@ def __init__(
         epsilon: float = 1e-5,
     ) -> None:
         """Initialize the MQOptimizer."""
+        delta_validate(delta=delta)
+        epsilon_validate(epsilon=epsilon)
+
         self._model = ModelName.get(model)
         self._objective = ObjectiveName.get(objective)
-        self._delta = delta_validate(delta)
+        self._delta = delta
         self._epsilon = epsilon
         self._get_params = _GET_PARAMS_FUNC.get(self._model)
 
@@ -179,6 +182,7 @@ def __optuna_objective(
     ) -> float:
         """Objective function for Optuna to minimize."""
         params = get_params_func(trial=trial)
+        params_validate(params=params)
         params = set_monotone_constraints(
             params=params,
             columns=self._dataset.columns,
diff --git a/mqboost/regressor.py b/mqboost/regressor.py
index 16d9d58..26d293b 100644
--- a/mqboost/regressor.py
+++ b/mqboost/regressor.py
@@ -6,6 +6,7 @@
 from mqboost.constraints import set_monotone_constraints
 from mqboost.dataset import MQDataset
 from mqboost.objective import MQObjective
+from mqboost.utils import params_validate
 
 __all__ = ["MQRegressor"]
 
@@ -46,6 +47,7 @@ def __init__(
         epsilon: float = 1e-5,
     ) -> None:
         """Initialize the MQRegressor."""
+        params_validate(params=params)
         self._params = params
         self._model = ModelName.get(model)
         self._objective = ObjectiveName.get(objective)
diff --git a/mqboost/utils.py b/mqboost/utils.py
index 8c9f592..9a9fa2e 100644
--- a/mqboost/utils.py
+++ b/mqboost/utils.py
@@ -4,7 +4,14 @@
 import numpy as np
 import pandas as pd
 
-from mqboost.base import AlphaLike, ValidationException, XdataLike, YdataLike
+from mqboost.base import (
+    AlphaLike,
+    MQStr,
+    ParamsLike,
+    ValidationException,
+    XdataLike,
+    YdataLike,
+)
 
 
 def alpha_validate(
@@ -49,7 +56,7 @@ def prepare_x(
     _alpha_repeat_count_list = [list(repeat(alpha, len(x))) for alpha in alphas]
     _alpha_repeat_list = list(chain.from_iterable(_alpha_repeat_count_list))
 
-    _repeated_x = pd.concat([x] * len(alphas), axis=0)
+    _repeated_x = pd.concat([x] * len(alphas), axis=0).reset_index(drop=True)
     _repeated_x = _repeated_x.assign(
         _tau=_alpha_repeat_list,
     )
@@ -64,7 +71,7 @@ def prepare_y(
     return np.concatenate(list(repeat(y, len(alphas))))
 
 
-def delta_validate(delta: float) -> float:
+def delta_validate(delta: float) -> None:
     """Validates the delta parameter ensuring it is a positive float and less than or equal to 0.05."""
     _delta_upper_bound: float = 0.05
 
@@ -77,4 +84,18 @@ def delta_validate(delta: float) -> float:
     if delta > _delta_upper_bound:
         warnings.warn("Delta should be 0.05 or less.")
 
-    return delta
+
+def epsilon_validate(epsilon: float) -> None:
+    if not isinstance(epsilon, float):
+        raise ValidationException("Epsilon is not float type")
+
+    if epsilon <= 0:
+        raise ValidationException("Epsilon must be positive")
+
+
+def params_validate(params: ParamsLike) -> None:
+    """Validates the model parameter ensuring its key dosen't contain 'objective'."""
+    if MQStr.obj.value in params:
+        raise ValidationException(
+            "The parameter named 'objective' must be excluded in params"
+        )
diff --git a/poetry.lock b/poetry.lock
index 4d32de4..c86f7b4 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -19,35 +19,6 @@ typing-extensions = ">=4"
 [package.extras]
 tz = ["backports.zoneinfo"]
 
-[[package]]
-name = "appnope"
-version = "0.1.4"
-description = "Disable App Nap on macOS >= 10.9"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
-    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
-]
-
-[[package]]
-name = "asttokens"
-version = "2.4.1"
-description = "Annotate AST trees with source code positions"
-optional = false
-python-versions = "*"
-files = [
-    {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
-    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
-]
-
-[package.dependencies]
-six = ">=1.12.0"
-
-[package.extras]
-astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
-test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
-
 [[package]]
 name = "black"
 version = "24.4.2"
@@ -94,70 +65,6 @@ d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
-[[package]]
-name = "cffi"
-version = "1.16.0"
-description = "Foreign Function Interface for Python calling C code."
-optional = false
-python-versions = ">=3.8"
-files = [
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 version = "3.4.0"
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 development = ["black", "flake8", "mypy", "pytest", "types-colorama"]
 
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-    {file = "debugpy-1.8.2.zip", hash = "sha256:95378ed08ed2089221896b9b3a8d021e642c24edc8fef20e5d4342ca8be65c00"},
-]
-
-[[package]]
-name = "decorator"
-version = "5.1.1"
-description = "Decorators for Humans"
-optional = false
-python-versions = ">=3.5"
-files = [
-    {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
-    {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
-]
+toml = ["tomli"]
 
 [[package]]
 name = "distlib"
@@ -295,20 +230,6 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
-[[package]]
-name = "executing"
-version = "2.0.1"
-description = "Get the currently executing AST node of a frame, and other information"
-optional = false
-python-versions = ">=3.5"
-files = [
-    {file = "executing-2.0.1-py2.py3-none-any.whl", hash = "sha256:eac49ca94516ccc753f9fb5ce82603156e590b27525a8bc32cce8ae302eb61bc"},
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-]
-
-[package.extras]
-tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
-
 [[package]]
 name = "filelock"
 version = "3.15.4"
@@ -411,94 +332,16 @@ files = [
 license = ["ukkonen"]
 
