classifier_comparison.rst
Scikit-learn Classifier Comparison¶
Original Code¶
Dependencies
For this example, you will need:
This example adapts the classifier comparison example from the scikit-learn documentation to use ZnTrack.
The original code looks like this:
Original Code
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import ListedColormap
from sklearn.datasets import make_circles, make_classification, make_moons
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
names = [
"Nearest Neighbors",
"Linear SVM",
"RBF SVM",
"Gaussian Process",
"Decision Tree",
"Random Forest",
"Neural Net",
"AdaBoost",
"Naive Bayes",
"QDA",
]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025, random_state=42),
SVC(gamma=2, C=1, random_state=42),
GaussianProcessClassifier(1.0 * RBF(1.0), random_state=42),
DecisionTreeClassifier(max_depth=5, random_state=42),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1, random_state=42),
MLPClassifier(alpha=1, max_iter=1000, random_state=42),
AdaBoostClassifier(random_state=42),
GaussianNB(),
QuadraticDiscriminantAnalysis(),
]
X, y = make_classification(
n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1
)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)
datasets = [
make_moons(noise=0.3, random_state=0),
make_circles(noise=0.2, factor=0.5, random_state=1),
linearly_separable,
]
figure = plt.figure(figsize=(27, 9))
i = 1
# iterate over datasets
for ds_cnt, ds in enumerate(datasets):
# preprocess dataset, split into training and test part
X, y = ds
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=42
)
x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
# just plot the dataset first
cm = plt.cm.RdBu
cm_bright = ListedColormap(["#FF0000", "#0000FF"])
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
if ds_cnt == 0:
ax.set_title("Input data")
# Plot the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
# Plot the testing points
ax.scatter(
X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k"
)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
ax.set_yticks(())
i += 1
# iterate over classifiers
for name, clf in zip(names, classifiers):
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
clf = make_pipeline(StandardScaler(), clf)
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
DecisionBoundaryDisplay.from_estimator(clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5)
# Plot the training points
ax.scatter(
X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k"
)
# Plot the testing points
ax.scatter(
X_test[:, 0],
X_test[:, 1],
c=y_test,
cmap=cm_bright,
edgecolors="k",
alpha=0.6,
)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
ax.set_yticks(())
if ds_cnt == 0:
ax.set_title(name)
ax.text(
x_max - 0.3,
y_min + 0.3,
("%.2f" % score).lstrip("0"),
size=15,
horizontalalignment="right",
)
i += 1
plt.tight_layout()
plt.show()
Converted Workflow with ZnTrack¶
We adapt the scikit-learn example to utilize ZnTrack. This allows us to store, share results, and reuse the code by better separating it into individual Node instances for each task. Additionally, we can optimize parameters and compare different classifiers more effectively using DVC infrastructure tools.
Here’s the graph structure for a single dataset and multiple classifiers:
flowchart TD Dataset --> TrainTestSplit subgraph Classifier KNeighborsClassifier SVC GaussianProcessClassifier DecisionTreeClassifier RandomForestClassifier a["..."] end TrainTestSplit --> KNeighborsClassifier --> Compare TrainTestSplit --> SVC --> Compare TrainTestSplit --> GaussianProcessClassifier --> Compare TrainTestSplit --> DecisionTreeClassifier --> Compare TrainTestSplit --> RandomForestClassifier --> Compare TrainTestSplit --> a["..."] --> Compare
ZnTrack Nodes
import dataclasses
import typing as t
from pathlib import Path
import zntrack
class CreateDataset(zntrack.Node):
params: dict = zntrack.params(default_factory=dict)
method: t.Literal["make_moons", "make_circles", "linearly_separable"] = (
zntrack.params()
)
x: t.Any = zntrack.outs()
y: t.Any = zntrack.outs()
def run(self) -> None:
import numpy as np
from sklearn.datasets import make_circles, make_classification, make_moons
if self.method == "make_moons":
self.x, self.y = make_moons(**self.params)
elif self.method == "make_circles":
self.x, self.y = make_circles(**self.params)
elif self.method == "linearly_separable":
X, y = make_classification(**self.params)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
self.x, self.y = X, y
else:
raise ValueError(f"Unknown method: {self.method}")
class TrainTestSplit(zntrack.Node):
x: t.Any = zntrack.deps()
y: t.Any = zntrack.deps()
x_train: t.Any = zntrack.outs()
y_train: t.Any = zntrack.outs()
x_test: t.Any = zntrack.outs()
y_test: t.Any = zntrack.outs()
test_size: float = zntrack.params(0.4)
random_state: int = zntrack.params(42)
def run(self) -> None:
from sklearn.model_selection import train_test_split
self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(
