Version v0.16.2 of nnetsauce is now available on your favorite platforms: PyPI, conda and GitHub for Python; R universe and GitHub for R. The updated documentation will be available next week (I’ve been using keras-autodoc so far for this purpose; it is now discontinued).

For Windows users: if you run into issues when trying to install nnetsauce (but you shouldn’t): remember that you can use the Windows Subsystem for Linux (WSL).

The changes in this new version are:

Regression-based classifiers
Automated Machine Learning (AutoML) for MTS (multivariate time series forecasting) (not in R yet)
AutoML for DeepMTS (multivariate time series forecasting) (not in R yet)
Subsample continuous and discrete responses

This post is about AutoML in nnetsauce using its 90 base classifiers, thus including regression-based classifiers. It’s a mix of these resources:

This PDF document describing the regression-based classifiers available in nnetsauce
Classification using linear regression
A classifier that’s very accurate (and deep)

The goal is to show that, with nnetsauce, it’s possible to calibrate a large number of classifiers and select the best one, using a few lines of code. AutoML for MTS (multivariate time series forecasting) and DeepMTS will be presented next week.

0 - Install packages

The notebook can be found on GitHub for reproducibility of the following results: https://github.com/Techtonique/nnetsauce/blob/master/nnetsauce/demo/thierrymoudiki_080124_lazy_classifier_deep_classifier.ipynb.

!pip install nnetsauce

Collecting nnetsauce
  Downloading nnetsauce-0.16.2-py2.py3-none-any.whl (152 kB)
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Installing collected packages: nnetsauce
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import os
import nnetsauce as ns
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer, load_iris, load_wine, load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, ConfusionMatrixDisplay
from time import time

1 - Lazy classifiers

1 - 1 breast cancer data

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 = .2, random_state = 123)

clf = ns.LazyClassifier(verbose=0, ignore_warnings=True)
start = time()
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
print(f"\n\n Elapsed: {time()-start} seconds \n")
model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)

display(models)

100%|██████████| 90/90 [01:21<00:00,  1.10it/s]

 Elapsed: 81.99524259567261 seconds

	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
Model
CustomClassifier(RandomForestClassifier)	0.99	0.99	0.99	0.99	1.96
CustomClassifier(SGDClassifier)	0.99	0.99	0.99	0.99	0.24
CustomClassifier(QuadraticDiscriminantAnalysis)	0.99	0.99	0.99	0.99	1.20
CustomClassifier(Perceptron)	0.99	0.99	0.99	0.99	1.38
SimpleMultitaskClassifier(BaggingRegressor)	0.99	0.99	0.99	0.99	0.58
...	...	...	...	...	...
SimpleMultitaskClassifier(Lasso)	0.64	0.50	0.50	0.50	0.56
SimpleMultitaskClassifier(LarsCV)	0.64	0.50	0.50	0.50	0.76
MultitaskClassifier(LassoLars)	0.64	0.50	0.50	0.50	2.11
SimpleMultitaskClassifier(Lars)	0.55	0.57	0.57	0.56	0.40
MultitaskClassifier(Lars)	0.54	0.56	0.56	0.55	0.53

85 rows × 5 columns

model_dictionary["CustomClassifier(RandomForestClassifier)"]

CustomClassifier(obj=RandomForestClassifier(random_state=42))

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

print(classification_report(y_test, model_dictionary["CustomClassifier(RandomForestClassifier)"].fit(X_train, y_train).predict(X_test)))

              precision    recall  f1-score   support

           0       1.00      0.98      0.99        41
           1       0.99      1.00      0.99        73

    accuracy                           0.99       114
   macro avg       0.99      0.99      0.99       114
weighted avg       0.99      0.99      0.99       114

ConfusionMatrixDisplay.from_estimator(model_dictionary["CustomClassifier(RandomForestClassifier)"], X_test, y_test)
plt.show()

xxx

1 - 2 - iris data

data = load_iris()
X = data.data
y= data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)

clf = ns.LazyClassifier(verbose=0, ignore_warnings=True)
start = time()
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
print(f"\n\n Elapsed: {time()-start} seconds \n")
model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)
display(models)

100%|██████████| 90/90 [00:10<00:00,  8.95it/s]

