def test_experiment_iris(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/iris.csv",
target="Species",
filter_type="remover",
model_features="",
one_hot_features="",
n_estimators=10,
criterion="gini",
max_depth=None,
max_features="auto",
class_weight=None,
method="predict_proba",
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
data = datasets.iris_testdata()
with server.Server() as s:
response = s.test(data=data)
names = response["names"]
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 8) # 4 features + 1 class + 3 probas
self.assertEqual(len(names), 8)
def test_experiment_iris(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/iris.csv",
target="Species",
filter_type="remover",
model_features="",
one_hot_features="",
time_left_for_this_task=30,
per_run_time_limit=30,
ensemble_size=5,
method="predict_proba",
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
data = datasets.iris_testdata()
with server.Server() as s:
response = s.test(data=data)
names = response["names"]
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 8) # 4 features + 1 class + 3 probas
self.assertEqual(len(names), 8)
def test_experiment_iris(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/iris.csv",
target="Species",
filter_type="remover",
model_features="",
ordinal_features="",
penalty="l2",
C=1.0,
fit_intercept=True,
class_weight=None,
solver="liblinear",
max_iter=100,
multi_class="auto",
method="predict_proba",
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
data = datasets.iris_testdata()
with server.Server() as s:
response = s.test(data=data)
names = response["names"]
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 8) # 4 features + 1 class + 3 probas
self.assertEqual(len(names), 8)
def test_experiment_iris(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/iris.csv",
target="Species",
filter_type="remover",
model_features="",
one_hot_features="",
C=1.0,
kernel="rbf",
degree=3,
gamma="auto",
probability=True,
max_iter=-1,
method="predict_proba",
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
data = datasets.iris_testdata()
with server.Server() as s:
response = s.test(data=data)
names = response["names"]
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 8) # 4 features + 1 class + 3 probas
self.assertEqual(len(names), 8)
def test_experiment_iris(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/iris.csv",
target="Species",
date=None,
group=["SepalLengthCm"],
budget=20,
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
data = datasets.iris_testdata()
with server.Server() as s:
response = s.test(data=data)
names = response["names"]
print(names)
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 12) # 4 original features + 8 new features
self.assertEqual(len(names), 12)
def test_experiment_iris(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/iris.csv",
target="Species",
filter_type="remover",
model_features="",
one_hot_features="",
hidden_layer_sizes=100,
activation="relu",
solver="adam",
learning_rate="constant",
max_iter=200,
shuffle=True,
method="predict_proba",
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
data = datasets.iris_testdata()
with server.Server() as s:
response = s.test(data=data)
names = response["names"]
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 8) # 4 features + 1 class + 3 probas
self.assertEqual(len(names), 8)
def test_experiment_iris(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/iris.csv",
filter_type = "remover",
model_features = "Species",
max_samples="auto",
contamination=0.1,
max_features=1.0,
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
data = datasets.iris_testdata()
with server.Server() as s:
response = s.test(data=data)
names = response["names"]
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 5) # 4 features + 1 anomaly score
self.assertEqual(len(names), 5)
def test_experiment_iris(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/iris.csv",
target="Species",
strategy_num="mean",
strategy_cat="most_frequent",
fillvalue_num=0,
fillvalue_cat="",
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
data = datasets.iris_testdata()
with server.Server() as s:
response = s.test(data=data)
names = response["names"]
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 4) # 4 features
self.assertEqual(len(names), 4)
def test_experiment_iris(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/iris.csv",
filter_type="remover",
model_features="Species",
n_clusters=3,
n_init=10,
max_iter=300,
algorithm="auto",
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
data = datasets.iris_testdata()
with server.Server() as s:
response = s.test(data=data)
names = response["names"]
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]),
8) # 4 features + 1 cluster + 3 distance to clusters
self.assertEqual(len(names), 8)
def test_experiment_iris(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/iris.csv",
target="Species",
cutoff=0.9,
threshold=0.0,
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
data = datasets.iris_testdata()
with server.Server() as s:
response = s.test(data=data)
names = response["names"]
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 3) # 4 features - 1 removed
self.assertEqual(len(names), 3)