self.x, self.y, test_size=self.test_size, random_state=self.random_state
)
@dataclasses.dataclass
class Model:
method: t.Literal[
"KNeighborsClassifier",
"SVC",
"GaussianProcessClassifier",
"DecisionTreeClassifier",
"RandomForestClassifier",
"MLPClassifier",
"AdaBoostClassifier",
"GaussianNB",
"QuadraticDiscriminantAnalysis",
]
params: dict = dataclasses.field(default_factory=dict)
name: str | None = None
def __post_init__(self):
if self.name is None:
self.name = self.method
def get_model(self):
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
if self.method == "KNeighborsClassifier":
return KNeighborsClassifier(**self.params)
elif self.method == "SVC":
return SVC(**self.params)
elif self.method == "GaussianProcessClassifier":
kernel = 1.0 * RBF(1.0)
return GaussianProcessClassifier(kernel=kernel, **self.params)
elif self.method == "DecisionTreeClassifier":
return DecisionTreeClassifier(**self.params)
elif self.method == "RandomForestClassifier":
return RandomForestClassifier(**self.params)
elif self.method == "MLPClassifier":
return MLPClassifier(**self.params)
elif self.method == "AdaBoostClassifier":
return AdaBoostClassifier(**self.params)
elif self.method == "GaussianNB":
return GaussianNB(**self.params)
elif self.method == "QuadraticDiscriminantAnalysis":
return QuadraticDiscriminantAnalysis(**self.params)
else:
raise ValueError(f"Unknown method: {self.method}")
class Classifier(zntrack.Node):
model: Model = zntrack.deps()
x: t.Any = zntrack.deps()
x_train: t.Any = zntrack.deps()
y_train: t.Any = zntrack.deps()
x_test: t.Any = zntrack.deps()
y_test: t.Any = zntrack.deps()
metrics: dict = zntrack.metrics()
figure_path: Path = zntrack.plots_path(zntrack.nwd / "figure.png")
def run(self):
model = self.model.get_model()
model.fit(self.x_train, self.y_train)
self.metrics = {"score": model.score(self.x_test, self.y_test)}
self.get_figure(model)
def get_figure(self, clf):
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.inspection import DecisionBoundaryDisplay
fig, ax = plt.subplots()
x_min, x_max = self.x[:, 0].min() - 0.5, self.x[:, 0].max() + 0.5
y_min, y_max = self.x[:, 1].min() - 0.5, self.x[:, 1].max() + 0.5
cm = plt.cm.RdBu
cm_bright = ListedColormap(["#FF0000", "#0000FF"])
DecisionBoundaryDisplay.from_estimator(
clf, self.x, cmap=cm, alpha=0.8, ax=ax, eps=0.5
)
# Plot the training points
ax.scatter(
self.x_train[:, 0],
self.x_train[:, 1],
c=self.y_train,
cmap=cm_bright,
edgecolors="k",
)
# Plot the testing points
ax.scatter(
self.x_test[:, 0],
self.x_test[:, 1],
c=self.y_test,
cmap=cm_bright,
edgecolors="k",
alpha=0.6,
)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
ax.set_yticks(())
self.figure_path.parent.mkdir(exist_ok=True, parents=True)
fig.savefig(self.figure_path, bbox_inches="tight")
class CombineFigures(zntrack.Node):
cfs: list[Classifier] = zntrack.deps()
combined_figure_path: Path = zntrack.plots_path(zntrack.nwd / "combined_figure.png")
def run(self):
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
# Determine grid size
n_cols = int(len(self.cfs) ** 0.5)
n_rows = (len(self.cfs) + n_cols - 1) // n_cols
# Create figure with tight spacing
fig, axs = plt.subplots(n_rows, n_cols, figsize=(n_cols * 4, n_rows * 4))
axs = np.array(axs).reshape(n_rows, n_cols) # Ensure axs is always iterable
# Remove unnecessary padding
fig.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0.0)
# Display images
for i, cf in enumerate(self.cfs):
img = Image.open(cf.figure_path)
ax = axs[i // n_cols, i % n_cols]
ax.imshow(img)
ax.set_title(
f"{cf.model.name} - {cf.metrics['score']:.2f}", fontsize=10, pad=2
)
ax.axis("off")
# Hide unused axes
for i in range(len(self.cfs), n_cols * n_rows):
axs[i // n_cols, i % n_cols].axis("off")
fig.savefig(self.combined_figure_path, bbox_inches="tight", pad_inches=0.1)
ZnTrack Workflow
import zntrack
# Store all outputs in the DVC cache and avoid GIT tracked Node outputs.
zntrack.config.ALWAYS_CACHE = True
from src import Classifier, CombineFigures, CreateDataset, Model, TrainTestSplit
project = zntrack.Project()
models = [
Model(
method="KNeighborsClassifier",
params={"n_neighbors": 3},
),
Model(
method="SVC",
params={"kernel": "linear", "C": 0.025, "random_state": 42},
name="Linear-SVM",
),
Model(
method="SVC",
params={"gamma": 2, "C": 1, "random_state": 42},
name="RBF-SVM",
),
Model(
method="GaussianProcessClassifier",
params={"random_state": 42},
),
Model(
method="DecisionTreeClassifier",
params={"max_depth": 5, "random_state": 42},
),
Model(
method="RandomForestClassifier",
params={
"max_depth": 5,
"n_estimators": 10,
"max_features": 1,
"random_state": 42,
},
),
Model(
method="MLPClassifier",
params={"alpha": 1, "max_iter": 1000, "random_state": 42},
),
Model(
method="AdaBoostClassifier",
params={"random_state": 42},
),
Model(
method="GaussianNB",
),
Model(
method="QuadraticDiscriminantAnalysis",
),
]
def classify(ds):
split = TrainTestSplit(x=ds.x, y=ds.y)
cfs = []
for model in models:
cfs.append(
Classifier(
model=model,
x=ds.x,
x_train=split.x_train,
