prettymetrics 0.0.1

Pretty Metrics bring the ROC, F1 scores and other details for all ML libraries

Pretty Metrics

Pretty Metrics bring the ROC, F1 scores and other details for all ML libraries

Credits: The base code is derived from LazyPredict (https://github.com/shankarpandala/lazypredict). As we see a lot of improvement in LazyPredict and the existing library is a bit outdated, we came up with this library. It can be LazyPredict++ as you will see this lib is updated and having more metrics.

• Free software: MIT license

• Documentation: https://prettymetrics.readthedocs.io.

Installation

To install Pretty Metrics:

pip install prettymetrics

or

pip install git+https://github.com/tactlabs/prettymetrics.git

Pip installing the library from local repository:

conda activate <env_name>

python setup.py install develop

Usage

To use Pretty Metrics in a project:

import prettymetrics

Classification

Example

from prettymetrics.clf import Classifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

data = load_breast_cancer()
X = data.data
y= data.target

X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =123)

clf = Classifier(verbose=0,ignore_warnings=True, custom_metric=None)
models,predictions = clf.fit(X_train, X_test, y_train, y_test)

print(models)


| Model                          |   Accuracy |   Balanced Accuracy |   ROC AUC |   F1 Score |   Time Taken |
|:-------------------------------|-----------:|--------------------:|----------:|-----------:|-------------:|
| LinearSVC                      |   0.989474 |            0.987544 |  0.987544 |   0.989462 |    0.0150008 |
| SGDClassifier                  |   0.989474 |            0.987544 |  0.987544 |   0.989462 |    0.0109992 |
| MLPClassifier                  |   0.985965 |            0.986904 |  0.986904 |   0.985994 |    0.426     |
| Perceptron                     |   0.985965 |            0.984797 |  0.984797 |   0.985965 |    0.0120046 |
| LogisticRegression             |   0.985965 |            0.98269  |  0.98269  |   0.985934 |    0.0200036 |
| LogisticRegressionCV           |   0.985965 |            0.98269  |  0.98269  |   0.985934 |    0.262997  |
| SVC                            |   0.982456 |            0.979942 |  0.979942 |   0.982437 |    0.0140011 |
| CalibratedClassifierCV         |   0.982456 |            0.975728 |  0.975728 |   0.982357 |    0.0350015 |
| PassiveAggressiveClassifier    |   0.975439 |            0.974448 |  0.974448 |   0.975464 |    0.0130005 |
| LabelPropagation               |   0.975439 |            0.974448 |  0.974448 |   0.975464 |    0.0429988 |
| LabelSpreading                 |   0.975439 |            0.974448 |  0.974448 |   0.975464 |    0.0310006 |
| RandomForestClassifier         |   0.97193  |            0.969594 |  0.969594 |   0.97193  |    0.033     |
| GradientBoostingClassifier     |   0.97193  |            0.967486 |  0.967486 |   0.971869 |    0.166998  |
| QuadraticDiscriminantAnalysis  |   0.964912 |            0.966206 |  0.966206 |   0.965052 |    0.0119994 |
| HistGradientBoostingClassifier |   0.968421 |            0.964739 |  0.964739 |   0.968387 |    0.682003  |
| RidgeClassifierCV              |   0.97193  |            0.963272 |  0.963272 |   0.971736 |    0.0130029 |
| RidgeClassifier                |   0.968421 |            0.960525 |  0.960525 |   0.968242 |    0.0119977 |
| AdaBoostClassifier             |   0.961404 |            0.959245 |  0.959245 |   0.961444 |    0.204998  |
| ExtraTreesClassifier           |   0.961404 |            0.957138 |  0.957138 |   0.961362 |    0.0270066 |
| KNeighborsClassifier           |   0.961404 |            0.95503  |  0.95503  |   0.961276 |    0.0560005 |
| BaggingClassifier              |   0.947368 |            0.954577 |  0.954577 |   0.947882 |    0.0559971 |
| BernoulliNB                    |   0.950877 |            0.951003 |  0.951003 |   0.951072 |    0.0169988 |
| LinearDiscriminantAnalysis     |   0.961404 |            0.950816 |  0.950816 |   0.961089 |    0.0199995 |
| GaussianNB                     |   0.954386 |            0.949536 |  0.949536 |   0.954337 |    0.0139935 |
| NuSVC                          |   0.954386 |            0.943215 |  0.943215 |   0.954014 |    0.019989  |
| DecisionTreeClassifier         |   0.936842 |            0.933693 |  0.933693 |   0.936971 |    0.0170023 |
| NearestCentroid                |   0.947368 |            0.933506 |  0.933506 |   0.946801 |    0.0160074 |
| ExtraTreeClassifier            |   0.922807 |            0.912168 |  0.912168 |   0.922462 |    0.0109999 |
| CheckingClassifier             |   0.361404 |            0.5      |  0.5      |   0.191879 |    0.0170043 |
| DummyClassifier                |   0.512281 |            0.489598 |  0.489598 |   0.518924 |    0.0119965 |

Regression

Example

from prettymetrics.reg import Regressor
from sklearn import datasets
from sklearn.utils import shuffle
import numpy as np

boston = datasets.load_boston()
X, y = shuffle(boston.data, boston.target, random_state=13)
X = X.astype(np.float32)

offset = int(X.shape[0] * 0.9)