 Elapsed: 10.071635246276855 seconds

	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
Model
MultitaskClassifier(AdaBoostRegressor)	0.97	0.97	None	0.97	0.16
CustomClassifier(SGDClassifier)	0.97	0.97	None	0.97	0.02
MultitaskClassifier(BayesianRidge)	0.97	0.97	None	0.97	0.04
MultitaskClassifier(DecisionTreeRegressor)	0.97	0.97	None	0.97	0.05
SimpleMultitaskClassifier(LinearRegression)	0.97	0.97	None	0.97	0.02
...	...	...	...	...	...
MultitaskClassifier(Lasso)	0.20	0.33	None	0.07	0.07
SimpleMultitaskClassifier(ElasticNet)	0.20	0.33	None	0.07	0.02
SimpleMultitaskClassifier(DummyRegressor)	0.20	0.33	None	0.07	0.02
CustomClassifier(DummyClassifier)	0.20	0.33	None	0.07	0.02
MultitaskClassifier(LassoLars)	0.20	0.33	None	0.07	0.06

85 rows × 5 columns

model_dictionary["MultitaskClassifier(AdaBoostRegressor)"]

CustomClassifier(obj=MultitaskClassifier(obj=AdaBoostRegressor()))

print(classification_report(y_test, model_dictionary["MultitaskClassifier(AdaBoostRegressor)"].fit(X_train, y_train).predict(X_test)))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        13
           1       0.86      1.00      0.92         6
           2       1.00      0.91      0.95        11

    accuracy                           0.97        30
   macro avg       0.95      0.97      0.96        30
weighted avg       0.97      0.97      0.97        30

ConfusionMatrixDisplay.from_estimator(model_dictionary["MultitaskClassifier(AdaBoostRegressor)"], X_test, y_test)
plt.show()

xxx

1 - 3 - wine data

data = load_wine()
X = data.data
y= data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)

clf = ns.LazyClassifier(verbose=0, ignore_warnings=True)
start = time()
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
print(f"\n\n Elapsed: {time()-start} seconds \n")
model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)
display(models)

100%|██████████| 90/90 [00:13<00:00,  6.66it/s]

 Elapsed: 13.523086071014404 seconds

	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
Model
MultitaskClassifier(AdaBoostRegressor)	1.00	1.00	None	1.00	0.51
SimpleMultitaskClassifier(ExtraTreesRegressor)	1.00	1.00	None	1.00	0.70
MultitaskClassifier(ElasticNetCV)	1.00	1.00	None	1.00	0.56
SimpleMultitaskClassifier(LassoLarsCV)	1.00	1.00	None	1.00	0.09
SimpleMultitaskClassifier(HuberRegressor)	1.00	1.00	None	1.00	0.18
...	...	...	...	...	...
MultitaskClassifier(Lars)	0.31	0.28	None	0.31	0.07
CustomClassifier(DummyClassifier)	0.31	0.33	None	0.14	0.02
MultitaskClassifier(Lasso)	0.31	0.33	None	0.14	0.06
MultitaskClassifier(LassoLars)	0.31	0.33	None	0.14	0.06
MultitaskClassifier(DummyRegressor)	0.31	0.33	None	0.14	0.05

83 rows × 5 columns

model_dictionary["MultitaskClassifier(AdaBoostRegressor)"]

CustomClassifier(obj=MultitaskClassifier(obj=AdaBoostRegressor()))

print(classification_report(y_test, model_dictionary["MultitaskClassifier(AdaBoostRegressor)"].fit(X_train, y_train).predict(X_test)))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00         8
           1       1.00      1.00      1.00        11
           2       1.00      1.00      1.00        17

    accuracy                           1.00        36
   macro avg       1.00      1.00      1.00        36
weighted avg       1.00      1.00      1.00        36

ConfusionMatrixDisplay.from_estimator(model_dictionary["MultitaskClassifier(AdaBoostRegressor)"], X_test, y_test)
plt.show()

xxx

1 - 4 - digits data

data = load_digits()
X = data.data
y= data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)

clf = ns.LazyClassifier(verbose=0, ignore_warnings=True)
start = time()
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
print(f"\n\n Elapsed: {time()-start} seconds \n")
model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)
display(models)