y_train=split.y_train,
x_test=split.x_test,
y_test=split.y_test,
name=model.name,
)
)
CombineFigures(cfs=cfs)
with project.group("moons"):
ds = CreateDataset(method="make_moons", params={"noise": 0.3, "random_state": 0})
classify(ds)
with project.group("circles"):
ds = CreateDataset(
method="make_circles", params={"noise": 0.2, "factor": 0.5, "random_state": 0}
)
classify(ds)
with project.group("linearly-separable"):
ds = CreateDataset(
method="linearly_separable",
params={
"n_features": 2,
"n_redundant": 0,
"n_informative": 2,
"random_state": 42,
"n_clusters_per_class": 1,
},
)
classify(ds)
project.repro()
Generated configuration files¶
dvc.yaml File
stages:
circles_AdaBoostClassifier:
cmd: zntrack run src.Classifier --name circles_AdaBoostClassifier
deps:
- nodes/circles/CreateDataset/x.json
- nodes/circles/TrainTestSplit/x_test.json
- nodes/circles/TrainTestSplit/x_train.json
- nodes/circles/TrainTestSplit/y_test.json
- nodes/circles/TrainTestSplit/y_train.json
metrics:
- nodes/circles/AdaBoostClassifier/metrics.json
- nodes/circles/AdaBoostClassifier/node-meta.json:
cache: true
outs:
- nodes/circles/AdaBoostClassifier/figure.png
params:
- circles_AdaBoostClassifier
circles_CombineFigures:
cmd: zntrack run src.CombineFigures --name circles_CombineFigures
deps:
- nodes/circles/AdaBoostClassifier/figure.png
- nodes/circles/AdaBoostClassifier/metrics.json
- nodes/circles/DecisionTreeClassifier/figure.png
- nodes/circles/DecisionTreeClassifier/metrics.json
- nodes/circles/GaussianNB/figure.png
- nodes/circles/GaussianNB/metrics.json
- nodes/circles/GaussianProcessClassifier/figure.png
- nodes/circles/GaussianProcessClassifier/metrics.json
- nodes/circles/KNeighborsClassifier/figure.png
- nodes/circles/KNeighborsClassifier/metrics.json
- nodes/circles/Linear-SVM/figure.png
- nodes/circles/Linear-SVM/metrics.json
- nodes/circles/MLPClassifier/figure.png
- nodes/circles/MLPClassifier/metrics.json
- nodes/circles/QuadraticDiscriminantAnalysis/figure.png
- nodes/circles/QuadraticDiscriminantAnalysis/metrics.json
- nodes/circles/RBF-SVM/figure.png
- nodes/circles/RBF-SVM/metrics.json
- nodes/circles/RandomForestClassifier/figure.png
- nodes/circles/RandomForestClassifier/metrics.json
metrics:
- nodes/circles/CombineFigures/node-meta.json:
cache: true
outs:
- nodes/circles/CombineFigures/combined_figure.png
circles_CreateDataset:
cmd: zntrack run src.CreateDataset --name circles_CreateDataset
metrics:
- nodes/circles/CreateDataset/node-meta.json:
cache: true
outs:
- nodes/circles/CreateDataset/x.json
- nodes/circles/CreateDataset/y.json
params:
- circles_CreateDataset
circles_DecisionTreeClassifier:
cmd: zntrack run src.Classifier --name circles_DecisionTreeClassifier
deps:
- nodes/circles/CreateDataset/x.json
- nodes/circles/TrainTestSplit/x_test.json
- nodes/circles/TrainTestSplit/x_train.json
- nodes/circles/TrainTestSplit/y_test.json
- nodes/circles/TrainTestSplit/y_train.json
metrics:
- nodes/circles/DecisionTreeClassifier/metrics.json
- nodes/circles/DecisionTreeClassifier/node-meta.json:
cache: true
outs:
- nodes/circles/DecisionTreeClassifier/figure.png
params:
- circles_DecisionTreeClassifier
circles_GaussianNB:
cmd: zntrack run src.Classifier --name circles_GaussianNB
deps:
- nodes/circles/CreateDataset/x.json
- nodes/circles/TrainTestSplit/x_test.json
- nodes/circles/TrainTestSplit/x_train.json
- nodes/circles/TrainTestSplit/y_test.json
- nodes/circles/TrainTestSplit/y_train.json
metrics:
- nodes/circles/GaussianNB/metrics.json
- nodes/circles/GaussianNB/node-meta.json:
cache: true
outs:
- nodes/circles/GaussianNB/figure.png
params:
- circles_GaussianNB
circles_GaussianProcessClassifier:
cmd: zntrack run src.Classifier --name circles_GaussianProcessClassifier
deps:
- nodes/circles/CreateDataset/x.json
- nodes/circles/TrainTestSplit/x_test.json
- nodes/circles/TrainTestSplit/x_train.json
- nodes/circles/TrainTestSplit/y_test.json
- nodes/circles/TrainTestSplit/y_train.json
metrics:
- nodes/circles/GaussianProcessClassifier/metrics.json
- nodes/circles/GaussianProcessClassifier/node-meta.json:
cache: true
outs:
- nodes/circles/GaussianProcessClassifier/figure.png
params:
- circles_GaussianProcessClassifier
circles_KNeighborsClassifier:
cmd: zntrack run src.Classifier --name circles_KNeighborsClassifier
deps:
- nodes/circles/CreateDataset/x.json
- nodes/circles/TrainTestSplit/x_test.json
- nodes/circles/TrainTestSplit/x_train.json
- nodes/circles/TrainTestSplit/y_test.json
- nodes/circles/TrainTestSplit/y_train.json
metrics:
- nodes/circles/KNeighborsClassifier/metrics.json
- nodes/circles/KNeighborsClassifier/node-meta.json:
cache: true
outs:
- nodes/circles/KNeighborsClassifier/figure.png
params:
- circles_KNeighborsClassifier
circles_Linear-SVM:
cmd: zntrack run src.Classifier --name circles_Linear-SVM
deps:
- nodes/circles/CreateDataset/x.json
- nodes/circles/TrainTestSplit/x_test.json
- nodes/circles/TrainTestSplit/x_train.json
- nodes/circles/TrainTestSplit/y_test.json
