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clara-transpiler-0.17.5
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مشاهده بیشترتوضیحات
-
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | clara-transpiler-0.17.5 |
| نام | clara-transpiler |
| نسخه کتابخانه | 0.17.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Sergio Branco | The Architech |
| ایمیل نویسنده | asergio.branco@gmail.com |
| آدرس صفحه اصلی | https://github.com/asergiobranco/clara |
| آدرس اینترنتی | https://pypi.org/project/clara-transpiler/ |
| مجوز | - |
# Clara
CLARA is a tool designed to help Machine Learning developers to build their models using High-Level languages (Python), but easily implement them in C. The goal is not to convert the code, but to convert the trained model itself (the object). Therefore, this is not a code converter, but a **code transpiler**.
The following algorithms are available
+ __Classification__
- MLP
- Decision Tree
- Support-Vector Machines (SVC & Nu)
- LinearSVM
- Gaussian Naive Bayes
- Complement Naive Bayes
- Multinomial Naive Bayes
- Categorical Naive Bayes
- Bernoulli Naive Bayes
+ __Regression__
- MLP
- Support-Vector Machines
+ __Decomposition__
- PCA
+ __Preprocessing__
- StandardScaler
- KernelCenterer
- MaxAbsScaler
- MinMaxScaler
- RobustScaler
## Transpiling Tools
| Python Class | Clara Class |
|:------------:|:-----------------:|
| *Decomposition* |
| [sklearn.decomposition.PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) | clara.transpiler.pca.PCATranspiler |
| *Neural Networks* ||
| [sklearn.neural_network.MLPClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html) | clara.transpiler.mlp.MLPCTranspiler|
| [sklearn.neural_network.MLPRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html) | clara.transpiler.mlp.MLPRTranspiler|
| *Decision Tree* ||
| [sklearn.tree.DecisionTreeClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) | clara.transpiler.tree.DecisionTreeClassifierTranspiler|
| *Support-Vector Machines* ||
| [sklearn.svm.SVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) | clara.transpiler.svm.SVCTranspiler|
| [sklearn.svm.NuSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html) | clara.transpiler.svm.SVCTranspiler|
| [sklearn.svm.LinearSVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVM.html) | clara.transpiler.svm.LinearSVMTranspiler |
| [sklearn.svm.SVR](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html) | clara.transpiler.svm.SVRTranspiler |
| *Naive Bayes* ||
| [sklearn.naive_bayes.GaussianNB ](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html) | clara.transpiler.naive_bayes.GaussianNBTranspiler |
| [sklearn.naive_bayes.ComplementNB ](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.ComplementNB.html) | clara.transpiler.naive_bayes.ComplementNBTranspiler |
| [sklearn.naive_bayes.MultinomialNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html) | clara.transpiler.naive_bayes.MultinomialNBTranspiler |
| [sklearn.naive_bayes.CategoricalNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.CategoricalNB.html) | clara.transpiler.naive_bayes.CategoricalNBTranspiler |
| [sklearn.naive_bayes.BernoulliNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html) | clara.transpiler.naive_bayes.BernoulliNBTranspiler |
| *Preprocessing* |
| [sklearn.preprocessing.StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) | clara.transpiler.preprocessing.StandardScalerTranspiler |
## Syntax
Besides the multiple available algorithms, the syntax to use in any of them is the same and shown in the snippet bellow.
```python
#The ML Algorithm you want to use
model = ScikitLearnClass()
# TRAIN YOUR MODEL FIRST!!!
model.fit()
# The correspondent CLARA TRanspiler Class
transpiler = ClaraClassTranspiler(model) #The correspondent Clara Class
# The C code to be exported to a .c file and compiled
# The code is of the model trained, therefore no retraining is needed.
c_code = transpiler.generate_code()
```
# PCA Transpiler
### Python Exporting
```python
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_wine
data = load_wine()
dataset = np.column_stack((data.data, data.target))
scale = StandardScaler()
pca = PCA(n_components=0.8)
X = scale.fit_transform(dataset[::,:-1])
pca.fit(X)
from clara.transpiler.pca import PCATranspiler
transpiler = PCATranspiler(pca)
code = transpiler.generate_code()
with open("pca.c", "w+") as fp:
fp.write(code)
```
# Test code in C
The results may vary, but if they should be the same!!