 [[package]]
-name = "importlib-metadata"
-version = "8.2.0"
-description = "Read metadata from Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "importlib_metadata-8.2.0-py3-none-any.whl", hash = "sha256:11901fa0c2f97919b288679932bb64febaeacf289d18ac84dd68cb2e74213369"},
-    {file = "importlib_metadata-8.2.0.tar.gz", hash = "sha256:72e8d4399996132204f9a16dcc751af254a48f8d1b20b9ff0f98d4a8f901e73d"},
-]
-
-[package.dependencies]
-zipp = ">=0.5"
-
-[package.extras]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
-
-[[package]]
-name = "ipykernel"
-version = "6.29.5"
-description = "IPython Kernel for Jupyter"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5"},
-    {file = "ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215"},
-]
-
-[package.dependencies]
-appnope = {version = "*", markers = "platform_system == \"Darwin\""}
-comm = ">=0.1.1"
-debugpy = ">=1.6.5"
-ipython = ">=7.23.1"
-jupyter-client = ">=6.1.12"
-jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
-matplotlib-inline = ">=0.1"
-nest-asyncio = "*"
-packaging = "*"
-psutil = "*"
-pyzmq = ">=24"
-tornado = ">=6.1"
-traitlets = ">=5.4.0"
-
-[package.extras]
-cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
-docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
-pyqt5 = ["pyqt5"]
-pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
-
-[[package]]
-name = "ipython"
-version = "8.18.1"
-description = "IPython: Productive Interactive Computing"
+name = "iniconfig"
+version = "2.0.0"
+description = "brain-dead simple config-ini parsing"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.7"
 files = [
-    {file = "ipython-8.18.1-py3-none-any.whl", hash = "sha256:e8267419d72d81955ec1177f8a29aaa90ac80ad647499201119e2f05e99aa397"},
-    {file = "ipython-8.18.1.tar.gz", hash = "sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27"},
+    {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
+    {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
-[package.dependencies]
-colorama = {version = "*", markers = "sys_platform == \"win32\""}
-decorator = "*"
-exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
-jedi = ">=0.16"
-matplotlib-inline = "*"
-pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-prompt-toolkit = ">=3.0.41,<3.1.0"
-pygments = ">=2.4.0"
-stack-data = "*"
-traitlets = ">=5"
-typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
-
-[package.extras]
-all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
-black = ["black"]
-doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
-kernel = ["ipykernel"]
-nbconvert = ["nbconvert"]
-nbformat = ["nbformat"]
-notebook = ["ipywidgets", "notebook"]
-parallel = ["ipyparallel"]
-qtconsole = ["qtconsole"]
-test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
-
 [[package]]
 name = "isort"
 version = "5.13.2"
@@ -513,25 +356,6 @@ files = [
 [package.extras]
 colors = ["colorama (>=0.4.6)"]
 
-[[package]]
-name = "jedi"
-version = "0.19.1"
-description = "An autocompletion tool for Python that can be used for text editors."
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
-    {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
-]
-
-[package.dependencies]
-parso = ">=0.8.3,<0.9.0"
-
-[package.extras]
-docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
-qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
-testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
-
 [[package]]
 name = "joblib"
 version = "1.4.2"
@@ -543,49 +367,6 @@ files = [
     {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
 ]
 
-[[package]]
-name = "jupyter-client"
-version = "8.6.2"
-description = "Jupyter protocol implementation and client libraries"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "jupyter_client-8.6.2-py3-none-any.whl", hash = "sha256:50cbc5c66fd1b8f65ecb66bc490ab73217993632809b6e505687de18e9dea39f"},
-    {file = "jupyter_client-8.6.2.tar.gz", hash = "sha256:2bda14d55ee5ba58552a8c53ae43d215ad9868853489213f37da060ced54d8df"},
-]
-
-[package.dependencies]
-importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
-jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
-python-dateutil = ">=2.8.2"
-pyzmq = ">=23.0"
-tornado = ">=6.2"
-traitlets = ">=5.3"
-
-[package.extras]
-docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
-test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
-
-[[package]]
-name = "jupyter-core"
-version = "5.7.2"
-description = "Jupyter core package. A base package on which Jupyter projects rely."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
-    {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
-]
-
-[package.dependencies]
-platformdirs = ">=2.5"
-pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
-traitlets = ">=5.3"
-
-[package.extras]
-docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
-test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
-
 [[package]]
 name = "lightgbm"
 version = "4.4.0"
@@ -699,20 +480,6 @@ files = [
     {file = "MarkupSafe-2.1.5.tar.gz", hash = "sha256:d283d37a890ba4c1ae73ffadf8046435c76e7bc2247bbb63c00bd1a709c6544b"},
 ]
 
-[[package]]
-name = "matplotlib-inline"
-version = "0.1.7"
-description = "Inline Matplotlib backend for Jupyter"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"},
-    {file = "matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90"},
-]
-
-[package.dependencies]
-traitlets = "*"
-
 [[package]]
 name = "mypy-extensions"
 version = "1.0.0"
@@ -724,17 +491,6 @@ files = [
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
 ]
 
-[[package]]
-name = "nest-asyncio"
-version = "1.6.0"
-description = "Patch asyncio to allow nested event loops"
-optional = false
-python-versions = ">=3.5"
-files = [
-    {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"},
-    {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
-]
-
 [[package]]
 name = "nodeenv"
 version = "1.9.1"
@@ -921,21 +677,6 @@ sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-d
 test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
 xml = ["lxml (>=4.9.2)"]
 
-[[package]]
-name = "parso"
-version = "0.8.4"
-description = "A Python Parser"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18"},
-    {file = "parso-0.8.4.tar.gz", hash = "sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d"},
-]
-
-[package.extras]
-qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
-testing = ["docopt", "pytest"]
-
 [[package]]
 name = "pathspec"
 version = "0.12.1"
@@ -947,20 +688,6 @@ files = [
     {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"},
 ]
 
-[[package]]
-name = "pexpect"
-version = "4.9.0"
-description = "Pexpect allows easy control of interactive console applications."
-optional = false
-python-versions = "*"
-files = [
-    {file = "pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523"},
-    {file = "pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f"},
-]
-
-[package.dependencies]
-ptyprocess = ">=0.5"
-
 [[package]]
 name = "platformdirs"
 version = "4.2.2"
@@ -978,19 +705,19 @@ test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-
 type = ["mypy (>=1.8)"]
 
 [[package]]
-name = "plotly"
-version = "5.22.0"
-description = "An open-source, interactive data visualization library for Python"
+name = "pluggy"
+version = "1.5.0"
+description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "plotly-5.22.0-py3-none-any.whl", hash = "sha256:68fc1901f098daeb233cc3dd44ec9dc31fb3ca4f4e53189344199c43496ed006"},
-    {file = "plotly-5.22.0.tar.gz", hash = "sha256:859fdadbd86b5770ae2466e542b761b247d1c6b49daed765b95bb8c7063e7469"},
+    {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"},
+    {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"},
 ]
 
-[package.dependencies]
-packaging = "*"
-tenacity = ">=6.2.0"
+[package.extras]
+dev = ["pre-commit", "tox"]
+testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pre-commit"
@@ -1011,97 +738,44 @@ pyyaml = ">=5.1"
 virtualenv = ">=20.10.0"
 
 [[package]]
-name = "prompt-toolkit"
-version = "3.0.47"
-description = "Library for building powerful interactive command lines in Python"
+name = "pytest"
+version = "8.3.2"
+description = "pytest: simple powerful testing with Python"
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.8"
 files = [
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-    {file = "pyzmq-26.0.3.tar.gz", hash = "sha256:dba7d9f2e047dfa2bca3b01f4f84aa5246725203d6284e3790f2ca15fba6b40a"},
-]
-
-[package.dependencies]
-cffi = {version = "*", markers = "implementation_name == \"pypy\""}
-
 [[package]]
 name = "scikit-learn"
 version = "1.5.1"
@@ -1496,40 +1047,6 @@ postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
 sqlcipher = ["sqlcipher3_binary"]
 