- nodes/circles/TrainTestSplit/y_train.json
metrics:
- nodes/circles/Linear-SVM/metrics.json
- nodes/circles/Linear-SVM/node-meta.json:
cache: true
outs:
- nodes/circles/Linear-SVM/figure.png
params:
- circles_Linear-SVM
circles_MLPClassifier:
cmd: zntrack run src.Classifier --name circles_MLPClassifier
deps:
- nodes/circles/CreateDataset/x.json
- nodes/circles/TrainTestSplit/x_test.json
- nodes/circles/TrainTestSplit/x_train.json
- nodes/circles/TrainTestSplit/y_test.json
- nodes/circles/TrainTestSplit/y_train.json
metrics:
- nodes/circles/MLPClassifier/metrics.json
- nodes/circles/MLPClassifier/node-meta.json:
cache: true
outs:
- nodes/circles/MLPClassifier/figure.png
params:
- circles_MLPClassifier
circles_QuadraticDiscriminantAnalysis:
cmd: zntrack run src.Classifier --name circles_QuadraticDiscriminantAnalysis
deps:
- nodes/circles/CreateDataset/x.json
- nodes/circles/TrainTestSplit/x_test.json
- nodes/circles/TrainTestSplit/x_train.json
- nodes/circles/TrainTestSplit/y_test.json
- nodes/circles/TrainTestSplit/y_train.json
metrics:
- nodes/circles/QuadraticDiscriminantAnalysis/metrics.json
- nodes/circles/QuadraticDiscriminantAnalysis/node-meta.json:
cache: true
outs:
- nodes/circles/QuadraticDiscriminantAnalysis/figure.png
params:
- circles_QuadraticDiscriminantAnalysis
circles_RBF-SVM:
cmd: zntrack run src.Classifier --name circles_RBF-SVM
deps:
- nodes/circles/CreateDataset/x.json
- nodes/circles/TrainTestSplit/x_test.json
- nodes/circles/TrainTestSplit/x_train.json
- nodes/circles/TrainTestSplit/y_test.json
- nodes/circles/TrainTestSplit/y_train.json
metrics:
- nodes/circles/RBF-SVM/metrics.json
- nodes/circles/RBF-SVM/node-meta.json:
cache: true
outs:
- nodes/circles/RBF-SVM/figure.png
params:
- circles_RBF-SVM
circles_RandomForestClassifier:
cmd: zntrack run src.Classifier --name circles_RandomForestClassifier
deps:
- nodes/circles/CreateDataset/x.json
- nodes/circles/TrainTestSplit/x_test.json
- nodes/circles/TrainTestSplit/x_train.json
- nodes/circles/TrainTestSplit/y_test.json
- nodes/circles/TrainTestSplit/y_train.json
metrics:
- nodes/circles/RandomForestClassifier/metrics.json
- nodes/circles/RandomForestClassifier/node-meta.json:
cache: true
outs:
- nodes/circles/RandomForestClassifier/figure.png
params:
- circles_RandomForestClassifier
circles_TrainTestSplit:
cmd: zntrack run src.TrainTestSplit --name circles_TrainTestSplit
deps:
- nodes/circles/CreateDataset/x.json
- nodes/circles/CreateDataset/y.json
metrics:
- nodes/circles/TrainTestSplit/node-meta.json:
cache: true
outs:
- nodes/circles/TrainTestSplit/x_test.json
- nodes/circles/TrainTestSplit/x_train.json
- nodes/circles/TrainTestSplit/y_test.json
- nodes/circles/TrainTestSplit/y_train.json
params:
- circles_TrainTestSplit
linearly-separable_AdaBoostClassifier:
cmd: zntrack run src.Classifier --name linearly-separable_AdaBoostClassifier
deps:
- nodes/linearly-separable/CreateDataset/x.json
- nodes/linearly-separable/TrainTestSplit/x_test.json
- nodes/linearly-separable/TrainTestSplit/x_train.json
- nodes/linearly-separable/TrainTestSplit/y_test.json
- nodes/linearly-separable/TrainTestSplit/y_train.json
metrics:
- nodes/linearly-separable/AdaBoostClassifier/metrics.json
- nodes/linearly-separable/AdaBoostClassifier/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/AdaBoostClassifier/figure.png
params:
- linearly-separable_AdaBoostClassifier
linearly-separable_CombineFigures:
cmd: zntrack run src.CombineFigures --name linearly-separable_CombineFigures
deps:
- nodes/linearly-separable/AdaBoostClassifier/figure.png
- nodes/linearly-separable/AdaBoostClassifier/metrics.json
- nodes/linearly-separable/DecisionTreeClassifier/figure.png
- nodes/linearly-separable/DecisionTreeClassifier/metrics.json
- nodes/linearly-separable/GaussianNB/figure.png
- nodes/linearly-separable/GaussianNB/metrics.json
- nodes/linearly-separable/GaussianProcessClassifier/figure.png
- nodes/linearly-separable/GaussianProcessClassifier/metrics.json
- nodes/linearly-separable/KNeighborsClassifier/figure.png
- nodes/linearly-separable/KNeighborsClassifier/metrics.json
- nodes/linearly-separable/Linear-SVM/figure.png
- nodes/linearly-separable/Linear-SVM/metrics.json
- nodes/linearly-separable/MLPClassifier/figure.png
- nodes/linearly-separable/MLPClassifier/metrics.json
- nodes/linearly-separable/QuadraticDiscriminantAnalysis/figure.png
- nodes/linearly-separable/QuadraticDiscriminantAnalysis/metrics.json
- nodes/linearly-separable/RBF-SVM/figure.png
- nodes/linearly-separable/RBF-SVM/metrics.json
- nodes/linearly-separable/RandomForestClassifier/figure.png
- nodes/linearly-separable/RandomForestClassifier/metrics.json
metrics:
- nodes/linearly-separable/CombineFigures/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/CombineFigures/combined_figure.png
linearly-separable_CreateDataset:
cmd: zntrack run src.CreateDataset --name linearly-separable_CreateDataset
metrics:
- nodes/linearly-separable/CreateDataset/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/CreateDataset/x.json
