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English version / Version en français
English version
A new version of nnetsauce, version 0.4.0
, is now available on Pypi and for R. As usual, you can install it on Python by using the following commands (command line):
pip install nnetsauce
And if you’re using R, it’s still (R console):
library(devtools)
devtools::install_github("thierrymoudiki/nnetsauce/R-package")
library(nnetsauce)
The R version may be slightly lagging behind on some features; feel free to signal it on GitHub or contact me directly. This new release is accompanied by a few goodies:
1) New features, detailed in the changelog.
2) A refreshed web page containing all the information about package installation, use, interface’s work-in-progress documentation, and contribution to package development.
3) A specific RSS feed related to nnetsauce on this blog (there’s still a general feed containing everything here).
4) A working paper related to Bayesianrvfl2Regressor
, Ridge2Regressor
, Ridge2Classifier
, and Ridge2MultitaskClassifier
: Quasi-randomized networks for regression and classification, with two shrinkage parameters. About Ridge2Classifier
specifically, you can also consult this other paper.
Among nnetsauce’s new features, there’s a new model class called MultitaskClassifier
, briefly described in the first paper from point 4). It’s a multitask classification model based on regression models, with shared covariates. What does that mean? We use the figure below to start the explanation:
Imagine that we have 4 fruits at our disposal, and we would like to classify them as avocados (is an avocado a fruit?), apples or tomatoes, by looking at their color and shapes. What we called covariates before in model description are color
and shape
, also known as explanatory variables or predictors. The column containing fruit names in the figure – on the left – is a so-called response; a variable that MultitaskClassifier
must learn to classify (which is typically much larger). This raw response is transformed into a one-hot encoded one – on the right.
Instead of one response vector, we now have three different responses. And instead of one classification problem on one response, three different two-class classification problems on three responses: is this fruit an apple or not? Is this fruit a tomato or not? Is this fruit an avocado or not? All these three problems share the same covariates: color
and shape
.
MultitaskClassifier
can use any regressor (meaning, a statistical learning model for continuous responses) to solve these three problems; with the same regressor being used for all three of them – which is a priori a relatively strong hypothesis. Regressor’s predictions on each response are interpreted as raw probabilities that the fruit is either one of them or not.
We now use MultitaskClassifier
on breast cancer data, as we did in this post for AdaBoostClassifier
, to illustrate how it works. The R version for this code would be almost identical, replacing “.”’s by “$”’s.
Import packages:
import nnetsauce as ns
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics
from time import time
Model fitting on training set:
breast_cancer = load_breast_cancer()
Z = breast_cancer.data
t = breast_cancer.target
# Training/testing datasets (using reproducibility seed)
np.random.seed(123)
X_train, X_test, y_train, y_test = train_test_split(Z, t, test_size=0.2)
# Linear Regression is used (can be anything, but must be a regression model)
regr = LinearRegression()
fit_obj = ns.MultitaskClassifier(regr, n_hidden_features=5,
n_clusters=2, type_clust="gmm")
# Model fitting on training set
start = time()
fit_obj.fit(X_train, y_train)
print(time() - start)
# Model accuracy on test set
print(fit_obj.score(X_test, y_test))
# Area under the curve
print(fit_obj.score(X_test, y_test, scoring="roc_auc"))
These results can be found in nnetsauce/demo/. MultitaskClassifier
’s accuracy on this dataset is 99.1%, and other indicators such as precision are equal to 99% on this dataset too. Let’s visualize the missclassification results, as we did in a previous post, on the same dataset.
In this case with MultitaskClassifier
, and no advanced hyperparameter tweaking, there is one patient out of 114 who is missclassified. A robust way to understand MultitaskClassifier
’s accuracy on this dataset using these same parameters, could be to repeat the same procedure for multiple random reproducibility seeds (see code, the training and testing sets randomly change when we change the seed
, and we change MultitaskClassifier
’s seed too).
We obtain the results below for 100 reproducibility seeds. The accuracy is always at least 90%, mostly 95% and quite sometimes, higher than 98% (with no advanced hyperparameter tweaking).
French version
Une nouvelle version de nnetsauce, la version 0.4.0, est maintenant disponible sur Pypi et pour R. Comme d’habitude, vous pouvez l’installer sur Python en utilisant les commandes suivantes (ligne de commande):
pip install nnetsauce
Et si vous utilisez R, c’est toujours (console R):
library(devtools)
devtools::install_github("thierrymoudiki/nnetsauce/R-package")
library(nnetsauce)
La version R peut être légèrement en retard sur certaines fonctionnalités; n’hésitez pas à le signaler sur GitHub ou à me contacter directement en cas de problème. Cette nouvelle version s’accompagne de quelques goodies:
1) Nouvelles fonctionnalités, détaillées dans le fichier changelog.