-[[package]]
-name = "stack-data"
-version = "0.6.3"
-description = "Extract data from python stack frames and tracebacks for informative displays"
-optional = false
-python-versions = "*"
-files = [
-    {file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"},
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-]
-
-[package.dependencies]
-asttokens = ">=2.1.0"
-executing = ">=1.2.0"
-pure-eval = "*"
-
-[package.extras]
-tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
-
-[[package]]
-name = "tenacity"
-version = "8.5.0"
-description = "Retry code until it succeeds"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "tenacity-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687"},
-    {file = "tenacity-8.5.0.tar.gz", hash = "sha256:8bc6c0c8a09b31e6cad13c47afbed1a567518250a9a171418582ed8d9c20ca78"},
-]
-
-[package.extras]
-doc = ["reno", "sphinx"]
-test = ["pytest", "tornado (>=4.5)", "typeguard"]
-
 [[package]]
 name = "threadpoolctl"
 version = "3.5.0"
@@ -1552,26 +1069,6 @@ files = [
     {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
 ]
 
-[[package]]
-name = "tornado"
-version = "6.4.1"
-description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:163b0aafc8e23d8cdc3c9dfb24c5368af84a81e3364745ccb4427669bf84aec8"},
-    {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:6d5ce3437e18a2b66fbadb183c1d3364fb03f2be71299e7d10dbeeb69f4b2a14"},
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-    {file = "tornado-6.4.1.tar.gz", hash = "sha256:92d3ab53183d8c50f8204a51e6f91d18a15d5ef261e84d452800d4ff6fc504e9"},
-]
-
 [[package]]
 name = "tqdm"
 version = "4.66.4"
@@ -1592,21 +1089,6 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
-[[package]]
-name = "traitlets"
-version = "5.14.3"
-description = "Traitlets Python configuration system"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
-    {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
-]
-
-[package.extras]
-docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
-test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
-
 [[package]]
 name = "typing-extensions"
 version = "4.12.2"
@@ -1649,17 +1131,6 @@ platformdirs = ">=3.9.1,<5"
 docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
 test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
 
-[[package]]
-name = "wcwidth"
-version = "0.2.13"
-description = "Measures the displayed width of unicode strings in a terminal"
-optional = false
-python-versions = "*"
-files = [
-    {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
-    {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
-]
-
 [[package]]
 name = "xgboost"
 version = "2.1.0"
@@ -1690,22 +1161,7 @@ plotting = ["graphviz", "matplotlib"]
 pyspark = ["cloudpickle", "pyspark", "scikit-learn"]
 scikit-learn = ["scikit-learn"]
 
-[[package]]
-name = "zipp"
-version = "3.19.2"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
-    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
-]
-
-[package.extras]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
-
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9"
-content-hash = "0001c60594d7516c9e84638144859a56ecadd977abcb99a52d672e1e32a154d0"
+content-hash = "1dfeb7a6167b3f8c016453de2427c13baa8b13c53cf3407c87fbe809078a48b0"
diff --git a/pyproject.toml b/pyproject.toml
index c604330..a5ec010 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,11 +18,11 @@ scikit-learn = "^1.5.1"
 
