def test_vDF_density(self, iris_vd):
# testing vDataFrame[].density
try:
create_verticapy_schema(iris_vd._VERTICAPY_VARIABLES_["cursor"])
except:
pass
for kernel in ["gaussian", "logistic", "sigmoid", "silverman"]:
result = iris_vd["PetalLengthCm"].density(
kernel=kernel,
nbins=20,
color="b",
)
assert max(result.get_default_bbox_extra_artists()[1].get_data()
[1]) < 0.25
plt.close("all")
for kernel in ["gaussian", "logistic", "sigmoid", "silverman"]:
result = iris_vd["PetalLengthCm"].density(
kernel=kernel,
nbins=20,
by="Species",
color="b",
)
assert len(result.get_default_bbox_extra_artists()) < 20
plt.close("all")
# testing vDataFrame.density
for kernel in ["gaussian", "logistic", "sigmoid", "silverman"]:
result = iris_vd.density(
kernel=kernel,
nbins=20,
color="b",
)
assert max(result.get_default_bbox_extra_artists()[5].get_data()
[1]) < 0.37
plt.close("all")
def model(titanic_vd):
create_verticapy_schema()
model_class = DBSCAN("DBSCAN_model_test", )
model_class.drop()
model_class.fit("public.titanic", ["age", "fare"])
yield model_class
model_class.drop()
def model(titanic_vd):
create_verticapy_schema()
model_class = KNeighborsClassifier("knn_model_test", )
model_class.drop()
model_class.fit("public.titanic", ["age", "fare"], "survived")
yield model_class
model_class.drop()
def model(commodities_vd):
create_verticapy_schema()
model_class = VAR("var_model_test", p=1)
model_class.drop()
model_class.fit("public.commodities", ["gold", "oil"], "date")
yield model_class
model_class.drop()
def model(titanic_vd):
create_verticapy_schema()
model_class = KernelDensity("KernelDensity_model_test",)
model_class.drop()
model_class.fit("public.titanic", ["age", "fare"])
yield model_class
model_class.drop()
def model(titanic_vd):
create_verticapy_schema()
model_class = LocalOutlierFactor("lof_model_test",)
model_class.drop()
model_class.fit("public.titanic", ["age", "fare"])
yield model_class
model_class.drop()
def model(titanic_vd):
create_verticapy_schema()
model_class = CountVectorizer("model_test_countvectorizer", )
model_class.drop()
model_class.fit("public.titanic", ["name"])
yield model_class
model_class.drop()
def model(titanic_vd):
create_verticapy_schema()
model_class = NearestCentroid("nc_model_test", )
model_class.drop()
model_class.fit("public.titanic", ["age", "fare"], "survived")
yield model_class
model_class.drop()
def model(base, amazon_vd):
try:
create_verticapy_schema(base.cursor)
except:
pass
model_class = SARIMAX("sarimax_model_test", cursor=base.cursor, p=1, d=1, q=1, s=12, P=1, D=1, Q=1, max_pik=20)
model_class.drop()
model_class.fit("public.amazon", "number", "date",)
yield model_class
model_class.drop()
def model(base, titanic_vd):
try:
create_verticapy_schema(base.cursor)
except:
pass
model_class = KNeighborsClassifier("knn_model_test", cursor=base.cursor)
model_class.drop()
model_class.fit("public.titanic", [
"age",
"fare",
], "survived")
yield model_class
model_class.drop()
def model(base, titanic_vd):
try:
create_verticapy_schema(base.cursor)
except:
pass
model_class = NearestCentroid("nc_model_test", cursor=base.cursor)
model_class.drop()
model_class.fit("public.titanic", [
"age",
"fare",
], "survived")
yield model_class
model_class.drop()
def model(amazon_vd):
create_verticapy_schema()
model_class = SARIMAX("sarimax_model_test",
p=1,
d=1,
q=1,
s=12,
P=1,
D=1,
Q=1,
max_pik=20)
model_class.drop()
model_class.fit("public.amazon", "number", "date")
yield model_class
model_class.drop()
def model(base, titanic_vd):
try:
create_verticapy_schema(base.cursor)
except:
pass
model_class = LocalOutlierFactor("lof_model_test", cursor=base.cursor)
model_class.drop()
model_class.fit(
"public.titanic",
[
"age",
"fare",
],
)
yield model_class
model_class.drop()
def model(base, titanic_vd):
try:
create_verticapy_schema(base.cursor)
except:
pass
model_class = KernelDensity("KernelDensity_model_test", cursor=base.cursor)
model_class.drop()
model_class.fit(
"public.titanic",
[
"age",
"fare",
],
)
yield model_class
model_class.drop()
def model(base, commodities_vd):
try:
create_verticapy_schema(base.cursor)
except:
pass
model_class = VAR(
"var_model_test",
cursor=base.cursor,
p=1,
)
model_class.drop()
model_class.fit(
"public.commodities",
["gold", "oil"],
"date",
)
yield model_class
model_class.drop()