2) Une page Web actualisée contenant toutes les informations sur l’installation, l’utilisation, la documentation de travail en cours, et la contribution au développement de l’outil.
3) Un flux RSS spécifique lié à nnetsauce sur ce blog (il existe toujours un flux général contenant tous les autres sujets, ici).
4) Un document de travail relatif à Bayesianrvfl2Regressor, Ridge2Regressor, Ridge2Classifier et Ridge2MultitaskClassifier: Quasi-randomized networks for regression and classification, with two shrinkage parameters. À propos de Ridge2Classifier en particulier, vous pouvez également consulter cet autre article.
Parmi les nouvelles fonctionnalités, il existe une nouvelle classe de modèle appelée MultitaskClassifier, brièvement décrite dans le premier article du point 4). Il s’agit d’un modèle de classification multitâche basé sur des modèles de régression, avec des covariables partagées. Qu’est-ce que cela signifie? Nous utilisons la figure ci-dessous pour commencer l’explication:
Imaginez que nous ayons 4 fruits à notre disposition, et nous aimerions les classer comme avocats (un avocat est-il un fruit?), pommes ou tomates, en regardant leur couleur et leur forme. Ce que nous appelions auparavant les covariables dans la description du modèle sont la couleur et la forme, également appelées variables explicatives ou prédicteurs. La colonne contenant les noms des fruits sur la figure – à gauche – est la réponse; la variable que MultitaskClassifier doit apprendre à classer (qui contient généralement beaucoup plus d’observations). Cette réponse brute est transformée en une réponse codée – à droite.
Au lieu d’une réponse, nous avons maintenant trois réponses différentes. Et au lieu d’un problème de classification sur une réponse, trois problèmes de classification à deux classes différents, sur trois réponses: ce fruit est-il ou non une pomme? Ce fruit est-il ou non une tomate? Ce fruit est-il ou non un avocat? Ces trois problèmes partagent les mêmes covariables: la couleur et la forme.
MultitaskClassifier peut utiliser n’importe quel modèle de régression (c’est-à-dire un modèle d’apprentissage statistique pour des réponses continues) pour résoudre ces trois problèmes simultanément; avec le même modèle de régression utilisé pour les trois - ce qui est a priori une hypothèse relativement forte. Les prédictions du modèle de régression sur chaque réponse sont alors interprétées comme des probabilités brutes que le fruit soit ou non, dans l’une ou l’autre des classes.
Nous utilisons maintenant MultitaskClassifier sur des données de cancer du sein, comme nous l’avions fait dans cet article pour AdaBoostClassifier. La version R de ce code serait presque identique, il s’agirait essentiellement de remplacer les «.» ’S par des « $ »’ s.
Import des packages:
import nnetsauce as ns
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics
from time import time
Ajustement du modèle sur l’ensemble d’entraînement:
breast_cancer = load_breast_cancer()
Z = breast_cancer.data
t = breast_cancer.target
# Training/testing datasets (using reproducibility seed)
np.random.seed(123)
X_train, X_test, y_train, y_test = train_test_split(Z, t, test_size=0.2)
# Linear Regression is used (can be anything, but must be a regression model)
regr = LinearRegression()
fit_obj = ns.MultitaskClassifier(regr, n_hidden_features=5,
n_clusters=2, type_clust="gmm")
# Model fitting on training set
start = time()
fit_obj.fit(X_train, y_train)
print(time() - start)
# Model accuracy on test set
print(fit_obj.score(X_test, y_test))
# Area under the curve
print(fit_obj.score(X_test, y_test, scoring="roc_auc"))
Les résultats de ce traitement se trouvent dans ce notebook. La précision du MultitaskClassifier sur cet ensemble de données est de 99,1%, et d’autres indicateurs sont également de l’ordre de 99% sur ces données. Visualisons maintenant comment les observations sont bien ou mal classées en fonction de leur classe réelle, comme nous l’avions fait dans un post précédent, sur le même ensemble de données.