 
 [tool.poetry.group.dev.dependencies]
-plotly = "^5.22.0"
 pre-commit = "^3.7.1"
 isort = "^5.13.2"
 black = "^24.4.2"
-ipykernel = "^6.29.5"
+pytest = "^8.3.2"
+pytest-cov = "^5.0.0"
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/tests/test_base.py b/tests/test_base.py
new file mode 100644
index 0000000..89f7051
--- /dev/null
+++ b/tests/test_base.py
@@ -0,0 +1,75 @@
+import lightgbm as lgb
+import numpy as np
+import pandas as pd
+import pytest
+import xgboost as xgb
+
+from mqboost.base import (
+    FUNC_TYPE,
+    FittingException,
+    ModelName,
+    ObjectiveName,
+    TypeName,
+    ValidationException,
+    _lgb_predict_dtype,
+)
+
+
+# Test Enum behavior
+def test_model_name_enum():
+    assert ModelName.get("lightgbm") == ModelName.lightgbm
+    assert ModelName.get("xgboost") == ModelName.xgboost
+
+    with pytest.raises(ValueError):
+        ModelName.get("invalid_model")
+
+
+def test_objective_name_enum():
+    assert ObjectiveName.get("check") == ObjectiveName.check
+    assert ObjectiveName.get("huber") == ObjectiveName.huber
+    assert ObjectiveName.get("approx") == ObjectiveName.approx
+
+    with pytest.raises(ValueError):
+        ObjectiveName.get("invalid_objective")
+
+
+# Test FUNC_TYPE
+def test_func_type_for_lgb():
+    assert FUNC_TYPE[ModelName.lightgbm][TypeName.train_dtype] == lgb.Dataset
+    assert isinstance(
+        FUNC_TYPE[ModelName.lightgbm][TypeName.predict_dtype](pd.DataFrame([1, 2, 3])),
+        pd.DataFrame,
+    )
+    assert isinstance(
+        FUNC_TYPE[ModelName.lightgbm][TypeName.predict_dtype](pd.Series([1, 2, 3])),
+        pd.Series,
+    )
+    assert isinstance(
+        FUNC_TYPE[ModelName.lightgbm][TypeName.predict_dtype](np.array([1, 2, 3])),
+        np.ndarray,
+    )
+    assert FUNC_TYPE[ModelName.lightgbm][TypeName.constraints_type] == list
+
+
+def test_func_type_for_xgb():
+    assert FUNC_TYPE[ModelName.xgboost][TypeName.train_dtype] == xgb.DMatrix
+    assert FUNC_TYPE[ModelName.xgboost][TypeName.predict_dtype] == xgb.DMatrix
+    assert FUNC_TYPE[ModelName.xgboost][TypeName.constraints_type] == tuple
+
+
+# Test _lgb_predict_dtype
+def test_lgb_predict_dtype():
+    data = pd.DataFrame([1, 2, 3])
+    assert _lgb_predict_dtype(data) is data
+
+    array_data = np.array([1, 2, 3])
+    assert _lgb_predict_dtype(array_data) is array_data
+
+
+# Test custom exceptions
+def test_custom_exceptions():
+    with pytest.raises(FittingException):
+        raise FittingException("Fitting failed")
+
+    with pytest.raises(ValidationException):
+        raise ValidationException("Validation failed")
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
new file mode 100644
index 0000000..5daaa83
--- /dev/null
+++ b/tests/test_constraints.py
@@ -0,0 +1,64 @@
+import pandas as pd
+
+from mqboost.base import ModelName, MQStr
+from mqboost.constraints import set_monotone_constraints
+
+
+# Test function for setting monotone constraints
+def test_set_monotone_constraints_with_existing_constraints_lgb():
+    params = {
+        MQStr.mono.value: [1, -1],
+    }
+    columns = pd.Index(["feature_1", "feature_2", "_tau"])
+    model_name = ModelName.lightgbm
+    updated_params = set_monotone_constraints(params, columns, model_name)
+    expected_constraints = [1, -1, 1]
+    assert updated_params[MQStr.mono.value] == expected_constraints
+
+
+def test_set_monotone_constraints_without_existing_constraints_lgb():
+    params = {}
+    columns = pd.Index(["feature_1", "feature_2", "_tau"])
+    model_name = ModelName.lightgbm
+    updated_params = set_monotone_constraints(params, columns, model_name)
+    expected_constraints = [0, 0, 1]
+    assert updated_params[MQStr.mono.value] == expected_constraints
+
+
+def test_set_monotone_constraints_with_existing_constraints_xgb():
+    params = {
+        MQStr.mono.value: [1, -1],
+    }
+    columns = pd.Index(["feature_1", "feature_2", "_tau"])
+    model_name = ModelName.xgboost
+    updated_params = set_monotone_constraints(params, columns, model_name)
+    expected_constraints = (1, -1, 1)
+    assert updated_params[MQStr.mono.value] == expected_constraints
+
+
+def test_set_monotone_constraints_without_existing_constraints_xgb():
+    params = {}
+    columns = pd.Index(["feature_1", "feature_2", "_tau"])
+    model_name = ModelName.xgboost
+    updated_params = set_monotone_constraints(params, columns, model_name)
+    expected_constraints = (0, 0, 1)
+    assert updated_params[MQStr.mono.value] == expected_constraints
+
+
+# Edge case where no constraints or empty columns
+def test_set_monotone_constraints_with_empty_columns():
+    params = {}
+    columns = pd.Index([])
+    model_name = ModelName.lightgbm
+    updated_params = set_monotone_constraints(params, columns, model_name)
+    expected_constraints = []
+    assert updated_params[MQStr.mono.value] == expected_constraints
+
+
+def test_set_monotone_constraints_with_empty_params_and_columns():
+    params = {}
+    columns = pd.Index([])
+    model_name = ModelName.xgboost
+    updated_params = set_monotone_constraints(params, columns, model_name)
+    expected_constraints = ()
+    assert updated_params[MQStr.mono.value] == expected_constraints
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
new file mode 100644
index 0000000..666e236
--- /dev/null
+++ b/tests/test_dataset.py
@@ -0,0 +1,123 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from mqboost.base import FittingException, ModelName, ValidationException
+from mqboost.dataset import MQDataset
+
+
+def _concat(df: pd.DataFrame, concat_count: int):
+    return pd.concat([df] * concat_count, axis=0).reset_index(drop=True)
+
+
+# Test for MQDataset initialization
+def test_mqdataset_initialization_with_lgb():
+    data = pd.DataFrame({"feature_1": [1, 2, 3], "feature_2": [4, 5, 6]})
+    label = pd.Series([1, 2, 3])
+    alphas = [0.1, 0.2, 0.3]
+    dataset = MQDataset(
+        alphas=alphas, data=data, label=label, model=ModelName.lightgbm.value
+    )
+
+    assert dataset.nrow == 3
+    assert dataset.alphas == alphas
+    pd.testing.assert_frame_equal(
+        dataset.data,
+        _concat(data, 3).assign(_tau=[0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3]),
+    )
+    np.testing.assert_array_equal(dataset.label, np.concatenate([label] * len(alphas)))
+
+
+def test_mqdataset_initialization_with_xgb():
+    data = pd.DataFrame({"feature_1": [1, 2, 3], "feature_2": [4, 5, 6]})
+    label = pd.Series([1, 2, 3])
+    alphas = [0.1, 0.2]
+    dataset = MQDataset(
+        alphas=alphas, data=data, label=label, model=ModelName.xgboost.value
+    )
+
+    assert dataset.nrow == 3
+    assert dataset.alphas == alphas
+    pd.testing.assert_frame_equal(
+        dataset.data, _concat(data, 2).assign(_tau=[0.1, 0.1, 0.1, 0.2, 0.2, 0.2])
