def test_no_unselected_feature(self, get_iris_data):
ground_truth = get_iris_data[["sepal width (cm)"]]
dt_multi = DecisionTreeSupervisedDiscretiserMethod(
tree_params={
"max_depth": 3,
"random_state": 2020
},
mode="multi",
split_unselected_feat=False,
)
tree_discretiser = dt_multi.fit(
feat_names=[
"sepal length (cm)",
"sepal width (cm)",
"petal length (cm)",
"petal width (cm)",
],
dataframe=get_iris_data,
target_continuous=False,
target="target",
)
output = tree_discretiser.transform(get_iris_data[["sepal width (cm)"
]])
assert all(ground_truth == output)
def test_single_categorical(self, categorical_data):
df = categorical_data.copy(deep=True)
ground_truth = np.array(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2],
[2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
]
) # ground truth is generated by manually use DecionTree to extract thresholds
dt_single = DecisionTreeSupervisedDiscretiserMethod(
tree_params={"max_depth": 2},
mode="single",
)
tree_discretiser = dt_single.fit(
feat_names=["petal width (cm)"],
dataframe=df,
target_continuous=False,
target="target",
)
discretiser_output = tree_discretiser.transform(
df[["petal width (cm)"]]).values
assert (ground_truth == discretiser_output.reshape(-1, 15)).all()
def test_transform_shuffled_indices(self, get_iris_data):
data = get_iris_data.copy(deep=True)
ground_truth = np.array([
[0, 0, 1, 0, 1],
[0, 0, 0, 0, 0],
[0, 1, 1, 2, 1],
[2, 1, 1, 0, 1],
[1, 2, 1, 0, 2],
[2, 2, 2, 0, 0],
[1, 1, 0, 2, 2],
[1, 2, 0, 2, 2],
[2, 2, 0, 1, 2],
[1, 1, 2, 1, 1],
])
sample_data = data[["sepal length (cm)",
"target"]].sample(50, random_state=2021)
dt_single = DecisionTreeSupervisedDiscretiserMethod(
tree_params={"max_depth": 2},
mode="single",
)
dt_single.fit(
feat_names=["sepal length (cm)"],
dataframe=sample_data,
target_continuous=False,
target="target",
)
discretiser_output = dt_single.transform(
sample_data[["sepal length (cm)"]])
assert discretiser_output.isnull().values.sum() == 0
assert (ground_truth == discretiser_output.values.reshape(10, 5)).all()
def test_multi_fit(self, get_iris_data):
ground_truth_petal_length = np.array(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2],
[2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
[2, 1, 2, 1, 2, 2, 0, 1, 2, 2, 2, 2, 2, 2, 2],
[2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
]
) # ground truth is generated by manually use DecionTree to extract thresholds
ground_truth_petal_width = np.array(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2],
[2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
]
) # ground truth is generated by manually use DecionTree to extract thresholds
iris = get_iris_data.copy(deep=True)
dt_multi = DecisionTreeSupervisedDiscretiserMethod(tree_params={
"max_depth": 3,
"random_state": 2020
},
mode="multi")
tree_discretiser = dt_multi.fit(
feat_names=[
"sepal length (cm)",
"sepal width (cm)",
"petal length (cm)",
"petal width (cm)",
],
dataframe=iris,
target_continuous=False,
target="target",
)
discretiser_petal_length = tree_discretiser.transform(
iris[["petal length (cm)"]]).values
