tessl/pypi-yellowbrick

A suite of visual analysis and diagnostic tools for machine learning.

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Yellowbrick

Name: tessl/pypi-yellowbrick
Author: tessl

A comprehensive machine learning visualization library that extends scikit-learn with publication-quality visualizations for machine learning model evaluation, selection, and interpretation. Yellowbrick provides visual diagnostic tools called "Visualizers" that combine scikit-learn with matplotlib to streamline the machine learning workflow from data exploration through model interpretation.

Package Information

Package Name: yellowbrick
Language: Python
Installation: pip install yellowbrick
Scikit-learn Integration: Compatible with scikit-learn 0.20+
Dependencies: matplotlib, scipy, scikit-learn, numpy

Core Imports

import yellowbrick

Direct imports from yellowbrick:

from yellowbrick import ROCAUC, ClassBalance, ClassificationScoreVisualizer
from yellowbrick import anscombe, datasaurus
from yellowbrick import set_aesthetic, set_style, set_palette, color_palette

Common pattern for visualizers:

from yellowbrick.classifier import ROCAUC, ConfusionMatrix
from yellowbrick.regressor import ResidualsPlot
from yellowbrick.cluster import KElbow

Functional API imports:

from yellowbrick.classifier import roc_auc, confusion_matrix
from yellowbrick.regressor import residuals_plot

Basic Usage

from yellowbrick.classifier import ROCAUC
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification

# Generate sample data
X, y = make_classification(n_samples=1000, n_features=20, n_classes=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Create and fit model
model = LogisticRegression()

# Visualize ROC/AUC curves
visualizer = ROCAUC(model, classes=['Class 0', 'Class 1'])
visualizer.fit(X_train, y_train)
visualizer.score(X_test, y_test)
visualizer.show()

# Using functional API
from yellowbrick.classifier import roc_auc
roc_auc(model, X_train, y_train, X_test, y_test, classes=['Class 0', 'Class 1'])

Architecture

Yellowbrick follows the scikit-learn API design with Visualizers that inherit from sklearn.base.BaseEstimator:

Base Classes: Visualizer, ModelVisualizer, ScoreVisualizer provide the foundation
Visualizer Pattern: All visualizers implement fit(), score(), and show() methods
Pipeline Integration: Visualizers can be used in scikit-learn pipelines
Dual API: Both class-based and functional APIs for flexibility
Matplotlib Integration: Built on matplotlib with consistent styling and themes

Capabilities

Classification Analysis

Comprehensive visualizers for evaluating classification models including ROC curves, confusion matrices, classification reports, class prediction errors, precision-recall curves, and discrimination thresholds.

class ROCAUC(ClassificationScoreVisualizer):
    def __init__(self, estimator, ax=None, micro=True, macro=True, per_class=True, binary=False, classes=None, encoder=None, is_fitted="auto", force_model=False, **kwargs): ...
    def fit(self, X, y, **kwargs): ...
    def score(self, X, y, **kwargs): ...

class ConfusionMatrix(ClassificationScoreVisualizer):
    def __init__(self, estimator, ax=None, sample_weight=None, percent=False, classes=None, encoder=None, cmap="YlOrRd", fontsize=None, is_fitted="auto", force_model=False, **kwargs): ...
    def fit(self, X, y, **kwargs): ...
    def score(self, X, y, **kwargs): ...

class ClassificationReport(ClassificationScoreVisualizer):
    def __init__(self, estimator, classes=None, **kwargs): ...
    def fit(self, X, y, **kwargs): ...
    def score(self, X, y, **kwargs): ...

# Functional APIs
def roc_auc(estimator, X_train, y_train, X_test=None, y_test=None, **kwargs): ...
def confusion_matrix(estimator, X_train, y_train, X_test=None, y_test=None, **kwargs): ...
def classification_report(estimator, X_train, y_train, X_test=None, y_test=None, **kwargs): ...

Classification Analysis

Regression Analysis

Diagnostic visualizers for regression models including residuals plots, prediction error plots, alpha selection for regularized models, and Cook's distance for influence analysis.

class ResidualsPlot(RegressionScoreVisualizer):
    def __init__(self, estimator, **kwargs): ...
    def fit(self, X, y, **kwargs): ...
    def score(self, X, y, **kwargs): ...

class PredictionError(RegressionScoreVisualizer):
    def __init__(self, estimator, **kwargs): ...
    def fit(self, X, y, **kwargs): ...
    def score(self, X, y, **kwargs): ...

class AlphaSelection(RegressionScoreVisualizer):
    def __init__(self, estimator, **kwargs): ...
    def fit(self, X, y, **kwargs): ...
    def score(self, X, y, **kwargs): ...

# Functional APIs
def residuals_plot(estimator, X_train, y_train, X_test=None, y_test=None, **kwargs): ...
def prediction_error(estimator, X_train, y_train, X_test=None, y_test=None, **kwargs): ...