+    )
+    np.testing.assert_array_equal(dataset.label, np.concatenate([label] * len(alphas)))
+
+
+def test_mqdataset_initialization_with_invalid_alpha():
+    data = pd.DataFrame({"feature_1": [1, 2, 3], "feature_2": [4, 5, 6]})
+
+    with pytest.raises(ValidationException, match="Alpha is not ascending order"):
+        MQDataset(alphas=[0.3, 0.2], data=data)
+
+
+def test_mqdataset_initialization_without_label():
+    data = pd.DataFrame({"feature_1": [1, 2, 3], "feature_2": [4, 5, 6]})
+    alphas = [0.1, 0.2]
+    dataset = MQDataset(alphas=alphas, data=data, model=ModelName.lightgbm.value)
+
+    assert dataset.nrow == 3
+    assert dataset.alphas == alphas
+
+    pd.testing.assert_frame_equal(
+        dataset.data, _concat(data, 2).assign(_tau=[0.1, 0.1, 0.1, 0.2, 0.2, 0.2])
+    )
+
+    # Ensure label is not available
+    with pytest.raises(
+        FittingException, match="Fitting is impossible since label is None"
+    ):
+        dataset.label
+
+
+# Test properties
+def test_mqdataset_train_predict_dtype():
+    data = pd.DataFrame({"feature_1": [1, 2, 3], "feature_2": [4, 5, 6]})
+    alphas = [0.1, 0.2]
+    dataset = MQDataset(alphas=alphas, data=data, model=ModelName.lightgbm.value)
+    assert dataset.train_dtype == dataset._train_dtype
+    assert dataset.predict_dtype == dataset._predict_dtype
+
+    dataset = MQDataset(alphas=alphas, data=data, model=ModelName.xgboost.value)
+    assert dataset.train_dtype == dataset._train_dtype
+    assert dataset.predict_dtype == dataset._predict_dtype
+
+
+def test_mqdataset_columns_property():
+    data = pd.DataFrame({"feature_1": [1, 2, 3], "feature_2": [4, 5, 6]})
+    alphas = [0.1, 0.2]
+
+    dataset = MQDataset(alphas=alphas, data=data, model=ModelName.lightgbm.value)
+    assert list(dataset.columns) == [
+        "feature_1",
+        "feature_2",
+        "_tau",
+    ]
+
+
+def test_mqdataset_dtype_lgb():
+    data = pd.DataFrame({"feature_1": [1, 2, 3], "feature_2": [4, 5, 6]})
+    label = pd.Series([1, 2, 3])
+    alphas = [0.1, 0.2]
+    dataset = MQDataset(
+        alphas=alphas, data=data, label=label, model=ModelName.lightgbm.value
+    )
+
+    dtrain = dataset.dtrain
+    dpredict = dataset.dpredict
+    assert isinstance(dtrain, dataset.train_dtype)
+    assert isinstance(dpredict, pd.DataFrame)
+
+
+def test_mqdataset_dtype_xgb():
+    data = pd.DataFrame({"feature_1": [1, 2, 3], "feature_2": [4, 5, 6]})
+    label = pd.Series([1, 2, 3])
+    alphas = [0.1, 0.2]
+    dataset = MQDataset(
+        alphas=alphas, data=data, label=label, model=ModelName.xgboost.value
+    )
+
+    dtrain = dataset.dtrain
+    dpredict = dataset.dpredict
+    assert isinstance(dtrain, dataset.train_dtype)
+    assert isinstance(dpredict, dataset.predict_dtype)
diff --git a/tests/test_objective.py b/tests/test_objective.py
new file mode 100644
index 0000000..cbe5e80
--- /dev/null
+++ b/tests/test_objective.py
@@ -0,0 +1,200 @@
+import numpy as np
+import pytest
+
+from mqboost.base import ModelName, ObjectiveName, ValidationException
+from mqboost.objective import (
+    MQObjective,
+    _eval_check_loss,
+    _lgb_eval_loss,
+    _xgb_eval_loss,
+    approx_loss_grad_hess,
+    check_loss_grad_hess,
+    huber_loss_grad_hess,
+)
+
+
+@pytest.fixture
+def dummy_data():
+    class DummyDtrain:
+        def __init__(self, label):
+            self.label = label
+
+        def get_label(self):
+            return self.label
+
+    return DummyDtrain
+
+
+alphas = [0.1, 0.5, 0.9]
+y_pred = np.array([1.0, 1.5, 2.0, 2.5, 3.0] * 3)
+y_true = np.array([1.01, 1.51, 1.98, 2.53, 3.05] * 3)
+
+
+# Test MQObjective Initialization
+def test_mqobjective_check_loss_initialization():
+    """Test MQObjective initialization with check loss."""
+
+    mq_objective = MQObjective(
+        alphas=alphas,
+        objective=ObjectiveName.check,
+        model=ModelName.xgboost,
+        delta=0.0,
+        epsilon=0.0,
+    )
+    assert mq_objective.fobj is not None
+    assert mq_objective.feval is not None
+    assert callable(mq_objective.fobj)
+    assert callable(mq_objective.feval)
+
+
+def test_mqobjective_huber_loss_initialization():
+    """Test MQObjective initialization with huber loss."""
+    delta = 0.1
+    mq_objective = MQObjective(
+        alphas=alphas,
+        objective=ObjectiveName.huber,
+        model=ModelName.lightgbm,
+        delta=delta,
+        epsilon=0.0,
+    )
+    assert mq_objective.fobj is not None
+    assert callable(mq_objective.fobj)
+
+
+def test_mqobjective_approx_loss_initialization():
+    """Test MQObjective initialization with approx loss."""
+    epsilon = 0.1
+    mq_objective = MQObjective(
+        alphas=alphas,
+        objective=ObjectiveName.approx,
+        model=ModelName.xgboost,
+        delta=0.0,
+        epsilon=epsilon,
+    )
+    assert mq_objective.fobj is not None
+    assert callable(mq_objective.fobj)
+
+
+# Test loss functions: check, huber, approx
+def test_check_loss_grad_hess(dummy_data):
+    """Test check loss gradient and Hessian calculation."""
+    dtrain = dummy_data(y_true)
+    grads, hess = check_loss_grad_hess(y_pred=y_pred, dtrain=dtrain, alphas=alphas)
+    # fmt: off
+    expected_grads = [-0.1, -0.1, 0.9, -0.1, -0.1, -0.5, -0.5, 0.5, -0.5, -0.5, -0.9, -0.9, 0.1, -0.9, -0.9]
+    # fmt: on
+    np.testing.assert_almost_equal(grads, np.array(expected_grads))
+    assert grads.shape == hess.shape
+    assert len(grads) == len(y_pred)
+
+
+# fmt: off
+@pytest.mark.parametrize(
+    "delta, expected_grads",
+    [
+        (0.01, [-0.1, -0.1, 0.9, -0.1, -0.1, -0.5, -0.5, 0.5, -0.5, -0.5, -0.9, -0.9, 0.1, -0.9, -0.9]),
+        (0.02, [0.001, 0.001, 0.9, -0.1, -0.1, 0.005, 0.005, 0.5, -0.5, -0.5, 0.009, 0.009, 0.1, -0.9, -0.9]),
+        (0.05, [0.001, 0.001, 0.018, 0.003, 0.005, 0.005, 0.005, 0.01, 0.015, 0.025, 0.009, 0.009, 0.002, 0.027, 0.045]),
+    ],
+)
+# fmt: on
+def test_huber_loss_grad_hess(dummy_data, delta, expected_grads):
+    """Test huber loss gradient and Hessian calculation with multiple datasets and deltas."""
+    dtrain = dummy_data(y_true)
+    grads, hess = huber_loss_grad_hess(
+        y_pred=y_pred, dtrain=dtrain, alphas=alphas, delta=delta
+    )
+
+    np.testing.assert_almost_equal(grads, np.array(expected_grads))
+    assert grads.shape == hess.shape
+    assert len(grads) == len(y_pred)
+
+
+# fmt: off
+@pytest.mark.parametrize(
+    "epsilon, expected_grads, expected_hess",
+    [
+        (
+            0.01,
+            [0.15, 0.15, 0.7333, 0.025, -0.0167, -0.25, -0.25, 0.3333, -0.375, -0.4167, -0.65, -0.65, -0.0667, -0.775, -0.8167],
+            [25.0, 25.0, 16.6666, 12.5, 8.3333, 25.0, 25.0, 16.6666, 12.5, 8.33333333, 25.0, 25.0, 16.6666, 12.5, 8.33333333],
+        ),
+        (
+            0.005,