100%|██████████| 90/90 [03:47<00:00,  2.53s/it]

 Elapsed: 227.8047935962677 seconds

	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
Model
CustomClassifier(SVC)	0.99	0.99	None	0.99	0.99
CustomClassifier(ExtraTreesClassifier)	0.98	0.98	None	0.98	1.26
MultitaskClassifier(ExtraTreesRegressor)	0.98	0.98	None	0.98	7.42
SimpleMultitaskClassifier(ExtraTreesRegressor)	0.98	0.98	None	0.98	6.63
CustomClassifier(LogisticRegressionCV)	0.98	0.98	None	0.98	5.03
...	...	...	...	...	...
MultitaskClassifier(LassoLars)	0.08	0.10	None	0.01	2.20
MultitaskClassifier(Lasso)	0.08	0.10	None	0.01	2.07
MultitaskClassifier(DummyRegressor)	0.08	0.10	None	0.01	2.39
MultitaskClassifier(ElasticNet)	0.08	0.10	None	0.01	1.89
SimpleMultitaskClassifier(DummyRegressor)	0.08	0.10	None	0.01	1.19

83 rows × 5 columns

model_dictionary["CustomClassifier(SVC)"]

CustomClassifier(obj=SVC(random_state=42))

print(classification_report(y_test, model_dictionary["CustomClassifier(SVC)"].fit(X_train, y_train).predict(X_test)))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        39
           1       0.94      1.00      0.97        34
           2       1.00      0.97      0.99        36
           3       1.00      1.00      1.00        33
           4       0.95      1.00      0.98        42
           5       1.00      0.95      0.97        37
           6       1.00      1.00      1.00        43
           7       1.00      1.00      1.00        31
           8       1.00      0.95      0.97        37
           9       0.97      1.00      0.98        28

    accuracy                           0.99       360
   macro avg       0.99      0.99      0.99       360
weighted avg       0.99      0.99      0.99       360

ConfusionMatrixDisplay.from_estimator(model_dictionary["CustomClassifier(SVC)"], X_test, y_test)
plt.show()

xxx

ConfusionMatrixDisplay.from_estimator(model_dictionary["CustomClassifier(LogisticRegressionCV)"], X_test, y_test)
plt.show()

xxx

2 - Lazy Deep classifiers

2 - 1 breast cancer data

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 = .2, random_state = 123)

clf = ns.LazyDeepClassifier(n_layers=3, verbose=0, ignore_warnings=True)
start = time()
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
print(f"\n\n Elapsed: {time()-start} seconds \n")
model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)

display(models)

100%|██████████| 90/90 [03:43<00:00,  2.48s/it]

 Elapsed: 223.3978340625763 seconds

	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
Model
SimpleMultitaskClassifier(MLPRegressor)	0.99	0.99	0.99	0.99	3.77
MultitaskClassifier(BaggingRegressor)	0.99	0.99	0.99	0.99	3.45
SimpleMultitaskClassifier(BaggingRegressor)	0.99	0.99	0.99	0.99	1.94
CustomClassifier(Perceptron)	0.99	0.99	0.99	0.99	1.54
CustomClassifier(MLPClassifier)	0.99	0.99	0.99	0.99	3.19
...	...	...	...	...	...
MultitaskClassifier(LassoLars)	0.64	0.50	0.50	0.50	2.25
SimpleMultitaskClassifier(Lars)	0.54	0.54	0.54	0.55	3.24
MultitaskClassifier(Lars)	0.51	0.51	0.51	0.52	2.02
SimpleMultitaskClassifier(RANSACRegressor)	0.39	0.52	0.52	0.26	2.86
MultitaskClassifier(RANSACRegressor)	0.36	0.50	0.50	0.19	5.04

85 rows × 5 columns

model_dictionary["SimpleMultitaskClassifier(MLPRegressor)"]

CustomClassifier(obj=CustomClassifier(obj=CustomClassifier(obj=SimpleMultitaskClassifier(obj=MLPRegressor()))))

print(classification_report(y_test, model_dictionary["SimpleMultitaskClassifier(MLPRegressor)"].fit(X_train, y_train).predict(X_test)))

              precision    recall  f1-score   support

           0       1.00      0.98      0.99        41
           1       0.99      1.00      0.99        73

    accuracy                           0.99       114
   macro avg       0.99      0.99      0.99       114
weighted avg       0.99      0.99      0.99       114