- nodes/linearly-separable/CreateDataset/y.json
params:
- linearly-separable_CreateDataset
linearly-separable_DecisionTreeClassifier:
cmd: zntrack run src.Classifier --name linearly-separable_DecisionTreeClassifier
deps:
- nodes/linearly-separable/CreateDataset/x.json
- nodes/linearly-separable/TrainTestSplit/x_test.json
- nodes/linearly-separable/TrainTestSplit/x_train.json
- nodes/linearly-separable/TrainTestSplit/y_test.json
- nodes/linearly-separable/TrainTestSplit/y_train.json
metrics:
- nodes/linearly-separable/DecisionTreeClassifier/metrics.json
- nodes/linearly-separable/DecisionTreeClassifier/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/DecisionTreeClassifier/figure.png
params:
- linearly-separable_DecisionTreeClassifier
linearly-separable_GaussianNB:
cmd: zntrack run src.Classifier --name linearly-separable_GaussianNB
deps:
- nodes/linearly-separable/CreateDataset/x.json
- nodes/linearly-separable/TrainTestSplit/x_test.json
- nodes/linearly-separable/TrainTestSplit/x_train.json
- nodes/linearly-separable/TrainTestSplit/y_test.json
- nodes/linearly-separable/TrainTestSplit/y_train.json
metrics:
- nodes/linearly-separable/GaussianNB/metrics.json
- nodes/linearly-separable/GaussianNB/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/GaussianNB/figure.png
params:
- linearly-separable_GaussianNB
linearly-separable_GaussianProcessClassifier:
cmd: zntrack run src.Classifier --name linearly-separable_GaussianProcessClassifier
deps:
- nodes/linearly-separable/CreateDataset/x.json
- nodes/linearly-separable/TrainTestSplit/x_test.json
- nodes/linearly-separable/TrainTestSplit/x_train.json
- nodes/linearly-separable/TrainTestSplit/y_test.json
- nodes/linearly-separable/TrainTestSplit/y_train.json
metrics:
- nodes/linearly-separable/GaussianProcessClassifier/metrics.json
- nodes/linearly-separable/GaussianProcessClassifier/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/GaussianProcessClassifier/figure.png
params:
- linearly-separable_GaussianProcessClassifier
linearly-separable_KNeighborsClassifier:
cmd: zntrack run src.Classifier --name linearly-separable_KNeighborsClassifier
deps:
- nodes/linearly-separable/CreateDataset/x.json
- nodes/linearly-separable/TrainTestSplit/x_test.json
- nodes/linearly-separable/TrainTestSplit/x_train.json
- nodes/linearly-separable/TrainTestSplit/y_test.json
- nodes/linearly-separable/TrainTestSplit/y_train.json
metrics:
- nodes/linearly-separable/KNeighborsClassifier/metrics.json
- nodes/linearly-separable/KNeighborsClassifier/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/KNeighborsClassifier/figure.png
params:
- linearly-separable_KNeighborsClassifier
linearly-separable_Linear-SVM:
cmd: zntrack run src.Classifier --name linearly-separable_Linear-SVM
deps:
- nodes/linearly-separable/CreateDataset/x.json
- nodes/linearly-separable/TrainTestSplit/x_test.json
- nodes/linearly-separable/TrainTestSplit/x_train.json
- nodes/linearly-separable/TrainTestSplit/y_test.json
- nodes/linearly-separable/TrainTestSplit/y_train.json
metrics:
- nodes/linearly-separable/Linear-SVM/metrics.json
- nodes/linearly-separable/Linear-SVM/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/Linear-SVM/figure.png
params:
- linearly-separable_Linear-SVM
linearly-separable_MLPClassifier:
cmd: zntrack run src.Classifier --name linearly-separable_MLPClassifier
deps:
- nodes/linearly-separable/CreateDataset/x.json
- nodes/linearly-separable/TrainTestSplit/x_test.json
- nodes/linearly-separable/TrainTestSplit/x_train.json
- nodes/linearly-separable/TrainTestSplit/y_test.json
- nodes/linearly-separable/TrainTestSplit/y_train.json
metrics:
- nodes/linearly-separable/MLPClassifier/metrics.json
- nodes/linearly-separable/MLPClassifier/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/MLPClassifier/figure.png
params:
- linearly-separable_MLPClassifier
linearly-separable_QuadraticDiscriminantAnalysis:
cmd: zntrack run src.Classifier --name linearly-separable_QuadraticDiscriminantAnalysis
deps:
- nodes/linearly-separable/CreateDataset/x.json
- nodes/linearly-separable/TrainTestSplit/x_test.json
- nodes/linearly-separable/TrainTestSplit/x_train.json
- nodes/linearly-separable/TrainTestSplit/y_test.json
- nodes/linearly-separable/TrainTestSplit/y_train.json
metrics:
- nodes/linearly-separable/QuadraticDiscriminantAnalysis/metrics.json
- nodes/linearly-separable/QuadraticDiscriminantAnalysis/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/QuadraticDiscriminantAnalysis/figure.png
params:
- linearly-separable_QuadraticDiscriminantAnalysis
linearly-separable_RBF-SVM:
cmd: zntrack run src.Classifier --name linearly-separable_RBF-SVM
deps:
- nodes/linearly-separable/CreateDataset/x.json
- nodes/linearly-separable/TrainTestSplit/x_test.json
- nodes/linearly-separable/TrainTestSplit/x_train.json
- nodes/linearly-separable/TrainTestSplit/y_test.json
- nodes/linearly-separable/TrainTestSplit/y_train.json
metrics:
- nodes/linearly-separable/RBF-SVM/metrics.json