+            [0.0667, 0.0667, 0.8, -0.02857, -0.0545, -0.3333, -0.3333, 0.4, -0.4286, -0.4545, -0.7333, -0.73333, 0.0, -0.8286, -0.8545],
+            [33.3333, 33.3333, 20.0, 14.2857, 9.0909, 33.3333, 33.3333, 20.0, 14.2857, 9.0909, 33.3333, 33.3333, 20.0, 14.2857, 9.0909],
+        ),
+        (
+            0.001,
+            [-0.0545, -0.0545, 0.8762, -0.0838, -0.0902, -0.4545, -0.4545, 0.4762, -0.4839, -0.4902, -0.8545, -0.8545, 0.0762, -0.8839, -0.8902],
+            [45.4545, 45.4545, 23.8095, 16.1290, 9.8039, 45.4545, 45.4545, 23.8095, 16.1290, 9.8039, 45.4545, 45.4545, 23.8095, 16.1290, 9.8039],
+        ),
+    ],
+)
+# fmt: on
+def test_approx_loss_grad_hess(dummy_data, epsilon, expected_grads, expected_hess):
+    """Test approx loss gradient and Hessian calculation."""
+    dtrain = dummy_data(y_true)
+    grads, hess = approx_loss_grad_hess(
+        y_pred=y_pred, dtrain=dtrain, alphas=alphas, epsilon=epsilon
+    )
+    np.testing.assert_almost_equal(grads, np.array(expected_grads), decimal=4)
+    np.testing.assert_almost_equal(hess, np.array(expected_hess), decimal=4)
+    assert grads.shape == hess.shape
+    assert len(grads) == len(y_pred)
+
+
+# Test evaluation functions
+def test_eval_check_loss(dummy_data):
+    """Test evaluation of the check loss."""
+    dtrain = dummy_data(y_true)
+    loss = _eval_check_loss(y_pred=y_pred, dtrain=dtrain, alphas=alphas)
+    np.testing.assert_almost_equal(loss, 0.012)
+    assert isinstance(loss, float)
+    assert loss > 0
+
+
+def test_xgb_eval_loss(dummy_data):
+    """Test XGBoost evaluation function."""
+    dtrain = dummy_data(y_true)
+    metric_name, loss = _xgb_eval_loss(y_pred=y_pred, dtrain=dtrain, alphas=alphas)
+    assert metric_name == "check_loss"
+    assert isinstance(loss, float)
+
+
+def test_lgb_eval_loss(dummy_data):
+    """Test LightGBM evaluation function."""
+    dtrain = dummy_data(y_true)
+    metric_name, loss, higher_better = _lgb_eval_loss(
+        y_pred=y_pred, dtrain=dtrain, alphas=alphas
+    )
+    assert metric_name == "check_loss"
+    assert isinstance(loss, float)
+    assert higher_better is False
+
+
+# Test error handling for invalid parameters
+def test_invalid_delta_for_huber():
+    """Test that invalid delta for Huber loss raises an exception."""
+    alphas = [0.1, 0.5, 0.9]
+    with pytest.raises(ValidationException):
+        MQObjective(
+            alphas=alphas,
+            objective=ObjectiveName.huber,
+            model=ModelName.xgboost,
+            delta=-0.1,  # Invalid delta (negative)
+            epsilon=0.0,
+        )
+
+
+def test_invalid_epsilon_for_approx():
+    """Test that invalid epsilon for approx loss raises an exception."""
+    alphas = [0.1, 0.5, 0.9]
+    with pytest.raises(ValidationException):
+        MQObjective(
+            alphas=alphas,
+            objective=ObjectiveName.approx,
+            model=ModelName.xgboost,
+            delta=0.0,
+            epsilon=-0.01,  # Invalid epsilon (negative)
+        )
diff --git a/tests/test_optimize.py b/tests/test_optimize.py
new file mode 100644
index 0000000..b9b89af
--- /dev/null
+++ b/tests/test_optimize.py
@@ -0,0 +1,208 @@
+import numpy as np
+import optuna
+import pandas as pd
+import pytest
+
+from mqboost.base import FittingException, ModelName, ObjectiveName, ValidationException
+from mqboost.dataset import MQDataset
+from mqboost.objective import MQObjective
+from mqboost.optimize import MQOptimizer, _lgb_get_params, _xgb_get_params
+
+
+@pytest.fixture
+def sample_data():
+    """Generates sample data for testing."""
+    X = pd.DataFrame(np.random.rand(100, 5), columns=[f"feature_{i}" for i in range(5)])
+    y = np.random.rand(100)
+    alphas = [0.1, 0.5, 0.9]
+    dataset = MQDataset(alphas=alphas, data=X, label=y, model=ModelName.lightgbm.value)
+    return dataset
+
+
+@pytest.fixture
+def sample_valid_data():
+    """Generates sample validation data."""
+    X_valid = pd.DataFrame(
+        np.random.rand(20, 5), columns=[f"feature_{i}" for i in range(5)]
+    )
+    y_valid = np.random.rand(20)
+    alphas = [0.1, 0.5, 0.9]
+    valid_set = MQDataset(
+        alphas=alphas, data=X_valid, label=y_valid, model=ModelName.lightgbm.value
+    )
+    return valid_set
+
+
+# Test MQOptimizer Initialization
+def test_mqoptimizer_initialization():
+    """Test initialization with valid parameters."""
+    optimizer = MQOptimizer(
+        model=ModelName.lightgbm.value,
+        objective=ObjectiveName.check.value,
+        delta=0.01,
+        epsilon=1e-5,
+    )
+    assert optimizer._model == ModelName.lightgbm
+    assert optimizer._objective == ObjectiveName.check
+    assert optimizer._delta == 0.01
+    assert optimizer._epsilon == 1e-5
+
+
+def test_mqoptimizer_invalid_model():
+    """Test initialization with an invalid model."""
+    with pytest.raises(ValueError):
+        MQOptimizer(model="invalid_model")
+
+
+def test_mqoptimizer_invalid_objective():
+    """Test initialization with an invalid objective."""
+    with pytest.raises(ValueError):
+        MQOptimizer(objective="invalid_objective")
+
+
+def test_mqoptimizer_invalid_delta():
+    """Test initialization with invalid delta value."""
+    with pytest.raises(ValidationException):
+        MQOptimizer(delta=-0.01)
+
+
+def test_mqoptimizer_invalid_epsilon():
+    """Test initialization with invalid epsilon value."""
+    with pytest.raises(ValidationException):
+        MQOptimizer(epsilon=-1e-5)
+
+
+# Test optimize_params method
+def test_optimize_params_with_default_get_params(sample_data):
+    """Test optimize_params with default get_params function."""
+    optimizer = MQOptimizer()
+    best_params = optimizer.optimize_params(dataset=sample_data, n_trials=1)
+    assert isinstance(best_params, dict)
+    assert optimizer._is_optimized
+
+
+def test_optimize_params_with_custom_get_params(sample_data):
+    """Test optimize_params with a custom get_params function."""
+    optimizer = MQOptimizer()
+
+    def custom_get_params(trial):
+        return {
+            "learning_rate": trial.suggest_float("learning_rate", 0.01, 0.1),
+            "num_leaves": trial.suggest_int("num_leaves", 10, 50),
+        }
+
+    best_params = optimizer.optimize_params(
+        dataset=sample_data, n_trials=1, get_params_func=custom_get_params
+    )
+    assert "learning_rate" in best_params
+    assert "num_leaves" in best_params
+
+
+def test_optimize_params_with_valid_set(sample_data, sample_valid_data):
+    """Test optimize_params with a provided validation set."""
+    optimizer = MQOptimizer()
+    best_params = optimizer.optimize_params(
+        dataset=sample_data, n_trials=1, valid_set=sample_valid_data
+    )
+    assert isinstance(best_params, dict)
+    assert optimizer._is_optimized
+
+
+def test_optimize_params_without_optimization():
