A classifier that's very accurate (and deep)

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Version v0.15.0 of nnetsauce is now available on your favorite platforms: PyPI, conda and GitHub for Python; R universe and GitHub for R.

The changes in this version are mostly related to Automated Machine learning (AutoML):

• lazy prediction for classification and regression: see this post for more details on the subject (and remember to use pip install nnetsauce directly, instead of installing from a GitHub branch named lazy-predict)
• lazy prediction for multivariate time series (MTS): see this post for more details on the subject (and remember to use pip install nnetsauce directly, instead of installing from a GitHub branch named lazy-predict)
• lazy prediction with deep quasi-randomized nnetworks will be described in this post

Note that in the example below, for the offically released version (v0.15.0), Gradient boosting classifiers are available. This doesn’t change the best model chosen by the algorithm (which is never Gradient boosting, because the deep model is already doing “too much”). Not sure if Gradient boosting will be kept as a default model here because, in this context, it’s relatively slow.

To finish, 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).

Contents

• 0 - Install and import packages
• 1 - breast cancer data
• 2 - iris data
• 3 - wine data
• 4 - digits data
• 5 - R examples

Here is a jupyter notebook allowing you to reproduce these results. Do not hesitate to modify these examples by choosing – in LazyDeepClassifier – a different number of layers n_layers, or the number of engineered features per layer, n_hidden_features.

0 - Install and import packages

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!pip install nnetsauce --upgrade

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 - breast cancer data

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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%|██████████| 27/27 [00:44<00:00,  1.65s/it]



 Elapsed: 44.49836850166321 seconds 

	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
Model
Perceptron	0.99	0.99	0.99	0.99	2.15
SGDClassifier	0.98	0.98	0.98	0.98	1.99
RandomForestClassifier	0.98	0.98	0.98	0.98	2.76
PassiveAggressiveClassifier	0.98	0.98	0.98	0.98	1.89
LogisticRegression	0.98	0.98	0.98	0.98	1.14
ExtraTreesClassifier	0.98	0.98	0.98	0.98	3.13
BaggingClassifier	0.97	0.97	0.97	0.97	1.62
LabelPropagation	0.97	0.97	0.97	0.97	1.69
LabelSpreading	0.97	0.97	0.97	0.97	1.08
AdaBoostClassifier	0.97	0.97	0.97	0.97	3.42
LinearSVC	0.97	0.97	0.97	0.97	1.65
KNeighborsClassifier	0.97	0.97	0.97	0.97	1.07
SVC	0.97	0.96	0.96	0.97	1.07
CalibratedClassifierCV	0.97	0.96	0.96	0.97	1.82
DecisionTreeClassifier	0.96	0.96	0.96	0.96	1.50
QuadraticDiscriminantAnalysis	0.96	0.95	0.95	0.96	1.16
LinearDiscriminantAnalysis	0.96	0.95	0.95	0.96	1.39
ExtraTreeClassifier	0.96	0.94	0.94	0.96	0.83
RidgeClassifier	0.96	0.94	0.94	0.96	3.15
RidgeClassifierCV	0.96	0.94	0.94	0.96	1.51
GaussianNB	0.93	0.90	0.90	0.93	1.50
NearestCentroid	0.93	0.90	0.90	0.93	3.00
NuSVC	0.93	0.90	0.90	0.93	1.27
BernoulliNB	0.91	0.89	0.89	0.91	1.15
DummyClassifier	0.64	0.50	0.50	0.50	0.96

<svg xmlns="http://www.w3.org/2000/svg" height="24px"viewBox="0 0 24 24" width="24px"> </svg>

model_dictionary["Perceptron"]

CustomClassifier(obj=CustomClassifier(obj=CustomClassifier(obj=Perceptron(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["Perceptron"].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["Perceptron"], X_test, y_test)
plt.show()

2 - iris data

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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%|██████████| 27/27 [00:05<00:00,  4.51it/s]