ConfusionMatrixDisplay.from_estimator(model_dictionary["SimpleMultitaskClassifier(MLPRegressor)"], X_test, y_test)
plt.show()

xxx

2 - 2 - iris data

data = load_iris()
X = data.data
y= data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)

clf = ns.LazyDeepClassifier(n_layers=3, verbose=0, ignore_warnings=True)
start = time()
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
print(f"\n\n Elapsed: {time()-start} seconds \n")
model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)
display(models)

100%|██████████| 90/90 [00:25<00:00,  3.54it/s]

 Elapsed: 25.443820476531982 seconds

	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
Model
CustomClassifier(BaggingClassifier)	1.00	1.00	None	1.00	0.12
CustomClassifier(DecisionTreeClassifier)	1.00	1.00	None	1.00	0.08
MultitaskClassifier(MLPRegressor)	1.00	1.00	None	1.00	0.85
MultitaskClassifier(BaggingRegressor)	0.97	0.97	None	0.97	0.25
MultitaskClassifier(ElasticNetCV)	0.97	0.97	None	0.97	1.34
...	...	...	...	...	...
SimpleMultitaskClassifier(Lasso)	0.20	0.33	None	0.07	0.09
MultitaskClassifier(Lasso)	0.20	0.33	None	0.07	0.15
SimpleMultitaskClassifier(ElasticNet)	0.20	0.33	None	0.07	0.10
SimpleMultitaskClassifier(DummyRegressor)	0.20	0.33	None	0.07	0.08
MultitaskClassifier(LassoLars)	0.20	0.33	None	0.07	0.13

85 rows × 5 columns

model_dictionary["CustomClassifier(BaggingClassifier)"]

CustomClassifier(obj=CustomClassifier(obj=CustomClassifier(obj=BaggingClassifier(random_state=42))))

print(classification_report(y_test, model_dictionary["CustomClassifier(BaggingClassifier)"].fit(X_train, y_train).predict(X_test)))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        13
           1       1.00      1.00      1.00         6
           2       1.00      1.00      1.00        11

    accuracy                           1.00        30
   macro avg       1.00      1.00      1.00        30
weighted avg       1.00      1.00      1.00        30

ConfusionMatrixDisplay.from_estimator(model_dictionary["CustomClassifier(BaggingClassifier)"], X_test, y_test)
plt.show()

xxx

2 - 3 - wine data

data = load_wine()
X = data.data
y= data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)

clf = ns.LazyDeepClassifier(n_layers=3, verbose=0, ignore_warnings=True)
start = time()
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
print(f"\n\n Elapsed: {time()-start} seconds \n")
model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)
display(models)

100%|██████████| 90/90 [00:27<00:00,  3.26it/s]

 Elapsed: 27.655547857284546 seconds

	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
Model
MultitaskClassifier(LassoCV)	1.00	1.00	None	1.00	1.36
SimpleMultitaskClassifier(LinearRegression)	1.00	1.00	None	1.00	0.10
MultitaskClassifier(PassiveAggressiveRegressor)	1.00	1.00	None	1.00	0.19
MultitaskClassifier(SGDRegressor)	1.00	1.00	None	1.00	0.20
SimpleMultitaskClassifier(AdaBoostRegressor)	1.00	1.00	None	1.00	0.52
...	...	...	...	...	...
MultitaskClassifier(Lars)	0.31	0.38	None	0.27	0.21
CustomClassifier(DummyClassifier)	0.31	0.33	None	0.14	0.10
MultitaskClassifier(Lasso)	0.31	0.33	None	0.14	0.16
MultitaskClassifier(LassoLars)	0.31	0.33	None	0.14	0.18
MultitaskClassifier(DummyRegressor)	0.31	0.33	None	0.14	0.45

83 rows × 5 columns

model_dictionary["MultitaskClassifier(LassoCV)"]