discretiser_petal_width = tree_discretiser.transform(
iris[["petal width (cm)"]]).values
assert (ground_truth_petal_length == discretiser_petal_length.reshape(
-1, 15)).all()
assert (ground_truth_petal_width == discretiser_petal_width.reshape(
-1, 15)).all()
def test_single_continuous(self, continuous_data):
diabete = continuous_data.copy(deep=True)
dt_single = DecisionTreeSupervisedDiscretiserMethod(
tree_params={"max_depth": 2},
mode="single",
)
tree_discretiser = dt_single.fit(
feat_names=["s6"],
dataframe=diabete,
target_continuous=True,
target="target",
)
discretiser_output = tree_discretiser.transform(diabete[["s6"]]).values
ground_truth = np.array(
[
[1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0],
[1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 3, 0, 0],
[1, 2, 0, 2, 1, 0, 1, 1, 0, 1, 1, 1, 1],
[1, 2, 1, 0, 2, 1, 0, 0, 0, 1, 1, 1, 1],
[1, 2, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1],
[1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1],
[0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 2, 1, 1],
[1, 1, 0, 0, 1, 1, 2, 0, 0, 1, 2, 1, 1],
[0, 1, 0, 1, 2, 2, 1, 0, 1, 2, 1, 1, 1],
[3, 2, 1, 1, 1, 2, 2, 1, 1, 0, 0, 0, 2],
[2, 0, 1, 1, 0, 2, 1, 1, 2, 1, 1, 3, 1],
[0, 1, 0, 1, 2, 1, 0, 1, 1, 2, 1, 1, 2],
[0, 1, 0, 1, 0, 2, 1, 2, 1, 1, 0, 2, 3],
[1, 1, 0, 2, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[1, 2, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1],
[1, 1, 2, 0, 1, 2, 1, 1, 1, 2, 1, 1, 2],
[2, 1, 0, 1, 1, 1, 0, 2, 2, 2, 1, 1, 0],
[0, 1, 0, 1, 1, 0, 1, 0, 1, 2, 1, 1, 0],
[2, 2, 1, 1, 1, 2, 1, 2, 0, 0, 1, 0, 0],
[0, 2, 1, 2, 2, 1, 2, 2, 1, 1, 0, 2, 1],
[1, 1, 1, 1, 1, 2, 1, 1, 2, 0, 1, 1, 0],
[2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1],
[1, 1, 1, 0, 2, 0, 0, 1, 0, 1, 0, 0, 1],
[1, 2, 2, 1, 1, 1, 0, 1, 2, 0, 2, 1, 1],
[1, 1, 0, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1],
[2, 1, 1, 1, 2, 1, 1, 2, 1, 0, 0, 2, 1],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 3],
[0, 0, 1, 2, 1, 0, 0, 0, 2, 2, 1, 2, 1],
[2, 2, 2, 1, 2, 1, 0, 1, 1, 1, 0, 1, 1],
[0, 0, 1, 1, 0, 2, 1, 0, 1, 1, 0, 0, 0],
[2, 0, 2, 1, 1, 0, 0, 1, 0, 1, 1, 2, 2],
[2, 1, 1, 0, 1, 2, 1, 0, 1, 2, 1, 1, 1],
[2, 1, 1, 1, 2, 1, 1, 0, 1, 0, 2, 1, 3],
[0, 1, 1, 2, 1, 1, 0, 0, 1, 2, 1, 1, 1],
]
) # ground truth is generated by manually use DecionTree to extract thresholds
assert (ground_truth == discretiser_output.reshape(-1, 13)).all()
def test_transform_all_multi(self, get_iris_data):
data = get_iris_data.copy(deep=True)
sepal_length = data["sepal length (cm)"]
sepal_width = data["sepal width (cm)"]
petal_length = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2],
[2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
[2, 1, 2, 1, 2, 2, 0, 1, 2, 2, 2, 2, 2, 2, 2],
[2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
])
petal_width = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2],
[2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
])
dt_multi = DecisionTreeSupervisedDiscretiserMethod(mode="multi",
tree_params={
"max_depth": 3,
"random_state":
2020
})
tree_discretiser = dt_multi.fit(
feat_names=[
