def test_GroupPCADecisionTreeClassifier():
klass = GroupPCADecisionTreeClassifier
model_kwargs = {}
np.random.seed(1)
verif_model(
df1,
df2,
y1,
klass,
model_kwargs,
all_types=(DataTypes.DataFrame, DataTypes.NumpyArray
), # , DataTypes.SparseArray, DataTypes.SparseDataFrame),
is_classifier=True,
)
def test_RandomRotationForestClassifier():
klass = RandomRotationForestClassifier
model_kwargs = {}
np.random.seed(2)
verif_model(
df1,
df2,
y1,
klass,
model_kwargs,
all_types=(DataTypes.DataFrame, DataTypes.NumpyArray
), # , DataTypes.SparseArray, DataTypes.SparseDataFrame),
is_classifier=True,
)
def test_GroupPCADecisionTreeRegressor():
# klass = DecisionTreeClassifier
klass = GroupPCADecisionTreeRegressor
model_kwargs = {}
np.random.seed(3)
verif_model(
df1_reg,
df2_reg,
y1_reg,
klass,
model_kwargs,
all_types=(DataTypes.DataFrame, DataTypes.NumpyArray
), # , DataTypes.SparseArray, DataTypes.SparseDataFrame),
is_classifier=False,
)
def test_WaveRandomForestRegressor():
klass = WaveRandomForestRegressor
model_kwargs = {}
np.random.seed(4)
verif_model(
df1_reg,
df2_reg,
y1_reg,
klass,
model_kwargs,
all_types=(DataTypes.DataFrame, DataTypes.NumpyArray
), # , DataTypes.SparseArray, DataTypes.SparseDataFrame),
is_classifier=False,
)
def test_GroupPCADecisionTreeClassifier_with_params(random_state, max_depth,
criterion, pca_bootstrap):
klass = GroupPCADecisionTreeClassifier
model_kwargs = {
"max_depth": max_depth,
"criterion": criterion,
"pca_bootstrap": pca_bootstrap,
"random_state": random_state,
}
verif_model(
df1,
df2,
y1,
klass,
model_kwargs,
all_types=(DataTypes.DataFrame, DataTypes.NumpyArray
), # , DataTypes.SparseArray, DataTypes.SparseDataFrame),
is_classifier=True,
)
def test_RandomRotationForestRegressor_with_params(random_state, max_depth,
criterion, pca_bootstrap):
klass = RandomRotationForestRegressor
model_kwargs = {
"max_depth": max_depth,
"criterion": criterion,
"pca_bootstrap": pca_bootstrap,
"random_state": random_state,
}
verif_model(
df1,
df2,
y1,
klass,
model_kwargs,
all_types=(DataTypes.DataFrame, DataTypes.NumpyArray
), # , DataTypes.SparseArray, DataTypes.SparseDataFrame),
is_classifier=False,
)
def test_WaveRandomForestClassifier_with_params(random_state, max_depth,
criterion, nodes_to_keep):
klass = WaveRandomForestClassifier
model_kwargs = {
"max_depth": max_depth,
"criterion": criterion,
"nodes_to_keep": nodes_to_keep,
"random_state": random_state,
}
verif_model(
df1,
df2,
y1,
klass,
model_kwargs,
all_types=(DataTypes.DataFrame, DataTypes.NumpyArray
), # , DataTypes.SparseArray, DataTypes.SparseDataFrame),
is_classifier=True,
)