```c
int main(int argc, const char * argv[]) {
// insert code here...
double sample[N_FEATURES] = { 1.51861254, -0.5622498 , 0.23205254, -1.16959318, 1.91390522,
0.80899739, 1.03481896, -0.65956311, 1.22488398, 0.25171685,
0.36217728, 1.84791957, 1.01300893};
double scores[N_COMPONENTS] = {0};
double inverse_sample[N_FEATURES] = {0};
calculate_scores(sample, scores);
printf("\nScores\n");
for(int i = 0; i < N_COMPONENTS; i++){
printf("%f\t", scores[i]);
}
printf("\n\nInverse Transform\n");
inverse(scores, inverse_sample);
for(int i = 0; i < N_FEATURES; i++){
printf("%f\t", inverse_sample[i]);
}
printf("\n\n");
pca_dimensions_t val;
val = calculate_dimensions(sample);
printf("T2 = %f, Q-Residuals: %f\n\n", val.hoteling2, val.q_residuals);
}
```
# MLP Transpiler
Multi-Layer Perceptron are the basis of Neural Networks and Deep Learning. Our tools provides a way to transpile MLPs for regression and classification problems.
* **Note:** At the current time, binary classifications are not working... Sorry*
## MLPClassifier
```python
from sklearn.neural_network import MLPClassifier
from sklearn.datasets import load_wine
import numpy as np
from clara.transpiler.mlp import MLPCTranspiler
data = load_wine()
dataset = np.column_stack((data.data, data.target))
mlp = MLPClassifier(hidden_layer_sizes=(30, 10))
mlp.fit(ddataset.data, dataset.target)
transpiler = MLPCTranspiler(mlp)
code = transpiler.generate_code()
with open("mlp.c", "w+") as fp:
fp.write(code)
```
## MLPRegressor
```python
from sklearn.neural_network import MLPRegressor
from sklearn.datasets import load_boston
import numpy as np
from clara.transpiler.mlp import MLPRTranspiler
data = load_boston()
dataset = np.column_stack((data.data, data.target))
mlp = MLPClassifier(hidden_layer_sizes=(30, 10))
mlp.fit(dataset.data, dataset.target)
transpiler = MLPRTranspiler(mlp)
code = transpiler.generate_code()
with open("mlp.c", "w+") as fp:
fp.write(code)
```
# Test code in C
```c
int main(){
double s[N_FEATURES] = {14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0};
int class;
for(int i = 0; i<N_FEATURES; i++){
sample[i] = s[i];
}
class = predict(sample);
return 0;
}
```
# Cite Us
Please, if you use our tool in any of your projects, cite us. This will help us improve and look at what people may need! Thanks!
DOI: [10.5281/zenodo.3930335](https://doi.org/10.5281/zenodo.3930335)
`Sérgio Branco. (2020, July 4). CLARA - Embedded ML Tools (Version v0.0.1). Zenodo. http://doi.org/10.5281/zenodo.3930336`
```
@software{sergio_branco_2020_3930336,
author = {Sérgio Branco},
title = {CLARA - Embedded ML Tools},
month = jul,
year = 2020,
publisher = {Zenodo},
version = {v0.0.1},
doi = {10.5281/zenodo.3930336},
url = {https://doi.org/10.5281/zenodo.3930336}
}
```
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.4 | Python |
نحوه نصب
نصب پکیج whl clara-transpiler-0.17.5:
pip install clara-transpiler-0.17.5.whl
نصب پکیج tar.gz clara-transpiler-0.17.5:
pip install clara-transpiler-0.17.5.tar.gz