mlplot 0.0.3

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mlplot

Machine learning evaluation plots using matplotlib and sklearn.

Install

pip install mlplot

ML Plot runs with python 3.5 and above! (using format strings and type annotations)

Contributing

Create a PR!

Plots

Work was inspired by sklearn model evaluation.

Classification

ROC with AUC number

from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.roc_curve()

https://github.com/sbarton272/mlplot/blob/master/tests/output/tests.evaluation.test_classification.test_calibration.png?raw=true

Calibration

from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.calibration()

Precision-Recall

from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.precision_recall(x_axis='recall')
eval.precision_recall(x_axis='thresold')

Distribution

from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.distribution()

Confusion Matrix

from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.confusion_matrix(threshold=0.5)

Classification Report

from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.report_table()

Regression

Scatter Plot

from mlplot.evaluation import RegressionEvaluation
eval = RegressionEvaluation(y_true, y_pred, class_names, model_name)
eval.scatter()

Residuals Plot

from mlplot.evaluation import RegressionEvaluation
eval = RegressionEvaluation(y_true, y_pred, class_names, model_name)
eval.residuals()

Residuals Histogram

from mlplot.evaluation import RegressionEvaluation
eval = RegressionEvaluation(y_true, y_pred, class_names, model_name)
eval.residuals_histogram()

Regression Report

from mlplot.evaluation import RegressionEvaluation
eval = RegressionEvaluation(y_true, y_pred, class_names, model_name)
eval.report_table()

Forecasts

• TBD

Rankings

• TBD

Development

Publish to pypi

python setup.py sdist bdist_wheel
twine upload --repository-url https://upload.pypi.org/legacy/ dist/*

Design

Basic interface thoughts

from mlplot.evaluation import ClassificationEvaluation
from mlplot.evaluation import RegressorEvaluation
from mlplot.evaluation import MultiClassificationEvaluation
from mlplot.evaluation import MultiRegressorEvaluation
from mlplot.evaluation import ModelComparison
from mlplot.feature_evaluation import *

eval = ClassificationEvaluation(y_true, y_pred)
ax = eval.roc_curve()
auc = eval.auc_score()
f1_score = eval.f1_score()
ax = eval.confusion_matrix(threshold=0.7)

• ModelEvaluation base class
• ClassificationEvaluation class
  • take in y_true, y_pred, class names, model_name
• RegressorEvaluation class
• MultiClassificationEvaluation class
• ModelComparison
  • takes in two evaluations of the same type

TODO

• Fix distribution plot, make lines
• Add legend with R2 to regression plots
• Add tests for regression comparison
• Split apart files for comparison classes
• Add comparisons to README