X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]

reg = Regressor(verbose=0, ignore_warnings=False, custom_metric=None)
models, predictions = reg.fit(X_train, X_test, y_train, y_test)

print(models)


| Model                         | Adjusted R-Squared | R-Squared |  RMSE | Time Taken |
|:------------------------------|-------------------:|----------:|------:|-----------:|
| SVR                           |               0.83 |      0.88 |  2.62 |       0.01 |
| BaggingRegressor              |               0.83 |      0.88 |  2.63 |       0.03 |
| NuSVR                         |               0.82 |      0.86 |  2.76 |       0.03 |
| RandomForestRegressor         |               0.81 |      0.86 |  2.78 |       0.21 |
| XGBRegressor                  |               0.81 |      0.86 |  2.79 |       0.06 |
| GradientBoostingRegressor     |               0.81 |      0.86 |  2.84 |       0.11 |
| ExtraTreesRegressor           |               0.79 |      0.84 |  2.98 |       0.12 |
| AdaBoostRegressor             |               0.78 |      0.83 |  3.04 |       0.07 |
| HistGradientBoostingRegressor |               0.77 |      0.83 |  3.06 |       0.17 |
| PoissonRegressor              |               0.77 |      0.83 |  3.11 |       0.01 |
| LGBMRegressor                 |               0.77 |      0.83 |  3.11 |       0.07 |
| KNeighborsRegressor           |               0.77 |      0.83 |  3.12 |       0.01 |
| DecisionTreeRegressor         |               0.65 |      0.74 |  3.79 |       0.01 |
| MLPRegressor                  |               0.65 |      0.74 |  3.80 |       1.63 |
| HuberRegressor                |               0.64 |      0.74 |  3.84 |       0.01 |
| GammaRegressor                |               0.64 |      0.73 |  3.88 |       0.01 |
| LinearSVR                     |               0.62 |      0.72 |  3.96 |       0.01 |
| RidgeCV                       |               0.62 |      0.72 |  3.97 |       0.01 |
| BayesianRidge                 |               0.62 |      0.72 |  3.97 |       0.01 |
| Ridge                         |               0.62 |      0.72 |  3.97 |       0.01 |
| TransformedTargetRegressor    |               0.62 |      0.72 |  3.97 |       0.01 |
| LinearRegression              |               0.62 |      0.72 |  3.97 |       0.01 |
| ElasticNetCV                  |               0.62 |      0.72 |  3.98 |       0.04 |
| LassoCV                       |               0.62 |      0.72 |  3.98 |       0.06 |
| LassoLarsIC                   |               0.62 |      0.72 |  3.98 |       0.01 |
| LassoLarsCV                   |               0.62 |      0.72 |  3.98 |       0.02 |
| Lars                          |               0.61 |      0.72 |  3.99 |       0.01 |
| LarsCV                        |               0.61 |      0.71 |  4.02 |       0.04 |
| SGDRegressor                  |               0.60 |      0.70 |  4.07 |       0.01 |
| TweedieRegressor              |               0.59 |      0.70 |  4.12 |       0.01 |
| GeneralizedLinearRegressor    |               0.59 |      0.70 |  4.12 |       0.01 |
| ElasticNet                    |               0.58 |      0.69 |  4.16 |       0.01 |
| Lasso                         |               0.54 |      0.66 |  4.35 |       0.02 |
| RANSACRegressor               |               0.53 |      0.65 |  4.41 |       0.04 |
| OrthogonalMatchingPursuitCV   |               0.45 |      0.59 |  4.78 |       0.02 |
| PassiveAggressiveRegressor    |               0.37 |      0.54 |  5.09 |       0.01 |
| GaussianProcessRegressor      |               0.23 |      0.43 |  5.65 |       0.03 |
| OrthogonalMatchingPursuit     |               0.16 |      0.38 |  5.89 |       0.01 |
| ExtraTreeRegressor            |               0.08 |      0.32 |  6.17 |       0.01 |
| DummyRegressor                |              -0.38 |     -0.02 |  7.56 |       0.01 |
| LassoLars                     |              -0.38 |     -0.02 |  7.56 |       0.01 |
| KernelRidge                   |             -11.50 |     -8.25 | 22.74 |       0.01 |

History

0.2.8 (2021-02-06)

• Removed StackingRegressor and CheckingClassifier.

• Added provided_models method.

• Added adjusted r-squared metric.

• Added cardinality check to split categorical columns into low and high cardinality features.

• Added different transformation pipeline for low and high cardinality features.

• Included all number dtypes as inputs.

• Fixed dependencies.

• Improved documentation.

0.2.7 (2020-07-09)

• Removed catboost regressor and classifier

0.2.6 (2020-01-22)

• Added xgboost, lightgbm, catboost regressors and classifiers

0.2.5 (2020-01-20)

• Removed troublesome regressors from list of CLASSIFIERS

0.2.4 (2020-01-19)

• Removed troublesome regressors from list of REGRESSORS

• Added feature to input custom metric for evaluation

• Added feature to return predictions as dataframe

• Added model training time for each model

0.2.3 (2019-11-22)

• Removed TheilSenRegressor from list of REGRESSORS

• Removed GaussianProcessClassifier from list of CLASSIFIERS

0.2.2 (2019-11-18)

• Fixed automatic deployment issue.

0.2.1 (2019-11-18)

• Release of Regression feature.

0.2.0 (2019-11-17)

• Release of Classification feature.

0.1.0 (2019-11-16)

• First release on PyPI.