"sepal length (cm)",
"sepal width (cm)",
"petal length (cm)",
"petal width (cm)",
],
dataframe=data,
target="target",
target_continuous=False,
)
output_df = tree_discretiser.transform(data)
assert (output_df["sepal length (cm)"].values == sepal_length).all()
assert (output_df["sepal width (cm)"].values == sepal_width).all()
assert (output_df["petal length (cm)"].values.reshape(
-1, 15) == petal_length).all()
assert (output_df["petal width (cm)"].values.reshape(
-1, 15) == petal_width).all()
def test_keep_unselected_feature(self, get_iris_data):
ground_truth = np.array(
[
[4, 2, 3, 3, 4, 6, 4, 4, 1, 3, 4, 4, 2, 2, 6],
[6, 6, 4, 5, 5, 4, 4, 4, 3, 4, 2, 4, 4, 4, 3],
[3, 4, 6, 6, 3, 3, 4, 4, 2, 4, 4, 0, 3, 4, 5],
[2, 5, 3, 4, 3, 3, 3, 3, 0, 1, 1, 3, 0, 1, 1],
[0, 2, 0, 1, 1, 3, 2, 1, 0, 1, 3, 1, 1, 1, 1],
[2, 1, 2, 1, 1, 0, 0, 1, 1, 2, 4, 3, 0, 2, 1],
[1, 2, 1, 0, 1, 2, 1, 1, 1, 1, 3, 1, 2, 1, 2],
[2, 1, 1, 1, 4, 3, 1, 2, 1, 1, 3, 2, 5, 1, 0],
[3, 1, 1, 1, 3, 3, 1, 2, 1, 2, 1, 5, 1, 1, 1],
[2, 4, 3, 2, 3, 3, 3, 1, 3, 3, 2, 1, 2, 4, 2],
]
) # ground truth is generated by manually use DecionTree to extract thresholds
dt_multi = DecisionTreeSupervisedDiscretiserMethod(
tree_params={
"max_depth": 3,
"random_state": 2020
},
mode="multi",
split_unselected_feat=True,
)
tree_discretiser = dt_multi.fit(
feat_names=[
"sepal length (cm)",
"sepal width (cm)",
"petal length (cm)",
"petal width (cm)",
],
dataframe=get_iris_data,
target_continuous=False,
target="target",
)
output = tree_discretiser.transform(get_iris_data[["sepal width (cm)"
]]).values
assert (ground_truth == output.reshape(-1, 15)).all()
def _discretise_features(self, X: pd.DataFrame) -> pd.DataFrame:
"""
Helper method to discretise input data using parameters in
`discretiser_kwargs` and `discretiser_alg`.
The splitting thresholds are extracted from the training data
Args:
X (pd.DataFrame): a dataframe to be discretised
Returns:
a discretised version of the input dataframe
"""
X = X.copy()
for col in self.discretiser_alg.keys():
if self.discretiser_alg[col] == "unsupervised":
if self.discretiser_kwargs[col]["method"] == "fixed":
X[col] = Discretiser(
**self.discretiser_kwargs[col]).transform(
X[col].values)
else:
discretiser = Discretiser(
**self.discretiser_kwargs[col]).fit(
self._discretise_data[col].values)
X[col] = discretiser.transform(X[col].values)
else:
if self.discretiser_alg[col] == "tree":
discretiser = DecisionTreeSupervisedDiscretiserMethod(
mode="single",
tree_params=self.discretiser_kwargs[col])
elif self.discretiser_alg[col] == "mdlp":
discretiser = MDLPSupervisedDiscretiserMethod(
self.discretiser_kwargs[col])
discretiser.fit(
dataframe=self._discretise_data,
feat_names=[col],
target=self._target_name,
target_continuous=False,
)
X[col] = discretiser.transform(X[[col]])
return X
def test_default_args(self):
dt_multi = DecisionTreeSupervisedDiscretiserMethod()
params = dt_multi.get_params()
assert params["tree_params"]["max_depth"] == 2
def test_invalid_mode(self):
with pytest.raises(KeyError):
DecisionTreeSupervisedDiscretiserMethod(
tree_params={"max_depth": 2}, mode="invalid")