 Elapsed: 5.992894172668457 seconds 

	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
Model
BaggingClassifier	1.00	1.00	None	1.00	0.18
DecisionTreeClassifier	1.00	1.00	None	1.00	0.11
KNeighborsClassifier	0.97	0.97	None	0.97	0.10
CalibratedClassifierCV	0.97	0.97	None	0.97	0.24
SGDClassifier	0.97	0.97	None	0.97	0.21
ExtraTreeClassifier	0.97	0.97	None	0.97	0.10
RidgeClassifier	0.97	0.97	None	0.97	1.05
RidgeClassifierCV	0.97	0.97	None	0.97	0.21
RandomForestClassifier	0.97	0.97	None	0.97	0.88
LogisticRegression	0.97	0.97	None	0.97	0.14
LinearSVC	0.97	0.97	None	0.97	0.11
PassiveAggressiveClassifier	0.93	0.94	None	0.93	0.11
Perceptron	0.93	0.94	None	0.93	0.11
BernoulliNB	0.93	0.94	None	0.93	0.10
LabelSpreading	0.93	0.91	None	0.93	0.31
LabelPropagation	0.93	0.91	None	0.93	0.20
ExtraTreesClassifier	0.93	0.91	None	0.93	0.45
GaussianNB	0.90	0.91	None	0.90	0.10
AdaBoostClassifier	0.90	0.91	None	0.90	0.38
SVC	0.87	0.88	None	0.87	0.19
NuSVC	0.87	0.88	None	0.87	0.12
NearestCentroid	0.87	0.88	None	0.87	0.10
LinearDiscriminantAnalysis	0.63	0.67	None	0.54	0.20
QuadraticDiscriminantAnalysis	0.20	0.33	None	0.07	0.11
DummyClassifier	0.20	0.33	None	0.07	0.10

<svg xmlns="http://www.w3.org/2000/svg" height="24px"viewBox="0 0 24 24" width="24px"> </svg>

model_dictionary["BaggingClassifier"]

CustomClassifier(obj=CustomClassifier(obj=CustomClassifier(obj=BaggingClassifier(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["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["BaggingClassifier"], X_test, y_test)
plt.show()

3 - wine data

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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%|██████████| 27/27 [00:05<00:00,  5.09it/s]



 Elapsed: 5.312330007553101 seconds 

	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
Model
RidgeClassifierCV	1.00	1.00	None	1.00	0.15
RidgeClassifier	1.00	1.00	None	1.00	0.13
Perceptron	1.00	1.00	None	1.00	0.15
SVC	0.97	0.98	None	0.97	0.13
SGDClassifier	0.97	0.98	None	0.97	0.13
CalibratedClassifierCV	0.97	0.98	None	0.97	0.27
PassiveAggressiveClassifier	0.97	0.98	None	0.97	0.14
LogisticRegression	0.97	0.98	None	0.97	0.16
LinearSVC	0.97	0.98	None	0.97	0.14
BaggingClassifier	0.97	0.97	None	0.97	0.21
RandomForestClassifier	0.97	0.97	None	0.97	0.61
LinearDiscriminantAnalysis	0.97	0.97	None	0.97	0.16
LabelSpreading	0.94	0.94	None	0.94	0.44
LabelPropagation	0.94	0.94	None	0.94	0.26
ExtraTreesClassifier	0.94	0.94	None	0.94	0.50
KNeighborsClassifier	0.92	0.92	None	0.92	0.13
ExtraTreeClassifier	0.89	0.88	None	0.89	0.13
DecisionTreeClassifier	0.83	0.87	None	0.83	0.12
GaussianNB	0.86	0.85	None	0.85	0.13
NearestCentroid	0.86	0.81	None	0.86	0.13
NuSVC	0.83	0.79	None	0.84	0.13
AdaBoostClassifier	0.81	0.78	None	0.81	0.43
BernoulliNB	0.81	0.75	None	0.80	0.15
QuadraticDiscriminantAnalysis	0.53	0.58	None	0.52	0.13
DummyClassifier	0.31	0.33	None	0.14	0.13