- nodes/linearly-separable/RBF-SVM/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/RBF-SVM/figure.png
params:
- linearly-separable_RBF-SVM
linearly-separable_RandomForestClassifier:
cmd: zntrack run src.Classifier --name linearly-separable_RandomForestClassifier
deps:
- nodes/linearly-separable/CreateDataset/x.json
- nodes/linearly-separable/TrainTestSplit/x_test.json
- nodes/linearly-separable/TrainTestSplit/x_train.json
- nodes/linearly-separable/TrainTestSplit/y_test.json
- nodes/linearly-separable/TrainTestSplit/y_train.json
metrics:
- nodes/linearly-separable/RandomForestClassifier/metrics.json
- nodes/linearly-separable/RandomForestClassifier/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/RandomForestClassifier/figure.png
params:
- linearly-separable_RandomForestClassifier
linearly-separable_TrainTestSplit:
cmd: zntrack run src.TrainTestSplit --name linearly-separable_TrainTestSplit
deps:
- nodes/linearly-separable/CreateDataset/x.json
- nodes/linearly-separable/CreateDataset/y.json
metrics:
- nodes/linearly-separable/TrainTestSplit/node-meta.json:
cache: true
outs:
- nodes/linearly-separable/TrainTestSplit/x_test.json
- nodes/linearly-separable/TrainTestSplit/x_train.json
- nodes/linearly-separable/TrainTestSplit/y_test.json
- nodes/linearly-separable/TrainTestSplit/y_train.json
params:
- linearly-separable_TrainTestSplit
moons_AdaBoostClassifier:
cmd: zntrack run src.Classifier --name moons_AdaBoostClassifier
deps:
- nodes/moons/CreateDataset/x.json
- nodes/moons/TrainTestSplit/x_test.json
- nodes/moons/TrainTestSplit/x_train.json
- nodes/moons/TrainTestSplit/y_test.json
- nodes/moons/TrainTestSplit/y_train.json
metrics:
- nodes/moons/AdaBoostClassifier/metrics.json
- nodes/moons/AdaBoostClassifier/node-meta.json:
cache: true
outs:
- nodes/moons/AdaBoostClassifier/figure.png
params:
- moons_AdaBoostClassifier
moons_CombineFigures:
cmd: zntrack run src.CombineFigures --name moons_CombineFigures
deps:
- nodes/moons/AdaBoostClassifier/figure.png
- nodes/moons/AdaBoostClassifier/metrics.json
- nodes/moons/DecisionTreeClassifier/figure.png
- nodes/moons/DecisionTreeClassifier/metrics.json
- nodes/moons/GaussianNB/figure.png
- nodes/moons/GaussianNB/metrics.json
- nodes/moons/GaussianProcessClassifier/figure.png
- nodes/moons/GaussianProcessClassifier/metrics.json
- nodes/moons/KNeighborsClassifier/figure.png
- nodes/moons/KNeighborsClassifier/metrics.json
- nodes/moons/Linear-SVM/figure.png
- nodes/moons/Linear-SVM/metrics.json
- nodes/moons/MLPClassifier/figure.png
- nodes/moons/MLPClassifier/metrics.json
- nodes/moons/QuadraticDiscriminantAnalysis/figure.png
- nodes/moons/QuadraticDiscriminantAnalysis/metrics.json
- nodes/moons/RBF-SVM/figure.png
- nodes/moons/RBF-SVM/metrics.json
- nodes/moons/RandomForestClassifier/figure.png
- nodes/moons/RandomForestClassifier/metrics.json
metrics:
- nodes/moons/CombineFigures/node-meta.json:
cache: true
outs:
- nodes/moons/CombineFigures/combined_figure.png
moons_CreateDataset:
cmd: zntrack run src.CreateDataset --name moons_CreateDataset
metrics:
- nodes/moons/CreateDataset/node-meta.json:
cache: true
outs:
- nodes/moons/CreateDataset/x.json
- nodes/moons/CreateDataset/y.json
params:
- moons_CreateDataset
moons_DecisionTreeClassifier:
cmd: zntrack run src.Classifier --name moons_DecisionTreeClassifier
deps:
- nodes/moons/CreateDataset/x.json
- nodes/moons/TrainTestSplit/x_test.json
- nodes/moons/TrainTestSplit/x_train.json
- nodes/moons/TrainTestSplit/y_test.json
- nodes/moons/TrainTestSplit/y_train.json
metrics:
- nodes/moons/DecisionTreeClassifier/metrics.json
- nodes/moons/DecisionTreeClassifier/node-meta.json:
cache: true
outs:
- nodes/moons/DecisionTreeClassifier/figure.png
params:
- moons_DecisionTreeClassifier
moons_GaussianNB:
cmd: zntrack run src.Classifier --name moons_GaussianNB
deps:
- nodes/moons/CreateDataset/x.json
- nodes/moons/TrainTestSplit/x_test.json
- nodes/moons/TrainTestSplit/x_train.json
- nodes/moons/TrainTestSplit/y_test.json
- nodes/moons/TrainTestSplit/y_train.json
metrics:
- nodes/moons/GaussianNB/metrics.json
- nodes/moons/GaussianNB/node-meta.json:
cache: true
outs:
- nodes/moons/GaussianNB/figure.png
params:
- moons_GaussianNB
moons_GaussianProcessClassifier:
cmd: zntrack run src.Classifier --name moons_GaussianProcessClassifier
deps:
- nodes/moons/CreateDataset/x.json
- nodes/moons/TrainTestSplit/x_test.json
- nodes/moons/TrainTestSplit/x_train.json
- nodes/moons/TrainTestSplit/y_test.json
- nodes/moons/TrainTestSplit/y_train.json
metrics:
- nodes/moons/GaussianProcessClassifier/metrics.json
- nodes/moons/GaussianProcessClassifier/node-meta.json:
cache: true
outs:
- nodes/moons/GaussianProcessClassifier/figure.png
params:
- moons_GaussianProcessClassifier
moons_KNeighborsClassifier:
cmd: zntrack run src.Classifier --name moons_KNeighborsClassifier
deps:
- nodes/moons/CreateDataset/x.json
- nodes/moons/TrainTestSplit/x_test.json
- nodes/moons/TrainTestSplit/x_train.json