+    """Test accessing best_params before optimization is completed."""
+    optimizer = MQOptimizer()
+    with pytest.raises(FittingException, match="Optimization is not completed."):
+        _ = optimizer.best_params
+
+
+def test_study_property_before_optimization():
+    """Test accessing study property before optimization."""
+    optimizer = MQOptimizer()
+    assert optimizer.study is None
+
+
+def test_study_property_after_optimization(sample_data):
+    """Test accessing study property after optimization."""
+    optimizer = MQOptimizer()
+    optimizer.optimize_params(dataset=sample_data, n_trials=1)
+    assert isinstance(optimizer.study, optuna.Study)
+
+
+def test_mqobjective_property(sample_data):
+    """Test MQObj property after initialization."""
+    optimizer = MQOptimizer()
+    optimizer._MQObj = MQObjective(
+        alphas=sample_data.alphas,
+        objective=optimizer._objective,
+        model=optimizer._model,
+        delta=optimizer._delta,
+        epsilon=optimizer._epsilon,
+    )
+    assert isinstance(optimizer.MQObj, MQObjective)
+
+
+def test_optimize_params_invalid_dataset():
+    """Test optimize_params with an invalid dataset."""
+    optimizer = MQOptimizer()
+    with pytest.raises(AttributeError):
+        optimizer.optimize_params(dataset=None, n_trials=1)
+
+
+def test_optimize_params_invalid_n_trials(sample_data):
+    """Test optimize_params with invalid n_trials."""
+    optimizer = MQOptimizer()
+    with pytest.raises(ValueError):
+        optimizer.optimize_params(dataset=sample_data, n_trials=0)
+
+
+def test_lgb_get_params():
+    """Test the default LightGBM get_params function."""
+    trial = optuna.trial.FixedTrial(
+        {
+            "learning_rate": 0.05,
+            "max_depth": 5,
+            "lambda_l1": 0.1,
+            "lambda_l2": 0.1,
+            "num_leaves": 31,
+            "feature_fraction": 0.8,
+            "bagging_fraction": 0.8,
+            "bagging_freq": 5,
+        }
+    )
+    params = _lgb_get_params(trial)
+    expected_keys = {
+        "verbose",
+        "learning_rate",
+        "max_depth",
+        "lambda_l1",
+        "lambda_l2",
+        "num_leaves",
+        "feature_fraction",
+        "bagging_fraction",
+        "bagging_freq",
+    }
+    assert set(params.keys()) == expected_keys
+
+
+def test_xgb_get_params():
+    """Test the default XGBoost get_params function."""
+    trial = optuna.trial.FixedTrial(
+        {
+            "learning_rate": 0.05,
+            "max_depth": 5,
+            "reg_lambda": 0.1,
+            "reg_alpha": 0.1,
+            "subsample": 0.8,
+            "colsample_bytree": 0.8,
+        }
+    )
+    params = _xgb_get_params(trial)
+    expected_keys = {
+        "learning_rate",
+        "max_depth",
+        "reg_lambda",
+        "reg_alpha",
+        "subsample",
+        "colsample_bytree",
+    }
+    assert set(params.keys()) == expected_keys
diff --git a/tests/test_regressor.py b/tests/test_regressor.py
new file mode 100644
index 0000000..07eede8
--- /dev/null
+++ b/tests/test_regressor.py
@@ -0,0 +1,179 @@
+import lightgbm as lgb
+import numpy as np
+import pytest
+import xgboost as xgb
+
+from mqboost.base import FittingException, ModelName, ObjectiveName
+from mqboost.regressor import MQDataset, MQRegressor
+
+# Test data and helper functions
+
+
+@pytest.fixture
+def dummy_dataset_lgb():
+    X = np.random.rand(100, 10)
+    y = np.random.rand(100)
+    alphas = [0.1, 0.49, 0.5, 0.51, 0.9]
+    return MQDataset(alphas=alphas, data=X, label=y, model=ModelName.lightgbm.value)
+
+
+@pytest.fixture
+def dummy_eval_set_lgb():
+    X_val = np.random.rand(50, 10)
+    y_val = np.random.rand(50)
+    alphas = [0.1, 0.49, 0.5, 0.51, 0.9]
+    return MQDataset(
+        alphas=alphas, data=X_val, label=y_val, model=ModelName.lightgbm.value
+    )
+
+
+@pytest.fixture
+def dummy_dataset_xgb():
+    X = np.random.rand(100, 10)
+    y = np.random.rand(100)
+    alphas = [0.1, 0.49, 0.5, 0.51, 0.9]
+    return MQDataset(alphas=alphas, data=X, label=y, model=ModelName.xgboost.value)
+
+
+@pytest.fixture
+def dummy_eval_set_xgb():
+    X_val = np.random.rand(50, 10)
+    y_val = np.random.rand(50)
+    alphas = [0.1, 0.49, 0.5, 0.51, 0.9]
+    return MQDataset(
+        alphas=alphas, data=X_val, label=y_val, model=ModelName.xgboost.value
+    )
+
+
+def test_mqregressor_initialization():
+    params = {"learning_rate": 0.1, "num_leaves": 31}
+    regressor = MQRegressor(
+        params=params,
+        model=ModelName.lightgbm.value,
+        objective=ObjectiveName.check.value,
+        delta=0.01,
+        epsilon=1e-5,
+    )
+    assert regressor._params == params
+    assert regressor._model == ModelName.lightgbm
+    assert regressor._objective == ObjectiveName.check
+    assert regressor._delta == 0.01
+    assert regressor._epsilon == 1e-5
+
+
+def test_invalid_model_initialization():
+    params = {"learning_rate": 0.1, "num_leaves": 31}
+    with pytest.raises(ValueError):
+        MQRegressor(
+            params=params,
+            model="invalid_model",
+            objective=ObjectiveName.check.value,
+            delta=0.01,
+            epsilon=1e-5,
+        )
+
+
+def test_invalid_objective_initialization():
+    params = {"learning_rate": 0.1, "num_leaves": 31}
+    with pytest.raises(ValueError):
+        MQRegressor(
+            params=params,
+            model=ModelName.lightgbm.value,
+            objective="invalid_objective",
+            delta=0.01,
+            epsilon=1e-5,
+        )
+
+
+# Test MQRegressor Fit Method
+def test_mqregressor_fit_lgb(dummy_dataset_lgb, dummy_eval_set_lgb):
+    params = {"learning_rate": 0.1, "num_leaves": 31}
+    regressor = MQRegressor(params=params, model=ModelName.lightgbm.value)
+    regressor.fit(dataset=dummy_dataset_lgb, eval_set=dummy_eval_set_lgb)
+
+    assert regressor._fitted is True
+    assert isinstance(regressor.model, lgb.Booster)
+
+
+def test_mqregressor_fit_xgb(dummy_dataset_xgb, dummy_eval_set_xgb):
+    params = {"learning_rate": 0.1, "max_depth": 6}
+    regressor = MQRegressor(params=params, model=ModelName.xgboost.value)
+    regressor.fit(dataset=dummy_dataset_xgb, eval_set=dummy_eval_set_xgb)
+
+    assert regressor._fitted is True
+    assert isinstance(regressor.model, xgb.Booster)
+
+
+def test_fit_without_eval_set_lgb(dummy_dataset_lgb):
+    params = {"learning_rate": 0.1, "num_leaves": 31}
+    regressor = MQRegressor(params=params, model=ModelName.lightgbm.value)
+
+    regressor.fit(dataset=dummy_dataset_lgb)
+    assert regressor._fitted is True
+    assert isinstance(regressor.model, lgb.Booster)
+
+
+def test_fit_without_eval_set_xgb(dummy_dataset_xgb):
+    params = {"learning_rate": 0.1, "max_depth": 6}