<svg xmlns="http://www.w3.org/2000/svg" height="24px"viewBox="0 0 24 24" width="24px"> </svg>

model_dictionary["RidgeClassifierCV"]

CustomClassifier(obj=CustomClassifier(obj=CustomClassifier(obj=RidgeClassifierCV())))

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["RidgeClassifierCV"].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["RidgeClassifierCV"], X_test, y_test)
plt.show()

4 - digits data

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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%|██████████| 27/27 [01:41<00:00,  3.75s/it]



 Elapsed: 101.15918207168579 seconds 

	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
Model
SVC	0.99	0.99	None	0.99	3.47
LogisticRegression	0.97	0.97	None	0.97	3.67
CalibratedClassifierCV	0.97	0.97	None	0.97	8.78
RandomForestClassifier	0.97	0.97	None	0.97	4.46
LinearDiscriminantAnalysis	0.96	0.96	None	0.96	4.78
LinearSVC	0.96	0.96	None	0.96	4.18
ExtraTreesClassifier	0.96	0.96	None	0.96	3.50
PassiveAggressiveClassifier	0.96	0.96	None	0.96	2.89
KNeighborsClassifier	0.96	0.96	None	0.96	3.60
SGDClassifier	0.96	0.96	None	0.96	2.62
RidgeClassifier	0.94	0.94	None	0.94	4.45
Perceptron	0.94	0.94	None	0.94	4.38
RidgeClassifierCV	0.94	0.94	None	0.93	3.02
NuSVC	0.93	0.93	None	0.93	4.55
BaggingClassifier	0.92	0.92	None	0.92	4.84
LabelPropagation	0.91	0.91	None	0.92	2.63
LabelSpreading	0.91	0.91	None	0.92	3.64
QuadraticDiscriminantAnalysis	0.88	0.88	None	0.88	2.39
NearestCentroid	0.85	0.85	None	0.85	4.27
DecisionTreeClassifier	0.83	0.83	None	0.83	3.36
BernoulliNB	0.82	0.82	None	0.82	2.85
GaussianNB	0.81	0.81	None	0.80	4.63
ExtraTreeClassifier	0.81	0.80	None	0.81	3.35
AdaBoostClassifier	0.39	0.37	None	0.32	5.05
DummyClassifier	0.08	0.10	None	0.01	4.98

<svg xmlns="http://www.w3.org/2000/svg" height="24px"viewBox="0 0 24 24" width="24px"> </svg>

model_dictionary["SVC"]

CustomClassifier(obj=CustomClassifier(obj=CustomClassifier(obj=SVC(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["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["SVC"], X_test, y_test)
plt.show()

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

5 - R examples

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I hope this is not too technical: please note that installing the R version of nnetsauce will create an environment in the current directory (more precisely, a folder named r-reticulate will be created where you’ll install the package).

Classification

remotes::install_github("Techtonique/nnetsauce_r") # install nnetsauce from GitHub

library(datasets)

set.seed(123)
X <- as.matrix(iris[, 1:4])
y <- as.integer(iris$Species) - 1L

(index_train <- base::sample.int(n = nrow(X),
                                 size = floor(0.8*nrow(X)),
                                 replace = FALSE))
X_train <- X[index_train, ]
y_train <- y[index_train]
X_test <- X[-index_train, ]
y_test <- y[-index_train]

obj <- nnetsauce::LazyDeepClassifier(n_layers = 3L)
res <- obj$fit(X_train, X_test, y_train, y_test)
print(res[[1]]) # best accuracy must be 1.0

Regression

X <- MASS::Boston[,-14] # dataset has an ethical problem
y <- MASS::Boston$medv

set.seed(13)
(index_train <- base::sample.int(n = nrow(X),
                                 size = floor(0.8*nrow(X)),
                                 replace = FALSE))
X_train <- X[index_train, ]
y_train <- y[index_train]
X_test <- X[-index_train, ]
y_test <- y[-index_train]

obj <- nnetsauce::LazyDeepRegressor(n_layers = 3L, n_clusters=2L)
res <- obj$fit(X_train, X_test, y_train, y_test)
print(res[[1]]) # best RMSE must be close to 2.8, try n_clusters=3L for 2.69