Dans ce cas, avec MultitaskClassifier et aucun ajustement avancé des hyperparamètres, il n’y a qu’un patient sur 114 qui est mal classé. Un moyen robuste de comprendre la précision de MultitaskClassifier sur cet ensemble de données en utilisant ces mêmes paramètres, pourrait être de répéter la même procédure pour plusieurs graines de reproductibilité aléatoire (voir le code, les ensembles d’apprentissage et de test changent de manière aléatoire lorsque nous changeons la graine seed
, et nous changeons aussi la graine de MultitaskClassifier ).
Nous obtenons les résultats ci-dessous pour 100 graines de reproductibilité. La précision est toujours d’au moins 90%, la plupart du temps égale à 95% et assez souvent, supérieure à 98% (sans ajustement avancé des hyperparamètres).
Note: I am currently looking for a gig. You can hire me on Malt or send me an email: thierry dot moudiki at pm dot me. I can do descriptive statistics, data preparation, feature engineering, model calibration, training and validation, and model outputs’ interpretation. I am fluent in Python, R, SQL, Microsoft Excel, Visual Basic (among others) and French. My résumé? Here!
For attribution, please cite this work as:
T. Moudiki (2020-02-28). Version 0.4.0 of nnetsauce, with fruits and breast cancer classification . Retrieved from https://thierrymoudiki.github.io/blog/2020/02/28/python/quasirandomizednn/r/nnetsauce-new-version
BibTeX citation (remove empty spaces)@misc{ tmoudiki20200228, author = { T. Moudiki }, title = { Version 0.4.0 of nnetsauce, with fruits and breast cancer classification }, url = { https://thierrymoudiki.github.io/blog/2020/02/28/python/quasirandomizednn/r/nnetsauce-new-version }, year = { 2020 } }
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- Bayesian forecasting for uni/multivariate time series Dec 4, 2020
- Generalized nonlinear models in nnetsauce Nov 28, 2020
- Boosting nonlinear penalized least squares Nov 21, 2020
- Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
- NEWS Oct 30, 2020
- A glimpse into my PhD journey Oct 23, 2020
- Submitting R package to CRAN Oct 16, 2020
- Simulation of dependent variables in ESGtoolkit Oct 9, 2020
- Forecasting lung disease progression Oct 2, 2020
- New nnetsauce Sep 25, 2020
- Technical documentation Sep 18, 2020
- A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
- Back next week, and a few announcements Sep 4, 2020
- Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
- LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
- nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
- Maximizing your tip as a waiter (Part 2) Jul 10, 2020
- New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
- Announcements Jun 26, 2020
- Parallel AdaOpt classification Jun 19, 2020
- Comments section and other news Jun 12, 2020
- Maximizing your tip as a waiter Jun 5, 2020
- AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
- AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
- AdaOpt May 15, 2020
- Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
- Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
- Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
- Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
- Grid search cross-validation using crossval Apr 10, 2020
- Documentation for the querier, a query language for Data Frames Apr 3, 2020
- Time series cross-validation using crossval Mar 27, 2020
- On model specification, identification, degrees of freedom and regularization Mar 20, 2020
- Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
- R notebooks for nnetsauce Mar 6, 2020
- Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
- Create a specific feed in your Jekyll blog Feb 21, 2020
- Git/Github for contributing to package development Feb 14, 2020
- Feedback forms for contributing Feb 7, 2020
- nnetsauce for R Jan 31, 2020
- A new version of nnetsauce (v0.3.1) Jan 24, 2020
- ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
- Search bar, new year 2020 Jan 10, 2020
- 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
- Understanding model interactions with the `teller` Dec 13, 2019
- Using the `teller` on a classifier Dec 6, 2019
- Benchmarking the querier's verbs Nov 29, 2019
- Composing the querier's verbs for data wrangling Nov 22, 2019
- Comparing and explaining model predictions with the teller Nov 15, 2019
- Tests for the significance of marginal effects in the teller Nov 8, 2019
- Introducing the teller Nov 1, 2019
- Introducing the querier Oct 25, 2019
- Prediction intervals for nnetsauce models Oct 18, 2019
- Using R in Python for statistical learning/data science Oct 11, 2019
- Model calibration with `crossval` Oct 4, 2019
- Bagging in the nnetsauce Sep 25, 2019
- Adaboost learning with nnetsauce Sep 18, 2019
- Change in blog's presentation Sep 4, 2019
- nnetsauce on Pypi Jun 5, 2019
- More nnetsauce (examples of use) May 9, 2019
- nnetsauce Mar 13, 2019
- crossval Mar 13, 2019
- test Mar 10, 2019
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