+    regressor = MQRegressor(params=params, model=ModelName.xgboost.value)
+    regressor.fit(dataset=dummy_dataset_xgb)
+
+    assert regressor._fitted is True
+    assert isinstance(regressor.model, xgb.Booster)
+
+
+# Test MQRegressor Predict Method
+def test_predict_lgb(dummy_dataset_lgb):
+    params = {"learning_rate": 0.1, "num_leaves": 31}
+    regressor = MQRegressor(params=params, model=ModelName.lightgbm.value)
+
+    regressor.fit(dataset=dummy_dataset_lgb)
+    predictions = regressor.predict(dataset=dummy_dataset_lgb)
+    assert predictions.shape == (len(dummy_dataset_lgb.alphas), dummy_dataset_lgb.nrow)
+
+
+def test_predict_xgb(dummy_dataset_xgb):
+    params = {"learning_rate": 0.1, "max_depth": 6}
+    regressor = MQRegressor(params=params, model=ModelName.xgboost.value)
+
+    regressor.fit(dataset=dummy_dataset_xgb)
+    predictions = regressor.predict(dataset=dummy_dataset_xgb)
+
+    assert predictions.shape == (len(dummy_dataset_xgb.alphas), dummy_dataset_xgb.nrow)
+
+
+def test_predict_without_fit(dummy_dataset_lgb):
+    params = {"learning_rate": 0.1, "num_leaves": 31}
+    regressor = MQRegressor(params=params, model=ModelName.lightgbm.value)
+
+    with pytest.raises(FittingException):
+        regressor.predict(dataset=dummy_dataset_lgb)
+
+
+# Test Monotonicity Constraints
+def test_monotone_constraints_called_lgb(dummy_dataset_lgb):
+    params = {"learning_rate": 0.1, "num_leaves": 31}
+    regressor = MQRegressor(params=params, model=ModelName.lightgbm.value)
+
+    regressor.fit(dataset=dummy_dataset_lgb)
+    predictions = regressor.predict(dataset=dummy_dataset_lgb)
+    assert np.all(
+        [
+            np.all(predictions[k] <= predictions[k + 1])
+            for k in range(len(predictions) - 1)
+        ]
+    )
+
+
+def test_monotone_constraints_called_xgb(dummy_dataset_xgb):
+    params = {"learning_rate": 0.1, "max_depth": 6}
+    regressor = MQRegressor(params=params, model=ModelName.xgboost.value)
+
+    regressor.fit(dataset=dummy_dataset_xgb)
+    predictions = regressor.predict(dataset=dummy_dataset_xgb)
+    assert np.all(
+        [
+            np.all(predictions[k] <= predictions[k + 1])
+            for k in range(len(predictions) - 1)
+        ]
+    )
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 0000000..1b778d5
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,174 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from mqboost.base import MQStr, ValidationException
+from mqboost.utils import (
+    alpha_validate,
+    delta_validate,
+    params_validate,
+    prepare_x,
+    prepare_y,
+)
+
+
+# Test for alpha_validate
+def test_alpha_validate_single_alpha():
+    alphas = 0.3
+    result = alpha_validate(alphas)
+    assert result == [0.3]
+
+
+def test_alpha_validate_multiple_alphas():
+    alphas = [0.1, 0.2, 0.3]
+    result = alpha_validate(alphas)
+    assert result == alphas
+
+
+def test_alpha_validate_raises_on_zero_or_one_alpha():
+    with pytest.raises(ValidationException, match="Alpha cannot be 0 or 1"):
+        alpha_validate([0.0, 0.3])
+    with pytest.raises(ValidationException, match="Alpha cannot be 0 or 1"):
+        alpha_validate([0.3, 1.0])
+
+
+def test_alpha_validate_raises_on_non_ascending_alphas():
+    with pytest.raises(ValidationException, match="Alpha is not ascending order"):
+        alpha_validate([0.3, 0.2, 0.1])
+
+
+def test_alpha_validate_raises_on_duplicate_alphas():
+    with pytest.raises(ValidationException, match="Duplicated alpha exists"):
+        alpha_validate([0.1, 0.2, 0.2])
+
+
+def test_alpha_validate_raises_on_empty_alphas():
+    with pytest.raises(ValidationException, match="Input alpha is not valid"):
+        alpha_validate([])
+
+
+# Test for prepare_x
+def test_prepare_x_with_dataframe():
+    x = pd.DataFrame(
+        {
+            "feature_1": [1, 2],
+            "feature_2": [3, 4],
+        }
+    )
+    alphas = [0.1, 0.2]
+    result = prepare_x(x, alphas)
+
+    expected = pd.DataFrame(
+        {
+            "feature_1": [1, 2, 1, 2],
+            "feature_2": [3, 4, 3, 4],
+            "_tau": [0.1, 0.1, 0.2, 0.2],
+        }
+    )
+
+    pd.testing.assert_frame_equal(result, expected)
+
+
+def test_prepare_x_with_series():
+    x = pd.Series([1, 2, 3])
+    alphas = [0.1, 0.2]
+    result = prepare_x(x, alphas)
+
+    expected = pd.DataFrame(
+        {
+            0: [1, 2, 3, 1, 2, 3],
+            "_tau": [0.1, 0.1, 0.1, 0.2, 0.2, 0.2],
+        }
+    )
+
+    pd.testing.assert_frame_equal(result, expected)
+
+
+def test_prepare_x_with_array():
+    x = np.array([[1, 2], [3, 4]])
+    alphas = [0.1, 0.2]
+    result = prepare_x(x, alphas)
+
+    expected = pd.DataFrame(
+        {
+            0: [1, 3, 1, 3],
+            1: [2, 4, 2, 4],
+            "_tau": [0.1, 0.1, 0.2, 0.2],
+        }
+    )
+
+    pd.testing.assert_frame_equal(result, expected)
+
+
+def test_prepare_x_raises_on_invalid_column_name():
+    x = pd.DataFrame({"_tau": [1, 2], "feature_1": [3, 4]})
+    alphas = [0.1, 0.2]
+
+    with pytest.raises(ValidationException, match="Column name '_tau' is not allowed."):
+        prepare_x(x, alphas)
+
+
+# Test for prepare_y
+def test_prepare_y_with_list():
+    y = [1, 2, 3]
+    alphas = [0.1, 0.2]
+    result = prepare_y(y, alphas)
+
+    expected = np.array([1, 2, 3, 1, 2, 3])
+
+    np.testing.assert_array_equal(result, expected)
+
+
+def test_prepare_y_with_array():
+    y = np.array([1, 2, 3])
+    alphas = [0.1, 0.2]
+    result = prepare_y(y, alphas)
+
+    expected = np.array([1, 2, 3, 1, 2, 3])
+
+    np.testing.assert_array_equal(result, expected)
+
+
+def test_prepare_y_with_series():
+    y = pd.Series([1, 2, 3])
+    alphas = [0.1, 0.2]
+    result = prepare_y(y, alphas)
+
+    expected = np.array([1, 2, 3, 1, 2, 3])
+
+    np.testing.assert_array_equal(result, expected)
+
+
+# Test for delta_validate
+def test_delta_validate_valid_delta():
+    delta = 0.04
+    assert delta_validate(delta) is None
+
+
+def test_delta_validate_invalid_type():
+    with pytest.raises(ValidationException, match="Delta is not float type"):
+        delta_validate(1)
+
+
+def test_delta_validate_negative_delta():
+    with pytest.raises(ValidationException, match="Delta must be positive"):
+        delta_validate(-0.01)
+
+
+def test_delta_validate_exceeds_upper_bound():
+    delta = 0.06
+    with pytest.warns(UserWarning, match="Delta should be 0.05 or less."):
+        delta_validate(delta)
+
+
+# Test for params validate
+def test_set_params_validate_raises_validation_exception():
+    params = {
+        MQStr.obj.value: "regression",
+        MQStr.mono.value: [1, -1],
+    }
+    with pytest.raises(
+        ValidationException,
+        match="The parameter named 'objective' must be excluded in params",
+    ):
+        params_validate(params)