MissingValues Dispersion
The MissingValues Dispersion visualizer creates a chart that maps the position of missing values by the order of the index.
Without Targets Supplied
import numpy as np
from sklearn.datasets import make_classification
from yellowbrick.contrib.missing import MissingValuesDispersion
X, y = make_classification(
n_samples=400, n_features=10, n_informative=2, n_redundant=3,
n_classes=2, n_clusters_per_class=2, random_state=854
)
# assign some NaN values
X[X > 1.5] = np.nan
features = ["Feature {}".format(str(n)) for n in range(10)]
visualizer = MissingValuesDispersion(features=features)
visualizer.fit(X)
visualizer.show()
(Source code, png, pdf)
With Targets (y) Supplied
import numpy as np
from sklearn.datasets import make_classification
from yellowbrick.contrib.missing import MissingValuesDispersion
X, y = make_classification(
n_samples=400, n_features=10, n_informative=2, n_redundant=3,
n_classes=2, n_clusters_per_class=2, random_state=854
)
# assign some NaN values
X[X > 1.5] = np.nan
features = ["Feature {}".format(str(n)) for n in range(10)]
# Instantiate the visualizer
visualizer = MissingValuesDispersion(features=features)
visualizer.fit(X, y=y) # supply the targets via y
visualizer.show()
(Source code, png, pdf)
API Reference
Dispersion visualizer for locations of missing values by column against index position.
- class yellowbrick.contrib.missing.dispersion.MissingValuesDispersion(alpha=0.5, marker='|', classes=None, **kwargs)[source]
Bases:
MissingDataVisualizerThe Missing Values Dispersion visualizer shows the locations of missing (nan) values in the feature dataset by the order of the index.
When y targets are supplied to fit, the output dispersion plot is color coded according to the target y that the element refers to.
- Parameters
- alphafloat, default: 0.5
A value for bending elments with the background.
- markermatplotlib marker, default: |
The marker used for each element coordinate in the plot
- classeslist, default: None
A list of class names for the legend. If classes is None and a y value is passed to fit then the classes are selected from the target vector.
- kwargsdict
Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers.
Examples
>>> from yellowbrick.contrib.missing import MissingValuesDispersion >>> visualizer = MissingValuesDispersion() >>> visualizer.fit(X, y=y) >>> visualizer.show()
- Attributes
- features_np.array
The feature labels ranked according to their importance
- classes_np.array
The class labels for each of the target values
- draw(X, y, **kwargs)[source]
Called from the fit method, this method creates a scatter plot that draws each instance as a class or target colored point, whose location is determined by the feature data set.
If y is not None, then it draws a scatter plot where each class is in a different color.
- draw_multi_dispersion_chart(nan_locs)[source]
Draws a multi dimensional dispersion chart, each color corresponds to a different target variable.