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80. Did you ask ChatGPT about who you are? Apr 16, 2023
81. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
82. Simple interfaces to the forecasting API Nov 23, 2022
83. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
84. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
85. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
86. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
87. A Machine Learning workflow using Techtonique Jun 6, 2022
88. Super Mario Bros © in the browser using PyScript May 8, 2022
89. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
90. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
91. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
92. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
93. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
94. Forecasting with `ahead` (Python version) Dec 13, 2021
95. Tuning and interpreting LSBoost Nov 15, 2021
96. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
97. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
98. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
99. Forecasting with `ahead` Oct 15, 2021
100. Classification using linear regression Sep 26, 2021
101. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
102. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
103. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
104. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
105. Hyperparameters tuning with GPopt Jun 11, 2021
106. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
107. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
108. Bayesian Optimization with GPopt Apr 16, 2021
109. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
110. Explaining xgboost predictions with the teller Mar 12, 2021
111. An infinity of time series models in nnetsauce Mar 6, 2021
112. New activation functions in mlsauce's LSBoost Feb 12, 2021
113. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
114. A deeper learning architecture in nnetsauce Dec 18, 2020
115. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
116. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
117. Generalized nonlinear models in nnetsauce Nov 28, 2020
118. Boosting nonlinear penalized least squares Nov 21, 2020
119. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
120. NEWS Oct 30, 2020
121. A glimpse into my PhD journey Oct 23, 2020
122. Submitting R package to CRAN Oct 16, 2020
123. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
124. Forecasting lung disease progression Oct 2, 2020
125. New nnetsauce Sep 25, 2020
126. Technical documentation Sep 18, 2020
127. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
128. Back next week, and a few announcements Sep 4, 2020
129. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
130. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
131. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
132. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
133. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
134. Announcements Jun 26, 2020
135. Parallel AdaOpt classification Jun 19, 2020
136. Comments section and other news Jun 12, 2020
137. Maximizing your tip as a waiter Jun 5, 2020
138. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
139. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
140. AdaOpt May 15, 2020
141. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
142. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
143. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
144. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
145. Grid search cross-validation using crossval Apr 10, 2020
146. Documentation for the querier, a query language for Data Frames Apr 3, 2020
147. Time series cross-validation using crossval Mar 27, 2020
148. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
149. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
150. R notebooks for nnetsauce Mar 6, 2020
151. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
152. Create a specific feed in your Jekyll blog Feb 21, 2020
153. Git/Github for contributing to package development Feb 14, 2020
154. Feedback forms for contributing Feb 7, 2020
155. nnetsauce for R Jan 31, 2020
156. A new version of nnetsauce (v0.3.1) Jan 24, 2020
157. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
158. Search bar, new year 2020 Jan 10, 2020
159. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
160. Understanding model interactions with the `teller` Dec 13, 2019
161. Using the `teller` on a classifier Dec 6, 2019
162. Benchmarking the querier's verbs Nov 29, 2019
163. Composing the querier's verbs for data wrangling Nov 22, 2019
164. Comparing and explaining model predictions with the teller Nov 15, 2019
165. Tests for the significance of marginal effects in the teller Nov 8, 2019
166. Introducing the teller Nov 1, 2019
167. Introducing the querier Oct 25, 2019
168. Prediction intervals for nnetsauce models Oct 18, 2019
169. Using R in Python for statistical learning/data science Oct 11, 2019
170. Model calibration with `crossval` Oct 4, 2019
171. Bagging in the nnetsauce Sep 25, 2019
172. Adaboost learning with nnetsauce Sep 18, 2019
173. Change in blog's presentation Sep 4, 2019
174. nnetsauce on Pypi Jun 5, 2019
175. More nnetsauce (examples of use) May 9, 2019
176. nnetsauce Mar 13, 2019
177. crossval Mar 13, 2019
178. test Mar 10, 2019