Machine learning evaluation plots using matplotlib and sklearn.
pip install mlplot
ML Plot runs with python 3.5 and above! (using format strings and type annotations)
Create a PR!
Work was inspired by sklearn model evaluation.
from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.roc_curve()
from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.calibration()
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')
from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.distribution()
from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.confusion_matrix(threshold=0.5)
from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.report_table()
from mlplot.evaluation import RegressionEvaluation
eval = RegressionEvaluation(y_true, y_pred, class_names, model_name)
eval.scatter()
from mlplot.evaluation import RegressionEvaluation
eval = RegressionEvaluation(y_true, y_pred, class_names, model_name)
eval.residuals()
from mlplot.evaluation import RegressionEvaluation
eval = RegressionEvaluation(y_true, y_pred, class_names, model_name)
eval.residuals_histogram()
from mlplot.evaluation import RegressionEvaluation
eval = RegressionEvaluation(y_true, y_pred, class_names, model_name)
eval.report_table()
- TBD
- TBD
python setup.py sdist bdist_wheel
twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
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
- 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