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Project description
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()
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
Project details
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mlplot-0.0.3.tar.gz
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mlplot-0.0.3-py3-none-any.whl
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