- nodes/moons/TrainTestSplit/y_test.json
- nodes/moons/TrainTestSplit/y_train.json
metrics:
- nodes/moons/KNeighborsClassifier/metrics.json
- nodes/moons/KNeighborsClassifier/node-meta.json:
cache: true
outs:
- nodes/moons/KNeighborsClassifier/figure.png
params:
- moons_KNeighborsClassifier
moons_Linear-SVM:
cmd: zntrack run src.Classifier --name moons_Linear-SVM
deps:
- nodes/moons/CreateDataset/x.json
- nodes/moons/TrainTestSplit/x_test.json
- nodes/moons/TrainTestSplit/x_train.json
- nodes/moons/TrainTestSplit/y_test.json
- nodes/moons/TrainTestSplit/y_train.json
metrics:
- nodes/moons/Linear-SVM/metrics.json
- nodes/moons/Linear-SVM/node-meta.json:
cache: true
outs:
- nodes/moons/Linear-SVM/figure.png
params:
- moons_Linear-SVM
moons_MLPClassifier:
cmd: zntrack run src.Classifier --name moons_MLPClassifier
deps:
- nodes/moons/CreateDataset/x.json
- nodes/moons/TrainTestSplit/x_test.json
- nodes/moons/TrainTestSplit/x_train.json
- nodes/moons/TrainTestSplit/y_test.json
- nodes/moons/TrainTestSplit/y_train.json
metrics:
- nodes/moons/MLPClassifier/metrics.json
- nodes/moons/MLPClassifier/node-meta.json:
cache: true
outs:
- nodes/moons/MLPClassifier/figure.png
params:
- moons_MLPClassifier
moons_QuadraticDiscriminantAnalysis:
cmd: zntrack run src.Classifier --name moons_QuadraticDiscriminantAnalysis
deps:
- nodes/moons/CreateDataset/x.json
- nodes/moons/TrainTestSplit/x_test.json
- nodes/moons/TrainTestSplit/x_train.json
- nodes/moons/TrainTestSplit/y_test.json
- nodes/moons/TrainTestSplit/y_train.json
metrics:
- nodes/moons/QuadraticDiscriminantAnalysis/metrics.json
- nodes/moons/QuadraticDiscriminantAnalysis/node-meta.json:
cache: true
outs:
- nodes/moons/QuadraticDiscriminantAnalysis/figure.png
params:
- moons_QuadraticDiscriminantAnalysis
moons_RBF-SVM:
cmd: zntrack run src.Classifier --name moons_RBF-SVM
deps:
- nodes/moons/CreateDataset/x.json
- nodes/moons/TrainTestSplit/x_test.json
- nodes/moons/TrainTestSplit/x_train.json
- nodes/moons/TrainTestSplit/y_test.json
- nodes/moons/TrainTestSplit/y_train.json
metrics:
- nodes/moons/RBF-SVM/metrics.json
- nodes/moons/RBF-SVM/node-meta.json:
cache: true
outs:
- nodes/moons/RBF-SVM/figure.png
params:
- moons_RBF-SVM
moons_RandomForestClassifier:
cmd: zntrack run src.Classifier --name moons_RandomForestClassifier
deps:
- nodes/moons/CreateDataset/x.json
- nodes/moons/TrainTestSplit/x_test.json
- nodes/moons/TrainTestSplit/x_train.json
- nodes/moons/TrainTestSplit/y_test.json
- nodes/moons/TrainTestSplit/y_train.json
metrics:
- nodes/moons/RandomForestClassifier/metrics.json
- nodes/moons/RandomForestClassifier/node-meta.json:
cache: true
outs:
- nodes/moons/RandomForestClassifier/figure.png
params:
- moons_RandomForestClassifier
moons_TrainTestSplit:
cmd: zntrack run src.TrainTestSplit --name moons_TrainTestSplit
deps:
- nodes/moons/CreateDataset/x.json
- nodes/moons/CreateDataset/y.json
metrics:
- nodes/moons/TrainTestSplit/node-meta.json:
cache: true
outs:
- nodes/moons/TrainTestSplit/x_test.json
- nodes/moons/TrainTestSplit/x_train.json
- nodes/moons/TrainTestSplit/y_test.json
- nodes/moons/TrainTestSplit/y_train.json
params:
- moons_TrainTestSplit
params.yaml File
circles_AdaBoostClassifier:
model:
_cls: src.Model
method: AdaBoostClassifier
name: AdaBoostClassifier
params:
random_state: 42
circles_CreateDataset:
method: make_circles
params:
factor: 0.5
noise: 0.2
random_state: 0
circles_DecisionTreeClassifier:
model:
_cls: src.Model
method: DecisionTreeClassifier
name: DecisionTreeClassifier
params:
max_depth: 5
random_state: 42
circles_GaussianNB:
model:
_cls: src.Model
method: GaussianNB
name: GaussianNB
params: {}
circles_GaussianProcessClassifier:
model:
_cls: src.Model
method: GaussianProcessClassifier
name: GaussianProcessClassifier
params:
random_state: 42
circles_KNeighborsClassifier:
model:
_cls: src.Model
method: KNeighborsClassifier
name: KNeighborsClassifier
params:
n_neighbors: 3
circles_Linear-SVM:
model:
_cls: src.Model
method: SVC
name: Linear-SVM
params:
C: 0.025
kernel: linear
random_state: 42
circles_MLPClassifier:
model:
_cls: src.Model
method: MLPClassifier
name: MLPClassifier
params:
alpha: 1
max_iter: 1000
random_state: 42
circles_QuadraticDiscriminantAnalysis:
model:
_cls: src.Model
method: QuadraticDiscriminantAnalysis
name: QuadraticDiscriminantAnalysis
params: {}
circles_RBF-SVM:
model:
_cls: src.Model
method: SVC
name: RBF-SVM
params:
C: 1
gamma: 2
random_state: 42
circles_RandomForestClassifier:
model:
_cls: src.Model
method: RandomForestClassifier
name: RandomForestClassifier
params:
max_depth: 5
max_features: 1
n_estimators: 10
random_state: 42
circles_TrainTestSplit:
random_state: 42
test_size: 0.4
linearly-separable_AdaBoostClassifier:
model:
_cls: src.Model
method: AdaBoostClassifier
name: AdaBoostClassifier
params:
random_state: 42
linearly-separable_CreateDataset:
method: linearly_separable
params:
n_clusters_per_class: 1
n_features: 2
n_informative: 2
n_redundant: 0
random_state: 42
linearly-separable_DecisionTreeClassifier:
