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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.model_evaluation import ClassifierEvaluation
eval = ClassifierEvaluation(y_true, y_pred, class_names, model_name)
eval.roc_curve()
Calibration
from mlplot.model_evaluation import ClassifierEvaluation
eval = ClassifierEvaluation(y_true, y_pred, class_names, model_name)
eval.calibration()
Precision-Recall
from mlplot.model_evaluation import ClassifierEvaluation
eval = ClassifierEvaluation(y_true, y_pred, class_names, model_name)
eval.precision_recall(x_axis='recall')
eval.precision_recall(x_axis='thresold')
Distribution
from mlplot.model_evaluation import ClassifierEvaluation
eval = ClassifierEvaluation(y_true, y_pred, class_names, model_name)
eval.distribution()
Confusion Matrix
from mlplot.model_evaluation import ClassifierEvaluation
eval = ClassifierEvaluation(y_true, y_pred, class_names, model_name)
eval.confusion_matrix(threshold=0.5)
Classification Report
from mlplot.model_evaluation import ClassifierEvaluation
eval = ClassifierEvaluation(y_true, y_pred, class_names, model_name)
eval.report_table()
Regression
- Full report
- Mean sqr error
- Mean abs error
- Target mean, std
- R2
- Residual plot
- Scatter plot
- Histogram of regressor
Library Cleanup
- Try in a notebook
- Logging
- Default matplotlib setup
- Multi-model comparison
- Report to generate multiple plots at once
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.model_evaluation import ClassifierEvaluation
from mlplot.model_evaluation import RegressorEvaluation
from mlplot.model_evaluation import MultiClassifierEvaluation
from mlplot.model_evaluation import MultiRegressorEvaluation
from mlplot.model_evaluation import ModelComparison
from mlplot.feature_evaluation import *
eval = ClassifierEvaluation(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
- ClassifierEvaluation class
- take in y_true, y_pred, class names, model_name
- RegressorEvaluation class
- MultiClassifierEvaluation class
- ModelComparison
- takes in two evaluations of the same type
Project details
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Source Distribution
mlplot-0.0.1.tar.gz
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mlplot-0.0.1-py3-none-any.whl
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