Regression Analysis

Clustering Analysis

Visualizers for clustering evaluation including elbow method for optimal K selection, silhouette analysis, and intercluster distance mapping.

class KElbow(ClusteringScoreVisualizer):
    def __init__(self, estimator, k=10, metric='distortion', **kwargs): ...
    def fit(self, X, y=None, **kwargs): ...

class SilhouetteVisualizer(ClusteringScoreVisualizer):
    def __init__(self, estimator, **kwargs): ...
    def fit(self, X, y=None, **kwargs): ...

class InterclusterDistance(ClusteringScoreVisualizer):
    def __init__(self, estimator, **kwargs): ...
    def fit(self, X, y=None, **kwargs): ...

# Functional APIs
def kelbow_visualizer(estimator, X, k=10, **kwargs): ...
def silhouette_visualizer(estimator, X, **kwargs): ...

Clustering Analysis

Feature Analysis

Tools for feature selection, analysis, and visualization including feature ranking, correlation analysis, PCA decomposition, manifold learning, and parallel coordinates.

class Rank1D(Visualizer):
    def __init__(self, algorithm='shapiro', **kwargs): ...
    def fit(self, X, y=None, **kwargs): ...

class Rank2D(Visualizer):
    def __init__(self, algorithm='pearson', **kwargs): ...
    def fit(self, X, y=None, **kwargs): ...

class PCA(Visualizer):
    def __init__(self, scale=True, proj_features=True, **kwargs): ...
    def fit(self, X, y=None, **kwargs): ...

class ParallelCoordinates(Visualizer):
    def __init__(self, classes=None, **kwargs): ...
    def fit(self, X, y=None, **kwargs): ...

# Functional APIs
def rank1d(X, y=None, algorithm='shapiro', **kwargs): ...
def rank2d(X, y=None, algorithm='pearson', **kwargs): ...
def pca_decomposition(X, y=None, **kwargs): ...

Feature Analysis

Model Selection

Visualizers for model selection and hyperparameter tuning including learning curves, validation curves, cross-validation scores, and feature importance analysis.

class LearningCurve(ModelVisualizer):
    def __init__(self, estimator, **kwargs): ...
    def fit(self, X, y, **kwargs): ...

class ValidationCurve(ModelVisualizer):
    def __init__(self, estimator, param_name, param_range, **kwargs): ...
    def fit(self, X, y, **kwargs): ...

class FeatureImportances(ModelVisualizer):
    def __init__(self, estimator, **kwargs): ...
    def fit(self, X, y, **kwargs): ...

class CVScores(ModelVisualizer):
    def __init__(self, estimator, **kwargs): ...
    def fit(self, X, y, **kwargs): ...

# Functional APIs
def learning_curve(estimator, X, y, **kwargs): ...
def validation_curve(estimator, X, y, param_name, param_range, **kwargs): ...
def feature_importances(estimator, X, y, **kwargs): ...

Model Selection

Text Analysis

Specialized visualizers for text analysis and natural language processing including t-SNE/UMAP embeddings, frequency distributions, part-of-speech analysis, and word correlation plots.

class TSNEVisualizer(Visualizer):
    def __init__(self, **kwargs): ...
    def fit(self, X, y=None, **kwargs): ...

class FreqDistVisualizer(Visualizer):
    def __init__(self, **kwargs): ...
    def fit(self, corpus, **kwargs): ...

class DispersionPlot(Visualizer):
    def __init__(self, **kwargs): ...
    def fit(self, corpus, **kwargs): ...

# Functional APIs
def tsne(X, y=None, **kwargs): ...
def freqdist(corpus, **kwargs): ...
def dispersion(corpus, **kwargs): ...

Text Analysis

Data Loading and Utilities

Built-in datasets for learning and testing, plus utility functions for data management and visualization styling.

# Dataset loaders
def load_concrete(): ...
def load_energy(): ...
def load_credit(): ...
def load_occupancy(): ...
def load_mushroom(): ...
def load_hobbies(): ...
def load_bikeshare(): ...

# Style management
def set_aesthetic(aesthetic='whitegrid'): ...
def set_palette(palette='flatui'): ...
def color_palette(palette=None): ...

# Demo functions
def anscombe(): ...
def datasaurus(): ...

Data Loading and Utilities

Types

from enum import Enum

class TargetType(Enum):
    AUTO = "auto"
    SINGLE = "single" 
    DISCRETE = "discrete"
    CONTINUOUS = "continuous"
    UNKNOWN = "unknown"

# Base visualizer classes
class Visualizer:
    def __init__(self, ax=None, fig=None, size=None, color=None, title=None, **kwargs): ...
    def fit(self, X, y=None, **kwargs): ...
    def transform(self, X): ...
    def show(self, outpath=None, **kwargs): ...
    def finalize(self, **kwargs): ...

class ModelVisualizer(Visualizer):
    def __init__(self, estimator, ax=None, fig=None, is_fitted="auto", **kwargs): ...

class ScoreVisualizer(ModelVisualizer):
    def score(self, X, y, **kwargs): ...