model:
_cls: src.Model
method: DecisionTreeClassifier
name: DecisionTreeClassifier
params:
max_depth: 5
random_state: 42
linearly-separable_GaussianNB:
model:
_cls: src.Model
method: GaussianNB
name: GaussianNB
params: {}
linearly-separable_GaussianProcessClassifier:
model:
_cls: src.Model
method: GaussianProcessClassifier
name: GaussianProcessClassifier
params:
random_state: 42
linearly-separable_KNeighborsClassifier:
model:
_cls: src.Model
method: KNeighborsClassifier
name: KNeighborsClassifier
params:
n_neighbors: 3
linearly-separable_Linear-SVM:
model:
_cls: src.Model
method: SVC
name: Linear-SVM
params:
C: 0.025
kernel: linear
random_state: 42
linearly-separable_MLPClassifier:
model:
_cls: src.Model
method: MLPClassifier
name: MLPClassifier
params:
alpha: 1
max_iter: 1000
random_state: 42
linearly-separable_QuadraticDiscriminantAnalysis:
model:
_cls: src.Model
method: QuadraticDiscriminantAnalysis
name: QuadraticDiscriminantAnalysis
params: {}
linearly-separable_RBF-SVM:
model:
_cls: src.Model
method: SVC
name: RBF-SVM
params:
C: 1
gamma: 2
random_state: 42
linearly-separable_RandomForestClassifier:
model:
_cls: src.Model
method: RandomForestClassifier
name: RandomForestClassifier
params:
max_depth: 5
max_features: 1
n_estimators: 10
random_state: 42
linearly-separable_TrainTestSplit:
random_state: 42
test_size: 0.4
moons_AdaBoostClassifier:
model:
_cls: src.Model
method: AdaBoostClassifier
name: AdaBoostClassifier
params:
random_state: 42
moons_CreateDataset:
method: make_moons
params:
noise: 0.3
random_state: 0
moons_DecisionTreeClassifier:
model:
_cls: src.Model
method: DecisionTreeClassifier
name: DecisionTreeClassifier
params:
max_depth: 5
random_state: 42
moons_GaussianNB:
model:
_cls: src.Model
method: GaussianNB
name: GaussianNB
params: {}
moons_GaussianProcessClassifier:
model:
_cls: src.Model
method: GaussianProcessClassifier
name: GaussianProcessClassifier
params:
random_state: 42
moons_KNeighborsClassifier:
model:
_cls: src.Model
method: KNeighborsClassifier
name: KNeighborsClassifier
params:
n_neighbors: 3
moons_Linear-SVM:
model:
_cls: src.Model
method: SVC
name: Linear-SVM
params:
C: 0.025
kernel: linear
random_state: 42
moons_MLPClassifier:
model:
_cls: src.Model
method: MLPClassifier
name: MLPClassifier
params:
alpha: 1
max_iter: 1000
random_state: 42
moons_QuadraticDiscriminantAnalysis:
model:
_cls: src.Model
method: QuadraticDiscriminantAnalysis
name: QuadraticDiscriminantAnalysis
params: {}
moons_RBF-SVM:
model:
_cls: src.Model
method: SVC
name: RBF-SVM
params:
C: 1
gamma: 2
random_state: 42
moons_RandomForestClassifier:
model:
_cls: src.Model
method: RandomForestClassifier
name: RandomForestClassifier
params:
max_depth: 5
max_features: 1
n_estimators: 10
random_state: 42
moons_TrainTestSplit:
random_state: 42
test_size: 0.4
zntrack.json File
{
"moons_CreateDataset": {
"nwd": {
"_type": "pathlib.Path",
"value": "nodes/moons/CreateDataset"
}
},
"moons_TrainTestSplit": {
"nwd": {
"_type": "pathlib.Path",
"value": "nodes/moons/TrainTestSplit"
},
"x": {
"_type": "znflow.Connection",
"value": {
"instance": {
"_type": "zntrack.Node",
"value": {
"module": "src",
"name": "moons_CreateDataset",
"cls": "CreateDataset",
"remote": null,
"rev": null
}
},
"attribute": "x",
"item": null
}
},
"y": {
"_type": "znflow.Connection",
"value": {
"instance": {
"_type": "zntrack.Node",
"value": {
"module": "src",
"name": "moons_CreateDataset",
"cls": "CreateDataset",
"remote": null,
"rev": null
}
},
"attribute": "y",
"item": null
}
}
},
"moons_KNeighborsClassifier": {
"nwd": {
"_type": "pathlib.Path",
"value": "nodes/moons/KNeighborsClassifier"
},
"model": {
"_type": "@dataclasses.dataclass",
"value": {
"module": "src",
"cls": "Model"
}
},
"x": {
"_type": "znflow.Connection",
"value": {
"instance": {
"_type": "zntrack.Node",
"value": {
"module": "src",
"name": "moons_CreateDataset",
"cls": "CreateDataset",
"remote": null,
"rev": null
}
},
"attribute": "x",
"item": null
}
},
"x_train": {
"_type": "znflow.Connection",
"value": {
"instance": {
"_type": "zntrack.Node",
"value": {
"module": "src",
"name": "moons_TrainTestSplit",
"cls": "TrainTestSplit",
"remote": null,
"rev": null
}
},
"attribute": "x_train",
"item": null
}
},
"y_train": {
"_type": "znflow.Connection",
"value": {
"instance": {
"_type": "zntrack.Node",
"value": {
"module": "src",
"name": "moons_TrainTestSplit",
"cls": "TrainTestSplit",
"remote": null,
"rev": null
}
},
"attribute": "y_train",
"item": null
}
},
"x_test": {
"_type": "znflow.Connection",
"value": {
"instance": {
"_type": "zntrack.Node",
"value": {
"module": "src",
"name": "moons_TrainTestSplit",
"cls": "TrainTestSplit",
"remote": null,
"rev": null
}
},
"attribute": "x_test",
"item": null
}
},
"y_test": {
"_type": "znflow.Connection",
"value": {
"instance": {
"_type": "zntrack.Node",
"value": {
"module": "src",
"name": "moons_TrainTestSplit",
"cls": "TrainTestSplit",
"remote": null,
"rev": null
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