CustomClassifier(obj=CustomClassifier(obj=CustomClassifier(obj=MultitaskClassifier(obj=LassoCV()))))

print(classification_report(y_test, model_dictionary["MultitaskClassifier(LassoCV)"].fit(X_train, y_train).predict(X_test)))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00         8
           1       1.00      1.00      1.00        11
           2       1.00      1.00      1.00        17

    accuracy                           1.00        36
   macro avg       1.00      1.00      1.00        36
weighted avg       1.00      1.00      1.00        36

ConfusionMatrixDisplay.from_estimator(model_dictionary["MultitaskClassifier(LassoCV)"], X_test, y_test)
plt.show()

xxx

2 - 4 - digits data

data = load_digits()
X = data.data
y= data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)

clf = ns.LazyDeepClassifier(n_layers=3, verbose=0, ignore_warnings=True)
start = time()
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
print(f"\n\n Elapsed: {time()-start} seconds \n")
model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)
display(models)

100%|██████████| 90/90 [10:05<00:00,  6.72s/it]

 Elapsed: 605.1929349899292 seconds

	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
Model
CustomClassifier(SVC)	0.99	0.99	None	0.99	4.41
SimpleMultitaskClassifier(AdaBoostRegressor)	0.98	0.98	None	0.98	12.88
MultitaskClassifier(ExtraTreesRegressor)	0.98	0.98	None	0.98	17.08
SimpleMultitaskClassifier(ExtraTreesRegressor)	0.98	0.98	None	0.98	19.48
CustomClassifier(LogisticRegressionCV)	0.98	0.98	None	0.98	15.02
...	...	...	...	...	...
SimpleMultitaskClassifier(DummyRegressor)	0.08	0.10	None	0.01	3.48
MultitaskClassifier(DummyRegressor)	0.08	0.10	None	0.01	6.17
MultitaskClassifier(ElasticNet)	0.08	0.10	None	0.01	5.31
CustomClassifier(DummyClassifier)	0.08	0.10	None	0.01	4.52
MultitaskClassifier(LassoLars)	0.08	0.10	None	0.01	4.03

83 rows × 5 columns

model_dictionary["CustomClassifier(SVC)"]

CustomClassifier(obj=CustomClassifier(obj=CustomClassifier(obj=SVC(random_state=42))))

print(classification_report(y_test, model_dictionary["CustomClassifier(SVC)"].fit(X_train, y_train).predict(X_test)))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        39
           1       0.94      1.00      0.97        34
           2       1.00      0.97      0.99        36
           3       1.00      1.00      1.00        33
           4       0.95      1.00      0.98        42
           5       1.00      0.95      0.97        37
           6       1.00      1.00      1.00        43
           7       1.00      1.00      1.00        31
           8       1.00      0.95      0.97        37
           9       0.97      1.00      0.98        28

    accuracy                           0.99       360
   macro avg       0.99      0.99      0.99       360
weighted avg       0.99      0.99      0.99       360

print(classification_report(y_test, model_dictionary["CustomClassifier(LogisticRegressionCV)"].fit(X_train, y_train).predict(X_test)))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        39
           1       0.87      1.00      0.93        34
           2       1.00      1.00      1.00        36
           3       1.00      1.00      1.00        33
           4       0.98      0.98      0.98        42
           5       1.00      0.97      0.99        37
           6       1.00      0.95      0.98        43
           7       0.97      1.00      0.98        31
           8       1.00      0.89      0.94        37
           9       0.97      1.00      0.98        28

    accuracy                           0.98       360
   macro avg       0.98      0.98      0.98       360
weighted avg       0.98      0.98      0.98       360

ConfusionMatrixDisplay.from_estimator(model_dictionary["CustomClassifier(SVC)"], X_test, y_test)
plt.show()

xxx

ConfusionMatrixDisplay.from_estimator(model_dictionary["CustomClassifier(LogisticRegressionCV)"], X_test, y_test)
plt.show()

xxx

A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce

0 - Install packages

1 - Lazy classifiers

1 - 1 breast cancer data

1 - 2 - iris data

1 - 3 - wine data

1 - 4 - digits data

2 - Lazy Deep classifiers

2 - 1 breast cancer data

2 - 2 - iris data

2 - 3 - wine data

2 - 4 - digits data